fix: whisper transcription compatibility with newer transformers (#274)

- Use getattr for max_length to handle removed WhisperConfig attribute
- Cast input_features to model dtype to fix float16 mismatch

.dockerignore

@@ -0,0 +1,34 @@
+# Include any files or directories that you don't want to be copied to your
+# container here (e.g., local build artifacts, temporary files, etc.).
+#
+# For more help, visit the .dockerignore file reference guide at
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+**/.DS_Store
+**/__pycache__
+**/.venv
+**/.classpath
+**/.dockerignore
+**/.env
+**/.git
+**/.gitignore
+**/.project
+**/.settings
+**/.toolstarget
+**/.vs
+**/.vscode
+**/*.*proj.user
+**/*.dbmdl
+**/*.jfm
+**/bin
+**/charts
+**/docker-compose*
+**/compose*
+**/Dockerfile*
+**/node_modules
+**/npm-debug.log
+**/obj
+**/secrets.dev.yaml
+**/values.dev.yaml
+LICENSE
+README.md

.editorconfig

@@ -0,0 +1,2 @@
+[*]
+end_of_line = lf

.fdignore

@@ -0,0 +1,7 @@
+**/GFPGAN/inputs/**
+**/GFPGAN/tests/**
+**/frame_interpolation/photos/*
+moment.gif
+node.zip
+.DS_Store
+

.github/CODEOWNERS

@@ -0,0 +1,5 @@
+* @melMass
+extern/GFPGAN/* @TencentARC
+extern/SadTalker/* @OpenTalker
+nodes/deep_bump.py @HugoTini
+web/imageFeed.js @pythongosssss @melMass

.github/FUNDING.yml

@@ -0,0 +1,14 @@
+# These are supported funding model platforms
+
+github: [melMass]
+custom: ["https://www.buymeacoffee.com/melmass"]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,85 @@
+name: 🐞 Bug Report
+title: '[bug] '
+description: Report a bug
+labels: ['type: 🐛 bug', 'status: 🧹 needs triage']
+assignees:
+  - melMass
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        ## Before submiting an issue
+        - Make sure to read the README & INSTALL instructions.  
+        - Please search for [existing issues](https://github.com/melMass/comfy_mtb/issues?q=is%3Aissue) around your problem before filing a report.
+        - Optionally check the `#mtb-nodes` channel on the Banodoco discord:  
+          [![](https://dcbadge.vercel.app/api/server/AXhsabmDhn?style=flat)](https://discord.gg/IAXhsabmDhn)
+
+        ### Try using the debug mode to get more info
+
+        If you use the env variable `MTB_DEBUG=true`, debug message from the extension will appear in the terminal.
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the bug
+      description: A clear description of what the bug is. Include screenshots if applicable.
+      placeholder: Bug description
+    validations:
+      required: true
+
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Reproduction
+      description: Steps to reproduce the behavior.
+      placeholder: |
+        1. Add node xxx ...
+        2. Connect to xxx ...
+        3. See error
+
+  - type: textarea
+    id: expected-behavior
+    attributes:
+      label: Expected behavior
+      description: A clear description of what you expected to happen.
+
+  - type: dropdown
+    id: os
+    attributes:
+      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
+
+  - type: textarea
+    id: logs
+    attributes:
+      label: Console output
+      description: Paste the console output without backticks
+      render: sh
+
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,35 @@
+name: 💡 Feature Request
+title: "[feat] "
+description: Suggest an idea
+labels: ["type: 🤚 feature request"]
+
+body:
+  - type: textarea
+    id: problem
+    attributes:
+      label: Describe the problem
+      description: A clear description of the problem this feature would solve
+      placeholder: "I'm always frustrated when..."
+    validations:
+      required: true
+
+  - type: textarea
+    id: solution
+    attributes:
+      label: "Describe the solution you'd like"
+      description: A clear description of what change you would like
+      placeholder: "I would like to..."
+    validations:
+      required: true
+
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Alternatives considered
+      description: "Any alternative solutions you've considered"
+
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.

.github/workflows/package_wheels.yml

@@ -27,17 +27,15 @@ jobs:
     steps:
       - name: ♻️ Checking out the repository
         uses: actions/checkout@v3
-      - name: "🐍 Setting up Python"
+      - name: '🐍 Setting up Python'
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10.9"
+          python-version: '3.10.9'
 
       - name: 📦 Building and Bundling wheels
         shell: bash
         run: |
-          python -m pip wheel --no-cache-dir -r requirements-wheels.txt -w ./wheels > build.log
-
-          cat build.log
+          python -m pip wheel --no-cache-dir -r reqs.txt -w ./wheels 2>&1 | tee build.log
 
           # find source wheels
           packages=$(cat build.log | awk -F 'Building wheels for collected packages: ' '{print $2}')
@@ -45,6 +43,13 @@ jobs:
 
           IFS=', ' read -r -a package_array <<< "$packages"
 
+          # Save reversed package_array to wheel_order.txt
+          reversed_array=()
+          for ((idx=${#package_array[@]}-1; idx>=0; idx--)); do
+            reversed_array+=("${package_array[idx]}")
+          done
+          printf '%s\n' "${reversed_array[@]}" > ./wheels/wheel_order.txt
+
           printf "Autodetect this source package: \e[32m%s\e[0m\n" "${package_array[@]}"
 
           # Iterate through the wheel files and remove those that are not source built
@@ -71,4 +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') }}

.github/workflows/publish_action.yml

@@ -0,0 +1,22 @@
+name: 📦 Publish to Comfy registry
+on:
+  workflow_dispatch:
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'melMass' }}
+    steps:
+      - name: ♻️ Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: 📦 Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          skip_checkout: 'true'
+          personal_access_token: ${{ secrets.COMFY_REGISTRY_TOKEN }}

.github/workflows/release.yml

@@ -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
@@ -27,8 +27,36 @@ 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
+      #     find ${{ env.repo_name }} -type d -empty >> .release_ignore
+      #   shell: bash
+
+      - name: 🗑️ Remove files and directories listed in .release_ignore
+        shell: bash
+        run: |
+          release_ignore="${{ env.repo_name }}/.release_ignore"
+          if [ -f "$release_ignore" ]; then
+            while IFS= read -r entry || [ -n "$entry" ]; do
+              target="${{ env.repo_name }}/$entry"
+              if [ -e "$target" ]; then
+                if [ -f "$target" ]; then
+                  rm "$target"
+                elif [ -d "$target" ]; then
+                  rm -r "$target"
+                fi
+              else
+                echo "Warning: $entry does not exist in the repository. Skipping removal."
+              fi
+            done < "$release_ignore"
+          else
+            echo "No .release_ignore file found. Skipping removal of files and directories."
+          fi
+
       - name: 📦 Building custom comfy nodes
         shell: bash
         run: |
@@ -70,10 +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

.github/workflows/test_embedded.yml

@@ -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

.gitignore

@@ -1,3 +1,16 @@
 __pycache__
 *.py[cod]
-*.onnx
+*.onnx
+
+wheels/
+node_modules/
+compose.yaml
+comfy_mtb.wsb
+Dockerfile
+
+.DS_Store
+node.zip
+
+# I store the gh-pages worktrees (src & build) there
+.worktrees
+comfy.lock

.gitmodules

@@ -1,3 +1,12 @@
-[submodule "extern/SadTalker"]
-	path = extern/SadTalker
-	url = https://github.com/OpenTalker/SadTalker.git
+[submodule "extern/google-FILM"]
+	path = extern/frame_interpolation
+	url = https://github.com/google-research/frame-interpolation
+[submodule "extern/GFPGAN"]
+	path = extern/GFPGAN
+	url = https://github.com/TencentARC/GFPGAN.git
+[submodule "extern/frame_interpolation"]
+	path = extern/frame_interpolation
+	url = https://github.com/google-research/frame-interpolation
+[submodule "wiki"]
+	path = wiki
+	url = https://github.com/melMass/comfy_mtb.wiki.git

.nvim.lua

@@ -0,0 +1,10 @@
+-- HACK: this should theorically not be needed since the lsp should read from the pyproject
+-- tried: ruff-lsp or basedpyright
+
+local comfyRoot = vim.fn.expand("%:p:h:h:h")
+
+if not vim.env.PYTHONPATH or vim.env.PYTHONPATH == "" then
+	vim.env.PYTHONPATH = comfyRoot
+else
+	vim.env.PYTHONPATH = vim.env.PYTHONPATH .. ";" .. comfyRoot
+end

.pre-commit-config.yaml

@@ -0,0 +1,8 @@
+default_language_version:
+  python: python3.10
+repos:
+  - repo: https://github.com/melmass/hooks
+    rev: e8c6c18175ed4f6e30f23991de7989411e09c73b
+    hooks:
+      - id: fix-trailing-whitespace
+      - id: bump-version

.prettierrc

@@ -0,0 +1,6 @@
+{
+  "semi": false,
+  "singleQuote": true,
+  "tabWidth": 2,
+  "useTabs": false
+}

.release_ignore

@@ -0,0 +1,4 @@
+extern/frame_interpolation/moment.gif
+extern/frame_interpolation/photos
+extern/GFPGAN/inputs
+.git

CHANGELOG.md

@@ -0,0 +1,638 @@
+# Changelog
+
+This is an automated changelog based on the commits in this repository.
+
+Check the notes in the [releases](https://github.com/melMass/comfy_mtb/releases) for more information.
+## [main] - 2025-04-16
+
+### Bug Fixes
+
+- 🐛 note+ breaking wfs ([af42134](https://github.com/melMass/comfy_mtb/commit/af421340286b234e4c0cfcd4143a9d8726ebf3d1))
+- 🐛 ColorCorrect clamp issue ([a85e57b](https://github.com/melMass/comfy_mtb/commit/a85e57b18c7d3c765131873ffff523244ca9be73))
+- 🐛 Whisper chunks processing ([8bf3545](https://github.com/melMass/comfy_mtb/commit/8bf3545fec5b2a180607d40394b025a1e09c14b6))
+- 🐛 stackImages move to device ([d87e52e](https://github.com/melMass/comfy_mtb/commit/d87e52ea2c112fd95f257dcd6a54a5db77a34fc3))
+- 🐛 bbox upscale from center ([55261bd](https://github.com/melMass/comfy_mtb/commit/55261bda7c33d088b62c5483e4483201e5a9ce77))
+- 🐛 add MASK support for PickFromBatch ([0d264b9](https://github.com/melMass/comfy_mtb/commit/0d264b90a78d5a6719fb3ce71f4e9a642db4c950))
+- 🐛 use addDOMWidget for Debug node ([46af602](https://github.com/melMass/comfy_mtb/commit/46af6027d6c87d0c29b8bb0fd1cc1dbdae993629))
+- 🐛 use "modern" notation in toDevice ([b7ca8ed](https://github.com/melMass/comfy_mtb/commit/b7ca8ed1c6e117b71afd7696f55dcc3dbd5bad08))
+- 🐛 handle missing submodules ([d61da30](https://github.com/melMass/comfy_mtb/commit/d61da304099ff5e4528e4beb1ecc2eb83cabaaa1))
+- 🐛 add warnings about what each IO mode can do ([6608c0b](https://github.com/melMass/comfy_mtb/commit/6608c0b6d1cf8f7a9901214096f8c78bfe17056f))
+- 🐛 soft deprecate compression h264 ([a757e1c](https://github.com/melMass/comfy_mtb/commit/a757e1c98b2abbd2221a15b77e89d772e02d1d82))
+- 🐛 limit packages allowed to be installed from API ([d6e004c](https://github.com/melMass/comfy_mtb/commit/d6e004cce2c32f8e48b868e66b89f82da4887dc3))
+- 🐛 ensure default settings (io sidebar) ([ed17fa2](https://github.com/melMass/comfy_mtb/commit/ed17fa2ef4688aadf305a6d51b32c13a0efd22d6))
+- 🐛 spawn colour picker at pointer location ([e5482ae](https://github.com/melMass/comfy_mtb/commit/e5482aee5e3de07e8f055b3edc0fccc0e0f75c14)) by [@webfiltered](https://github.com/webfiltered) in [#223](https://github.com/melMass/comfy_mtb/pull/223)
+- 🐛 i/o sidebar for custom paths ([62469a4](https://github.com/melMass/comfy_mtb/commit/62469a4dd96e32509171aad74fcae8d2bb0ec593))
+
+### Features
+
+- ⚡ add BatchFromFolder ([9618513](https://github.com/melMass/comfy_mtb/commit/96185132b83c182032e9f6e822561eb5699af517))
+- ⚡ add use_normalized to TransformBatch2D ([d4a31bf](https://github.com/melMass/comfy_mtb/commit/d4a31bf19c2863df8dfc4cb9a3cd6683304949e4))
+- [**breaking**] ⚡ add support for masks in BatchFLoatMath ([fc7ba08](https://github.com/melMass/comfy_mtb/commit/fc7ba084f6ed7880e88e28eb448ab0bd7d796824))
+- ✨ add use_normalized to TransformImage ([4516aa9](https://github.com/melMass/comfy_mtb/commit/4516aa9cb4fcb12c946999d6dcc1501cc09011a3))
+- ✨ add regex support for String Replace ([78946b0](https://github.com/melMass/comfy_mtb/commit/78946b0fa3c3cf5dfcee8c7c4c0921b722d09d1e)) by [@poetryiii](https://github.com/poetryiii) in [#233](https://github.com/melMass/comfy_mtb/pull/233)
+- ✨ update diarization to 3.1 ([c30408f](https://github.com/melMass/comfy_mtb/commit/c30408f96d4df9c7d35545654401162090a74305)) by [@numz](https://github.com/numz)
+- ✨ add "workflow" query to /mtb/view endpoint ([eb7cf89](https://github.com/melMass/comfy_mtb/commit/eb7cf89f173b2342b04e7b61dca3d12cfaf65bdb))
+- ✨ add stretch_x and stretch_y to TransformImage ([22fce6f](https://github.com/melMass/comfy_mtb/commit/22fce6fdda135cbb1f1aad42c86aae166cba81b5))
+- ✨ add AudioDuration node ([f471497](https://github.com/melMass/comfy_mtb/commit/f47149746ac1e418cda2007c38aafbb03946ce22))
+- ✨ basic whisper nodes ([83cfc5c](https://github.com/melMass/comfy_mtb/commit/83cfc5c723d1a572af67ad14b52be4f8371a3c5f))
+- ✨ add BboxForDimensions ([9405784](https://github.com/melMass/comfy_mtb/commit/940578476438eaa6a42e0056f1b7b319ee585334))
+- ✨ improve the debug node ([cf7a9c4](https://github.com/melMass/comfy_mtb/commit/cf7a9c41e81e8dd461ab9dfa3c05bb8e2cdf2a67))
+- ✨ add BatchImageToSublist and counterpart ([00173fa](https://github.com/melMass/comfy_mtb/commit/00173fa3fbca4c5b1ff3016cc5139705ce61ec20))
+- ✨ add TensorOps ([a8cf465](https://github.com/melMass/comfy_mtb/commit/a8cf4650ff5cbd4975ef954b5829c772ee53250c))
+- ✨ live update outputs grid ([7f7a62f](https://github.com/melMass/comfy_mtb/commit/7f7a62f832c865a13b9181daee79d3cfc21581e2)) by [@christian-byrne](https://github.com/christian-byrne) in [#229](https://github.com/melMass/comfy_mtb/pull/229)
+- ✨ add SaveImage passthrough ([0eeb707](https://github.com/melMass/comfy_mtb/commit/0eeb707f34f51142def8e0ef7d351ee5028cb5e0))
+- ✨ add filtering to TransformImage ([bae26a0](https://github.com/melMass/comfy_mtb/commit/bae26a07fb02dd518c621eba28986a51c5d086bc))
+- ✨ add support for video in I/O sidebar ([c92d99a](https://github.com/melMass/comfy_mtb/commit/c92d99a8a37a64cfc285296f21452c4927a22774))
+- ✨ add an extra static input to Stack Images ([3f6d082](https://github.com/melMass/comfy_mtb/commit/3f6d08294096918d50101a19083f9134305cc8c9)) in [#222](https://github.com/melMass/comfy_mtb/pull/222)
+- ✨ add support for subdirs (i/o sidebar) ([52bd76e](https://github.com/melMass/comfy_mtb/commit/52bd76e19c8bd7e72986900e5dbfade0457ef7e0))
+- ✨ add Batch Sequence Nodes ([827c64c](https://github.com/melMass/comfy_mtb/commit/827c64c43d52ebfb8acd2e5c4491c4b66e6b8f40))
+- ✨ add support for more formats (I/O sidebar) ([8c629be](https://github.com/melMass/comfy_mtb/commit/8c629bee186b5ac991058018a788e4a836eef630))
+
+### Miscellaneous Tasks
+
+- 🧹 bump version ([d093d76](https://github.com/melMass/comfy_mtb/commit/d093d76efd87474a3ca82858147255038060ab17))
+- 🧹 small adjustments ([01107c4](https://github.com/melMass/comfy_mtb/commit/01107c45f8539ff7c579e08e2a9075d93781b9a2))
+- 🤖 update publish action workflow with permissions and version constraints ([0e48aaa](https://github.com/melMass/comfy_mtb/commit/0e48aaa3e4f1e440a5d7ab42df56b728ced03aca)) by [@robinjhuang](https://github.com/robinjhuang) in [#237](https://github.com/melMass/comfy_mtb/pull/237)
+- 🧹 basic standalone detection ([3a25526](https://github.com/melMass/comfy_mtb/commit/3a25526e818a1af8f886d2ad5c27101c4a0caa8b))
+- 🧹 rename type ([edcb3da](https://github.com/melMass/comfy_mtb/commit/edcb3da08bff66f9adcef8dcd37c3925e64d0135))
+- 🧹 update env file ([fc908ba](https://github.com/melMass/comfy_mtb/commit/fc908ba0a528523b7c1e37e34fb32f430746de0d))
+- 🧹 dev ([9a94371](https://github.com/melMass/comfy_mtb/commit/9a943714aada107bfd236e00fa1063872db7a834))
+- 🧹 apply formatting ([58ae89f](https://github.com/melMass/comfy_mtb/commit/58ae89f8e0f0f8b42825722a6aebc04da39847b1))
+
+### Refactor
+
+- 📦 add model autodownload ([147edcf](https://github.com/melMass/comfy_mtb/commit/147edcfcbc09dd27a0c787f9da568fb850c3308a))
+
+### Wip
+
+- 🚧 loop drawing ([ead4b34](https://github.com/melMass/comfy_mtb/commit/ead4b34e6dd03ea4ed309b246ef31c995325aa08))
+
+## New Contributors
+* [@poetryiii](https://github.com/poetryiii) made their first contribution in [#233](https://github.com/melMass/comfy_mtb/pull/233)
+* [@numz](https://github.com/numz) made their first contribution in [#](https://github.com/melMass/comfy_mtb/pull/)
+* [@webfiltered](https://github.com/webfiltered) made their first contribution in [#223](https://github.com/melMass/comfy_mtb/pull/223)
+## [0.2.0] - 2024-12-08
+
+### Bug Fixes
+
+- 🐛 remove mtb sidebar ([b0d52f7](https://github.com/melMass/comfy_mtb/commit/b0d52f73051368df6de2d1e10ad28ca56df72803))
+- 🐛 always enable the I/O sidebar ([ec1cb1a](https://github.com/melMass/comfy_mtb/commit/ec1cb1ac17d14670aa756dfb1ae7542397b12559))
+- 🐛 ui shifts on animation builder ([ecbb220](https://github.com/melMass/comfy_mtb/commit/ecbb220de6a05f2e506ec43f2b786be983166157))
+- 🐛 category for settings ([b6fa571](https://github.com/melMass/comfy_mtb/commit/b6fa571fd2096ace60d03cab42dba9ca37d0cb27)) in [#211](https://github.com/melMass/comfy_mtb/pull/211)
+- 🐛 new UI issues ([f272526](https://github.com/melMass/comfy_mtb/commit/f272526bfc5da95e95d42cb4c613a0b9585b2577))
+- 🐛 disable old BOOL widget (legacy) ([8596b81](https://github.com/melMass/comfy_mtb/commit/8596b8184edb484c907475a77ac1dc9e4a5c92af))
+- 🐛 pass ONNX providers explicitely ([43092e4](https://github.com/melMass/comfy_mtb/commit/43092e44a4ea17f90fcfb12372da634fe4b79557))
+- 🐛 typo in mtb_widgets error catch ([80b5a0c](https://github.com/melMass/comfy_mtb/commit/80b5a0ca7459763e7662421bccd8636976eefddd)) by [@christian-byrne](https://github.com/christian-byrne) in [#197](https://github.com/melMass/comfy_mtb/pull/197)
+- 🐛 doc widget sidebar offset in the new ui ([81b3bc1](https://github.com/melMass/comfy_mtb/commit/81b3bc1651f06ad2fa7938f810d3f406f5e7c41c))
+- 🐛 don't fallback to eval ([997d2fb](https://github.com/melMass/comfy_mtb/commit/997d2fb13af6aadf36873ea2ea3317e56f405aef))
+- 🐛 rework main utils ([c99b081](https://github.com/melMass/comfy_mtb/commit/c99b0812ab4a4183ef9298fb8a7c954bc7c858b2))
+- 🐛 MaskToImage ([821a0ef](https://github.com/melMass/comfy_mtb/commit/821a0ef42735a0a97ab82be22a4fdc67c9cfc80e))
+
+### Documentation
+
+- 📚 update wiki ([e17c6e2](https://github.com/melMass/comfy_mtb/commit/e17c6e29f5111bf5085b1fe6f764cfd1aae709f2))
+- 📚 remove link ([5bc125d](https://github.com/melMass/comfy_mtb/commit/5bc125d2f08470c8900dfd89deca721835848917))
+- 📚 clean readme ([333f646](https://github.com/melMass/comfy_mtb/commit/333f646ab1959d2c944fb046275cc93a545d557c))
+
+### Features
+
+- ✨ add h264 compression node ([e32d1e0](https://github.com/melMass/comfy_mtb/commit/e32d1e02df5e3a9351f829513f7ee3ffb2934be4))
+- ✨ add postshot nodes ([27e03fa](https://github.com/melMass/comfy_mtb/commit/27e03fa23efffda461c6975b15fe3964de476cb3))
+- ✨ improve the I/O sidebar ([cd9e614](https://github.com/melMass/comfy_mtb/commit/cd9e614b1a385d6b06eacfaad62def1d69f09808)) in [#193](https://github.com/melMass/comfy_mtb/pull/193)
+- ✨ add UpscaleBBoxBy ([74af5c6](https://github.com/melMass/comfy_mtb/commit/74af5c6499ef5dd73ce66c4c21b8c3507d69b037))
+- ✨ simplified sidebar and backend ([22f7c30](https://github.com/melMass/comfy_mtb/commit/22f7c3037345a866c9ff0b06f6689748021cee63))
+- ✨ add Interpolate Condition ([0133fb9](https://github.com/melMass/comfy_mtb/commit/0133fb93bc944d0dd7593b89b36e5b2676d9397a))
+- ✨ dump of wip things... ([cf7d305](https://github.com/melMass/comfy_mtb/commit/cf7d30507e7e449c4489e6a1ca159d3d0486bc55))
+- ✨ use the new parser for documentations ([4e593bb](https://github.com/melMass/comfy_mtb/commit/4e593bb30be561e39f1790e3514f60bb39e5a261))
+- ✨ add @mtb/markdown-parser bundles ([097ca33](https://github.com/melMass/comfy_mtb/commit/097ca33b8e7b27148e183e91712dc34d98d1a69b))
+- ✨ add VitMatte nodes ([896a025](https://github.com/melMass/comfy_mtb/commit/896a025006f9c7809c5e0776393a28f908be8950))
+- ✨ add ColorCorrectGPU ([9651a70](https://github.com/melMass/comfy_mtb/commit/9651a7034120589b059329b21688708e42772453))
+- ✨ add Swap FG/BG colors to MaskToImage ([57683c3](https://github.com/melMass/comfy_mtb/commit/57683c3c7d299a117a26526d52de4c26f2ec0f69))
+- ✨ add Extract coordinates ([f99f92e](https://github.com/melMass/comfy_mtb/commit/f99f92e8f7b2d6fac56f7f40049715910e15cfee))
+- ✨ add AudioCut ([5681b46](https://github.com/melMass/comfy_mtb/commit/5681b464adce395086712b61159b2694150b8027))
+- ✨ add AudioStack ([8d0fcee](https://github.com/melMass/comfy_mtb/commit/8d0fcee2f3decc1cbbf3b850332e6b2a022e1377))
+- ✨ add AudioSequence node ([1078fc6](https://github.com/melMass/comfy_mtb/commit/1078fc6f0fb225b52536f25ec6a9fa0456a90595))
+- ✨ add Split Bbox node ([9007a70](https://github.com/melMass/comfy_mtb/commit/9007a70aa0d6b2ead0f68f7aff8ae8e3c4f3624f))
+- ✨ update lerp example ([1a0ebd5](https://github.com/melMass/comfy_mtb/commit/1a0ebd5173687784f279a9c2184c89fb3be01dc5))
+
+### Miscellaneous Tasks
+
+- 🧹 bump minor ([50cb6f5](https://github.com/melMass/comfy_mtb/commit/50cb6f5ed6e5d9fecb9733ef3f7852b8500005e9))
+- 🧹 add worktree to gitignores ([9ccf572](https://github.com/melMass/comfy_mtb/commit/9ccf572a158caeab9bff53853e8f6fb85b76776d))
+- 🧹 remove dupe code ([e099d58](https://github.com/melMass/comfy_mtb/commit/e099d581a7627c3a66d2e3e6df3a701b0e5f31b7))
+- 🧹 update externs ([784fb01](https://github.com/melMass/comfy_mtb/commit/784fb0145b7421e2730b52237ce6a8b63b189191))
+- 🧹 add pathlibed inputs to utils ([a825504](https://github.com/melMass/comfy_mtb/commit/a825504bdd67e3461be8118119e0becc35f8af40))
+- 🧹 disable Constant ([22190cd](https://github.com/melMass/comfy_mtb/commit/22190cd25ee590595f8f19e75a9a6c539699622b))
+- 🧹 new ui is default, flag for old ui ([a976adb](https://github.com/melMass/comfy_mtb/commit/a976adbb39a13b4cd76f224ebba40c604900c862))
+- 🧹 add methods to shared ([f8829fc](https://github.com/melMass/comfy_mtb/commit/f8829fcb373e0f9bc4f0ad36c939f372349943bf))
+- 🧹 add an old_ui flag to my launcher ([dbdf276](https://github.com/melMass/comfy_mtb/commit/dbdf27664cd207dbbc69b8d635adcd59ed8d269a))
+- 🧹 move qrcode to his own file ([7d5569e](https://github.com/melMass/comfy_mtb/commit/7d5569e5c1e0f0b6ccb505a02f74640139d6aaf9))
+
+## [0.1.6] - 2024-07-03
+
+### Bug Fixes
+
+- 🐛 menu callback issue ([d64fac4](https://github.com/melMass/comfy_mtb/commit/d64fac4b74e0590acde5e3b8edd4a2f715448cf5))
+
+### Documentation
+
+- 📚 Update requirements file in INSTALL.md ([f25f6bd](https://github.com/melMass/comfy_mtb/commit/f25f6bdcd13d50f9d383065321320b0ce6a03214)) by [@elthariel](https://github.com/elthariel) in [#186](https://github.com/melMass/comfy_mtb/pull/186)
+
+### Features
+
+- ✨ add alpha channel support for faceswap/restore ([d6343e1](https://github.com/melMass/comfy_mtb/commit/d6343e1860f46947e93758f8bba03857c9326b38))
+
+### Miscellaneous Tasks
+
+- 🧹 better classname extraction ([d687497](https://github.com/melMass/comfy_mtb/commit/d687497d8041ab5d77bd31909592def6e4d0e7f6))
+- 🤖 limit release only to tags ([4eebdd8](https://github.com/melMass/comfy_mtb/commit/4eebdd8b8bff73c3db4f0248da8dac7d67cb310b))
+- 🧹 runner ([fb34671](https://github.com/melMass/comfy_mtb/commit/fb34671ee6fe80b965fe576c279ed1ff77a358f2))
+- 🤖 only publish on tag ([f1b4846](https://github.com/melMass/comfy_mtb/commit/f1b484617a917d38d9b3658d8920aa7dec672a79))
+- 🧹 small fixes ([4507842](https://github.com/melMass/comfy_mtb/commit/4507842a706141977a6a68945c36e977c358d91a))
+
+## New Contributors
+* [@elthariel](https://github.com/elthariel) made their first contribution in [#186](https://github.com/melMass/comfy_mtb/pull/186)
+## [0.1.5] - 2024-06-21
+
+### Bug Fixes
+
+- 🐛 keep the last model match instead of first ([1edc2cd](https://github.com/melMass/comfy_mtb/commit/1edc2cd10de81297e7a895009d358813e79b70ba))
+- 🐛 properly initialize the curve value ([35622e3](https://github.com/melMass/comfy_mtb/commit/35622e3a5e58103a8f5b150556b85e97e31555e1))
+- 🐛 ImageCompare improvements ([acc2d68](https://github.com/melMass/comfy_mtb/commit/acc2d687d596bf82c2075f9a24003eacf18adfe7)) by [@christian-byrne](https://github.com/christian-byrne) in [#176](https://github.com/melMass/comfy_mtb/pull/176)
+- 🐛 repetitive warning ([780c52f](https://github.com/melMass/comfy_mtb/commit/780c52f03aca3079a1b695510341486720004bec)) by [@vxkj1211](https://github.com/vxkj1211) in [#177](https://github.com/melMass/comfy_mtb/pull/177)
+- 🐛 add back was conversion node ([349a852](https://github.com/melMass/comfy_mtb/commit/349a8524c6f7fcab4a124cacb60bfbef1463cf1b))
+- 🐛 drag lag on documentation resize handle ([15330ea](https://github.com/melMass/comfy_mtb/commit/15330eab655f66214d3c25fd237679f090175c32))
+- 🐛 kwarg typo ([1571782](https://github.com/melMass/comfy_mtb/commit/1571782d012b83bce32a065e700f9a587db234d2))
+- 🐛 seed of PlotBatchFloat ([5b40302](https://github.com/melMass/comfy_mtb/commit/5b4030288d43c79859c9706a12aa0f8b7dea190f))
+- 🐛 forceInput for FLOAT <-> FLOATS converters ([5a0ef0d](https://github.com/melMass/comfy_mtb/commit/5a0ef0dadd01fd5937ed0715d829d6a456f96318))
+- 🐛 FLOAT always need options to be set ([967e72f](https://github.com/melMass/comfy_mtb/commit/967e72fc66780685f8192cb8fe13ba66b9326f63))
+- 🐛 remove doc if opened on node delete ([bee3f47](https://github.com/melMass/comfy_mtb/commit/bee3f47a14ddb92b3760098666bf75dc7d37f1e4))
+- 🐛 for documentation on HiDPI ([b11346a](https://github.com/melMass/comfy_mtb/commit/b11346aba88d9f1dac3b6b42c691979cc0978b6f))
+- 🐛 never remove input 0 of dynamic inputs ([30982fa](https://github.com/melMass/comfy_mtb/commit/30982fa48829c3fc2a6745ce5a07537a3d94b2f9))
+- 🐛 use the same fix as dynamicInputs for debug ([92b7990](https://github.com/melMass/comfy_mtb/commit/92b79906cd2ee1b4ca3ff25378d7786b5a47cb75))
+- 🐛 missing numberInput ([76f365b](https://github.com/melMass/comfy_mtb/commit/76f365b5eee165c76f3da7d2e3950786685bc08b))
+- 🐛 better curve ([da67e76](https://github.com/melMass/comfy_mtb/commit/da67e766c2f700dd9e2f51a5bafe07c612904f5d))
+- 🐛 prepend MTB_ to all classes ([b1d74ad](https://github.com/melMass/comfy_mtb/commit/b1d74adb15166e3e5eb9cf92d6148e4644bed346))
+- 🐛 dynamic connections ([652ac3f](https://github.com/melMass/comfy_mtb/commit/652ac3f3b971582b02115177fd6f7a9d3d7295df))
+- 🐛 remaining issue before merge ([100067a](https://github.com/melMass/comfy_mtb/commit/100067a645194366426f29b085bf25d0623f4fac))
+- 🐛 debug issues ([7807449](https://github.com/melMass/comfy_mtb/commit/7807449e6dcc01cfdb7f0eb818569184c8b41af2))
+- 🐛 errors when insightface's folder missing ([e838c04](https://github.com/melMass/comfy_mtb/commit/e838c04758402250fd3464d6cd6a6f872e8cef29))
+- 🐛 typo ([e40ad7a](https://github.com/melMass/comfy_mtb/commit/e40ad7a574f961ebe1f338b97214da5cbadcc529))
+- 🐛 better defaults (cont) ([1da483a](https://github.com/melMass/comfy_mtb/commit/1da483a8baa6a893f1adb05ef79b90c4412c3834))
+- 🐛 better defaults for Autopan ([5eff38b](https://github.com/melMass/comfy_mtb/commit/5eff38b387d22206d39c08e435806f9d03992feb))
+- 🐛 dynamic inputs ([9ab20a0](https://github.com/melMass/comfy_mtb/commit/9ab20a0ab50b1656ded9a84c13769fd2d547f2d2))
+- 🐛 bundle ace editor ([7c35582](https://github.com/melMass/comfy_mtb/commit/7c3558273bebc0754c802720e705232f220a0da4))
+- 🐛 image to mask ([f16d576](https://github.com/melMass/comfy_mtb/commit/f16d576f6f0e83fc2fafd2d1f29b2edeb00d3197))
+- 🐛 prepend MTB to classnames ([e56508c](https://github.com/melMass/comfy_mtb/commit/e56508c2078155f053e7f11d538a048df6a5b18b))
+- 🐛 allow smaller values in BatchTransform ([9a4b27d](https://github.com/melMass/comfy_mtb/commit/9a4b27d2e05e8ebe31f58a21db94bd3a54ed23d9))
+- 🐛 add category for virtual note+ ([eeac8c0](https://github.com/melMass/comfy_mtb/commit/eeac8c002ad1f9e461418fb66b9338e969259e58))
+- 🐛 make image feed of by default ([df0a98b](https://github.com/melMass/comfy_mtb/commit/df0a98b94a4a9388811bc8786e820ec892919c1a))
+- 🐛 support batch masks (colored image node) ([2465ffb](https://github.com/melMass/comfy_mtb/commit/2465ffb0d3b052fb78559394dbb550bba59b97a3))
+- 🐛 support pillow < 10 ([48f91b7](https://github.com/melMass/comfy_mtb/commit/48f91b74e2c7ef6d31c094eafa5332784a275a8b))
+- 🐛 image rotation bug ([54ff658](https://github.com/melMass/comfy_mtb/commit/54ff6583ded0ed4054f8e5d7fadf0b2350259dce)) by [@hongminpark](https://github.com/hongminpark) in [#154](https://github.com/melMass/comfy_mtb/pull/154)
+- 🐛 font fallback ([9fccdee](https://github.com/melMass/comfy_mtb/commit/9fccdee82d721e88c64d2292c209fec869524dd2))
+- ✨ optional inputs of colored image ([cd32f26](https://github.com/melMass/comfy_mtb/commit/cd32f26b167088d6b489e43b260c187ea5e4d223)) by [@ScottNealon](https://github.com/ScottNealon) in [#147](https://github.com/melMass/comfy_mtb/pull/147)
+- 📝 adds a way to not load the imagefeed ([501c330](https://github.com/melMass/comfy_mtb/commit/501c3301056b2851555cccd75ab3ff15b1ab8e0c))
+- 🐛 colored image mask input ([30c4311](https://github.com/melMass/comfy_mtb/commit/30c4311b69f6481a34f968cb67a9b5ce5d2e9fda))
+- 🐛 handle font cache errors ([c43a661](https://github.com/melMass/comfy_mtb/commit/c43a661ba31dcd7720b4f32d8e96760e6191fbd9))
+- 💄 register the COLOR type even for external extensions ([12b134a](https://github.com/melMass/comfy_mtb/commit/12b134ab4c937c192aaf4a3667d9885dd4fe43ca))
+- ✨ mask crop output ([59a361a](https://github.com/melMass/comfy_mtb/commit/59a361af5870b8ffc984c6680dd3282d3553dcf9))
+- 🚑️ thread font loading ([e4da832](https://github.com/melMass/comfy_mtb/commit/e4da832b99bd640b72c31b67178a3168e3238fa0))
+- 📦 changed way of creating bbox from mask ([14ee9e2](https://github.com/melMass/comfy_mtb/commit/14ee9e23c009ab55fa3b2fc6ec60fb683c46d57d)) by [@Yurchikian](https://github.com/Yurchikian) in [#124](https://github.com/melMass/comfy_mtb/pull/124)
+- ✨ expose invert of bboxfrommask ([53cb503](https://github.com/melMass/comfy_mtb/commit/53cb503866da6d83b47eaeb8073039ace2ae0a95))
+- ✨ less strict csv parsing ([d5c4c5f](https://github.com/melMass/comfy_mtb/commit/d5c4c5f2649ecdb4bf7b517c5b33bbf8df753047))
+- 🐛 fit number regression ([c8658df](https://github.com/melMass/comfy_mtb/commit/c8658dfbdd3a0ca8c3e88cd1adfddc55c7444045))
+- 🐛 remove uneeded installs ([4e07450](https://github.com/melMass/comfy_mtb/commit/4e07450bcabb0105b5610e52f7d4692ea07f9c1d))
+- 🐛 import issue ([255ac03](https://github.com/melMass/comfy_mtb/commit/255ac036bab1d776301857843d0e7a85e9a9dcb8))
+- 🐛 wrong output for bbox ([8d12b59](https://github.com/melMass/comfy_mtb/commit/8d12b59844958fbc696d01d51162f97262664ae9))
+- 🚑️ fallback when symlink detection fails ([278f22c](https://github.com/melMass/comfy_mtb/commit/278f22c2093b6eca63d2d00f7936774918707e4e))
+- ✨ handle malformed styles.csv ([e6f6502](https://github.com/melMass/comfy_mtb/commit/e6f65026735770df8aced4a3acb75550ff1c84da))
+- 🐛 encoding ([5af2840](https://github.com/melMass/comfy_mtb/commit/5af284067c65042bcdfff04a5d5a2360bf9e4af7))
+- ⚡️ add the cli deps ([bb90e04](https://github.com/melMass/comfy_mtb/commit/bb90e0415f6a1ececbf468815dc0f5959d9a34e8))
+- 🚑️ check for symlink ([25b933c](https://github.com/melMass/comfy_mtb/commit/25b933c698b250a411549d2600fae49bec225b7a))
+- 🚑️ remove problematic dependencies ([5dfea51](https://github.com/melMass/comfy_mtb/commit/5dfea51dd8db2a4829e559eadeda22374b51c8a4))
+- 🐛 batch support ([f1ff9fc](https://github.com/melMass/comfy_mtb/commit/f1ff9fc7c4684ad673c3178df3b8142dcf0b16ac))
+- 🐛 automatically disable tiling if seamless is on ([4605f74](https://github.com/melMass/comfy_mtb/commit/4605f74f370d4d221ab1d50f21b72910fa6909c7))
+- 🐛 debug node ([dc500b7](https://github.com/melMass/comfy_mtb/commit/dc500b788e885205f017956da6a71a677f822941))
+- ⚡️ hack to handle prompt validation ([d49b257](https://github.com/melMass/comfy_mtb/commit/d49b2578c247dcba9b09b374d99f5cc45cac172d))
+- ✨ deepbump update ([87b245c](https://github.com/melMass/comfy_mtb/commit/87b245c6a6895490e3612b235879fa90b62dea2b))
+- 👷 user folder_paths to retrieve comfy root ([38df58a](https://github.com/melMass/comfy_mtb/commit/38df58a78c363ef2657011893d4d811676b1c664))
+- 🐛 typo ([90aee83](https://github.com/melMass/comfy_mtb/commit/90aee83797a863cf4797cdbe187f949061cbd176))
+- 🐛 do not resolve symlink for "here" ([a50b11b](https://github.com/melMass/comfy_mtb/commit/a50b11bdaa66f4e805811b1676c937ade11318c2))
+- ✏️ use Union to allow support for <3.10 ([88a2779](https://github.com/melMass/comfy_mtb/commit/88a277968745ac990406b14d300a8ada9c575b11)) by [@M1kep](https://github.com/M1kep) in [#91](https://github.com/melMass/comfy_mtb/pull/91)
+- ⚡️ simplify widgets cleanup ([cdd098e](https://github.com/melMass/comfy_mtb/commit/cdd098e10258401402b8023c9143532cfa4a1745))
+- ✨ don't assume the install was ran ([cc43654](https://github.com/melMass/comfy_mtb/commit/cc43654af2987bc8860557caa99cde91e8309b21))
+- 🐛 install ([616b2bf](https://github.com/melMass/comfy_mtb/commit/616b2bfc6c629cef1d30cb0d717bd805c3a086aa))
+- 🐛 properly escape paths ([22cac9b](https://github.com/melMass/comfy_mtb/commit/22cac9b2d95910197941b73e7548735470bd3b17))
+- 🐛 use relative paths in JS ([e2773ff](https://github.com/melMass/comfy_mtb/commit/e2773ff22e43e7756ad618344a03d661a576cf35))
+- 💄 BatchFromHistory when "listening" ([3b07984](https://github.com/melMass/comfy_mtb/commit/3b07984716402fbbf5da41020bf73befd52e7ebf))
+- ✨ save gif widget removal ([fe8f519](https://github.com/melMass/comfy_mtb/commit/fe8f519f8860b0610d8cafcd9b843b4171c2b3d4))
+
+### Documentation
+
+- 📚 update the wiki ([fa3199b](https://github.com/melMass/comfy_mtb/commit/fa3199be2b87bf3cb7484a0fee32a8ac099adc65))
+- 📚 update wiki submodule ([49cea8d](https://github.com/melMass/comfy_mtb/commit/49cea8d94508b27781506e3b5509c65e1d84e80f))
+- 📚 add the wiki as a submodule ([5998924](https://github.com/melMass/comfy_mtb/commit/59989249260a9c579ec851c50534b58f3f02cd61))
+- 📚 missing doc ([c9836a8](https://github.com/melMass/comfy_mtb/commit/c9836a87f6823db1d53e56997417f3cbe8cc4727))
+- 📚 use flat icon ([991af4f](https://github.com/melMass/comfy_mtb/commit/991af4f45ff8c660b2c45466bb219186699170ed))
+- 📚 add banodoco channel link ([9ce34b4](https://github.com/melMass/comfy_mtb/commit/9ce34b47fd99b18db7997ccce44e6063f00b6801))
+- 📚 udpate changelog ([8221c49](https://github.com/melMass/comfy_mtb/commit/8221c49942bd87c14d5063066315a449a1fee86e))
+- 📝 add changelog ([0d817bf](https://github.com/melMass/comfy_mtb/commit/0d817bf326b4a22e2221264a414af50c3b7048b9))
+- 📄 add note+ screenshot ([90d9636](https://github.com/melMass/comfy_mtb/commit/90d96366c8b7637b55d1b4f88cb9aca217c1414b))
+- 📝 add cover image ([6b993b8](https://github.com/melMass/comfy_mtb/commit/6b993b84071bbb80ba1b8bd63576f31e35d05590))
+- 📝 fix image size ([3e8c2fe](https://github.com/melMass/comfy_mtb/commit/3e8c2fe789925e7017c2f8c8d9164c139588aba4))
+- 📝 add image ([3e93ea6](https://github.com/melMass/comfy_mtb/commit/3e93ea6f2c73353891b1a3f6223b5730bc69df37))
+- 📝 explain optional nodes ([cea0b08](https://github.com/melMass/comfy_mtb/commit/cea0b08eb044756ab1b408f630435095b8969d36))
+- 📝 add the example previews from the wiki ([8f90986](https://github.com/melMass/comfy_mtb/commit/8f909864bfaa9f2d0fbdcf3942eacb9d78ee8fb8))
+- 📝 update node list ([4917e31](https://github.com/melMass/comfy_mtb/commit/4917e31c427c74d28c830fd7b2423cab393ba0f8))
+- 📝 add some deprecation warnings and recommendations ([e11df9d](https://github.com/melMass/comfy_mtb/commit/e11df9d45c81d93f4334841de036b4aa3364375a))
+- 📝 add a reference to SlickComfy for colab ([bb35098](https://github.com/melMass/comfy_mtb/commit/bb35098c656b0b2d30909b83df0a3b65c5975f78))
+
+### Features
+
+- ✨ add ModelPruner (wip) ([43d65ae](https://github.com/melMass/comfy_mtb/commit/43d65ae68c97e077117b17b7c9d1936583f965eb))
+- ✨ Use dynamic contrast in Color Correct ([6abac2e](https://github.com/melMass/comfy_mtb/commit/6abac2e4706a3d937420213e01468bae10cc2017)) by [@christian-byrne](https://github.com/christian-byrne) in [#180](https://github.com/melMass/comfy_mtb/pull/180)
+- ✨ StackImages add support for batch mismatch ([5060c56](https://github.com/melMass/comfy_mtb/commit/5060c561353e43624ec164cb73fce7d1d422f765))
+- ✨ add BatchFloatMath ([f9d2ebf](https://github.com/melMass/comfy_mtb/commit/f9d2ebf91d09fc214fecf7501a5490b33c30aca2))
+- ✨ add FLOATS to INTS ([1b7ae27](https://github.com/melMass/comfy_mtb/commit/1b7ae27cc1907bfba3c5166ec2c61547babd2e0a))
+- ✨ debug dict ([63ee25d](https://github.com/melMass/comfy_mtb/commit/63ee25d001d4c94aa95dc8b39008f5d943f2ab45))
+- ✨ add Swap BG/FG color menu item ([1caf7c1](https://github.com/melMass/comfy_mtb/commit/1caf7c18c372651b2be7227eb77e2251d963693d))
+- ✨ BatchFloatFit the batch version of FitNumber ([ab58c36](https://github.com/melMass/comfy_mtb/commit/ab58c362124f0f4b3178534ca78cb924fb881534))
+- ✨ add FloatToFloats (the counterpart) ([78a86da](https://github.com/melMass/comfy_mtb/commit/78a86daaf71dab5be34b90b13491460854718485))
+- ✨ add some FLOATS batch nodes ([2159395](https://github.com/melMass/comfy_mtb/commit/2159395389429c5f7012e660b41fad48d376b39f))
+- ✨ poc of the doc widget idea ([fac7529](https://github.com/melMass/comfy_mtb/commit/fac7529d1f7b6fc4b3b2e7f6022ebb23ec71169d))
+- ✨ add the backend node for Constant ([dff5b22](https://github.com/melMass/comfy_mtb/commit/dff5b2201d73c1a91d4b5864e3b974e68846a011))
+- ✨ add Constant node ([cbb5dd2](https://github.com/melMass/comfy_mtb/commit/cbb5dd2cf810d5648a64eae370dba610336b99d5))
+- ✨ add FloatsToFloat ([6ebecfd](https://github.com/melMass/comfy_mtb/commit/6ebecfd8cf1dc3779384e565a65baa9dceb43660))
+- ✨ add AutoPanEquilateral ([3513937](https://github.com/melMass/comfy_mtb/commit/35139371e84d715423015e05d1b4a6c1d88b0eb5))
+- ✨ add MatchDimensions ([5db3ebe](https://github.com/melMass/comfy_mtb/commit/5db3ebedb9d38470c82544e45970775193add05c))
+- ✨ add equilateral example ([8d65556](https://github.com/melMass/comfy_mtb/commit/8d65556c37f33d1c496504db92574805916dd613))
+- ✨ enhance tiling tools ([ba73fc6](https://github.com/melMass/comfy_mtb/commit/ba73fc6af7039a4629a73cdc36a8c8736dc27c9d))
+- ✨ add FLOATS support to blur ([92c810c](https://github.com/melMass/comfy_mtb/commit/92c810c5036f7a2b3f84a3fde8c81e6a2b046b07))
+- ✨ add "tube" to Batch Shape ([f658fc3](https://github.com/melMass/comfy_mtb/commit/f658fc31e040141209384d98dfe84b766fe4ae11))
+- ✨ note+ editor themes ([133da70](https://github.com/melMass/comfy_mtb/commit/133da705c94af2dfb3d2f38c0d9c2723c72cacf7))
+- ✨ add ffmpeg gif export ([1b29aad](https://github.com/melMass/comfy_mtb/commit/1b29aad360116e631b7b4d34e98a5a631f134977)) by [@huanggou666](https://github.com/huanggou666) in [#159](https://github.com/melMass/comfy_mtb/pull/159)
+- ✨ add "To Device" ([c28181f](https://github.com/melMass/comfy_mtb/commit/c28181f1615d2e183767aa76cc2350934330e546))
+- ✨ add note+ example ([90f3bc2](https://github.com/melMass/comfy_mtb/commit/90f3bc2d953b299ea34e9e3a925f1a824b488855))
+- 💄 node+ improvements ([4b29395](https://github.com/melMass/comfy_mtb/commit/4b29395000254382882c0d1be115b2ed80cd7c99))
+- 📝 add note plus ([605c8db](https://github.com/melMass/comfy_mtb/commit/605c8db320e1531c6347f6888606fa50d8eb268b))
+- 🚧 add playlist nodes ([cf96572](https://github.com/melMass/comfy_mtb/commit/cf965727e8e7064328704d88cd0410c61f1e686e))
+- 🚨 add missing node ([16c1a59](https://github.com/melMass/comfy_mtb/commit/16c1a59312b1d9841f5f8a814eff93a1ddf04edb))
+- ✨ Math Expression node ([142624e](https://github.com/melMass/comfy_mtb/commit/142624eea616a5622387b1b641c02605455ee6f1))
+- 🚀 add optional inputs to colored image ([049983d](https://github.com/melMass/comfy_mtb/commit/049983dbe2dbce6b772908468c4042d2bfde5eb2))
+- ✨ Add support for extra_model_paths.yaml ([d7b8ac8](https://github.com/melMass/comfy_mtb/commit/d7b8ac8e0c98b0d7a2e21889d35aad9f6b093560))
+- ✨ add batch shake ([af94203](https://github.com/melMass/comfy_mtb/commit/af94203d1b461d934ca1c44211ca0f71a5d05d48))
+- ✨ enhance concat images ([a798eb0](https://github.com/melMass/comfy_mtb/commit/a798eb07d0d891cfbd47013b442ef2fa3d7cc5bc))
+- 💄 add a few more batch nodes ([c1d42de](https://github.com/melMass/comfy_mtb/commit/c1d42de0fcde86d2a167fb4b5e781ee987814da2))
+- ✨ Batch node utilities ([cef5023](https://github.com/melMass/comfy_mtb/commit/cef5023efc17366a2e937ef43944de3587707fac))
+- 🚨 Image Stack node (horizontal and vertical stack) ([bb3277d](https://github.com/melMass/comfy_mtb/commit/bb3277d85f4ca21735cb1f5237cb1430db88c183))
+- 🚀 add seamless model hack ([21acc87](https://github.com/melMass/comfy_mtb/commit/21acc87ff0a84b7588f4b5aae0aeb5ae94bbbfbe))
+- 🔧 debug handle a few more types ([638498c](https://github.com/melMass/comfy_mtb/commit/638498c6b47c2b2cab82f76aec1f3d46df67f263))
+- 🎨 Add an editor for the styles loader ([2faa2f2](https://github.com/melMass/comfy_mtb/commit/2faa2f2a148a4dbf5525e4945f688a239f244546))
+- ✨ add a static assets path ([6a00d1d](https://github.com/melMass/comfy_mtb/commit/6a00d1da5a8a5fa47af1bf1ab5d3cd206c599841))
+- ✨ add  Interpolate Clip Sequential ([a71c273](https://github.com/melMass/comfy_mtb/commit/a71c273baf450ad7e2a7e032451f015d3be3e9e9))
+
+### Miscellaneous Tasks
+
+- 🧹 add fields for the registry ([bb5682a](https://github.com/melMass/comfy_mtb/commit/bb5682aa6da923859db33830c2e46f24b19199a1))
+- 🧹 add pre-commit ([59612fd](https://github.com/melMass/comfy_mtb/commit/59612fd8110a888f0081433242a2b5a5f7e46da6))
+- 🧹 migrate from poetry to setuptools ([dfd17f6](https://github.com/melMass/comfy_mtb/commit/dfd17f6d783e784df7dab38d185c747b4c04d1d0))
+- 🧹 remove logs ([1070edd](https://github.com/melMass/comfy_mtb/commit/1070edd0245fb235183d5f38cd1bebf6e0405f97))
+- 🧹 add more pyproject meta ([644371e](https://github.com/melMass/comfy_mtb/commit/644371e5b5a2b8260fc5c6f699465b0bc1c81d57))
+- 🤖 move at the proper location ([f3d468c](https://github.com/melMass/comfy_mtb/commit/f3d468cfc238f13905a13a7b2225e3711129c64d))
+- 🤖 add CI to publish to ComfyUI Registry ([6cd448b](https://github.com/melMass/comfy_mtb/commit/6cd448b026956cdf3f1b81e93724b295316fbf09)) by [@haohaocreates](https://github.com/haohaocreates) in [#182](https://github.com/melMass/comfy_mtb/pull/182)
+- 🧹 add ComfyUI registry to pyproject.toml ([5951c90](https://github.com/melMass/comfy_mtb/commit/5951c90b10f9b77b2b617e83efe0112f43c8daef)) by [@haohaocreates](https://github.com/haohaocreates) in [#181](https://github.com/melMass/comfy_mtb/pull/181)
+- 🧹 update types ([96a0da9](https://github.com/melMass/comfy_mtb/commit/96a0da9dbd051d1fcf8b332c54ed2d307d8ae0dd))
+- 🧹 use a gettattr fallback ([a344cdc](https://github.com/melMass/comfy_mtb/commit/a344cdcba9823ca1fb0762795068039b1e1cf0ab))
+- 🧹 cleanup js ([64cc4e9](https://github.com/melMass/comfy_mtb/commit/64cc4e9649853023d645245bea1e1ceb11073f01))
+- 🧹 add savedatabundle js part ([edd7c3f](https://github.com/melMass/comfy_mtb/commit/edd7c3f5d075b640e9cdb067ebfe51c42ff61791))
+- 🧹 wip dynamic multitype ([71bfdd6](https://github.com/melMass/comfy_mtb/commit/71bfdd61d731ce15f9bd0bb19d65b5af208d5dcf))
+- 🧹 applied some linting ([fe49312](https://github.com/melMass/comfy_mtb/commit/fe49312cbef03c6540304448fa88aa7a88391efa))
+- 📝 header links not parsed ([514c0d2](https://github.com/melMass/comfy_mtb/commit/514c0d2eda9990435eb18258d4bbd1aa137feb3d))
+- 📝 hardcode links in changelog ([915b744](https://github.com/melMass/comfy_mtb/commit/915b7444a9db83f349d83b636304af0d276f529f))
+- 🔖 local updates ([6c5e5d3](https://github.com/melMass/comfy_mtb/commit/6c5e5d36379bdab223b4503e42b7956b55a82ab0))
+- 📝 update node list ([dd27f99](https://github.com/melMass/comfy_mtb/commit/dd27f990c72fa94aff205eb314a8ea360f57479e))
+- ✨ update node_list ([537a0d8](https://github.com/melMass/comfy_mtb/commit/537a0d8108d0caa3ab2daeafd1d25d680214ef26))
+- ✨ local stuff ([9afad1a](https://github.com/melMass/comfy_mtb/commit/9afad1a1680073006d946be10f8c97b75ddfe253))
+- 📝 fix update issue template ([da290db](https://github.com/melMass/comfy_mtb/commit/da290dbcf2952a56be9334f7bf9dc4d8fa64a21d))
+- 📝 update issue template ([b949bb4](https://github.com/melMass/comfy_mtb/commit/b949bb406bc1929634600465ea389eaedefe6e6f))
+
+### Refactor
+
+- ⚡️ small local fixes ([bcac665](https://github.com/melMass/comfy_mtb/commit/bcac66508d2e788cc437da289d1ccede19465b8c))
+- 🗑️ remove unused code in install script ([5b75436](https://github.com/melMass/comfy_mtb/commit/5b75436610c6312adf47c6baa3e9fe9cc7d56dcf))
+
+### Merge
+
+- 🔀 pull request #109 from melMass/dev/0.2.0 ([87e301d](https://github.com/melMass/comfy_mtb/commit/87e301d120a542d5aabe544bec10d38dbd19b2f6)) in [#109](https://github.com/melMass/comfy_mtb/pull/109)
+- 🔀 pull request #86 from melMass/feature/styles-editor ([cbdb816](https://github.com/melMass/comfy_mtb/commit/cbdb816164900061ddaa1671f4287763d0b79ee1)) in [#86](https://github.com/melMass/comfy_mtb/pull/86)
+
+### Wip
+
+- 🚧 curve widget logic fixed ([e312b02](https://github.com/melMass/comfy_mtb/commit/e312b02ad2f8334e87654a20b0114837df229371))
+- 🚧 dump3 ([eedbb4b](https://github.com/melMass/comfy_mtb/commit/eedbb4bc6581bef85c746307fe9d53360ea45bcf))
+- 🚧 dump ([fa23975](https://github.com/melMass/comfy_mtb/commit/fa2397585fff4f54bcf17f0b0e0083c427b34fa8))
+- 🚧 dump ([0d0fb8e](https://github.com/melMass/comfy_mtb/commit/0d0fb8e13a5da54a44a96a04607f7a349f8fdb03))
+- 🚧 add text template node ([af2175a](https://github.com/melMass/comfy_mtb/commit/af2175a1fc0c2fb29ef3493f242fe45ec6fcabac))
+
+## New Contributors
+* [@haohaocreates](https://github.com/haohaocreates) made their first contribution in [#182](https://github.com/melMass/comfy_mtb/pull/182)
+* [@vxkj1211](https://github.com/vxkj1211) made their first contribution in [#177](https://github.com/melMass/comfy_mtb/pull/177)
+* [@huanggou666](https://github.com/huanggou666) made their first contribution in [#159](https://github.com/melMass/comfy_mtb/pull/159)
+* [@hongminpark](https://github.com/hongminpark) made their first contribution in [#154](https://github.com/melMass/comfy_mtb/pull/154)
+* [@ScottNealon](https://github.com/ScottNealon) made their first contribution in [#147](https://github.com/melMass/comfy_mtb/pull/147)
+* [@Yurchikian](https://github.com/Yurchikian) made their first contribution in [#124](https://github.com/melMass/comfy_mtb/pull/124)
+* [@M1kep](https://github.com/M1kep) made their first contribution in [#91](https://github.com/melMass/comfy_mtb/pull/91)
+## [0.1.4] - 2023-08-12
+
+### Bug Fixes
+
+- 🚀 pending fixes ([ea5d73d](https://github.com/melMass/comfy_mtb/commit/ea5d73d48cfa4046f48a52609cff7f754d8364ed))
+- 🚑️ image resize infinite loop ([30d6cfe](https://github.com/melMass/comfy_mtb/commit/30d6cfe81292d0f7702544b3c2cbad1820c4a926))
+- ✨ update example files ([610afe0](https://github.com/melMass/comfy_mtb/commit/610afe031f21d737b2fd5128e4be7100b6666181))
+- 🐛 simplify install steps ([4fc84d6](https://github.com/melMass/comfy_mtb/commit/4fc84d615dd0f546442c3537f00c52366db4ca9b))
+- ✨ refactor ([8523392](https://github.com/melMass/comfy_mtb/commit/8523392df74c586dc940841ddbb5069943b16f7d))
+- 🐛 debug rgba ([40560f8](https://github.com/melMass/comfy_mtb/commit/40560f8154d3ddeabf708be4d111370648d466ac))
+- 🎨 rename fun to generate ([e7f72f9](https://github.com/melMass/comfy_mtb/commit/e7f72f9825da58254e3084b4ba91f76e6cf2cf5f))
+- ✨ refactor existing ([1144466](https://github.com/melMass/comfy_mtb/commit/11444662b9198861b62aff06a08b9c9ea01dd8bd))
+- ⚡️ move getbatchfromhistory to graphutils ([2eccba4](https://github.com/melMass/comfy_mtb/commit/2eccba4e33b21d1d080cb2f415f76a93488120f0))
+- 🚧 wip dependency installer UI ([630b492](https://github.com/melMass/comfy_mtb/commit/630b492347f75d7308b31a000061b41d7dfa4a10))
+- 🐛 image feed zorder ([0fb2d4d](https://github.com/melMass/comfy_mtb/commit/0fb2d4da90a7e65f82b3f9c8942a68e360456cf7))
+- ⬇️ download_antelopev2 ([4dd5321](https://github.com/melMass/comfy_mtb/commit/4dd532185223a1fa5978446e7bb75d32d77ebdb5))
+- 🚑️ frontend pushed too early ([91f60d4](https://github.com/melMass/comfy_mtb/commit/91f60d4c463c474ac10e868e8e73e13fa019856b))
+- 🚑️ missing input ([84ac8ac](https://github.com/melMass/comfy_mtb/commit/84ac8ac852aeb962029bfd8369fe5ed59a203977))
+- 🐛 shell command bug ([3d5075f](https://github.com/melMass/comfy_mtb/commit/3d5075fea2e219a179271c9810017c7e38bff6cc))
+- 🚑️ remove pipe mode from the install.py ([b854a30](https://github.com/melMass/comfy_mtb/commit/b854a302ce4708d2ad2dac249860308dbdcae5a6))
+- ⚡️ colab install ([36d8e6b](https://github.com/melMass/comfy_mtb/commit/36d8e6bdb06edab72ccfb686266d2e644a9f028c))
+- 🚑️ install typo ([ffa1a87](https://github.com/melMass/comfy_mtb/commit/ffa1a87b9184df5a3699a6118714b39d359bde4d))
+
+### Documentation
+
+- 📝 link the actual action instead of badge ([098d74a](https://github.com/melMass/comfy_mtb/commit/098d74a3cd8449d836569a074995e20d775c6728))
+- 📝 add action badge ([e74314b](https://github.com/melMass/comfy_mtb/commit/e74314b04eb218c140482ccf704b61af06db3f4d))
+
+### Features
+
+- 💫 export to prores -> export with ffmpeg ([a4d99d9](https://github.com/melMass/comfy_mtb/commit/a4d99d966b1207191243a9749385b998d1a9c6b1))
+- 🔥 add any to string & refactor ([dbdb872](https://github.com/melMass/comfy_mtb/commit/dbdb872b74e18c16feb44bd037abc3aafbb4700f))
+- ✨ add UI for interpolate clip sequential ([5ec5511](https://github.com/melMass/comfy_mtb/commit/5ec551143302b2a94ca82e477f684ecee23f1459))
+- ✨ add portable reqs ([3f14b16](https://github.com/melMass/comfy_mtb/commit/3f14b1676d28f5ffa1f47fda00b9bc244951045c))
+- ✨ add border extension ([fb64484](https://github.com/melMass/comfy_mtb/commit/fb644847ca434123e8e8e4991d33949fd31e3cbe))
+- ✨ use PIL for gif saving ([2bc7ae8](https://github.com/melMass/comfy_mtb/commit/2bc7ae88bf4cdfa575d11233c0e6f7b07f9dfd23))
+- 🎨 update node list ([a54d7d5](https://github.com/melMass/comfy_mtb/commit/a54d7d5346c272898dd4e67c65495de7325ab3a0))
+- ✨ install fix ([512de60](https://github.com/melMass/comfy_mtb/commit/512de6023e55f2cc47516bf44436efe22157273f)) in [#41](https://github.com/melMass/comfy_mtb/pull/41)
+
+### Miscellaneous Tasks
+
+- 💄 encoding ([49c64c7](https://github.com/melMass/comfy_mtb/commit/49c64c74eb3e99f456b563bbd79e3fe47a85c70d))
+- 🚀 only fetch controlnet_preprocessor deps ([414beb9](https://github.com/melMass/comfy_mtb/commit/414beb99a1f9bf719eca6ac139c9b2ccdfd6d743))
+- 🚀 add controlnetpreprocessors to tests ([63b3aec](https://github.com/melMass/comfy_mtb/commit/63b3aece2ba05adc2b655afeb41e3d47e7887b33))
+- ✨ remove unused input ([d4f791d](https://github.com/melMass/comfy_mtb/commit/d4f791d7a14ba9cb8abd7c95ba70b081fee5fb7c))
+- ✨ use the same cwd as manager ([2ff0467](https://github.com/melMass/comfy_mtb/commit/2ff04672daff773d52e1552dca1bf616bc32daa6))
+- 🎨 no brace glob ([bbfcb62](https://github.com/melMass/comfy_mtb/commit/bbfcb62c398de39058bcb6e18161425059d53e8e))
+- 🎨 extract txt ([a22fd01](https://github.com/melMass/comfy_mtb/commit/a22fd01d664276e4cd833ae1326feeece1d1deaf))
+- 🎨 also push wheels_order to releases ([8e5b776](https://github.com/melMass/comfy_mtb/commit/8e5b7765cc0c6730bd5517ccfd56e817ea39bd3a))
+- 🚧 more info for bug reports ([3dadc11](https://github.com/melMass/comfy_mtb/commit/3dadc119f44fca1029ec4b349d71ce99fb20a4b6))
+- ✨ individual wheels ([346ff64](https://github.com/melMass/comfy_mtb/commit/346ff649d50c9f0286ad2243938406fefb62853b))
+
+### Refactor
+
+- 🚧 tidy ([4f30829](https://github.com/melMass/comfy_mtb/commit/4f30829e06c41b3685644bfe7bece07e0bcfb70e))
+- ♻️ get batch from history ([13d255a](https://github.com/melMass/comfy_mtb/commit/13d255a730b08c4903647875350b9b3dcd61b4a6))
+
+### Revert
+
+- 💄 use BOOLEAN instead of BOOL ([cfb3b23](https://github.com/melMass/comfy_mtb/commit/cfb3b237cf64b512414a17f71e6d89c3355aa8ef))
+
+### Testing
+
+- 🧪 remove sha input ([c5bbe83](https://github.com/melMass/comfy_mtb/commit/c5bbe83008bb194cbd6ad5e3dc70cb3850b18985))
+- 🧪 ci for comfy embedded ([7b3afca](https://github.com/melMass/comfy_mtb/commit/7b3afca8179760e35e8a6fbf742080dee13e4fc7))
+
+### Merge
+
+- 🔀 pull request #50 from melMass/dev/august-refactor ([2ecd470](https://github.com/melMass/comfy_mtb/commit/2ecd4700d77c0727e6b5d2124e0a6ebd48ec96ed)) in [#50](https://github.com/melMass/comfy_mtb/pull/50)
+
+## [0.1.3] - 2023-07-29
+
+### Bug Fixes
+
+- 🔥 manage pip from install only, remove requirements.txt ([247fbfb](https://github.com/melMass/comfy_mtb/commit/247fbfbc216b8259d607e0699d5b990b6a06ca71)) in [#38](https://github.com/melMass/comfy_mtb/pull/38)
+- 🎨 use image ratio for imagefeed ([f5cd56c](https://github.com/melMass/comfy_mtb/commit/f5cd56ce861c8c0a931744ae6cf2b96e9c8bca06))
+
+### Documentation
+
+- 📝 update imagefeed preview ([cbcacbe](https://github.com/melMass/comfy_mtb/commit/cbcacbe3c92ebb5f74d046b83504c3723710f130))
+- 📝 fix typo and add more details ([7c020ba](https://github.com/melMass/comfy_mtb/commit/7c020bab288aa7d17dc937b5f102319d43c3ebb3))
+
+### Miscellaneous Tasks
+
+- ✨ use wheel order if present ([9b24edd](https://github.com/melMass/comfy_mtb/commit/9b24eddd9c51004af08d7ac6ff2b6473dd3ee161))
+- ✨ store order of install for wheels ([5053142](https://github.com/melMass/comfy_mtb/commit/505314294f02e7c19ac95e4d0ed37fd397a54b46))
+
+## [0.1.2] - 2023-07-28
+
+### Bug Fixes
+
+- ✨ various small things ([0e311cf](https://github.com/melMass/comfy_mtb/commit/0e311cf2c64cf2b4861d4cc612a3409390e3039a))
+- 📝 last release ([889f08c](https://github.com/melMass/comfy_mtb/commit/889f08c08b721be8fdb4e4d7eacc47169d5692d6)) in [#36](https://github.com/melMass/comfy_mtb/pull/36)
+- 📝 narrow requirements ([5d661b2](https://github.com/melMass/comfy_mtb/commit/5d661b2509fecf3940c3c0fab25b16ec0eae7a2d))
+- ✨ Separate FaceAnalysis model loading ([d143e83](https://github.com/melMass/comfy_mtb/commit/d143e83dba3bffa16e1b98d7ad1e9cf92dc94db2))
+- ⚡️ update examples to match wiki ([3dfe98c](https://github.com/melMass/comfy_mtb/commit/3dfe98c7957df48723380de85e1242a424ec23de))
+
+### Documentation
+
+- 📝 add readme for web extensions features ([be162a2](https://github.com/melMass/comfy_mtb/commit/be162a20477258627fa0d742c97a478bd085ff4f))
+- 📝 link to the proper lang instructions ([232cf89](https://github.com/melMass/comfy_mtb/commit/232cf8966cc20291b60c68f487dfd37bf6aa4dfa)) in [#33](https://github.com/melMass/comfy_mtb/pull/33)
+- 📝 update readmes ([96a0618](https://github.com/melMass/comfy_mtb/commit/96a0618c5990a8559a9e2dd17c868d3465b8ca90))
+
+### Miscellaneous Tasks
+
+- 🎉 bump version ([9e751a2](https://github.com/melMass/comfy_mtb/commit/9e751a242f4e9afee3dc5c871c414b29b9706ff6))
+- 👷 remove stale example ([c237737](https://github.com/melMass/comfy_mtb/commit/c2377374201fc34b107c8b7db1cdeb2f483d1e18))
+- 🐛 fix size ([c0cc557](https://github.com/melMass/comfy_mtb/commit/c0cc5572d8c727568eca8a3d0f116a1f540c31ff))
+
+## [0.1.1] - 2023-07-24
+
+### Bug Fixes
+
+- 🎨 improve a bit the HTML response of endpoints ([50d51c7](https://github.com/melMass/comfy_mtb/commit/50d51c70d04e49e9df524975c171288c0fc0b20f))
+- 🐛 caching issues ([55c9736](https://github.com/melMass/comfy_mtb/commit/55c9736a9b2ca036926be4b06406121bfb9ebad2))
+- 🔥 remove notice ([abf1e82](https://github.com/melMass/comfy_mtb/commit/abf1e82adb9fac8cd70d5c409baad55309ef6fe1))
+- 🔥 use BOOL everywhere ([a393793](https://github.com/melMass/comfy_mtb/commit/a393793cfa93721eac46295723076a1dda940dcd))
+
+### Documentation
+
+- 📝 added lang links ([bbdac97](https://github.com/melMass/comfy_mtb/commit/bbdac97e49af4e90d22eeec3f63b96ecc126ffcf))
+- 📝 add comfyforum example ([10d0503](https://github.com/melMass/comfy_mtb/commit/10d05031b1791ab3534cf838be6eb75df638dfb6))
+
+### Features
+
+- 🚧 jupyter seems to require an __init__ there ([9a4eda3](https://github.com/melMass/comfy_mtb/commit/9a4eda3ef573bf382c13515f67ae8a415bf61abd))
+- ⚡️ use notify ([a2ecc11](https://github.com/melMass/comfy_mtb/commit/a2ecc11ebde79c2403959bf09c258f3a2465894a))
+- ✨ first version of Notify ([7e9c97e](https://github.com/melMass/comfy_mtb/commit/7e9c97ecb48672b25e5ed17b9b35dba9208ac311))
+- ⚡️ add an "actions" endpoint ([3de160a](https://github.com/melMass/comfy_mtb/commit/3de160af25b516c02aaa8cc32baec16e9ef358fb))
+- ✨ add Unsplash Image node ([8d3cc39](https://github.com/melMass/comfy_mtb/commit/8d3cc39b72dff1b5eb61bf7e2e395753c138ec8a))
+- ✨ add back Save Tensors ([7142b28](https://github.com/melMass/comfy_mtb/commit/7142b284adc7fba9a1bdafd1a52621bfc168bde1))
+- ✨ add TransformImage node ([11128ff](https://github.com/melMass/comfy_mtb/commit/11128ff85a7e0b4a54f405548969c2478da26df6))
+
+### Miscellaneous Tasks
+
+- 🚀 bump version ([cf86552](https://github.com/melMass/comfy_mtb/commit/cf865529ab64b350cd7af964b41160e7d130d12d))
+- 🚀 Remove large files from release ([3b9190a](https://github.com/melMass/comfy_mtb/commit/3b9190a69b002b8933c097fd6655bb4fe07264d2))
+
+### Refactor
+
+- ✨ cleaned up frontend code a bit ([3801a44](https://github.com/melMass/comfy_mtb/commit/3801a443bc1e89c70fdb35ce0b1724d86fa22928))
+- ⚡️ remove empty inits ([21729b2](https://github.com/melMass/comfy_mtb/commit/21729b2784a50fcaf24a63ac283bdae475a53ce7))
+
+### Merge
+
+- 🔀 pull request #32 from melMass/dev/next ([8695cd3](https://github.com/melMass/comfy_mtb/commit/8695cd3f1b6d27b5cd6c616ed1215ea2f25c5304)) in [#32](https://github.com/melMass/comfy_mtb/pull/32)
+
+## [0.1.0] - 2023-07-22
+
+### Bug Fixes
+
+- 🔥 properly match built wheels ([119b4d6](https://github.com/melMass/comfy_mtb/commit/119b4d6e16c2a90db1664ccaac748507feb73ea0)) in [#30](https://github.com/melMass/comfy_mtb/pull/30)
+- ✨ also try to copy web if symlink fails ([0df55de](https://github.com/melMass/comfy_mtb/commit/0df55def29fb992751010f6b8a707699f230ff37))
+- ✨ install process tested in comfy-manager (embed, colab) ([b40730d](https://github.com/melMass/comfy_mtb/commit/b40730ddbc3f8e3e7d5a17e9e9e4526ff37977fd))
+- 🚀 try to support remote install too ([3c66de2](https://github.com/melMass/comfy_mtb/commit/3c66de2500a89efd2d2e3af88fc58429af725789))
+- 💄 save gif issues ([7335003](https://github.com/melMass/comfy_mtb/commit/7335003346e83666c5dee631b8e6b15586d871e7))
+- 🚑️ always use latest for now ([fccf313](https://github.com/melMass/comfy_mtb/commit/fccf31348994ab6e344a1ab00a8f9998309f9319))
+- 🐛 install logic ([7e301e2](https://github.com/melMass/comfy_mtb/commit/7e301e2a067d41cba9b8ef357496dd1df94e4cdd))
+- 🎉 remove tests & add missing docs ([4e6b877](https://github.com/melMass/comfy_mtb/commit/4e6b87719989aa144946c5c9a43b9398c20bf11e))
+- ⚡️ update node_list ([c794d6a](https://github.com/melMass/comfy_mtb/commit/c794d6a071778220d654b526d2edfddcc79752fc))
+- 🚑️ set debug level from endpoint ([18402e3](https://github.com/melMass/comfy_mtb/commit/18402e3be1ab47e10109cfd2dff18863a1ee56f7))
+- 🐛 add base64 prefix to outputs ([0950f99](https://github.com/melMass/comfy_mtb/commit/0950f9914c9bbed7c89f3de33a967cb76f9d0bbb))
+- 🎨 refactor and add Gif preview on node ([c2e8379](https://github.com/melMass/comfy_mtb/commit/c2e83794faeb8da708c98908882e38b2a42827bd))
+- ✨ Various widgets issues ([27500ca](https://github.com/melMass/comfy_mtb/commit/27500ca432d686774b991045b7cffc58c0b67faf))
+- 🔥 deprecate some nodes and fix image list ([9aa934f](https://github.com/melMass/comfy_mtb/commit/9aa934f70ff6adf91efb26aa8e5cb21ec575196a))
+- 🐛 crop nodes ([67d3783](https://github.com/melMass/comfy_mtb/commit/67d3783ac9186da6bba4b7dc7e8dc3d5db5a1b0f))
+- 🐛 tensor2pil ([8a59508](https://github.com/melMass/comfy_mtb/commit/8a59508ff91d6b2d9ca287ef1c054ec5755337a4))
+- ⚡️ a few missing __doc__ ([ab09cca](https://github.com/melMass/comfy_mtb/commit/ab09ccadd905bebbf1b7b2d992e96e36fc60d68a))
+- ⚡️ from tensor2np always returning a list ([6168b3a](https://github.com/melMass/comfy_mtb/commit/6168b3a2ac38b5eebed3daf9e52df5742abf6813))
+- 🚑️ TF by default fills vram ([c225da5](https://github.com/melMass/comfy_mtb/commit/c225da5f298acb4cb2b39022382543c0c966d428))
+- ✨ leftovers ([da3e6f4](https://github.com/melMass/comfy_mtb/commit/da3e6f47c6073e73cf9d3a3cd23ba5ccbe1fedce))
+- ✨ handle non fork gdown in model dll ([95797e8](https://github.com/melMass/comfy_mtb/commit/95797e823e12e62ae8758753f60c34afbe19ec90))
+- ✨ properly add the submodules ([00510ed](https://github.com/melMass/comfy_mtb/commit/00510ed0b8583dd64518daa67d963582f4f029d3))
+- 📌 remove sad talker for now ([1622cbc](https://github.com/melMass/comfy_mtb/commit/1622cbcb9d51ddd0e1a8b4d87ba47b99327163eb))
+- 🎨 narrow requirements ([1a92ef7](https://github.com/melMass/comfy_mtb/commit/1a92ef734dd4271efc875856038f9e3b6b9ded6c))
+- 🚀 use the comfy util to handle graph interruption ([9752f3e](https://github.com/melMass/comfy_mtb/commit/9752f3e9dec9aa59cfa809aa14f0151594c03858))
+- 🔥 much faster (using GPU) on windows ([2f455aa](https://github.com/melMass/comfy_mtb/commit/2f455aaca55c0a044735c768b295d077b2f5b8d6))
+- 🐛 uint8 to uint16 ([be5a655](https://github.com/melMass/comfy_mtb/commit/be5a655cfaba1794f7b09c82d65de42e2b031720))
+- ✨ add missing requirements ([b779bc3](https://github.com/melMass/comfy_mtb/commit/b779bc39ac19f779aeb73c98b916671d1d16806f))
+- 📝 don't propagate base logs ([7fd99c2](https://github.com/melMass/comfy_mtb/commit/7fd99c25c4e50566def5c5166a9d9059b1febfa6))
+- 🐛 bg upscaler in gfpgan ([fee48ad](https://github.com/melMass/comfy_mtb/commit/fee48adff3d66960cb17836f3f4efbfd0c8740c4))
+- 📝 separate debug / info better ([e24863d](https://github.com/melMass/comfy_mtb/commit/e24863d1f9f63f367a2b392e6228ffa42927b71b))
+- 🔥 change log level of the base logger ([7538c2c](https://github.com/melMass/comfy_mtb/commit/7538c2c4bad8390a32226dc0a5a6ef978b00d201))
+- ✨ handle externs dynamicly ([6ef308a](https://github.com/melMass/comfy_mtb/commit/6ef308a87062c91e2d7249c05d96c6fb76e5a6c4))
+- 🐛 separate faceswap model load ([8e267c0](https://github.com/melMass/comfy_mtb/commit/8e267c0204ce5abe8e113fd401234d49f377646a))
+
+### Documentation
+
+- 📝 fold each comfy mode ([3c3c438](https://github.com/melMass/comfy_mtb/commit/3c3c4380bd1a3f0eed5216b835e076c26fce2f88))
+- 📝 add more description to examples ([46eab5c](https://github.com/melMass/comfy_mtb/commit/46eab5ca2f0e04d872d87c849b11551fd219bdb9))
+- 📝 add model notice ([cbe67ed](https://github.com/melMass/comfy_mtb/commit/cbe67edd4befb7260be01fa09af8448e5bcf5680))
+- 📝 add preview for examples ([b5176ca](https://github.com/melMass/comfy_mtb/commit/b5176ca0ee489ada52b6632f68b794b4f709d5ba))
+- 📝 add jp and cn (using deep translation) ([da559b9](https://github.com/melMass/comfy_mtb/commit/da559b9eaf135a49c0ab9bfa45573baf0c18dfb2))
+- 📝 update readme ([b0fb522](https://github.com/melMass/comfy_mtb/commit/b0fb5222cb19e4004533d3367863be5c9ce8e72b)) in [#15](https://github.com/melMass/comfy_mtb/pull/15)
+- 📝 update README.md ([f8dc768](https://github.com/melMass/comfy_mtb/commit/f8dc768635a2d21f6ff81b42c418724c432159bf))
+- 📝 updated instructions ([c3b9fd4](https://github.com/melMass/comfy_mtb/commit/c3b9fd4afedbb46748aef17b40e167a4cfad65f5))
+
+### Features
+
+- ✨ update install instructions ([7be37db](https://github.com/melMass/comfy_mtb/commit/7be37dbbfac45e8038f94ced8a2fa8ec2b06fb34))
+- 🚀 add install script ([dad3966](https://github.com/melMass/comfy_mtb/commit/dad3966ba219c1998e4fc7f6e641864fb0e7c3e8))
+- 🚧 add my CLIs ([44eaae5](https://github.com/melMass/comfy_mtb/commit/44eaae5c79f4dbec344053d945e7275be5c3c0a5))
+- ✨comfy_widget shared utils ([91bb95d](https://github.com/melMass/comfy_mtb/commit/91bb95da914468de533b040324700c7f9707e4fb))
+- 🚀 debug node ([b27b8ef](https://github.com/melMass/comfy_mtb/commit/b27b8ef91fe7335b1df3766547edaf4b9625ae4d))
+- ✨ add FitNumber node ([aa551eb](https://github.com/melMass/comfy_mtb/commit/aa551ebe57801c69010815119fe21e19a858780c))
+- 🔥 add API endpoints ([95afbdb](https://github.com/melMass/comfy_mtb/commit/95afbdbf76e66897e632252d876384ada9acf153))
+- ✨ categorize ([d2b3962](https://github.com/melMass/comfy_mtb/commit/d2b396236a10fe620ebebabd5a22c36159921913))
+- 🚀 add a few examples ([b9c1d3d](https://github.com/melMass/comfy_mtb/commit/b9c1d3df7a1460fe9ffa84f6f9ea0cfb5409de1a))
+- ✨ added a way to export the node list ([5f5297f](https://github.com/melMass/comfy_mtb/commit/5f5297f80debc77f3fda2f0d37b3acff8419140d))
+- ✨ WIP batch from history ([cde7293](https://github.com/melMass/comfy_mtb/commit/cde72938d5ffd09179f5974676e12d4599a8d6ff))
+- ✨ extract node names using ast ([38f6147](https://github.com/melMass/comfy_mtb/commit/38f61473bc23b4c5d4efc5048d54c059565a6fa0))
+- 🔥 add batch support for load image sequence ([3faadc4](https://github.com/melMass/comfy_mtb/commit/3faadc4b8a5049cb8c264b8a3d50565adec405f1))
+- 🎨 add support for image.size(0) == 0 ([629e2b5](https://github.com/melMass/comfy_mtb/commit/629e2b5f5fbebe4e79e8b7a4cff2de6017e79225))
+- ✨ image feed ([99eb5ae](https://github.com/melMass/comfy_mtb/commit/99eb5ae0c7413f6ab1f24cfc8337c9b1b2d9824c))
+- ✨ FILM interpolation nodes ([e04e77e](https://github.com/melMass/comfy_mtb/commit/e04e77eb097735ec1369dec51238cdcc5abe39b7))
+- ✨ add an headless option for model downloads ([217e8a1](https://github.com/melMass/comfy_mtb/commit/217e8a1546d06b97250d99612ac6bdb5ce89e155))
+- 🐛 support batch count > 1 for restore face ([8ef48a0](https://github.com/melMass/comfy_mtb/commit/8ef48a013a8d6b832b8c0c7dcabc1b78c27ff207))
+- 🚧 wrapper for GFPGAN bg upscaler ([88cdcc6](https://github.com/melMass/comfy_mtb/commit/88cdcc6a87dae452924e8915eccdadc69d7d136e))
+- ✨ add GFPGAN (FaceRestore) ([3a6e545](https://github.com/melMass/comfy_mtb/commit/3a6e5450502f3b1d7c505178fc9ba337cd95c39e))
+
+### Miscellaneous Tasks
+
+- ✨ before categorize ([0cc54e5](https://github.com/melMass/comfy_mtb/commit/0cc54e58ec86c28354cae37e14f39e831c13ea02))
+- ✨ add more issue templates ([710a638](https://github.com/melMass/comfy_mtb/commit/710a638a8187ef08254478f684307dccdebcded2)) in [#25](https://github.com/melMass/comfy_mtb/pull/25)
+- ✨ add bug report template ([f927bc7](https://github.com/melMass/comfy_mtb/commit/f927bc7c9a82951e6df4763433732f20ea87e9cb))
+- 🍻 create FUNDING.yml ([f634fe0](https://github.com/melMass/comfy_mtb/commit/f634fe0e6b2db28138e4bd7932fbfc8606a0f033))
+- 🍻 add bmc to readme ([cd1b603](https://github.com/melMass/comfy_mtb/commit/cd1b603565464fe98a718e1fbaa8c7cd84057576))
+- 📝 extra files from another branch ([b78be8f](https://github.com/melMass/comfy_mtb/commit/b78be8fd3cd36666fd94a3ab08eca11cce526043))
+- 🚀 push leftovers ([4c41fe7](https://github.com/melMass/comfy_mtb/commit/4c41fe7af9f8e16d895eb06223349e1294dd4698))
+
+### Refactor
+
+- ♻️ removes a few nodes, moved other around ([4d8ddac](https://github.com/melMass/comfy_mtb/commit/4d8ddaca320ce483640d030618e70730b3453df2))
+- ♻️ remove test ([68c250e](https://github.com/melMass/comfy_mtb/commit/68c250e890dacae9f627b0d266ad6dcab0fa0c8b))
+- 🚧 remove color_widget ([e480d07](https://github.com/melMass/comfy_mtb/commit/e480d071171cffa789930620f1e7ccc76473bf93))
+
+### Testing
+
+- 🔧 pipe detection ([ee17d57](https://github.com/melMass/comfy_mtb/commit/ee17d57c3d6d71fda1a5acc2cf85f936c525bc87))
+
+### Install
+
+- 🚧 handle symlink errors ([d982b69](https://github.com/melMass/comfy_mtb/commit/d982b69a58c05ccead9c49370764beaa4549992a))
+
+### Merge
+
+- 🔀 pull request #22 from melMass/dev/next-release ([c34de0a](https://github.com/melMass/comfy_mtb/commit/c34de0ab351b2c95d7fa4fab4487155bee6bfa3a)) in [#22](https://github.com/melMass/comfy_mtb/pull/22)
+- 🎉 pull request #11 from dev/frame_interpolation ([1e28606](https://github.com/melMass/comfy_mtb/commit/1e28606427bcc8d895b87eaa6cd4147ab6d9a11f)) in [#11](https://github.com/melMass/comfy_mtb/pull/11)
+- 🎉 pull request #8 from dev/small-fixes ([7585624](https://github.com/melMass/comfy_mtb/commit/7585624de5895eb34c6a520d4dab18b47e64b6ca)) in [#8](https://github.com/melMass/comfy_mtb/pull/8)
+
+## [0.0.1] - 2023-06-28
+
+### Bug Fixes
+
+- 🤦 add missing file ([e2c4561](https://github.com/melMass/comfy_mtb/commit/e2c456147c260b4e9d583662e3bb9d6d9a019a5e))
+- ✨ small edits ([bcf55ca](https://github.com/melMass/comfy_mtb/commit/bcf55ca9a3a07067be3319182501f7b635e5d2ba))
+- ⚡️ add support for batch in roop ([2dae020](https://github.com/melMass/comfy_mtb/commit/2dae02056a11ddfe1f84ee040818028177e404b5))
+- 🔥 various preparing for the first tag ([793784a](https://github.com/melMass/comfy_mtb/commit/793784a5fd08e8a70d670fc8edbc3bb5b6e13e67))
+- 🐛 various bugs ([afd0843](https://github.com/melMass/comfy_mtb/commit/afd08431458e3bbb14a25c84a87408113edf5db5))
+- ⚡️ add missing controls to QRCode ([7e86b0e](https://github.com/melMass/comfy_mtb/commit/7e86b0ed4d300021517f6c5cf28a45012497b5c5))
+
+### Documentation
+
+- 📝 add rembg screenshot ([9a2d523](https://github.com/melMass/comfy_mtb/commit/9a2d52325f87ecf6342ef4897da919006755b9db))
+- 📝 add a few screenshots ([e162336](https://github.com/melMass/comfy_mtb/commit/e162336cd366d39cd4b96f05b3c9c68eecec3dc4))
+- 📝 update readme ([7f3070d](https://github.com/melMass/comfy_mtb/commit/7f3070debbc3330da50ff845621ce299894cf862))
+
+### Features
+
+- 💄 faceswap node using roop ([966a14b](https://github.com/melMass/comfy_mtb/commit/966a14b40d88f4fccfb2eaa5ff9b222f0eedd7cb))
+- ✨ sync local changes ([647bf9e](https://github.com/melMass/comfy_mtb/commit/647bf9e94195c279a620c74c2253471b9c4b90f7))
+- ✨ bbox from alpha ([37abf8a](https://github.com/melMass/comfy_mtb/commit/37abf8aad12f4711c6d82c6be4be6fa3578e7af5))
+- ✨ a111 like style loader ([f59b68e](https://github.com/melMass/comfy_mtb/commit/f59b68e3ad92841a4d189d8dddf7b41e915c9b4e))
+- ✨ add a color type and widget ([9a2e986](https://github.com/melMass/comfy_mtb/commit/9a2e986327c34227a707beab6d9929b0a05e41e6))
+- ✨ add a few nodes ([811443b](https://github.com/melMass/comfy_mtb/commit/811443b92161815db1cdff81898e8834dcd6fbfa))
+- ✨ add SadTalker as a submodule ([3fb8716](https://github.com/melMass/comfy_mtb/commit/3fb871651b12bce62d8e911bd3884f417f80c937))
+- 🚨 push local changes ([6cac344](https://github.com/melMass/comfy_mtb/commit/6cac344f6fb15ebb902acee70ee71edc585ec4bc))
+- ⚡️ initial commit ([1ae3bbc](https://github.com/melMass/comfy_mtb/commit/1ae3bbc89ae6e0d2e8c61122485bd0df837e17c2))
+
+### Miscellaneous Tasks
+
+- 🚀 add gh action ([572b4d5](https://github.com/melMass/comfy_mtb/commit/572b4d52bce1398660d4d7ca0c5c48c11e0128e3)) in [#4](https://github.com/melMass/comfy_mtb/pull/4)
+
+[main]: https://github.com/melMass/comfy_mtb/compare/v0.2.0..main
+[0.2.0]: https://github.com/melMass/comfy_mtb/compare/v0.1.6..v0.2.0
+[0.1.6]: https://github.com/melMass/comfy_mtb/compare/v0.1.5..v0.1.6
+[0.1.5]: https://github.com/melMass/comfy_mtb/compare/v0.1.4..v0.1.5
+[0.1.4]: https://github.com/melMass/comfy_mtb/compare/v0.1.3..v0.1.4
+[0.1.3]: https://github.com/melMass/comfy_mtb/compare/v0.1.2..v0.1.3
+[0.1.2]: https://github.com/melMass/comfy_mtb/compare/v0.1.1..v0.1.2
+[0.1.1]: https://github.com/melMass/comfy_mtb/compare/v0.1.0..v0.1.1
+[0.1.0]: https://github.com/melMass/comfy_mtb/compare/v0.0.1..v0.1.0
+

