⬆️ Bump version: 0.1.4 → 0.1.5

See #184 for details

.editorconfig

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,10 +1,10 @@
 name: 🐞 Bug Report
-title: "[bug] "
+title: '[bug] '
 description: Report a bug
-labels: ["type: 🐛 bug", "status: 🧹 needs triage"]
+labels: ['type: 🐛 bug', 'status: 🧹 needs triage']
 assignees:
   - melMass
-  
+
 body:
   - type: markdown
     attributes:
@@ -12,6 +12,8 @@ body:
         ## 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
 
@@ -54,7 +56,7 @@ body:
       default: 0
     validations:
       required: true
-      
+
   - type: dropdown
     id: comfy_mode
     attributes:

.github/workflows/publish_action.yml

@@ -0,0 +1,20 @@
+name: 📦 Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: ♻️ Check out code
+        uses: actions/checkout@v4
+      - name: 📦 Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.COMFY_REGISTRY_TOKEN }}

.gitignore

@@ -2,4 +2,7 @@ __pycache__
 *.py[cod]
 *.onnx
 wheels/
-node_modules/
+node_modules/
+compose.yaml
+comfy_mtb.wsb
+Dockerfile

.gitmodules

@@ -7,3 +7,6 @@
 [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

CHANGELOG.md

@@ -0,0 +1,402 @@
+# 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] - 2024-03-07
+
+### Bug Fixes
+
+- 🐛 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
+
+- 📝 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 "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
+
+- 🧹 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
+
+- 🚧 add text template node ([af2175a](https://github.com/melMass/comfy_mtb/commit/af2175a1fc0c2fb29ef3493f242fe45ec6fcabac))
+
+## New Contributors
+* [@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/v0.1.4..main
+[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
+

README.md

@@ -15,6 +15,11 @@
 
 [**Install Guide**](./INSTALL.md) | [**Examples**](https://github.com/melMass/comfy_mtb/wiki/Examples)
 
+There is now a dedicated `#mtb-nodes` channel on the Banodoco discord:  
+[![](https://dcbadge.vercel.app/api/server/AXhsabmDhn?style=flat)](https://discord.gg/IAXhsabmDhn)
+
+---
+
 Welcome to the MTB Nodes project! This codebase is open for you to explore and utilize as you wish. Its primary purpose is to build proof-of-concepts (POCs) for implementation in [MLOPs](https://github.com/Bismuth-Consultancy-BV/MLOPs). Many nodes in this project are inspired by existing community contributions or built-in functionalities.
 
 Before proceeding, please be aware of the licenses associated with certain libraries used in this project. For example, the `deepbump` library is licensed under [GPLv3](https://github.com/HugoTini/DeepBump/blob/master/LICENSE).
@@ -41,6 +46,7 @@ mtb add a few widgets like `COLOR`
 A few nodes have the concept of "dynamic" inputs:  
 <img alt="dynamic inputs" width=450  src="https://github.com/melMass/comfy_mtb/assets/7041726/10b3976e-b212-4968-91eb-f34c02bb80c3" />
 
+<!-- NOTE: Here it should just be some examples and warnings, move the rest to the wiki -->
 
 # Node List
 

__init__.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
 ###
 # File: __init__.py
 # Project: comfy_mtb
@@ -7,21 +6,27 @@
 # Copyright (c) 2023 Mel Massadian
 #
 ###
+
+__version__ = "0.1.5"
+
 import os
 
-# todo: don't override this if the user has that setup already
-os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
-os.environ["TF_GPU_ALLOCATOR"] = "cuda_malloc_async"
+# 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 os
 import shutil
 import traceback
 from importlib import reload
+from pathlib import Path
 
 from aiohttp import web
 from server import PromptServer
@@ -37,14 +42,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {}
 NODE_CLASS_MAPPINGS_DEBUG = {}
 WEB_DIRECTORY = "./web"
 
-__version__ = "0.2.0"
 
-
-def extract_nodes_from_source(filename):
+def extract_nodes_from_source(filename: Path):
     source_code = ""
 
-    with open(filename, "r", encoding="utf8") as file:
-        source_code = file.read()
+    source_code = filename.read_text(encoding="utf-8")
 
     nodes = []
 
@@ -69,7 +71,7 @@ def extract_nodes_from_source(filename):
 
 
 def load_nodes():
-    errors = []
+    errors: list[str] = []
     nodes = []
     nodes_failed = []
 
@@ -81,14 +83,16 @@ 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 {module_name} because {error_message}"
                 )
@@ -97,7 +101,7 @@ def load_nodes():
 
     if errors:
         log.debug(
-            f"Some nodes failed to load:\n\t"
+            "Some nodes failed to load:\n\t"
             + "\n\t".join(errors)
             + "\n\n"
             + "Check that you properly installed the dependencies.\n"
@@ -108,25 +112,82 @@ def load_nodes():
 
 
 # - REGISTER WEB EXTENSIONS
-web_extensions_root = 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() 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}\nPlease manually remove it from disk ({web_mtb}) and restart the server."
-        )
+    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}\nPlease 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("-")]
+    )
+
+
+def classname_to_wiki(s: str):
+    classname = s.replace("MTB_", "")
+    parts = []
+    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 = node_class.__name__
+    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
@@ -147,7 +208,7 @@ for node_class in nodes:
             )
 
 log.debug(
-    f"Loaded the following nodes:\n\t"
+    "Loaded the following nodes:\n\t"
     + "\n\t".join(
         f"{cyan_text(k)}: {blue_text(get_summary(doc)) if doc else '-'}"
         for k, doc in NODE_CLASS_MAPPINGS_DEBUG.items()
@@ -182,6 +243,12 @@ if hasattr(PromptServer, "instance"):
         "/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
@@ -270,7 +337,7 @@ if hasattr(PromptServer, "instance"):
                 <a href="/mtb/manage">manage</a>
                 <a href="/mtb/debug">debug</a>
                 <a href="/mtb/status">status</a>
-            </div>            
+            </div>
             """
             return web.Response(
                 text=endpoint.render_base_template("MTB", html_response),

biome.json

@@ -0,0 +1,22 @@
+{
+    "$schema": "https://biomejs.dev/schemas/1.6.1/schema.json",
+    "organizeImports": {
+        "enabled": true
+    },
+    "linter": {
+        "enabled": true,
+        "rules": {
+            "recommended": true
+        }
+    },
+    "formatter": {
+        "lineEnding": "lf"
+    },
+    "javascript": {
+        "formatter": {
+            "quoteStyle": "single",
+            "semicolons": "asNeeded",
+            "indentWidth": 2
+        }
+    }
+}

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

@@ -3,12 +3,17 @@ import csv
 from aiohttp import web
 
 from .log import mklog
-from .utils import backup_file, here, import_install, reqs_map, run_command, styles_dir
+from .utils import (
+    backup_file,
+    import_install,
+    reqs_map,
+    run_command,
+    styles_dir,
+)
 
 endlog = mklog("mtb endpoint")
 
 # - ACTIONS
-import platform
 import sys
 from pathlib import Path
 
@@ -22,7 +27,9 @@ def ACTIONS_installDependency(dependency_names=None):
     # reqs = []
     resolved_names = [reqs_map.get(name, name) for name in dependency_names]
     try:
-        run_command([Path(sys.executable), "-m", "pip", "install"] + resolved_names)
+        run_command(
+            [Path(sys.executable), "-m", "pip", "install"] + resolved_names
+        )
         return {"success": True}
 
     except Exception as e:
@@ -44,9 +51,9 @@ def ACTIONS_installDependency(dependency_names=None):
 
 
 def ACTIONS_getStyles(style_name=None):
-    from .nodes.conditions import StylesLoader
+    from .nodes.conditions import MTB_StylesLoader
 
-    styles = StylesLoader.options
+    styles = MTB_StylesLoader.options
     match_list = ["name"]
     if styles:
         filtered_styles = {
@@ -55,7 +62,9 @@ def ACTIONS_getStyles(style_name=None):
             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.get(
+                style_name, {"error": "Style not found"}
+            )
         return filtered_styles
     return {"error": "No styles found"}
 
@@ -75,7 +84,9 @@ def ACTIONS_saveStyle(data):
             break
 
     if not target:
-        endlog.warning(f"Could not determine the target file for {data.keys()}")
+        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)
@@ -103,11 +114,16 @@ async def do_action(request) -> web.Response:
         return web.json_response({"result": result})
 
     available_methods = [
-        attr[len("ACTIONS_") :] for attr in globals() if attr.startswith("ACTIONS_")
+        attr[len("ACTIONS_") :]
+        for attr in globals()
+        if attr.startswith("ACTIONS_")
     ]
 
     return web.json_response(
-        {"error": "Invalid method name.", "available_methods": available_methods}
+        {
+            "error": "Invalid method name.",
+            "available_methods": available_methods,
+        }
     )
 
 
@@ -127,7 +143,7 @@ def csv_editor():
 
     style_files = {}
     for file in inputs:
-        with open(file, "r", encoding="utf8") as f:
+        with open(file, encoding="utf8") as f:
             parsed = csv.reader(f)
             style_files[file.name] = []
             for row in parsed:
@@ -235,7 +251,9 @@ def add_split_pane(left_content, right_content, vertical=True):
 
 
 def add_dropdown(title, options):
-    option_str = "\n".join([f"<option value='{opt}'>{opt}</option>" for opt in options])
+    option_str = "\n".join(
+        [f"<option value='{opt}'>{opt}</option>" for opt in options]
+    )
     return f"""
     <select>
         <option disabled selected>{title}</option>
@@ -254,11 +272,15 @@ def render_table(table_dict, sort=True, title=None):
         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 += (
+                    f"{dependencies_button(name,item['dependencies'])}"
+                )
 
                 table_rows += "</td></tr>"
             else:
-                table_rows += f"<tr><td>{name}</td><td>{render_table(item)}</td></tr>"
+                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:

env.nu

@@ -0,0 +1,147 @@
+# NOTE: This file is only use for development you can ignore it
+
+use path.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 $env.COMFY_MTB
+    cd web_source
+    npm run dev
+}
+
+
+# start the comfy server
+export def "comfy start" [--clean, --listen] {
+    let root = get_root --clean=($clean)
+    cd $root
+    MTB_DEBUG=true python main.py --port 3000 --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"
+    cd $root
+    let branch_name = (git rev-parse --abbrev-ref HEAD | str trim)
+    print $"(ansi yellow_italic)Backing up and removing models symlinks(ansi reset)"
+
+    cd $models
+
+    # find all symlinks
+    let links = (ls -la | 
+        where not ($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 }
+	}
+
+    cd $root
+
+    print $"(ansi yellow_italic)Checking out to master(ansi reset)"
+    git checkout master
+
+    print $"(ansi yellow_italic)Fetching and pulling remote updates(ansi reset)"
+    git fetch
+    git pull
+
+    print $"(ansi yellow_italic)Back to our branch \(($branch_name)\)(ansi reset)"
+    git checkout -
+
+    if $rebase {
+        print $"(ansi yellow_italic)Rebasing changes(ansi reset)"
+        git rebase master
+
+    } else {
+        print $"(ansi yellow_italic)Merging changes(ansi reset)"
+        git merge master
+    }
+
+    print $"(ansi yellow_italic)Linking back the models(ansi reset)"
+    
+    cd $models
+    # resymlink them
+    open links.nuon | each {|p| link -a $p.target $p.name }
+
+    let commit_count = (git rev-list --count $branch_name $"^origin/($branch_name)")
+
+
+    print $"(ansi green_bold)Update successful \(($commit_count) new commits\)(ansi reset)"
+
+
+}
+
+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
+    }
+
+    print $choices
+
+    let filtered = $choices | wrap name | upsert enabled {|p| not ($p.name | str ends-with ".disabled")}
+            
+    print $filtered 
+    $filtered | each {|f|
+        let new_name = ($f.name | str replace ".disabled" "")
+       
+        let new_name = if $f.enabled {
+            $"($new_name).disabled"
+        } else {
+            $new_name
+        }
+        print $"Moving ($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 .
+}
+
+
+
+export-env {
+  $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'
+  path-add ($env.CUDA_ROOT | path join bin)
+  overlay use ../../.venv/Scripts/activate.nu 
+}
+
+

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}

log.py

@@ -36,7 +36,7 @@ class Formatter(logging.Formatter):
         return formatter.format(record)
 
 
-def mklog(name, level=base_log_level):
+def mklog(name: str, level: int = base_log_level):
     logger = logging.getLogger(name)
     logger.setLevel(level)
 
@@ -58,23 +58,23 @@ def mklog(name, level=base_log_level):
 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 cyan_text(text):
+def cyan_text(text: str):
     return f"\033[96m{text}\033[0m"
 
 
-def get_label(label):
+def get_label(label: str):
     if label.startswith("MTB_"):
         label = label[4:]
     words = re.findall(r"(?:^|[A-Z])[a-z]*", label)

nodes/__init__.py

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

nodes/animation.py

@@ -1,8 +1,8 @@
 from ..log import log
 
 
-class AnimationBuilder:
-    """Convenient way to manage basic animation maths at the core of many of my workflows"""
+class MTB_AnimationBuilder:
+    """Simple maths for animation."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -21,6 +21,36 @@ class AnimationBuilder:
     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,
@@ -41,4 +71,4 @@ class AnimationBuilder:
         return (frame, scaled, raw_loop, (frame == (total_frames - 1)))
 
 
-__nodes__ = [AnimationBuilder]
+__nodes__ = [MTB_AnimationBuilder]

nodes/batch.py

@@ -1,19 +1,16 @@
-import math
-import os
-from pathlib import Path
-from typing import List
+from io import BytesIO
 
 import cv2
-import folder_paths
 import numpy as np
 import torch
+from PIL import Image
 
 from ..log import log
-from ..utils import apply_easing, pil2tensor
-from .transform import TransformImage
+from ..utils import EASINGS, apply_easing, pil2tensor
+from .transform import MTB_TransformImage
 
 
-def hex_to_rgb(hex_color, bgr=False):
+def hex_to_rgb(hex_color: str, bgr: bool = False):
     hex_color = hex_color.lstrip("#")
     if bgr:
         return tuple(int(hex_color[i : i + 2], 16) for i in (4, 2, 0))
@@ -21,7 +18,158 @@ def hex_to_rgb(hex_color, bgr=False):
     return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
 