CODE_OF_CONDUCT.md

@@ -0,0 +1,52 @@
+# Code of Conduct
+
+## Our Commitment
+
+We are committed to creating a welcoming and inclusive community for everyone. We believe that a diverse and respectful community is essential for fostering creativity and innovation. We expect all members of our community to adhere to this Code of Conduct.
+
+## Our Expectations
+
+This Code of Conduct applies to all interactions within the mtb community, including:
+
+*   Public communication channels (e.g., GitHub issues, pull requests, discussions, social media)
+*   Private communication channels (e.g., direct messages, email)
+*   In-person events (if any)
+
+We expect all members to:
+
+*   **Be respectful and considerate:** Treat others with kindness and empathy.
+*   **Be inclusive:** Welcome and respect people of all backgrounds, identities, and experiences.
+*   **Be constructive:** Focus on providing helpful and positive feedback.
+*   **Be mindful of your language:** Avoid using offensive, discriminatory, or harassing language.
+*   **Respect privacy:** Do not share personal information without consent.
+
+## Unacceptable Behavior
+
+The following behaviors are not tolerated:
+
+*   Offensive, discriminatory, or harassing language or conduct
+*   Personal attacks or insults
+*   Spamming or trolling
+*   Sharing of malicious or inappropriate content
+*   Disrupting the community or hindering collaboration
+*   Violating the privacy of others
+
+## Reporting Violations
+
+If you experience or witness a violation of this Code of Conduct, please report it to @melmass.  All reports will be treated confidentially and investigated promptly.
+
+## Enforcement
+
+Violations of this Code of Conduct may result in the following actions:
+
+*   Warning
+*   Removal from the community
+*   Ban from the community
+
+## License
+[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](code_of_conduct.md)
+
+## Contact
+
+If you have any questions or concerns about this Code of Conduct, please contact @melmass.
+

CONTRIBUTING.md

@@ -0,0 +1,62 @@
+# Contributing to mtb
+
+Thank you for your interest in contributing to mtb!  We appreciate your help in making this project better.  This document outlines how you can contribute to the project.
+
+## Project Overview
+
+This project is a collection of custom nodes for ComfyUI, tailored specifically for animation workflows. It aims to provide a streamlined and user-friendly experience for creating animations within the ComfyUI environment.
+
+## Ways to Contribute
+
+We welcome all kinds of contributions! Here's how you can get involved:
+
+*   **Bug Reports:**  If you encounter any issues, please create a new issue on GitHub.  Please include clear steps to reproduce the bug, along with any relevant error messages, workflows or screenshots.
+*   **Feature Requests:**  Have an idea for a new node or feature?  Create a new issue to discuss it!  Please describe the feature in detail, and explain how it would benefit the project.
+*   **Documentation Improvements:**  Help us improve the documentation by fixing errors, adding examples, or clarifying explanations.
+*   **Code Contributions:**  We welcome contributions to the codebase!  Please see the "Development Setup" and "File Structure" sections below for more information.
+*   **Testing:**  Help us ensure the stability and reliability of the project by testing new features and bug fixes.
+*   **Refactoring:**  Help us improve the codebase by refactoring existing code to improve readability, maintainability, and performance.
+
+## Development Setup
+
+```sh
+git clone --recursive https://github.com/melmass/comfy_mtb
+```
+
+## File Structure
+
+Understanding the project structure is crucial for making effective contributions.
+
+*   **`./nodes/*.py`:** This directory contains the definitions for all custom nodes. Nodes are automatically registered when a file defines an array named `__nodes__` containing the node classes.  Make sure your node follows the ComfyUI node definition structure.
+*   **`./web/*.js`:** This directory contains all the frontend JavaScript code for the extension's user interface.
+*   **`./wiki`:** This directory is a Git submodule that contains the project's Wiki documentation, written in Markdown.  Node documentation should be created or updated in the corresponding Markdown files within this submodule. This is then referenced by the UI for in-GUI help
+
+## Coding Style
+
+We use **Ruff** for code formatting to ensure consistency.  Please run Ruff on your code before submitting a pull request.  No specific configuration is required, so the default Ruff settings will be used.
+
+## Contribution Workflow
+
+1.  **Create a Branch:** Create a new branch for your feature or fix.  Use a descriptive branch name (e.g., `feature/new-node`, `fix/bug-in-ui`). **Do not fork the main branch directly.**
+2.  **Make Changes:** Implement your changes in your branch.
+3.  **Run Tests:** (Add instructions on how to run tests if available.)
+4.  **Format Code:** Run Ruff on your code to ensure it is properly formatted.
+5.  **Create a Pull Request:** Submit a pull request to the `main` branch.  Please provide a clear and concise description of your changes.
+
+## Code of Conduct
+
+We are committed to creating a welcoming and inclusive community.  We expect all contributors to adhere to a respectful and professional code of conduct. (Consider adding a link to a CODE_OF_CONDUCT.md file or a standard code of conduct.)
+
+## Tools and Libraries
+
+*   **Python:** The primary programming language for this project.
+*   **ComfyUI:** The underlying framework for the custom nodes.
+
+## Current Focus
+
+We are currently focused on a major refactor to clean up the project's codebase. Contributions related to this effort are particularly welcome!
+
+## Thank You!
+
+Thank you for considering contributing to mtb! Your contributions are greatly appreciated.  We look forward to reviewing your pull requests!
+

INSTALL.md

@@ -0,0 +1,85 @@
+# Installation
+- [Installation](#installation)
+  - [Automatic Install (Recommended)](#automatic-install-recommended)
+    - [ComfyUI Manager](#comfyui-manager)
+    - [Virtual Env](#virtual-env)
+  - [Models Download](#models-download)
+  - [Old installation method (MANUAL)](#old-installation-method-manual)
+    - [Dependencies](#dependencies)
+
+## Automatic Install (Recommended)
+
+### ComfyUI Manager
+
+As of version 0.1.0, this extension is meant to be installed with the [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager), which helps a lot with handling the various install issues faced by various environments.
+
+### Virtual Env
+There is also an experimental one liner install using the following command from ComfyUI's root. It will download the code, install the dependencies and run the install script:
+
+```bash
+curl -sSL "https://raw.githubusercontent.com/username/repo/main/install.py" | python3 -
+```
+
+## Models Download
+Some nodes require extra models to be downloaded, you can interactively do it using the same python environment as above:
+```bash
+python scripts/download_models.py
+```
+
+then follow the prompt or just press enter to download every models.
+
+> **Note**
+> You can use the following to download all models without prompt:
+  ```bash
+  python scripts/download_models.py -y
+  ```
+
+
+## Old installation method (MANUAL)
+### Dependencies
+<details><summary><h4>Custom Virtualenv (I use this mainly)</h4></summary>
+    
+1. Make sure you are in the Python environment you use for ComfyUI.
+2. Install the required dependencies by running the following command:
+  ```bash
+  pip install -r comfy_mtb/requirements.txt
+  ```
+
+</details>
+
+<details><summary><h4>Comfy-portable / standalone (from ComfyUI releases)</h4></summary>
+    
+If you use the `python-embeded` from ComfyUI standalone then you are not able to pip install dependencies with binaries when they don't have wheels, in this case check the last [release](https://github.com/melMass/comfy_mtb/releases) there is a bundle for linux and windows with prebuilt wheels (only the ones that require building from source), check [this issue (#1)](https://github.com/melMass/comfy_mtb/issues/1) for more info.
+![image](https://github.com/melMass/comfy_mtb/assets/7041726/2934fa14-3725-427c-8b9e-2b4f60ba1b7b)
+
+
+
+</details>
+
+<details><summary><h4>Google Colab</h4></summary>
+
+Add a new code cell just after the **Run ComfyUI with localtunnel (Recommended Way)** header (before the code cell)
+![preview of where to add it on colab](https://github.com/melMass/comfy_mtb/assets/7041726/35df2ef1-14f9-44cd-aa65-353829188cd7)
+
+
+```python
+# download the nodes
+!git clone --recursive https://github.com/melMass/comfy_mtb.git custom_nodes/comfy_mtb
+
+# download all models
+!python custom_nodes/comfy_mtb/scripts/download_models.py -y
+
+# install the dependencies
+!pip install -r custom_nodes/comfy_mtb/reqs.txt -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html
+```
+If after running this, colab complains about needing to restart runtime, do it, and then do not rerun earlier cells, just the one to run the localtunnel. (you might have to add a cell with `%cd ComfyUI` first...)
+
+
+> **Note**:
+> If you don't need all models, remove the `-y` as collab actually supports user input: ![image](https://github.com/melMass/comfy_mtb/assets/7041726/40fc3602-f1d4-432a-98fd-ce2240f5ad06)
+
+> **Preview**
+> ![image](https://github.com/melMass/comfy_mtb/assets/7041726/b5b2b2d9-f1e8-4c43-b1db-7dfc5e07be86)
+
+</details>
+

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Mel Massadian
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,59 +1,17 @@
-## MTB Nodes
+# MTB Nodes
 
-Feel free to do whatever you want with this codebase, I'm mainly using Comfy to build POCs to implement in [MLOPs](https://github.com/Bismuth-Consultancy-BV/MLOPs). And a lot of nodes are inspired by existing ones from the community or builtin 
-Just beware of the licenses of some libraries (deepbump for instance is [GPLv3](https://github.com/HugoTini/DeepBump/blob/master/LICENSE))
-
-## Install
-
-From within the python environment you already use for ComfyUI install the requirements.
-```bash
-pip install -r comfy_mtb/requirements.txt
-```
-
-## Screenshots
-
-- **FaceSwap [roop]** (using [roop](https://github.com/s0md3v/roop/))  
-  The face index allow you to choose which face to replace as you can see here:
-  ![ComfyUI_909](https://github.com/melMass/comfy_mtb/assets/7041726/2e9d6066-c466-4a01-bd6c-315f7f1e8b42)
-
-- **Style Loader**: A111 like csv styles in Comfy  
-  ![image](https://github.com/melMass/comfy_mtb/assets/7041726/02fe3211-18ee-4e54-a029-931388f5fde8)
-
-- **Color Correction**: basic color correction node  
-  ![image](https://github.com/melMass/comfy_mtb/assets/7041726/7c20ac83-31ff-40ea-a1a0-06c2acefb2ef)
-
-- **Image Remove Background [RemBG]**: (using [rembg](https://github.com/danielgatis/rembg))
-  ![image](https://github.com/melMass/comfy_mtb/assets/7041726/e69253b4-c03c-45e9-92b5-aa46fb887be8)
+> [!NOTE]
+> master/main is outdated for now to keep backward compatibility, the next version is being worked on in
+> [`dev/0.6.0`](https://github.com/melMass/comfy_mtb/tree/dev/0.6.0)
 
 
+[![embedded test](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml/badge.svg)](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml)
 
-### Node List
+![home](https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873)
 
-- `Latent Lerp`: Linear Interpolate between two latents,
-- `Int to Number`: Supplement for WASSuite number nodes,
-- `Bounding Box`: BBox constructor (custom type),
-- `Crop`: Crop image from BBox,
-- `Uncrop`: Uncrop image from BBox,
-- `ImageBlur`: Blur the input image,
-- `Denoise`: Denoise the input image,
-- `ImageCompare`: Compare image,
-- `RGB to HSV`: -,
-- `HSV to RGB`: -,
-- `Color Correct`: Basic color correction tools,
-- `Modulo`: Modulo (useful for loops),
-- `Deglaze Image`: taken from [FN16](https://github.com/Fannovel16/FN16-ComfyUI-nodes/blob/main/DeglazeImage.py),
-- `Smart Step`: A very basic node to get step percent to use in KSampler advanced,
+<!-- omit in toc -->
+<a href="https://www.buymeacoffee.com/melmass" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 32px !important;width: 140px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>
+
+[**Wiki**](https://github.com/melMass/comfy_mtb/wiki) | [**Install Guide**](./INSTALL.md) | [**Examples**](https://github.com/melMass/comfy_mtb/wiki/Examples)
 
 
-### Comfy Resources
-
-**Guides**:
-- [Official Examples (eng)](https://comfyanonymous.github.io/ComfyUI_examples/)
-- [ComfyUI Community Manual (eng)](https://blenderneko.github.io/ComfyUI-docs/) by @BlenderNeko
-  
-- [Tomoaki's personal Wiki (jap)](https://comfyui.creamlab.net/guides/) by @tjhayasaka
-
-**Extensions and Custom Nodes**:
-- [Plugins for Comfy List (eng)](https://github.com/WASasquatch/comfyui-plugins) by @WASasquatch
-
-- [ComfyUI tag on CivitAI (eng)](https://civitai.com/tag/comfyui)

__init__.py

@@ -1,16 +1,91 @@
-import traceback
-from .log import log, blue_text, get_summary, get_label
-from .utils import here
-import importlib
+#!/usr/bin/env python3
+###
+# File: __init__.py
+# Project: comfy_mtb
+# Author: Mel Massadian
+# Copyright (c) 2023-2025 Mel Massadian
+#
+###
+
+__version__ = "0.5.4"
+
 import os
 
-NODE_CLASS_MAPPINGS = {}
-NODE_CLASS_MAPPINGS_DEBUG = {}
+from aiohttp.web_request import Request
+
+# TODO: don't override this if the user has that setup already
+if not os.environ.get("TF_FORCE_GPU_ALLOW_GROWTH"):
+    os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+
+if not os.environ.get("TF_GPU_ALLOCATOR"):
+    os.environ["TF_GPU_ALLOCATOR"] = "cuda_malloc_async"
+
+import ast
+import contextlib
+import importlib
+import json
+import logging
+import shutil
+import traceback
+from importlib import reload
+from pathlib import Path
+
+from aiohttp import web
+
+IN_COMFY = False
+
+PromptServer = None
+
+try:
+    from server import PromptServer
+
+    IN_COMFY = True
+except ModuleNotFoundError:
+    IN_COMFY = False
+
+
+from .endpoint import endlog
+from .install import get_node_dependencies
+from .log import blue_text, cyan_text, get_label, get_summary, log
+from .utils import comfy_dir, here
+
+NODE_CLASS_MAPPINGS: dict[str, type] = {}
+NODE_DISPLAY_NAME_MAPPINGS: dict[str, str] = {}
+NODE_CLASS_MAPPINGS_DEBUG: dict[str, str | None] = {}
+WEB_DIRECTORY = "./web"
+
+
+def extract_nodes_from_source(filename: Path):
+    source_code = ""
+    source_code = filename.read_text(encoding="utf-8")
+    nodes: list[str] = []
+
+    try:
+        parsed = ast.parse(source_code)
+        for node in ast.walk(parsed):
+            if isinstance(node, ast.Assign) and len(node.targets) == 1:
+                target = node.targets[0]
+                if isinstance(target, ast.Name) and target.id == "__nodes__":
+                    value = ast.get_source_segment(source_code, node.value)
+                    if value:
+                        node_value = ast.parse(value).body[0].value
+                        if isinstance(node_value, ast.List | ast.Tuple):
+                            nodes.extend(
+                                str(element.id)
+                                for element in node_value.elts
+                                if isinstance(element, ast.Name)
+                            )
+                        break
+    except SyntaxError:
+        log.error(f"Failed to parse ast from: {filename}")
+    return nodes
 
 
 def load_nodes():
-    errors = []
-    nodes = []
+    errors: list[str] = []
+    nodes: list[type] = []
+    nodes_failed: list[str] = []
+
     for filename in (here / "nodes").iterdir():
         if filename.suffix == ".py":
             module_name = filename.stem
@@ -19,56 +94,506 @@ def load_nodes():
                 module = importlib.import_module(
                     f".nodes.{module_name}", package=__package__
                 )
-                _nodes = getattr(module, "__nodes__")
+                _nodes = getattr(module, "__nodes__", [])
                 nodes.extend(_nodes)
-
                 log.debug(f"Imported {module_name} nodes")
 
             except AttributeError:
+                log.debug(f"Skipping wip module {module_name}")
                 pass  # wip nodes
             except Exception:
                 error_message = traceback.format_exc().splitlines()[-1]
-                errors.append(f"Failed to import {module_name} because {error_message}")
+
+                errors.append(
+                    f"Failed to import module {module_name} because {error_message}"
+                )
+                # Read __nodes__ variable from the source file
+                nodes_failed.extend(extract_nodes_from_source(filename))
 
     if errors:
-        log.error(
-            f"Some nodes failed to load:\n\t"
+        log.debug(
+            "Some nodes failed to load:\n\t"
             + "\n\t".join(errors)
             + "\n\n"
             + "Check that you properly installed the dependencies.\n"
             + "If you think this is a bug, please report it on the github page (https://github.com/melMass/comfy_mtb/issues)"
         )
 
-    return nodes
+    return (nodes, nodes_failed)
 
 
 # - REGISTER WEB EXTENSIONS
-web_extensions_root = utils.comfy_dir / "web" / "extensions"
-web_mtb = web_extensions_root / "mtb"
+def uninstall_old_web_extensions():
+    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}")
-elif web_extensions_root.exists():
-    os.symlink((here / "web"), web_mtb.as_posix())
-else:
-    log.error(
-        f"Comfy root probably not found automatically, please copy the folder {web_mtb} manually in the web/extensions folder of ComfyUI"
+    if web_mtb.exists() and hasattr(nodes, "EXTENSION_WEB_DIRS"):
+        try:
+            if web_mtb.is_symlink():
+                web_mtb.unlink()
+            else:
+                shutil.rmtree(web_mtb)
+        except Exception as e:
+            log.warning(
+                f"""Failed to remove web mtb directory: {e}
+Please manually remove it from disk ({web_mtb}) and restart the server."""
+            )
+
+
+# uninstall_old_web_extensions()
+
+
+# - GATHER WIKI PAGES
+def wiki_to_classname(s: str):
+    wiki_name = s.replace("nodes-", "", 1)
+    return "MTB_" + "".join(
+        [part.capitalize() for part in wiki_name.split("-")]
     )
 
-# - REGISTER NODES
-nodes = load_nodes()
-for node_class in nodes:
-    class_name = node_class.__name__
-    class_name = node_class.__name__
-    node_name = f"{get_label(class_name)} (mtb)"
-    NODE_CLASS_MAPPINGS[node_name] = node_class
-    NODE_CLASS_MAPPINGS_DEBUG[node_name] = node_class.__doc__
 
+def classname_to_wiki(s: str):
+    classname = s.replace("MTB_", "")
+    parts: list[str] = []
+    start = 0
+    for i in range(1, len(classname)):
+        if classname[i].isupper():
+            parts.append(classname[start:i].lower())
+            start = i
+    parts.append(classname[start:].lower())
+    return "nodes-" + "-".join(parts)
+
+
+wiki = here / "wiki"
+node_docs = {}
+if wiki.exists() and wiki.is_dir():
+    node_docs = {
+        wiki_to_classname(x.stem): x.read_text(encoding="utf-8")
+        for x in (wiki / "nodes").glob("*.md")
+    }
+
+
+# - REGISTER NODES
+MTB_EXPORT = os.environ.get("MTB_EXPORT")
+
+nodes, failed = load_nodes()
+for node_class in nodes:
+    class_name: str = node_class.__name__
+    linked_doc = node_docs.get(class_name)
+
+    if not hasattr(node_class, "DESCRIPTION"):
+        if linked_doc:
+            log.debug(f"Found linked doc for {class_name}, using it")
+            node_class.DESCRIPTION = linked_doc
+        elif node_class.__doc__:
+            log.debug(f"Using __doc__ as description for {class_name}")
+            node_class.DESCRIPTION = node_class.__doc__
+            if MTB_EXPORT:
+                wiki_name = classname_to_wiki(class_name)
+                _ = (wiki / "nodes" / (wiki_name + ".md")).write_text(
+                    node_class.__doc__, encoding="utf-8"
+                )
+
+        else:
+            log.debug(
+                f"None of the methods could retrieve documentation for {class_name}"
+            )
+
+    node_label = f"{get_label(class_name)} (mtb)"
+    NODE_CLASS_MAPPINGS[node_label] = node_class
+    NODE_DISPLAY_NAME_MAPPINGS[class_name] = node_label
+    NODE_CLASS_MAPPINGS_DEBUG[node_label] = node_class.__doc__
+
+    # TODO: I removed this, I find it more convenient to write without spaces
+    # but it breaks every of my workflows
+    # TODO (cont): and until I find a way to automate the conversion
+    # I'll leave it like this
+
+    if os.environ.get("MTB_EXPORT"):
+        with open(here / "node_list.json", "w") as f:
+            _ = f.write(
+                json.dumps(
+                    {
+                        k: NODE_CLASS_MAPPINGS_DEBUG[k]
+                        for k in sorted(NODE_CLASS_MAPPINGS_DEBUG.keys())
+                    },
+                    indent=4,
+                )
+            )
 
 log.debug(
-    f"Loaded the following nodes:\n\t"
+    "Loaded the following nodes:\n\t"
     + "\n\t".join(
-        f"{k}: {blue_text(get_summary(doc)) if doc else '-'}"
+        f"{cyan_text(k)}: {blue_text(get_summary(doc)) if doc else '-'}"
         for k, doc in NODE_CLASS_MAPPINGS_DEBUG.items()
     )
 )
+
+log.info(f"loaded {cyan_text(str(len(nodes)))} nodes successfuly")
+
+if failed:
+    with contextlib.suppress(Exception):
+        base_url, port = utils.get_server_info()
+        log.info(
+            f"Some nodes ({len(failed)}) could not be loaded. This can be ignored, but go to http://{base_url}:{port}/mtb if you want more information."
+        )
+        log.debug(failed)
+
+
+# - ENDPOINT
+
+
+# TODO: move that away and simplify existing endpoints
+
+
+def register_routes():
+    if not PromptServer:
+        log.error("No prompt server, are you inside comfy?")
+
+    if PromptServer.instance.app.frozen:
+        log.warning(
+            "The router is frozen and cannot be further edited."
+            "If you are hot reloading mtb this is expected."
+        )
+        return
+
+    img_cache = None
+    prompt_cache = None
+
+    import asyncio
+    import os
+    from io import BytesIO
+
+    from PIL import Image
+
+    with contextlib.suppress(ImportError):
+        from cachetools import TTLCache
+
+        img_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+        prompt_cache = TTLCache(maxsize=100, ttl=5)  # 1 min TTL
+
+    node_dependency_mapping = get_node_dependencies()
+
+    PromptServer.instance.app.router.add_static(
+        "/mtb-assets/", path=(here / "html").as_posix()
+    )
+
+    # NOTE: we add an extra static path to avoid comfy mechanism
+    # that loads every script in web.
+    PromptServer.instance.app.add_routes(
+        [web.static("/mtb_async", (here / "web_async").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):
+        from . import endpoint
+
+        reload(endpoint)
+
+        endlog.debug("Getting node registration status")
+        # Check if the request prefers HTML content
+        if "text/html" in request.headers.get("Accept", ""):
+            # # Return an HTML page
+            html_response = endpoint.render_table(
+                NODE_CLASS_MAPPINGS_DEBUG, title="Registered"
+            )
+            html_response += endpoint.render_table(
+                {
+                    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(
+                text=endpoint.render_base_template("MTB", html_response),
+                content_type="text/html",
+            )
+
+        return web.json_response(
+            {
+                "registered": NODE_CLASS_MAPPINGS_DEBUG,
+                "failed": failed,
+            }
+        )
+
+    @PromptServer.instance.routes.post("/mtb/server-info")
+    async def set_server_info(request: Request):
+        json_data: dict[str, bool] = await request.json()
+        enabled = json_data.get("debug")
+        if enabled:
+            os.environ["MTB_DEBUG"] = "true"
+            log.setLevel(logging.DEBUG)
+            log.debug("Debug mode set from API (/mtb/debug POST route)")
+
+        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'}"}
+        )
+
+    @PromptServer.instance.routes.get("/mtb")
+    async def get_home(request: Request):
+        from . import endpoint
+
+        _ = reload(endpoint)
+        # Check if the request prefers HTML content
+        if "text/html" in request.headers.get("Accept", ""):
+            # # Return an HTML page
+            html_response = """
+            <div class="flex-container menu">
+                <a href="/mtb/manage">manage</a>
+                <a href="/mtb/server-info">Server Info</a>
+                <a href="/mtb/status">status</a>
+            </div>
+            """
+            return web.Response(
+                text=endpoint.render_base_template("MTB", html_response),
+                content_type="text/html",
+            )
+
+        # Return JSON for other requests
+        return web.json_response({"message": "Welcome to MTB!"})
+
+    def get_cached_image(file_path: str, preview_params=None, channel=None):
+        cache_key = (file_path, preview_params, channel)
+        if img_cache and (cache_key in img_cache):
+            return img_cache[cache_key]
+
+        with Image.open(file_path) as img:
+            info = img.info
+            if preview_params:
+                img = process_preview(img, preview_params)
+            if channel:
+                img = process_channel(img, channel)
+            if prompt_cache:
+                prompt_cache[cache_key] = info
+            if img_cache:
+                img_cache[cache_key] = img.getvalue()
+                return img_cache[cache_key]
+
+            return img.getvalue()
+
+    def process_preview(img: Image.Image, preview_params):
+        image_format, quality, width = preview_params
+        quality = int(quality)
+
+        if width:
+            width = int(width)
+            img.thumbnail((width, int(width * img.height / img.width)))
+
+        buffer = BytesIO()
+        img.save(
+            buffer, format=image_format, quality=quality, metadata=img.info
+        )
+        buffer.seek(0)
+        return buffer
+
+    def process_channel(img: Image.Image, channel: str):
+        if channel == "rgb":
+            if img.mode == "RGBA":
+                r, g, b, _ = img.split()
+                img = Image.merge("RGB", (r, g, b))
+            else:
+                img = img.convert("RGB")
+        elif channel == "a":
+            if img.mode == "RGBA":
+                _, _, _, a = img.split()
+            else:
+                a = Image.new("L", img.size, 255)
+            img = Image.new("RGBA", img.size)
+            img.putalpha(a)
+
+        buffer = BytesIO()
+        img.save(buffer, format="PNG")
+        _ = buffer.seek(0)
+        return buffer
+
+    async def get_image_response(
+        file, filename: str, preview_info=None, channel=None
+    ):
+        img = await asyncio.to_thread(
+            get_cached_image, file, preview_info, channel
+        )
+        return web.Response(
+            body=img,
+            content_type="image/webp" if preview_info else "image/png",
+            headers={"Content-Disposition": f'filename="{filename}"'},
+        )
+
+    # TODO: Embed the metadatas somehow so we can drag and drop
+    #       to load workflows in the sidebar
+    @PromptServer.instance.routes.get("/mtb/view")
+    async def view_image(request: Request):
+        import folder_paths
+
+        filename = request.rel_url.query.get("filename")
+        if not filename:
+            return web.Response(status=404)
+
+        filename, output_dir = folder_paths.annotated_filepath(filename)
+        if filename[0] == "/" or ".." in filename:
+            return web.Response(status=400)
+
+        if output_dir is None:
+            rtype = request.rel_url.query.get("type", "output")
+            output_dir = folder_paths.get_directory_by_type(rtype)
+
+        if output_dir is None:
+            return web.Response(status=400)
+
+        if "subfolder" in request.rel_url.query:
+            full_output_dir = os.path.join(
+                output_dir, request.rel_url.query["subfolder"]
+            )
+            if (
+                os.path.commonpath(
+                    (os.path.abspath(full_output_dir), output_dir)
+                )
+                != output_dir
+            ):
+                return web.Response(status=403)
+            output_dir = full_output_dir
+
+        filename = os.path.basename(filename)
+        file = os.path.join(output_dir, filename)
+
+        if not os.path.isfile(file):
+            return web.Response(status=404)
+
+        ret_workflow = request.rel_url.query.get("workflow")
+
+        if ret_workflow:
+            image = Image.open(file)
+            prompt = image.info.get("prompt", "")
+            workflow = image.info.get("workflow", "")
+
+            if workflow:
+                workflow = json.loads(workflow)
+
+            if prompt:
+                prompt = json.loads(prompt)
+
+            return web.json_response(
+                {
+                    "prompt": prompt,
+                    "workflow": workflow,
+                }
+            )
+
+        preview_info = None
+        if "preview" in request.rel_url.query:
+            preview_params = request.rel_url.query["preview"].split(";")
+            image_format = (
+                preview_params[0]
+                if preview_params[0] in ["webp", "jpeg"]
+                else "webp"
+            )
+            quality = (
+                int(preview_params[1])
+                if len(preview_params) > 1 and preview_params[1].isdigit()
+                else 90
+            )
+            width = request.rel_url.query.get("width")
+            preview_info = (image_format, quality, width)
+
+        channel = request.rel_url.query.get("channel")
+
+        return await get_image_response(file, filename, preview_info, channel)
+
+    @PromptServer.instance.routes.get("/mtb/server-info")
+    async def get_debug(request: Request):
+        from . import endpoint
+
+        _ = reload(endpoint)
+        isdebug = "MTB_DEBUG" in os.environ
+        exposed = "MTB_EXPOSE" in os.environ
+
+        def render_property(name: str, val: str):
+            return f"""<strong>{name}:</strong>
+                <p>
+                    {val}
+                </p>"""
+
+        # Check if the request prefers HTML content
+        if "text/html" in request.headers.get("Accept", ""):
+            # # Return an HTML page
+            html_response = ""
+
+            html_response += render_property(
+                "Debug", "Enabled" if isdebug else "Disabled"
+            )
+
+            html_response += render_property("Exposed", str(exposed))
+
+            return web.Response(
+                text=endpoint.render_base_template(
+                    "Server Info", html_response
+                ),
+                content_type="text/html",
+            )
+
+        # Return JSON for other requests
+        return web.json_response({"exposed": exposed, "debug": isdebug})
+
+    @PromptServer.instance.routes.get("/mtb/actions")
+    async def no_route(request: Request):
+        from . import endpoint
+
+        if "text/html" in request.headers.get("Accept", ""):
+            html_response = """
+            <h1>Actions has no get for now...</h1>
+            """
+            return web.Response(
+                text=endpoint.render_base_template("Actions", html_response),
+                content_type="text/html",
+            )
+        return web.json_response({"message": "actions has no get for now"})
+
+    @PromptServer.instance.routes.post("/mtb/actions")
+    async def do_action(request: Request):
+        from . import endpoint
+
+        reload(endpoint)
+
+        return await endpoint.do_action(request)
+
+
+if IN_COMFY and hasattr(PromptServer, "instance"):
+    register_routes()
+
+
+# - WAS Dictionary
+MANIFEST = {
+    "name": "MTB Nodes",  # The title that will be displayed on Node Class menu,. and Node Class view
+    "version": (0, 1, 0),  # Version of the custom_node or sub module
+    "author": "Mel Massadian",  # Author or organization of the custom_node or sub module
+    "project": "https://github.com/melMass/comfy_mtb",  # The address that the `name` value will link to on Node Class Views
+    "description": "Set of nodes that enhance your animation workflow and provide a range of useful tools including features such as manipulating bounding boxes, perform color corrections, swap faces in images, interpolate frames for smooth animation, export to ProRes format, apply various image operations, work with latent spaces, generate QR codes, and create normal and height maps for textures.",
+}

biome.json

@@ -0,0 +1,38 @@
+{
+  "$schema": "https://biomejs.dev/schemas/2.0.5/schema.json",
+  "assist": { "actions": { "source": { "organizeImports": "on" } } },
+  "linter": {
+    "enabled": true,
+    "rules": {
+      "recommended": true,
+      "suspicious": {
+        "noConsole": { "level": "warn", "options": { "allow": ["log"] } }
+      },
+      "style": {
+        "noParameterAssign": "off",
+        "noShoutyConstants": "warn",
+        "useNamingConvention": "off",
+        "useAsConstAssertion": "error",
+        "useDefaultParameterLast": "error",
+        "useEnumInitializers": "error",
+        "useSelfClosingElements": "error",
+        "useSingleVarDeclarator": "error",
+        "noUnusedTemplateLiteral": "error",
+        "useNumberNamespace": "error",
+        "noInferrableTypes": "error",
+        "noUselessElse": "error"
+      }
+    }
+  },
+  "formatter": {
+    "indentStyle": "space",
+    "indentWidth": 2,
+    "lineEnding": "lf"
+  },
+  "javascript": {
+    "formatter": {
+      "quoteStyle": "single",
+      "semicolons": "asNeeded"
+    }
+  }
+}

cliff.toml

@@ -0,0 +1,83 @@
+[changelog]
+header = """
+# Changelog\n
+This is an automated changelog based on the commits in this repository.
+
+Check the notes in the [releases](https://github.com/melMass/comfy_mtb/releases) for more information.
+"""
+# https://keats.github.io/tera/docs/#introduction
+body = """
+{% if version -%}\
+    ## [{{ version | trim_start_matches(pat="v") }}] - {{ timestamp | date(format="%Y-%m-%d") }}
+{% else %}\
+    ## [Unreleased]
+{% endif -%}\
+
+{% for group, commits in commits | group_by(attribute="group") %}
+    ### {{ group | upper_first }}
+    {% for commit in commits %}
+        - {% if commit.breaking %}[**breaking**] {% endif %}{{ commit.message | upper_first | trim }} ([{{ commit.id | truncate(length=7, end="") }}](<REPO>/commit/{{ commit.id }}))\
+            {% if commit.github.username and commit.github.username != remote.github.owner %} by [@{{ commit.github.username }}](https://github.com/{{ commit.github.username }}){%- endif -%}
+            {% if commit.github.pr_number %} in [#{{ commit.github.pr_number }}](<REPO>/pull/{{ commit.github.pr_number }}){%- endif -%}
+    {% endfor %}
+{% endfor %}
+
+{%- if github.contributors | filter(attribute="is_first_time", value=true) | length != 0 %}
+  ## New Contributors
+{%- endif -%}
+
+{% for contributor in github.contributors | filter(attribute="is_first_time", value=true) %}
+  * [@{{ contributor.username }}](https://github.com/{{ contributor.username }}) made their first contribution in [#{{ contributor.pr_number }}](<REPO>/pull/{{ contributor.pr_number }})\
+{%- endfor %}\n
+"""
+footer = """
+{% for release in releases -%}
+    {% if release.version -%}
+        {% if release.previous.version -%}
+            [{{ release.version | trim_start_matches(pat="v") }}]: \
+                <REPO>/compare/{{ release.previous.version }}..{{ release.version }}
+        {% endif -%}
+    {% else -%}
+        [unreleased]: <REPO>/compare/{{ release.previous.version }}..HEAD
+    {% endif -%}
+{% endfor %}
+"""
+trim = true
+postprocessors = [
+  { pattern = '<REPO>', replace = "https://github.com/melMass/comfy_mtb" }, # replace repository URL
+]
+
+[git]
+# https://www.conventionalcommits.org
+conventional_commits = true
+filter_unconventional = true
+split_commits = false
+commit_preprocessors = [
+  # { pattern = '\((\w+\s)?#([0-9]+)\)', replace = "([#${2}](<REPO>/issues/${2}))" }, # replace issue numbers
+  { pattern = '\((\w+\s)?#([0-9]+)\)', replace = "" },
+]
+commit_parsers = [
+  { message = "^feat", group = "Features" },
+  { message = "^fix", group = "Bug Fixes" },
+  { message = "^doc", group = "Documentation" },
+  { message = "^perf", group = "Performance" },
+  { message = "^refactor", group = "Refactor" },
+  { message = "^style", group = "Styling" },
+  { message = "^test", group = "Testing" },
+  { message = "^chore\\(release\\): prepare for", skip = true },
+  { message = "^chore\\(deps\\)", skip = true },
+  { message = "^chore\\(pr\\)", skip = true },
+  { message = "^chore\\(pull\\)", skip = true },
+  { message = "^chore|ci", group = "Miscellaneous Tasks" },
+  { body = ".*security", group = "Security" },
+  { message = "^revert", group = "Revert" },
+]
+protect_breaking_commits = false
+filter_commits = false
+tag_pattern = "v[0-9].*"
+topo_order = false
+sort_commits = "newest"
+
+[remote.github]
+owner = "melMass"
+repo = "comfy_mtb"

endpoint.py

@@ -0,0 +1,495 @@
+import csv
+import secrets
+import sys
+import urllib.parse
+from pathlib import Path
+from typing import Any, Literal
+
+import folder_paths
+from aiohttp import web
+
+from .install import get_node_dependencies
+from .log import mklog
+from .utils import (
+    SortMode,
+    backup_file,
+    build_glob_patterns,
+    glob_multiple,
+    reqs_map,
+    run_command,
+    styles_dir,
+)
+
+endlog = mklog("mtb endpoint")
+
+# - ACTIONS
+
+
+def ACTIONS_installDependency(dependency_names: list[str] | None = None):
+    if dependency_names is None:
+        # return web.Response(text="No dependency name provided", status=400)
+        return {"error": "No dependency name provided"}
+
+    endlog.debug(f"Received Install Dependency request for {dependency_names}")
+    # reqs = []
+    resolved_names = [reqs_map.get(name, name) for name in dependency_names]
+    allowed_deps = list(
+        {d for dep in get_node_dependencies().values() for d in dep}
+    )
+    for dep in dependency_names:
+        if dep not in allowed_deps:
+            return {
+                "error": f"Unknown dependency: {dep}, you can only use this endpoint to install {allowed_deps}"
+            }
+    try:
+        run_command(
+            [Path(sys.executable), "-m", "pip", "install"] + resolved_names
+        )
+        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_getUserImageFolders():
+    input_dir = Path(folder_paths.get_input_directory())
+    output_dir = Path(folder_paths.get_output_directory())
+
+    input_subdirs = [x.name for x in input_dir.iterdir() if x.is_dir()]
+    output_subdirs = [x.name for x in output_dir.iterdir() if x.is_dir()]
+
+    return {"input": input_subdirs, "output": output_subdirs}
+
+
+def ACTIONS_getUserVideos(
+    size=256, count=200, offset=0, sort: str | None = None
+):
+    count = count or 1000
+    video_extensions = ["webm", "mp4", "mkv", "mov"]
+    entries = {}
+    patterns = build_glob_patterns(video_extensions)
+    input_dir = Path(folder_paths.get_input_directory())
+    entries = glob_multiple(input_dir, patterns)
+
+    sort_mode = SortMode.from_str(sort)
+
+    if sort_mode:
+        sort_key = {
+            SortMode.MODIFIED: lambda x: x.stat().st_mtime,
+            SortMode.MODIFIED_REVERSE: lambda x: x.stat().st_mtime,
+            SortMode.NAME: lambda x: x.name,
+            SortMode.NAME_REVERSE: lambda x: x.name,
+        }.get(sort_mode)
+        if sort_key:
+            reverse = sort_mode in (SortMode.MODIFIED, SortMode.NAME_REVERSE)
+            entries = sorted(entries, key=sort_key, reverse=reverse)
+
+    videos = {
+        video.name: (
+            f"/view?force_rate=0&frame_load_cap=0&skip_first_frames=0&select_every_nth=1&filename={urllib.parse.quote_plus(video.name)}&type=input&format=video&force_size={size}x?"
+        )
+        for i, video in enumerate(entries)
+        if offset <= i < offset + count
+    }
+    return videos
+
+
+def ACTIONS_getUserImages(
+    mode: Literal["input", "output"],
+    target_width: int | str | None = None,
+    count=1000,
+    offset=0,
+    sort: str | None = None,
+    include_subfolders: bool = False,
+    subfolder: str | None = None,
+    # IIRC I copied this from Comfy base
+    # just keeping it until I properly checked implications
+    salt_urls=False,
+):
+    # enabled = "MTB_EXPOSE" in os.environ
+    # if not enabled:
+    #     return {"error": "Session not authorized to getInputs"}
+
+    imgs = {}
+    count = count or 1000
+    target_width = int(target_width) if target_width else None
+
+    input_dir = Path(folder_paths.get_input_directory())
+    output_dir = Path(folder_paths.get_output_directory())
+
+    entry_dir: Path = input_dir if mode == "input" else output_dir
+    if subfolder:
+        entry_dir = entry_dir / subfolder
+
+        if not entry_dir.exists():
+            return {
+                "error": f"Subfolder {entry_dir.name} doesn't exists in {entry_dir.parent.as_posix()}"
+            }
+    supported = ["png", "jpg", "jpeg", "webp", "gif"]
+
+    entries = {}
+    patterns = build_glob_patterns(supported, recursive=include_subfolders)
+    entries = glob_multiple(entry_dir, patterns)
+
+    sort_mode = SortMode.from_str(sort)
+
+    if sort_mode:
+        sort_key = {
+            SortMode.MODIFIED: lambda x: x.stat().st_mtime,
+            SortMode.MODIFIED_REVERSE: lambda x: x.stat().st_mtime,
+            SortMode.NAME: lambda x: x.name,
+            SortMode.NAME_REVERSE: lambda x: x.name,
+        }.get(sort_mode)
+        if sort_key:
+            reverse = sort_mode in (SortMode.MODIFIED, SortMode.NAME_REVERSE)
+            entries = sorted(entries, key=sort_key, reverse=reverse)
+
+    imgs = {
+        img.name: (
+            f"/mtb/view?filename={img.name}{f'&width={target_width}' if target_width and target_width > 0 else ''}&type={mode}&subfolder={subfolder or ''}"
+            f"{img.parent.relative_to(entry_dir) if include_subfolders else ''}"
+            f"&preview={f'&rand={secrets.randbelow(424242)}' if salt_urls else ''}"
+        )
+        for i, img in enumerate(entries)
+        if offset <= i < offset + count
+    }
+    return imgs
+
+
+def ACTIONS_getStyles(style_name=None):
+    from .nodes.conditions import MTB_StylesLoader
+
+    styles = MTB_StylesLoader.options
+    match_list = ["name"]
+    if styles:
+        filtered_styles = {
+            key: value
+            for key, value in styles.items()
+            if not key.startswith("__") and key not in match_list
+        }
+        if style_name:
+            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.Request) -> web.Response:
+    endlog.debug("Init action request")
+    request_data = await request.json()
+    name = request_data.get("name")
+    args = request_data.get("args")
+
+    endlog.debug(f"Received action request: {name} {args}")
+
+    method_name = f"ACTIONS_{name}"
+    method = globals().get(method_name)
+
+    if callable(method):
+        result = None
+        if args:
+            result = method(*args) if isinstance(args, list) else method(args)
+        else:
+            result = method()
+
+        endlog.debug(f"Action result: {result}")
+        return web.json_response({"result": result})
+
+    available_methods = [
+        attr[len("ACTIONS_") :]
+        for attr in globals()
+        if attr.startswith("ACTIONS_")
+    ]
+
+    return web.json_response(
+        {
+            "error": "Invalid method name.",
+            "available_methods": available_methods,
+        }
+    )
+
+
+# - HTML UTILS
+
+
+def dependencies_button(name: str, dependencies: list[str]) -> str:
+    deps = ",".join([f"'{x}'" for x in dependencies])
+    return f"""
+        <button
+            class="dependency-button"
+            onclick="window.mtb_action('installDependency',[{deps}])"
+        >Install {name} deps</button>
+        """
+
+
+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, 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: str, content: str):
+    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: str, right_content: str, *, vertical: bool = 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: str, options: list[str]):
+    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: dict[str, Any], sort=True, title=None):
+    table_list = sorted(
+        table_dict.items(), key=lambda item: item[0]
+    )  # Sort the dictionary by keys
+
+    table_rows = ""
+    for name, item in table_list:
+        if isinstance(item, dict):
+            if "dependencies" in item:
+                table_rows += f"<tr><td>{name}</td><td>"
+                table_rows += (
+                    f"{dependencies_button(name, item['dependencies'])}"
+                )
+
+                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>
+            <thead>
+                <tr>
+                    <th>Name</th>
+                    <th>Description</th>
+                </tr>
+            </thead>
+            <tbody>
+                {table_rows}
+            </tbody>
+        </table>
+        </div>
+        """
+
+
+def render_base_template(title: str, content: str):
+    github_icon_svg = """<svg xmlns="http://www.w3.org/2000/svg" fill="whitesmoke" height="3em" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>"""
+    return f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>{title}</title>
+        <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>
+        <div class="mtb_logo">
+            <img
+                src="https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873"
+                alt="Comfy MTB Logo" height="70" width="128">
+            <span class="title">Comfy MTB</span></div>
+            <a style="width:128px;text-align:center" href="https://www.github.com/melmass/comfy_mtb">
+                {github_icon_svg}
+            </a>
+        </header>
+
+        <main>
+            {content}
+        </main>
+
+        <footer>
+            <!-- Shared footer content here -->
+        </footer>
+    </body>
+
+    </html>
+    """

env.nu

@@ -0,0 +1,238 @@
+# NOTE: This file is only use for development you can ignore it
+
+use private/log.nu
+
+def get_root [--clean] {
+    if $clean {
+        $env.COMFY_CLEAN_ROOT
+    } else {
+        $env.COMFY_ROOT
+    }
+}
+
+export def "comfy build-web" [] {
+    cd $env.COMFY_MTB
+    cd web_source
+    npm run build
+    cp dist/*.js ../web/dist
+}
+
+export def "comfy dev-web" [] {
+    cd $env.COMFY_MTB
+    cd web_source
+    npm run dev
+}
+
+export def "daily run"  [] {
+  let res = (comfy update --rebase)
+  comfy update --clean
+  comfy update_extensions
+
+  daily commit $res.from $res.to
+}
+
+def short-date [] {
+  format date "%Y-%m-%d"
+}
+
+# was daily run today?
+export def "daily was-run" [] {
+
+  let daily = ($env.COMFY_MTB | path join daily.nuon)
+
+  if ($daily | path exists) {
+    let last = (open $daily | sort-by date | get date  | last | short-date)
+    let today = (date now | short-date)
+    return ($last == $today)
+  }
+  return false
+}
+
+export def "daily commit"  [from:string, to:string] {
+  let daily = ($env.COMFY_MTB | path join daily.nuon)
+  let commit = [{date: (date now) from:$from to:$to}]
+
+  let dailies = (if ($daily | path exists) {
+    open $daily | append $commit
+  } else {
+      $commit
+  })
+
+  $dailies | save -f $daily
+  log success "Commited daily check"
+}
+
+# start the comfy server
+export def "comfy start" [--clean,--old-ui, --listen, --skip-daily(-s)] {
+  if (not (daily was-run)) and not $skip_daily {
+      log info "Running daily checks"
+      daily run
+    }
+    let root = get_root --clean=($clean)
+    cd $root
+
+    log info "Running Server"
+
+    MTB_DEBUG=true python main.py --port 3000 ...(if $old_ui { ["--front-end-version", "Comfy-Org/ComfyUI_legacy_frontend@latest"]} else {[ --front-end-version Comfy-Org/ComfyUI_frontend@latest]}) --preview-method auto ...(if $listen {["--listen"]} else {[]})
+}
+
+# update comfy itself and merge master in current branch
+export def "comfy update" [
+    --clean # ??
+    --rebase # Rebase instead of merge
+] {
+  let root = get_root --clean=$clean
+
+  let models = $"($root)/models"
+  let inputs = $"($root)/input"
+
+  cd $root
+
+  let branch_name = (git rev-parse --abbrev-ref HEAD | str trim)
+  let current_commit = (git rev-parse HEAD | str trim)
+
+  log info "Backing up and removing models symlinks"
+
+  # preparing root for pull
+  if not $clean {
+    git checkout pyproject.toml
+    cd $models
+    # find and store all symlinks
+    let links = (ls -la |
+      where not ($it.target | is-empty) |
+      select name target |
+      sort-by name)
+
+
+    if not ($links | is-empty) {
+      $links | save -f links.nuon
+      # remove them
+      open links.nuon | each {|p| rm $p.name }
+    }
+  } else {
+    # just remove symlinks
+    rm $models
+    rm $inputs
+  }
+
+  cd $root
+
+  log info $"Checking out to master"
+  git checkout master
+
+  log info "Fetching and pulling remote updates"
+  if ($clean) {
+    # from the local base repo master
+    git fetch local master # $branch_name # master
+    git pull local master # $branch_name # master
+  } else {
+    git fetch
+    git pull
+  }
+
+  let new_commit = (git rev-parse HEAD | str trim)
+
+  log info $"Back to our branch \(($branch_name)\)"
+  git checkout -
+
+  if $current_commit == $new_commit {
+    log warn "No changes upstream"
+  } else {
+    if $rebase {
+      log info "Rebasing changes"
+      git rebase master
+
+    } else {
+      log info "Merging changes"
+      git merge master
+    }
+  }
+
+  log info "Linking back the models"
+
+  if not $clean {
+    rm pyproject.toml
+    cp pyproject-mel.toml pyproject.toml
+    cd $models
+
+    # resymlink them
+    open links.nuon | each {|p| link -a $p.target $p.name }
+  } else {
+    let master = (get_root)
+    link ($master | path join models) $models
+    link ($master | path join input) $inputs
+  }
+
+  let commit_count = (git rev-list --count $branch_name $"^origin/($branch_name)")
+
+  log success $"Update successful \(($commit_count) new commits\)"
+
+  return {from:$current_commit to:$new_commit}
+
+
+}
+
+export def "comfy toggle_extensions" [--clean] {
+    let root = get_root --clean=($clean)
+    cd $root
+    cd custom_nodes
+    let exts = (ls | where type in ["dir","symlink"] | get name)
+    let choices = ($exts | input list -m "choose extension to toggle")
+    if ($choices | is-empty) {
+        return
+    }
+
+    log info "Choices" $choices
+
+    let filtered = $choices | wrap name | upsert enabled {|p| not ($p.name | str ends-with ".disabled")}
+
+    log info "Filtered" $filtered
+    $filtered | each {|f|
+        let new_name = ($f.name | str replace ".disabled" "")
+
+        let new_name = if $f.enabled {
+            $"($new_name).disabled"
+        } else {
+            $new_name
+        }
+        log info $"Moving ($f.name) to ($new_name)"
+        mv $f.name $new_name
+    }
+}
+
+# git pull all extensions
+export def "comfy update_extensions" [--clean] {
+    let root = get_root --clean=($clean)
+    cd $root
+    cd custom_nodes
+    git multipull . -s -q
+}
+
+def --env path-add [pth] {
+    $env.PATH = ($env.PATH | append ($pth | path expand))
+
+}
+
+
+export-env {
+  $env.PYTHONUTF8 = 1
+  $env.COMFY_MTB = ("." | path expand)
+  # $env.CUDA_ROOT =  'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\'
+
+  $env.CUDA_HOME = $env.CUDA_ROOT
+
+  $env.COMFY_ROOT = ("../.." | path expand)
+  $env.COMFY_CLEAN_ROOT =  ($env.COMFY_ROOT | path dirname | path join ComfyClean)
+
+  path-add 'C:/Portable/TensorRT-8.6.0.12/lib'
+
+  if $nu.os-info.family == 'windows' {
+    path-add 'G:\BIN\TensorRT-10.7.0.23\lib'
+    path-add 'G:\BIN\cudnn-windows-x86_64-9.6.0.74_cuda12-archive\bin'
+  }
+
+  path-add ($env.CUDA_ROOT | path join bin)
+  overlay use ../../.venv/Scripts/activate.nu
+}
+
+

errors.py

@@ -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,
+        )

examples/05-seamless_texture.json

@@ -0,0 +1,905 @@
+{
+  "last_node_id": 97,
+  "last_link_id": 179,
+  "nodes": [
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -1165.8749246009997,
+        30
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4,
+            158
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "Closeup texture of rocks"
+      ],
+      "color": "#432",
+      "bgcolor": "#653",
+      "shape": 1
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -1175.8749246009997,
+        250
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "((drawing, cartoon, painting, sketch, blur, depth of field, dof))"
+      ],
+      "color": "#432",
+      "bgcolor": "#653",
+      "shape": 1
+    },
+    {
+      "id": 89,
+      "type": "Reroute",
+      "pos": [
+        350,
+        803
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 176
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "IMAGE",
+          "links": [
+            167
+          ]
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -1740,
+        236
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            170
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "revAnimated_v122.safetensors"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 63,
+      "type": "SaveImage",
+      "pos": [
+        1315,
+        18
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 115
+        }
+      ],
+      "title": "Normal",
+      "properties": {},
+      "widgets_values": [
+        "Normal"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 67,
+      "type": "SaveImage",
+      "pos": [
+        2095,
+        22
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 119
+        }
+      ],
+      "title": "Curvature",
+      "properties": {},
+      "widgets_values": [
+        "Curvature"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 69,
+      "type": "SaveImage",
+      "pos": [
+        1560,
+        1290
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 17,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 121
+        }
+      ],
+      "title": "Depth",
+      "properties": {},
+      "widgets_values": [
+        "Height"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 91,
+      "type": "Model Patch Seamless (mtb)",
+      "pos": [
+        -1150,
+        -146
+      ],
+      "size": [
+        430.8000183105469,
+        78
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 170
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Original Model (passthrough)",
+          "type": "MODEL",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "Patched Model",
+          "type": "MODEL",
+          "links": [
+            169
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Model Patch Seamless (mtb)"
+      },
+      "widgets_values": [
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 93,
+      "type": "PreviewImage",
+      "pos": [
+        1115,
+        -597
+      ],
+      "size": [
+        451.3526306152344,
+        478.3444519042969
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 179
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 43,
+      "type": "VAELoader",
+      "pos": [
+        -598.2757622278747,
+        577.3595309932109
+      ],
+      "size": [
+        387.48089599609375,
+        70.60645294189453
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            174
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "vae-ft-mse-840000-ema-pruned.safetensors"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 97,
+      "type": "Image Tile Offset (mtb)",
+      "pos": [
+        617,
+        -598
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 178
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            179
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Image Tile Offset (mtb)"
+      },
+      "widgets_values": [
+        2
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 96,
+      "type": "Vae Decode (mtb)",
+      "pos": [
+        -52,
+        40
+      ],
+      "size": [
+        315,
+        126
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 173
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 174
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            175,
+            176,
+            178
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Vae Decode (mtb)"
+      },
+      "widgets_values": [
+        true,
+        false,
+        512
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 46,
+      "type": "SaveImage",
+      "pos": [
+        533,
+        25
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 175
+        }
+      ],
+      "title": "Albedo",
+      "properties": {},
+      "widgets_values": [
+        "Albedo"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 74,
+      "type": "EmptyLatentImage",
+      "pos": [
+        -1075.8749246009997,
+        480
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            132
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        768,
+        768,
+        1
+      ],
+      "color": "#323",
+      "bgcolor": "#535",
+      "shape": 1
+    },
+    {
+      "id": 62,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        727,
+        801
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 167
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            115,
+            118,
+            122
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Color to Normals",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 66,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        1626,
+        808
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 118
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            119
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Normals to Curvature",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 68,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        1185,
+        1288
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 122
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            121
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Normals to Height",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        -518.2757622278748,
+        47.359530993211024
+      ],
+      "size": [
+        315,
+        474
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 169
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 132
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            173
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        1001,
+        "fixed",
+        28,
+        8,
+        "dpmpp_2m",
+        "normal",
+        1
+      ],
+      "color": "#222",
+      "bgcolor": "#000",
+      "shape": 1
+    }
+  ],
+  "links": [
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
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+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      115,
+      62,
+      0,
+      63,
+      0,
+      "IMAGE"
+    ],
+    [
+      118,
+      62,
+      0,
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+      0,
+      "IMAGE"
+    ],
+    [
+      119,
+      66,
+      0,
+      67,
+      0,
+      "IMAGE"
+    ],
+    [
+      121,
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+      0,
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+      0,
+      "IMAGE"
+    ],
+    [
+      122,
+      62,
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+      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,
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+      0,
+      "*"
+    ],
+    [
+      178,
+      96,
+      0,
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+      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
+}

examples/08-note-plus.json

@@ -0,0 +1 @@
+{"last_node_id":9,"last_link_id":0,"nodes":[{"id":9,"type":"Note Plus (mtb)","pos":[332, 139, 0, 0, 0, 0, 0, 0, 0, 0],"size":[573.4446126650389, 1292.5298919072263],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note+ (mtb)","properties":{},"widgets_values":["# Note+ Demo\n\n# Images \nyou can resize them (see showdown syntax)\n\n![Minion](https://octodex.github.com/images/minion.png =120x*)\n\n# iFrame (embeds)\n<iframe src=\"https://www.youtube.com/embed/tgbNymZ7vqY\">\n</iframe>\n\n# Headings\n\n# h1 Heading:smile:\n\n## h2 Heading\n\n### h3 Heading\n\n#### h4 Heading\n\n##### h5 Heading\n\n###### h6 Heading\n\n# Tables\n\nColons can be used to align columns.\n\n| Tables|Are|Cool |\n| ------------- |:-----------:| ----:|\n| col 3 is| right-aligned | $1600 |\n| col 2 is| centered| $12 |\n| zebra stripes | are neat|$1 |\n\nEmphasis, aka italics, with _asterisks_ or _underscores_.\n\nStrong emphasis, aka bold, with **asterisks** or **underscores**.\n\nCombined emphasis with **asterisks and _underscores_**.\n\nStrikethrough uses two tildes. ~~Scratch this.~~\n\n**This is bold text**\n\n**This is bold text**\n\n_This is italic text_\n\n_This is italic text_\n\n~~Strikethrough~~\n\n1. First ordered list item\n2. Another item\n\n- Unordered sub-list.\n\n1. Actual numbers don't matter, just that it's a number\n1. Ordered sub-list\n1. And another item.\n1.\n\n- [x] Finish my changes\n- [] Push my commits to GitHub\n- [] Open a pull request\n- [x] mentions:@melmass, #refs, [links](), **formatting**, and <del>tags</del> supported\n- [x] list syntax required (any unordered or ordered list supported)\n- [x] this is a complete item\n- [] this is an incomplete item\n","markdown","*{\ncolor:whitesmoke;\n}\n\nh1{\ncolor:cyan;\n}\nh2{\ncolor:yellow;\n}\nh3{\ncolor:pink;\n}\n\nstrong{\ncolor:red;\n}"],"color":"#223","bgcolor":"#335","shape":1}],"links":[],"groups":[],"config":{},"extra":{},"version":0.4}

examples/README.md

@@ -0,0 +1,12 @@
+# Examples
+All the examples use the [RevAnimated model 1.22](https://civitai.com/models/7371?modelVersionId=46846)
+## 01 Faceswap
+
+This example showcase the `Face Swap` & `Restore Face` nodes to replace the character with Georges Lucas's face.
+The face reference image is using the `Load Image From Url` node to avoid bundling input images.
+
+<img src="https://github.com/melMass/comfy_mtb/assets/7041726/272af7d6-f01c-478e-a82f-926e772d7209" width=500/>
+
+## 02 FILM interpolation
+This example showcase the FILM interpolation implementation. Here we do text replacement on the condition of two distinct images sharing the same model, input latent & seed to get relatively close images.  
+<img src="https://github.com/melMass/comfy_mtb/assets/7041726/4c28dd87-89fc-4d27-910a-0a1fcf28cdc0" width=500/>

html/js/foldable.js

@@ -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 = '▽' // Down arrow
+  } else {
+    content.style.display = 'none'
+    symbol.innerHTML = '▷' // Right arrow
+  }
+}