 
-class BatchMake:
+class MTB_BatchFloatMath:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "reverse": ("BOOLEAN", {"default": False}),
+                "operation": (
+                    ["add", "sub", "mul", "div", "pow", "abs"],
+                    {"default": "add"},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
+
+    def execute(self, reverse: bool, operation: str, **kwargs: list[float]):
+        res: list[float] = []
+        vals = list(kwargs.values())
+
+        if reverse:
+            vals = vals[::-1]
+
+        ref_count = len(vals[0])
+        for v in vals:
+            if len(v) != ref_count:
+                raise ValueError(
+                    f"All values must have the same length (current: {len(v)}, ref: {ref_count}"
+                )
+
+        match operation:
+            case "add":
+                for i in range(ref_count):
+                    result = sum(v[i] for v in vals)
+                    res.append(result)
+            case "sub":
+                for i in range(ref_count):
+                    result = vals[0][i] - sum(v[i] for v in vals[1:])
+                    res.append(result)
+            case "mul":
+                for i in range(ref_count):
+                    result = vals[0][i] * vals[1][i]
+                    res.append(result)
+            case "div":
+                for i in range(ref_count):
+                    result = vals[0][i] / vals[1][i]
+                    res.append(result)
+            case "pow":
+                for i in range(ref_count):
+                    result: float = vals[0][i] ** vals[1][i]
+                    res.append(result)
+            case "abs":
+                for i in range(ref_count):
+                    result = abs(vals[0][i])
+                    res.append(result)
+            case _:
+                log.info(f"For now this mode ({operation}) is not implemented")
+
+        return (res,)
+
+
+class MTB_BatchFloatNormalize:
+    """Normalize the values in the list of floats"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"floats": ("FLOATS",)},
+        }
+
+    RETURN_TYPES = ("FLOATS",)
+    RETURN_NAMES = ("normalized_floats",)
+    CATEGORY = "mtb/batch"
+    FUNCTION = "execute"
+
+    def execute(
+        self,
+        floats: list[float],
+    ):
+        min_value = min(floats)
+        max_value = max(floats)
+
+        normalized_floats = [
+            (x - min_value) / (max_value - min_value) for x in floats
+        ]
+        log.debug(f"Floats: {floats}")
+        log.debug(f"Normalized Floats: {normalized_floats}")
+
+        return (normalized_floats,)
+
+
+class MTB_BatchTimeWrap:
+    """Remap a batch using a time curve (FLOATS)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "target_count": ("INT", {"default": 25, "min": 2}),
+                "frames": ("IMAGE",),
+                "curve": ("FLOATS",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "FLOATS")
+    RETURN_NAMES = ("image", "interpolated_floats")
+    CATEGORY = "mtb/batch"
+    FUNCTION = "execute"
+
+    def execute(
+        self, target_count: int, frames: torch.Tensor, curve: list[float]
+    ):
+        """Apply time warping to a list of video frames based on a curve."""
+        log.debug(f"Input frames shape: {frames.shape}")
+        log.debug(f"Curve: {curve}")
+
+        total_duration = sum(curve)
+
+        log.debug(f"Total duration: {total_duration}")
+
+        B, H, W, C = frames.shape
+
+        log.debug(f"Batch Size: {B}")
+
+        normalized_times = np.linspace(0, 1, target_count)
+        interpolated_curve = np.interp(
+            normalized_times, np.linspace(0, 1, len(curve)), curve
+        ).tolist()
+        log.debug(f"Interpolated curve: {interpolated_curve}")
+
+        interpolated_frame_indices = [
+            (B - 1) * value for value in interpolated_curve
+        ]
+        log.debug(f"Interpolated frame indices: {interpolated_frame_indices}")
+
+        rounded_indices = [
+            int(round(idx)) for idx in interpolated_frame_indices
+        ]
+        rounded_indices = np.clip(rounded_indices, 0, B - 1)
+
+        # Gather frames based on interpolated indices
+        warped_frames = []
+        for index in rounded_indices:
+            warped_frames.append(frames[index].unsqueeze(0))
+
+        warped_tensor = torch.cat(warped_frames, dim=0)
+        log.debug(f"Warped frames shape: {warped_tensor.shape}")
+        return (warped_tensor, interpolated_curve)
+
+
+class MTB_BatchMake:
     """Simply duplicates the input frame as a batch"""
 
     @classmethod
@@ -44,7 +192,7 @@ class BatchMake:
         return (image.repeat(count, 1, 1, 1),)
 
 
-class BatchShape:
+class MTB_BatchShape:
     """Generates a batch of 2D shapes with optional shading (experimental)"""
 
     @classmethod
@@ -53,8 +201,8 @@ class BatchShape:
             "required": {
                 "count": ("INT", {"default": 1}),
                 "shape": (
-                    ["Box", "Circle", "Diamond"],
-                    {"default": "Box"},
+                    ["Box", "Circle", "Diamond", "Tube"],
+                    {"default": "Circle"},
                 ),
                 "image_width": ("INT", {"default": 512}),
                 "image_height": ("INT", {"default": 512}),
@@ -62,6 +210,7 @@ class BatchShape:
                 "color": ("COLOR", {"default": "#ffffff"}),
                 "bg_color": ("COLOR", {"default": "#000000"}),
                 "shade_color": ("COLOR", {"default": "#000000"}),
+                "thickness": ("INT", {"default": 5}),
                 "shadex": ("FLOAT", {"default": 0.0}),
                 "shadey": ("FLOAT", {"default": 0.0}),
             },
@@ -81,12 +230,13 @@ class BatchShape:
         color,
         bg_color,
         shade_color,
+        thickness,
         shadex,
         shadey,
     ):
-        print(f"COLOR: {color}")
-        print(f"BG_COLOR: {bg_color}")
-        print(f"SHADE_COLOR: {shade_color}")
+        log.debug(f"COLOR: {color}")
+        log.debug(f"BG_COLOR: {bg_color}")
+        log.debug(f"SHADE_COLOR: {shade_color}")
 
         # Parse color input to BGR tuple for OpenCV
         color = hex_to_rgb(color)
@@ -95,7 +245,9 @@ class BatchShape:
         res = []
         for x in range(count):
             # Initialize an image canvas
-            canvas = np.full((image_height, image_width, 3), bg_color, dtype=np.uint8)
+            canvas = np.full(
+                (image_height, image_width, 3), bg_color, dtype=np.uint8
+            )
             mask = np.zeros((image_height, image_width), dtype=np.uint8)
 
             # Compute the center point of the shape
@@ -119,13 +271,27 @@ class BatchShape:
                 )
                 cv2.fillPoly(mask, [pts], 255)
 
+            elif shape == "Tube":
+                cv2.ellipse(
+                    mask,
+                    center,
+                    (shape_size // 2, shape_size // 2),
+                    0,
+                    0,
+                    360,
+                    255,
+                    thickness,
+                )
+
             # Color the shape
             canvas[mask == 255] = color
 
             # Apply shading effects to a separate shading canvas
             shading = np.zeros_like(canvas, dtype=np.float32)
             shading[:, :, 0] = shadex * np.linspace(0, 1, image_width)
-            shading[:, :, 1] = shadey * np.linspace(0, 1, image_height).reshape(-1, 1)
+            shading[:, :, 1] = shadey * np.linspace(
+                0, 1, image_height
+            ).reshape(-1, 1)
             shading_canvas = cv2.addWeighted(
                 canvas.astype(np.float32), 1, shading, 1, 0
             ).astype(np.uint8)
@@ -137,7 +303,7 @@ class BatchShape:
         return (pil2tensor(res),)
 
 
-class BatchFloatFill:
+class MTB_BatchFloatFill:
     """Fills a batch float with a single value until it reaches the target length"""
 
     @classmethod
@@ -158,7 +324,9 @@ class BatchFloatFill:
     def fill_floats(self, floats, direction, value, count):
         size = len(floats)
         if size > count:
-            raise ValueError(f"Size ({size}) is less then target count ({count})")
+            raise ValueError(
+                f"Size ({size}) is less then target count ({count})"
+            )
 
         rem = count - size
         if direction == "tail":
@@ -168,30 +336,33 @@ class BatchFloatFill:
         return (floats,)
 
 
-class BatchFloatAssemble:
+class MTB_BatchFloatAssemble:
     """Assembles mutiple batches of floats into a single stream (batch)"""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {"required": {"reverse": ("BOOLEAN", {"default": False})}}
 
-    FUNCTION = "assemble_floats"
     RETURN_TYPES = ("FLOATS",)
     CATEGORY = "mtb/batch"
+    FUNCTION = "assemble_floats"
+
+    def assemble_floats(self, reverse: bool, **kwargs: list[float]):
+        res: list[float] = []
 
-    def assemble_floats(self, reverse, **kwargs):
-        res = []
         if reverse:
             for x in reversed(kwargs.values()):
-                res += x
+                if x:
+                    res += x
         else:
             for x in kwargs.values():
-                res += x
+                if x:
+                    res += x
 
         return (res,)
 
 
-class BatchFloat:
+class MTB_BatchFloat:
     """Generates a batch of float values with interpolation"""
 
     @classmethod
@@ -202,9 +373,9 @@ class BatchFloat:
                     ["Single", "Steps"],
                     {"default": "Steps"},
                 ),
-                "count": ("INT", {"default": 1}),
-                "min": ("FLOAT", {"default": 0.0}),
-                "max": ("FLOAT", {"default": 1.0}),
+                "count": ("INT", {"default": 2}),
+                "min": ("FLOAT", {"default": 0.0, "step": 0.001}),
+                "max": ("FLOAT", {"default": 1.0, "step": 0.001}),
                 "easing": (
                     [
                         "Linear",
@@ -240,6 +411,10 @@ class BatchFloat:
     CATEGORY = "mtb/batch"
 
     def set_floats(self, mode, count, min, max, easing):
+        if mode == "Steps" and count == 1:
+            raise ValueError(
+                "Steps mode requires at least a count of 2 values"
+            )
         keyframes = []
         if mode == "Single":
             keyframes = [min] * count
@@ -254,14 +429,17 @@ class BatchFloat:
         return (keyframes,)
 
 
-class BatchMerge:
+class MTB_BatchMerge:
     """Merges multiple image batches with different frame counts"""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "fusion_mode": (["add", "multiply", "average"], {"default": "average"}),
+                "fusion_mode": (
+                    ["add", "multiply", "average"],
+                    {"default": "average"},
+                ),
                 "fill": (["head", "tail"], {"default": "tail"}),
             }
         }
@@ -270,7 +448,7 @@ class BatchMerge:
     FUNCTION = "merge_batches"
     CATEGORY = "mtb/batch"
 
-    def merge_batches(self, fusion_mode, fill, **kwargs):
+    def merge_batches(self, fusion_mode: str, fill: str, **kwargs):
         images = kwargs.values()
         max_frames = max(img.shape[0] for img in images)
 
@@ -279,7 +457,9 @@ class BatchMerge:
             frame_count = img.shape[0]
             if frame_count < max_frames:
                 fill_frame = img[0] if fill == "head" else img[-1]
-                fill_frames = fill_frame.repeat(max_frames - frame_count, 1, 1, 1)
+                fill_frames = fill_frame.repeat(
+                    max_frames - frame_count, 1, 1, 1
+                )
                 adjusted_batch = (
                     torch.cat((fill_frames, img), dim=0)
                     if fill == "head"
@@ -305,7 +485,7 @@ class BatchMerge:
         return (merged_image,)
 
 
-class Batch2dTransform:
+class MTB_Batch2dTransform:
     """Transform a batch of images using a batch of keyframes"""
 
     @classmethod
@@ -332,9 +512,12 @@ class Batch2dTransform:
     FUNCTION = "transform_batch"
     CATEGORY = "mtb/batch"
 
-    def get_num_elements(self, param) -> int:
+    def get_num_elements(
+        self, param: None | torch.Tensor | list[torch.Tensor] | list[float]
+    ) -> int:
         if isinstance(param, torch.Tensor):
             return torch.numel(param)
+
         elif isinstance(param, list):
             return len(param)
 
@@ -343,32 +526,42 @@ class Batch2dTransform:
     def transform_batch(
         self,
         image: torch.Tensor,
-        border_handling,
-        constant_color,
-        x=None,
-        y=None,
-        zoom=None,
-        angle=None,
-        shear=None,
+        border_handling: str,
+        constant_color: str,
+        x: list[float] | None = None,
+        y: list[float] | None = None,
+        zoom: list[float] | None = None,
+        angle: list[float] | None = None,
+        shear: list[float] | None = None,
     ):
         if all(
-            self.get_num_elements(param) <= 0 for param in [x, y, zoom, angle, shear]
+            self.get_num_elements(param) <= 0
+            for param in [x, y, zoom, angle, shear]
         ):
-            raise ValueError("At least one transform parameter must be provided")
+            raise ValueError(
+                "At least one transform parameter must be provided"
+            )
 
-        keyframes = {"x": [], "y": [], "zoom": [], "angle": [], "shear": []}
+        keyframes: dict[str, list[float]] = {
+            "x": [],
+            "y": [],
+            "zoom": [],
+            "angle": [],
+            "shear": [],
+        }
 
         default_vals = {"x": 0, "y": 0, "zoom": 1.0, "angle": 0, "shear": 0}
 
-        if self.get_num_elements(x) > 0:
+        if x and self.get_num_elements(x) > 0:
             keyframes["x"] = x
-        if self.get_num_elements(y) > 0:
+        if y and self.get_num_elements(y) > 0:
             keyframes["y"] = y
-        if self.get_num_elements(zoom) > 0:
-            keyframes["zoom"] = zoom
-        if self.get_num_elements(angle) > 0:
+        if zoom and self.get_num_elements(zoom) > 0:
+            # some easing types like elastic can pull back... maybe it should abs the value?
+            keyframes["zoom"] = [max(x, 0.00001) for x in zoom]
+        if angle and self.get_num_elements(angle) > 0:
             keyframes["angle"] = angle
-        if self.get_num_elements(shear) > 0:
+        if shear and self.get_num_elements(shear) > 0:
             keyframes["shear"] = shear
 
         for name, values in keyframes.items():
@@ -380,7 +573,7 @@ class Batch2dTransform:
             if count == 0:
                 keyframes[name] = [default_vals[name]] * image.shape[0]
 
-        transformer = TransformImage()
+        transformer = MTB_TransformImage()
         res = [
             transformer.transform(
                 image[i].unsqueeze(0),
@@ -397,10 +590,218 @@ class Batch2dTransform:
         return (torch.cat(res, dim=0),)
 
 
+class MTB_BatchFloatFit:
+    """Fit a list of floats using a source and target range"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "values": ("FLOATS", {"forceInput": True}),
+                "clamp": ("BOOLEAN", {"default": False}),
+                "auto_compute_source": ("BOOLEAN", {"default": False}),
+                "source_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "source_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
+                "target_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "target_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
+                "easing": (
+                    EASINGS,
+                    {"default": "Linear"},
+                ),
+            }
+        }
+
+    FUNCTION = "fit_range"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+    DESCRIPTION = "Fit a list of floats using a source and target range"
+
+    def fit_range(
+        self,
+        values: list[float],
+        clamp: bool,
+        auto_compute_source: bool,
+        source_min: float,
+        source_max: float,
+        target_min: float,
+        target_max: float,
+        easing: str,
+    ):
+        if auto_compute_source:
+            source_min = min(values)
+            source_max = max(values)
+
+        from .graph_utils import MTB_FitNumber
+
+        res = []
+        fit_number = MTB_FitNumber()
+        for value in values:
+            (transformed_value,) = fit_number.set_range(
+                value,
+                clamp,
+                source_min,
+                source_max,
+                target_min,
+                target_max,
+                easing,
+            )
+            res.append(transformed_value)
+
+        return (res,)
+
+
+class MTB_PlotBatchFloat:
+    """Plot floats"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "width": ("INT", {"default": 768}),
+                "height": ("INT", {"default": 768}),
+                "point_size": ("INT", {"default": 4}),
+                "seed": ("INT", {"default": 1}),
+                "start_at_zero": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("plot",)
+    FUNCTION = "plot"
+    CATEGORY = "mtb/batch"
+
+    def plot(
+        self,
+        width: int,
+        height: int,
+        point_size: int,
+        seed: int,
+        start_at_zero: bool,
+        interactive_backend: bool = False,
+        **kwargs,
+    ):
+        import matplotlib
+
+        # NOTE: This is for notebook usage or tests, i.e not exposed to comfy that should always use Agg
+        if not interactive_backend:
+            matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        fig, ax = plt.subplots(figsize=(width / 100, height / 100), dpi=100)
+        fig.set_edgecolor("black")
+        fig.patch.set_facecolor("#2e2e2e")
+        # Setting background color and grid
+        ax.set_facecolor("#2e2e2e")  # Dark gray background
+        ax.grid(color="gray", linestyle="-", linewidth=0.5, alpha=0.5)
+
+        # Finding global min and max across all lists for scaling the plot
+        all_values = [value for values in kwargs.values() for value in values]
+        global_min = min(all_values)
+        global_max = max(all_values)
+
+        y_padding = 0.05 * (global_max - global_min)
+        ax.set_ylim(global_min - y_padding, global_max + y_padding)
+
+        max_length = max(len(values) for values in kwargs.values())
+        if start_at_zero:
+            x_values = np.linspace(0, max_length - 1, max_length)
+        else:
+            x_values = np.linspace(1, max_length, max_length)
+
+        ax.set_xlim(1, max_length)  # Set X-axis limits
+        np.random.seed(seed)
+        colors = np.random.rand(len(kwargs), 3)  # Generate random RGB values
+        for color, (label, values) in zip(colors, kwargs.items()):
+            ax.plot(x_values[: len(values)], values, label=label, color=color)
+        ax.legend(
+            title="Legend",
+            title_fontsize="large",
+            fontsize="medium",
+            edgecolor="black",
+            loc="best",
+        )
+
+        # Setting labels and title
+        ax.set_xlabel("Time", fontsize="large", color="white")
+        ax.set_ylabel("Value", fontsize="large", color="white")
+        ax.set_title(
+            "Plot of Values over Time", fontsize="x-large", color="white"
+        )
+
+        # Adjusting tick colors to be visible on dark background
+        ax.tick_params(colors="white")
+
+        # Changing color of the axes border
+        for _, spine in ax.spines.items():
+            spine.set_edgecolor("white")
+
+        # Rendering the plot into a NumPy array
+        buf = BytesIO()
+        plt.savefig(buf, format="png", bbox_inches="tight")
+        buf.seek(0)
+        image = Image.open(buf)
+        plt.close(fig)  # Closing the figure to free up memory
+
+        return (pil2tensor(image),)
+
+    def draw_point(self, image, point, color, point_size):
+        x, y = point
+        y = image.shape[0] - 1 - y  # Invert Y-coordinate
+        half_size = point_size // 2
+        x_start, x_end = (
+            max(0, x - half_size),
+            min(image.shape[1], x + half_size + 1),
+        )
+        y_start, y_end = (
+            max(0, y - half_size),
+            min(image.shape[0], y + half_size + 1),
+        )
+        image[y_start:y_end, x_start:x_end] = color
+
+    def draw_line(self, image, start, end, color):
+        x1, y1 = start
+        x2, y2 = end
+
+        # Invert Y-coordinate
+        y1 = image.shape[0] - 1 - y1
+        y2 = image.shape[0] - 1 - y2
+
+        dx = x2 - x1
+        dy = y2 - y1
+        is_steep = abs(dy) > abs(dx)
+        if is_steep:
+            x1, y1 = y1, x1
+            x2, y2 = y2, x2
+        swapped = False
+        if x1 > x2:
+            x1, x2 = x2, x1
+            y1, y2 = y2, y1
+            swapped = True
+        dx = x2 - x1
+        dy = y2 - y1
+        error = int(dx / 2.0)
+        y = y1
+        ystep = None
+        if y1 < y2:
+            ystep = 1
+        else:
+            ystep = -1
+        for x in range(x1, x2 + 1):
+            coord = (y, x) if is_steep else (x, y)
+            image[coord] = color
+            error -= abs(dy)
+            if error < 0:
+                y += ystep
+                error += dx
+        if swapped:
+            image[(x1, y1)] = color
+            image[(x2, y2)] = color
+
+
 DEFAULT_INTERPOLANT = lambda t: t * t * t * (t * (t * 6 - 15) + 10)
 
 
-class BatchShake:
+class MTB_BatchShake:
     """Applies a shaking effect to batches of images."""
 