html/js/saveTableData.js

@@ -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)
+    })
+}

html/js/splitPane.js

@@ -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'
+    }
+  }
+}

html/js/tabSwitch.js

@@ -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'
+}

html/style.css

@@ -0,0 +1,228 @@
+html {
+    height: 100%;
+    margin: 0;
+    padding: 0;
+    background-color: rgb(33, 33, 33);
+    color: whitesmoke;
+}
+
+a {
+    color: whitesmoke;
+
+}
+
+.table-container {
+    width: 70%;
+    height: 100%;
+    overflow: auto;
+}
+
+table {
+    width: 100%;
+    border-collapse: collapse;
+}
+
+th,
+td {
+    padding: 10px;
+    text-align: left;
+}
+
+th {
+    background-color: rgb(45, 45, 45);
+    /* Light gray background for header row */
+    font-weight: bold;
+}
+
+tr:nth-child(even) {
+    background-color: rgb(45, 45, 45);
+    /* Alternate row background color */
+}
+
+tr:hover {
+    background-color: #797979;
+    /* Highlight color on hover */
+}
+
+td:nth-child(2) {
+    /* Applies to the second column (Description) */
+    width: 80%;
+    /* Adjust the width as needed */
+    word-wrap: break-word;
+    /* Allow long words to be broken and wrapped to the next line */
+}
+
+.mtb_logo {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+
+/* Styling for WebKit-based browsers (Chrome, Edge) */
+.table-container::-webkit-scrollbar {
+    width: 10px;
+    /* Set the width of the scrollbar */
+}
+
+.table-container::-webkit-scrollbar-thumb {
+    background-color: #797979;
+    /* Color of the scrollbar thumb */
+}
+
+/* Styling for Firefox */
+.table-container {
+    scrollbar-width: thin;
+    /* Set the width of the scrollbar */
+}
+
+.table-container::-webkit-scrollbar-thumb {
+    background-color: #797979;
+    /* Color of the scrollbar thumb */
+}
+
+/* Optionally, you can also style the scrollbar track (background) */
+.table-container::-webkit-scrollbar-track {
+    background-color: #f2f2f2;
+}
+
+
+
+body {
+    margin: 0;
+    padding: 0;
+    font-family: monospace;
+    height: 100%;
+    background-color: rgb(33, 33, 33);
+
+}
+
+.title {
+    font-size: 2.5em;
+    font-weight: 700;
+
+}
+
+header {
+    display: flex;
+    align-items: center;
+    vertical-align: middle;
+    justify-content: space-between;
+    background-color: rgb(12, 12, 12);
+    padding: 1em;
+    margin: 0;
+}
+
+main {
+    display: flex;
+    align-items: center;
+    vertical-align: middle;
+    justify-content: center;
+    padding: 1em;
+    margin: 0;
+    /* height: 80%; */
+}
+
+.flex-container {
+    display: flex;
+    flex-direction: column;
+}
+
+.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;
+}

install.py

@@ -0,0 +1,456 @@
+import argparse
+import ast
+import os
+import platform
+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 = Path(sys.executable)
+
+# - detect mode
+mode = None
+if os.environ.get("COLAB_GPU"):
+    mode = "colab"
+elif "python_embeded" in str(executable):
+    mode = "embeded"
+elif ".venv" in str(executable):
+    mode = "venv"
+
+
+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",
+    # Add more mappings as needed
+}
+
+
+def get_node_dependencies():
+    restore_deps = ["basicsr"]
+    onnx_deps = ["onnxruntime"]
+    swap_deps = ["insightface"] + onnx_deps
+    quant_deps = ["bitsandbytes"]
+    io_deps = ["av"]
+    return {
+        "QrCode": ["qrcode"],
+        "DeepBump": onnx_deps,
+        "FaceSwap": swap_deps,
+        "LoadFaceSwapModel": swap_deps,
+        "LoadFaceAnalysisModel": restore_deps,
+        "Quantize": quant_deps,
+        "SaveGif": io_deps,
+    }
+
+
+# endregion
+
+# region ansi
+# ANSI escape sequences for text styling
+ANSI_FORMATS = {
+    "reset": "\033[0m",
+    "bold": "\033[1m",
+    "dim": "\033[2m",
+    "italic": "\033[3m",
+    "underline": "\033[4m",
+    "blink": "\033[5m",
+    "reverse": "\033[7m",
+    "strike": "\033[9m",
+}
+
+ANSI_COLORS = {
+    "black": "\033[30m",
+    "red": "\033[31m",
+    "green": "\033[32m",
+    "yellow": "\033[33m",
+    "blue": "\033[34m",
+    "magenta": "\033[35m",
+    "cyan": "\033[36m",
+    "white": "\033[37m",
+    "bright_black": "\033[30;1m",
+    "bright_red": "\033[31;1m",
+    "bright_green": "\033[32;1m",
+    "bright_yellow": "\033[33;1m",
+    "bright_blue": "\033[34;1m",
+    "bright_magenta": "\033[35;1m",
+    "bright_cyan": "\033[36;1m",
+    "bright_white": "\033[37;1m",
+    "bg_black": "\033[40m",
+    "bg_red": "\033[41m",
+    "bg_green": "\033[42m",
+    "bg_yellow": "\033[43m",
+    "bg_blue": "\033[44m",
+    "bg_magenta": "\033[45m",
+    "bg_cyan": "\033[46m",
+    "bg_white": "\033[47m",
+    "bg_bright_black": "\033[40;1m",
+    "bg_bright_red": "\033[41;1m",
+    "bg_bright_green": "\033[42;1m",
+    "bg_bright_yellow": "\033[43;1m",
+    "bg_bright_blue": "\033[44;1m",
+    "bg_bright_magenta": "\033[45;1m",
+    "bg_bright_cyan": "\033[46;1m",
+    "bg_bright_white": "\033[47;1m",
+}
+
+
+def apply_format(text, *formats):
+    """Apply ANSI escape sequences for the specified formats to the given text."""
+    formatted_text = text
+    for format in formats:
+        formatted_text = f"{ANSI_FORMATS.get(format, '')}{formatted_text}{ANSI_FORMATS.get('reset', '')}"
+    return formatted_text
+
+
+def apply_color(text, color=None, background=None):
+    """Apply ANSI escape sequences for the specified color and background to the given text."""
+    formatted_text = text
+    if color:
+        formatted_text = f"{ANSI_COLORS.get(color, '')}{formatted_text}{ANSI_FORMATS.get('reset', '')}"
+    if background:
+        formatted_text = f"{ANSI_COLORS.get(background, '')}{formatted_text}{ANSI_FORMATS.get('reset', '')}"
+    return formatted_text
+
+
+def print_formatted(text, *formats, color=None, background=None, **kwargs):
+    """Print the given text with the specified formats, color, and background."""
+    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(
+        " " * len(encoded_header)
+        if kwargs.get("no_header")
+        else apply_color(apply_format(encoded_header, "bold"), color="yellow"),
+        encoded_text,
+        file=file,
+    )
+
+
+# endregion
+
+
+# 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,
+    )
+
+    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!")
+
+
+def is_pipe():
+    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
+def get_local_version():
+    init_file = os.path.join(os.path.dirname(__file__), "__init__.py")
+    if os.path.isfile(init_file):
+        with open(init_file) as f:
+            tree = ast.parse(f.read())
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Assign):
+                    for target in node.targets:
+                        if (
+                            isinstance(target, ast.Name)
+                            and target.id == "__version__"
+                            and isinstance(node.value, ast.Str)
+                        ):
+                            return node.value.s
+    return None
+
+
+def download_file(url, file_name):
+    with requests.get(url, stream=True) as response:
+        response.raise_for_status()
+        total_size = int(response.headers.get("content-length", 0))
+        with (
+            open(file_name, "wb") as file,
+            tqdm(
+                desc=file_name.stem,
+                total=total_size,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+            ) as progress_bar,
+        ):
+            for chunk in response.iter_content(chunk_size=8192):
+                file.write(chunk)
+                progress_bar.update(len(chunk))
+
+
+def try_import(requirement):
+    dependency = requirement.name.strip()
+    import_name = pip_map.get(dependency, dependency)
+    installed = False
+
+    pip_name = dependency
+    pip_spec = "".join(specs[0]) if (specs := requirement.specs) else ""
+    try:
+        with suppress_std():
+            import_module(import_name)
+        print_formatted(
+            f"\t✅ Package {pip_name} already installed (import name: '{import_name}').",
+            "bold",
+            color="green",
+            no_header=True,
+        )
+        installed = True
+    except ImportError:
+        print_formatted(
+            f"\t⛔ Package {pip_name} is missing (import name: '{import_name}').",
+            "bold",
+            color="red",
+            no_header=True,
+        )
+
+    return (installed, pip_name, pip_spec, import_name)
+
+
+def import_or_install(requirement, dry=False):
+    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_install_name} would be installed (import name: '{import_name}').",
+                color="yellow",
+            )
+        else:
+            try:
+                run_command(
+                    [executable, "-m", "pip", "install", pip_install_name]
+                )
+                print_formatted(
+                    f"Package {pip_install_name} installed successfully using pip package name  (import name: '{import_name}')",
+                    "bold",
+                    color="green",
+                )
+            except subprocess.CalledProcessError as e:
+                print_formatted(
+                    f"Failed to install package {pip_install_name} using pip package name  (import name: '{import_name}'). Error: {str(e)}",
+                    "bold",
+                    color="red",
+                )
+
+
+def get_github_assets(tag=None):
+    if tag:
+        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/tags/{tag}"
+    else:
+        tag_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+    response = requests.get(tag_url)
+    if response.status_code == 404:
+        # print_formatted(
+        #     f"Tag version '{apply_color(version,'cyan')}' not found for {owner}/{repo} repository."
+        # )
+        print_formatted("Error retrieving the release assets.", color="red")
+        sys.exit()
+
+    tag_data = response.json()
+    tag_name = tag_data["name"]
+
+    return tag_data, tag_name
+
+
+# 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(
+            "mtb doesn't need an install script anymore.",
+            "italic",
+            color="yellow",
+        )
+        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
+
+    # 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)",
+    )
+
+    print_formatted("mtb install", "bold", color="yellow")
+
+    args = parser.parse_args()
+
+    print_formatted(f"Detected environment: {apply_color(mode, 'cyan')}")
+
+    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()
+
+        else:
+            repo_dir = clone_dir / repo_name
+            if not repo_dir.exists():
+                print_formatted(
+                    f"Cloning to {repo_dir}...", "italic", color="yellow"
+                )
+                run_command(
+                    ["git", "clone", "--recursive", repo_url, repo_dir]
+                )
+            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
+
+    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()

log.py

@@ -1,9 +1,20 @@
 import logging
+import os
 import re
 
+base_log_level = logging.DEBUG if os.environ.get("MTB_DEBUG") else logging.INFO
+
+
+# Custom object that discards the output
+class NullWriter:
+    def write(self, text):
+        pass
+
 
 class Formatter(logging.Formatter):
     grey = "\x1b[38;20m"
+    cyan = "\x1b[36;20m"
+    purple = "\x1b[35;20m"
     yellow = "\x1b[33;20m"
     red = "\x1b[31;20m"
     bold_red = "\x1b[31;1m"
@@ -12,8 +23,8 @@ class Formatter(logging.Formatter):
     format = "[%(name)s] | %(levelname)s -> %(message)s"
 
     FORMATS = {
-        logging.DEBUG: grey + format + reset,
-        logging.INFO: grey + format + reset,
+        logging.DEBUG: purple + format + reset,
+        logging.INFO: cyan + format + reset,
         logging.WARNING: yellow + format + reset,
         logging.ERROR: red + format + reset,
         logging.CRITICAL: bold_red + format + reset,
@@ -25,35 +36,52 @@ class Formatter(logging.Formatter):
         return formatter.format(record)
 
 
-def mklog(name, level=logging.DEBUG):
+def mklog(name: str, level: int = base_log_level):
     logger = logging.getLogger(name)
     logger.setLevel(level)
-    # create console handler with a higher log level
+
+    for handler in logger.handlers:
+        logger.removeHandler(handler)
+
     ch = logging.StreamHandler()
-    ch.setLevel(logging.DEBUG)
-
+    ch.setLevel(level)
     ch.setFormatter(Formatter())
-
     logger.addHandler(ch)
+
+    # Disable log propagation
+    logger.propagate = False
+
     return logger
 
 
 # - The main app logger
-log = mklog(__package__)
+log = mklog(__package__, base_log_level)
 
 
-def log_user(arg):
-    print("\033[34mComfy MTB Utils:\033[0m {arg}")
+def log_user(arg: str):
+    print(f"\033[34mComfy MTB Utils:\033[0m {arg}")
 
 
-def get_summary(docstring):
+def get_summary(docstring: str):
     return docstring.strip().split("\n\n", 1)[0]
 
 
-def blue_text(text):
+def blue_text(text: str):
     return f"\033[94m{text}\033[0m"
 
 
-def get_label(label):
-    words = re.findall(r"(?:^|[A-Z])[a-z]*", label)
+def cyan_text(text: str):
+    return f"\033[96m{text}\033[0m"
+
+
+def get_label(label: str):
+    if label.startswith("MTB_"):
+        label = label[4:]
+
+    words = re.findall(
+        r"(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|(?<=[A-Za-z])(?=[0-9])|(?<=[0-9])(?=[A-Za-z]))",
+        label,
+    )
+    reformatted_label = re.sub(r"([A-Z]+)", r" \1", label).strip()
+    words = reformatted_label.split()
     return " ".join(words).strip()

node_list.json

@@ -0,0 +1,62 @@
+{
+    "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"
+}

nodes/__init__.py

@@ -0,0 +1 @@
+"""MTB Nodes module."""

nodes/animation.py

@@ -0,0 +1,74 @@
+from ..log import log
+
+
+class MTB_AnimationBuilder:
+    """Simple maths for animation."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "total_frames": ("INT", {"default": 100, "min": 0}),
+                # "fps": ("INT", {"default": 12, "min": 0}),
+                "scale_float": ("FLOAT", {"default": 1.0, "min": 0.0}),
+                "loop_count": ("INT", {"default": 1, "min": 0}),
+                "raw_iteration": ("INT", {"default": 0, "min": 0}),
+                "raw_loop": ("INT", {"default": 0, "min": 0}),
+            },
+        }
+
+    RETURN_TYPES = ("INT", "FLOAT", "INT", "BOOLEAN")
+    RETURN_NAMES = ("frame", "0-1 (scaled)", "count", "loop_ended")
+    CATEGORY = "mtb/animation"
+    FUNCTION = "build_animation"
+    DESCRIPTION = """
+# Animation Builder
+
+Check the
+[wiki page](https://github.com/melMass/comfy_mtb/wiki/nodes-animation-builder)
+for more info.
+
+
+- This basic example should help to understand the meaning of
+its inputs and outputs thanks to the [debug](nodes-debug) node.
+
+![](https://github.com/melMass/comfy_mtb/assets/7041726/2b5c7e4f-372d-4494-9e73-abb2daa7cb36)
+
+- In this other example Animation Builder is used in combination with
+[Batch From History](https://github.com/melMass/comfy_mtb/wiki/nodes-batch-from-history)
+to create a zoom-in animation on a static image
+
+![](https://github.com/melMass/comfy_mtb/assets/7041726/77d37da1-0a8e-4519-a493-dfdef7f755ea)
+
+## Inputs
+
+| name | description |
+| ---- | :----------:|
+| total_frames | The number of frame to queue (this is multiplied by the `loop_count`)|
+| scale_float | Convenience input to scale the normalized `current value` (a float between 0 and 1 lerp over the current queue length) |
+| loop_count | The number of loops to queue |
+| **Reset Button** | resets the internal counters, although the node is though around using its queue button it should still work fine when using the regular queue button of comfy |
+| **Queue Button** | Convenience button to run the queues (`total_frames` * `loop_count`) |
+
+"""
+
+    def build_animation(
+        self,
+        total_frames=100,
+        # fps=12,
+        scale_float=1.0,
+        loop_count=1,  # set in js
+        raw_iteration=0,  # set in js
+        raw_loop=0,  # set in js
+    ):
+        frame = raw_iteration % (total_frames)
+        scaled = (frame / (total_frames - 1)) * scale_float
+        # if frame == 0:
+        #     log.debug("Reseting history")
+        #     PromptServer.instance.prompt_queue.wipe_history()
+        log.debug(f"frame: {frame}/{total_frames}  scaled: {scaled}")
+
+        return (frame, scaled, raw_loop, (frame == (total_frames - 1)))
+
+
+__nodes__ = [MTB_AnimationBuilder]

nodes/audio.py

@@ -0,0 +1,923 @@
+from typing import TYPE_CHECKING, Any, TypedDict
+
+import torch
+import torchaudio
+from comfy.model_management import get_torch_device
+from huggingface_hub import snapshot_download
+
+if TYPE_CHECKING:
+    from transformers import (
+        WhisperForConditionalGeneration,
+        WhisperProcessor,
+    )
+
+from ..log import log
+from ..utils import get_model_path
+
+WHISPER_SAMPLE_RATE = 16000
+
+
+class AudioTensor(TypedDict):
+    """Comfy's representation of AUDIO data."""
+
+    sample_rate: int
+    waveform: torch.Tensor
+
+
+class WhisperData(TypedDict):
+    """Whisper transcription data with timestamps and speaker info."""
+
+    text: str
+    chunks: list[dict[str, Any]]
+    language: str
+
+
+AudioData = AudioTensor | list[AudioTensor]
+
+
+class MtbAudio:
+    """Base class for audio processing."""
+
+    @classmethod
+    def is_stereo(
+        cls,
+        audios: AudioData,
+    ) -> bool:
+        if isinstance(audios, list):
+            return any(cls.is_stereo(audio) for audio in audios)
+        else:
+            return audios["waveform"].shape[1] == 2
+
+    @staticmethod
+    def resample(audio: AudioTensor, common_sample_rate: int) -> AudioTensor:
+        current_rate = audio["sample_rate"]
+        if current_rate != common_sample_rate:
+            log.debug(
+                f"Resampling audio from {current_rate} to {common_sample_rate}"
+            )
+            resampler = torchaudio.transforms.Resample(
+                orig_freq=current_rate, new_freq=common_sample_rate
+            )
+            return {
+                "sample_rate": common_sample_rate,
+                "waveform": resampler(audio["waveform"]),
+            }
+        else:
+            return audio
+
+    @staticmethod
+    def to_stereo(audio: AudioTensor) -> AudioTensor:
+        if audio["waveform"].shape[1] == 1:
+            return {
+                "sample_rate": audio["sample_rate"],
+                "waveform": torch.cat(
+                    [audio["waveform"], audio["waveform"]], dim=1
+                ),
+            }
+        else:
+            return audio
+
+    @classmethod
+    def preprocess_audios(
+        cls, audios: list[AudioTensor]
+    ) -> tuple[list[AudioTensor], bool, int]:
+        max_sample_rate = max([audio["sample_rate"] for audio in audios])
+
+        resampled_audios = [
+            cls.resample(audio, max_sample_rate) for audio in audios
+        ]
+
+        is_stereo = cls.is_stereo(audios)
+        if is_stereo:
+            audios = [cls.to_stereo(audio) for audio in resampled_audios]
+
+        return (audios, is_stereo, max_sample_rate)
+
+
+class WhisperPipeline(TypedDict):
+    """Whisper model pipeline."""
+
+    processor: "WhisperProcessor"
+    model: "WhisperForConditionalGeneration"
+
+
+class MTB_LoadWhisper:
+    """Load Whisper model and processor."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model_size": (
+                    [
+                        "tiny",
+                        "small",
+                        "medium",
+                        "medium.en",
+                        "base",
+                        "large",
+                        "large-v2",
+                        "large-v3",
+                        "large-v3-turbo",
+                    ],
+                    {"default": "tiny"},
+                ),
+            },
+            "optional": {
+                "download_missing": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": (
+                            "Download missing models if missing,"
+                            "otherwise they must be in ComfyUI/models/whisper"
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("WHISPER_PIPELINE",)
+    RETURN_NAMES = ("pipeline",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "load"
+
+    def load(self, model_size="tiny", download_missing=False):
+        """Load Whisper model and processor."""
+        from transformers import (
+            WhisperForConditionalGeneration,
+            WhisperProcessor,
+        )
+
+        whisper_dir = get_model_path("whisper")
+        tag = f"whisper-{model_size}"
+        model_dir = whisper_dir / tag
+
+        if not (whisper_dir.exists() or model_dir.exists()):
+            if not download_missing:
+                raise RuntimeError(
+                    "Models not found and download_missing=False"
+                )
+            else:
+                whisper_dir.mkdir(exist_ok=True)
+                model_dir.mkdir(exist_ok=True)
+
+                snapshot_download(
+                    repo_id=f"openai/{tag}",
+                    resume_download=True,
+                    ignore_patterns=["*.msgpack", "*.bin", "*.h5"],
+                    local_dir=model_dir.as_posix(),
+                    local_dir_use_symlinks=False,
+                )
+
+        device = get_torch_device()
+        log.debug(
+            f"Loading Whisper model {model_size} on {device} from {model_dir}"
+        )
+
+        processor = WhisperProcessor.from_pretrained(model_dir.as_posix())
+        model = WhisperForConditionalGeneration.from_pretrained(
+            model_dir.as_posix()
+        ).to(device)
+
+        model.eval()
+        model.requires_grad_(False)
+
+        return ({"processor": processor, "model": model},)
+
+
+class MTB_AudioToText(MtbAudio):
+    """Transcribe audio to text using Whisper."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "pipeline": ("WHISPER_PIPELINE",),
+                "audio": ("AUDIO",),
+                "language": (
+                    ["auto"]
+                    + sorted(
+                        [
+                            "en",
+                            "fr",
+                            "es",
+                            "de",
+                            "it",
+                            "pt",
+                            "nl",
+                            "ru",
+                            "zh",
+                            "ja",
+                            "ko",
+                        ]
+                    ),
+                    {"default": "auto"},
+                ),
+                "return_timestamps": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "WHISPER_OUTPUT")
+    FUNCTION = "transcribe"
+    CATEGORY = "mtb/audio"
+
+    def transcribe(
+        self,
+        pipeline: WhisperPipeline,
+        audio: AudioTensor,
+        language="auto",
+        return_timestamps=True,
+    ):
+        """Transcribe audio to text using Whisper."""
+        processor = pipeline["processor"]
+        model = pipeline["model"]
+        device = model.device
+
+        audio = self.resample(audio, WHISPER_SAMPLE_RATE)
+
+        waveform = audio["waveform"]
+        log.debug(f"Processed waveform shape: {waveform.shape}")
+
+        # - Mono: [1, 1, samples] or [1, samples] or [samples]
+        # - Stereo: [1, 2, samples] or [2, samples] or [samples, 2]
+        if len(waveform.shape) == 3:
+            waveform = waveform.squeeze(0)
+
+        if len(waveform.shape) == 2:
+            if waveform.shape[0] == 2:  # [channels, samples]
+                waveform = waveform.mean(dim=0)
+            elif waveform.shape[1] == 2:  # [samples, channels]
+                waveform = waveform.mean(dim=1)
+            else:  # mono
+                waveform = waveform.squeeze(0)
+
+        sample_rate = audio["sample_rate"]
+        chunk_duration = 30
+        chunk_samples = chunk_duration * sample_rate
+        total_samples = waveform.shape[-1]
+        total_duration = total_samples / sample_rate
+
+        log.debug(f"Audio duration: {total_duration:.2f}s")
+
+        all_tokens = []
+        all_text = []
+        chunk_offsets = []
+
+        last_time = 0.0
+        accumulated_offset = 0.0
+
+        for chunk_start in range(0, total_samples, chunk_samples):
+            chunk_end = min(chunk_start + chunk_samples, total_samples)
+            chunk_waveform = waveform[chunk_start:chunk_end]
+            chunk_offset = chunk_start / sample_rate
+            chunk_offsets.append(chunk_offset)
+
+            log.debug(
+                f"Processing chunk {chunk_offset:.1f}s - {chunk_end / sample_rate:.1f}s"
+            )
+
+            max_length = getattr(model.config, "max_length", None) or 448
+            attention_mask = torch.ones((1, max_length))
+
+            input_features = processor(
+                chunk_waveform,
+                sampling_rate=sample_rate,
+                return_tensors="pt",
+            ).input_features.to(device=device, dtype=model.dtype)
+
+            with torch.no_grad():
+                predicted_ids = model.generate(
+                    input_features,
+                    attention_mask=attention_mask.to(device),
+                    task="transcribe",
+                    language=None if language == "auto" else language,
+                    return_timestamps=return_timestamps,
+                    no_repeat_ngram_size=3,
+                    num_beams=5,
+                    length_penalty=1.0,
+                    max_length=max_length,
+                )
+
+            chunk_tokens = processor.tokenizer.convert_ids_to_tokens(
+                predicted_ids[0]
+            )
+
+            adjusted_tokens = []
+            for token in chunk_tokens:
+                if token.startswith("<|") and token.endswith("|>"):
+                    try:
+                        time_str = token[2:-2]
+                        if time_str.replace(".", "").isdigit():
+                            time_val = float(time_str)
+
+                            # If this timestamp is less than the last one, we've started a new sequence
+                            if time_val < last_time:
+                                accumulated_offset += last_time
+
+                            adjusted_time = time_val + accumulated_offset
+                            adjusted_tokens.append(f"<|{adjusted_time:.2f}|>")
+                            last_time = time_val
+                        else:
+                            adjusted_tokens.append(token)
+                    except ValueError:
+                        adjusted_tokens.append(token)
+                else:
+                    adjusted_tokens.append(token)
+
+            all_tokens.extend(adjusted_tokens)
+            chunk_text = processor.batch_decode(
+                predicted_ids, skip_special_tokens=True
+            )[0]
+            all_text.append(chunk_text)
+
+        detected_language = "en"
+        if language == "auto":
+            try:
+                log.debug("Detecting language")
+                with torch.no_grad():
+                    first_chunk_features = processor(
+                        waveform[:chunk_samples],
+                        sampling_rate=sample_rate,
+                        return_tensors="pt",
+                    ).input_features.to(device)
+
+                    predicted_probs = model.detect_language(
+                        first_chunk_features
+                    )[0]
+                    language_token = processor.tokenizer.convert_ids_to_tokens(
+                        predicted_probs.argmax(-1).item()
+                    )
+                    detected_language = (
+                        language_token[2:-2]
+                        if language_token.startswith("<|")
+                        else "en"
+                    )
+                    log.debug(f"Detected language: {detected_language}")
+
+            except Exception as e:
+                log.warning(f"Language detection failed: {e}")
+
+        full_transcription = " ".join(all_text)
+
+        whisper_output = {
+            "text": full_transcription,
+            "language": detected_language,
+            "tokens": all_tokens,
+            "audio": audio,
+            "chunk_offsets": chunk_offsets,
+        }
+
+        return full_transcription, whisper_output
+
+
+class MTB_ProcessWhisperOutput:
+    """Process Whisper output into timestamped chunks."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "whisper_output": ("WHISPER_OUTPUT",),
+                "min_chunk_length": (
+                    "FLOAT",
+                    {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "WHISPER_CHUNKS")
+    FUNCTION = "process"
+    CATEGORY = "mtb/audio"
+
+    def process(self, whisper_output, min_chunk_length=0.0):
+        """Process Whisper output into timestamped chunks."""
+        tokens = whisper_output["tokens"]
+        audio = whisper_output["audio"]
+        timestamp_tokens = []
+
+        audio_duration = audio["waveform"].shape[-1] / audio["sample_rate"]
+        log.debug(f"Audio duration: {audio_duration:.2f}s")
+
+        for i, token in enumerate(tokens):
+            if token.startswith("<|") and token.endswith("|>"):
+                try:
+                    time_str = token[2:-2]
+                    if time_str.replace(".", "").isdigit():
+                        time_val = float(time_str)
+                        if 0 <= time_val <= audio_duration:
+                            timestamp_tokens.append((i, time_val))
+                            log.debug(f"Token {i}: {time_val}")
+                except ValueError:
+                    continue
+
+        chunks = []
+        if len(timestamp_tokens) > 1:
+            for i in range(len(timestamp_tokens) - 1):
+                start_pos, start_time = timestamp_tokens[i]
+                end_pos, end_time = timestamp_tokens[i + 1]
+
+                if end_time - start_time < min_chunk_length:
+                    continue
+
+                chunk_tokens = tokens[start_pos + 1 : end_pos]
+                text = " ".join(
+                    t
+                    for t in chunk_tokens
+                    if not (t.startswith("<|") and t.endswith("|>"))
+                )
+
+                if text.strip():
+                    chunks.append(
+                        {
+                            "text": text.strip(),
+                            "timestamp": [start_time, end_time],
+                        }
+                    )
+
+        if timestamp_tokens:
+            start_pos, start_time = timestamp_tokens[-1]
+            if start_pos < len(tokens) - 1:
+                text = " ".join(
+                    t
+                    for t in tokens[start_pos + 1 :]
+                    if not (t.startswith("<|") and t.endswith("|>"))
+                )
+                if text.strip():
+                    if chunks:
+                        prev_chunk = chunks[-1]
+                        prev_duration = (
+                            prev_chunk["timestamp"][1]
+                            - prev_chunk["timestamp"][0]
+                        )
+                        end_time = min(
+                            start_time + prev_duration, audio_duration
+                        )
+                    else:
+                        end_time = audio_duration
+
+                    if (
+                        end_time > start_time
+                        and end_time - start_time >= min_chunk_length
+                    ):
+                        chunks.append(
+                            {
+                                "text": text.strip(),
+                                "timestamp": [start_time, end_time],
+                            }
+                        )
+
+        result = {
+            "text": whisper_output["text"],
+            "chunks": chunks,
+            "language": whisper_output["language"],
+        }
+
+        return whisper_output["text"], result
+
+
+class MTB_AudioCut(MtbAudio):
+    """Basic audio cutter, values are in ms."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "length": (
+                    ("FLOAT"),
+                    {
+                        "default": 1000.0,
+                        "min": 0.0,
+                        "max": 999999.0,
+                        "step": 1,
+                    },
+                ),
+                "offset": (
+                    ("FLOAT"),
+                    {"default": 0.0, "min": 0.0, "max": 999999.0, "step": 1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("cut_audio",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "cut"
+
+    def cut(self, audio: AudioTensor, length: float, offset: float):
+        sample_rate = audio["sample_rate"]
+        start_idx = int(offset * sample_rate / 1000)
+        end_idx = min(
+            start_idx + int(length * sample_rate / 1000),
+            audio["waveform"].shape[-1],
+        )
+        cut_waveform = audio["waveform"][:, :, start_idx:end_idx]
+
+        return (
+            {
+                "sample_rate": sample_rate,
+                "waveform": cut_waveform,
+            },
+        )
+
+
+class MTB_AudioStack(MtbAudio):
+    """Stack/Overlay audio inputs (dynamic inputs).
+    - pad audios to the longest inputs.
+    - resample audios to the highest sample rate in the inputs.
+    - convert them all to stereo if one of the inputs is.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {}}
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("stacked_audio",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "stack"
+
+    def stack(self, **kwargs: AudioTensor) -> tuple[AudioTensor]:
+        audios, is_stereo, max_rate = self.preprocess_audios(
+            list(kwargs.values())
+        )
+
+        max_length = max([audio["waveform"].shape[-1] for audio in audios])
+
+        padded_audios: list[torch.Tensor] = []
+        for audio in audios:
+            padding = torch.zeros(
+                (
+                    1,
+                    2 if is_stereo else 1,
+                    max_length - audio["waveform"].shape[-1],
+                )
+            )
+            padded_audio = torch.cat([audio["waveform"], padding], dim=-1)
+            padded_audios.append(padded_audio)
+
+        stacked_waveform = torch.stack(padded_audios, dim=0).sum(dim=0)
+
+        return (
+            {
+                "sample_rate": max_rate,
+                "waveform": stacked_waveform,
+            },
+        )
+
+
+class MTB_AudioSequence(MtbAudio):
+    """Sequence audio inputs (dynamic inputs).
+    - adding silence_duration between each segment
+      can now also be negative to overlap the clips, safely bound
+      to the the input length.
+    - resample audios to the highest sample rate in the inputs.
+    - convert them all to stereo if one of the inputs is.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "silence_duration": (
+                    ("FLOAT"),
+                    {"default": 0.0, "min": -999.0, "max": 999, "step": 0.01},
+                )
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("sequenced_audio",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "sequence"
+
+    def sequence(self, silence_duration: float, **kwargs: AudioTensor):
+        audios, is_stereo, max_rate = self.preprocess_audios(
+            list(kwargs.values())
+        )
+
+        sequence: list[torch.Tensor] = []
+        for i, audio in enumerate(audios):
+            if i > 0:
+                if silence_duration > 0:
+                    silence = torch.zeros(
+                        (
+                            1,
+                            2 if is_stereo else 1,
+                            int(silence_duration * max_rate),
+                        )
+                    )
+                    sequence.append(silence)
+                elif silence_duration < 0:
+                    overlap = int(abs(silence_duration) * max_rate)
+                    previous_audio = sequence[-1]
+                    overlap = min(
+                        overlap,
+                        previous_audio.shape[-1],
+                        audio["waveform"].shape[-1],
+                    )
+                    if overlap > 0:
+                        overlap_part = (
+                            previous_audio[:, :, -overlap:]
+                            + audio["waveform"][:, :, :overlap]
+                        )
+                        sequence[-1] = previous_audio[:, :, :-overlap]
+                        sequence.append(overlap_part)
+                        audio["waveform"] = audio["waveform"][:, :, overlap:]
+
+            sequence.append(audio["waveform"])
+
+        sequenced_waveform = torch.cat(sequence, dim=-1)
+        return (
+            {
+                "sample_rate": max_rate,
+                "waveform": sequenced_waveform,
+            },
+        )
+
+
+class MTB_AudioResample(MtbAudio):
+    """Resample audio to a different sample rate."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "sample_rate": (
+                    "INT",
+                    {
+                        "default": 16000,
+                        "min": 1000,
+                        "max": 192000,
+                        "step": 100,
+                        "tooltip": "Target sample rate in Hz. Whisper requires 16000.",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("resampled_audio",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "resample_audio"
+
+    def resample_audio(
+        self, audio: AudioTensor, sample_rate: int
+    ) -> tuple[AudioTensor]:
+        resampled = self.resample(audio, sample_rate)
+        return (resampled,)
+
+
+class MTB_AudioIsolateSpeaker(MtbAudio):
+    """Isolate or mute specific speakers using WhisperData"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "whisper_data": ("WHISPER_CHUNKS",),
+                "target_speaker": ("STRING", {"default": "SPEAKER_00"}),
+                "mode": (["isolate", "mute"], {"default": "isolate"}),
+                "fade_ms": (
+                    "FLOAT",
+                    {
+                        "default": 100.0,
+                        "min": 0.0,
+                        "max": 1000.0,
+                        "step": 10,
+                        "tooltip": "Fade duration in milliseconds to avoid clicks",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("processed_audio",)
+    CATEGORY = "mtb/audio"
+    FUNCTION = "process_audio"
+
+    def process_audio(
+        self,
+        audio: AudioTensor,
+        whisper_data: WhisperData,
+        target_speaker: str,
+        mode: str = "isolate",
+        fade_ms: float = 100.0,
+    ) -> tuple[AudioTensor]:
+        fade_samples = int((fade_ms / 1000.0) * audio["sample_rate"])
+
+        mask = (
+            torch.zeros_like(audio["waveform"])
+            if mode == "isolate"
+            else torch.ones_like(audio["waveform"])
+        )
+
+        for chunk in whisper_data["chunks"]:
+            if not chunk.get("speaker"):
+                continue
+
+            speaker_present = target_speaker in chunk["speaker"]
+            if (mode == "isolate" and speaker_present) or (
+                mode == "mute" and not speaker_present
+            ):
+                start_sample = int(
+                    chunk["timestamp"][0] * audio["sample_rate"]
+                )
+                end_sample = int(chunk["timestamp"][1] * audio["sample_rate"])
+
+                mask[:, start_sample:end_sample] = 1.0
+
+        if fade_samples > 0:
+            fade = torch.linspace(0, 1, fade_samples)
+
+            transitions = torch.where(mask[0, 1:] != mask[0, :-1])[0] + 1
+
+            for trans_idx in transitions:
+                if (
+                    trans_idx >= fade_samples
+                    and trans_idx <= mask.shape[1] - fade_samples
+                ):
+                    if mask[0, trans_idx] == 1:
+                        mask[:, trans_idx : trans_idx + fade_samples] *= fade
+                    else:
+                        mask[:, trans_idx - fade_samples : trans_idx] *= (
+                            fade.flip(0)
+                        )
+
+        processed_waveform = audio["waveform"] * mask
+
+        return (
+            {
+                "sample_rate": audio["sample_rate"],
+                "waveform": processed_waveform,
+            },
+        )
+
+
+class MTB_ProcessWhisperDiarization:
+    """Process Whisper chunks with speaker diarization using either pyannote or NeMo."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "whisper_chunks": ("WHISPER_CHUNKS",),
+                "audio": ("AUDIO",),
+                "backend": (["pyannote", "nemo"], {"default": "pyannote"}),
+                "num_speakers": (
+                    "INT",
+                    {"default": 2, "min": 1, "max": 10, "step": 1},
+                ),
+            },
+            "optional": {
+                "device": (["cuda", "cpu"], {"default": "cuda"}),
+            },
+        }
+
+    RETURN_TYPES = ("WHISPER_CHUNKS",)
+    FUNCTION = "process"
+    CATEGORY = "mtb/audio"
+
+    def process_pyannote(self, audio, num_speakers, device):
+        """Process audio using pyannote backend."""
+        try:
+            from pyannote.audio import Pipeline
+            from pyannote.audio.pipelines.utils.hook import ProgressHook
+        except ImportError:
+            raise ImportError(
+                "pyannote.audio not found. Install with: pip install pyannote.audio"
+            )
+
+        pipeline = Pipeline.from_pretrained(
+            "pyannote/speaker-diarization-3.1", use_auth_token=None
+        )
+        pipeline.to(torch.device(device))
+        with ProgressHook() as hook:
+            diarization = pipeline(
+                {
+                    "waveform": audio["waveform"][0],
+                    "sample_rate": audio["sample_rate"],
+                },
+                num_speakers=num_speakers,
+                hook=hook,
+            )
+
+        speaker_segments = []
+        for turn, _, speaker in diarization.itertracks(yield_label=True):
+            speaker_segments.append(
+                {
+                    "start": turn.start,
+                    "end": turn.end,
+                    "speaker": speaker,
+                }
+            )
+
+        return speaker_segments
+
+    def process_nemo(self, audio, num_speakers, device):
+        """Process audio using NeMo backend."""
+        try:
+            import nemo.collections.asr as nemo_asr
+        except ImportError:
+            raise ImportError(
+                "NeMo not found. Install with: pip install nemo_toolkit[asr]"
+            )
+
+        model = nemo_asr.models.ClusteringDiarizer.from_pretrained(
+            "nvidia/speakerverification_en_titanet_large"
+        ).to(device)
+
+        diarization = model.diarize(
+            audio=audio["waveform"][0],
+            sample_rate=audio["sample_rate"],
+            num_speakers=num_speakers,
+        )
+
+        speaker_segments = []
+        for segment in diarization:
+            speaker_segments.append(
+                {
+                    "start": segment["start"],
+                    "end": segment["end"],
+                    "speaker": f"SPEAKER_{segment['speaker']}",
+                }
+            )
+
+        return speaker_segments
+
+    def process(
+        self,
+        whisper_chunks,
+        audio,
+        backend="pyannote",
+        num_speakers=2,
+        device="cuda",
+    ):
+        if backend == "pyannote":
+            speaker_segments = self.process_pyannote(
+                audio, num_speakers, device
+            )
+        else:  # nemo
+            speaker_segments = self.process_nemo(audio, num_speakers, device)
+
+        for chunk in whisper_chunks["chunks"]:
+            chunk_start, chunk_end = chunk["timestamp"]
+            chunk_speakers = set()
+            for segment in speaker_segments:
+                if (
+                    segment["start"] <= chunk_end
+                    and segment["end"] >= chunk_start
+                ):
+                    chunk_speakers.add(segment["speaker"])
+
+            if chunk_speakers:
+                chunk["speaker"] = list(chunk_speakers)[0]
+            else:
+                chunk["speaker"] = "unknown"
+
+        return (whisper_chunks,)
+
+
+class MTB_AudioDuration:
+    """Get audio duration in milliseconds."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("duration_ms",)
+    FUNCTION = "get_duration"
+    CATEGORY = "mtb/audio"
+
+    def get_duration(self, audio):
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        duration_ms = int((waveform.shape[-1] / sample_rate) * 1000)
+        log.debug(
+            f"Audio duration: {duration_ms}ms ({duration_ms / 1000:.2f}s)"
+        )
+
+        return (duration_ms,)
+
+
+__nodes__ = [
+    MTB_AudioSequence,
+    MTB_AudioStack,
+    MTB_AudioCut,
+    MTB_AudioResample,
+    MTB_AudioIsolateSpeaker,
+    MTB_LoadWhisper,
+    MTB_AudioToText,
+    MTB_ProcessWhisperOutput,
+    MTB_ProcessWhisperDiarization,
+    MTB_AudioDuration,
+]

nodes/clock.py

@@ -0,0 +1,190 @@
+import time
+import uuid
+from collections import OrderedDict
+from typing import Any, TypedDict
+
+from comfy.comfy_types.node_typing import IO as CIO
+from server import PromptServer
+
+from ..log import log
+
+
+class Clock(TypedDict):
+    name: str
+    start: float
+    end: float | None
+
+
+active_timers: OrderedDict[str, Clock] = OrderedDict()
+
+# TODO: lower this
+MAX_CLOCKS = 50
+
+
+class MTB_StartClock:
+    """
+    Starts a profiling clock with a given name.
+
+    Outputs a unique ID that must be passed to EndClock.
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Clock A"}),
+                "cache": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Cache the clock ID, this means the node will follow Comfy's default invalidation system. If False it will always invalidate / mark the node as 'dirty'",
+                    },
+                ),
+            },
+            "optional": {
+                "passthrough": (CIO.ANY,),
+            },
+        }
+
+    RETURN_TYPES = (
+        CIO.ANY,
+        "STRING",
+    )
+    RETURN_NAMES = (
+        "passthrough",
+        "clock_id",
+    )
+    FUNCTION = "start_timer"
+    CATEGORY = "mtb/utils"
+
+    def start_timer(
+        self, *, name: str, passthrough: Any | None = None, **kwargs
+    ):
+        global active_timers
+
+        if len(active_timers) >= MAX_CLOCKS:
+            # get oldest clock
+            removed_key = None
+            for key, clock_data in active_timers.items():
+                if clock_data["end"] is not None:
+                    removed_key = key
+                    break
+            if removed_key:
+                removed_clock = active_timers.pop(removed_key)
+                log.info(
+                    f"[Profiling] Evicted finished clock '{removed_clock['name']}' (ID: {removed_key}) due to limit ({MAX_CLOCKS})."
+                )
+            else:
+                removed_key, removed_clock = active_timers.popitem(last=False)
+                log.warning(
+                    f"[Profiling] Evicted running clock '{removed_clock['name']}' (ID: {removed_key}) due to limit ({MAX_CLOCKS})."
+                )
+
+        clock_id = str(uuid.uuid4())
+        start_time = time.perf_counter()
+
+        active_timers[clock_id] = {
+            "start": start_time,
+            "name": name,
+            "end": None,
+        }
+
+        active_timers.move_to_end(clock_id)
+
+        log.debug(f"[Profiling] Clock '{name}' (ID: {clock_id}) started.")
+
+        return (
+            passthrough,
+            clock_id,
+        )
+
+    @classmethod
+    def IS_CHANGED(
+        cls, *, name: str, cache: bool = False, passthrough: Any | None = None
+    ):
+        if not cache:
+            return float("Nan")
+
+        return {"name": name, "cache": cache, "passthrough": passthrough}
+
+
+class MTB_EndClock:
+    """
+    Stops a profiling clock identified by its ID and returns the elapsed time in milliseconds.
+
+    Errors if the clock ID is not found or already stopped.
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "clock_id": (
+                    "STRING",
+                    {"forceInput": True},
+                ),
+            },
+            "optional": {
+                "passthrough": (CIO.ANY,),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = (
+        CIO.ANY,
+        "STRING",
+        "FLOAT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "passthrough",
+        "name",
+        "seconds",
+        "milliseconds",
+    )
+    FUNCTION = "end_timer"
+    CATEGORY = "mtb/utils"
+
+    def end_timer(self, clock_id: str, passthrough, unique_id=None):
+        global active_timers
+
+        if clock_id not in active_timers:
+            raise ValueError(
+                f"Error: Clock with ID '{clock_id}' not found. "
+                "Ensure StartClock was executed for this ID and proper passthrough chaining."
+            )
+
+        clock = active_timers[clock_id]
+        if clock.get("end") is not None:
+            return (passthrough, clock["name"], clock["end"])
+
+        start_time = clock["start"]
+        end_time = time.perf_counter()
+
+        duration_seconds = end_time - start_time
+        duration_ms = int(duration_seconds * 1000)
+        clock["end"] = duration_ms
+
+        active_timers.move_to_end(clock_id)
+
+        log.debug(
+            f"[Profiling] Clock '{clock['name']}' (ID: {clock_id}) stopped. Elapsed: {duration_ms}ms"
+        )
+        if unique_id:
+            PromptServer.instance.send_progress_text(
+                f"Clock '{clock['name']}' took {duration_seconds:.4f} seconds",
+                unique_id,
+            )
+
+        return (passthrough, clock["name"], duration_seconds, duration_ms)
+
+
+__nodes__ = [MTB_StartClock, MTB_EndClock]

nodes/conditions.py

@@ -1,18 +1,232 @@
-from ..utils import pil2tensor
-from ..utils import here
-from ..log import log
-import folder_paths
-from pathlib import Path
-import shutil
 import csv
+import shutil
+from pathlib import Path
+
+import folder_paths
+import torch
+
+from ..log import log
+from ..utils import here
+
+Conditioning = list[tuple[torch.Tensor, dict[str, torch.Tensor]]]
 
 
-class SmartStep:
+def check_condition(conditioning: Conditioning):
+    has_cn = False
+    if len(conditioning) > 1:
+        log.warn(
+            "More than one conditioning was provided. Only the first one will be used."
+        )
+    first = conditioning[0]
+    cond, kwargs = first
+
+    log.debug("Conditioning Shape")
+    log.debug(cond.shape)
+    log.debug("Conditioning keys")
+    log.debug([f"\t{k} - {type(kwargs[k])}" for k in kwargs])
+    if "control" in kwargs:
+        log.debug("Conditioning contains a controlnet")
+        has_cn = True
+    if "pooled_output" not in kwargs:
+        raise ValueError(
+            "Conditioning is not valid. Missing 'pooled_output' key."
+        )
+    return has_cn
+
+
+class MTB_InterpolateCondition:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "blend": (
+                    "FLOAT",
+                    {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "mtb/conditioning"
+    FUNCTION = "execute"
+
+    def execute(
+        self, blend: float, **kwargs: Conditioning
+    ) -> tuple[Conditioning]:
+        blend = max(0.0, min(1.0, blend))
+
+        conditions: list[Conditioning] = list(kwargs.values())
+        num_conditions = len(conditions)
+
+        if num_conditions < 2:
+            raise ValueError("At least two conditioning inputs are required.")
+
+        segment_length = 1.0 / (num_conditions - 1)
+
+        segment_index = min(int(blend // segment_length), num_conditions - 2)
+
+        local_blend = (
+            blend - (segment_index * segment_length)
+        ) / segment_length
+
+        cond_from = conditions[segment_index]
+        cond_to = conditions[segment_index + 1]
+
+        from_cn = check_condition(cond_from)
+        to_cn = check_condition(cond_to)
+
+        if from_cn and to_cn:
+            raise ValueError(
+                "Interpolating conditions cannot both contain ControlNets"
+            )
+
+        try:
+            interpolated_condition = [
+                (1.0 - local_blend) * c_from + local_blend * c_to
+                for c_from, c_to in zip(
+                    cond_from[0][0], cond_to[0][0], strict=False
+                )
+            ]
+        except Exception as e:
+            print(f"Error during interpolation: {e}")
+            raise
+
+        pooled_from = cond_from[0][1].get(
+            "pooled_output",
+            torch.zeros_like(
+                next(iter(cond_from[0][1].values()), torch.tensor([]))
+            ),
+        )
+
+        pooled_to = cond_to[0][1].get(
+            "pooled_output",
+            torch.zeros_like(
+                next(iter(cond_from[0][1].values()), torch.tensor([]))
+            ),
+        )
+
+        interpolated_pooled = (
+            1.0 - local_blend
+        ) * pooled_from + local_blend * pooled_to
+
+        res = {"pooled_output": interpolated_pooled}
+
+        if from_cn:
+            res["control"] = cond_from[0][1]["control"]
+            res["control_apply_to_uncond"] = cond_from[0][1][
+                "control_apply_to_uncond"
+            ]
+        if to_cn:
+            res["control"] = cond_to[0][1]["control"]
+            res["control_apply_to_uncond"] = cond_to[0][1][
+                "control_apply_to_uncond"
+            ]
+
+        return ([(torch.stack(interpolated_condition), res)],)
+
+
+class MTB_InterpolateClipSequential:
+    @classmethod
+    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 MTB_SmartStep:
     """Utils to control the steps start/stop of the KAdvancedSampler in percentage"""
 
-    def __init__(self):
-        pass
-
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -35,7 +249,7 @@ class SmartStep:
     RETURN_TYPES = ("INT", "INT", "INT")
     RETURN_NAMES = ("step", "start", "end")
     FUNCTION = "do_step"
-    CATEGORY = "conditioning"
+    CATEGORY = "mtb/conditioning"
 
     def do_step(self, step, start_percent, end_percent):
         start = int(step * start_percent / 100)
@@ -57,42 +271,56 @@ def install_default_styles(force=False):
     return dest_style
 
 
-class StylesLoader:
+class MTB_StylesLoader:
     """Load csv files and populate a dropdown from the rows (à la A111)"""
 
     options = {}
 
-    def __init__(self):
-        pass
-
     @classmethod
     def INPUT_TYPES(cls):
-        input_dir = Path(folder_paths.base_path) / "styles"
-        if not input_dir.exists():
-            install_default_styles()
+        if not cls.options:
+            input_dir = Path(folder_paths.base_path) / "styles"
+            if not input_dir.exists():
+                install_default_styles()
+
+            if not (
+                files := [f for f in input_dir.iterdir() if f.suffix == ".csv"]
+            ):
+                log.warn(
+                    "No styles found in the styles folder, place at least one csv file in the styles folder at the root of ComfyUI (for instance ComfyUI/styles/mystyle.csv)"
+                )
+
+            for file in files:
+                with open(file, encoding="utf8") as f:
+                    parsed = csv.reader(f)
+                    for i, row in enumerate(parsed):
+                        # log.debug(f"Adding style {row[0]}")
+                        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)})")
 
-        if not (files := [f for f in input_dir.iterdir() if f.suffix == ".csv"]):
-            log.error(
-                "No styles found in the styles folder, place at least one csv file in the styles folder"
-            )
-            return {
-                "required": {
-                    "style_name": (["error"],),
-                }
-            }
-        for file in files:
-            with open(file, "r", encoding="utf8") as f:
-                parsed = csv.reader(f)
-                for row in parsed:
-                    log.debug(f"Adding style {row[0]}")
-                    cls.options[row[0]] = (row[1], row[2])
         return {
             "required": {
                 "style_name": (list(cls.options.keys()),),
             }
         }
 
-    CATEGORY = "conditioning"
+    CATEGORY = "mtb/conditioning"
 
     RETURN_TYPES = ("STRING", "STRING")
     RETURN_NAMES = ("positive", "negative")
@@ -102,100 +330,9 @@ 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):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        fonts = list(Path(folder_paths.base_path).glob("**/*.ttf"))
-        if not fonts:
-            log.error(
-                "No fonts found in the fonts folder, place at least one ttf file in the fonts folder"
-            )
-            return {
-                "required": {
-                    "font": (["error"],),
-                }
-            }
-        for font in fonts:
-            log.debug(f"Adding font {font}")
-            cls.fonts[font.stem] = font.as_posix()
-
-        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": 100, "step": 1},
-                ),
-                "width": (
-                    "INT",
-                    {"default": 512, "min": 1, "max": 1000, "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 = "utils"
-
-    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__ = [
+    MTB_SmartStep,
+    MTB_StylesLoader,
+    MTB_InterpolateClipSequential,
+    MTB_InterpolateCondition,
+]

nodes/constant.py

@@ -0,0 +1,27 @@
+import json
+
+from ..log import log
+
+
+class MTB_Constant:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"Value": ("*",)},
+        }
+
+    RETURN_TYPES = ("*",)
+    RETURN_NAMES = ("output",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
+
+    def execute(
+        self,
+        **kwargs,
+    ):
+        log.debug("Received kwargs")
+        log.debug(json.dumps(kwargs, check_circular=True))
+        return (kwargs.get("Value"),)
+
+
+# __nodes__ = [MTB_Constant]

nodes/crop.py

@@ -1,20 +1,36 @@
+from typing import NamedTuple
+
 import torch
-from ..utils import tensor2pil, pil2tensor
-from PIL import Image, ImageFilter, ImageDraw
-import numpy as np
+import torchvision.transforms.functional as TF
+
+from ..log import log
 
 
-class BoundingBox:
-    """The bounding box (BBOX) custom type used by other nodes"""
-    def __init__(self):
-        pass
+class BoundingBox(NamedTuple):
+    """The bounding box tuple."""
+
+    x: int
+    y: int
+    width: int
+    height: int
+
+
+class MTB_Bbox:
+    """A literal bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "x": ("INT", {"default": 0, "max": 10000000, "min": 0, "step": 1}),
-                "y": ("INT", {"default": 0, "max": 10000000, "min": 0, "step": 1}),
+                # "bbox": ("BBOX",),
+                "x": (
+                    "INT",
+                    {"default": 0, "max": 10000000, "min": 0, "step": 1},
+                ),
+                "y": (
+                    "INT",
+                    {"default": 0, "max": 10000000, "min": 0, "step": 1},
+                ),
                 "width": (
                     "INT",
                     {"default": 256, "max": 10000000, "min": 0, "step": 1},
@@ -28,25 +44,74 @@ class BoundingBox:
 
     RETURN_TYPES = ("BBOX",)
     FUNCTION = "do_crop"
-    CATEGORY = "image/crop"
+    CATEGORY = "mtb/crop"
 
-    def do_crop(self, x, y, width, height):
-        return (x, y, width, height)
+    def do_crop(
+        self, x: int, y: int, width: int, height: int
+    ) -> tuple[BoundingBox]:  # bbox
+        return (BoundingBox(x, y, width, height),)
 
 
-class BBoxFromMask:
-    """From a mask extract the bounding box"""
-    def __init__(self):
-        pass
+class MTB_SplitBbox:
+    """Split the components of a bbox."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"bbox": ("BBOX",)},
+        }
+
+    CATEGORY = "mtb/crop"
+    FUNCTION = "split_bbox"
+    RETURN_TYPES = ("INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("x", "y", "width", "height")
+
+    def split_bbox(self, bbox: BoundingBox) -> BoundingBox:
+        return bbox
+
+
+class MTB_UpscaleBboxBy:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bbox": ("BBOX",),
+                "scale": ("FLOAT", {"default": 1.0}),
+            },
+        }
+
+    CATEGORY = "mtb/crop"
+    RETURN_TYPES = ("BBOX",)
+
+    FUNCTION = "upscale"
+
+    def upscale(self, bbox: BoundingBox, scale: float) -> tuple[BoundingBox]:
+        x, y, width, height = bbox
+
+        center_x = x + width / 2
+        center_y = y + height / 2
+
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+
+        new_x = int(center_x - new_width / 2)
+        new_y = int(center_y - new_height / 2)
+
+        return (BoundingBox(new_x, new_y, new_width, new_height),)
+
+
+class MTB_BboxFromMask:
+    """From a mask extract the bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "mask": ("MASK",),
+                "invert": ("BOOLEAN", {"default": False}),
             },
             "optional": {
-                "image": ("IMAGE",),
+                "image": ("IMAGE", {"tooltip": "Optional image"}),
             },
         }
 
@@ -59,41 +124,49 @@ class BBoxFromMask:
         "image (optional)",
     )
     FUNCTION = "extract_bounding_box"
-    CATEGORY = "image/crop"
+    CATEGORY = "mtb/crop"
 
-    def extract_bounding_box(self, mask: torch.Tensor, image=None):
+    def extract_bounding_box(
+        self,
+        mask: torch.Tensor,
+        *,
+        invert: bool = False,
+        image: torch.Tensor | None = None,
+    ) -> tuple[BoundingBox, torch.Tensor | None]:
+        mask = 1 - mask if invert else mask
+        non_zero_indices = torch.nonzero(mask)
 
-        mask = tensor2pil(mask)
+        if non_zero_indices.numel() == 0:
+            log.warning(
+                "BboxFromMask: Mask is empty. Returning a (0,0,0,0) bbox."
+            )
+            return (BoundingBox(0, 0, 0, 0), image)
 
-        alpha_channel = np.array(mask)
-        non_zero_indices = np.nonzero(alpha_channel)
+        min_coords = torch.min(non_zero_indices, dim=0).values
+        max_coords = torch.max(non_zero_indices, dim=0).values
 
-        min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-        min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+        min_y, min_x = min_coords[1].item(), min_coords[2].item()
+        max_y, max_x = max_coords[1].item(), max_coords[2].item()
 
-        # Create a bounding box tuple
-        if image != None:
-            # Convert the image to a NumPy array
-            image = image.numpy()
-            # Crop the image from the bounding box
-            image = image[:, min_y:max_y, min_x:max_x]
-            image = torch.from_numpy(image)
+        width = max_x - min_x + 1
+        height = max_y - min_y + 1
 
-        bounding_box = (min_x, min_y, max_x - min_x, max_y - min_y)
-        return (
-            bounding_box,
-            image,
+        bounding_box = BoundingBox(
+            int(min_x), int(min_y), int(width), int(height)
         )
 
+        cropped_image = None
+        if image is not None:
+            cropped_image = image[:, min_y : max_y + 1, min_x : max_x + 1, :]
 
-class Crop:
-    """Crops an image and an optional mask to a given bounding box
+        return (bounding_box, cropped_image)
+
+
+class MTB_Crop:
+    """Crop an image and an optional mask to a given bounding box.
 
-    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
     The BBOX input takes precedence over the tuple input
     """
-    def __init__(self):
-        pass
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -103,8 +176,14 @@ class Crop:
             },
             "optional": {
                 "mask": ("MASK",),
-                "x": ("INT", {"default": 0, "max": 10000000, "min": 0, "step": 1}),
-                "y": ("INT", {"default": 0, "max": 10000000, "min": 0, "step": 1}),
+                "x": (
+                    "INT",
+                    {"default": 0, "max": 10000000, "min": 0, "step": 1},
+                ),
+                "y": (
+                    "INT",
+                    {"default": 0, "max": 10000000, "min": 0, "step": 1},
+                ),
                 "width": (
                     "INT",
                     {"default": 256, "max": 10000000, "min": 0, "step": 1},
@@ -120,37 +199,83 @@ class Crop:
     RETURN_TYPES = ("IMAGE", "MASK", "BBOX")
     FUNCTION = "do_crop"
 
-    CATEGORY = "image/crop"
+    CATEGORY = "mtb/crop"
 
     def do_crop(
-        self, image: torch.Tensor, mask=None, x=0, y=0, width=256, height=256, bbox=None
+        self,
+        image: torch.Tensor,
+        *,
+        mask: torch.Tensor | None = None,
+        x: int = 0,
+        y: int = 0,
+        width: int = 256,
+        height: int = 256,
+        bbox: BoundingBox | None = None,
     ):
-
-        image = image.numpy()
-        if mask:
-            mask = mask.numpy()
-
-        if bbox != None:
+        if bbox is not None:
             x, y, width, height = bbox
 
+        if width <= 0 or height <= 0:
+            log.error(
+                "Crop dimensions must be positive. Check the BBOX or widget inputs."
+            )
+            return (
+                torch.zeros_like(image),
+                torch.zeros_like(mask) if mask is not None else None,
+                (x, y, width, height),
+            )
+
         cropped_image = image[:, y : y + height, x : x + width, :]
-        cropped_mask = mask[y : y + height, x : x + width] if mask != None else None
-        crop_data = (x, y, width, height)
+        cropped_mask = (
+            mask[:, y : y + height, x : x + width]
+            if mask is not None
+            else None
+        )
+        crop_data = BoundingBox(x, y, width, height)
 
         return (
-            torch.from_numpy(cropped_image),
-            torch.from_numpy(cropped_mask) if mask != None else None,
+            cropped_image,
+            cropped_mask if cropped_mask is not None else None,
             crop_data,
         )
 
 
-class Uncrop:
-    """Uncrops an image to a given bounding box
+# def calculate_intersection(rect1, rect2):
+#     x_left = max(rect1[0], rect2[0])
+#     y_top = max(rect1[1], rect2[1])
+#     x_right = min(rect1[2], rect2[2])
+#     y_bottom = min(rect1[3], rect2[3])
 
-    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
-    The BBOX input takes precedence over the tuple input"""
-    def __init__(self):
-        pass
+#     return (x_left, y_top, x_right, y_bottom)
+
+
+def bbox_check(bbox: BoundingBox, target_size: tuple[int, int] | None = None):
+    if not target_size:
+        return bbox
+
+    new_bbox = BoundingBox(
+        bbox.x,
+        bbox.y,
+        min(target_size[0] - bbox.x, bbox.width),
+        min(target_size[1] - bbox.y, bbox.height),
+    )
+    if new_bbox != bbox:
+        log.warning(f"BBox too big, constrained to {new_bbox}")
+
+    return new_bbox
+
+
+def bbox_to_region(
+    bbox: BoundingBox, target_size: tuple[int, int] | None = None
+):
+    bbox = bbox_check(bbox, target_size)
+
+    # to region
+    return (bbox.x, bbox.y, bbox.x + bbox.width, bbox.y + bbox.height)
+
+
+class MTB_Uncrop:
+    """Uncrop an image to a given bounding box."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -167,56 +292,164 @@ class Uncrop:
         }
 
     RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "do_crop"
+    FUNCTION = "do_uncrop"
+    CATEGORY = "mtb/crop"
 
-    CATEGORY = "image/crop"
-
-    def do_crop(self, image, crop_image, bbox, border_blending):
-        def inset_border(image, border_width=20, border_color=(0)):
-            width, height = image.size
-            bordered_image = Image.new(image.mode, (width, height), border_color)
-            bordered_image.paste(image, (0, 0))
-            draw = ImageDraw.Draw(bordered_image)
-            draw.rectangle(
-                (0, 0, width - 1, height - 1), outline=border_color, width=border_width
+    def do_uncrop(
+        self,
+        image: torch.Tensor,
+        crop_image: torch.Tensor,
+        bbox: BoundingBox,
+        border_blending: float = 0.25,
+    ):
+        if len(image) > 1 and len(image) != len(crop_image):
+            raise ValueError(
+                "Uncrop: Batch size of background 'image' must be 1 or match the 'crop_image' batch size."
             )
-            return bordered_image
+        import comfy.utils
 
-        image = tensor2pil(image)
-        crop_img = tensor2pil(crop_image)
-        crop_img = crop_img.convert("RGB")
+        pbar = comfy.utils.ProgressBar(4)
 
-        # uncrop the image based on the bounding box
-        bb_x, bb_y, bb_width, bb_height = bbox
+        device = image.device
 
-        if border_blending > 1.0:
-            border_blending = 1.0
-        elif border_blending < 0.0:
-            border_blending = 0.0
+        log.debug(f"Working on device: {device}")
 
-        blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
+        crop_image = crop_image.to(device)
 
-        blend = image.convert("RGBA")
-        mask = Image.new("L", image.size, 0)
+        if len(image) == 1 and len(crop_image) > 1:
+            image = image.repeat(len(crop_image), 1, 1, 1)
 
-        mask_block = Image.new("L", (bb_width, bb_height), 255)
-        mask_block = inset_border(mask_block, int(blend_ratio / 2), (0))
+        batch_size, bg_h, bg_w, _ = image.shape
+        _, fg_h, fg_w, _ = crop_image.shape
+        x, y, width, height = bbox
 
-        mask.paste(mask_block, (bb_x, bb_y, bb_x + bb_width, bb_y + bb_height))
-        blend.paste(crop_img, (bb_x, bb_y, bb_x + bb_width, bb_y + bb_height))
+        if (width, height) != (fg_w, fg_h):
+            log.warning(
+                f"Uncrop: crop_image size {(fg_w, fg_h)} "
+                "differs from bbox {(width, height)}. Resizing to fit bbox."
+            )
 
-        mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
-        mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
+        resized_crop = crop_image.permute(0, 3, 1, 2)
+        resized_crop = torch.nn.functional.interpolate(
+            resized_crop,
+            size=(height, width),
+            mode="bicubic",
+            align_corners=False,
+        )
+        resized_crop = resized_crop.permute(0, 2, 3, 1)
 
-        blend.putalpha(mask)
-        image = Image.alpha_composite(image.convert("RGBA"), blend)
+        pbar.update(1)
+        # paste coords
+        paste_x1 = max(x, 0)
+        paste_y1 = max(y, 0)
+        paste_x2 = min(x + width, bg_w)
+        paste_y2 = min(y + height, bg_h)
 
-        return (pil2tensor(image.convert("RGB")),)
+        # region from crop (bound)
+        crop_x1 = max(0, -x)
+        crop_y1 = max(0, -y)
+        crop_x2 = crop_x1 + (paste_x2 - paste_x1)
+        crop_y2 = crop_y1 + (paste_y2 - paste_y1)
+
+        if paste_x1 >= paste_x2 or paste_y1 >= paste_y2:
+            log.warning(
+                "Uncrop: BBOX is entirely outside the image boundaries. Returning original image."
+            )
+            return (image,)
+
+        pbar.update(1)
+        source_slice = resized_crop[:, crop_y1:crop_y2, crop_x1:crop_x2, :]
+
+        final_image = image.clone()
+        final_image[:, paste_y1:paste_y2, paste_x1:paste_x2, :] = source_slice
+
+        pbar.update(1)
+
+        blend_radius = int(max(width, height) * border_blending * 0.5)
+        if blend_radius > 0:
+            _device = device
+            if torch.cuda.is_available():
+                _device = torch.device("cuda")
+
+            log.debug("Processing blending")
+            alpha_mask = torch.zeros((batch_size, bg_h, bg_w), device=_device)
+            alpha_mask[:, paste_y1:paste_y2, paste_x1:paste_x2] = 1.0
+
+            kernel_size = 2 * blend_radius + 1
+
+            log.debug("Gaussian blur...")
+            alpha_mask = TF.gaussian_blur(
+                alpha_mask.unsqueeze(1), kernel_size=[kernel_size, kernel_size]
+            ).squeeze(1)
+            alpha_mask = alpha_mask.unsqueeze(-1)
+
+            log.debug("Applying blending")
+            final_image = final_image.to(_device) * alpha_mask + image.to(
+                _device
+            ) * (1.0 - alpha_mask)
+
+        pbar.update(1)
+        return (final_image.to(device),)
+
+
+class MTB_BBoxForceDimensions:
+    """
+    Resize a BBOX to new dimensions while keeping its center.
+
+    Optionally constrains the BBOX to stay within image boundaries.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bbox": ("BBOX",),
+                "width": ("INT", {"default": 512, "min": 1, "max": 8192}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 8192}),
+                "constrain_to_image": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    CATEGORY = "mtb/crop"
+    RETURN_TYPES = ("BBOX",)
+    FUNCTION = "force_dimensions"
+
+    def force_dimensions(
+        self,
+        *,
+        bbox: tuple[int, int, int, int],
+        width: int,
+        height: int,
+        constrain_to_image: bool = True,
+        image: torch.Tensor | None = None,
+    ) -> tuple[tuple[int, int, int, int]]:
+        x, y, curr_width, curr_height = bbox
+
+        center_x = x + curr_width // 2
+        center_y = y + curr_height // 2
+
+        new_x = center_x - width // 2
+        new_y = center_y - height // 2
+
+        if constrain_to_image and image is not None:
+            img_height, img_width = image.shape[1:3]
+            new_x = max(0, min(new_x, img_width - width))
+            new_y = max(0, min(new_y, img_height - height))
+            width = min(width, img_width)
+            height = min(height, img_height)
+
+        return ((new_x, new_y, width, height),)
 
 
 __nodes__ = [
-    BBoxFromMask,
-    BoundingBox,
-    Crop,
-    Uncrop
-]
+    MTB_BboxFromMask,
+    MTB_Bbox,
+    MTB_Crop,
+    MTB_Uncrop,
+    MTB_SplitBbox,
+    MTB_UpscaleBboxBy,
+    MTB_BBoxForceDimensions,
+]

nodes/curve.py

@@ -0,0 +1,93 @@
+import json
+
+from ..log import log
+
+
+def deserialize_curve(curve):
+    if isinstance(curve, str):
+        curve = json.loads(curve)
+    return curve
+
+
+def serialize_curve(curve):
+    if not isinstance(curve, str):
+        curve = json.dumps(curve)
+    return curve
+
+
+class MTB_Curve:
+    """A basic FLOAT_CURVE input node."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "curve": ("FLOAT_CURVE",),
+            },
+        }
+
+    RETURN_TYPES = ("FLOAT_CURVE",)
+    FUNCTION = "do_curve"
+
+    CATEGORY = "mtb/curve"
+
+    def do_curve(self, curve):
+        log.debug(f"Curve: {curve}")
+        return (curve,)
+
+
+class MTB_CurveToFloat:
+    """Convert a FLOAT_CURVE to a FLOAT or FLOATS"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "curve": ("FLOAT_CURVE", {"forceInput": True}),
+                "steps": ("INT", {"default": 10, "min": 2}),
+            },
+        }
+
+    RETURN_TYPES = ("FLOATS", "FLOAT")
+    FUNCTION = "do_curve"
+
+    CATEGORY = "mtb/curve"
+
+    def do_curve(self, curve, steps):
+        log.debug(f"Curve: {curve}")
+
+        # sort by x (should be handled by the widget)
+        sorted_points = sorted(curve.items(), key=lambda item: item[1]["x"])
+        # Extract X and Y values
+        x_values = [point[1]["x"] for point in sorted_points]
+        y_values = [point[1]["y"] for point in sorted_points]
+        # Calculate step size
+        step_size = (max(x_values) - min(x_values)) / (steps - 1)
+
+        # Interpolate Y values for each step
+        interpolated_y_values = []
+        for step in range(steps):
+            current_x = min(x_values) + step_size * step
+
+            # Find the indices of the two points between which the current_x falls
+            idx1 = max(idx for idx, x in enumerate(x_values) if x <= current_x)
+            idx2 = min(idx for idx, x in enumerate(x_values) if x >= current_x)
+
+            # If the current_x matches one of the points, no interpolation is needed
+            if current_x == x_values[idx1]:
+                interpolated_y_values.append(y_values[idx1])
+            elif current_x == x_values[idx2]:
+                interpolated_y_values.append(y_values[idx2])
+            else:
+                # Interpolate Y value using linear interpolation
+                y1 = y_values[idx1]
+                y2 = y_values[idx2]
+                x1 = x_values[idx1]
+                x2 = x_values[idx2]
+                interpolated_y = y1 + (y2 - y1) * (current_x - x1) / (x2 - x1)
+                interpolated_y_values.append(interpolated_y)
+
+        return (interpolated_y_values, interpolated_y_values)
+
+
+__nodes__ = [MTB_Curve, MTB_CurveToFloat]

nodes/debug.py

@@ -0,0 +1,268 @@
+import base64
+import io
+import json
+from pathlib import Path
+
+import folder_paths
+import torch
+
+from ..log import log
+from ..utils import tensor2pil
+
+
+def get_detailed_type_info(obj):
+    type_info = []
+
+    type_name = type(obj).__name__
+    type_info.append(f"Type: {type_name}")
+
+    if isinstance(obj, torch.Tensor):
+        type_info.extend(
+            [
+                f"Shape: {obj.shape}",
+                f"Dtype: {obj.dtype}",
+                f"Device: {obj.device}",
+                f"Requires grad: {obj.requires_grad}",
+                f"Stride: {obj.stride()}",
+                f"Contiguous: {obj.is_contiguous()}",
+            ]
+        )
+    elif isinstance(obj, (list, tuple)):
+        type_info.extend(
+            [
+                f"Length: {len(obj)}",
+                f"Container type: {type_name}",
+            ]
+        )
+        if obj:
+            type_info.append(f"Element type: {type(obj[0]).__name__}")
+    elif isinstance(obj, dict):
+        type_info.extend(
+            [
+                f"Length: {len(obj)}",
+                f"Keys: {list(obj.keys())}",
+            ]
+        )
+    elif hasattr(obj, "__dict__"):
+        attributes = [attr for attr in dir(obj) if not attr.startswith("_")]
+        type_info.append(f"Attributes: {attributes}")
+
+    return type_info
+
+
+# region processors
+def process_tensor(tensor: torch.Tensor, as_type=False):
+    log.debug(f"Tensor: {tensor.shape}")
+
+    if as_type:
+        return {
+            "text": [f"Tensor of shape {tensor.shape} of type {tensor.dtype}"]
+        }
+
+    is_mask = len(tensor.shape) == 3
+
+    if is_mask:
+        tensor = tensor.unsqueeze(-1).repeat(1, 1, 1, 3)
+
+    image = tensor2pil(tensor)
+    b64_imgs = []
+    for im in image:
+        if is_mask:
+            im = im.convert("L")
+
+        buffered = io.BytesIO()
+        im.save(buffered, format="PNG")
+        b64_imgs.append(
+            "data:image/png;base64,"
+            + base64.b64encode(buffered.getvalue()).decode("utf-8")
+        )
+
+    return {"b64_images": b64_imgs}
+
+
+def process_list(anything, as_type=False):
+    text = []
+    if not anything:
+        return {"text": []}
+
+    if as_type:
+        type_info = get_detailed_type_info(anything)
+        type_info.extend(get_detailed_type_info(anything[0]))
+        return {"text": type_info}
+
+    first_element = anything[0]
+    if (
+        isinstance(first_element, list)
+        and first_element
+        and isinstance(first_element[0], torch.Tensor)
+    ):
+        text.append(
+            "List of List of Tensors: "
+            f"{first_element[0].shape} (x{len(anything)})"
+        )
+
+    elif isinstance(first_element, torch.Tensor):
+        text.append(
+            f"List of Tensors: {first_element.shape} (x{len(anything)})"
+        )
+    else:
+        text.append(f"Array ({len(anything)}): {anything}")
+
+    return {"text": text}
+
+
+def process_dict(anything, as_type=False):
+    text = []
+    if as_type:
+        return {"text": get_detailed_type_info(anything)}
+
+    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}")
+
+    elif "waveform" in anything:
+        is_empty = (
+            "(empty) " if torch.count_nonzero(anything["samples"]) == 0 else ""
+        )
+
+        text.append(
+            f"Audio Samples: {anything['waveform'].shape}{is_empty} | sample rate {anything['sample_rate']}"
+        )
+
+    else:
+        log.debug(f"Unhandled dict: {anything.keys()}")
+        text.append(json.dumps(anything, indent=2))
+
+    return {"text": text}
+
+
+def process_bool(anything, as_type=False):
+    return {"text": ["True" if anything else "False"]}
+
+
+def process_text(anything, as_type=False):
+    if as_type:
+        return {"text": get_detailed_type_info(anything)}
+
+    return {"text": [str(anything)]}
+
+
+# endregion
+
+
+class MTB_Debug:
+    """Experimental node to debug any Comfy values.
+
+    support for more types and widgets is planned.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"output_to_console": ("BOOLEAN", {"default": False})},
+            "optional": {"as_detailed_types": ("BOOLEAN", {"default": False})},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "do_debug"
+    CATEGORY = "mtb/debug"
+    OUTPUT_NODE = True
+
+    def do_debug(
+        self, output_to_console: bool, as_detailed_types: bool, **kwargs
+    ):
+        output = {"ui": {"items": []}}
+
+        if output_to_console:
+            for k, v in kwargs.items():
+                log.info(f"{k}: {v}")
+
+        for input_name, anything in kwargs.items():
+            processor = processors.get(type(anything), process_text)
+
+            processed = processor(anything, as_detailed_types)
+
+            item = {
+                "input": input_name,
+                **processed,
+            }
+            output["ui"]["items"].append(item)
+
+        return output
+
+
+class MTB_SaveTensors:
+    """Save torch tensors (image, mask or latent) to disk.
+
+    useful to debug things outside comfy.
+    """
+
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "mtb/debug"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "filename_prefix": ("STRING", {"default": "ComfyPickle"}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+                "latent": ("LATENT",),
+            },
+        }
+
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+    CATEGORY = "mtb/debug"
+
+    def save(
+        self,
+        filename_prefix,
+        image: torch.Tensor | None = None,
+        mask: torch.Tensor | None = None,
+        latent: torch.Tensor | None = None,
+    ):
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            filename_prefix,
+        ) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder = Path(full_output_folder)
+        if image is not None:
+            image_file = f"{filename}_image_{counter:05}.pt"
+            torch.save(image, full_output_folder / image_file)
+            # np.save(full_output_folder/ image_file, image.cpu().numpy())
+
+        if mask is not None:
+            mask_file = f"{filename}_mask_{counter:05}.pt"
+            torch.save(mask, full_output_folder / mask_file)
+            # np.save(full_output_folder/ mask_file, mask.cpu().numpy())
+
+        if latent is not None:
+            # for latent we must use pickle
+            latent_file = f"{filename}_latent_{counter:05}.pt"
+            torch.save(latent, full_output_folder / latent_file)
+            # pickle.dump(latent, open(full_output_folder/ latent_file, "wb"))
+
+            # np.save(full_output_folder / latent_file,
+            # latent[""].cpu().numpy())
+
+        return f"{filename_prefix}_{counter:05}"
+
+
+processors = {
+    torch.Tensor: process_tensor,
+    list: process_list,
+    dict: process_dict,
+    bool: process_bool,
+}
+
+__nodes__ = [MTB_Debug, MTB_SaveTensors]

nodes/deep_bump.py

@@ -1,23 +1,57 @@
-import onnxruntime as ort
+import tempfile
+from pathlib import Path
+
 import numpy as np
-import pathlib
+
+# torch must be imported prior to onnx for the CUDAProvider.
+import torch  # isort:skip
 import onnxruntime as ort
-import numpy as np
-from .. import utils as utils_inference
-from ..log import log
+from PIL import Image
+
+from ..errors import ModelNotFound
+from ..log import mklog
+from ..utils import (
+    download_model,
+    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):
-    """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'.
+def color_to_normals(
+    color_img,
+    overlap,
+    progress_callback,
+    *,
+    save_temp=False,
+    auto_download=False,
+):
+    """Compute 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(
+        "Converting color image to grayscale by taking "
+        f"the mean over color channels: {img.shape}"
+    )
 
     # Split image in tiles
     log.debug("DeepBump Color → Normals : tilling")
@@ -28,72 +62,130 @@ 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():
+        if not auto_download:
+            raise ModelNotFound(f"deepbump ({model})")
+        log.debug("Downloading models...")
+        download_model(
+            "https://github.com/HugoTini/DeepBump/raw/master/deepbump256.onnx",
+            "deepbump",
+        )
+
+    providers = [
+        "TensorrtExecutionProvider",
+        "CUDAExecutionProvider",
+        "CoreMLProvider",
+        "CPUExecutionProvider",
+    ]
+    available_providers = [
+        provider
+        for provider in providers
+        if provider in ort.get_available_providers()
+    ]
+
+    if not available_providers:
+        raise RuntimeError(
+            "No valid ONNX Runtime providers available on this machine."
+        )
+    log.debug(f"Using ONNX providers: {available_providers}")
+    ort_session = ort.InferenceSession(
+        model.as_posix(), providers=available_providers
+    )
 
     # Predict normal map for each tile
     log.debug("DeepBump Color → Normals : generating")
-    pred_tiles = utils_inference.tiles_infer(
+    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
 
 
 # - NORMALS to CURVATURE
 def conv_1d(array, kernel_1d):
-    """Performs row by row 1D convolutions of the given 2D image with the given 1D kernel."""
+    """Perform row by row 1D convolutions.
 
+    of the given 2D image with the given 1D kernel.
+    """
     # Input kernel length must be odd
     k_l = len(kernel_1d)
+
     assert k_l % 2 != 0
     # Convolution is repeat-padded
     extended = np.pad(array, k_l // 2, mode="wrap")
     # Output has same size as input (padded, valid-mode convolution)
     output = np.empty(array.shape)
     for i in range(array.shape[0]):
-        output[i] = np.convolve(extended[i + (k_l // 2)], kernel_1d, mode="valid")
+        output[i] = np.convolve(
+            extended[i + (k_l // 2)], kernel_1d, mode="valid"
+        )
 
     return output * -1
 
 
 def gaussian_kernel(length, sigma):
-    """Returns a 1D gaussian kernel of size 'length'."""
-
+    """Return a 1D gaussian kernel of size 'length'."""
     space = np.linspace(-(length - 1) / 2, (length - 1) / 2, length)
     kernel = np.exp(-0.5 * np.square(space) / np.square(sigma))
     return kernel / np.sum(kernel)
 
 
 def normalize(np_array):
-    """Normalize all elements of the given numpy array to [0,1]"""
-
-    return (np_array - np.min(np_array)) / (np.max(np_array) - np.min(np_array))
+    """Normalize all elements of the given numpy array to [0,1]."""
+    return (np_array - np.min(np_array)) / (
+        np.max(np_array) - np.min(np_array)
+    )
 
 
 def normals_to_curvature(normals_img, blur_radius, progress_callback):
-    """Computes a curvature map from the given normal map. 'normals_img' must be a numpy array
-    in C,H,W format (with C as RGB). 'blur_radius' must be one of 'SMALLEST', 'SMALLER', 'SMALL',
-    'MEDIUM', 'LARGE', 'LARGER', 'LARGEST'."""
+    """Compute a curvature map from the given normal map.
 
+    'normals_img' must be a numpy array in C,H,W format (with C as RGB).
+    'blur_radius' must be one of:
+        'SMALLEST', 'SMALLER', 'SMALL', 'MEDIUM', 'LARGE', 'LARGER', 'LARGEST'.
+    """
     # Convolutions on normal map red & green channels
     if progress_callback is not None:
         progress_callback(0, 4)
@@ -118,8 +210,12 @@ def normals_to_curvature(normals_img, blur_radius, progress_callback):
         "LARGER": 1 / 8,
         "LARGEST": 1 / 4,
     }
-    assert blur_radius in blur_factors
-    blur_radius_px = int(np.mean(normals_img.shape[1:3]) * blur_factors[blur_radius])
+    if blur_radius not in blur_factors:
+        raise ValueError(f"{blur_radius} not found in {blur_factors}")
+
+    blur_radius_px = int(
+        np.mean(normals_img.shape[1:3]) * blur_factors[blur_radius]
+    )
 
     # If blur radius too small, do not blur
     if blur_radius_px < 2:
@@ -156,8 +252,9 @@ def normals_to_grad(normals_img):
 
 def copy_flip(grad_x, grad_y):
     """Concat 4 flipped copies of input gradients (makes them wrap).
-    Output is twice bigger in both dimensions."""
 
+    Output is twice bigger in both dimensions.
+    """
     grad_x_top = np.hstack([grad_x, -np.flip(grad_x, axis=1)])
     grad_x_bottom = np.hstack([np.flip(grad_x, axis=0), -np.flip(grad_x)])
     new_grad_x = np.vstack([grad_x_top, grad_x_bottom])
@@ -171,7 +268,6 @@ def copy_flip(grad_x, grad_y):
 
 def frankot_chellappa(grad_x, grad_y, progress_callback=None):
     """Frankot-Chellappa depth-from-gradient algorithm."""
-
     if progress_callback is not None:
         progress_callback(0, 3)
 
@@ -211,8 +307,8 @@ def frankot_chellappa(grad_x, grad_y, progress_callback=None):
 def normals_to_height(normals_img, seamless, progress_callback):
     """Computes a height map from the given normal map. 'normals_img' must be a numpy array
     in C,H,W format (with C as RGB). 'seamless' is a bool that should indicates if 'normals_img'
-    is seamless."""
-
+    is seamless.
+    """
     # Flip height axis
     flip_img = np.flip(normals_img, axis=1)
 
@@ -226,7 +322,9 @@ def normals_to_height(normals_img, seamless, progress_callback):
         grad_x, grad_y = copy_flip(grad_x, grad_y)
 
     # Compute height
-    pred_img = frankot_chellappa(-grad_x, grad_y, progress_callback=progress_callback)
+    pred_img = frankot_chellappa(
+        -grad_x, grad_y, progress_callback=progress_callback
+    )
 
     # Cut to valid part if gradients were expanded
     if not seamless:
@@ -238,19 +336,20 @@ def normals_to_height(normals_img, seamless, progress_callback):
 
 
 # - ADDON
-class DeepBump:
+class MTB_DeepBump:
     """Normal & height maps generation from single pictures"""
 
-    def __init__(self):
-        pass
-
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "image": ("IMAGE",),
                 "mode": (
-                    ["Color to Normals", "Normals to Curvature", "Normals to Height"],
+                    [
+                        "Color to Normals",
+                        "Normals to Curvature",
+                        "Normals to Height",
+                    ],
                 ),
                 "color_to_normals_overlap": (["SMALL", "MEDIUM", "LARGE"],),
                 "normals_to_curvature_blur_radius": (
@@ -264,44 +363,67 @@ class DeepBump:
                         "LARGEST",
                     ],
                 ),
-                "normals_to_height_seamless": (["TRUE", "FALSE"],),
+                "normals_to_height_seamless": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "auto_download": ("BOOLEAN", {"default": True}),
             },
         }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "apply"
 
-    CATEGORY = "image processing"
+    CATEGORY = "mtb/textures"
 
     def apply(
         self,
+        *,
         image,
         mode="Color to Normals",
         color_to_normals_overlap="SMALL",
         normals_to_curvature_blur_radius="SMALL",
-        normals_to_height_seamless="TRUE",
+        normals_to_height_seamless=True,
+        auto_download=False,
     ):
-        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 == "TRUE", None
-            )
+            # Apply processing
+            if mode == "Color to Normals":
+                out_img = color_to_normals(
+                    in_img,
+                    color_to_normals_overlap,
+                    None,
+                    auto_download=auto_download,
+                )
+            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]
+__nodes__ = [MTB_DeepBump]

nodes/faceenhance.py

@@ -0,0 +1,299 @@
+import os
+
+import comfy
+import comfy.utils
+import cv2
+import folder_paths
+import numpy as np
+import torch
+from comfy import model_management
+from PIL import Image
+
+from ..log import NullWriter, log
+from ..utils import get_model_path, np2tensor, pil2tensor, tensor2np
+
+
+class MTB_LoadFaceEnhanceModel:
+    """Loads a GFPGan or RestoreFormer model for face enhancement."""
+
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def get_models_root(cls):
+        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):
+        fr_models_path, um_models_path = cls.get_models_root()
+
+        if fr_models_path is None and um_models_path is None:
+            if not hasattr(cls, "_warned"):
+                log.warning("Face restoration models not found.")
+                cls._warned = True
+            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 fr_models_path.iterdir()
+            if x.name.endswith(".pth")
+            and ("GFPGAN" in x.name or "RestoreFormer" in x.name)
+        ]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model_name": (
+                    [x.name for x in cls.get_models()],
+                    {"default": "None"},
+                ),
+                "upscale": ("INT", {"default": 1}),
+            },
+            "optional": {"bg_upsampler": ("UPSCALE_MODEL", {"default": None})},
+        }
+
+    RETURN_TYPES = ("FACEENHANCE_MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "load_model"
+    CATEGORY = "mtb/facetools"
+    DEPRECATED = True
+
+    def load_model(self, model_name, upscale=2, bg_upsampler=None):
+        from gfpgan import GFPGANer
+
+        basic = "RestoreFormer" not in model_name
+
+        fr_root, um_root = self.get_models_root()
+
+        if bg_upsampler is not None:
+            log.warning(
+                f"Upscale value overridden to {bg_upsampler.scale} from bg_upsampler"
+            )
+            upscale = bg_upsampler.scale
+            bg_upsampler = BGUpscaleWrapper(bg_upsampler)
+
+        sys.stdout = NullWriter()
+        model = GFPGANer(
+            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
+            bg_upsampler=bg_upsampler,
+        )
+
+        sys.stdout = sys.__stdout__
+        return (model,)
+
+
+class BGUpscaleWrapper:
+    def __init__(self, upscale_model) -> None:
+        self.upscale_model = upscale_model
+
+    def enhance(self, img: Image.Image, outscale=2):
+        device = model_management.get_torch_device()
+        self.upscale_model.to(device)
+
+        tile = 128 + 64
+        overlap = 8
+
+        imgt = np2tensor(img)
+        imgt = imgt.movedim(-1, -3).to(device)
+
+        steps = imgt.shape[0] * comfy.utils.get_tiled_scale_steps(
+            imgt.shape[3],
+            imgt.shape[2],
+            tile_x=tile,
+            tile_y=tile,
+            overlap=overlap,
+        )
+
+        log.debug(f"Steps: {steps}")
+
+        pbar = comfy.utils.ProgressBar(steps)
+
+        s = comfy.utils.tiled_scale(
+            imgt,
+            lambda a: self.upscale_model(a),
+            tile_x=tile,
+            tile_y=tile,
+            overlap=overlap,
+            upscale_amount=self.upscale_model.scale,
+            pbar=pbar,
+        )
+
+        self.upscale_model.cpu()
+        s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
+        return (tensor2np(s)[0],)
+
+
+import sys
+
+
+class MTB_RestoreFace:
+    """Uses GFPGan to restore faces"""
+
+    def __init__(self) -> None:
+        pass
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "restore"
+    CATEGORY = "mtb/facetools"
+    DEPRECATED = True
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "model": ("FACEENHANCE_MODEL",),
+                # Input are aligned faces
+                "aligned": ("BOOLEAN", {"default": False}),
+                # Only restore the center face
+                "only_center_face": ("BOOLEAN", {"default": False}),
+                # Adjustable weights
+                "weight": ("FLOAT", {"default": 0.5}),
+                "save_tmp_steps": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "preserve_alpha": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    def do_restore(
+        self,
+        image: torch.Tensor,
+        model,
+        aligned,
+        only_center_face,
+        weight,
+        save_tmp_steps,
+        preserve_alpha: bool = False,
+    ) -> torch.Tensor:
+        pimage = tensor2np(image)[0]
+        width, height = pimage.shape[1], pimage.shape[0]
+        source_img = cv2.cvtColor(np.array(pimage), cv2.COLOR_RGB2BGR)
+
+        alpha_channel = None
+        if (
+            preserve_alpha and image.size(-1) == 4
+        ):  # Check if the image has an alpha channel
+            alpha_channel = pimage[:, :, 3]
+            pimage = pimage[:, :, :3]  # Remove alpha channel for processing
+
+        sys.stdout = NullWriter()
+        cropped_faces, restored_faces, restored_img = model.enhance(
+            source_img,
+            has_aligned=aligned,
+            only_center_face=only_center_face,
+            paste_back=True,
+            # TODO: weight has no effect in 1.3 and 1.4 (only tested these for now...)
+            weight=weight,
+        )
+        sys.stdout = sys.__stdout__
+        log.warning(f"Weight value has no effect for now. (value: {weight})")
+
+        if save_tmp_steps:
+            self.save_intermediate_images(
+                cropped_faces, restored_faces, height, width
+            )
+        output = None
+        if restored_img is not None:
+            restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
+            output = Image.fromarray(restored_img)
+
+            if alpha_channel is not None:
+                alpha_resized = Image.fromarray(alpha_channel).resize(
+                    output.size, Image.LANCZOS
+                )
+                output.putalpha(alpha_resized)
+            # imwrite(restored_img, save_restore_path)
+
+        return pil2tensor(output)
+
+    def restore(
+        self,
+        image: torch.Tensor,
+        model,
+        aligned=False,
+        only_center_face=False,
+        weight=0.5,
+        save_tmp_steps=True,
+        preserve_alpha: bool = False,
+    ) -> tuple[torch.Tensor]:
+        out = [
+            self.do_restore(
+                image[i],
+                model,
+                aligned,
+                only_center_face,
+                weight,
+                save_tmp_steps,
+                preserve_alpha,
+            )
+            for i in range(image.size(0))
+        ]
+
+        return (torch.cat(out, dim=0),)
+
+    def get_step_image_path(self, step, idx):
+        (
+            full_output_folder,
+            filename,
+            counter,
+            _subfolder,
+            _filename_prefix,
+        ) = folder_paths.get_save_image_path(
+            f"{step}_{idx:03}",
+            folder_paths.temp_directory,
+        )
+        file = f"{filename}_{counter:05}_.png"
+
+        return os.path.join(full_output_folder, file)
+
+    def save_intermediate_images(
+        self, cropped_faces, restored_faces, height, width
+    ):
+        for idx, (cropped_face, restored_face) in enumerate(
+            zip(cropped_faces, restored_faces, strict=False)
+        ):
+            face_id = idx + 1
+            file = self.get_step_image_path("cropped_faces", face_id)
+            cv2.imwrite(file, cropped_face)
+
+            file = self.get_step_image_path("cropped_faces_restored", face_id)
+            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)
+            cv2.imwrite(file, cmp_img)
+
+
+__nodes__ = [MTB_RestoreFace, MTB_LoadFaceEnhanceModel]

nodes/faceswap.py

@@ -1,29 +1,105 @@
+# Optional face enhance nodes
 # region imports
-from ifnude import detect
+import sys
 from pathlib import Path
-from PIL import Image
-from typing import List, Set, Tuple
-import cv2
-import folder_paths
-import glob
-import insightface
-import numpy as np
-import onnxruntime
-import os
-import tempfile
-import torch
 
-from ..utils import pil2tensor, tensor2pil
-from ..log import mklog
+import comfy.model_management as model_management
+import numpy as np
+import torch
+from PIL import Image
+
+from ..errors import ModelNotFound
+from ..log import NullWriter, mklog
+from ..utils import download_antelopev2, get_model_path, pil2tensor, tensor2pil
 
 # endregion
 
-logger = mklog(__name__)
-providers = onnxruntime.get_available_providers()
+log = mklog(__name__)
+
+
+class MTB_LoadFaceAnalysisModel:
+    """Loads a face analysis model"""
+
+    models = []
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "faceswap_model": (
+                    ["antelopev2", "buffalo_l", "buffalo_m", "buffalo_sc"],
+                    {"default": "buffalo_l"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("FACE_ANALYSIS_MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "mtb/facetools"
+    DEPRECATED = True
+
+    def load_model(self, faceswap_model: str):
+        import insightface
+
+        if faceswap_model == "antelopev2":
+            download_antelopev2()
+
+        face_analyser = insightface.app.FaceAnalysis(
+            name=faceswap_model,
+            root=get_model_path("insightface").as_posix(),
+        )
+        return (face_analyser,)
+
+
+class MTB_LoadFaceSwapModel:
+    """Loads a faceswap model"""
+
+    @staticmethod
+    def get_models() -> list[Path]:
+        models_path = get_model_path("insightface")
+        if models_path.exists():
+            models = models_path.iterdir()
+            return [x for x in models if x.suffix in [".onnx", ".pth"]]
+        return []
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "faceswap_model": (
+                    [x.name for x in cls.get_models()],
+                    {"default": "None"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("FACESWAP_MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "mtb/facetools"
+    DEPRECATED = True
+
+    def load_model(self, faceswap_model: str):
+        import onnxruntime
+        from insightface.model_zoo.inswapper import INSwapper
+
+        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(
+                model_path,
+                onnxruntime.InferenceSession(
+                    path_or_bytes=model_path,
+                    providers=onnxruntime.get_available_providers(),
+                ),
+            ),
+        )
 
 
 # region roop node
-class FaceSwap:
+class MTB_FaceSwap:
     """Face swap using deepinsight/insightface models"""
 
     model = None
@@ -32,13 +108,6 @@ class FaceSwap:
     def __init__(self) -> None:
         pass
 
-    @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
-
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -46,39 +115,58 @@ class FaceSwap:
                 "image": ("IMAGE",),
                 "reference": ("IMAGE",),
                 "faces_index": ("STRING", {"default": "0"}),
-                "faceswap_model": (
-                    [x.name for x in cls.get_models()],
+                "faceanalysis_model": (
+                    "FACE_ANALYSIS_MODEL",
                     {"default": "None"},
                 ),
+                "faceswap_model": ("FACESWAP_MODEL", {"default": "None"}),
+            },
+            "optional": {
+                "preserve_alpha": ("BOOLEAN", {"default": True}),
             },
-            "optional": {"debug": (["true", "false"], {"default": "false"})},
         }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "swap"
-    CATEGORY = "face"
+    CATEGORY = "mtb/facetools"
+    DEPRECATED = True
 
     def swap(
         self,
         image: torch.Tensor,
         reference: torch.Tensor,
         faces_index: str,
-        faceswap_model: str,
-        debug: str,
+        faceanalysis_model,
+        faceswap_model,
+        preserve_alpha=False,
     ):
         def do_swap(img):
-            img = tensor2pil(img)
-            ref = tensor2pil(reference)
+            model_management.throw_exception_if_processing_interrupted()
+            img = tensor2pil(img)[0]
+            ref = tensor2pil(reference)[0]
+
+            alpha_channel = None
+            if preserve_alpha and img.mode == "RGBA":
+                alpha_channel = img.getchannel("A")
+                img = img.convert("RGB")
+
             face_ids = {
-                int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
+                int(x)
+                for x in faces_index.strip(",").split(",")
+                if x.isnumeric()
             }
-            model = self.getFaceSwapModel(faceswap_model)
-            swapped = swap_face(ref, img, model, face_ids)
+            sys.stdout = NullWriter()
+            swapped = swap_face(
+                faceanalysis_model, ref, img, faceswap_model, face_ids
+            )
+            sys.stdout = sys.__stdout__
+            if alpha_channel:
+                swapped.putalpha(alpha_channel)
             return pil2tensor(swapped)
 
         batch_count = image.size(0)
 
-        logger.info(f"Running insightface swap (batch size: {batch_count})")
+        log.info(f"Running insightface swap (batch size: {batch_count})")
 
         if reference.size(0) != 1:
             raise ValueError("Reference image must have batch size 1")
@@ -86,38 +174,31 @@ class FaceSwap:
             image = do_swap(image)
 
         else:
-            image = [do_swap(image[i]) for i in range(batch_count)]
-            image = torch.cat(image, dim=0)
+            image_batch = [do_swap(image[i]) for i in range(batch_count)]
+            image = torch.cat(image_batch, dim=0)
 
         return (image,)
 
-    def getFaceSwapModel(self, model_path: str):
-        model_path = os.path.join(folder_paths.models_dir, "insightface", model_path)
-        if self.model_path is None or self.model_path != model_path:
-            logger.info(f"Loading model {model_path}")
-            self.model_path = model_path
-            self.model = insightface.model_zoo.get_model(
-                model_path, providers=providers
-            )
-        else:
-            logger.info("Using cached model")
-
-        logger.info("Model loaded")
-        return self.model
-
 
 # endregion
 
 
 # region face swap utils
-def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640)):
-    face_analyser = insightface.app.FaceAnalysis(name="buffalo_l", providers=providers)
+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(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]
@@ -125,59 +206,53 @@ def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640)):
         return None
 
 
-def convert_to_sd(img) -> Tuple[bool, str]:
-    chunks = detect(img)
-    shapes = [chunk["score"] > 0.7 for chunk in chunks]
-    return [any(shapes), tempfile.NamedTemporaryFile(delete=False, suffix=".png")]
-
-
 def swap_face(
-    source_img: Image.Image,
-    target_img: Image.Image,
-    face_swapper_model=None,
-    faces_index: Set[int] = None,
+    face_analyser,
+    source_img: Image.Image | list[Image.Image],
+    target_img: Image.Image | list[Image.Image],
+    face_swapper_model,
+    faces_index: set[int] | None = None,
 ) -> Image.Image:
+    import cv2
+
     if faces_index is None:
         faces_index = {0}
-    logger.info(f"Swapping faces: {faces_index}")
+    log.debug(f"Swapping faces: {faces_index}")
     result_image = target_img
-    converted = convert_to_sd(target_img)
-    scale, fn = converted[0], converted[1]
-    if face_swapper_model is not None and not scale:
-        if isinstance(source_img, str):  # source_img is a base64 string
-            import base64, io
 
-            if (
-                "base64," in source_img
-            ):  # check if the base64 string has a data URL scheme
-                base64_data = source_img.split("base64,")[-1]
-                img_bytes = base64.b64decode(base64_data)
-            else:
-                # if no data URL scheme, just decode
-                img_bytes = base64.b64decode(source_img)
-            source_img = Image.open(io.BytesIO(img_bytes))
-        source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
-        target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
-        source_face = get_face_single(source_img, face_index=0)
+    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
+        )
         if source_face is not None:
-            result = target_img
+            result = cv_target_img
 
             for face_num in faces_index:
-                target_face = get_face_single(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:
-                    result = face_swapper_model.get(result, target_face, source_face)
+                    sys.stdout = NullWriter()
+                    result = face_swapper_model.get(
+                        result, target_face, source_face
+                    )
+                    sys.stdout = sys.__stdout__
                 else:
-                    logger.warning(f"No target face found for {face_num}")
+                    log.warning(f"No target face found for {face_num}")
 
-            result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+            result_image = Image.fromarray(
+                cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
+            )
         else:
-            logger.warning("No source face found")
+            log.warning("No source face found")
     else:
-        logger.error("No face swap model provided")
+        log.error("No face swap model provided")
     return result_image
 