     @classmethod
@@ -442,17 +843,21 @@ class BatchShake:
             interpolant: The interpolation function, defaults to
                 t*t*t*(t*(t*6 - 15) + 10).
 
-        Returns:
+        Returns
+        -------
             A numpy array of shape shape with the generated noise.
 
-        Raises:
+        Raises
+        ------
             ValueError: If shape is not a multiple of res.
         """
         interpolant = interpolant or DEFAULT_INTERPOLANT
         delta = (res[0] / shape[0], res[1] / shape[1])
         d = (shape[0] // res[0], shape[1] // res[1])
         grid = (
-            np.mgrid[0 : res[0] : delta[0], 0 : res[1] : delta[1]].transpose(1, 2, 0)
+            np.mgrid[0 : res[0] : delta[0], 0 : res[1] : delta[1]].transpose(
+                1, 2, 0
+            )
             % 1
         )
         # Gradients
@@ -471,7 +876,9 @@ class BatchShake:
         n00 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1])) * g00, 2)
         n10 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1])) * g10, 2)
         n01 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1] - 1)) * g01, 2)
-        n11 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1] - 1)) * g11, 2)
+        n11 = np.sum(
+            np.dstack((grid[:, :, 0] - 1, grid[:, :, 1] - 1)) * g11, 2
+        )
         # Interpolation
         t = interpolant(grid)
         n0 = n00 * (1 - t[:, :, 0]) + t[:, :, 0] * n10
@@ -504,11 +911,13 @@ class BatchShake:
             interpolant: The, interpolation function, defaults to
                 t*t*t*(t*(t*6 - 15) + 10).
 
-        Returns:
+        Returns
+        -------
             A numpy array of fractal noise and of shape shape generated by
             combining several octaves of perlin noise.
 
-        Raises:
+        Raises
+        ------
             ValueError: If shape is not a multiple of
                 (lacunarity**(octaves-1)*res).
         """
@@ -519,7 +928,10 @@ class BatchShake:
         amplitude = 1
         for _ in range(octaves):
             noise += amplitude * self.generate_perlin_noise_2d(
-                shape, (frequency * res[0], frequency * res[1]), tileable, interpolant
+                shape,
+                (frequency * res[0], frequency * res[1]),
+                tileable,
+                interpolant,
             )
             frequency *= lacunarity
             amplitude *= persistence
@@ -594,7 +1006,7 @@ class BatchShake:
         # rotations = torch.tensor(rotations, dtype=torch.float32)
 
         # Create an instance of Batch2dTransform
-        transform = Batch2dTransform()
+        transform = MTB_Batch2dTransform()
 
         log.debug(
             f"Applying shaking with parameters: \nposition {position_amount_x}, {position_amount_y}\nrotation {rotation_amount}\nfrequency {frequency}\noctaves {octaves}"
@@ -614,12 +1026,17 @@ class BatchShake:
 
 
 __nodes__ = [
-    BatchFloat,
-    Batch2dTransform,
-    BatchShape,
-    BatchMake,
-    BatchFloatAssemble,
-    BatchFloatFill,
-    BatchMerge,
-    BatchShake,
+    MTB_BatchFloat,
+    MTB_Batch2dTransform,
+    MTB_BatchShape,
+    MTB_BatchMake,
+    MTB_BatchFloatAssemble,
+    MTB_BatchFloatFill,
+    MTB_BatchFloatNormalize,
+    MTB_BatchMerge,
+    MTB_BatchShake,
+    MTB_PlotBatchFloat,
+    MTB_BatchTimeWrap,
+    MTB_BatchFloatFit,
+    MTB_BatchFloatMath,
 ]

nodes/conditions.py

@@ -1,4 +1,5 @@
-import csv, shutil
+import csv
+import shutil
 from pathlib import Path
 
 import folder_paths
@@ -7,7 +8,7 @@ from ..log import log
 from ..utils import here
 
 
-class InterpolateClipSequential:
+class MTB_InterpolateClipSequential:
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -28,7 +29,12 @@ class InterpolateClipSequential:
     CATEGORY = "mtb/conditioning"
 
     def interpolate_encodings_sequential(
-        self, base_text, text_to_replace, clip, interpolation_strength, **replacements
+        self,
+        base_text,
+        text_to_replace,
+        clip,
+        interpolation_strength,
+        **replacements,
     ):
         log.debug(f"Received interpolation_strength: {interpolation_strength}")
 
@@ -63,20 +69,30 @@ class InterpolateClipSequential:
             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)
+            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)
+            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)
+        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)
+        cond_to, pooled_to = clip.encode_from_tokens(
+            tokens, return_pooled=True
+        )
 
         # - Linearly interpolate between the two conditions
         interpolated_condition = (
@@ -86,10 +102,12 @@ class InterpolateClipSequential:
             1.0 - local_strength
         ) * pooled_from + local_strength * pooled_to
 
-        return ([[interpolated_condition, {"pooled_output": interpolated_pooled}]],)
+        return (
+            [[interpolated_condition, {"pooled_output": interpolated_pooled}]],
+        )
 
 
-class SmartStep:
+class MTB_SmartStep:
     """Utils to control the steps start/stop of the KAdvancedSampler in percentage"""
 
     @classmethod
@@ -136,7 +154,7 @@ 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 = {}
@@ -148,13 +166,15 @@ class StylesLoader:
             if not input_dir.exists():
                 install_default_styles()
 
-            if not (files := [f for f in input_dir.iterdir() if f.suffix == ".csv"]):
+            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, "r", encoding="utf8") as f:
+                with open(file, encoding="utf8") as f:
                     parsed = csv.reader(f)
                     for i, row in enumerate(parsed):
                         log.debug(f"Adding style {row[0]}")
@@ -193,4 +213,4 @@ class StylesLoader:
         return (self.options[style_name][0], self.options[style_name][1])
 
 
-__nodes__ = [SmartStep, StylesLoader, InterpolateClipSequential]
+__nodes__ = [MTB_SmartStep, MTB_StylesLoader, MTB_InterpolateClipSequential]

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

@@ -1,12 +1,12 @@
 import numpy as np
 import torch
-from PIL import Image, ImageChops, ImageDraw, ImageFilter
+from PIL import Image, ImageDraw, ImageFilter
 
 from ..log import log
 from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
 
 
-class Bbox:
+class MTB_Bbox:
     """The bounding box (BBOX) custom type used by other nodes"""
 
     @classmethod
@@ -14,8 +14,14 @@ class Bbox:
         return {
             "required": {
                 # "bbox": ("BBOX",),
-                "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},
@@ -31,12 +37,11 @@ class Bbox:
     FUNCTION = "do_crop"
     CATEGORY = "mtb/crop"
 
-    def do_crop(self, x, y, width, height):  # bbox
+    def do_crop(self, x: int, y: int, width: int, height: int):  # bbox
         return ((x, y, width, height),)
-        # return bbox
 
 
-class BboxFromMask:
+class MTB_BboxFromMask:
     """From a mask extract the bounding box"""
 
     @classmethod
@@ -62,7 +67,9 @@ class BboxFromMask:
     FUNCTION = "extract_bounding_box"
     CATEGORY = "mtb/crop"
 
-    def extract_bounding_box(self, mask: torch.Tensor, invert: bool, image=None):
+    def extract_bounding_box(
+        self, mask: torch.Tensor, invert: bool, image=None
+    ):
         # if image != None:
         #     if mask.size(0) != image.size(0):
         #         if mask.size(0) != 1:
@@ -103,7 +110,7 @@ class BboxFromMask:
         )
 
 
-class Crop:
+class MTB_Crop:
     """Crops 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
@@ -118,8 +125,14 @@ 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},
@@ -138,7 +151,14 @@ class 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=None,
+        x=0,
+        y=0,
+        width=256,
+        height=256,
+        bbox=None,
     ):
         image = image.numpy()
         if mask is not None:
@@ -151,13 +171,17 @@ class Crop:
         cropped_mask = None
         if mask is not None:
             cropped_mask = (
-                mask[:, y : y + height, x : x + width] if mask is not None else None
+                mask[:, y : y + height, x : x + width]
+                if mask is not None
+                else None
             )
         crop_data = (x, y, width, height)
 
         return (
             torch.from_numpy(cropped_image),
-            torch.from_numpy(cropped_mask) if cropped_mask is not None else None,
+            torch.from_numpy(cropped_mask)
+            if cropped_mask is not None
+            else None,
             crop_data,
         )
 
@@ -194,11 +218,12 @@ def bbox_to_region(bbox, target_size=None):
     return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
 
 
-class Uncrop:
+class MTB_Uncrop:
     """Uncrops an image 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"""
+    The BBOX input takes precedence over the tuple input
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -222,11 +247,15 @@ class Uncrop:
     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 = 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
+                (0, 0, width - 1, height - 1),
+                outline=border_color,
+                width=border_width,
             )
             return bordered_image
 
@@ -249,7 +278,9 @@ class Uncrop:
             # uncrop the image based on the bounding box
             bb_x, bb_y, bb_width, bb_height = bbox
 
-            paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+            paste_region = bbox_to_region(
+                (bb_x, bb_y, bb_width, bb_height), img.size
+            )
             # log.debug(f"Paste region: {paste_region}")
             # new_region = adjust_paste_region(img.size, paste_region)
             # log.debug(f"Adjusted paste region: {new_region}")
@@ -275,12 +306,16 @@ class Uncrop:
 
             mask.paste(mask_block, paste_region)
             log.debug(f"Blend size: {blend.size} | kind {blend.mode}")
-            log.debug(f"Crop image size: {crop_img.size} | kind {crop_img.mode}")
+            log.debug(
+                f"Crop image size: {crop_img.size} | kind {crop_img.mode}"
+            )
             log.debug(f"BBox: {paste_region}")
             blend.paste(crop_img, paste_region)
 
             mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
-            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
+            mask = mask.filter(
+                ImageFilter.GaussianBlur(radius=blend_ratio / 4)
+            )
 
             blend.putalpha(mask)
             img = Image.alpha_composite(img.convert("RGBA"), blend)
@@ -289,4 +324,4 @@ class Uncrop:
         return (pil2tensor(out_images),)
 
 
-__nodes__ = [BboxFromMask, Bbox, Crop, Uncrop]
+__nodes__ = [MTB_BboxFromMask, MTB_Bbox, MTB_Crop, MTB_Uncrop]

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

@@ -1,5 +1,6 @@
 import base64
 import io
+import json
 from pathlib import Path
 from typing import Optional
 
@@ -39,11 +40,16 @@ def process_list(anything):
         and isinstance(first_element[0], torch.Tensor)
     ):
         text.append(
-            f"List of List of Tensors: {first_element[0].shape} (x{len(anything)})"
+            "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)})")
+        text.append(
+            f"List of Tensors: {first_element.shape} (x{len(anything)})"
+        )
+    else:
+        text.append(f"Array ({len(anything)}): {anything}")
 
     return {"text": text}
 
@@ -51,9 +57,14 @@ def process_list(anything):
 def process_dict(anything):
     text = []
     if "samples" in anything:
-        is_empty = "(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
+        is_empty = (
+            "(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
+        )
         text.append(f"Latent Samples: {anything['samples'].shape} {is_empty}")
 
+    else:
+        text.append(json.dumps(anything, indent=2))
+
     return {"text": text}
 
 
@@ -68,8 +79,11 @@ def process_text(anything):
 # endregion
 
 
-class Debug:
-    """Experimental node to debug any Comfy values, support for more types and widgets is planned"""
+class MTB_Debug:
+    """Experimental node to debug any Comfy values.
+
+    support for more types and widgets is planned.
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -82,7 +96,7 @@ class Debug:
     CATEGORY = "mtb/debug"
     OUTPUT_NODE = True
 
-    def do_debug(self, output_to_console, **kwargs):
+    def do_debug(self, output_to_console: bool, **kwargs):
         output = {
             "ui": {"b64_images": [], "text": []},
             # "result": ("A"),
@@ -95,23 +109,25 @@ class Debug:
             bool: process_bool,
         }
         if output_to_console:
-            print("bouh!")
+            for k, v in kwargs.items():
+                log.info(f"{k}: {v}")
 
         for anything in kwargs.values():
             processor = processors.get(type(anything), process_text)
+
             processed_data = processor(anything)
 
             for ui_key, ui_value in processed_data.items():
                 output["ui"][ui_key].extend(ui_value)
-            # log.debug(
-            #     f"Processed input {k}, found {len(processed_data.get('b64_images', []))} images and {len(processed_data.get('text', []))} text items."
-            # )
 
         return output
 
 
-class SaveTensors:
-    """Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy"""
+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()
@@ -166,9 +182,10 @@ class SaveTensors:
             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())
+            # np.save(full_output_folder / latent_file,
+            # latent[""].cpu().numpy())
 
         return f"{filename_prefix}_{counter:05}"
 
 
-__nodes__ = [Debug, SaveTensors]
+__nodes__ = [MTB_Debug, MTB_SaveTensors]

nodes/deep_bump.py

@@ -8,7 +8,13 @@ from PIL import Image
 
 from ..errors import ModelNotFound
 from ..log import mklog
-from ..utils import get_model_path, tensor2pil, tiles_infer, tiles_merge, tiles_split
+from ..utils import (
+    get_model_path,
+    tensor2pil,
+    tiles_infer,
+    tiles_merge,
+    tiles_split,
+)
 
 # Disable MS telemetry
 ort.disable_telemetry_events()
@@ -16,9 +22,13 @@ log = mklog(__name__)
 
 
 # - COLOR to NORMALS
-def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
-    """Computes a normal map from the given color map. 'color_img' must be a numpy array
-    in C,H,W format (with C as RGB). 'overlap' must be one of 'SMALL', 'MEDIUM', 'LARGE'.
+def color_to_normals(
+    color_img, overlap, progress_callback, *, save_temp=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
 
@@ -31,7 +41,8 @@ def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
         )
 
     log.debug(
-        f"Converting color image to grayscale by taking the mean over color channels: {img.shape}"
+        "Converting color image to grayscale by taking "
+        f"the mean over color channels: {img.shape}"
     )
 
     # Split image in tiles
@@ -62,13 +73,15 @@ def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
 
     # Predict normal map for each tile
     log.debug("DeepBump Color → Normals : generating")
-    pred_tiles = tiles_infer(tiles, ort_session, progress_callback=progress_callback)
+    pred_tiles = tiles_infer(
+        tiles, ort_session, progress_callback=progress_callback
+    )
 
     if temp_dir:
         for i, pred_tile in enumerate(pred_tiles):
-            Image.fromarray((pred_tile.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
-                temp_dir / f"pred_tile_{i}.png"
-            )
+            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")
@@ -80,17 +93,17 @@ def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
     )
 
     if temp_dir:
-        Image.fromarray((pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
-            temp_dir / "merged_img.png"
-        )
+        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 = normalize(pred_img)
 
     if temp_dir:
-        Image.fromarray((pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)).save(
-            temp_dir / "final_img.png"
-        )
+        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}")
 
@@ -99,40 +112,47 @@ def color_to_normals(color_img, overlap, progress_callback, save_temp=False):
 
 # - 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)
@@ -157,8 +177,12 @@ 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:
@@ -195,8 +219,9 @@ 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])
@@ -210,7 +235,6 @@ 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)
 
@@ -250,8 +274,8 @@ 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)
 
@@ -265,7 +289,9 @@ 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:
@@ -277,7 +303,7 @@ def normals_to_height(normals_img, seamless, progress_callback):
 
 
 # - ADDON
-class DeepBump:
+class MTB_DeepBump:
     """Normal & height maps generation from single pictures"""
 
     @classmethod
@@ -286,7 +312,11 @@ class DeepBump:
             "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": (
@@ -311,6 +341,7 @@ class DeepBump:
 
     def apply(
         self,
+        *,
         image,
         mode="Color to Normals",
         color_to_normals_overlap="SMALL",
@@ -329,13 +360,17 @@ class DeepBump:
 
             # Apply processing
             if mode == "Color to Normals":
-                out_img = color_to_normals(in_img, color_to_normals_overlap, None)
+                out_img = color_to_normals(
+                    in_img, color_to_normals_overlap, None
+                )
             if mode == "Normals to Curvature":
                 out_img = normals_to_curvature(
                     in_img, normals_to_curvature_blur_radius, None
                 )
             if mode == "Normals to Height":
-                out_img = normals_to_height(in_img, normals_to_height_seamless, None)
+                out_img = normals_to_height(
+                    in_img, normals_to_height_seamless, None
+                )
 
             if out_img is not None:
                 log.debug(f"Output image shape: {out_img.shape}")
@@ -351,4 +386,4 @@ class DeepBump:
         return (torch.cat(out_images, dim=0),)
 
 
-__nodes__ = [DeepBump]
+__nodes__ = [MTB_DeepBump]

nodes/faceenhance.py

@@ -1,6 +1,4 @@
 import os
-from pathlib import Path
-from typing import Tuple
 
 import comfy
 import comfy.utils
@@ -9,14 +7,13 @@ import folder_paths
 import numpy as np
 import torch
 from comfy import model_management
-from gfpgan import GFPGANer
 from PIL import Image
 
 from ..log import NullWriter, log
 from ..utils import get_model_path, np2tensor, pil2tensor, tensor2np
 
 
-class LoadFaceEnhanceModel:
+class MTB_LoadFaceEnhanceModel:
     """Loads a GFPGan or RestoreFormer model for face enhancement."""
 
     def __init__(self) -> None:
@@ -37,15 +34,17 @@ class LoadFaceEnhanceModel:
         fr_models_path, um_models_path = cls.get_models_root()
 
         if fr_models_path is None and um_models_path is None:
-            log.warning("Face restoration models not found.")
+            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"
-            )
+            # 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
@@ -81,6 +80,8 @@ class LoadFaceEnhanceModel:
     CATEGORY = "mtb/facetools"
 
     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()
@@ -98,7 +99,9 @@ class LoadFaceEnhanceModel:
                 (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
+            arch="clean"
+            if basic
+            else "RestoreFormer",  # or original for v1.0 only
             channel_multiplier=2,  # 1 for v1.0 only
             bg_upsampler=bg_upsampler,
         )
@@ -122,7 +125,11 @@ class BGUpscaleWrapper:
         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
+            imgt.shape[3],
+            imgt.shape[2],
+            tile_x=tile,
+            tile_y=tile,
+            overlap=overlap,
         )
 
         log.debug(f"Steps: {steps}")
@@ -147,7 +154,7 @@ class BGUpscaleWrapper:
 import sys
 
 
-class RestoreFace:
+class MTB_RestoreFace:
     """Uses GFPGan to restore faces"""
 
     def __init__(self) -> None:
@@ -176,7 +183,7 @@ class RestoreFace:
     def do_restore(
         self,
         image: torch.Tensor,
-        model: GFPGANer,
+        model,
         aligned,
         only_center_face,
         weight,
@@ -199,10 +206,14 @@ class RestoreFace:
         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)
+            self.save_intermediate_images(
+                cropped_faces, restored_faces, height, width
+            )
         output = None
         if restored_img is not None:
-            output = Image.fromarray(cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB))
+            output = Image.fromarray(
+                cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
+            )
             # imwrite(restored_img, save_restore_path)
 
         return pil2tensor(output)
@@ -210,15 +221,20 @@ class RestoreFace:
     def restore(
         self,
         image: torch.Tensor,
-        model: GFPGANer,
+        model,
         aligned=False,
         only_center_face=False,
         weight=0.5,
         save_tmp_steps=True,
-    ) -> Tuple[torch.Tensor]:
+    ) -> tuple[torch.Tensor]:
         out = [
             self.do_restore(
-                image[i], model, aligned, only_center_face, weight, save_tmp_steps
+                image[i],
+                model,
+                aligned,
+                only_center_face,
+                weight,
+                save_tmp_steps,
             )
             for i in range(image.size(0))
         ]
@@ -240,7 +256,9 @@ class RestoreFace:
 
         return os.path.join(full_output_folder, file)
 
-    def save_intermediate_images(self, cropped_faces, restored_faces, height, width):
+    def save_intermediate_images(
+        self, cropped_faces, restored_faces, height, width
+    ):
         for idx, (cropped_face, restored_face) in enumerate(
             zip(cropped_faces, restored_faces)
         ):
@@ -258,4 +276,4 @@ class RestoreFace:
             cv2.imwrite(file, cmp_img)
 
 
-__nodes__ = [RestoreFace, LoadFaceEnhanceModel]
+__nodes__ = [MTB_RestoreFace, MTB_LoadFaceEnhanceModel]

nodes/faceswap.py

@@ -22,7 +22,7 @@ from ..utils import download_antelopev2, get_model_path, pil2tensor, tensor2pil
 log = mklog(__name__)
 
 
-class LoadFaceAnalysisModel:
+class MTB_LoadFaceAnalysisModel:
     """Loads a face analysis model"""
 
     models = []
@@ -48,18 +48,21 @@ class LoadFaceAnalysisModel:
 
         face_analyser = insightface.app.FaceAnalysis(
             name=faceswap_model,
-            root=get_model_path("insightface"),
+            root=get_model_path("insightface").as_posix(),
         )
         return (face_analyser,)
 
 
-class LoadFaceSwapModel:
+class MTB_LoadFaceSwapModel:
     """Loads a faceswap model"""
 
     @staticmethod
-    def get_models() -> List[Path]:
-        models_path = get_model_path("insightface").iterdir()
-        return [x for x in models_path if x.suffix in [".onnx", ".pth"]]
+    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):
@@ -94,7 +97,7 @@ class LoadFaceSwapModel:
 
 
 # region roop node
-class FaceSwap:
+class MTB_FaceSwap:
     """Face swap using deepinsight/insightface models"""
 
     model = None
@@ -110,7 +113,10 @@ class FaceSwap:
                 "image": ("IMAGE",),
                 "reference": ("IMAGE",),
                 "faces_index": ("STRING", {"default": "0"}),
-                "faceanalysis_model": ("FACE_ANALYSIS_MODEL", {"default": "None"}),
+                "faceanalysis_model": (
+                    "FACE_ANALYSIS_MODEL",
+                    {"default": "None"},
+                ),
                 "faceswap_model": ("FACESWAP_MODEL", {"default": "None"}),
             },
             "optional": {},
@@ -133,10 +139,14 @@ class FaceSwap:
             img = tensor2pil(img)[0]
             ref = tensor2pil(reference)[0]
             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()
             }
             sys.stdout = NullWriter()
-            swapped = swap_face(faceanalysis_model, ref, img, faceswap_model, face_ids)
+            swapped = swap_face(
+                faceanalysis_model, ref, img, faceswap_model, face_ids
+            )
             sys.stdout = sys.__stdout__
             return pil2tensor(swapped)
 
@@ -170,7 +180,10 @@ def get_face_single(
         log.debug("No face ed, trying again with smaller image")
         det_size_half = (det_size[0] // 2, det_size[1] // 2)
         return get_face_single(
-            face_analyser, img_data, face_index=face_index, det_size=det_size_half
+            face_analyser,
+            img_data,
+            face_index=face_index,
+            det_size=det_size_half,
         )
 
     try:
@@ -194,7 +207,9 @@ def swap_face(
     if face_swapper_model is not None:
         cv_source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
         cv_target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
-        source_face = get_face_single(face_analyser, cv_source_img, face_index=0)
+        source_face = get_face_single(
+            face_analyser, cv_source_img, face_index=0
+        )
         if source_face is not None:
             result = cv_target_img
 
@@ -204,12 +219,16 @@ def swap_face(
                 )
                 if target_face is not None:
                     sys.stdout = NullWriter()
-                    result = face_swapper_model.get(result, target_face, source_face)
+                    result = face_swapper_model.get(
+                        result, target_face, source_face
+                    )
                     sys.stdout = sys.__stdout__
                 else:
                     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:
             log.warning("No source face found")
     else:
@@ -220,4 +239,4 @@ def swap_face(
 # endregion face swap utils
 
 
-__nodes__ = [FaceSwap, LoadFaceSwapModel, LoadFaceAnalysisModel]
+__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

@@ -1,11 +1,8 @@
-import threading
-from typing import cast
-
 import qrcode
 from PIL import Image
 
 from ..log import log
-from ..utils import comfy_dir, pil2tensor
+from ..utils import comfy_dir, font_path, pil2tensor
 
 # class MtbExamples:
 #     """MTB Example Images"""
@@ -55,16 +52,25 @@ from ..utils import comfy_dir, pil2tensor
 #         return m.digest().hex()
 
 
-class UnsplashImage:
+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}),
+                "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"}),
@@ -107,7 +113,7 @@ class UnsplashImage:
             return (None,)
 
 
-class QrCode:
+class MTB_QrCode:
     """Basic QR Code generator"""
 
     @classmethod
@@ -124,8 +130,14 @@ 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}),
+                "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}),
             }
         }
@@ -134,7 +146,9 @@ class QrCode:
     FUNCTION = "do_qr"
     CATEGORY = "mtb/generate"
 
-    def do_qr(self, url, width, height, error_correct, box_size, border, invert):
+    def do_qr(
+        self, url, width, height, error_correct, box_size, border, invert
+    ):
         log.warning(
             "This node will soon be deprecated, there are much better alternatives like https://github.com/coreyryanhanson/comfy-qr"
         )
@@ -159,7 +173,9 @@ class QrCode:
         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 = img = qr.make_image(
+            back_color=back_color, fill_color=fill_color
+        )
 
         # that we now resize without filtering
         code = code.resize((width, height), Image.NEAREST)
@@ -174,43 +190,51 @@ def bbox_dim(bbox):
     return width, height
 
 
-class TextToImage:
-    """Utils to convert text to image using a font
+# 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
+        # - 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 = []
+        fonts = [font_path]
 
         for extension in font_extensions:
-            fonts.extend(comfy_dir.glob(f"**/{extension}"))
-
-        if not fonts:
-            log.warn(
-                "> No fonts found in the comfy folder, place at least one font file somewhere in ComfyUI's hierarchy"
-            )
-        else:
-            log.debug(f"> Found {len(fonts)} fonts")
+            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}")
-            TextToImage.fonts[font.stem] = font.as_posix()
+            MTB_TextToImage.fonts[font.stem] = font.as_posix()
 
     @classmethod
     def INPUT_TYPES(cls):
         if not cls.fonts:
-            thread = threading.Thread(target=cls.CACHE_FONTS)
-            thread.start()
+            cls.CACHE_FONTS()
         else:
             log.debug(f"Using cached fonts (count: {len(cls.fonts)})")
         return {
@@ -220,13 +244,15 @@ class TextToImage:
                     {"default": "Hello world!"},
                 ),
                 "font": ((sorted(cls.fonts.keys())),),
-                "wrap": (
-                    "INT",
-                    {"default": 120, "min": 0, "max": 8096, "step": 1},
+                "wrap": ("BOOLEAN", {"default": True}),
+                "trim": ("BOOLEAN", {"default": True}),
+                "line_height": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0, "step": 0.1},
                 ),
                 "font_size": (
                     "INT",
-                    {"default": 12, "min": 1, "max": 2500, "step": 1},
+                    {"default": 32, "min": 1, "max": 2500, "step": 1},
                 ),
                 "width": (
                     "INT",
@@ -246,6 +272,18 @@ class TextToImage:
                 ),
                 "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},
+                ),
             }
         }
 
@@ -256,9 +294,11 @@ class TextToImage:
 
     def text_to_image(
         self,
-        text,
+        text: str,
         font,
         wrap,
+        trim,
+        line_height,
         font_size,
         width,
         height,
@@ -266,6 +306,9 @@ class TextToImage:
         background,
         h_align="left",
         v_align="top",
+        h_offset=0,
+        v_offset=0,
+        h_coverage=100,
     ):
         import textwrap
 
@@ -273,52 +316,56 @@ class TextToImage:
 
         font_path = self.fonts[font]
 
+        text = (
+            text.encode("ascii", "ignore").decode().strip() if trim else text
+        )
         # Handle word wrapping
         if wrap:
-            lines = textwrap.wrap(text, width=wrap)
+            wrap_width = (((width / 100) * h_coverage) / font_size) * 2
+            lines = textwrap.wrap(text, width=wrap_width)
         else:
             lines = [text]
-        font = ImageFont.truetype(font_path, font_size)
-        # font = ImageFont.truetype(font_path, font_size)
-        # if wrap == 0:
-        #     wrap = width / font_size
-
+        font = ImageFont.truetype(font_path, size=font_size)
         log.debug(f"Lines: {lines}")
         img = Image.new("RGBA", (width, height), background)
         draw = ImageDraw.Draw(img)
 
-        text_height = sum(font.getsize(line)[1] for line in lines)
+        line_height_px = line_height * font_size
 
         # Vertical alignment
         if v_align == "top":
-            y_text = 0
+            y_text = v_offset
         elif v_align == "center":
-            y_text = (height - text_height) // 2
+            y_text = ((height - (line_height_px * len(lines))) // 2) + v_offset
         else:  # bottom
-            y_text = height - text_height
+            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)
 
         # Draw each line of text
         for line in lines:
-            line_width, line_height = font.getsize(line)
-
+            line_width = get_width(line)
             # Horizontal alignment
             if h_align == "left":
-                x_text = 0
+                x_text = h_offset
             elif h_align == "center":
-                x_text = (width - line_width) // 2
+                x_text = ((width - line_width) // 2) + h_offset
             else:  # right
-                x_text = width - line_width
+                x_text = (width - line_width) - h_offset
 
-            draw.text((x_text, y_text), line, color, font=font)
-            y_text += line_height
+            draw.text((x_text, y_text), line, fill=color, font=font)
+            y_text += line_height_px
 
-        # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png'))
         return (pil2tensor(img),)
 
 
 __nodes__ = [
-    QrCode,
-    UnsplashImage,
-    TextToImage
+    MTB_QrCode,
+    MTB_UnsplashImage,
+    MTB_TextToImage,
     #  MtbExamples,
 ]

nodes/graph_utils.py

@@ -1,11 +1,26 @@
-import io, json, urllib.parse, urllib.request
+import io
+import json
+import urllib.parse
+import urllib.request
+from math import pi
+from typing import Optional
 