 
 # endregion face swap utils
 
 
-__nodes__ = [FaceSwap]
+__nodes__ = [MTB_FaceSwap, MTB_LoadFaceSwapModel, MTB_LoadFaceAnalysisModel]

nodes/filter.py

@@ -0,0 +1,69 @@
+import torch
+
+
+class MTB_FilterZ:
+    """Filters an image based on a depth map"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "depth": ("IMAGE",),
+                "to_black": ("BOOLEAN", {"default": True}),
+                "threshold": (
+                    "FLOAT",
+                    {"default": 0.5, "step": 0.01, "min": 0.0, "max": 1.0},
+                ),
+                "invert": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "filter"
+    CATEGORY = "mtb/filters"
+
+    def filter(
+        self,
+        image: torch.Tensor,
+        depth: torch.Tensor,
+        to_black,
+        threshold: float,
+        invert,
+    ):
+        # Normalize depth map to be in range [0, 1]
+        depth_normalized = (depth - depth.min()) / (depth.max() - depth.min())
+
+        # Calculate the difference from the threshold
+        diff_from_threshold = torch.abs(depth_normalized - threshold)
+
+        out_img = None
+
+        if to_black:
+            if invert:
+                soft_mask = diff_from_threshold >= threshold
+            else:
+                soft_mask = diff_from_threshold <= threshold
+
+            out_img = image.clone()
+            out_img[soft_mask] = 0
+            return (out_img,)
+        else:
+            alpha_channel = 1 - diff_from_threshold / threshold
+            alpha_channel = torch.clamp(alpha_channel, 0, 1)
+
+            if invert:
+                # Invert the alpha channel
+                alpha_channel = 1 - alpha_channel
+
+            # Ensure alpha_channel has the correct shape
+            # It should have the shape [batch_size, height, width, 1]
+            alpha_channel = alpha_channel.unsqueeze(-1)
+
+            # Combine RGB channels with alpha channel
+            out_img = torch.cat((image, alpha_channel), dim=-1)
+
+        return (out_img,)
+
+
+__nodes__ = [MTB_FilterZ]

nodes/generate.py

@@ -0,0 +1,438 @@
+import io
+
+import requests
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+from ..log import log
+from ..utils import comfy_dir, font_path, pil2tensor
+
+# class MtbExamples:
+#     """MTB Example Images"""
+
+#     def __init__(self):
+#         pass
+
+#     @classmethod
+#     @lru_cache(maxsize=1)
+#     def get_root(cls):
+#         return here / "examples" / "samples"
+
+#     @classmethod
+#     def INPUT_TYPES(cls):
+#         input_dir = cls.get_root()
+#         files = [f.name for f in input_dir.iterdir() if f.is_file()]
+#         return {
+#             "required": {"image": (sorted(files),)},
+#         }
+
+#     RETURN_TYPES = ("IMAGE", "MASK")
+#     FUNCTION = "do_mtb_examples"
+#     CATEGORY = "fun"
+
+#     def do_mtb_examples(self, image, index):
+#         image_path = (self.get_root() / image).as_posix()
+
+#         i = Image.open(image_path)
+#         i = ImageOps.exif_transpose(i)
+#         image = i.convert("RGB")
+#         image = np.array(image).astype(np.float32) / 255.0
+#         image = torch.from_numpy(image)[None,]
+#         if "A" in i.getbands():
+#             mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
+#             mask = 1.0 - torch.from_numpy(mask)
+#         else:
+#             mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+#         return (image, mask)
+
+#     @classmethod
+#     def IS_CHANGED(cls, image):
+#         image_path = (cls.get_root() / image).as_posix()
+
+#         m = hashlib.sha256()
+#         with open(image_path, "rb") as f:
+#             m.update(f.read())
+#         return m.digest().hex()
+
+
+class MTB_UnsplashImage:
+    """Unsplash Image given a keyword and a size"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "width": (
+                    "INT",
+                    {"default": 512, "max": 8096, "min": 0, "step": 1},
+                ),
+                "height": (
+                    "INT",
+                    {"default": 512, "max": 8096, "min": 0, "step": 1},
+                ),
+                "random_seed": (
+                    "INT",
+                    {"default": 0, "max": 1e5, "min": 0, "step": 1},
+                ),
+            },
+            "optional": {
+                "keyword": ("STRING", {"default": "nature"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "do_unsplash_image"
+    CATEGORY = "mtb/generate"
+
+    def do_unsplash_image(self, width, height, random_seed, keyword=None):
+        base_url = "https://source.unsplash.com/random/"
+
+        if width and height:
+            base_url += f"/{width}x{height}"
+
+        if keyword:
+            keyword = keyword.replace(" ", "%20")
+            base_url += f"?{keyword}&{random_seed}"
+        else:
+            base_url += f"?&{random_seed}"
+        try:
+            log.debug(f"Getting unsplash image from {base_url}")
+            response = requests.get(base_url)
+            response.raise_for_status()
+
+            image = Image.open(io.BytesIO(response.content))
+            return (
+                pil2tensor(
+                    image,
+                ),
+            )
+
+        except requests.exceptions.RequestException as e:
+            print("Error retrieving image:", e)
+            return (None,)
+
+
+def bbox_dim(bbox):
+    left, upper, right, lower = bbox
+    width = right - left
+    height = lower - upper
+    return width, height
+
+
+# TODO: Auto install the base font to ComfyUI/fonts
+
+
+class MTB_TextToImage:
+    """Utils to convert text to image using a font.
+
+    The tool looks for any .ttf file in the Comfy folder hierarchy.
+    """
+
+    fonts = {}
+    DESCRIPTION = """# Text to Image
+
+This node look for any font files in comfy_dir/fonts.
+by default it fallsback to a default font.
+
+![img](https://i.imgur.com/3GT92hy.gif)
+"""
+
+    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 = [font_path]
+
+        for extension in font_extensions:
+            try:
+                if comfy_dir.exists():
+                    fonts.extend(comfy_dir.glob(f"fonts/**/{extension}"))
+                else:
+                    log.warn(f"Directory {comfy_dir} does not exist.")
+            except Exception as e:
+                log.error(f"Error during font caching: {e}")
+
+        for font in fonts:
+            log.debug(f"Adding font {font}")
+            MTB_TextToImage.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": ("BOOLEAN", {"default": True}),
+                "trim": ("BOOLEAN", {"default": True}),
+                "line_height": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0, "step": 0.1},
+                ),
+                "font_size": (
+                    "INT",
+                    {"default": 32, "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", "widgetType": "MTB_COLOR"},
+                ),
+                "background": (
+                    "COLOR",
+                    {"default": "white", "widgetType": "MTB_COLOR"},
+                ),
+                "h_align": (("left", "center", "right"), {"default": "left"}),
+                "v_align": (("top", "center", "bottom"), {"default": "top"}),
+                "h_offset": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 8096, "step": 1},
+                ),
+                "v_offset": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 8096, "step": 1},
+                ),
+                "h_coverage": (
+                    "INT",
+                    {"default": 100, "min": 1, "max": 100, "step": 1},
+                ),
+            },
+            "optional": {
+                "whisper_chunks": ("WHISPER_CHUNKS",),
+                "fps": (
+                    "INT",
+                    {"default": 24, "min": 1, "max": 60, "step": 1},
+                ),
+                "fade_duration": (
+                    "FLOAT",
+                    {"default": 0.5, "min": 0.0, "max": 5.0, "step": 0.1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "text_to_image"
+    CATEGORY = "mtb/generate"
+
+    def create_animation_frames(
+        self,
+        chunks,
+        base_image,
+        font,
+        font_size,
+        color,
+        width,
+        height,
+        fps,
+        fade_duration,
+    ):
+        """Create animation frames from Whisper chunks."""
+        if not chunks or not chunks.get("chunks"):
+            return [base_image]
+
+        frames = []
+        total_duration = chunks["chunks"][-1]["timestamp"][1]
+        frame_count = int(total_duration * fps)
+        fade_frames = int(fade_duration * fps)
+
+        for frame_idx in range(frame_count):
+            time = frame_idx / fps
+            frame = base_image.copy()
+            draw = ImageDraw.Draw(frame)
+
+            active_chunks = []
+            for chunk in chunks["chunks"]:
+                start, end = chunk["timestamp"]
+                if start <= time <= end:
+                    fade_in_alpha = min(
+                        1.0, (time - start) * fps / fade_frames
+                    )
+                    fade_out_alpha = min(1.0, (end - time) * fps / fade_frames)
+                    alpha = min(fade_in_alpha, fade_out_alpha)
+                    active_chunks.append((chunk["text"], alpha))
+
+            y = height // 4
+            for text, alpha in active_chunks:
+                # Create a temporary image for the text with alpha
+                text_img = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+                text_draw = ImageDraw.Draw(text_img)
+
+                text_draw.text(
+                    (width // 2, y),
+                    text,
+                    font=font,
+                    fill=color,
+                    anchor="mm",
+                )
+
+                text_img.putalpha(
+                    Image.fromarray(
+                        (torch.ones((height, width)) * (alpha * 255))
+                        .byte()
+                        .numpy()
+                    )
+                )
+
+                frame = Image.alpha_composite(frame, text_img)
+                y += font_size * 1.5
+
+            frames.append(frame)
+
+        return frames
+
+    def text_to_image(
+        self,
+        text: str,
+        font,
+        wrap,
+        trim,
+        line_height,
+        font_size,
+        width,
+        height,
+        color,
+        background,
+        h_align="left",
+        v_align="top",
+        h_offset=0,
+        v_offset=0,
+        h_coverage=100,
+        whisper_chunks=None,
+        fps=24,
+        fade_duration=0.5,
+    ):
+        """Convert text to image, with optional animation support."""
+        import textwrap
+
+        from PIL import ImageColor
+
+        font_path = self.fonts[font]
+        font = ImageFont.truetype(font_path, size=font_size)
+
+        try:
+            if isinstance(color, str):
+                color = ImageColor.getrgb(color)
+            if isinstance(background, str):
+                background = ImageColor.getrgb(background)
+
+            if len(color) == 3:
+                color = color + (255,)
+            if len(background) == 3:
+                background = background + (255,)
+        except ValueError as e:
+            log.error(f"Color parsing error: {e}")
+            color = (255, 255, 255, 255)
+            background = (0, 0, 0, 255)
+
+        def render_text(text_to_render, alpha=None):
+            if trim:
+                text_to_render = text_to_render.strip()
+            if wrap:
+                wrap_width = (((width / 100) * h_coverage) / font_size) * 2
+                lines = textwrap.wrap(text_to_render, width=wrap_width)
+            else:
+                lines = [text_to_render]
+
+            img = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+            draw = ImageDraw.Draw(img)
+
+            line_height_px = line_height * font_size
+
+            if v_align == "top":
+                y_text = v_offset
+            elif v_align == "center":
+                y_text = (
+                    (height - (line_height_px * len(lines))) // 2
+                ) + v_offset
+            else:
+                y_text = (height - (line_height_px * len(lines))) - v_offset
+
+            def get_width(line):
+                if hasattr(font, "getsize"):
+                    return font.getsize(line)[0]
+                else:
+                    return font.getlength(line)
+
+            for line in lines:
+                line_width = get_width(line)
+                if h_align == "left":
+                    x_text = h_offset
+                elif h_align == "center":
+                    x_text = ((width - line_width) // 2) + h_offset
+                else:
+                    x_text = (width - line_width) - h_offset
+
+                text_color = color
+                if alpha is not None:
+                    text_color = tuple(
+                        list(color[:3]) + [int(alpha * color[3])]
+                    )
+
+                draw.text((x_text, y_text), line, fill=text_color, font=font)
+                y_text += line_height_px
+
+            return img
+
+        base_img = Image.new("RGBA", (width, height), background)
+
+        if whisper_chunks and whisper_chunks.get("chunks"):
+            frames = []
+            total_duration = whisper_chunks["chunks"][-1]["timestamp"][1]
+            frame_count = int(total_duration * fps)
+            fade_frames = int(fade_duration * fps)
+
+            for frame_idx in range(frame_count):
+                time = frame_idx / fps
+                frame = base_img.copy()
+
+                active_chunks = []
+                for chunk in whisper_chunks["chunks"]:
+                    start, end = chunk["timestamp"]
+                    if start <= time <= end:
+                        fade_in_alpha = min(
+                            1.0, (time - start) * fps / fade_frames
+                        )
+                        fade_out_alpha = min(
+                            1.0, (end - time) * fps / fade_frames
+                        )
+                        alpha = min(fade_in_alpha, fade_out_alpha)
+                        active_chunks.append((chunk["text"], alpha))
+
+                for chunk_text, alpha in active_chunks:
+                    chunk_img = render_text(
+                        chunk_text.encode("ascii", "ignore").decode(), alpha
+                    )
+                    frame = Image.alpha_composite(frame, chunk_img)
+
+                frames.append(frame)
+
+            frame_tensors = [pil2tensor(frame) for frame in frames]
+            return (torch.cat(frame_tensors, dim=0),)
+        else:
+            text_img = render_text(text)
+            result = Image.alpha_composite(base_img, text_img)
+            return (pil2tensor(result),)
+
+
+__nodes__ = [
+    MTB_UnsplashImage,
+    MTB_TextToImage,
+    #  MtbExamples,
+]

nodes/graph_utils.py

@@ -1,69 +1,936 @@
+import io
+import json
+import re
+import urllib.parse
+import urllib.request
+from math import pi
+from typing import Any
+
+import comfy.model_management as model_management
+import comfy.utils
+import numpy as np
 import torch
-import folder_paths
-import os
+from comfy.comfy_types.node_typing import IO as CIO
+from PIL import Image
+
+from ..log import log
+from ..utils import (
+    EASINGS,
+    apply_easing,
+    get_server_info,
+    numpy_NFOV,
+    pil2tensor,
+    tensor2np,
+)
 
 
-class SaveTensors:
-    """Debug node that will probably be removed in the future"""
+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()
 
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
+    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 MTB_ToDevice:
+    """Send a image or mask tensor to the given device."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        devices = ["cpu"]
+        if torch.backends.mps.is_available():
+            devices.append("mps")
+        if torch.cuda.is_available():
+            devices.append("cuda:0")
+            for i in range(1, torch.cuda.device_count()):
+                devices.append(f"cuda:{i}")
+            devices.append("cuda")
+
+        return {
+            "required": {
+                "ignore_errors": ("BOOLEAN", {"default": False}),
+                "device": (
+                    devices,
+                    {
+                        "default": "cuda"
+                        if torch.cuda.is_available()
+                        else "cpu"
+                    },
+                ),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("images", "masks")
+    CATEGORY = "mtb/utils"
+    FUNCTION = "to_device"
+
+    def to_device(
+        self,
+        *,
+        ignore_errors: bool = False,
+        device: str = "cuda",
+        image: torch.Tensor | None = None,
+        mask: torch.Tensor | None = None,
+    ):
+        if not ignore_errors and image is None and mask is None:
+            raise ValueError(
+                "You must either provide an image or a mask,"
+                + " use ignore_error to passthrough"
+            )
+        if (
+            device.startswith("cuda")
+            and ":" not in device
+            and device != "cuda"
+        ):
+            device = f"cuda:{device[4:]}"
+
+        try:
+            if image is not None:
+                image = image.to(device)
+            if mask is not None:
+                mask = mask.to(device)
+        except RuntimeError as e:
+            if not ignore_errors:
+                raise RuntimeError(
+                    f"Failed to move tensor to device {device}: {str(e)}"
+                ) from e
+            log.warning(
+                f"Failed to move tensor to device {device}, ignoring: {str(e)}"
+            )
+        return (image, mask)
+
+
+# class MTB_ApplyTextTemplate:
+class MTB_ApplyTextTemplate:
+    """
+    Experimental node to interpolate strings from inputs.
+
+    Interpolation just requires {}, for instance:
+
+    Some string {var_1} and {var_2}
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "filename_prefix": ("STRING", {"default": "ComfyPickle"}),
-            },
-            "optional": {
-                "image": ("IMAGE",),
-                "mask": ("MASK",),
-                "latent": ("LATENT",),
+                "template": ("STRING", {"default": "", "multiline": True}),
             },
         }
 
-    FUNCTION = "save"
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-    CATEGORY = "utils"
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("string",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
 
-    def save(
-        self,
-        filename_prefix,
-        image: torch.Tensor = None,
-        mask: torch.Tensor = None,
-        latent: torch.Tensor = None,
+    def execute(self, *, template: str, **kwargs):
+        res = f"{template}"
+        for k, v in kwargs.items():
+            res = res.replace(f"{{{k}}}", f"{v}")
+
+        return (res,)
+
+
+class MTB_MatchDimensions:
+    """Match images dimensions along the given dimension, preserving aspect ratio."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "source": ("IMAGE",),
+                "reference": ("IMAGE",),
+                "match": (["height", "width"], {"default": "height"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT")
+    RETURN_NAMES = ("image", "new_width", "new_height")
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
+
+    def execute(
+        self, source: torch.Tensor, reference: torch.Tensor, match: str
     ):
-        (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            filename_prefix,
-        ) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        import torchvision.transforms.functional as VF
 
-        if image is not None:
-            image_file = f"{filename}_image_{counter:05}.pt"
-            torch.save(image, os.path.join(full_output_folder, image_file))
-            # np.save(os.path.join(full_output_folder, image_file), image.cpu().numpy())
+        _batch_size, height, width, _channels = source.shape
+        _rbatch_size, rheight, rwidth, _rchannels = reference.shape
 
-        if mask is not None:
-            mask_file = f"{filename}_mask_{counter:05}.pt"
-            torch.save(mask, os.path.join(full_output_folder, mask_file))
-            # np.save(os.path.join(full_output_folder, mask_file), mask.cpu().numpy())
+        source_aspect_ratio = width / height
+        # reference_aspect_ratio = rwidth / rheight
 
-        if latent is not None:
-            # for latent we must use pickle
-            latent_file = f"{filename}_latent_{counter:05}.pt"
-            torch.save(latent, os.path.join(full_output_folder, latent_file))
-            # pickle.dump(latent, open(os.path.join(full_output_folder, latent_file), "wb"))
+        source = source.permute(0, 3, 1, 2)
+        reference = reference.permute(0, 3, 1, 2)
 
-            # np.save(os.path.join(full_output_folder, latent_file), latent[""].cpu().numpy())
+        if match == "height":
+            new_height = rheight
+            new_width = int(rheight * source_aspect_ratio)
+        else:
+            new_width = rwidth
+            new_height = int(rwidth / source_aspect_ratio)
 
-        return f"{filename_prefix}_{counter:05}"
+        resized_images = [
+            VF.resize(
+                source[i],
+                (new_height, new_width),
+                antialias=True,
+                interpolation=Image.BICUBIC,
+            )
+            for i in range(_batch_size)
+        ]
+        resized_source = torch.stack(resized_images, dim=0)
+        resized_source = resized_source.permute(0, 2, 3, 1)
+
+        return (resized_source, new_width, new_height)
+
+
+class MTB_FloatToFloats:
+    """Conversion utility for compatibility with other extensions (AD, IPA, Fitz are using FLOAT to represent list of floats.)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "float": ("FLOAT", {"default": 0.0, "forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("FLOATS",)
+    RETURN_NAMES = ("floats",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "convert"
+
+    def convert(self, float: float):
+        return (float,)
+
+
+class MTB_FloatsToInts:
+    """Conversion utility for compatibility with frame interpolation."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "floats": ("FLOATS", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("INTS", "INT")
+    CATEGORY = "mtb/utils"
+    FUNCTION = "convert"
+
+    def convert(self, floats: list[float]):
+        vals = [int(x) for x in floats]
+        return (vals, vals)
+
+
+class MTB_FloatsToFloat:
+    """Conversion utility for compatibility with other extensions (AD, IPA, Fitz are using FLOAT to represent list of floats.)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "floats": ("FLOATS",),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("float",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "convert"
+
+    def convert(self, floats):
+        return (floats,)
+
+
+class MTB_AutoPanEquilateral:
+    """Generate a 360 panning video from an equilateral image."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "equilateral_image": ("IMAGE",),
+                "fovX": ("FLOAT", {"default": 45.0}),
+                "fovY": ("FLOAT", {"default": 45.0}),
+                "elevation": ("FLOAT", {"default": 0.5}),
+                "frame_count": ("INT", {"default": 100}),
+                "width": ("INT", {"default": 768}),
+                "height": ("INT", {"default": 512}),
+            },
+            "optional": {
+                "floats_fovX": ("FLOATS",),
+                "floats_fovY": ("FLOATS",),
+                "floats_elevation": ("FLOATS",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "generate_frames"
+
+    def check_floats(self, f: list[float] | None, expected_count: int):
+        if f:
+            if len(f) == expected_count:
+                return True
+            return False
+        return True
+
+    def generate_frames(
+        self,
+        equilateral_image: torch.Tensor,
+        fovX: float,
+        fovY: float,
+        elevation: float,
+        frame_count: int,
+        width: int,
+        height: int,
+        floats_fovX: list[float] | None = None,
+        floats_fovY: list[float] | None = None,
+        floats_elevation: list[float] | None = None,
+    ):
+        source = tensor2np(equilateral_image)
+
+        if len(source) > 1:
+            log.warn(
+                "You provided more than one image in the equilateral_image input, only the first will be used."
+            )
+        if not all(
+            [
+                self.check_floats(x, frame_count)
+                for x in [floats_fovX, floats_fovY, floats_elevation]
+            ]
+        ):
+            raise ValueError(
+                "You provided less than the expected number of fovX, fovY, or elevation values."
+            )
+
+        source = source[0]
+        frames = []
+
+        pbar = comfy.utils.ProgressBar(frame_count)
+        for i in range(frame_count):
+            rotation_angle = (i / frame_count) * 2 * pi
+
+            if floats_elevation:
+                elevation = floats_elevation[i]
+
+            if floats_fovX:
+                fovX = floats_fovX[i]
+
+            if floats_fovY:
+                fovY = floats_fovY[i]
+
+            fov = [fovX / 100, fovY / 100]
+            center_point = [rotation_angle / (2 * pi), elevation]
+
+            nfov = numpy_NFOV(fov, height, width)
+            frame = nfov.to_nfov(source, center_point=center_point)
+
+            frames.append(frame)
+
+            model_management.throw_exception_if_processing_interrupted()
+            pbar.update(1)
+
+        return (pil2tensor(frames),)
+
+
+class MTB_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 MTB_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 MTB_StringReplace:
+    """Basic string replacement with regex support."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string": ("STRING", {"forceInput": True}),
+                "old": ("STRING", {"default": ""}),
+                "new": ("STRING", {"default": ""}),
+                "use_regex": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    FUNCTION = "replace_str"
+    RETURN_TYPES = ("STRING",)
+    CATEGORY = "mtb/string"
+
+    def replace_str(self, string: str, old: str, new: str, use_regex: bool):
+        log.debug(f"Current string: {string}")
+        log.debug(f"Find string: {old}")
+        log.debug(f"Replace string: {new}")
+        log.debug(f"Use regex: {use_regex}")
+
+        if use_regex:
+            try:
+                string = re.sub(old, new, string)
+            except re.error as e:
+                raise ValueError(f"Regex error: {e}") from e
+        else:
+            string = string.replace(old, new)
+
+        log.debug(f"New string: {string}")
+
+        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, only supports literal_eval"
+    )
+
+    def eval_expression(self, expression: str, **kwargs):
+        from ast import literal_eval
+
+        for key, value in kwargs.items():
+            log.debug(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 Exception as e:
+            raise ValueError(
+                f"Math expression only support literal_eval now: {e}"
+            )
+
+        return (result, int(result))
+
+
+class MTB_FitNumber:
+    """Fit the input float using a source and target range"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0, "forceInput": True}),
+                "clamp": ("BOOLEAN", {"default": False}),
+                "source_min": (
+                    "FLOAT",
+                    {"default": 0.0, "step": 0.01, "min": -1e5},
+                ),
+                "source_max": (
+                    "FLOAT",
+                    {"default": 1.0, "step": 0.01, "min": -1e5},
+                ),
+                "target_min": (
+                    "FLOAT",
+                    {"default": 0.0, "step": 0.01, "min": -1e5},
+                ),
+                "target_max": (
+                    "FLOAT",
+                    {"default": 1.0, "step": 0.01, "min": -1e5},
+                ),
+                "easing": (
+                    EASINGS,
+                    {"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,
+        value: float,
+        clamp: bool,
+        source_min: float,
+        source_max: float,
+        target_min: float,
+        target_max: float,
+        easing: str,
+    ):
+        if source_min == source_max:
+            normalized_value = 0
+        else:
+            normalized_value = (value - source_min) / (source_max - source_min)
+        if clamp:
+            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,)
+
+
+class MTB_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})},
+            "optional": {
+                "on_mismatch": (
+                    ["Error", "Smallest", "Largest"],
+                    {"default": "Smallest"},
+                )
+            },
+        }
+
+    def concatenate_tensors(
+        self,
+        reverse: bool,
+        on_mismatch: str = "Smallest",
+        **kwargs: torch.Tensor,
+    ) -> tuple[torch.Tensor]:
+        tensors = list(kwargs.values())
+
+        if on_mismatch == "Error":
+            shapes = [tensor.shape for tensor in tensors]
+            if not all(shape == shapes[0] for shape in shapes):
+                raise ValueError(
+                    "All input tensors must have the same shape when on_mismatch is 'Error'."
+                )
+
+        else:
+            import torch.nn.functional as F
+
+            if on_mismatch == "Smallest":
+                target_shape = min(
+                    (tensor.shape for tensor in tensors),
+                    key=lambda s: (s[1], s[2]),
+                )
+            else:  # on_mismatch == "Largest"
+                target_shape = max(
+                    (tensor.shape for tensor in tensors),
+                    key=lambda s: (s[1], s[2]),
+                )
+
+            target_height, target_width = target_shape[1], target_shape[2]
+
+            resized_tensors = []
+            for tensor in tensors:
+                if (
+                    tensor.shape[1] != target_height
+                    or tensor.shape[2] != target_width
+                ):
+                    resized_tensor = F.interpolate(
+                        tensor.permute(0, 3, 1, 2),
+                        size=(target_height, target_width),
+                        mode="bilinear",
+                        align_corners=False,
+                    )
+                    resized_tensor = resized_tensor.permute(0, 2, 3, 1)
+                    resized_tensors.append(resized_tensor)
+                else:
+                    resized_tensors.append(tensor)
+
+            tensors = resized_tensors
+
+        concatenated = torch.cat(tensors, dim=0)
+
+        return (concatenated,)
+
+
+class MTB_TensorOps:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "tensor": ("IMAGE",),
+                "operation": (
+                    [
+                        "multiply",
+                        "divide",
+                        "add",
+                        "subtract",
+                        "power",
+                        "clamp",
+                        "abs",
+                        "log",
+                        "exp",
+                        "convert_dtype",
+                        "normalize_range",
+                        "normalize_per_channel",
+                    ],
+                    {"default": "multiply"},
+                ),
+                "value": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": -1000000.0,
+                        "max": 1000000.0,
+                        "step": 0.01,
+                    },
+                ),
+                "source_min": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": -1000000.0,
+                        "max": 1000000.0,
+                        "step": 0.01,
+                    },
+                ),
+                "source_max": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": -1000000.0,
+                        "max": 1000000.0,
+                        "step": 0.01,
+                    },
+                ),
+                "target_min": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": -1000000.0,
+                        "max": 1000000.0,
+                        "step": 0.01,
+                    },
+                ),
+                "target_max": (
+                    "FLOAT",
+                    {
+                        "default": 16.0,
+                        "min": -1000000.0,
+                        "max": 1000000.0,
+                        "step": 0.01,
+                    },
+                ),
+                "dtype": (
+                    ["uint8", "float32", "float16", "bfloat16"],
+                    {"default": "float32"},
+                ),
+                "use_mean": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "target_tensor": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply"
+    CATEGORY = "mtb/tensor_ops"
+
+    def apply(
+        self,
+        tensor,
+        operation="multiply",
+        value=1.0,
+        source_min=0.0,
+        source_max=1.0,
+        target_min=0.0,
+        target_max=1.0,
+        dtype="float32",
+        use_mean=False,
+        target_tensor=None,
+    ):
+        log.debug(
+            f"Input tensor stats: shape={tensor.shape}, dtype={tensor.dtype}, range=[{tensor.min().item():.6f}, {tensor.max().item():.6f}]"
+        )
+        if operation == "normalize_per_channel":
+            if target_tensor is None:
+                raise ValueError(
+                    "Target tensor required for per-channel normalization"
+                )
+
+            result = tensor.clone()
+            for c in range(tensor.shape[-1]):
+                if use_mean:
+                    source_mean = tensor[..., c].mean()
+                    target_mean = target_tensor[..., c].mean()
+                    scale = target_mean / source_mean
+                    result[..., c] = tensor[..., c] * scale
+                else:
+                    source_min = tensor[..., c].min()
+                    source_max = tensor[..., c].max()
+                    target_min = target_tensor[..., c].min()
+                    target_max = target_tensor[..., c].max()
+
+                    normalized = (tensor[..., c] - source_min) / (
+                        source_max - source_min
+                    )
+                    result[..., c] = (
+                        normalized * (target_max - target_min) + target_min
+                    )
+
+                log.debug(
+                    f"Channel {c} - Scale: source=[{source_min:.6f}, {source_max:.6f}], target=[{target_min:.6f}, {target_max:.6f}]"
+                )
+
+        elif operation == "normalize_range":
+            if target_tensor is not None:
+                target_min = target_tensor.min().item()
+                target_max = target_tensor.max().item()
+                log.debug(
+                    f"Using target tensor range: [{target_min:.6f}, {target_max:.6f}]"
+                )
+
+            normalized = (tensor - source_min) / (source_max - source_min)
+            result = normalized * (target_max - target_min) + target_min
+        elif operation == "convert_dtype":
+            if dtype == "float32":
+                result = tensor.float()
+            elif dtype == "float16":
+                result = tensor.half()
+            elif dtype == "bfloat16":
+                result = tensor.bfloat16()
+
+        else:
+            result = tensor
+            if operation == "multiply":
+                result = tensor * value
+            elif operation == "divide":
+                result = tensor / value if value != 0 else tensor
+            elif operation == "add":
+                result = tensor + value
+            elif operation == "subtract":
+                result = tensor - value
+            elif operation == "power":
+                result = torch.pow(tensor, value)
+            elif operation == "clamp":
+                if target_tensor is not None:
+                    result = torch.clamp(
+                        tensor,
+                        target_tensor.min().item(),
+                        target_tensor.max().item(),
+                    )
+                else:
+                    result = torch.clamp(tensor, source_min, source_max)
+            elif operation == "abs":
+                result = torch.abs(tensor)
+            elif operation == "log":
+                result = torch.log(tensor.clamp(min=1e-10))
+            elif operation == "exp":
+                result = torch.exp(tensor)
+
+        log.debug(
+            f"Output tensor stats: shape={result.shape}, dtype={result.dtype}, range=[{result.min().item():.6f}, {result.max().item():.6f}]"
+        )
+        return (result,)
+
+
+class MTB_GetItem:
+    """Generic index based getter for common types"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "container": (CIO.ANY,),
+                "index": ("INT", {"default": 0}),
+            }
+        }
+
+    RETURN_TYPES = (CIO.ANY,)
+    RETURN_NAMES = ("item",)
+    FUNCTION = "get_item"
+    CATEGORY = "mtb/utils"
+
+    def get_item(self, container: Any, index: int):
+        if "__getitem__" in dir(container):
+            log.debug(f"Container is {type(container)}")
+            res = container[index]
+            if type(res) is torch.Tensor:
+                res = res.unsqueeze(0)
+
+            return (res,)
+
+
+class MTB_BooleanNot:
+    """Inverts a boolean."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bool_in": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("inverted_bool",)
+    FUNCTION = "invert"
+    CATEGORY = "mtb/utils"
+
+    def invert(self, bool_in: bool):
+        return (not bool_in,)
 
 
 __nodes__ = [
-    SaveTensors,
+    MTB_StringReplace,
+    MTB_FitNumber,
+    MTB_GetBatchFromHistory,
+    MTB_AnyToString,
+    MTB_ConcatImages,
+    MTB_MathExpression,
+    MTB_ToDevice,
+    MTB_ApplyTextTemplate,
+    MTB_MatchDimensions,
+    MTB_AutoPanEquilateral,
+    MTB_FloatsToFloat,
+    MTB_FloatToFloats,
+    MTB_FloatsToInts,
+    MTB_TensorOps,
+    MTB_BooleanNot,
+    MTB_GetItem,
 ]

nodes/image_interpolation.py

@@ -0,0 +1,131 @@
+from pathlib import Path
+
+import comfy
+import comfy.model_management as model_management
+import comfy.utils
+import numpy as np
+import tensorflow as tf
+import torch
+from frame_interpolation.eval import interpolator, util
+
+from ..errors import ModelNotFound
+from ..log import log
+from ..utils import get_model_path
+
+
+class MTB_LoadFilmModel:
+    """Loads a FILM model
+
+    [DEPRECATED] Use ComfyUI-FrameInterpolation instead
+    """
+
+    @staticmethod
+    def get_models() -> list[Path]:
+        models_paths = get_model_path("FILM").iterdir()
+
+        return [x for x in models_paths if x.suffix in [".onnx", ".pth"]]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "film_model": (
+                    ["L1", "Style", "VGG"],
+                    {"default": "Style"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("FILM_MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "mtb/frame iterpolation"
+    DEPRECATED = True
+
+    def load_model(self, film_model: str):
+        model_path = get_model_path("FILM", film_model)
+        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"
+
+        if not model_path.exists():
+            log.error(f"Model {model_path} does not exist")
+            raise ValueError(f"Model {model_path} does not exist")
+
+        log.info(f"Loading model {model_path}")
+
+        return (interpolator.Interpolator(model_path.as_posix(), None),)
+
+
+class MTB_FilmInterpolation:
+    """Google Research FILM frame interpolation for large motion
+
+    [DEPRECATED] Use ComfyUI-FrameInterpolation instead
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "interpolate": ("INT", {"default": 2, "min": 1, "max": 50}),
+                "film_model": ("FILM_MODEL",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "do_interpolation"
+    CATEGORY = "mtb/frame iterpolation"
+    DEPRECATED = True
+
+    def do_interpolation(
+        self,
+        images: torch.Tensor,
+        interpolate: int,
+        film_model: interpolator.Interpolator,
+    ):
+        n = images.size(0)
+        # check if images is an empty tensor and return it...
+        if n == 0:
+            return (images,)
+
+        # check if tensorflow GPU is available
+        available_gpus = tf.config.list_physical_devices("GPU")
+        if not len(available_gpus):
+            log.warning(
+                "Tensorflow GPU not available, falling back to CPU this will be very slow"
+            )
+        else:
+            log.debug(f"Tensorflow GPU available, using {available_gpus}")
+
+        num_frames = (n - 1) * (2 ** (interpolate) - 1)
+        log.debug(f"Will interpolate into {num_frames} frames")
+
+        in_frames = [images[i] for i in range(n)]
+        out_tensors = []
+
+        pbar = comfy.utils.ProgressBar(num_frames)
+
+        for frame in util.interpolate_recursively_from_memory(
+            in_frames, interpolate, film_model
+        ):
+            out_tensors.append(
+                torch.from_numpy(frame)
+                if isinstance(frame, np.ndarray)
+                else frame
+            )
+            model_management.throw_exception_if_processing_interrupted()
+            pbar.update(1)
+
+        out_tensors = torch.cat(
+            [tens.unsqueeze(0) for tens in out_tensors], dim=0
+        )
+
+        log.debug(f"Returning {len(out_tensors)} tensors")
+        log.debug(f"Output shape {out_tensors.shape}")
+        log.debug(f"Output type {out_tensors.dtype}")
+        return (out_tensors,)
+
+
+__nodes__ = [MTB_LoadFilmModel, MTB_FilmInterpolation]

nodes/image_utils.py

@@ -0,0 +1,294 @@
+import json
+import os
+
+import numpy as np
+import torch
+from comfy.cli_args import args
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+
+from ..log import log
+
+
+class MTB_StackImages:
+    """Stack the input images horizontally or vertically."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"vertical": ("BOOLEAN", {"default": False})},
+            "optional": {
+                "match_method": (
+                    ["error", "smallest", "largest"],
+                    {"default": "error"},
+                ),
+                "output_rgb": (
+                    "BOOLEAN",
+                    {"default": True, "tooltip": "Output RGB instead of RGBA"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stack"
+    CATEGORY = "mtb/image utils"
+
+    def stack(self, vertical, match_method="error", output_rgb=True, **kwargs):
+        if not kwargs:
+            raise ValueError("At least one tensor must be provided.")
+
+        tensors = list(kwargs.values())
+        log.debug(
+            f"Stacking {len(tensors)} tensors "
+            f"{'vertically' if vertical else 'horizontally'}"
+        )
+
+        target_device = tensors[0].device
+
+        normalized_tensors = [
+            self.normalize_to_rgba(tensor.to(target_device))
+            for tensor in tensors
+        ]
+
+        max_batch_size = max(tensor.shape[0] for tensor in normalized_tensors)
+        normalized_tensors = [
+            self.duplicate_frames(tensor, max_batch_size)
+            for tensor in normalized_tensors
+        ]
+        if match_method != "error":
+            if vertical:
+                # match widths
+                widths = [tensor.shape[2] for tensor in normalized_tensors]
+                target_width = (
+                    min(widths) if match_method == "smallest" else max(widths)
+                )
+                normalized_tensors = [
+                    self.resize_tensor(tensor, width=target_width)
+                    for tensor in normalized_tensors
+                ]
+            else:
+                # match heights
+                heights = [tensor.shape[1] for tensor in normalized_tensors]
+                target_height = (
+                    min(heights)
+                    if match_method == "smallest"
+                    else max(heights)
+                )
+                normalized_tensors = [
+                    self.resize_tensor(tensor, height=target_height)
+                    for tensor in normalized_tensors
+                ]
+        else:
+            if vertical:
+                width = normalized_tensors[0].shape[2]
+                if any(
+                    tensor.shape[2] != width for tensor in normalized_tensors
+                ):
+                    raise ValueError(
+                        "All tensors must have the same width "
+                        "for vertical stacking."
+                    )
+            else:
+                height = normalized_tensors[0].shape[1]
+                if any(
+                    tensor.shape[1] != height for tensor in normalized_tensors
+                ):
+                    raise ValueError(
+                        "All tensors must have the same height "
+                        "for horizontal stacking."
+                    )
+
+        dim = 1 if vertical else 2
+
+        stacked_tensor = torch.cat(normalized_tensors, dim=dim)
+
+        if output_rgb:
+            stacked_tensor = stacked_tensor[:, :, :, :3]
+
+        return (stacked_tensor,)
+
+    def normalize_to_rgba(self, tensor):
+        """Normalize tensor to have 4 channels (RGBA)."""
+        _, _, _, channels = tensor.shape
+        # already RGBA
+        if channels == 4:
+            return tensor
+        # RGB to RGBA
+        elif channels == 3:
+            alpha_channel = torch.ones(
+                tensor.shape[:-1] + (1,), device=tensor.device
+            )
+            return torch.cat((tensor, alpha_channel), dim=-1)
+        else:
+            raise ValueError(
+                "Tensor has an unsupported number of channels: "
+                "expected 3 (RGB) or 4 (RGBA)."
+            )
+
+    def duplicate_frames(self, tensor, target_batch_size):
+        """Duplicate frames in tensor to match the target batch size."""
+        current_batch_size = tensor.shape[0]
+        if current_batch_size < target_batch_size:
+            duplication_factors: int = target_batch_size // current_batch_size
+            duplicated_tensor = tensor.repeat(duplication_factors, 1, 1, 1)
+            remaining_frames = target_batch_size % current_batch_size
+            if remaining_frames > 0:
+                duplicated_tensor = torch.cat(
+                    (duplicated_tensor, tensor[:remaining_frames]), dim=0
+                )
+            return duplicated_tensor
+        else:
+            return tensor
+
+    def resize_tensor(self, tensor, width=None, height=None):
+        """Resize tensor to specified width or height while maintaining aspect ratio."""
+        current_height, current_width = tensor.shape[1:3]
+
+        if width is not None and width != current_width:
+            scale_factor = width / current_width
+            new_height = int(current_height * scale_factor)
+            new_width = width
+        elif height is not None and height != current_height:
+            scale_factor = height / current_height
+            new_width = int(current_width * scale_factor)
+            new_height = height
+        else:
+            return tensor
+
+        resized = torch.nn.functional.interpolate(
+            tensor.permute(0, 3, 1, 2),
+            size=(new_height, new_width),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        return resized.permute(0, 2, 3, 1)
+
+
+class MTB_PickFromBatch:
+    """Pick a specific number of images from a batch.
+
+    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}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "pick_from_batch"
+    CATEGORY = "mtb/image utils"
+
+    def pick_from_batch(self, image, from_direction, count, mask=None):
+        batch_size = image.size(0)
+
+        # Limit count to the available number of images in the batch
+        count = min(count, batch_size)
+
+        selected_masks = None
+
+        if from_direction == "end":
+            selected_tensors = image[-count:]
+            if mask is not None:
+                selected_masks = mask[-count:]
+        else:
+            selected_tensors = image[:count]
+            if mask is not None:
+                selected_masks = mask[:count]
+
+        return (selected_tensors, selected_masks)
+
+
+import folder_paths
+
+
+class MTB_SaveImage:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save."}),
+                "filename_prefix": (
+                    "STRING",
+                    {
+                        "default": "ComfyUI",
+                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                    },
+                ),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "save_images"
+
+    # OUTPUT_NODE = True
+
+    CATEGORY = "mtb/image utils"
+    DESCRIPTION = """Saves the input images to your ComfyUI output directory.
+    This behaves exactly like the native SaveImage node but isn't an output node.
+    The reason I made this is to allow 'inlining' image save in loops for instance,
+    using the native node there wouldn't run for each iteration of the loop."""
+
+    def save_images(
+        self,
+        images,
+        filename_prefix="ComfyUI",
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix,
+                self.output_dir,
+                images[0].shape[1],
+                images[0].shape[0],
+            )
+        )
+        results = list()
+        for batch_number, image in enumerate(images):
+            i = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace(
+                "%batch_num%", str(batch_number)
+            )
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
+            img.save(
+                os.path.join(full_output_folder, file),
+                pnginfo=metadata,
+                compress_level=self.compress_level,
+            )
+            results.append(
+                {"filename": file, "subfolder": subfolder, "type": self.type}
+            )
+            counter += 1
+
+        return {"ui": {"images": results}, "result": (images,)}
+
+
+__nodes__ = [MTB_StackImages, MTB_PickFromBatch, MTB_SaveImage]

nodes/io.py

@@ -0,0 +1,439 @@
+import json
+import subprocess
+import uuid
+from pathlib import Path
+
+import comfy.model_management as model_management
+import comfy.utils
+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
+
+
+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 MTB_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"
+    EXPERIMENTAL = True
+
+    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 MTB_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"
+    EXPERIMENTAL = True
+
+    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 MTB_ExportWithFfmpeg:
+    """Export with FFmpeg (Experimental).
+
+    [DEPRACATED] Use VHS nodes instead
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "images": ("IMAGE",),
+                "playlist": ("PLAYLIST",),
+            },
+            "required": {
+                "fps": ("FLOAT", {"default": 24, "min": 1}),
+                "prefix": ("STRING", {"default": "export"}),
+                "format": (
+                    ["mov", "mp4", "mkv", "gif", "avi"],
+                    {"default": "mov"},
+                ),
+                "codec": (
+                    ["prores_ks", "libx264", "libx265", "gif"],
+                    {"default": "prores_ks"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("VIDEO",)
+    OUTPUT_NODE = True
+    FUNCTION = "export_prores"
+    DEPRECATED = True
+    CATEGORY = "mtb/IO"
+
+    def export_prores(
+        self,
+        fps: float,
+        prefix: str,
+        format: str,
+        codec: str,
+        images: torch.Tensor | None = None,
+        playlist: list[str] | None = None,
+    ):
+        file_ext = format
+        file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}"
+
+        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])}")
+        log.debug(f"Exporting {len(frames)} frames")
+        height, width, channels = frames[0].shape
+        has_alpha = channels == 4
+        out_path = (output_dir / file_id).as_posix()
+
+        if codec == "gif":
+            command = [
+                "ffmpeg",
+                "-f",
+                "image2pipe",
+                "-vcodec",
+                "png",
+                "-r",
+                str(fps),
+                "-i",
+                "-",
+                "-vcodec",
+                "gif",
+                "-y",
+                out_path,
+            ]
+            process = subprocess.Popen(command, stdin=subprocess.PIPE)
+            for frame in frames:
+                model_management.throw_exception_if_processing_interrupted()
+                Image.fromarray(frame).save(process.stdin, "PNG")
+
+            process.stdin.close()
+            process.wait()
+            return (out_path,)
+        else:
+            if has_alpha:
+                if codec in ["prores_ks", "libx264", "libx265"]:
+                    pix_fmt = (
+                        "yuva444p" if codec == "prores_ks" else "yuva420p"
+                    )
+                    frames = [
+                        frame.astype(np.uint16) * 257 for frame in frames
+                    ]
+                else:
+                    log.warning(
+                        f"Alpha channel not supported for codec {codec}. Alpha will be ignored."
+                    )
+                    frames = [
+                        frame[:, :, :3].astype(np.uint16) * 257
+                        for frame in frames
+                    ]
+                    pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
+            else:
+                pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
+                frames = [frame.astype(np.uint16) * 257 for frame in frames]
+
+        # Prepare the FFmpeg command
+        command = [
+            "ffmpeg",
+            "-y",
+            "-f",
+            "rawvideo",
+            "-vcodec",
+            "rawvideo",
+            "-s",
+            f"{width}x{height}",
+            "-pix_fmt",
+            pix_fmt,
+            "-r",
+            str(fps),
+            "-i",
+            "-",
+            "-c:v",
+            codec,
+        ]
+        if codec == "prores_ks":
+            command.extend(["-profile:v", "4444"])
+
+        command.extend(
+            [
+                "-r",
+                str(fps),
+                "-y",
+                out_path,
+            ]
+        )
+
+        process = subprocess.Popen(command, stdin=subprocess.PIPE)
+
+        pbar = comfy.utils.ProgressBar(len(frames))
+
+        for frame in frames:
+            process.stdin.write(frame.tobytes())
+            pbar.update(1)
+
+        process.stdin.close()
+        process.wait()
+
+        return (out_path,)
+
+
+def prepare_animated_batch(
+    batch: torch.Tensor,
+    pingpong=False,
+    resize_by=1.0,
+    resample_filter: Image.Resampling | None = 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 MTB_SaveGif:
+    """Save the images from the batch as a GIF.
+
+    [DEPRACATED] Use VHS nodes instead
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "fps": ("INT", {"default": 12, "min": 1, "max": 120}),
+                "resize_by": ("FLOAT", {"default": 1.0, "min": 0.1}),
+                "optimize": ("BOOLEAN", {"default": False}),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "resample_filter": (list(PIL_FILTER_MAP.keys()),),
+                "use_ffmpeg": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    CATEGORY = "mtb/IO"
+    FUNCTION = "save_gif"
+    DEPRECATED = True
+
+    def save_gif(
+        self,
+        image,
+        fps=12,
+        resize_by=1.0,
+        optimize=False,
+        pingpong=False,
+        resample_filter=None,
+        use_ffmpeg=False,
+    ):
+        if image.size(0) == 0:
+            return ("",)
+
+        if resample_filter is not None:
+            resample_filter = PIL_FILTER_MAP.get(resample_filter)
+
+        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"
+
+        if use_ffmpeg:
+            # Use FFmpeg to create the GIF from PIL images
+            command = [
+                "ffmpeg",
+                "-f",
+                "image2pipe",
+                "-vcodec",
+                "png",
+                "-r",
+                str(fps),
+                "-i",
+                "-",
+                "-vcodec",
+                "gif",
+                "-y",
+                out_path,
+            ]
+            process = subprocess.Popen(command, stdin=subprocess.PIPE)
+            for image in pil_images:
+                model_management.throw_exception_if_processing_interrupted()
+                image.save(process.stdin, "PNG")
+            process.stdin.close()
+            process.wait()
+
+        else:
+            pil_images[0].save(
+                out_path,
+                save_all=True,
+                append_images=pil_images[1:],
+                optimize=optimize,
+                duration=int(1000 / fps),
+                loop=0,
+            )
+        results = [
+            {"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}
+        ]
+        return {"ui": {"gif": results}}
+
+
+__nodes__ = [
+    MTB_SaveGif,
+    MTB_ExportWithFfmpeg,
+    MTB_AddToPlaylist,
+    MTB_ReadPlaylist,
+]

nodes/latent_processing.py

@@ -1,9 +1,8 @@
 import torch
 
-class LatentLerp:
+
+class MTB_LatentLerp:
     """Linear interpolation (blend) between two latent vectors"""
-    def __init__(self):
-        pass
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -11,14 +10,17 @@ class LatentLerp:
             "required": {
                 "A": ("LATENT",),
                 "B": ("LATENT",),
-                "t": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "t": (
+                    "FLOAT",
+                    {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01},
+                ),
             }
         }
 
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "lerp_latent"
 
-    CATEGORY = "latent"
+    CATEGORY = "mtb/latent"
 
     def lerp_latent(self, A, B, t):
         a = A.copy()
@@ -28,6 +30,7 @@ class LatentLerp:
 
         return (a,)
 
+
 __nodes__ = [
-    LatentLerp,
-]
+    MTB_LatentLerp,
+]

nodes/legacy.py

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

nodes/ltx.py

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

nodes/mask.py

@@ -1,57 +1,113 @@
-from rembg import remove
-from ..utils import pil2tensor, tensor2pil
+import comfy.utils
 from PIL import Image
 
-class ImageRemoveBackgroundRembg:
-    def __init__(self):
-        pass
+from ..utils import pil2tensor, tensor2pil
+
+
+class MTB_ImageRemoveBackgroundRembg:
+    """Removes the background from the input using Rembg."""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "image": ("IMAGE",),
-                "alpha_matting": (["True","False"], {"default":"False"},),
-                "alpha_matting_foreground_threshold": ("INT", {"default":240, "min": 0, "max": 255},),
-                "alpha_matting_background_threshold": ("INT", {"default":10, "min": 0, "max": 255},),
-                "alpha_matting_erode_size": ("INT", {"default":10, "min": 0, "max": 255},),
-                "post_process_mask": (["True","False"], {"default":"False"},),
-                "bgcolor": ("COLOR", {"default":"black"},),
-                
+                "alpha_matting": (
+                    "BOOLEAN",
+                    {"default": False},
+                ),
+                "alpha_matting_foreground_threshold": (
+                    "INT",
+                    {"default": 240, "min": 0, "max": 255},
+                ),
+                "alpha_matting_background_threshold": (
+                    "INT",
+                    {"default": 10, "min": 0, "max": 255},
+                ),
+                "alpha_matting_erode_size": (
+                    "INT",
+                    {"default": 10, "min": 0, "max": 255},
+                ),
+                "post_process_mask": (
+                    "BOOLEAN",
+                    {"default": False},
+                ),
+                "bgcolor": (
+                    "COLOR",
+                    {"default": "#000000","widgetType": "MTB_COLOR"},
+                ),
             },
         }
 