+import comfy.model_management as model_management
+import comfy.utils
 import numpy as np
 import torch
+import torchvision.transforms.functional as F
 from PIL import Image
 
 from ..log import log
-from ..utils import apply_easing, get_server_info, pil2tensor
+from ..utils import (
+    EASINGS,
+    apply_easing,
+    get_server_info,
+    numpy_NFOV,
+    pil2tensor,
+    tensor2np,
+)
 
 
 def get_image(filename, subfolder, folder_type):
@@ -22,10 +37,296 @@ def get_image(filename, subfolder, folder_type):
         return io.BytesIO(response.read())
 
 
-class GetBatchFromHistory:
+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")
+            for i in range(torch.cuda.device_count()):
+                devices.append(f"cuda{i}")
+
+        return {
+            "required": {
+                "ignore_errors": ("BOOLEAN", {"default": False}),
+                "device": (devices, {"default": "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=False,
+        device="cuda",
+        image: Optional[torch.Tensor] = None,
+        mask: Optional[torch.Tensor] = 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 image is not None:
+            image = image.to(device)
+        if mask is not None:
+            mask = mask.to(device)
+        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": {
+                "template": ("STRING", {"default": "", "multiline": True}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("string",)
+    CATEGORY = "mtb/utils"
+    FUNCTION = "execute"
+
+    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
+    ):
+        _batch_size, height, width, _channels = source.shape
+        _rbatch_size, rheight, rwidth, _rchannels = reference.shape
+
+        source_aspect_ratio = width / height
+        # reference_aspect_ratio = rwidth / rheight
+
+        source = source.permute(0, 3, 1, 2)
+        reference = reference.permute(0, 3, 1, 2)
+
+        if match == "height":
+            new_height = rheight
+            new_width = int(rheight * source_aspect_ratio)
+        else:
+            new_width = rwidth
+            new_height = int(rwidth / source_aspect_ratio)
+
+        resized_images = [
+            F.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."""
+    Queue items without output are ignored in the count.
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -48,6 +349,7 @@ class GetBatchFromHistory:
 
     def load_from_history(
         self,
+        *,
         enable=True,
         count=0,
         offset=0,
@@ -85,7 +387,9 @@ class GetBatchFromHistory:
                 if "images" in node_output:
                     for image in node_output["images"]:
                         image_data = get_image(
-                            image["filename"], image["subfolder"], image["type"]
+                            image["filename"],
+                            image["subfolder"],
+                            image["type"],
                         )
                         output_images.append(image_data)
 
@@ -107,8 +411,8 @@ class GetBatchFromHistory:
         return pil2tensor(frames)
 
 
-class AnyToString:
-    """Tries to take any input and convert it to a string"""
+class MTB_AnyToString:
+    """Tries to take any input and convert it to a string."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -128,18 +432,22 @@ class AnyToString:
         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}",)
+            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()}",)
+            return (
+                f"Dictionary of {len(input)} items, with keys {input.keys()}",
+            )
 
         else:
             log.debug(f"Falling back to string conversion of {input}")
             return (str(input),)
 
 
-class StringReplace:
-    """Basic string replacement"""
+class MTB_StringReplace:
+    """Basic string replacement."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -182,10 +490,11 @@ class MTB_MathExpression:
     RETURN_TYPES = ("FLOAT", "INT")
     RETURN_NAMES = ("result (float)", "result (int)")
     CATEGORY = "mtb/math"
-    DESCRIPTION = "evaluate a simple math expression string (!! Fallsback to eval)"
+    DESCRIPTION = (
+        "evaluate a simple math expression string (!! Fallsback to eval)"
+    )
 
     def eval_expression(self, expression, **kwargs):
-        import math
         from ast import literal_eval
 
         for key, value in kwargs.items():
@@ -213,7 +522,7 @@ class MTB_MathExpression:
         return (result, int(result))
 
 
-class FitNumber:
+class MTB_FitNumber:
     """Fit the input float using a source and target range"""
 
     @classmethod
@@ -227,30 +536,7 @@ class FitNumber:
                 "target_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
                 "target_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
                 "easing": (
-                    [
-                        "Linear",
-                        "Sine In",
-                        "Sine Out",
-                        "Sine In/Out",
-                        "Quart In",
-                        "Quart Out",
-                        "Quart In/Out",
-                        "Cubic In",
-                        "Cubic Out",
-                        "Cubic In/Out",
-                        "Circ In",
-                        "Circ Out",
-                        "Circ In/Out",
-                        "Back In",
-                        "Back Out",
-                        "Back In/Out",
-                        "Elastic In",
-                        "Elastic Out",
-                        "Elastic In/Out",
-                        "Bounce In",
-                        "Bounce Out",
-                        "Bounce In/Out",
-                    ],
+                    EASINGS,
                     {"default": "Linear"},
                 ),
             }
@@ -286,8 +572,8 @@ class FitNumber:
         return (res,)
 
 
-class ConcatImages:
-    """Add images to batch"""
+class MTB_ConcatImages:
+    """Add images to batch."""
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "concatenate_tensors"
@@ -314,10 +600,17 @@ class ConcatImages:
 
 
 __nodes__ = [
-    StringReplace,
-    FitNumber,
-    GetBatchFromHistory,
-    AnyToString,
-    ConcatImages,
+    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,
 ]

nodes/image_interpolation.py

@@ -1,12 +1,9 @@
-import glob
-import os
 from pathlib import Path
 from typing import List
 
 import comfy
 import comfy.model_management as model_management
 import comfy.utils
-import folder_paths
 import numpy as np
 import tensorflow as tf
 import torch
@@ -17,7 +14,7 @@ from ..log import log
 from ..utils import get_model_path
 
 
-class LoadFilmModel:
+class MTB_LoadFilmModel:
     """Loads a FILM model"""
 
     @staticmethod
@@ -58,7 +55,7 @@ class LoadFilmModel:
         return (interpolator.Interpolator(model_path.as_posix(), None),)
 
 
-class FilmInterpolation:
+class MTB_FilmInterpolation:
     """Google Research FILM frame interpolation for large motion"""
 
     @classmethod
@@ -107,12 +104,16 @@ class FilmInterpolation:
             in_frames, interpolate, film_model
         ):
             out_tensors.append(
-                torch.from_numpy(frame) if isinstance(frame, np.ndarray) else frame
+                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)
+        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}")
@@ -120,4 +121,4 @@ class FilmInterpolation:
         return (out_tensors,)
 
 
-__nodes__ = [LoadFilmModel, FilmInterpolation]
+__nodes__ = [MTB_LoadFilmModel, MTB_FilmInterpolation]

nodes/image_processing.py

@@ -3,18 +3,17 @@ import json
 import math
 import os
 
-import cv2
 import folder_paths
 import numpy as np
 import torch
 import torch.nn.functional as F
-from PIL import Image
+from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from skimage.filters import gaussian
 from skimage.util import compare_images
 
 from ..log import log
-from ..utils import pil2tensor, tensor2np, tensor2pil
+from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
 
 # try:
 #     from cv2.ximgproc import guidedFilter
@@ -22,7 +21,9 @@ from ..utils import pil2tensor, tensor2np, tensor2pil
 #     log.warning("cv2.ximgproc.guidedFilter not found, use opencv-contrib-python")
 
 
-def gaussian_kernel(kernel_size: int, sigma_x: float, sigma_y: float, device=None):
+def gaussian_kernel(
+    kernel_size: int, sigma_x: float, sigma_y: float, device=None
+):
     x, y = torch.meshgrid(
         torch.linspace(-1, 1, kernel_size, device=device),
         torch.linspace(-1, 1, kernel_size, device=device),
@@ -34,7 +35,7 @@ def gaussian_kernel(kernel_size: int, sigma_x: float, sigma_y: float, device=Non
     return g / g.sum()
 
 
-class ColorCorrect:
+class MTB_ColorCorrect:
     """Various color correction methods"""
 
     @classmethod
@@ -85,7 +86,14 @@ class ColorCorrect:
 
     @staticmethod
     def contrast_adjustment_tensor(image, contrast):
-        contrasted = (image - 0.5) * contrast + 0.5
+        r, g, b = image.unbind(-1)
+        
+        # Using Adobe RGB luminance weights.
+        luminance_image = 0.33 * r + 0.71 * g + 0.06 * b
+        luminance_mean = torch.mean(luminance_image.unsqueeze(-1))
+
+        # Blend original with mean luminance using contrast factor as blend ratio.
+        contrasted = image * contrast + (1.0 - contrast) * luminance_mean        
         return torch.clamp(contrasted, 0.0, 1.0)
 
     @staticmethod
@@ -115,7 +123,9 @@ class ColorCorrect:
         return pil2tensor(out)
 
     @staticmethod
-    def hsv_adjustment_tensor_not_working(image: torch.Tensor, hue, saturation, value):
+    def hsv_adjustment_tensor_not_working(
+        image: torch.Tensor, hue, saturation, value
+    ):
         """Abandonning for now"""
         image = image.squeeze(0).permute(2, 0, 1)
 
@@ -192,7 +202,7 @@ class ColorCorrect:
         return (image,)
 
 
-class ImageCompare_:
+class MTB_ImageCompare:
     """Compare two images and return a difference image"""
 
     @classmethod
@@ -213,22 +223,61 @@ class ImageCompare_:
     CATEGORY = "mtb/image"
 
     def compare(self, imageA: torch.Tensor, imageB: torch.Tensor, mode):
-        imageA = imageA.numpy()
-        imageB = imageB.numpy()
+        if imageA.dim() == 4:
+            batch_count = imageA.size(0)
+            return (
+                torch.cat(
+                    tuple(
+                        self.compare(imageA[i], imageB[i], mode)[0]
+                        for i in range(batch_count)
+                    ),
+                    dim=0,
+                ),
+            )
 
-        imageA = imageA.squeeze()
-        imageB = imageB.squeeze()
+        num_channels_A = imageA.size(2)
+        num_channels_B = imageB.size(2)
 
-        image = compare_images(imageA, imageB, method=mode)
+        # handle RGBA/RGB mismatch
+        if num_channels_A == 3 and num_channels_B == 4:
+            imageA = torch.cat(
+                (imageA, torch.ones_like(imageA[:, :, 0:1])), dim=2
+            )
+        elif num_channels_B == 3 and num_channels_A == 4:
+            imageB = torch.cat(
+                (imageB, torch.ones_like(imageB[:, :, 0:1])), dim=2
+            )
+        match mode:
+            case "diff":
+                compare_image = torch.abs(imageA - imageB)
+            case "blend":
+                compare_image = 0.5 * (imageA + imageB)
+            case "checkerboard":
+                imageA = imageA.numpy()
+                imageB = imageB.numpy()
+                compared_channels = [
+                    torch.from_numpy(
+                        compare_images(
+                            imageA[:, :, i], imageB[:, :, i], method=mode
+                        )
+                    )
+                    for i in range(imageA.shape[2])
+                ]
 
-        image = np.expand_dims(image, axis=0)
-        return (torch.from_numpy(image),)
+                compare_image = torch.stack(compared_channels, dim=2)
+            case _:
+                compare_image = None
+                raise ValueError(f"Unknown mode {mode}")
+
+        compare_image = compare_image.unsqueeze(0)
+
+        return (compare_image,)
 
 
 import requests
 
 
-class LoadImageFromUrl_:
+class MTB_LoadImageFromUrl:
     """Load an image from the given URL"""
 
     @classmethod
@@ -251,10 +300,11 @@ class LoadImageFromUrl_:
     def load(self, url):
         # get the image from the url
         image = Image.open(requests.get(url, stream=True).raw)
+        image = ImageOps.exif_transpose(image)
         return (pil2tensor(image),)
 
 
-class Blur_:
+class MTB_Blur:
     """Blur an image using a Gaussian filter."""
 
     @classmethod
@@ -264,28 +314,55 @@ class Blur_:
                 "image": ("IMAGE",),
                 "sigmaX": (
                     "FLOAT",
-                    {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.01},
+                    {"default": 3.0, "min": 0.0, "max": 200.0, "step": 0.01},
                 ),
                 "sigmaY": (
                     "FLOAT",
-                    {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.01},
+                    {"default": 3.0, "min": 0.0, "max": 200.0, "step": 0.01},
                 ),
-            }
+            },
+            "optional": {"sigmasX": ("FLOATS",), "sigmasY": ("FLOATS",)},
         }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "blur"
     CATEGORY = "mtb/image processing"
 
-    def blur(self, image: torch.Tensor, sigmaX, sigmaY):
-        image = image.numpy()
-        image = image.transpose(1, 2, 3, 0)
-        image = gaussian(image, sigma=(sigmaX, sigmaY, 0, 0))
-        image = image.transpose(3, 0, 1, 2)
-        return (torch.from_numpy(image),)
+    def blur(
+        self, image: torch.Tensor, sigmaX, sigmaY, sigmasX=None, sigmasY=None
+    ):
+        image_np = image.numpy() * 255
+
+        blurred_images = []
+        if sigmasX is not None:
+            if sigmasY is None:
+                sigmasY = sigmasX
+            if len(sigmasX) != image.size(0):
+                raise ValueError(
+                    f"SigmasX must have same length as image, sigmasX is {len(sigmasX)} but the batch size is {image.size(0)}"
+                )
+
+            for i in range(image.size(0)):
+                blurred = gaussian(
+                    image_np[i],
+                    sigma=(sigmasX[i], sigmasY[i], 0),
+                    channel_axis=2,
+                )
+                blurred_images.append(blurred)
+
+            image_np = np.array(blurred_images)
+        else:
+            for i in range(image.size(0)):
+                blurred = gaussian(
+                    image_np[i], sigma=(sigmaX, sigmaY, 0), channel_axis=2
+                )
+                blurred_images.append(blurred)
+
+            image_np = np.array(blurred_images)
+        return (np2tensor(image_np).squeeze(0),)
 
 
-class Sharpen_:
+class MTB_Sharpen:
     """Sharpens an image using a Gaussian kernel."""
 
     @classmethod
@@ -344,11 +421,16 @@ class Sharpen_:
             (sharpen_radius, sharpen_radius, sharpen_radius, sharpen_radius),
             "reflect",
         )
-        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
+        sharpened = F.conv2d(
+            tensor_image, kernel, padding=center, groups=channels
+        )
 
         # Remove padding
         sharpened = sharpened[
-            :, :, sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius
+            :,
+            :,
+            sharpen_radius:-sharpen_radius,
+            sharpen_radius:-sharpen_radius,
         ]
 
         sharpened = sharpened.permute(0, 2, 3, 1)
@@ -383,7 +465,7 @@ class Sharpen_:
 #         return (np2tensor(deglaze_np_img(tensor2np(image))),)
 
 
-class MaskToImage:
+class MTB_MaskToImage:
     """Converts a mask (alpha) to an RGB image with a color and background"""
 
     @classmethod
@@ -403,12 +485,14 @@ class MaskToImage:
     FUNCTION = "render_mask"
 
     def render_mask(self, mask, color, background):
-        masks = tensor2np(mask)
+        masks = tensor2np(mask)[0]
         images = []
         for m in masks:
             _mask = Image.fromarray(m).convert("L")
 
-            log.debug(f"Converted mask to PIL Image format, size: {_mask.size}")
+            log.debug(
+                f"Converted mask to PIL Image format, size: {_mask.size}"
+            )
 
             image = Image.new("RGBA", _mask.size, color=color)
             # apply the mask
@@ -425,8 +509,8 @@ class MaskToImage:
         return (pil2tensor(images),)
 
 
-class ColoredImage:
-    """Constant color image of given size"""
+class MTB_ColoredImage:
+    """Constant color image of given size."""
 
     def __init__(self) -> None:
         pass
@@ -451,49 +535,101 @@ class ColoredImage:
 
     FUNCTION = "render_img"
 
+    def resize_and_crop(self, img, target_size):
+        # Calculate scaling factors for both dimensions
+        scale_x = target_size[0] / img.width
+        scale_y = target_size[1] / img.height
+
+        # Use the smaller scaling factor to maintain aspect ratio
+        scale = max(scale_x, scale_y)
+
+        # Resize the image based on calculated scale
+        new_size = (int(img.width * scale), int(img.height * scale))
+        img = img.resize(new_size, Image.LANCZOS)
+
+        # Calculate cropping coordinates
+        left = (img.width - target_size[0]) / 2
+        top = (img.height - target_size[1]) / 2
+        right = (img.width + target_size[0]) / 2
+        bottom = (img.height + target_size[1]) / 2
+
+        # Crop and return the image
+        return img.crop((left, top, right, bottom))
+
+    def resize_and_crop_thumbnails(self, img, target_size):
+        img.thumbnail(target_size, Image.LANCZOS)
+        left = (img.width - target_size[0]) / 2
+        top = (img.height - target_size[1]) / 2
+        right = (img.width + target_size[0]) / 2
+        bottom = (img.height + target_size[1]) / 2
+        return img.crop((left, top, right, bottom))
+
     def render_img(
-        self, color, width, height, foreground_image=None, foreground_mask=None
+        self,
+        color,
+        width,
+        height,
+        foreground_image: torch.Tensor | None = None,
+        foreground_mask: torch.Tensor | None = None,
     ):
         image = Image.new("RGBA", (width, height), color=color)
         output = []
         if foreground_image is not None:
-            if foreground_mask is None:
-                fg_images = tensor2pil(foreground_image)
-                for img in fg_images:
-                    if image.size != img.size:
+            fg_masks = [None] * foreground_image.size()[0]
+
+            if foreground_mask is not None:
+                fg_size = foreground_image.size()[0]
+                mask_size = foreground_mask.size()[0]
+
+                if fg_size == 1 and mask_size > fg_size:
+                    foreground_image = foreground_image.repeat(
+                        mask_size, 1, 1, 1
+                    )
+
+                if foreground_image.size()[0] != foreground_mask.size()[0]:
+                    raise ValueError(
+                        "Foreground image and mask must have same batch size"
+                    )
+                fg_masks = tensor2pil(foreground_mask.unsqueeze(-1))
+
+            fg_images = tensor2pil(foreground_image)
+
+            for fg_image, fg_mask in zip(fg_images, fg_masks):
+                # Resize and crop if dimensions mismatch
+                if fg_image.size != image.size:
+                    fg_image = self.resize_and_crop(fg_image, image.size)
+                    if fg_mask:
+                        fg_mask = self.resize_and_crop(fg_mask, image.size)
+
+                if fg_mask:
+                    output.append(
+                        Image.composite(
+                            fg_image.convert("RGBA"),
+                            image,
+                            fg_mask,
+                        ).convert("RGB")
+                    )
+                else:
+                    if fg_image.mode != "RGBA":
                         raise ValueError(
-                            f"Dimension mismatch: image {image.size}, img {img.size}"
+                            "Foreground image must be in 'RGBA' mode "
+                            f"when no mask is provided, got {fg_image.mode}"
                         )
+                    output.append(
+                        Image.alpha_composite(image, fg_image).convert("RGB")
+                    )
 
-                    if img.mode != "RGBA":
-                        raise ValueError(
-                            f"Foreground image must be in 'RGBA' mode when no mask is provided, got {img.mode}"
-                        )
-
-                    output.append(Image.alpha_composite(image, img).convert("RGB"))
-
-            elif foreground_image.size[0] != foreground_mask.size[0]:
-                raise ValueError("Foreground image and mask must have same batch size")
-            else:
-                fg_images = tensor2pil(foreground_image)
-                fg_masks = tensor2pil(foreground_mask)
-                output.extend(
-                    Image.composite(
-                        fg_image.convert("RGBA"),
-                        image,
-                        fg_mask,
-                    ).convert("RGB")
-                    for fg_image, fg_mask in zip(fg_images, fg_masks)
-                )
-        elif foreground_mask is not None:
-            log.warn("Mask ignored because no foreground image is given")
+        else:
+            if foreground_mask is not None:
+                log.warn("Mask ignored because no foreground image is given")
+            output.append(image.convert("RGB"))
 
         output = pil2tensor(output)
 
         return (output,)
 
 
-class ImagePremultiply:
+class MTB_ImagePremultiply:
     """Premultiply image with mask"""
 
     @classmethod
@@ -532,7 +668,7 @@ class ImagePremultiply:
         return (pil2tensor(out),)
 
 
-class ImageResizeFactor:
+class MTB_ImageResizeFactor:
     """Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features."""
 
     @classmethod
@@ -577,7 +713,9 @@ class ImageResizeFactor:
     ):
         # Check if the tensor has the correct dimension
         if len(image.shape) not in [3, 4]:  # HxWxC or BxHxWxC
-            raise ValueError("Expected image tensor of shape (H, W, C) or (B, H, W, C)")
+            raise ValueError(
+                "Expected image tensor of shape (H, W, C) or (B, H, W, C)"
+            )
 
         # Transpose to CxHxW or BxCxHxW for PyTorch
         if len(image.shape) == 3:
@@ -624,7 +762,7 @@ class ImageResizeFactor:
         return (resized_image,)
 
 
-class SaveImageGrid_:
+class MTB_SaveImageGrid:
     """Save all the images in the input batch as a grid of images."""
 
     def __init__(self):
@@ -689,7 +827,10 @@ class SaveImageGrid_:
             subfolder,
             filename_prefix,
         ) = folder_paths.get_save_image_path(
-            filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+            filename_prefix,
+            self.output_dir,
+            images[0].shape[1],
+            images[0].shape[0],
         )
         image_list = []
         batch_counter = counter
@@ -719,14 +860,18 @@ class SaveImageGrid_:
         file = f"{filename}_{counter:05}_.png"
         grid = self.create_image_grid(image_list)
         grid.save(
-            os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4
+            os.path.join(full_output_folder, file),
+            pnginfo=metadata,
+            compress_level=4,
         )
 
-        results = [{"filename": file, "subfolder": subfolder, "type": self.type}]
+        results = [
+            {"filename": file, "subfolder": subfolder, "type": self.type}
+        ]
         return {"ui": {"images": results}}
 
 
-class ImageTileOffset:
+class MTB_ImageTileOffset:
     """Mimics an old photoshop technique to check for seamless textures"""
 
     @classmethod
@@ -734,7 +879,8 @@ class ImageTileOffset:
         return {
             "required": {
                 "image": ("IMAGE",),
-                "tiles": ("INT", {"default": 2}),
+                "tilesX": ("INT", {"default": 2, "min": 1}),
+                "tilesY": ("INT", {"default": 2, "min": 1}),
             }
         }
 
@@ -744,17 +890,19 @@ class ImageTileOffset:
 
     FUNCTION = "tile_image"
 
-    def tile_image(self, image: torch.Tensor, tiles: int = 2):
-        if tiles < 1:
+    def tile_image(
+        self, image: torch.Tensor, tilesX: int = 2, tilesY: int = 2
+    ):
+        if tilesX < 1 or tilesY < 1:
             raise ValueError("The number of tiles must be at least 1.")
 
         batch_size, height, width, channels = image.shape
-        tile_height = height // tiles
-        tile_width = width // tiles
+        tile_height = height // tilesY
+        tile_width = width // tilesX
 
         output_image = torch.zeros_like(image)
 
-        for i, j in itertools.product(range(tiles), range(tiles)):
+        for i, j in itertools.product(range(tilesY), range(tilesX)):
             start_h = i * tile_height
             end_h = start_h + tile_height
             start_w = j * tile_width
@@ -762,8 +910,8 @@ class ImageTileOffset:
 
             tile = image[:, start_h:end_h, start_w:end_w, :]
 
-            output_start_h = (i + 1) % tiles * tile_height
-            output_start_w = (j + 1) % tiles * tile_width
+            output_start_h = (i + 1) % tilesY * tile_height
+            output_start_w = (j + 1) % tilesX * tile_width
             output_end_h = output_start_h + tile_height
             output_end_w = output_start_w + tile_width
 
@@ -775,16 +923,16 @@ class ImageTileOffset:
 
 
 __nodes__ = [
-    ColorCorrect,
-    ImageCompare_,
-    ImageTileOffset,
-    Blur_,
+    MTB_ColorCorrect,
+    MTB_ImageCompare,
+    MTB_ImageTileOffset,
+    MTB_Blur,
     # DeglazeImage,
-    MaskToImage,
-    ColoredImage,
-    ImagePremultiply,
-    ImageResizeFactor,
-    SaveImageGrid_,
-    LoadImageFromUrl_,
-    Sharpen_,
+    MTB_MaskToImage,
+    MTB_ColoredImage,
+    MTB_ImagePremultiply,
+    MTB_ImageResizeFactor,
+    MTB_SaveImageGrid,
+    MTB_LoadImageFromUrl,
+    MTB_Sharpen,
 ]

nodes/image_utils.py

@@ -3,8 +3,8 @@ import torch
 from ..log import log
 
 
-class StackImages:
-    """Stack the input images horizontally or vertically"""
+class MTB_StackImages:
+    """Stack the input images horizontally or vertically."""
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -20,26 +20,79 @@ class StackImages:
 
         tensors = list(kwargs.values())
         log.debug(
-            f"Stacking {len(tensors)} tensors {'vertically' if vertical else 'horizontally'}"
+            f"Stacking {len(tensors)} tensors "
+            f"{'vertically' if vertical else 'horizontally'}"
         )
-        log.debug(list(kwargs.keys()))
 
-        ref_shape = tensors[0].shape
-        for tensor in tensors[1:]:
-            if tensor.shape[1:] != ref_shape[1:]:
+        normalized_tensors = [
+            self.normalize_to_rgba(tensor) 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 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 dimensions except for the stacking dimension."
+                    "All tensors must have the same width "
+                    "for vertical stacking."
                 )
+            dim = 1
+        else:
+            height = normalized_tensors[0].shape[1]
+            if any(tensor.shape[1] != height for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same height "
+                    "for horizontal stacking."
+                )
+            dim = 2
 
-        dim = 1 if vertical else 2
-
-        stacked_tensor = torch.cat(tensors, dim=dim)
+        stacked_tensor = torch.cat(normalized_tensors, dim=dim)
 
         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
+            )  # Add an alpha channel
+            return torch.cat((tensor, alpha_channel), dim=-1)
+        else:
+            raise ValueError(
+                "Tensor has an unsupported number of channels: "
+                "expected 3 (RGB) or 4 (RGBA)."
+            )
 
-class PickFromBatch:
-    """Pick a specific number of images from a batch, either from the start or end."""
+    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
+
+
+class MTB_PickFromBatch:
+    """Pick a specific number of images from a batch.
+
+    either from the start or end.
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -62,7 +115,8 @@ class PickFromBatch:
         count = min(count, batch_size)
         if count < batch_size:
             log.warning(
-                f"Requested {count} images, but only {batch_size} are available."
+                f"Requested {count} images, "
+                f"but only {batch_size} are available."
             )
 
         if from_direction == "end":
@@ -73,4 +127,4 @@ class PickFromBatch:
         return (selected_tensors,)
 
 
-__nodes__ = [StackImages, PickFromBatch]
+__nodes__ = [MTB_StackImages, MTB_PickFromBatch]

nodes/io.py

@@ -1,4 +1,6 @@
-import json, subprocess, uuid
+import json
+import subprocess
+import uuid
 from pathlib import Path
 from typing import List, Optional
 
@@ -19,7 +21,7 @@ def get_playlist_path(playlist_name: str, persistant_playlist=False):
     return output_dir / "playlists" / session_id / f"{playlist_name}.json"
 
 
-class ReadPlaylist:
+class MTB_ReadPlaylist:
     """Read a playlist"""
 
     @classmethod
@@ -28,7 +30,10 @@ class ReadPlaylist:
             "required": {
                 "enable": ("BOOLEAN", {"default": True}),
                 "persistant_playlist": ("BOOLEAN", {"default": False}),
-                "playlist_name": ("STRING", {"default": "playlist_{index:04d}"}),
+                "playlist_name": (
+                    "STRING",
+                    {"default": "playlist_{index:04d}"},
+                ),
                 "index": ("INT", {"default": 0, "min": 0}),
             }
         }
@@ -38,7 +43,11 @@ class ReadPlaylist:
     CATEGORY = "mtb/IO"
 
     def read_playlist(
-        self, enable: bool, persistant_playlist: bool, playlist_name: str, index: int
+        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)
@@ -53,7 +62,7 @@ class ReadPlaylist:
         return (json.loads(playlist_path.read_text(encoding="utf-8")),)
 
 
-class AddToPlaylist:
+class MTB_AddToPlaylist:
     """Add a video to the playlist"""
 
     @classmethod
@@ -62,7 +71,10 @@ class AddToPlaylist:
             "required": {
                 "relative_paths": ("BOOLEAN", {"default": False}),
                 "persistant_playlist": ("BOOLEAN", {"default": False}),
-                "playlist_name": ("STRING", {"default": "playlist_{index:04d}"}),
+                "playlist_name": (
+                    "STRING",
+                    {"default": "playlist_{index:04d}"},
+                ),
                 "index": ("INT", {"default": 0, "min": 0}),
             }
         }
@@ -104,7 +116,7 @@ class AddToPlaylist:
         return ()
 
 
-class ExportWithFfmpeg:
+class MTB_ExportWithFfmpeg:
     """Export with FFmpeg (Experimental)"""
 
     @classmethod
@@ -115,12 +127,14 @@ class ExportWithFfmpeg:
                 "playlist": ("PLAYLIST",),
             },
             "required": {
-                # "frames": ("FRAMES",),
                 "fps": ("FLOAT", {"default": 24, "min": 1}),
                 "prefix": ("STRING", {"default": "export"}),
-                "format": (["mov", "mp4", "mkv", "avi"], {"default": "mov"}),
+                "format": (
+                    ["mov", "mp4", "mkv", "gif", "avi"],
+                    {"default": "mov"},
+                ),
                 "codec": (
-                    ["prores_ks", "libx264", "libx265"],
+                    ["prores_ks", "libx264", "libx265", "gif"],
                     {"default": "prores_ks"},
                 ),
             },
@@ -152,7 +166,9 @@ class ExportWithFfmpeg:
                 log.debug("Playlist is empty, skipping")
                 return ("",)
 
-            temp_playlist_path = output_dir / f"temp_playlist_{uuid.uuid4()}.txt"
+            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}"
             )
@@ -193,7 +209,32 @@ class ExportWithFfmpeg:
         log.debug(f"Frames type {type(frames[0])}")
         log.debug(f"Exporting {len(frames)} frames")
 
-        frames = [frame.astype(np.uint16) * 257 for frame in frames]
+        if codec == "gif":
+            out_path = (output_dir / file_id).as_posix()
+            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()
+        else:
+            frames = [frame.astype(np.uint16) * 257 for frame in frames]
 
         height, width, _ = frames[0].shape
 
@@ -266,7 +307,7 @@ def prepare_animated_batch(
 
 
 # todo: deprecate for apng
-class SaveGif:
+class MTB_SaveGif:
     """Save the images from the batch as a GIF"""
 
     @classmethod
@@ -278,9 +319,8 @@ class SaveGif:
                 "resize_by": ("FLOAT", {"default": 1.0, "min": 0.1}),
                 "optimize": ("BOOLEAN", {"default": False}),
                 "pingpong": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
                 "resample_filter": (list(PIL_FILTER_MAP.keys()),),
+                "use_ffmpeg": ("BOOLEAN", {"default": False}),
             },
         }
 
@@ -297,6 +337,7 @@ class SaveGif:
         optimize=False,
         pingpong=False,
         resample_filter=None,
+        use_ffmpeg=False,
     ):
         if image.size(0) == 0:
             return ("",)
@@ -315,18 +356,48 @@ class SaveGif:
         ruuid = ruuid.hex[:10]
         out_path = f"{folder_paths.output_directory}/{ruuid}.gif"
 
-        # Create the GIF from PIL images
-        pil_images[0].save(
-            out_path,
-            save_all=True,
-            append_images=pil_images[1:],
-            optimize=optimize,
-            duration=int(1000 / fps),
-            loop=0,
-        )
+        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()
 
-        results = [{"filename": f"{ruuid}.gif", "subfolder": "", "type": "output"}]
+        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__ = [SaveGif, ExportWithFfmpeg, AddToPlaylist, ReadPlaylist]
+__nodes__ = [
+    MTB_SaveGif,
+    MTB_ExportWithFfmpeg,
+    MTB_AddToPlaylist,
+    MTB_ReadPlaylist,
+]

nodes/latent_processing.py

@@ -1,7 +1,7 @@
 import torch
 
 
-class LatentLerp:
+class MTB_LatentLerp:
     """Linear interpolation (blend) between two latent vectors"""
 
     @classmethod
@@ -10,7 +10,10 @@ 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},
+                ),
             }
         }
 
@@ -29,5 +32,5 @@ class LatentLerp:
 
 
 __nodes__ = [
-    LatentLerp,
+    MTB_LatentLerp,
 ]

nodes/mask.py

@@ -5,7 +5,7 @@ from rembg import remove
 from ..utils import pil2tensor, tensor2pil
 
 
-class ImageRemoveBackgroundRembg:
+class MTB_ImageRemoveBackgroundRembg:
     """Removes the background from the input using Rembg."""
 