-    RETURN_TYPES = ("IMAGE","MASK","IMAGE",)
-    RETURN_NAMES = ("Image (rgba)","Mask","Image",)
+    RETURN_TYPES = (
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+    )
+    RETURN_NAMES = (
+        "Image (rgba)",
+        "Mask",
+        "Image",
+    )
     FUNCTION = "remove_background"
-    CATEGORY = "image"
+    CATEGORY = "mtb/image"
+
     # bgcolor: Optional[Tuple[int, int, int, int]]
-    def remove_background(self, image, alpha_matting, alpha_matting_foreground_threshold, alpha_matting_background_threshold, alpha_matting_erode_size, post_process_mask, bgcolor):
-        image = remove(
-                data=tensor2pil(image),
-                alpha_matting=alpha_matting == "True",
+    def remove_background(
+        self,
+        image,
+        alpha_matting,
+        alpha_matting_foreground_threshold,
+        alpha_matting_background_threshold,
+        alpha_matting_erode_size,
+        post_process_mask,
+        bgcolor,
+    ):
+        from rembg import remove
+
+        pbar = comfy.utils.ProgressBar(image.size(0))
+        images = tensor2pil(image)
+
+        out_img = []
+        out_mask = []
+        out_img_on_bg = []
+
+        for img in images:
+            img_rm = remove(
+                data=img,
+                alpha_matting=alpha_matting,
                 alpha_matting_foreground_threshold=alpha_matting_foreground_threshold,
                 alpha_matting_background_threshold=alpha_matting_background_threshold,
                 alpha_matting_erode_size=alpha_matting_erode_size,
                 session=None,
                 only_mask=False,
-                post_process_mask=post_process_mask == "True",
-                bgcolor=None
+                post_process_mask=post_process_mask,
+                bgcolor=None,
             )
-        
-        
-        # extract the alpha to a new image
-        mask = image.getchannel(3)
-        
-        # add our bgcolor behind the image
-        image_on_bg = Image.new("RGBA", image.size, bgcolor)
-        
-        image_on_bg.paste(image, mask=mask)
-        
-        
-        return (pil2tensor(image), pil2tensor(mask), pil2tensor(image_on_bg))
+
+            # extract the alpha to a new image
+            mask = img_rm.getchannel(3)
+
+            # add our bgcolor behind the image
+            image_on_bg = Image.new("RGBA", img_rm.size, bgcolor)
+
+            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)
+
+            pbar.update(1)
+
+        return (
+            pil2tensor(out_img),
+            pil2tensor(out_mask),
+            pil2tensor(out_img_on_bg),
+        )
 
 
 __nodes__ = [
-    ImageRemoveBackgroundRembg,
-]
+    MTB_ImageRemoveBackgroundRembg,
+]

nodes/model.py

@@ -0,0 +1,155 @@
+import copy
+
+import torch
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair
+
+from ..log import log
+
+
+class MTB_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"]),)
+
+
+def conv_forward(lyr, tensor, weight, bias):
+    step = lyr.timestep
+    if (lyr.paddingStartStep < 0 or step >= lyr.paddingStartStep) and (
+        lyr.paddingStopStep < 0 or step <= lyr.paddingStopStep
+    ):
+        working = F.pad(tensor, lyr.paddingX, mode=lyr.padding_modeX)
+        working = F.pad(working, lyr.paddingY, mode=lyr.padding_modeY)
+    else:
+        working = F.pad(tensor, lyr.paddingX, mode="constant")
+        working = F.pad(working, lyr.paddingY, mode="constant")
+
+    lyr.timestep += 1
+
+    return F.conv2d(
+        working, weight, bias, lyr.stride, _pair(0), lyr.dilation, lyr.groups
+    )
+
+
+class MTB_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",),
+                "startStep": ("INT", {"default": 0}),
+                "stopStep": ("INT", {"default": 999}),
+                "tilingX": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+                "tilingY": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "MODEL")
+    RETURN_NAMES = (
+        "Original Model (passthrough)",
+        "Patched Model",
+    )
+    FUNCTION = "hack"
+
+    CATEGORY = "mtb/textures"
+
+    def apply_circular(self, model, startStep, stopStep, x, y):
+        for layer in [
+            layer
+            for layer in model.modules()
+            if isinstance(layer, torch.nn.Conv2d)
+        ]:
+            layer.padding_modeX = "circular" if x else "constant"
+            layer.padding_modeY = "circular" if y else "constant"
+            layer.paddingX = (
+                layer._reversed_padding_repeated_twice[0],
+                layer._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            layer.paddingY = (
+                0,
+                0,
+                layer._reversed_padding_repeated_twice[2],
+                layer._reversed_padding_repeated_twice[3],
+            )
+            layer.paddingStartStep = startStep
+            layer.paddingStopStep = stopStep
+            layer.timestep = 0
+            layer._conv_forward = conv_forward.__get__(layer, torch.nn.Conv2d)
+
+        return model
+
+    def hack(
+        self,
+        model,
+        startStep,
+        stopStep,
+        tilingX,
+        tilingY,
+    ):
+        hacked_model = model.clone()
+        self.apply_circular(
+            hacked_model.model, startStep, stopStep, tilingX, tilingY
+        )
+        return (model, hacked_model)
+
+
+__nodes__ = [MTB_ModelPatchSeamless, MTB_VaeDecode]

nodes/number.py

@@ -1,26 +1,85 @@
-class IntToNumber:
-    """Node addon for the WAS Suite. Converts a "comfy" INT to a NUMBER."""
-
-    def __init__(self):
-        pass
+class MTB_IntToBool:
+    """Basic int to bool conversion"""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "int": ("INT", {"default": 0, "min": 0, "max": 1e9, "step": 1}),
+                "int": (
+                    "INT",
+                    {
+                        "default": 0,
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "int_to_bool"
+    CATEGORY = "mtb/number"
+
+    def int_to_bool(self, int):
+        return (bool(int),)
+
+
+class MTB_IntToNumber:
+    """Node addon for the WAS Suite. Converts a "comfy" INT to a NUMBER."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "int": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": -1e9,
+                        "max": 1e9,
+                        "step": 1,
+                        "forceInput": True,
+                    },
+                ),
             }
         }
 
     RETURN_TYPES = ("NUMBER",)
     FUNCTION = "int_to_number"
-    CATEGORY = "number"
+    CATEGORY = "mtb/number"
 
     def int_to_number(self, int):
-
         return (int,)
 
-__nodes__ = [
-    IntToNumber,
 
-]
+class MTB_FloatToNumber:
+    """Node addon for the WAS Suite. Converts a "comfy" FLOAT to a NUMBER."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "float": (
+                    "FLOAT",
+                    {
+                        "default": 0,
+                        "min": -1e9,
+                        "max": 1e9,
+                        "step": 1,
+                        "forceInput": True,
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("NUMBER",)
+    FUNCTION = "float_to_number"
+    CATEGORY = "mtb/number"
+
+    def float_to_number(self, float):
+        return (float,)
+
+
+__nodes__ = [
+    MTB_FloatToNumber,
+    MTB_IntToBool,
+    MTB_IntToNumber,
+]

nodes/postshot.py

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

nodes/prune.py

@@ -0,0 +1,360 @@
+from pathlib import Path
+
+import safetensors.torch
+import torch
+import tqdm
+
+from ..log import log
+from ..utils import Operation, Precision
+from ..utils import output_dir as comfy_out_dir
+
+PRUNE_DATA = {
+    "known_junk_prefix": [
+        "embedding_manager.embedder.",
+        "lora_te_text_model",
+        "control_model.",
+    ],
+    "nai_keys": {
+        "cond_stage_model.transformer.embeddings.": "cond_stage_model.transformer.text_model.embeddings.",
+        "cond_stage_model.transformer.encoder.": "cond_stage_model.transformer.text_model.encoder.",
+        "cond_stage_model.transformer.final_layer_norm.": "cond_stage_model.transformer.text_model.final_layer_norm.",
+    },
+}
+
+# position_ids in clip is int64. model_ema.num_updates is int32
+dtypes_to_fp16 = {torch.float32, torch.float64, torch.bfloat16}
+dtypes_to_bf16 = {torch.float32, torch.float64, torch.float16}
+dtypes_to_fp8 = {torch.float32, torch.float64, torch.bfloat16, torch.float16}
+
+
+class MTB_ModelPruner:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "unet": ("MODEL",),
+                "clip": ("CLIP",),
+                "vae": ("VAE",),
+            },
+            "required": {
+                "save_separately": ("BOOLEAN", {"default": False}),
+                "save_folder": ("STRING", {"default": "checkpoints/ComfyUI"}),
+                "fix_clip": ("BOOLEAN", {"default": True}),
+                "remove_junk": ("BOOLEAN", {"default": True}),
+                "ema_mode": (
+                    ("disabled", "remove_ema", "ema_only"),
+                    {"default": "remove_ema"},
+                ),
+                "precision_unet": (
+                    Precision.list_members(),
+                    {"default": Precision.FULL.value},
+                ),
+                "operation_unet": (
+                    Operation.list_members(),
+                    {"default": Operation.CONVERT.value},
+                ),
+                "precision_clip": (
+                    Precision.list_members(),
+                    {"default": Precision.FULL.value},
+                ),
+                "operation_clip": (
+                    Operation.list_members(),
+                    {"default": Operation.CONVERT.value},
+                ),
+                "precision_vae": (
+                    Precision.list_members(),
+                    {"default": Precision.FULL.value},
+                ),
+                "operation_vae": (
+                    Operation.list_members(),
+                    {"default": Operation.CONVERT.value},
+                ),
+            },
+        }
+
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+    CATEGORY = "mtb/prune"
+    FUNCTION = "prune"
+
+    def convert_precision(self, tensor: torch.Tensor, precision: Precision):
+        precision = Precision.from_str(precision)
+        log.debug(f"Converting to {precision}")
+        match precision:
+            case Precision.FP8:
+                if tensor.dtype in dtypes_to_fp8:
+                    return tensor.to(torch.float8_e4m3fn)
+                log.error(f"Cannot convert {tensor.dtype} to fp8")
+                return tensor
+            case Precision.FP16:
+                if tensor.dtype in dtypes_to_fp16:
+                    return tensor.half()
+                log.error(f"Cannot convert {tensor.dtype} to f16")
+                return tensor
+            case Precision.BF16:
+                if tensor.dtype in dtypes_to_bf16:
+                    return tensor.bfloat16()
+                log.error(f"Cannot convert {tensor.dtype} to bf16")
+                return tensor
+            case Precision.FULL | Precision.FP32:
+                return tensor
+
+    def is_sdxl_model(self, clip: dict[str, torch.Tensor] | None):
+        if clip:
+            return (any(k.startswith("conditioner.embedders") for k in clip),)
+        return False
+
+    def has_ema(self, unet: dict[str, torch.Tensor]):
+        return any(k.startswith("model_ema") for k in unet)
+
+    def fix_clip(self, clip: dict[str, torch.Tensor] | None):
+        if self.is_sdxl_model(clip):
+            log.warn("[fix clip] SDXL not supported")
+            return
+
+        if clip is None:
+            return
+
+        position_id_key = (
+            "cond_stage_model.transformer.text_model.embeddings.position_ids"
+        )
+        if position_id_key in clip:
+            correct = torch.Tensor([list(range(77))]).to(torch.int64)
+            now = clip[position_id_key].to(torch.int64)
+
+            broken = correct.ne(now)
+            broken = [i for i in range(77) if broken[0][i]]
+
+            if len(broken) != 0:
+                clip[position_id_key] = correct
+                log.info(f"[Converter] Fixed broken clip\n{broken}")
+            else:
+                log.info(
+                    "[Converter] Clip in this model is fine, skip fixing..."
+                )
+
+        else:
+            log.info("[Converter] Missing position id in model, try fixing...")
+            clip[position_id_key] = torch.Tensor([list(range(77))]).to(
+                torch.int64
+            )
+        return clip
+
+    def get_dicts(self, unet, clip, vae):
+        clip_sd = clip.get_sd()
+        state_dict = unet.model.state_dict_for_saving(
+            clip_sd, vae.get_sd(), None
+        )
+
+        unet = {
+            k: v
+            for k, v in state_dict.items()
+            if k.startswith("model.diffusion_model")
+        }
+        clip = {
+            k: v
+            for k, v in state_dict.items()
+            if k.startswith("cond_stage_model")
+            or k.startswith("conditioner.embedders")
+        }
+        vae = {
+            k: v
+            for k, v in state_dict.items()
+            if k.startswith("first_stage_model")
+        }
+
+        other = {
+            k: v
+            for k, v in state_dict.items()
+            if k not in unet and k not in vae and k not in clip
+        }
+
+        return (unet, clip, vae, other)
+
+    def do_remove_junk(self, tensors: dict[str, dict[str, torch.Tensor]]):
+        need_delete: list[str] = []
+        for layer in tensors:
+            for key in layer:
+                for jk in PRUNE_DATA["known_junk_prefix"]:
+                    if key.startswith(jk):
+                        need_delete.append(".".join([layer, key]))
+
+        for k in need_delete:
+            log.info(f"Removing junk data: {k}")
+            del tensors[k]
+
+        return tensors
+
+    def prune(
+        self,
+        *,
+        save_separately: bool,
+        save_folder: str,
+        fix_clip: bool,
+        remove_junk: bool,
+        ema_mode: str,
+        precision_unet: Precision,
+        precision_clip: Precision,
+        precision_vae: Precision,
+        operation_unet: str,
+        operation_clip: str,
+        operation_vae: str,
+        unet: dict[str, torch.Tensor] | None = None,
+        clip: dict[str, torch.Tensor] | None = None,
+        vae: dict[str, torch.Tensor] | None = None,
+    ):
+        operation = {
+            "unet": Operation.from_str(operation_unet),
+            "clip": Operation.from_str(operation_clip),
+            "vae": Operation.from_str(operation_vae),
+        }
+        precision = {
+            "unet": Precision.from_str(precision_unet),
+            "clip": Precision.from_str(precision_clip),
+            "vae": Precision.from_str(precision_vae),
+        }
+
+        unet, clip, vae, _other = self.get_dicts(unet, clip, vae)
+
+        out_dir = Path(save_folder)
+        folder = out_dir.parent
+        if not out_dir.is_absolute():
+            folder = (comfy_out_dir / save_folder).parent
+
+        if not folder.exists():
+            if folder.parent.exists():
+                folder.mkdir()
+            else:
+                raise FileNotFoundError(
+                    f"Folder {folder.parent} does not exist"
+                )
+
+        name = out_dir.name
+        save_name = f"{name}-{precision_unet}"
+        if ema_mode != "disabled":
+            save_name += f"-{ema_mode}"
+        if fix_clip:
+            save_name += "-clip-fix"
+
+        if (
+            any(o == Operation.CONVERT for o in operation.values())
+            and any(p == Precision.FP8 for p in precision.values())
+            and torch.__version__ < "2.1.0"
+        ):
+            raise NotImplementedError(
+                "PyTorch 2.1.0 or newer is required for fp8 conversion"
+            )
+
+        if not self.is_sdxl_model(clip):
+            for part in [unet, vae, clip]:
+                if part:
+                    nai_keys = PRUNE_DATA["nai_keys"]
+                    for k in list(part.keys()):
+                        for r in nai_keys:
+                            if isinstance(k, str) and k.startswith(r):
+                                new_key = k.replace(r, nai_keys[r])
+                                part[new_key] = part[k]
+                                del part[k]
+                                log.info(
+                                    f"[Converter] Fixed novelai error key {k}"
+                                )
+                                break
+
+            if fix_clip:
+                clip = self.fix_clip(clip)
+
+        ok: dict[str, dict[str, torch.Tensor]] = {
+            "unet": {},
+            "clip": {},
+            "vae": {},
+        }
+
+        def _hf(part: str, wk: str, t: torch.Tensor):
+            if not isinstance(t, torch.Tensor):
+                log.debug("Not a torch tensor, skipping key")
+                return
+
+            log.debug(f"Operation {operation[part]}")
+            if operation[part] == Operation.CONVERT:
+                ok[part][wk] = self.convert_precision(
+                    t, precision[part]
+                )  # conv_func(t)
+            elif operation[part] == Operation.COPY:
+                ok[part][wk] = t
+            elif operation[part] == Operation.DELETE:
+                return
+
+        log.info("[Converter] Converting model...")
+
+        for part_name, part in zip(
+            ["unet", "vae", "clip", "other"],
+            [unet, vae, clip],
+            strict=False,
+        ):
+            if part:
+                match ema_mode:
+                    case "remove_ema":
+                        for k, v in tqdm.tqdm(part.items()):
+                            if "model_ema." not in k:
+                                _hf(part_name, k, v)
+                    case "ema_only":
+                        if not self.has_ema(part):
+                            log.warn("No EMA to extract")
+                            return
+                        for k in tqdm.tqdm(part):
+                            ema_k = "___"
+                            try:
+                                ema_k = "model_ema." + k[6:].replace(".", "")
+                            except Exception:
+                                pass
+                            if ema_k in part:
+                                _hf(part_name, k, part[ema_k])
+                            elif not k.startswith("model_ema.") or k in [
+                                "model_ema.num_updates",
+                                "model_ema.decay",
+                            ]:
+                                _hf(part_name, k, part[k])
+                    case "disabled" | _:
+                        for k, v in tqdm.tqdm(part.items()):
+                            _hf(part_name, k, v)
+
+                if save_separately:
+                    if remove_junk:
+                        ok = self.do_remove_junk(ok)
+
+                    flat_ok = {
+                        k: v
+                        for _, subdict in ok.items()
+                        for k, v in subdict.items()
+                    }
+                    save_path = (
+                        folder / f"{part_name}-{save_name}.safetensors"
+                    ).as_posix()
+                    safetensors.torch.save_file(flat_ok, save_path)
+                    ok: dict[str, dict[str, torch.Tensor]] = {
+                        "unet": {},
+                        "clip": {},
+                        "vae": {},
+                    }
+
+        if save_separately:
+            return ()
+
+        if remove_junk:
+            ok = self.do_remove_junk(ok)
+
+        flat_ok = {
+            k: v for _, subdict in ok.items() for k, v in subdict.items()
+        }
+
+        try:
+            safetensors.torch.save_file(
+                flat_ok, (folder / f"{save_name}.safetensors").as_posix()
+            )
+        except Exception as e:
+            log.error(e)
+
+        return ()
+
+
+__nodes__ = [MTB_ModelPruner]

nodes/qrcode.py

@@ -1,13 +1,13 @@
 import qrcode
-from ..utils import pil2tensor
+import torch
 from PIL import Image
 
+from ..log import log
+from ..utils import pil2tensor
 
-class QrCode:
-    """Basic QR Code generator"""
 
-    def __init__(self):
-        pass
+class MTB_QrCode:
+    """Basic QR Code generator."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -23,17 +23,36 @@ class QrCode:
                     {"default": 256, "max": 8096, "min": 0, "step": 1},
                 ),
                 "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": (("True", "False"), {"default": "False"}),
+                "box_size": (
+                    "INT",
+                    {"default": 10, "max": 8096, "min": 0, "step": 1},
+                ),
+                "border": (
+                    "INT",
+                    {"default": 4, "max": 8096, "min": 0, "step": 1},
+                ),
+                "invert": (("BOOLEAN",), {"default": False}),
             }
         }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "do_qr"
-    CATEGORY = "fun"
+    CATEGORY = "mtb/generate"
 
-    def do_qr(self, url, width, height, error_correct, box_size, border, invert):
+    def do_qr(
+        self,
+        *,
+        url: str,
+        width: int,
+        height: int,
+        error_correct: str,
+        box_size: int,
+        border: int,
+        invert: bool,
+    ) -> tuple[torch.Tensor]:
+        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":
@@ -52,10 +71,10 @@ class QrCode:
         qr.add_data(url)
         qr.make(fit=True)
 
-        back_color = (255, 255, 255) if invert == "True" else (0, 0, 0)
-        fill_color = (0, 0, 0) if invert == "True" else (255, 255, 255)
+        back_color = (255, 255, 255) if invert else (0, 0, 0)
+        fill_color = (0, 0, 0) if invert else (255, 255, 255)
 
-        code = img = qr.make_image(back_color=back_color, fill_color=fill_color)
+        code = qr.make_image(back_color=back_color, fill_color=fill_color)
 
         # that we now resize without filtering
         code = code.resize((width, height), Image.NEAREST)
@@ -63,4 +82,4 @@ class QrCode:
         return (pil2tensor(code),)
 
 
-__nodes__ = [QrCode]
+__nodes__ = [MTB_QrCode]

nodes/transform.py

@@ -0,0 +1,220 @@
+from math import ceil, sqrt
+from typing import cast
+
+import torch
+import torchvision.transforms.functional as TF
+from PIL import Image
+
+from ..utils import hex_to_rgb, log, pil2tensor, tensor2pil
+
+
+class MTB_TransformImage:
+    """Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy
+
+    it return a tensor representing the transformed images with the same shape as the input tensor
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "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", "widgetType": "MTB_COLOR"},
+                ),
+            },
+            "optional": {
+                "filter_type": (
+                    [
+                        "nearest",
+                        "box",
+                        "bilinear",
+                        "hamming",
+                        "bicubic",
+                        "lanczos",
+                    ],
+                    {"default": "bilinear"},
+                ),
+                "stretch_x": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.001, "max": 10.0, "step": 0.01},
+                ),
+                "stretch_y": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.001, "max": 10.0, "step": 0.01},
+                ),
+                "use_normalized": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "If true, transform values are scaled to image dimensions.",
+                    },
+                ),
+            },
+        }
+
+    FUNCTION = "transform"
+    RETURN_TYPES = ("IMAGE",)
+    CATEGORY = "mtb/transform"
+
+    def transform(
+        self,
+        image: torch.Tensor,
+        x: float,
+        y: float,
+        zoom: float,
+        angle: float,
+        shear: float,
+        border_handling="edge",
+        constant_color=None,
+        filter_type="nearest",
+        stretch_x=1.0,
+        stretch_y=1.0,
+        use_normalized: bool = False,
+    ):
+        filter_map = {
+            "nearest": Image.NEAREST,
+            "box": Image.BOX,
+            "bilinear": Image.BILINEAR,
+            "hamming": Image.HAMMING,
+            "bicubic": Image.BICUBIC,
+            "lanczos": Image.LANCZOS,
+        }
+        resampling_filter = filter_map[filter_type]
+
+        _, frame_height, frame_width, _ = image.size()
+        if use_normalized:
+            x = float(x) * frame_width
+            y = float(y) * frame_height
+        x = int(x)
+        y = int(y)
+        angle = int(angle)
+
+        log.debug(
+            f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear} | stretch_x: {stretch_x}, stretch_y: {stretch_y}"
+        )
+
+        if image.size(0) == 0:
+            return (torch.zeros(0),)
+        transformed_images = []
+
+        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,
+                padding=padding,
+                padding_mode=border_handling,
+                fill=constant_color or 0,
+            )
+
+            if stretch_x != 1.0 or stretch_y != 1.0:
+                img = cast(
+                    Image.Image,
+                    TF.affine(
+                        img,
+                        angle=angle,
+                        scale=zoom,
+                        translate=[x, y],
+                        shear=shear,
+                        interpolation=resampling_filter,
+                    ),
+                )
+
+                width, height = img.size
+                center = (width // 2, height // 2)
+
+                stretch_x_factor = 1.0 / stretch_x
+                stretch_y_factor = 1.0 / stretch_y
+
+                matrix = [
+                    stretch_x_factor,
+                    0,
+                    center[0] - center[0] * stretch_x_factor,
+                    0,
+                    stretch_y_factor,
+                    center[1] - center[1] * stretch_y_factor,
+                ]
+
+                img = img.transform(
+                    img.size, Image.AFFINE, matrix, resampling_filter
+                )
+            else:
+                img = cast(
+                    Image.Image,
+                    TF.affine(
+                        img,
+                        angle=angle,
+                        scale=zoom,
+                        translate=[x, y],
+                        shear=shear,
+                        interpolation=resampling_filter,
+                    ),
+                )
+
+            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__ = [MTB_TransformImage]

nodes/video.py

@@ -1,105 +1,273 @@
+import hashlib
+import json
 import os
 import re
-import torch
+from pathlib import Path
+
+import folder_paths
 import numpy as np
-import hashlib
+import torch
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
-import folder_paths
-from pathlib import Path
-import json
 
 from ..log import log
-class LoadImageSequence:
+
+
+class MTB_LoadImageSequence:
     """Load an image sequence from a folder. The current frame is used to determine which image to load.
 
     Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.
+    Use -1 to load all matching frames as a batch.
+
     """
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "path": ("STRING",{"default":"videos/####.png"}),
-                "current_frame": ("INT",{"default":0, "min":0, "max": 9999999},),
-            }
+                "path": ("STRING", {"default": "videos/####.png"}),
+                "current_frame": (
+                    "INT",
+                    {"default": 0, "min": -1, "max": 9999999},
+                ),
+            },
+            "optional": {
+                "range": ("STRING", {"default": ""}),
+            },
         }
 
-    CATEGORY = "video"
+    CATEGORY = "mtb/IO"
     FUNCTION = "load_image"
-    RETURN_TYPES = ("IMAGE", "MASK", "INT",)
-    RETURN_NAMES = ("image", "mask", "current_frame",)
+    RETURN_TYPES = (
+        "IMAGE",
+        "MASK",
+        "INT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "image",
+        "mask",
+        "current_frame",
+        "total_frames",
+    )
+
+    def load_image(self, path=None, current_frame=0, range=""):
+        load_all = current_frame == -1
+        total_frames = 1
+
+        if range:
+            frames = self.get_frames_from_range(path, range)
+            imgs, masks = zip(*(img_from_path(frame) for frame in frames))
+            out_img = torch.cat(imgs, dim=0)
+            out_mask = torch.cat(masks, dim=0)
+            total_frames = len(imgs)
+            return (out_img, out_mask, -1, total_frames)
+
+        elif load_all:
+            log.debug(f"Loading all frames from {path}")
+            frames = resolve_all_frames(path)
+            log.debug(f"Found {len(frames)} frames")
+
+            imgs = []
+            masks = []
+
+            imgs, masks = zip(*(img_from_path(frame) for frame in frames))
+
+            out_img = torch.cat(imgs, dim=0)
+            out_mask = torch.cat(masks, dim=0)
+            total_frames = len(imgs)
+
+            return (out_img, out_mask, -1, total_frames)
 
-    def load_image(self, path=None, current_frame=0):
         log.debug(f"Loading image: {path}, {current_frame}")
-        print(f"Loading image: {path}, {current_frame}")
         resolved_path = resolve_path(path, current_frame)
         image_path = folder_paths.get_annotated_filepath(resolved_path)
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
-        image = i.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        if 'A' in i.getbands():
-            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-            mask = 1. - torch.from_numpy(mask)
+        image, mask = img_from_path(image_path)
+        return (image, mask, current_frame, total_frames)
+
+    def get_frames_from_range(self, path, range_str):
+        try:
+            start, end = map(int, range_str.split("-"))
+        except ValueError:
+            raise ValueError(
+                f"Invalid range format: {range_str}. Expected format is 'start-end'."
+            )
+
+        frames = resolve_all_frames(path)
+        total_frames = len(frames)
+
+        if start < 0 or end >= total_frames:
+            raise ValueError(
+                f"Range {range_str} is out of bounds. Total frames available: {total_frames}"
+            )
+
+        if "#" in path:
+            frame_regex = re.escape(path).replace(r"\#", r"(\d+)")
+            frame_number_regex = re.compile(frame_regex)
+
+            matching_frames = []
+            for frame in frames:
+                match = frame_number_regex.search(frame)
+
+                if match:
+                    frame_number = int(match.group(1))
+                    if start <= frame_number <= end:
+                        matching_frames.append(frame)
+
+            return matching_frames
         else:
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
-        return (image, mask, current_frame,)
+            log.warning(
+                f"Wildcard pattern or directory will use indexes instead of frame numbers for : {path}"
+            )
+
+            selected_frames = frames[start : end + 1]
+
+        return selected_frames
 
     @staticmethod
-    def IS_CHANGED(path="", current_frame=0):
+    def IS_CHANGED(path="", current_frame=0, range=""):
         print(f"Checking if changed: {path}, {current_frame}")
+        if range or current_frame == -1:
+            resolved_paths = resolve_all_frames(path)
+            timestamps = [
+                os.path.getmtime(folder_paths.get_annotated_filepath(p))
+                for p in resolved_paths
+            ]
+            combined_hash = hashlib.sha256(
+                "".join(map(str, timestamps)).encode()
+            )
+            return combined_hash.hexdigest()
         resolved_path = resolve_path(path, current_frame)
         image_path = folder_paths.get_annotated_filepath(resolved_path)
-        if os.path.exists(image_path): 
+        if os.path.exists(image_path):
             m = hashlib.sha256()
-            with open(image_path, 'rb') as f:
+            with open(image_path, "rb") as f:
                 m.update(f.read())
             return m.digest().hex()
         return "NONE"
 
     # @staticmethod
     # def VALIDATE_INPUTS(path="", current_frame=0):
-        
+
     #     print(f"Validating inputs: {path}, {current_frame}")
     #     resolved_path = resolve_path(path, current_frame)
     #     if not folder_paths.exists_annotated_filepath(resolved_path):
     #         return f"Invalid image file: {resolved_path}"
     #     return True
 
+
+import glob
+
+
+def img_from_path(path):
+    img = Image.open(path)
+    img = ImageOps.exif_transpose(img)
+    image = img.convert("RGB")
+    image = np.array(image).astype(np.float32) / 255.0
+    image = torch.from_numpy(image)[None,]
+    if "A" in img.getbands():
+        mask = np.array(img.getchannel("A")).astype(np.float32) / 255.0
+        mask = 1.0 - torch.from_numpy(mask)
+    else:
+        mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+    return (
+        image,
+        mask,
+    )
+
+
+def resolve_all_frames(path: str):
+    frames: list[str] = []
+    if "#" not in path:
+        pth = Path(path)
+        if pth.is_dir():
+            for f in pth.iterdir():
+                if f.suffix in [".jpg", ".png"]:
+                    frames.append(f.as_posix())
+        elif "*" in path:
+            frames = glob.glob(path)
+        else:
+            raise ValueError(
+                "The path doesn't contain a # or a * or is not a directory"
+            )
+        frames.sort()
+
+        return frames
+
+    pattern = path
+    folder_path, file_pattern = os.path.split(pattern)
+
+    log.debug(f"Resolving all frames in {folder_path}")
+    hash_count = file_pattern.count("#")
+    frame_pattern = re.sub(r"#+", "*", file_pattern)
+
+    log.debug(f"Found pattern: {frame_pattern}")
+
+    matching_files = glob.glob(os.path.join(folder_path, frame_pattern))
+
+    log.debug(f"Found {len(matching_files)} matching files")
+
+    frame_regex = re.escape(file_pattern).replace(r"\#", r"(\d+)")
+
+    frame_number_regex = re.compile(frame_regex)
+
+    for file in matching_files:
+        match = frame_number_regex.search(file)
+        if match:
+            frame_number = match.group(1)
+            log.debug(f"Found frame number: {frame_number}")
+            # resolved_file = pattern.replace("*" * frame_number.count("#"), frame_number)
+            frames.append(file)
+
+    frames.sort()  # Sort frames alphabetically
+    return frames
+
+
 def resolve_path(path, frame):
     hashes = path.count("#")
     padded_number = str(frame).zfill(hashes)
     return re.sub("#+", padded_number, path)
 
-class SaveImageSequence:
+
+class MTB_SaveImageSequence:
     """Save an image sequence to a folder. The current frame is used to determine which image to save.
 
     This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.
     """
+
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
         self.type = "output"
-        
 
     @classmethod
     def INPUT_TYPES(cls):
-        return {"required": {
-                    "images": ("IMAGE", ),
-                    "filename_prefix": ("STRING", {"default": "Sequence"}),
-                    "current_frame": ("INT", {"default": 0, "min": 0, "max": 9999999}),
-                    },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "filename_prefix": ("STRING", {"default": "Sequence"}),
+                "current_frame": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 9999999},
+                ),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
 
     RETURN_TYPES = ()
     FUNCTION = "save_images"
 
     OUTPUT_NODE = True
 
-    CATEGORY = "image"
+    CATEGORY = "mtb/IO"
 
-    def save_images(self, images, filename_prefix="Sequence", current_frame=0, prompt=None, extra_pnginfo=None):
+    def save_images(
+        self,
+        images,
+        filename_prefix="Sequence",
+        current_frame=0,
+        prompt=None,
+        extra_pnginfo=None,
+    ):
         # full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         # results = list()
         # for image in images:
@@ -120,30 +288,43 @@ class SaveImageSequence:
         #         "type": self.type
         #     })
         #     counter += 1
-    
+
         if len(images) > 1:
             raise ValueError("Can only save one image at a time")
-        
+
         resolved_path = Path(self.output_dir) / filename_prefix
         resolved_path.mkdir(parents=True, exist_ok=True)
-        
-        resolved_img = resolved_path / f"{filename_prefix}_{current_frame:05}.png"
-        
+
+        resolved_img = (
+            resolved_path / f"{filename_prefix}_{current_frame:05}.png"
+        )
+
         output_image = images[0].cpu().numpy()
-        img = Image.fromarray(np.clip(output_image * 255., 0, 255).astype(np.uint8))
+        img = Image.fromarray(
+            np.clip(output_image * 255.0, 0, 255).astype(np.uint8)
+        )
         metadata = PngInfo()
         if prompt is not None:
             metadata.add_text("prompt", json.dumps(prompt))
         if extra_pnginfo is not None:
             for x in extra_pnginfo:
                 metadata.add_text(x, json.dumps(extra_pnginfo[x]))
-                
+
         img.save(resolved_img, pnginfo=metadata, compress_level=4)
-        return { "ui": { "images": [ { "filename": resolved_img.name, "subfolder": resolved_path.name, "type": self.type } ] } }
-                
-                
-        
+        return {
+            "ui": {
+                "images": [
+                    {
+                        "filename": resolved_img.name,
+                        "subfolder": resolved_path.name,
+                        "type": self.type,
+                    }
+                ]
+            }
+        }
+
+
 __nodes__ = [
-    LoadImageSequence,
-    SaveImageSequence,
-]
+    MTB_LoadImageSequence,
+    MTB_SaveImageSequence,
+]

nodes/vitmatte.py

@@ -0,0 +1,141 @@
+import cv2
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+
+from ..utils import models_dir, np2tensor
+
+# TODO: check if I can make a torch script device independant
+# for now I forced it to use cuda.
+
+
+class MTB_LoadVitMatteModel:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "kind": (("Composition-1K", "Distinctions-646"),),
+                "autodownload": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    RETURN_TYPES = ("VITMATTE_MODEL",)
+    RETURN_NAMES = ("torch_script",)
+    CATEGORY = "mtb/vitmatte"
+    FUNCTION = "execute"
+
+    def execute(self, *, kind: str, autodownload: bool):
+        dest = models_dir / "vitmatte"
+        dest.mkdir(exist_ok=True)
+        name = "dis" if kind == "Distinctions-646" else "com"
+
+        file = hf_hub_download(
+            repo_id="melmass/pytorch-scripts",
+            filename=f"vitmatte_b_{name}.pt",
+            local_dir=dest.as_posix(),
+            local_files_only=not autodownload,
+        )
+        model = torch.jit.load(file).to("cuda")
+
+        return (model,)
+
+
+class MTB_GenerateTrimap:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                # "image": ("IMAGE",),
+                "mask": ("MASK",),
+                "erode": ("INT", {"default": 10}),
+                "dilate": ("INT", {"default": 10}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("trimap",)
+
+    CATEGORY = "mtb/vitmatte"
+    FUNCTION = "execute"
+
+    def execute(
+        self,
+        # image:torch.Tensor,
+        mask: torch.Tensor,
+        erode: int = 10,
+        dilate: int = 10,
+    ):
+        # TODO: not sure what's the most practical between IMAGE or MASK
+
+        # image = image.to("cuda").half()
+        mask = mask.to("cuda").half()
+
+        trimaps = []
+        for m in mask:
+            mask_arr = m.squeeze(0).to(torch.uint8).cpu().numpy() * 255
+            erode_kernel = np.ones((erode, erode), np.uint8)
+            dilate_kernel = np.ones((dilate, dilate), np.uint8)
+            eroded = cv2.erode(mask_arr, erode_kernel, iterations=5)
+            dilated = cv2.dilate(mask_arr, dilate_kernel, iterations=5)
+            trimap = np.zeros_like(mask_arr)
+            trimap[dilated == 255] = 128
+            trimap[eroded == 255] = 255
+            trimaps.append(trimap)
+
+        return (np2tensor(trimaps),)
+
+
+class MTB_ApplyVitMatte:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("VITMATTE_MODEL",),
+                "image": ("IMAGE",),
+                "trimap": ("IMAGE",),
+                "returns": (("RGB", "RGBA"),),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image (rgba)", "mask")
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
+
+    def execute(
+        self, model, image: torch.Tensor, trimap: torch.Tensor, returns: str
+    ):
+        im_count = image.shape[0]
+        tm_count = trimap.shape[0]
+
+        if im_count != tm_count:
+            raise ValueError("image and trimap must have the same batch size")
+
+        outputs_m: list[torch.Tensor] = []
+        outputs_i: list[torch.Tensor] = []
+        for i, im in enumerate(image):
+            tm = trimap[i].half().unsqueeze(2).permute(2, 0, 1).to("cuda")
+            im = im.half().permute(2, 0, 1).to("cuda")
+
+            inputs = {"image": im.unsqueeze(0), "trimap": tm.unsqueeze(0)}
+
+            fine_mask = model(inputs)
+            foreground = im * fine_mask + (1 - fine_mask)
+
+            if returns == "RGBA":
+                rgba_image = torch.cat(
+                    (foreground, fine_mask.unsqueeze(0)), dim=0
+                )
+                outputs_i.append(rgba_image.unsqueeze(0))
+            else:
+                outputs_i.append(foreground.unsqueeze(0))
+
+            outputs_m.append(fine_mask.unsqueeze(0))
+
+        result_m = torch.cat(outputs_m, dim=0)
+        result_i = torch.cat(outputs_i, dim=0)
+
+        return (result_i.permute(0, 2, 3, 1), result_m)
+
+
+__nodes__ = [MTB_LoadVitMatteModel, MTB_GenerateTrimap, MTB_ApplyVitMatte]

pyproject.toml

@@ -0,0 +1,182 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "comfy-mtb"
+version = "0.5.4"
+description = "Animation oriented nodes pack for ComfyUI."
+license = { text = "MIT" }
+readme = "README.md"
+# repository = ""
+# url = "https://github.com/melMass/comfy_mtb"
+authors = [{ name = "Mel Massadian", email = "mel@melmassadian.com" }]
+classifiers = [
+  "License :: OSI Approved :: MIT License",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Intended Audience :: Developers",
+]
+requires-python = ">=3.10"
+dependencies = [
+  "qrcode",
+  "cachetools",
+  "onnxruntime-gpu",
+  "requirements-parserx",
+  "rembg",
+  "imageio_ffmpeg",
+  "rich",
+  "rich_argparse",
+  "matplotlib",
+  "pillow",
+]
+optional-dependencies = { mel = [
+  "jupyterlab==4.1.6",
+], dev = [
+  "black[jupyter]",
+  "codespell",
+  "marimo",
+  "mypy",
+  "pre-commit",
+  "pytest",
+  "pytest-cov",
+  "pytest-random-order",
+  "ruff",
+], doc = [
+  "docutils==0.17.1",
+  "jupyter-book>=0.15",
+  "sphinx-autobuild",
+] }
+
+[project.urls]
+Homepage = "https://github.com/melMass/comfy_mtb"
+Documentation = "https://github.com/melMass/comfy_mtb/wiki"
+Repository = "https://github.com/melMass/comfy_mtb"
+Issues = "https://github.com/melMass/comfy_mtb/issues"
+
+[tool.comfy]
+PublisherId = "mel"
+DisplayName = "comfy-mtb"
+Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
+
+[tool.bumpversion]
+current_version = "0.5.1"
+parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
+serialize = ["{major}.{minor}.{patch}"]
+search = "{current_version}"
+replace = "{new_version}"
+regex = false
+ignore_missing_version = false
+ignore_missing_files = false
+tag = true
+sign_tags = true
+tag_name = "v{new_version}"
+tag_message = "⬆️ Bump version: {current_version} → {new_version}"
+allow_dirty = true
+commit = true
+message = "⬆️ Bump version: {current_version} → {new_version}"
+commit_args = ""
+
+[[tool.bumpversion.files]]
+filename = "__init__.py"
+search = "__version__ = \"{current_version}\""
+replace = "__version__ = \"{new_version}\""
+
+[[tool.bumpversion.files]]
+filename = "pyproject.toml"
+search = "version = \"{current_version}\""
+replace = "version = \"{new_version}\""
+
+# [[tool.bumpversion.files]]
+# filename = "your_package/__init__.py"
+# search = "__version__ = '{current_version}'"
+# replace = "__version__ = '{new_version}'"
+
+# INFO: All those remaining keys are meant for local dev
+[tool.pyright]
+include = ["."]
+exclude = [
+  "**/node_modules",
+  "**/__pycache__",
+  "src/experimental",
+  "src/typestubs",
+]
+ignore = ["src/oldstuff"]
+defineConstant = { DEBUG = true }
+extraPaths = ["python", "../.."]
+stubPath = "src/stubs"
+
+reportMissingImports = true
+reportMissingTypeStubs = false
+typeCheckingMode = "basic"
+
+pythonVersion = "3.10"
+pythonPlatform = "Windows"
+
+[tool.pytest.ini_options]
+log_level = "DEBUG"
+log_cli = true
+markers = [
+  "wip: tests that aren't fully finished yet",
+  "heavy: marks tests as heavy (deselect with '-m \"not heavy\"')",
+
+]
+filterwarnings = ["ignore::UserWarning", 'ignore::DeprecationWarning']
+
+[tool.isort]
+profile = "black"
+line_length = 88
+auto_identify_namespace_packages = false
+# NOTE:
+# pyright doesn't like implicit namespace + single line (related to https://github.com/microsoft/pyright/issues/2882?) but it's horible so I'll live with it
+force_single_line = false
+known_first_party = ["mtb"]
+extend_skip = ["archives"]
+combine_straight_imports = true
+
+[tool.coverage.run]
+parallel = true
+source = ["docs", "tests", "comfy-mtb"]
+
+[tool.coverage.report]
+fail_under = 90
+show_missing = true
+
+[tool.coverage.html]
+show_contexts = true
+
+[tool.ruff]
+line-length = 79
+extend-exclude = ["./docs/conf.py", "notebooks", "stubs"]
+
+[tool.ruff.lint]
+select = ["A", "B", "C", "D", "E", "F", "FBT", "I", "N", "S", "SIM", "UP", "W"]
+# NOTE:
+# D102 - undocumented-public-method (noisy)
+# D103 - undocumented-public-function (noisy)
+# D100 - undocumented-public-module (noisy)
+# N802 - invalid-function-name (forced by comfy's arch)
+ignore = ["D103", "D102", "D100", "N802"]
+
+[tool.ruff.lint.per-file-ignores]
+# imported but unused
+"__init__.py" = ["F401"]
+# use of assert detected
+"tests/*" = ["S101"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.mypy]
+pretty = true
+ignore_missing_imports = true
+# exclude auto generated file
+exclude = ["docs/conf.py"]
+
+[tool.codespell]
+# exclude auto generated file
+skip = "./docs/conf.py,poetry.lock"
+check-filenames = true

pyrightconfig.json

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

requirements-wheels.txt

@@ -1,3 +0,0 @@
-insightface==0.7.3
-mmcv==2.0.0
-mmdet==3.0.0

requirements.txt

@@ -1,9 +1,12 @@
-onnxruntime-gpu
-imageio
 qrcode[pil]
-numpy==1.23.5
-ifnude==0.0.3
-insightface==0.7.3
-mmcv==2.0.0
-mmdet==3.0.0
-rembg==2.0.37
+onnxruntime-gpu
+requirements-parser
+# opencv-contrib
+rembg
+imageio_ffmpeg
+rich
+rich_argparse
+matplotlib
+pillow
+cachetools
+transformers

scripts/a111_extract.py

@@ -0,0 +1,112 @@
+from pathlib import Path
+from PIL import Image
+from PIL.PngImagePlugin import PngImageFile, PngInfo
+import json
+from pprint import pprint
+import argparse
+from rich.console import Console
+from rich.progress import Progress
+from rich_argparse import RichHelpFormatter
+
+
+def parse_a111(params, verbose=False):
+    # params = [p.split(": ") for p in params.split("\n")]
+    params = params.split("\n")
+
+    prompt = params[0].strip()
+    neg = params[1].split(":")[1].strip()
+
+    settings = {}
+    try:
+        settings = {
+            s.split(":")[0].strip(): s.split(":")[1].strip()
+            for s in params[2].split(",")
+        }
+
+    except IndexError:
+        settings = {"raw": params[2].strip()}
+
+    if verbose:
+        print(f"PROMPT: {prompt}")
+        print(f"NEG: {neg}")
+        print("SETTINGS:")
+        pprint(settings, indent=4)
+
+    return {"prompt": prompt, "negative": neg, "settings": settings}
+
+
+import glob
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Crude metadata extractor from A111 pngs",
+        formatter_class=RichHelpFormatter
+    )
+    parser.add_argument("inputs", nargs="*", help="Input image files")
+    parser.add_argument("--output", help="Output JSON file")
+    parser.add_argument("-v", "--verbose", action="store_true", help="Verbose mode")
+    parser.add_argument(
+        "--glob", help="Enable glob pattern matching", metavar="PATTERN"
+    )
+
+    args = parser.parse_args()
+
+    # - checks
+    if not args.glob and not args.inputs:
+        parser.error("Either --glob flag or inputs must be provided.")
+    if args.glob:
+        glob_pattern = args.glob
+        try:
+            pattern_path = str(Path(glob_pattern).expanduser().resolve())
+
+            if not any(glob.glob(pattern_path)):
+                raise ValueError(f"No files found for glob pattern: {glob_pattern}")
+        except Exception as e:
+            console = Console()
+            console.print(
+                f"[bold red]Error: Invalid glob pattern '{glob_pattern}': {e}[/bold red]"
+            )
+
+            exit(1)
+    else:
+        glob_pattern = None
+
+    input_files = []
+
+    if glob_pattern:
+        input_files = list(glob.glob(str(Path(glob_pattern).expanduser().resolve())))
+    else:
+        input_files = [Path(p) for p in args.inputs]
+
+    console = Console()
+    console.print("Input Files:", style="bold", end=" ")
+    console.print(f"{len(input_files):03d} files", style="cyan")
+    # for input_file in args.inputs:
+    #     console.print(f"- {input_file}", style="cyan")
+    console.print("\nOutput File:", style="bold", end=" ")
+    console.print(f"{Path(args.output).resolve().absolute()}", style="cyan")
+
+    with Progress(console=console, auto_refresh=True) as progress:
+        # files = Path(pth).rglob("*.png")
+        unique_info = {}
+        last = None
+
+        task = progress.add_task("[cyan]Extracting meta...", total=len(input_files) + 1)
+        for p in input_files:
+            im = Image.open(p)
+            parsed = parse_a111(im.info["parameters"], args.verbose)
+
+            if parsed != last:
+                unique_info[Path(p).stem] = parsed
+
+            last = parsed
+            progress.update(task, advance=1)
+            progress.refresh()
+
+        unique_info = json.dumps(unique_info, indent=4)
+        with open(args.output, "w") as f:
+            f.write(unique_info)
+            progress.update(task, advance=1)
+            progress.refresh()
+
+    console.print("\nProcessing completed!", style="bold green")

scripts/comfy_meta.py

@@ -0,0 +1,213 @@
+import argparse
+import json
+from PIL import Image, PngImagePlugin
+from rich.console import Console
+from rich import print
+from rich_argparse import RichHelpFormatter
+import os
+from pathlib import Path
+
+console = Console()
+
+# BNK_CutoffSetRegions
+# BNK_CutoffRegionsToConditioning
+# BNK_CutoffBasePrompt
+
+
+# Extracts metadata from a PNG image and returns it as a dictionary
+def extract_metadata(image_path):
+    image = Image.open(image_path)
+    prompt = image.info.get("prompt", "")
+    workflow = image.info.get("workflow", "")
+
+    if workflow:
+        workflow = json.loads(workflow)
+
+    if prompt:
+        prompt = json.loads(prompt)
+
+    console.print(f"Metadata extracted from [cyan]{image_path}[/cyan].")
+
+    return {
+        "prompt": prompt,
+        "workflow": workflow,
+    }
+
+
+# Embeds metadata into a PNG image
+def embed_metadata(image_path, metadata):
+    image = Image.open(image_path)
+    o_metadata = image.info
+
+    pnginfo = PngImagePlugin.PngInfo()
+    if prompt := metadata.get("prompt"):
+        pnginfo.add_text("prompt", json.dumps(prompt))
+    elif "prompt" in o_metadata:
+        pnginfo.add_text("prompt", o_metadata["prompt"])
+
+    if workflow := metadata.get("workflow"):
+        pnginfo.add_text("workflow", json.dumps(workflow))
+    elif "workflow" in o_metadata:
+        pnginfo.add_text("workflow", o_metadata["workflow"])
+
+    imgp = Path(image_path)
+    output = imgp.with_stem(f"{imgp.stem}_comfy_embed")
+    index = 1
+    while output.exists():
+        output = imgp.with_stem(f"{imgp.stem}_{index}_comfy_embed").with_suffix(".png")
+        index += 1
+
+    image.save(output, pnginfo=pnginfo)
+    console.print(f"Metadata embedded into [cyan]{output}[/cyan].")
+
+
+# CLI subcommand: extract
+def extract(args):
+    input_files = []
+    for input_path in args.input:
+        if os.path.isdir(input_path):
+            folder_path = input_path
+            input_files.extend(
+                [
+                    os.path.join(folder_path, file_name)
+                    for file_name in os.listdir(folder_path)
+                    if file_name.lower().endswith((".png", ".jpg", ".jpeg"))
+                ]
+            )
+        else:
+            input_files.append(input_path)
+
+    if len(input_files) == 1:
+        metadata = extract_metadata(input_files[0])
+        if args.print_output:
+            print(json.dumps(metadata, indent=4))
+        else:
+            if not args.output:
+                output = Path(input_files[0]).with_suffix(".json")
+                index = 1
+                while output.exists():
+                    output = (
+                        Path(input_files[0])
+                        .with_stem(f"{Path(input_files[0]).stem}_{index}")
+                        .with_suffix(".json")
+                    )
+                    index += 1
+            else:
+                output = args.output
+            with open(output, "w") as file:
+                json.dump(metadata, file, indent=4)
+            console.print(f"Metadata extracted and saved to [cyan]{output}[/cyan].")
+    else:
+        metadata_dict = {}
+        for input_file in input_files:
+            metadata = extract_metadata(input_file)
+            filename = os.path.basename(input_file)
+            output = (
+                Path(args.output) / f"{filename}.json"
+                if args.output
+                else Path(input_file).with_suffix(".json")
+            )
+            index = 1
+            while output.exists():
+                output = Path(args.output).parent / f"{filename}_{index}.json"
+                index += 1
+            with open(output, "w") as file:
+                json.dump(metadata, file, indent=4)
+            metadata_dict[filename] = metadata
+        if args.output:
+            with open(args.output, "w") as file:
+                json.dump(metadata_dict, file, indent=4)
+            console.print(
+                f"Metadata extracted and saved to [cyan]{args.output}[/cyan]."
+            )
+        else:
+            console.print("Multiple metadata files created.")
+
+
+# CLI subcommand: embed
+def embed(args):
+    input_files = []
+    for input_path in args.input:
+        if os.path.isdir(input_path):
+            folder_path = input_path
+            input_files.extend(
+                [
+                    os.path.join(folder_path, file_name)
+                    for file_name in os.listdir(folder_path)
+                    if file_name.lower().endswith(".json")
+                ]
+            )
+        else:
+            input_files.append(input_path)
+
+    for input_file in input_files:
+        with open(input_file) as file:
+            metadata = json.load(file)
+        image_path = input_file.replace(".json", ".png")
+        if args.output:
+            output_dir = args.output
+            if os.path.isdir(output_dir):
+                output_path = os.path.join(output_dir, os.path.basename(image_path))
+                index = 1
+                while os.path.exists(output_path):
+                    output_path = os.path.join(
+                        output_dir,
+                        f"{os.path.basename(image_path)}_{index}.png",
+                    )
+                    index += 1
+            else:
+                output_path = output_dir
+        else:
+            output_path = image_path.replace(".png", "_comfy_embed.png")
+
+        embed_metadata(image_path, metadata)
+        # os.rename(image_path, output_path)
+        console.print(f"Metadata embedded into [cyan]{output_path}[/cyan].")
+
+
+if __name__ == "__main__":
+    # Create the main CLI parser
+    parser = argparse.ArgumentParser(
+        prog="image-metadata-cli", formatter_class=RichHelpFormatter
+    )
+    subparsers = parser.add_subparsers(title="subcommands")
+
+    # Parser for the "extract" subcommand
+    extract_parser = subparsers.add_parser(
+        "extract",
+        help="Extract metadata from PNG image(s) or folder",
+        formatter_class=RichHelpFormatter,
+    )
+    extract_parser.add_argument(
+        "input", nargs="+", help="Input PNG image file(s) or folder path"
+    )
+    extract_parser.add_argument(
+        "--print",
+        dest="print_output",
+        action="store_true",
+        help="Print the output to stdout",
+    )
+    extract_parser.add_argument("--output", help="Output JSON file(s) or directory")
+    extract_parser.set_defaults(func=extract)
+
+    # Parser for the "embed" subcommand
+    embed_parser = subparsers.add_parser(
+        "embed",
+        help="Embed metadata into PNG image(s) or folder",
+        formatter_class=RichHelpFormatter,
+    )
+    embed_parser.add_argument(
+        "input", nargs="+", help="Input JSON file(s) or folder path"
+    )
+    embed_parser.add_argument("--output", help="Output PNG image file(s) or directory")
+    embed_parser.set_defaults(func=embed)
+
+    # Parse the command-line arguments and execute the appropriate subcommand
+    args = parser.parse_args()
+    if hasattr(args, "func"):
+        try:
+            args.func(args)
+        except ValueError as e:
+            console.print(f"[bold red]Error:[/bold red] {str(e)}")
+    else:
+        parser.print_help()

scripts/download_models.py

@@ -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
@@ -26,6 +28,25 @@ models_to_download = {
         ],
         "destination": "insightface",
     },
+    "GFPGAN (face enhancement)": {
+        "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/v1.3.4/RestoreFormer.pth
+        ],
+        "destination": "face_restore",
+    },
+    "FILM: Frame Interpolation for Large Motion": {
+        "size": 402,
+        "download_url": [
+            "https://drive.google.com/drive/folders/131_--QrieM4aQbbLWrUtbO2cGbX8-war"
+        ],
+        "destination": "FILM",
+    },
 }
 
 console = Console()
@@ -41,6 +62,35 @@ def download_model(download_url, destination):
         return
 