     @classmethod
@@ -100,9 +100,13 @@ class ImageRemoveBackgroundRembg:
 
             pbar.update(1)
 
-        return (pil2tensor(out_img), pil2tensor(out_mask), pil2tensor(out_img_on_bg))
+        return (
+            pil2tensor(out_img),
+            pil2tensor(out_mask),
+            pil2tensor(out_img_on_bg),
+        )
 
 
 __nodes__ = [
-    ImageRemoveBackgroundRembg,
+    MTB_ImageRemoveBackgroundRembg,
 ]

nodes/model.py

@@ -1,11 +1,13 @@
 import copy
 
 import torch
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair
 
 from ..log import log
 
 
-class VaeDecode_:
+class MTB_VaeDecode:
     """Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"""
 
     @classmethod
@@ -29,7 +31,12 @@ class VaeDecode_:
     CATEGORY = "mtb/decode"
 
     def decode(
-        self, vae, samples, seamless_model, use_tiling_decoder=True, tile_size=512
+        self,
+        vae,
+        samples,
+        seamless_model,
+        use_tiling_decoder=True,
+        tile_size=512,
     ):
         if seamless_model:
             if use_tiling_decoder:
@@ -55,7 +62,25 @@ class VaeDecode_:
             return (vae.decode(samples["samples"]),)
 
 
-class ModelPatchSeamless:
+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
@@ -63,10 +88,16 @@ class ModelPatchSeamless:
         return {
             "required": {
                 "model": ("MODEL",),
-                "tiling": (
+                "startStep": ("INT", {"default": 0}),
+                "stopStep": ("INT", {"default": 999}),
+                "tilingX": (
                     "BOOLEAN",
                     {"default": True},
-                ),  # kept for testing not sure why it should be false
+                ),
+                "tilingY": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
             }
         }
 
@@ -79,21 +110,46 @@ class ModelPatchSeamless:
 
     CATEGORY = "mtb/textures"
 
-    def apply_circular(self, model, enable):
+    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
+            for layer in model.modules()
+            if isinstance(layer, torch.nn.Conv2d)
         ]:
-            layer.padding_mode = "circular" if enable else "zeros"
+            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,
-        tiling,
+        startStep,
+        stopStep,
+        tilingX,
+        tilingY,
     ):
         hacked_model = copy.deepcopy(model)
-        self.apply_circular(hacked_model.model, tiling)
+        self.apply_circular(
+            hacked_model.model, startStep, stopStep, tilingX, tilingY
+        )
         return (model, hacked_model)
 
 
-__nodes__ = [ModelPatchSeamless, VaeDecode_]
+__nodes__ = [MTB_ModelPatchSeamless, MTB_VaeDecode]

nodes/number.py

@@ -1,4 +1,4 @@
-class IntToBool:
+class MTB_IntToBool:
     """Basic int to bool conversion"""
 
     @classmethod
@@ -22,7 +22,7 @@ class IntToBool:
         return (bool(int),)
 
 
-class IntToNumber:
+class MTB_IntToNumber:
     """Node addon for the WAS Suite. Converts a "comfy" INT to a NUMBER."""
 
     @classmethod
@@ -50,7 +50,7 @@ class IntToNumber:
         return (int,)
 
 
-class FloatToNumber:
+class MTB_FloatToNumber:
     """Node addon for the WAS Suite. Converts a "comfy" FLOAT to a NUMBER."""
 
     @classmethod
@@ -77,11 +77,9 @@ class FloatToNumber:
     def float_to_number(self, float):
         return (float,)
 
-        return (int,)
-
 
 __nodes__ = [
-    FloatToNumber,
-    IntToBool,
-    IntToNumber,
+    MTB_FloatToNumber,
+    MTB_IntToBool,
+    MTB_IntToNumber,
 ]

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/transform.py

@@ -1,15 +1,16 @@
+from math import ceil, sqrt
+from typing import cast
+
 import torch
 import torchvision.transforms.functional as TF
-from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor
-from math import sqrt, ceil
-from typing import cast
 from PIL import Image
 
+from ..utils import hex_to_rgb, log, pil2tensor, tensor2pil
 
-class TransformImage:
+
+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
     """
 
@@ -18,10 +19,22 @@ class TransformImage:
         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}),
+                "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},
@@ -53,14 +66,21 @@ class TransformImage:
         y = int(y)
         angle = int(angle)
 
-        log.debug(f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}")
+        log.debug(
+            f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}"
+        )
 
         if image.size(0) == 0:
             return (torch.zeros(0),)
         transformed_images = []
-        frames_count, frame_height, frame_width, frame_channel_count = image.size()
+        frames_count, frame_height, frame_width, frame_channel_count = (
+            image.size()
+        )
 
-        new_height, new_width = int(frame_height * zoom), int(frame_width * zoom)
+        new_height, new_width = (
+            int(frame_height * zoom),
+            int(frame_width * zoom),
+        )
 
         log.debug(f"New height: {new_height}, New width: {new_width}")
 
@@ -74,7 +94,12 @@ class TransformImage:
         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)]
+        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}")
@@ -89,7 +114,9 @@ class TransformImage:
 
             img = cast(
                 Image.Image,
-                TF.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear),
+                TF.affine(
+                    img, angle=angle, scale=zoom, translate=[x, y], shear=shear
+                ),
             )
 
             left = abs(padding[0])
@@ -107,4 +134,4 @@ class TransformImage:
         return (pil2tensor(transformed_images),)
 
 
-__nodes__ = [TransformImage]
+__nodes__ = [MTB_TransformImage]

nodes/video.py

@@ -1,22 +1,24 @@
+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
@@ -28,7 +30,10 @@ class LoadImageSequence:
                     "INT",
                     {"default": 0, "min": -1, "max": 9999999},
                 ),
-            }
+            },
+            "optional": {
+                "range": ("STRING", {"default": ""}),
+            },
         }
 
     CATEGORY = "mtb/IO"
@@ -37,17 +42,28 @@ class LoadImageSequence:
         "IMAGE",
         "MASK",
         "INT",
+        "INT",
     )
     RETURN_NAMES = (
         "image",
         "mask",
         "current_frame",
+        "total_frames",
     )
 
-    def load_image(self, path=None, current_frame=0):
+    def load_image(self, path=None, current_frame=0, range=""):
         load_all = current_frame == -1
+        total_frames = 1
 
-        if load_all:
+        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")
@@ -55,33 +71,72 @@ class LoadImageSequence:
             imgs = []
             masks = []
 
-            for frame in frames:
-                img, mask = img_from_path(frame)
-                imgs.append(img)
-                masks.append(mask)
+            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,
-            )
+            return (out_img, out_mask, -1, total_frames)
 
         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)
         image, mask = img_from_path(image_path)
-        return (
-            image,
-            mask,
-            current_frame,
-        )
+        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:
+            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):
@@ -121,11 +176,28 @@ def img_from_path(path):
     )
 
 
-def resolve_all_frames(pattern):
+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}")
-    frames = []
     hash_count = file_pattern.count("#")
     frame_pattern = re.sub(r"#+", "*", file_pattern)
 
@@ -157,7 +229,7 @@ def resolve_path(path, frame):
     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.
@@ -173,7 +245,10 @@ class SaveImageSequence:
             "required": {
                 "images": ("IMAGE",),
                 "filename_prefix": ("STRING", {"default": "Sequence"}),
-                "current_frame": ("INT", {"default": 0, "min": 0, "max": 9999999}),
+                "current_frame": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 9999999},
+                ),
             },
             "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
         }
@@ -220,10 +295,14 @@ class SaveImageSequence:
         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, 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))
@@ -246,6 +325,6 @@ class SaveImageSequence:
 
 
 __nodes__ = [
-    LoadImageSequence,
-    SaveImageSequence,
+    MTB_LoadImageSequence,
+    MTB_SaveImageSequence,
 ]

pyproject.toml

@@ -0,0 +1,179 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "comfy-mtb"
+version = "0.1.5"
+description = "Animation oriented nodes pack for ComfyUI."
+license = "MIT"
+readme = "README.md"
+# repository = ""
+# url = "https://github.com/melMass/comfy_mtb"
+authors = [{ name = "Mel Massadian", email = "mel@melmassadian.com" }]
+classifiers = [
+  "License :: OSI Approved :: MIT License",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Intended Audience :: Developers",
+]
+requires-python = ">=3.10"
+dependencies = [
+  "qrcode",
+  "onnxruntime-gpu",
+  "requirements-parserx",
+  "rembg",
+  "imageio_ffmpeg",
+  "rich",
+  "rich_argparse",
+  "matplotlib",
+  "pillow",
+]
+optional-dependencies = { mel = [
+  "jupyterlab==4.1.6",
+], dev = [
+  "black[jupyter]",
+  "codespell",
+  "mypy",
+  "pre-commit",
+  "pytest",
+  "pytest-cov",
+  "pytest-random-order",
+  "ruff",
+], doc = [
+  "docutils==0.17.1",
+  "jupyter-book>=0.15",
+  "sphinx-autobuild",
+] }
+
+[project.urls]
+Homepage = "https://github.com/melMass/comfy_mtb"
+Documentation = "https://github.com/melMass/comfy_mtb/wiki"
+Repository = "https://github.com/melMass/comfy_mtb"
+Issues = "https://github.com/melMass/comfy_mtb/issues"
+
+[tool.comfy]
+PublisherId = "mel"
+DisplayName = "comfy-mtb"
+Icon = "https://avatars.githubusercontent.com/u/7041726?v=4"
+
+[tool.bumpversion]
+current_version = "0.1.5"
+parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
+serialize = ["{major}.{minor}.{patch}"]
+search = "{current_version}"
+replace = "{new_version}"
+regex = false
+ignore_missing_version = false
+ignore_missing_files = false
+tag = true
+sign_tags = true
+tag_name = "v{new_version}"
+tag_message = "⬆️ Bump version: {current_version} → {new_version}"
+allow_dirty = true
+commit = true
+message = "⬆️ Bump version: {current_version} → {new_version}"
+commit_args = ""
+
+[[tool.bumpversion.files]]
+filename = "__init__.py"
+search = "__version__ = \"{current_version}\""
+replace = "__version__ = \"{new_version}\""
+
+[[tool.bumpversion.files]]
+filename = "pyproject.toml"
+search = "version = \"{current_version}\""
+replace = "version = \"{new_version}\""
+
+# [[tool.bumpversion.files]]
+# filename = "your_package/__init__.py"
+# search = "__version__ = '{current_version}'"
+# replace = "__version__ = '{new_version}'"
+
+# INFO: All those remaining keys are meant for local dev
+[tool.pyright]
+include = ["."]
+exclude = [
+  "**/node_modules",
+  "**/__pycache__",
+  "src/experimental",
+  "src/typestubs",
+]
+ignore = ["src/oldstuff"]
+defineConstant = { DEBUG = true }
+extraPaths = ["python", "../.."]
+stubPath = "src/stubs"
+
+reportMissingImports = true
+reportMissingTypeStubs = false
+typeCheckingMode = "basic"
+
+pythonVersion = "3.10"
+pythonPlatform = "Windows"
+
+[tool.pytest.ini_options]
+log_level = "DEBUG"
+log_cli = true
+markers = [
+  "wip: tests that aren't fully finished yet",
+  "heavy: marks tests as heavy (deselect with '-m \"not heavy\"')",
+
+]
+filterwarnings = ["ignore::UserWarning", 'ignore::DeprecationWarning']
+
+[tool.isort]
+profile = "black"
+line_length = 88
+auto_identify_namespace_packages = false
+# NOTE:
+# pyright doesn't like implicit namespace + single line (related to https://github.com/microsoft/pyright/issues/2882?) but it's horible so I'll live with it
+force_single_line = false
+known_first_party = ["mtb"]
+extend_skip = ["archives"]
+combine_straight_imports = true
+
+[tool.coverage.run]
+parallel = true
+source = ["docs", "tests", "comfy-mtb"]
+
+[tool.coverage.report]
+fail_under = 90
+show_missing = true
+
+[tool.coverage.html]
+show_contexts = true
+
+[tool.ruff]
+line-length = 79
+select = ["A", "B", "C", "D", "E", "F", "FBT", "I", "N", "S", "SIM", "UP", "W"]
+# NOTE:
+# D102 - undocumented-public-method (noisy)
+# D103 - undocumented-public-function (noisy)
+# D100 - undocumented-public-module (noisy)
+# N802 - invalid-function-name (forced by comfy's arch)
+ignore = ["D103", "D102", "D100", "N802"]
+# exclude auto generated file
+extend-exclude = ["./docs/conf.py"]
+
+[tool.ruff.per-file-ignores]
+# imported but unused
+"__init__.py" = ["F401"]
+# use of assert detected
+"tests/*" = ["S101"]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
+
+[tool.mypy]
+pretty = true
+ignore_missing_imports = true
+# exclude auto generated file
+exclude = ["docs/conf.py"]
+
+[tool.codespell]
+# exclude auto generated file
+skip = "./docs/conf.py,poetry.lock"
+check-filenames = true

requirements.txt

@@ -5,4 +5,6 @@ requirements-parser
 rembg
 imageio_ffmpeg
 rich
-rich_argparse
+rich_argparse
+matplotlib
+pillow

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 +1,18 @@
+/**
+ * @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
+ */
+

utils.py

@@ -1,6 +1,17 @@
-import contextlib, functools, math, os, shlex, shutil, socket, subprocess, sys, uuid
+import contextlib
+import functools
+import importlib
+import math
+import os
+import shlex
+import shutil
+import socket
+import subprocess
+import sys
+import uuid
+from enum import Enum
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import TypeVar
 
 import folder_paths
 import numpy as np
@@ -34,6 +45,117 @@ def make_report():
 # endregion
 