     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:
+                gdown.download_folder(download_url, output=destination, resume=True)
+            except TypeError:
+                gdown.download_folder(download_url, output=destination)
+
+            return
+        # download from google drive
+        gdown.download(download_url, destination, quiet=False, resume=True)
+        return
+
     response = requests.get(download_url, stream=True)
     total_size = int(response.headers.get("content-length", 0))
 
@@ -93,7 +143,7 @@ def handle_interrupt():
     console.print("Interrupted by user.", style="bold red")
 
 
-def main(models_to_download):
+def main(models_to_download, skip_input=False):
     try:
         models_to_download_selected = {}
 
@@ -129,13 +179,16 @@ def main(models_to_download):
             console.print("No new models to download.")
             return
 
-        models_to_download_selected = ask_user_for_downloads(
-            models_to_download_selected
+        models_to_download_selected = (
+            ask_user_for_downloads(models_to_download_selected)
+            if not skip_input
+            else models_to_download_selected
         )
 
         for model_name, model_details in models_to_download_selected.items():
             download_url = model_details["download_url"]
             destination = model_details["destination"]
+            console.print(f"Downloading {model_name}...")
             download_model(download_url, destination)
 
     except KeyboardInterrupt:
@@ -143,4 +196,10 @@ def main(models_to_download):
 
 
 if __name__ == "__main__":
-    main(models_to_download)
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-y", "--yes", action="store_true", help="skip user input")
+
+    args = parser.parse_args()
+    main(models_to_download, args.yes)

scripts/interpolate_frames.py

@@ -0,0 +1,142 @@
+import glob
+from pathlib import Path
+import uuid
+import sys
+from typing import List
+
+sys.path.append((Path(__file__).parent / "extern").as_posix())
+
+
+import argparse
+from rich_argparse import RichHelpFormatter
+from rich.console import Console
+from rich.progress import Progress
+
+import numpy as np
+import subprocess
+
+
+def write_prores_444_video(output_file, frames: List[np.ndarray], fps):
+    # Convert float images to the range of 0-65535 (12-bit color depth)
+    frames = [(frame * 65535).clip(0, 65535).astype(np.uint16) for frame in frames]
+
+    height, width, _ = frames[0].shape
+
+    # Prepare the FFmpeg command
+    command = [
+        "ffmpeg",
+        "-y",  # Overwrite output file if it already exists
+        "-f",
+        "rawvideo",
+        "-vcodec",
+        "rawvideo",
+        "-s",
+        f"{width}x{height}",
+        "-pix_fmt",
+        "rgb48le",
+        "-r",
+        str(fps),
+        "-i",
+        "-",
+        "-c:v",
+        "prores_ks",
+        "-profile:v",
+        "4",
+        "-pix_fmt",
+        "yuva444p10le",
+        "-r",
+        str(fps),
+        "-y",  # Overwrite output file if it already exists
+        output_file,
+    ]
+
+    process = subprocess.Popen(command, stdin=subprocess.PIPE)
+
+    for frame in frames:
+        process.stdin.write(frame.tobytes())
+
+    process.stdin.close()
+    process.wait()
+
+
+if __name__ == "__main__":
+    default_output = f"./output_{uuid.uuid4()}.mov"
+    parser = argparse.ArgumentParser(
+        description="FILM frame interpolation", formatter_class=RichHelpFormatter
+    )
+    parser.add_argument("inputs", nargs="*", help="Input image files")
+    parser.add_argument("--output", help="Output JSON file", default=default_output)
+    parser.add_argument("-v", "--verbose", action="store_true", help="Verbose mode")
+    parser.add_argument(
+        "--glob", help="Enable glob pattern matching", metavar="PATTERN"
+    )
+    parser.add_argument(
+        "--interpolate", type=int, default=4, help="Time for interpolated frames"
+    )
+    parser.add_argument("--fps", type=int, default=30, help="Out FPS")
+    align = 64
+    block_width = 2
+    block_height = 2
+
+    args = parser.parse_args()
+
+    # - checks
+    if not args.glob and not args.inputs:
+        parser.error("Either --glob flag or inputs must be provided.")
+    if args.glob:
+        glob_pattern = args.glob
+        try:
+            pattern_path = str(Path(glob_pattern).expanduser().resolve())
+
+            if not any(glob.glob(pattern_path)):
+                raise ValueError(f"No files found for glob pattern: {glob_pattern}")
+        except Exception as e:
+            console = Console()
+            console.print(
+                f"[bold red]Error: Invalid glob pattern '{glob_pattern}': {e}[/bold red]"
+            )
+
+            exit(1)
+    else:
+        glob_pattern = None
+
+    input_files: List[Path] = []
+
+    if glob_pattern:
+        input_files = [
+            Path(p)
+            for p in list(glob.glob(str(Path(glob_pattern).expanduser().resolve())))
+        ]
+    else:
+        input_files = [Path(p) for p in args.inputs]
+
+    console = Console()
+    console.print("Input Files:", style="bold", end=" ")
+    console.print(f"{len(input_files):03d} files", style="cyan")
+    # for input_file in args.inputs:
+    #     console.print(f"- {input_file}", style="cyan")
+    console.print("\nOutput File:", style="bold", end=" ")
+    console.print(f"{Path(args.output).resolve().absolute()}", style="cyan")
+
+    with Progress(console=console, auto_refresh=True) as progress:
+        from frame_interpolation.eval import util
+        from frame_interpolation.eval import util, interpolator
+
+        # files = Path(pth).rglob("*.png")
+
+        model = interpolator.Interpolator(
+            "G:/MODELS/FILM/pretrained_models/film_net/Style", None
+        )  # [2,2]
+
+        task = progress.add_task("[cyan]Interpolating frames...", total=1)
+
+        frames = list(
+            util.interpolate_recursively_from_files(
+                [x.as_posix() for x in input_files], args.interpolate, model
+            )
+        )
+
+        # mediapy.write_video(args.output, frames, fps=args.fps)
+        write_prores_444_video(args.output, frames, fps=args.fps)
+        progress.update(task, advance=1)
+        progress.refresh()

scripts/update_changelog.nu

@@ -0,0 +1,2 @@
+$env.GITHUB_TOKEN = (gh auth token) 
+git cliff --tag main | save -f CHANGELOG.md

types/shared.d.ts

@@ -0,0 +1,126 @@
+// Some manual types I use to facilitate developing on top of
+// Comfy's Litegraph implementation.
+
+import type {
+  ContextMenuItem,
+  LGraphNode,
+  IWidget,
+  LGraph,
+} from '../../../web/types/litegraph'
+
+export type {
+  ComfyExtension,
+  ComfyObjectInfo,
+  ComfyObjectInfoConfig,
+} from '../../../web/types/comfy'
+
+export type {
+  ContextMenuItem,
+  IWidget,
+  LLink,
+  INodeInputSlot,
+  INodeOutputSlot,
+} from '../../../web/types/litegraph'
+
+export type VectorWidget = IWidget<number[], { default: number[] }>
+export interface NodeData {
+  category: str
+  description: str
+  display_name: str
+  input: NodeInput
+  name: str
+  output: [str]
+  output_is_list: [boolean]
+  output_name: [str]
+  output_node: boolean
+}
+
+export interface ComfyDialog {
+  element: Element
+  close: () => void
+  show: (html: str) => void
+}
+
+export interface ComfySettingsDialog {
+  app: ComfyApp
+  element: Element
+  settingsValues: Record<string, unknown>
+  settingsLookup: Record<string, unknown>
+  load: () => Promise<void>
+  setSettingValueAsync: (id: string, value: unknown) => Promise<void>
+}
+
+export interface ComfyUI {
+  app: ComfyApp
+  dialog: ComfyDialog
+  settings: ComfySettingsDialog
+  autoQueueMode: 'instant' | 'change'
+  batchCount: number
+  lastQueueSize: number
+  graphHasChanged: boolean
+  queue: ComfyList
+  history: ComfyList
+}
+
+/**Very incomplete Comfy App definition*/
+interface ComfyApp {
+  graph: LGraph
+  queueItems: { number: number; batchCount: number }[]
+  processingQueue: boolean
+  ui: ComfyUI
+  extensions: ComfyExtension[]
+  nodeOutputs: Record<string, unknown>
+  nodePreviewImages: Record<string, Image>
+  shiftDown: boolean
+  isImageNode: (node: LGraphNodeExtended) => boolean
+  queuePrompt: (number: number, batchCount: number) => Promise<void>
+  /** Loads workflow data from the specified file*/
+  handleFile: (file: File) => Promise<void>
+}
+
+export type { ComfyApp as App }
+
+export interface LGraphNodeExtension {
+  addDOMWidget: (
+    name: string,
+    type: string,
+    element: Element,
+    options: Record<string, unknown>,
+  ) => IWidget
+  onNodeCreated: () => void
+  getExtraMenuOptions: () => ContextMenuItem[]
+  prototype: LGraphNodeExtended
+}
+
+export type LGraphNodeExtended = LGraphNode & LGraphNodeExtension
+
+export interface NodeType /*extends LGraphNode*/ {
+  category: str
+  comfyClass: str
+  length: 0
+  name: str
+  nodeData: NodeData
+  prototype: LGraphNodeExtended
+  title: str
+  type: str
+}
+
+export interface NodeInput {
+  required: object
+}
+
+// NOTE: for prototype overriding
+export type OnDrawWidgetParams = Parameters<IWidget['draw']>
+export type OnDrawForegroundParams = Parameters<LGraphNode['onDrawForeground']>
+export type OnMouseDownParams = Parameters<LGraphNode['onMouseDown']>
+export type OnConnectionsChangeParams = Parameters<
+  LGraphNode['onConnectionsChange']
+>
+export type OnNodeCreatedParams = Parameters<
+  LGraphNodeExtension['onNodeCreated']
+>
+
+export interface DocumentationOptions {
+  icon_size?: number
+  icon_margin?: number
+}

types/typedefs.js

@@ -0,0 +1,65 @@
+/**
+ * @typedef {import("./shared.d.ts").NodeData} NodeData
+ * @typedef {import("./shared.d.ts").NodeType} NodeType
+ * @typedef {import("./shared.d.ts").DocumentationOptions} DocumentationOptions
+ * @typedef {import("./shared.d.ts").OnDrawForegroundParams} OnDrawForegroundParams
+ * @typedef {import("./shared.d.ts").OnMouseDownParams} OnMouseDownParams
+ * @typedef {import("./shared.d.ts").OnConnectionsChangeParams} OnConnectionsChangeParams
+ * @typedef {import("./shared.d.ts").ContextMenuItem} ContextMenuItem
+ * @typedef {import("./shared.d.ts").IWidget} IWidget
+ * @typedef {import("./shared.d.ts").VectorWidget} VectorWidget
+ * @typedef {import("./shared.d.ts").LGraphNodeExtended} LGraphNode
+ * @typedef {import("./shared.d.ts").LLink} LLink
+ * @typedef {import("./shared.d.ts").App} App
+ * @typedef {import("./shared.d.ts").OnDrawWidgetParams} OnDrawWidgetParams
+ * @typedef {import("./shared.d.ts").INodeInputSlot} INodeInputSlot
+ * @typedef {import("./shared.d.ts").INodeOutputSlot} INodeOutputSlot
+ */
+
+/**
+ * @typedef {Object} ResultItem
+ * @property {string} [filename] - The filename of the item.
+ * @property {string} [subfolder] - The subfolder of the item.
+ * @property {string} [type] - The type of the item.
+ */
+
+/**
+ * @typedef {Object} Outputs
+ * @property {ResultItem[]} [audio] - Audio result items.
+ * @property {ResultItem[]} [images] - Image result items.
+ * @property {ResultItem[]} [animated] - Animated result items.
+ */
+
+/**
+ * @typedef {Record<string, Outputs>} TaskOutput
+ * - A record mapping Node IDs to their Outputs.
+ */
+
+/**
+ * @typedef {Array} TaskPrompt
+ * @property {QueueIndex} [0] - The queue index.
+ * @property {PromptId} [1] - The unique prompt ID.
+ * @property {PromptInputs} [2] - The prompt inputs.
+ * @property {ExtraData} [3] - Extra data.
+ * @property {OutputsToExecute} [4] - The outputs to execute.
+ */
+
+/**
+ * @typedef {Object} HistoryTaskItem
+ * @property {'History'} taskType - The type of task.
+ * @property {TaskPrompt} prompt - The task prompt.
+ * @property {Status} [status] - The status of the task.
+ * @property {TaskOutput} outputs - The task outputs.
+ * @property {TaskMeta} [meta] - Optional task metadata.
+ */
+
+/**
+ * @typedef {Object} ExecInfo
+ * @property {number} queue_remaining - The number of items remaining in the queue.
+ */
+
+/**
+ * @typedef {Object} StatusWsMessageStatus
+ * @property {ExecInfo} exec_info - Execution information.
+ */
+

web/README.md

@@ -0,0 +1,36 @@
+## Core
+These 3 scripts cannot be used independently and must all be present to work, they are mostly enhancing the frontend of python nodes
+- `comfy_shared`: library of methods used in `mtb_widgets` and `debug`
+
+**mtb_widgets** define ui callbacks, and various widgets like the `COLOR` type:  
+<img src="https://github.com/melMass/comfy_mtb/assets/7041726/5dbcb714-e1e2-4be7-b0e2-68a6c38c83de" width=400/>
+
+or the `BOOL` type:  
+<img src="https://github.com/melMass/comfy_mtb/assets/7041726/7601366d-601c-4f4d-b735-1a4b076770b0" width=400/>
+
+There is also `Debug` which is a node that should be able to display any data input, it handle a few cases and fallback to the string representation of the
+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 
+
+- **imageFeed**: a fork of @pythongosssss ' s [image feed](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/tree/main/js), it adds support for: a lightbox to see images bigger, a way to load the current session history (in case of a web page reload), and different icons, most of the work come from the original script. 
+
+
+> **NOTE**
+> 
+> The original imagefeed got updated since and offer more options, ideally I would clean my lightbox thing and PR it to pythongoss later but in the meantime the script will detect if you already use the original one and not load this fork
+
+
+- ![imagefeed2-hd](https://github.com/melMass/comfy_mtb/assets/7041726/8539f46f-78e1-459a-a11c-fddd44e63ca9)
+
+
+- **notify**: a basic toast notification system that I use in some places accross mtb, it can be used by simply calling `window.MTB.notify("Hello world!")`  
+![extract](https://github.com/melMass/comfy_mtb/assets/7041726/450c67fc-a7e9-4bea-ae49-b610d693098d)

web/color_widget.js

@@ -1,195 +0,0 @@
-// Define the Color Picker widget class
-import parseCss from '/extensions/mtb/extern/parse-css.js'
-import { app } from "/scripts/app.js";
-import { ComfyWidgets } from "/scripts/widgets.js";
-
-export function CUSTOM_INT(node, inputName, val, func, config = {}) {
-	return {
-		widget: node.addWidget(
-			"number",
-			inputName,
-			val,
-			func, 
-			Object.assign({}, { min: 0, max: 4096, step: 640, precision: 0 }, config)
-		),
-	};
-}
-const dumb_call = (v,d,node) => {
-    console.log("dumb_call", {v,d,node});
-}
-function isColorBright (rgb, threshold=240) {
-    const brightess = getBrightness(rgb)
-  
-    return brightess > threshold
-  }
-  
-  function getBrightness (rgbObj) {
-    return Math.round(((parseInt(rgbObj[0]) * 299) + (parseInt(rgbObj[1]) * 587) + (parseInt(rgbObj[2]) * 114)) /1000)
-  }
-  
-
-/**
- * @returns {import("/types/litegraph").IWidget} widget
- */
-const custom = (key,val) => {
-    /** @type {import("/types/litegraph").IWidget} */
-    const widget = {}
-    // widget.y = 0;
-    widget.name = key;
-    widget.type = "COLOR";
-    widget.options = { default: "#ff0000" };
-    widget.value = val || "#ff0000";
-    widget.draw = function (ctx,
-        node,
-        widgetWidth,
-        widgetY,
-        height) {
-        const border = 3;
-        
-        // draw a rect with a border and a fill color
-        ctx.fillStyle = "#000";
-        ctx.fillRect(0, widgetY, widgetWidth, height);
-        ctx.fillStyle = this.value;
-        ctx.fillRect(border, widgetY + border, widgetWidth - border * 2, height - border * 2);
-        // write the input name
-        // choose the fill based on the luminoisty of this.value color
-        const color = parseCss(this.value.default || this.value)
-        if (!color) {
-            return
-        }
-        ctx.fillStyle = isColorBright(color.values, 125) ? "#000" : "#fff";
-
-
-        ctx.font = "14px Arial";
-        ctx.textAlign = "center";
-        ctx.fillText(this.name, widgetWidth * 0.5, widgetY + 14);
-
-
-
-        // ctx.strokeStyle = "#fff";
-        // ctx.strokeRect(border, widgetY + border, widgetWidth - border * 2, height - border * 2);
-        
-        
-        // ctx.fillStyle = "#000";
-        // ctx.fillRect(widgetWidth/2 - border / 2 , widgetY + border / 2 , widgetWidth/2 + border / 2, height + border / 2);
-        // ctx.fillStyle = this.value;
-        // ctx.fillRect(widgetWidth/2, widgetY, widgetWidth/2, height);
-
-    }
-    widget.mouse = function (e, pos, node) {
-        if (e.type === "pointerdown") {
-        console.log({e,pos,node})
-        // get widgets of type type : "COLOR"
-        const widgets = node.widgets.filter(w => w.type === "COLOR");
-
-        for (const w of widgets) {
-            // color picker
-            const rect = [w.last_y, w.last_y + 32];
-            console.log({rect,pos})
-            if (pos[1] > rect[0] && pos[1] < rect[1]) {
-                console.log("color picker", node)
-                const picker = document.createElement("input");
-                picker.type = "color";
-                picker.value = this.value;
-                // 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;
-                
-
-
-                document.body.appendChild(picker);
-                
-                picker.addEventListener("change", () => {
-                    this.value = picker.value;
-                    node.graph._version++;
-                    node.setDirtyCanvas(true, true);
-                    document.body.removeChild(picker);
-                });
-                
-                // simulate click with screen center
-                const pointer_event = new MouseEvent('click', {
-                    bubbles: false,
-                    // cancelable: true,
-                    pointerType: "mouse",
-                    clientX: window.innerWidth / 2,
-                    clientY: window.innerHeight / 2,
-                    x: window.innerWidth / 2,
-                    y: window.innerHeight / 2,
-                    offsetX: window.innerWidth / 2,
-                    offsetY: window.innerHeight / 2,
-                    screenX: window.innerWidth / 2,
-                    screenY: window.innerHeight / 2,
-
-
-                });
-                console.log(e)
-                picker.dispatchEvent(pointer_event);
-            
-            }}}}
-    widget.computeSize = function (width) {
-        return [width, 32];
-    }
-    return widget;
-}
-
-app.registerExtension({
-    name: "mtb.ColorPicker",
-    init: () => {
-        ComfyWidgets.COLOR = function () {
-            return {
-                widget:custom("color", "#ff0000")
-            };
-        };
-    },
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        
-        //console.log("mtb.ColorPicker", { nodeType, nodeData, app });
-        const rinputs = nodeData.input?.required; // object with key/value pairs, "0" is the type
-        // console.log(nodeData.name, { nodeType, nodeData, app });
-
-        if (!rinputs) return;
-
-       
-        let has_color = false;
-        for (const [key, input] of Object.entries(rinputs)) {
-            if (input[0] === "COLOR") {
-                has_color = true;                
-                // input[1] = { default: "#ff0000" };
-            
-            }}
-
-        if (!has_color) return;
-        
-        const onNodeCreated = nodeType.prototype.onNodeCreated;
-        nodeType.prototype.onNodeCreated = function () {
-            const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
-            this.serialize_widgets = true;
-        // if (rinputs[0] === "COLOR") {
-        // console.log(nodeData.name, { nodeType, nodeData, app });
-
-        // loop through the inputs to find the color inputs
-         for (const [key, input] of Object.entries(rinputs)) {
-            if (input[0] === "COLOR") {
-                this.addCustomWidget(custom(key,input[1]))
-            }
-        // }
-         }
-
-         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();
-                }
-            }
-        };
-        }
-    }
-});

web/constant.js

@@ -0,0 +1,496 @@
+import { app } from '../../scripts/app.js'
+import * as shared from './comfy_shared.js'
+import { infoLogger } from './comfy_shared.js'
+import { MtbWidgets } from './mtb_widgets.js'
+import { ComfyWidgets } from '../../scripts/widgets.js'
+import * as mtb_widgets from './mtb_widgets.js'
+
+/**
+ * @typedef {'number'|'string'|'vector2'|'vector3'|'vector4'|'color'} ConstantType
+ * @typedef {import ("../../../web/types/litegraph.d.ts").LGraphNode} Node
+ * @typedef {{x:number,y:number,z?:number,w?:number}} VectorValue
+ * @typedef {}
+ *
+ */
+
+/**
+ * @param {number} size - The number of axis of the vector (2,3 or 4)
+ * @param {number} val - The default scalar value to fill the vector with
+ * @returns {VectorValue} vector
+ * */
+const initVector = (size, val = 0.0) => {
+  const res = {}
+  for (let i = 0; i < size; i++) {
+    const axis = mtb_widgets.VECTOR_AXIS[i]
+    res[axis] = val
+  }
+  return res
+}
+
+/**
+ *
+ * @extends {Node}
+ * @classdesc Wrapper for the python node
+ */
+export class ConstantJs {
+  constructor(python_node) {
+    // this.uuid = shared.makeUUID()
+    const wrapper = this
+
+    python_node.shape = LiteGraph.BOX_SHAPE
+    python_node.serialize_widgets = true
+
+    const onNodeCreated = python_node.prototype.onNodeCreated
+    python_node.prototype.onNodeCreated = function () {
+      const r = onNodeCreated ? onNodeCreated.apply(this) : undefined
+
+      this.addProperty('type', 'number')
+      this.addProperty('value', 0)
+
+      this.removeInput(0)
+      this.removeOutput(0)
+
+      this.addOutput('Output', '*')
+
+      // bind our wrapper
+      this.configure = wrapper.configure.bind(this)
+      // this.applyToGraph = wrapper.applyToGraph.bind(this)
+      this.updateWidgets = wrapper.updateWidgets.bind(this)
+      this.convertValue = wrapper.convertValue.bind(this)
+      // this.updateOutput = wrapper.updateOutput.bind(this)
+      this.updateOutputType = wrapper.updateOutputType.bind(this)
+      // this.updateTargetWidgets = wrapper.updateTargetWidgets.bind(this)
+
+      this.addWidget(
+        'combo',
+        'Type',
+        this.properties.type,
+        (value) => {
+          this.properties.type = value
+          this.updateWidgets()
+          this.updateOutputType()
+        },
+        {
+          values: [
+            // 'number',
+            'float',
+            'int',
+            'string',
+            'vector2',
+            'vector3',
+            'vector4',
+            'color',
+          ],
+        },
+      )
+      this.updateWidgets()
+      this.updateOutputType()
+
+      for (let n = 0; n < this.inputs.length; n++) {
+        this.removeInput(n)
+      }
+      this.inputs = []
+      return r
+    }
+    return
+  }
+
+  // NOTE: this is called onPrompt
+  // applyToGraph() {
+  // infoLogger('Updating values for backend')
+  // this.updateTargetWidgets()
+  // }
+
+  // NOTE: deserialization happens here
+  configure(info) {
+    // super.configure(info)
+    infoLogger('Configure Constant', { info, node: this })
+
+    this.properties.type = info.properties.type
+    this.properties.value = info.properties.value
+
+    this.pos = info.pos
+    this.order = info.order
+
+    this.updateWidgets()
+    this.updateOutputType()
+  }
+
+  /**
+   * Convert the old value type to the new one, falling back to some default
+   * @param {ConstantType} propType - The target type
+   */
+  convertValue(propType) {
+    switch (propType) {
+      case 'color': {
+        if (typeof this.properties.value !== 'string') {
+          this.properties.value = '#ffffff'
+        } else if (this.properties.value[0] !== '#') {
+          this.properties.value = '#ff0000'
+        }
+        break
+      }
+      case 'int': {
+        if (typeof this.properties.value === 'object') {
+          this.properties.value = Number.parseInt(this.properties.value.x)
+        } else {
+          this.properties.value = Number.parseInt(this.properties.value) || 0
+        }
+        break
+      }
+      case 'float': {
+        if (typeof this.properties.value === 'object') {
+          this.properties.value = Number.parseFloat(this.properties.value.x)
+        } else {
+          this.properties.value =
+            Number.parseFloat(this.properties.value) || 0.0
+        }
+        break
+      }
+      case 'string': {
+        if (typeof this.properties.value !== 'string') {
+          this.properties.value = JSON.stringify(this.properties.value)
+        }
+        break
+      }
+      case 'vector2':
+      case 'vector3':
+      case 'vector4': {
+        const numInputs = Number.parseInt(propType.charAt(6))
+        if (!this.properties.value) {
+          this.properties.value = initVector(numInputs) // Array.from({ length: numInputs }, () => 0.0)
+        } else if (typeof this.properties.value === 'string') {
+          try {
+            const parsed = JSON.parse(this.properties.value)
+            const newVec = {}
+            for (
+              let i = 0;
+              i < Object.keys(mtb_widgets.VECTOR_AXIS).length;
+              i++
+            ) {
+              const axis = mtb_widgets.VECTOR_AXIS[i]
+              if (Object.keys(parsed).includes(axis)) {
+                newVec[axis] = parsed[axis]
+              }
+            }
+            this.properties.value = newVec
+          } catch (e) {
+            shared.errorLogger(e)
+            infoLogger(
+              `Couldn't parse string to vec (${this.properties.value})`,
+            )
+            this.properties.value = initVector(numInputs)
+          }
+        } else if (typeof this.properties.value === 'number') {
+          const newVec = initVector(numInputs)
+          newVec.x = Number.parseFloat(this.properties.value)
+          this.properties.value = newVec
+        }
+
+        if (
+          typeof this.properties.value === 'object' &&
+          Object.keys(this.properties.value).length !== numInputs
+        ) {
+          const current = Object.keys(this.properties.value)
+          if (current.length < numInputs) {
+            infoLogger('current value smaller than target, adjusting')
+            for (let index = current.length; index < numInputs; index++) {
+              this.properties.value[mtb_widgets.VECTOR_AXIS[index]] = 0.0
+            }
+          } else {
+            infoLogger('current value greater than target, adjusting')
+            const newVal = {}
+            for (let index = 0; index < numInputs; index++) {
+              newVal[mtb_widgets.VECTOR_AXIS[index]] =
+                this.properties.value[mtb_widgets.VECTOR_AXIS[index]]
+            }
+            this.properties.value = newVal
+          }
+        }
+        break
+      }
+      default:
+        break
+    }
+  }
+
+  /**
+   * Remove all widgets but the comboBox for selecting the type
+   * then recreate the appropriate widget from scratch
+   */
+  updateWidgets() {
+    // NOTE: Remove existing widgets
+    for (let i = 1; i < this.widgets.length; i++) {
+      const element = this.widgets[i]
+      if (element.onRemove) {
+        element.onRemove()
+      }
+      // element?.onRemove()
+    }
+
+    this.widgets.splice(1)
+    this.widgets[0].value = this.properties.type
+
+    this.convertValue(this.properties.type)
+
+    switch (this.properties.type) {
+      case 'color': {
+        const col_widget = this.addCustomWidget(
+          MtbWidgets.COLOR('Value', this.properties.value),
+        )
+        col_widget.callback = (col) => {
+          this.properties.value = col
+          // this.updateOutput()
+        }
+        break
+      }
+      case 'int': {
+        const f_widget = this.addCustomWidget(
+          ComfyWidgets.INT(
+            this,
+            'Value',
+            [
+              '',
+              {
+                default: this.properties.value,
+                callback: (val) => console.log('VALUE', val),
+              },
+            ],
+            app,
+          ),
+        )
+
+        f_widget.widget.callback = (val) => {
+          this.properties.value = val
+        }
+
+        break
+      }
+      case 'float': {
+        this.addWidget('number', 'Value', this.properties.value, (val) => {
+          this.properties.value = val
+        })
+        break
+      }
+      case 'string': {
+        mtb_widgets.addMultilineWidget(
+          this,
+          'Value',
+          {
+            defaultVal: this.properties.value,
+          },
+          (v) => {
+            this.properties.value = v
+            // this.updateOutput()
+          },
+        )
+        break
+      }
+      case 'vector2':
+      case 'vector3':
+      case 'vector4': {
+        const numInputs = Number.parseInt(this.properties.type.charAt(6))
+        const node = this
+        const v_widget = mtb_widgets.addVectorWidget(
+          this,
+          'Value',
+          this.properties.value, // value
+          numInputs, // vector_size
+          function (v) {
+            node.properties.value = v
+            // this.updateOutput()
+          },
+        )
+        break
+      }
+
+      // NOTE: this is not reached anymore, kept for reference
+      case 'number': {
+        if (typeof this.properties.value !== 'number') {
+          this.properties.value = 0.0
+        }
+        const n_widget = this.addWidget(
+          'number',
+          'Value',
+          this.properties.force_int
+            ? Number.parseInt(this.properties.value)
+            : this.properties.value,
+          (value) => {
+            this.properties.value = this.properties.force_int
+              ? Number.parseInt(value)
+              : value
+            // this.updateOutput()
+          },
+        )
+        //override the callback
+        const origCallback = n_widget.callback
+        const node = this
+        n_widget.callback = function (val) {
+          const r = origCallback ? origCallback.apply(this, [val]) : undefined
+          if (node.properties.force_int) {
+            // TODO: rework this, a it makes it harder to manipulate
+            this.value = Number.parseInt(this.value)
+            node.properties.value = Number.parseInt(this.value)
+          }
+          infoLogger('NEW NUMBER', this.value)
+          return r
+        }
+
+        this.addWidget(
+          'toggle',
+          'Convert to Integer',
+          this.properties.force_int,
+          (value) => {
+            this.properties.force_int = value
+            this.updateOutputType()
+          },
+        )
+        break
+      }
+      default:
+        break
+    }
+  }
+  onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) {
+    // super.onConnectionsChange(type, slotIndex, isConnected, link, ioSlot)
+    if (isConnected) {
+      this.updateTargetWidgets([link.id])
+    }
+  }
+
+  updateOutputType() {
+    infoLogger('Updating output type')
+    const rm_if_mismatch = (type) => {
+      if (this.outputs[0].type !== type) {
+        for (let i = 0; i < this.outputs.length; i++) {
+          this.removeOutput(i)
+        }
+        this.addOutput('output', type)
+        // this.setOutputDataType(0, type)
+      }
+    }
+    switch (this.properties.type) {
+      case 'color':
+        rm_if_mismatch('COLOR')
+        break
+      case 'float':
+        rm_if_mismatch('FLOAT')
+        break
+      case 'int':
+        rm_if_mismatch('INT')
+        break
+      case 'number':
+        if (this.properties.force_int) {
+          rm_if_mismatch('INT')
+        } else {
+          rm_if_mismatch('FLOAT')
+        }
+        break
+      case 'string':
+        rm_if_mismatch('STRING')
+        break
+      // case 'vector2':
+      // case 'vector3':
+      // case 'vector4':
+      //   rm_if_mismatch('FLOAT')
+      //   break
+      case 'vector2':
+        rm_if_mismatch('VECTOR2')
+        break
+      case 'vector3':
+        rm_if_mismatch('VECTOR3')
+        break
+      case 'vector4':
+        rm_if_mismatch('VECTOR4')
+        break
+      default:
+        break
+    }
+    // this.updateOutput()
+  }
+
+  /**
+   * NOTE: This feels hacky but seems to work fine
+   * since Constant is a virtual node.
+   */
+  updateTargetWidgets(u_links) {
+    infoLogger('Updating target widgets')
+    if (!app.graph.links) return
+    const links = u_links || this.outputs[0].links
+    if (!links) return
+    for (let i = 0; i < links.length; i++) {
+      const link = app.graph.links[links[i]]
+      const tgt_node = app.graph.getNodeById(link.target_id)
+      if (!tgt_node || !tgt_node.inputs) return
+      const tgt_input = tgt_node.inputs[link.target_slot]
+      if (!tgt_input) return
+      const tgt_widget = tgt_node.widgets.filter(
+        (w) => w.name === tgt_input.name,
+      )
+      // infoLogger('Constant Target Node', tgt_node)
+      // infoLogger('Constant Target Input', tgt_input)
+      if (!tgt_widget || tgt_widget.length === 0) return
+
+      tgt_widget[0].value = this.properties.value
+    }
+  }
+
+  updateOutput() {
+    infoLogger('Updating output value')
+    const value = this.properties.value
+
+    switch (this.properties.type) {
+      case 'color':
+        this.setOutputData(0, value)
+        break
+      case 'number':
+        if (this.properties.force_int) {
+          this.setOutputData(0, Number.parseInt(value))
+        } else {
+          this.setOutputData(0, Number.parseFloat(value))
+        }
+        break
+      case 'string':
+        this.setOutputData(0, value.toString())
+        break
+      case 'vector2':
+      case 'vector3':
+      case 'vector4':
+        this.setOutputData(0, value)
+        break
+
+      // case 'vector2':
+      //   this.setOutputData(0, value.slice(0, 2))
+      //   break
+      // case 'vector3':
+      //   this.setOutputData(0, value.slice(0, 3))
+      //   break
+      // case 'vector4':
+      //   this.setOutputData(0, value.slice(0, 4))
+      //   break
+      default:
+        break
+    }
+
+    infoLogger('New Value', this.value)
+
+    this.updateTargetWidgets()
+  }
+}
+app.registerExtension({
+  name: 'mtb.constant',
+
+  async beforeRegisterNodeDef(nodeType, nodeData, _app) {
+    if (nodeData.name === 'Constant (mtb)') {
+      new ConstantJs(nodeType)
+    }
+  },
+  // NOTE: old js only registration
+  //
+  // registerCustomNodes() {
+  //   LiteGraph.registerNodeType('Constant (mtb)', Constant)
+  //
+  //   Constant.category = 'mtb/utils'
+  //   Constant.title = 'Constant (mtb)'
+  // },
+})

web/curve_widget.js

@@ -0,0 +1,221 @@
+// Reference the shared typedefs file
+/// <reference path="../types/typedefs.js" />
+import { app } from '../../scripts/app.js'
+import { infoLogger } from './comfy_shared.js'
+
+function B0(t) {
+  return (1 - t) ** 3 / 6
+}
+function B1(t) {
+  return (3 * t ** 3 - 6 * t ** 2 + 4) / 6
+}
+function B2(t) {
+  return (-3 * t ** 3 + 3 * t ** 2 + 3 * t + 1) / 6
+}
+function B3(t) {
+  return t ** 3 / 6
+}
+class CurveWidget {
+  constructor(...args) {
+    const [inputName, opts] = args
+
+    this.name = inputName || 'Curve'
+
+    this.type = 'FLOAT_CURVE'
+    this.selectedPointIndex = null
+    this.options = opts
+    this.value = this.value || { 0: { x: 0, y: 0 }, 1: { x: 1, y: 1 } }
+  }
+
+  drawBSpline(ctx, width, height, posY) {
+    const n = this.value.length - 1
+    const numSegments = n - 2
+    const numPoints = this.value.length
+    if (numPoints < 4) {
+      this.drawLinear(ctx, width, height, posY)
+    } else {
+      for (let j = 0; j <= numSegments; j++) {
+        for (let t = 0; t <= 1; t += 0.01) {
+          let pt = this.getBSplinePoint(j, t)
+          let x = pt.x * width
+          let y = posY + height - pt.y * height
+
+          if (t === 0) ctx.moveTo(x, y)
+          else ctx.lineTo(x, y)
+        }
+      }
+      ctx.stroke()
+    }
+  }
+
+  drawLinear(ctx, width, height, posY) {
+    for (let i = 0; i < Object.keys(this.value).length - 1; i++) {
+      let p1 = this.value[i]
+      let p2 = this.value[i + 1]
+      ctx.moveTo(p1.x * width, posY + height - p1.y * height)
+      ctx.lineTo(p2.x * width, posY + height - p2.y * height)
+    }
+    ctx.stroke()
+  }
+  getBSplinePoint(i, t) {
+    // Control points for this segment
+    const p0 = this.value[i]
+    const p1 = this.value[i + 1]
+    const p2 = this.value[i + 2]
+    const p3 = this.value[i + 3]
+
+    const x = B0(t) * p0.x + B1(t) * p1.x + B2(t) * p2.x + B3(t) * p3.x
+    const y = B0(t) * p0.y + B1(t) * p1.y + B2(t) * p2.y + B3(t) * p3.y
+
+    return { x, y }
+  }
+  /**
+   * @param {OnDrawWidgetParams} args
+   */
+  draw(...args) {
+    const hide = this.type !== 'FLOAT_CURVE'
+    if (hide) {
+      return
+    }
+
+    const [ctx, node, width, posY, height] = args
+    const [cw, ch] = this.computeSize(width)
+
+    ctx.beginPath()
+    ctx.fillStyle = '#000'
+    ctx.strokeStyle = '#fff'
+    ctx.lineWidth = 2
+
+    // normalized coordinates -> canvas coordinates
+    for (let i = 0; i < Object.keys(this.value || {}).length - 1; i++) {
+      let p1 = this.value[i]
+      let p2 = this.value[i + 1]
+      ctx.moveTo(p1.x * cw, posY + ch - p1.y * ch)
+      ctx.lineTo(p2.x * cw, posY + ch - p2.y * ch)
+    }
+    ctx.stroke()
+
+    // points
+    Object.values(this.value || {}).forEach((point) => {
+      ctx.beginPath()
+      ctx.arc(point.x * cw, posY + ch - point.y * ch, 5, 0, 2 * Math.PI)
+      ctx.fill()
+    })
+  }
+
+  mouse(event, pos, node) {
+    let x = pos[0] - node.pos[0]
+    let y = pos[1] - node.pos[1]
+    const width = node.size[0]
+    const height = 300 // TODO: compute
+    const posY = node.pos[1]
+    const localPos = { x: pos[0], y: pos[1] - LiteGraph.NODE_WIDGET_HEIGHT }
+
+    if (event.type === LiteGraph.pointerevents_method + 'down') {
+      console.debug('Checking if a point was clicked')
+      const clickedPointIndex = this.detectPoint(localPos, width, height)
+      if (clickedPointIndex !== null) {
+        this.selectedPointIndex = clickedPointIndex
+      } else {
+        this.addPoint(localPos, width, height)
+      }
+      return true
+    } else if (
+      event.type === LiteGraph.pointerevents_method + 'move' &&
+      this.selectedPointIndex !== null
+    ) {
+      this.movePoint(this.selectedPointIndex, localPos, width, height)
+      return true
+    } else if (
+      event.type === LiteGraph.pointerevents_method + 'up' &&
+      this.selectedPointIndex !== null
+    ) {
+      this.selectedPointIndex = null
+      return true
+    }
+    return false
+  }
+  callback(...args) {
+    //value, that, node, pos, event) {
+
+  }
+
+  detectPoint(localPos, width, height) {
+    const threshold = 20 // TODO: extract
+    const keys = Object.keys(this.value)
+    for (let i = 0; i < keys.length; i++) {
+      const key = keys[i]
+      const p = this.value[key]
+      const px = p.x * width
+      const py = height - p.y * height
+      if (
+        Math.abs(localPos.x - px) < threshold &&
+        Math.abs(localPos.y - py) < threshold
+      ) {
+        return key
+      }
+    }
+    return null
+  }
+  addPoint(localPos, width, height) {
+    // add a new point based on click position
+    const normalizedPoint = {
+      x: localPos.x / width,
+      y: 1 - localPos.y / height,
+    }
+
+    const keys = Object.keys(this.value)
+    let insertIndex = keys.length
+    for (let i = 0; i < keys.length; i++) {
+      if (normalizedPoint.x < this.value[keys[i]].x) {
+        insertIndex = i
+        break
+      }
+    }
+    // shift
+    for (let i = keys.length; i > insertIndex; i--) {
+      this.value[i] = this.value[i - 1]
+    }
+
+    this.value[insertIndex] = normalizedPoint
+  }
+
+  movePoint(index, localPos, width, height) {
+    const point = this.value[index]
+    point.x = Math.max(0, Math.min(1, localPos.x / width))
+    point.y = Math.max(0, Math.min(1, 1 - localPos.y / height))
+
+    this.value[index] = point
+  }
+  computeSize(width) {
+    return [width, 300]
+  }
+
+  configure(data) {
+  }
+}
+
+app.registerExtension({
+  name: 'mtb.curves',
+  getCustomWidgets: () => {
+    return {
+      /**
+       * @param {LGraphNode} node
+       * @param {str} inputName
+       * @param {[str,*]} inputData
+       * @param {*} app
+       *
+       */
+      FLOAT_CURVE: (node, inputName, inputData, app) => {
+        // const c = node.widgets.find((w) => w.type === "FLOAT_CURVE")
+        const wid = node.addCustomWidget(new CurveWidget(inputName, inputData))
+
+        return {
+          widget: wid,
+          minWidth: 150,
+          minHeight: 30,
+        }
+      },
+    }
+  },
+})

web/debug.js

@@ -0,0 +1,202 @@
+/**
+ * File: debug.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+// Reference the shared typedefs file
+/// <reference path="../types/typedefs.js" />
+
+import { app } from '../../scripts/app.js'
+import * as shared from './comfy_shared.js'
+import * as mtb_ui from './mtb_ui.js'
+
+function escapeHtml(unsafe) {
+  return unsafe
+    .replace(/&/g, '&amp;')
+    .replace(/</g, '&lt;')
+    .replace(/>/g, '&gt;')
+    .replace(/"/g, '&quot;')
+    .replace(/'/g, '&#039;')
+}
+
+function createDebugSection(title) {
+  const section = mtb_ui.makeElement('div', {
+    margin: '8px 0',
+    padding: '8px',
+    borderRadius: '4px',
+    backgroundColor: 'rgba(0,0,0,0.2)',
+  })
+
+  const header = mtb_ui.makeElement('h3', {
+    margin: '0 0 8px 0',
+    padding: '4px 0',
+    borderBottom: '1px solid rgba(255,255,255,0.1)',
+    fontSize: '14px',
+    fontWeight: 'bold',
+    color: '#9f9',
+  })
+  header.textContent = title
+  section.appendChild(header)
+
+  return section
+}
+
+function createDebugContent(content, type) {
+  const wrapper = mtb_ui.makeElement('div', {
+    margin: '4px 0',
+  })
+
+  if (type === 'text') {
+    const text = mtb_ui.makeElement('p', {
+      margin: '2px 0',
+      fontFamily: 'monospace',
+      whiteSpace: 'pre-wrap',
+    })
+    text.innerHTML = content
+    wrapper.appendChild(text)
+  } else if (type === 'image') {
+    const img = mtb_ui.makeElement('img', {
+      width: '100%',
+      borderRadius: '2px',
+    })
+    img.src = content
+    wrapper.appendChild(img)
+  }
+
+  return wrapper
+}
+
+app.registerExtension({
+  name: 'mtb.Debug',
+
+  /**
+   * @param {NodeType} nodeType
+   * @param {NodeData} nodeData
+   * @param {*} app
+   */
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+    if (nodeData.name === 'Debug (mtb)') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function (...args) {
+        this.options = {}
+        const r = onNodeCreated ? onNodeCreated.apply(this, args) : undefined
+        this.addInput('anything_1', '*')
+        return r
+      }
+
+      const onConnectionsChange = nodeType.prototype.onConnectionsChange
+      /**
+       * @param {OnConnectionsChangeParams} args
+       */
+      nodeType.prototype.onConnectionsChange = function (...args) {
+        const [_type, index, connected, link_info, ioSlot] = args
+        const r = onConnectionsChange
+          ? onConnectionsChange.apply(this, args)
+          : undefined
+        // TODO: remove all widgets on disconnect once computed
+        shared.dynamic_connection(this, index, connected, 'anything_', '*', {
+          link: link_info,
+          ioSlot: ioSlot,
+        })
+
+        //- infer type
+        if (link_info) {
+          // const fromNode = this.graph._nodes.find(
+          // (otherNode) => otherNode.id === link_info.origin_id,
+          // )
+          // const fromNode = app.graph.getNodeById(link_info.origin_id)
+          const { from } = shared.nodesFromLink(this, link_info)
+          if (!from || this.inputs.length === 0) return
+          const type = from.outputs[link_info.origin_slot].type
+          this.inputs[index].type = type
+          // this.inputs[index].label = type.toLowerCase()
+        }
+        //- restore dynamic input
+        if (!connected) {
+          this.inputs[index].type = '*'
+          this.inputs[index].label = `anything_${index + 1}`
+        }
+        return r
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (...args) {
+        onExecuted?.apply(this, args)
+        const [data, ..._rest] = args
+
+        if (this.widgets) {
+          let tgt_len = this.widgets.length
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (
+              this.widgets[i].name !== 'output_to_console' &&
+              this.widgets[i].name !== 'as_detailed_types'
+            ) {
+              this.widgets[i].onRemove?.()
+              this.widgets[i].onRemoved?.()
+              tgt_len -= 1
+            }
+          }
+          this.widgets.length = tgt_len
+        }
+
+        const inputData = {}
+
+        const uiData = data.ui || data
+
+        if (uiData.items) {
+          uiData.items.forEach((item) => {
+            const inputName = item.input
+            if (!inputData[inputName]) {
+              inputData[inputName] = { text: [], b64_images: [] }
+            }
+            if (item.text) {
+              inputData[inputName].text.push(...item.text)
+            }
+            if (item.b64_images) {
+              inputData[inputName].b64_images.push(...item.b64_images)
+            }
+          })
+        }
+
+        let widgetI = 1
+        for (const [inputName, content] of Object.entries(inputData)) {
+          if (content.text.length === 0 && content.b64_images.length === 0) {
+            continue
+          }
+
+          const section = createDebugSection(inputName)
+
+          if (content.text.length > 0) {
+            content.text.forEach((text) => {
+              section.appendChild(createDebugContent(text, 'text'))
+            })
+          }
+
+          if (content.b64_images.length > 0) {
+            content.b64_images.forEach((img) => {
+              section.appendChild(createDebugContent(img, 'image'))
+            })
+          }
+
+          this.addDOMWidget(`debug_section_${widgetI}`, 'CUSTOM', section, {})
+          widgetI++
+        }
+
+        this.onRemoved = function () {
+          for (const widget of this.widgets) {
+            if (widget.canvas) {
+              widget.canvas.remove()
+            }
+            widget.onRemoved?.()
+            widget.onRemove?.()
+          }
+          shared.cleanupNode(this)
+        }
+      }
+    }
+  },
+})