 
+# region NFOV
+class numpy_NFOV:
+    def __init__(self, fov=None, height: int = 400, width: int = 800):
+        self.field_of_view = fov or [0.45, 0.45]
+        self.PI = np.pi
+        self.PI_2 = np.pi * 0.5
+        self.PI2 = np.pi * 2.0
+        self.height = height
+        self.width = width
+        self.screen_points = self._get_screen_img()
+
+    def _get_coord_rad(self, is_center_point, center_point=None):
+        if is_center_point:
+            center_point = np.array(center_point)
+            return (center_point * 2 - 1) * np.array([self.PI, self.PI_2])
+        else:
+            return (
+                (self.screen_points * 2 - 1)
+                * np.array([self.PI, self.PI_2])
+                * (np.ones(self.screen_points.shape) * self.field_of_view)
+            )
+
+    def _get_screen_img(self):
+        xx, yy = np.meshgrid(
+            np.linspace(0, 1, self.width), np.linspace(0, 1, self.height)
+        )
+        return np.array([xx.ravel(), yy.ravel()]).T
+
+    def _calc_spherical_to_gnomonic(self, converted_screen_coord):
+        x = converted_screen_coord.T[0]
+        y = converted_screen_coord.T[1]
+
+        rou = np.sqrt(x**2 + y**2)
+        c = np.arctan(rou)
+        sin_c = np.sin(c)
+        cos_c = np.cos(c)
+
+        lat = np.arcsin(
+            cos_c * np.sin(self.cp[1]) + (y * sin_c * np.cos(self.cp[1])) / rou
+        )
+        lon = self.cp[0] + np.arctan2(
+            x * sin_c,
+            rou * np.cos(self.cp[1]) * cos_c - y * np.sin(self.cp[1]) * sin_c,
+        )
+
+        lat = (lat / self.PI_2 + 1.0) * 0.5
+        lon = (lon / self.PI + 1.0) * 0.5
+
+        return np.array([lon, lat]).T
+
+    def _bilinear_interpolation(self, screen_coord):
+        uf = np.mod(screen_coord.T[0], 1) * self.frame_width  # long - width
+        vf = np.mod(screen_coord.T[1], 1) * self.frame_height  # lat - height
+
+        x0 = np.floor(uf).astype(int)  # coord of pixel to bottom left
+        y0 = np.floor(vf).astype(int)
+        x2 = np.add(
+            x0, np.ones(uf.shape).astype(int)
+        )  # coords of pixel to top right
+        y2 = np.add(y0, np.ones(vf.shape).astype(int))
+
+        base_y0 = np.multiply(y0, self.frame_width)
+        base_y2 = np.multiply(y2, self.frame_width)
+
+        A_idx = np.add(base_y0, x0)
+        B_idx = np.add(base_y2, x0)
+        C_idx = np.add(base_y0, x2)
+        D_idx = np.add(base_y2, x2)
+
+        flat_img = np.reshape(self.frame, [-1, self.frame_channel])
+
+        A = np.take(flat_img, A_idx, axis=0)
+        B = np.take(flat_img, B_idx, axis=0)
+        C = np.take(flat_img, C_idx, axis=0)
+        D = np.take(flat_img, D_idx, axis=0)
+
+        wa = np.multiply(x2 - uf, y2 - vf)
+        wb = np.multiply(x2 - uf, vf - y0)
+        wc = np.multiply(uf - x0, y2 - vf)
+        wd = np.multiply(uf - x0, vf - y0)
+
+        # interpolate
+        AA = np.multiply(A, np.array([wa, wa, wa]).T)
+        BB = np.multiply(B, np.array([wb, wb, wb]).T)
+        CC = np.multiply(C, np.array([wc, wc, wc]).T)
+        DD = np.multiply(D, np.array([wd, wd, wd]).T)
+        nfov = np.reshape(
+            np.round(AA + BB + CC + DD).astype(np.uint8),
+            [self.height, self.width, 3],
+        )
+
+        return nfov
+
+    def to_nfov(self, frame, center_point):
+        self.frame = frame
+        self.frame_height = frame.shape[0]
+        self.frame_width = frame.shape[1]
+        self.frame_channel = frame.shape[2]
+
+        self.cp = self._get_coord_rad(
+            center_point=center_point, is_center_point=True
+        )
+        converted_screen_coord = self._get_coord_rad(is_center_point=False)
+        return self._bilinear_interpolation(
+            self._calc_spherical_to_gnomonic(converted_screen_coord)
+        )
+
+
+# endregion
+
+
 # region SERVER Utilities
 class IPChecker:
     def __init__(self):
@@ -77,7 +199,9 @@ def get_server_info():
     base_url = args.listen
     if base_url == "0.0.0.0":
         log.debug("Server set to 0.0.0.0, we will try to resolve the host IP")
-        base_url = ip_checker.get_working_ip(f"http://{{}}:{args.port}/history")
+        base_url = ip_checker.get_working_ip(
+            f"http://{{}}:{args.port}/history"
+        )
         log.debug(f"Setting ip to {base_url}")
     return (base_url, args.port)
 
@@ -86,12 +210,120 @@ def get_server_info():
 
 
 # region MISC Utilities
+
+
+# TODO: use mtb.core directly instead of copying parts here
+T = TypeVar("T", bound="StringConvertibleEnum")
+
+
+class StringConvertibleEnum(Enum):
+    """Base class for enums with utility methods for string conversion and member listing."""
+
+    @classmethod
+    def from_str(cls: type[T], label: str | T) -> T:
+        """
+        Convert a string to the corresponding enum value (case sensitive).
+
+        Args:
+            label (Union[str, T]): The string or enum value to convert.
+
+        Returns
+        -------
+            T: The corresponding enum value.
+
+        Raises
+        ------
+            ValueError: If the label does not correspond to any enum member.
+        """
+        if isinstance(label, cls):
+            return label
+        if isinstance(label, str):
+            # from key
+            if label in cls.__members__:
+                return cls[label]
+
+            for member in cls:
+                if member.value == label:
+                    return member
+
+        raise ValueError(
+            f"Unknown label: '{label}'. Valid members: {list(cls.__members__.keys())}, "
+            f"valid values: {cls.list_members()}"
+        )
+
+    @classmethod
+    def to_str(cls: type[T], enum_value: T) -> str:
+        """
+        Convert an enum value to its string representation.
+
+        Args:
+            enum_value (T): The enum value to convert.
+
+        Returns
+        -------
+            str: The string representation of the enum value.
+
+        Raises
+        ------
+            ValueError: If the enum value is invalid.
+        """
+        if isinstance(enum_value, cls):
+            return enum_value.value
+        raise ValueError(f"Invalid Enum: {enum_value}")
+
+    @classmethod
+    def list_members(cls: type[T]) -> list[str]:
+        """
+        Return a list of string representations of all enum members.
+
+        Returns
+        -------
+            List[str]: List of all enum member values.
+        """
+        return [enum.value for enum in cls]
+
+    def __str__(self) -> str:
+        """
+        Returns the string representation of the enum value.
+
+        Returns
+        -------
+            str: The string representation of the enum value.
+        """
+        return self.value
+
+
+class Precision(StringConvertibleEnum):
+    FULL = "full"
+    FP32 = "fp32"
+    FP16 = "fp16"
+    BF16 = "bf16"
+    FP8 = "fp8"
+
+    def to_dtype(self):
+        match self:
+            case Precision.FP32 | Precision.FULL:
+                return torch.float32
+            case Precision.FP16:
+                return torch.float16
+            case Precision.BF16:
+                return torch.bfloat16
+            case Precision.FP8:
+                return torch.float8_e4m3fn
+
+
+class Operation(StringConvertibleEnum):
+    COPY = "copy"
+    CONVERT = "convert"
+    DELETE = "delete"
+
+
 def backup_file(
     fp: Path,
-    target: Optional[Path] = None,
+    target: Path | None = None,
     backup_dir: str = ".bak",
-    suffix: Optional[str] = None,
-    prefix: Optional[str] = None,
+    suffix: str | None = None,
+    prefix: str | None = None,
 ):
     if not fp.exists():
         raise FileNotFoundError(f"No file found at {fp}")
@@ -157,7 +389,9 @@ def run_command(cmd, ignored_lines_start=None):
     try:
         _run_command(shell_cmd, ignored_lines_start)
     except subprocess.CalledProcessError as e:
-        print(f"Command failed with return code: {e.returncode}", file=sys.stderr)
+        print(
+            f"Command failed with return code: {e.returncode}", file=sys.stderr
+        )
         print(e.stderr.strip(), file=sys.stderr)
 
     except KeyboardInterrupt:
@@ -191,12 +425,6 @@ def _run_command(shell_cmd, ignored_lines_start):
     print("Command executed successfully!")
 
 
-# todo use the requirements library
-reqs_map = {value: key for key, value in pip_map.items()}
-
-import importlib
-
-
 def import_install(package_name):
     package_spec = reqs_map.get(package_name, package_name)
 
@@ -205,7 +433,13 @@ def import_install(package_name):
 
     except Exception:  # (ImportError, ModuleNotFoundError):
         run_command(
-            [Path(sys.executable).as_posix(), "-m", "pip", "install", package_spec]
+            [
+                Path(sys.executable).as_posix(),
+                "-m",
+                "pip",
+                "install",
+                package_spec,
+            ]
         )
         importlib.import_module(package_name)
 
@@ -233,18 +467,27 @@ output_dir = Path(folder_paths.output_directory)
 styles_dir = comfy_dir / "styles"
 session_id = str(uuid.uuid4())
 # - Construct the path to the font file
-font_path = here / "font.ttf"
+font_path = here / "data" / "font.ttf"
 
 # - Add extern folder to path
 extern_root = here / "extern"
 add_path(extern_root)
+
 for pth in extern_root.iterdir():
     if pth.is_dir():
         add_path(pth)
 
 # - Add the ComfyUI directory and custom nodes path to the sys.path list
 add_path(comfy_dir)
-add_path((comfy_dir / "custom_nodes"))
+add_path(comfy_dir / "custom_nodes")
+
+
+# TODO: use the requirements library
+reqs_map = {value: key for key, value in pip_map.items()}
+
+# NOTE: store already logged warnings to only alert once.
+warned_messages: set[str] = set()
+
 
 PIL_FILTER_MAP = {
     "nearest": Image.Resampling.NEAREST,
@@ -258,7 +501,7 @@ PIL_FILTER_MAP = {
 
 
 # region TENSOR Utilities
-def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
+def tensor2pil(image: torch.Tensor) -> list[Image.Image]:
     batch_count = image.size(0) if len(image.shape) > 3 else 1
     if batch_count > 1:
         out = []
@@ -268,26 +511,30 @@ def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
 
     return [
         Image.fromarray(
-            np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+            np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(
+                np.uint8
+            )
         )
     ]
 
 
-def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
+def pil2tensor(image: Image.Image | list[Image.Image]) -> torch.Tensor:
     if isinstance(image, list):
         return torch.cat([pil2tensor(img) for img in image], dim=0)
 
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+    return torch.from_numpy(
+        np.array(image).astype(np.float32) / 255.0
+    ).unsqueeze(0)
 
 
-def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
+def np2tensor(img_np: np.ndarray | list[np.ndarray]) -> torch.Tensor:
     if isinstance(img_np, list):
         return torch.cat([np2tensor(img) for img in img_np], dim=0)
 
     return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
 
 
-def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
+def tensor2np(tensor: torch.Tensor) -> list[np.ndarray]:
     batch_count = tensor.size(0) if len(tensor.shape) > 3 else 1
     if batch_count > 1:
         out = []
@@ -295,23 +542,31 @@ def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
             out.extend(tensor2np(tensor[i]))
         return out
 
-    return [np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)]
+    return [
+        np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(
+            np.uint8
+        )
+    ]
 
 
 def pad(img, left, right, top, bottom):
     pad_width = np.array(((0, 0), (top, bottom), (left, right)))
-    print(f"pad_width: {pad_width}, shape: {pad_width.shape}")  # Debugging line
+    print(
+        f"pad_width: {pad_width}, shape: {pad_width.shape}"
+    )  # Debugging line
     return np.pad(img, pad_width, mode="wrap")
 
 
 def tiles_infer(tiles, ort_session, progress_callback=None):
     """Infer each tile with the given model. progress_callback will be called with
     arguments : current tile idx and total tiles amount (used to show progress on
-    cursor in Blender)."""
-
+    cursor in Blender).
+    """
     out_channels = 3  # normal map RGB channels
     tiles_nb = tiles.shape[0]
-    pred_tiles = np.empty((tiles_nb, out_channels, tiles.shape[2], tiles.shape[3]))
+    pred_tiles = np.empty(
+        (tiles_nb, out_channels, tiles.shape[2], tiles.shape[3])
+    )
 
     for i in range(tiles_nb):
         if progress_callback != None:
@@ -325,7 +580,6 @@ def tiles_infer(tiles, ort_session, progress_callback=None):
 
 def generate_mask(tile_size, stride_size):
     """Generates a pyramidal-like mask. Used for mixing overlapping predicted tiles."""
-
     tile_h, tile_w = tile_size
     stride_h, stride_w = stride_size
     ramp_h = tile_h - stride_h
@@ -364,8 +618,8 @@ def generate_mask(tile_size, stride_size):
 
 def corner_mask(side_length):
     """Generates the corner part of the pyramidal-like mask.
-    Currently, only for square shapes."""
-
+    Currently, only for square shapes.
+    """
     corner = np.zeros([side_length, side_length])
 
     for h in range(0, side_length):
@@ -401,8 +655,8 @@ def scaling_mask(side_length):
 
 def tiles_merge(tiles, stride_size, img_size, paddings):
     """Merges the list of tiles into one image. img_size is the original size, before
-    padding."""
-
+    padding.
+    """
     _, tile_h, tile_w = tiles[0].shape
     pad_left, pad_right, pad_top, pad_bottom = paddings
     height = img_size[1] + pad_top + pad_bottom
@@ -435,7 +689,8 @@ def tiles_merge(tiles, stride_size, img_size, paddings):
 
 def tiles_split(img, tile_size, stride_size):
     """Returns list of tiles from the given image and the padding used to fit the tiles
-    in it. Input image must have dimension C,H,W."""
+    in it. Input image must have dimension C,H,W.
+    """
     log.debug(f"Splitting img: tile {tile_size}, stride {stride_size} ")
     tile_h, tile_w = tile_size
     stride_h, stride_w = stride_size
@@ -492,7 +747,9 @@ def tiles_split(img, tile_size, stride_size):
 
 # region MODEL Utilities
 def download_antelopev2():
-    antelopev2_url = "https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8"
+    antelopev2_url = (
+        "https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8"
+    )
 
     try:
         import gdown
@@ -539,10 +796,11 @@ def get_model_path(fam, model=None):
     if res:
         if isinstance(res, list):
             if len(res) > 1:
-                log.warning(
-                    f"Found multiple match, we will pick the first {res[0]}\n{res}"
-                )
-            res = res[0]
+                warn_msg = f"Found multiple match, we will pick the last {res[-1]}\n{res}"
+                if warn_msg not in warned_messages:
+                    log.info(warn_msg)
+                    warned_messages.add(warn_msg)
+            res = res[-1]
         res = Path(res)
         log.debug(f"Resolved model path from folder_paths: {res}")
     else:
@@ -576,6 +834,32 @@ def create_uv_map_tensor(width=512, height=512):
 
 
 # region ANIMATION Utilities
+EASINGS = [
+    "Linear",
+    "Sine In",
+    "Sine Out",
+    "Sine In/Out",
+    "Quart In",
+    "Quart Out",
+    "Quart In/Out",
+    "Cubic In",
+    "Cubic Out",
+    "Cubic In/Out",
+    "Circ In",
+    "Circ Out",
+    "Circ In/Out",
+    "Back In",
+    "Back Out",
+    "Back In/Out",
+    "Elastic In",
+    "Elastic Out",
+    "Elastic In/Out",
+    "Bounce In",
+    "Bounce Out",
+    "Bounce In/Out",
+]
+
+
 def apply_easing(value, easing_type):
     if easing_type == "Linear":
         return value
@@ -603,7 +887,10 @@ def apply_easing(value, easing_type):
             return 1
         p = 0.3
         s = p / 4
-        return -(math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p))
+        return -(
+            math.pow(2, 10 * (t - 1))
+            * math.sin((t - 1 - s) * (2 * math.pi) / p)
+        )
 
     def easeOutElastic(t):
         if t == 0:
@@ -624,10 +911,13 @@ def apply_easing(value, easing_type):
         t = t * 2
         if t < 1:
             return -0.5 * (
-                math.pow(2, 10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+                math.pow(2, 10 * (t - 1))
+                * math.sin((t - 1 - s) * (2 * math.pi) / p)
             )
         return (
-            0.5 * math.pow(2, -10 * (t - 1)) * math.sin((t - 1 - s) * (2 * math.pi) / p)
+            0.5
+            * math.pow(2, -10 * (t - 1))
+            * math.sin((t - 1 - s) * (2 * math.pi) / p)
             + 1
         )
 

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

@@ -7,10 +7,12 @@
  *
  */
 
+// Reference the shared typedefs file
+/// <reference path="../types/typedefs.js" />
+
 import { app } from '../../scripts/app.js'
 
 import * as shared from './comfy_shared.js'
-import { log } from './comfy_shared.js'
 import { MtbWidgets } from './mtb_widgets.js'
 
 // TODO: respect inputs order...
@@ -25,10 +27,17 @@ function escapeHtml(unsafe) {
 }
 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 () {
+        this.options = {}
         const r = onNodeCreated
           ? onNodeCreated.apply(this, arguments)
           : undefined
@@ -37,24 +46,29 @@ app.registerExtension({
       }
 
       const onConnectionsChange = nodeType.prototype.onConnectionsChange
-      nodeType.prototype.onConnectionsChange = function (
-        type,
-        index,
-        connected,
-        link_info
-      ) {
+      /**
+       * @param {OnConnectionsChangeParams} args
+       */
+      nodeType.prototype.onConnectionsChange = function (...args) {
+        const [_type, index, connected, link_info, ioSlot] = args
         const r = onConnectionsChange
-          ? onConnectionsChange.apply(this, arguments)
+          ? onConnectionsChange.apply(this, args)
           : undefined
         // TODO: remove all widgets on disconnect once computed
-        shared.dynamic_connection(this, index, connected, 'anything_', '*')
+        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 type = fromNode.outputs[link_info.origin_slot].type
+          // 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()
         }
@@ -63,17 +77,16 @@ app.registerExtension({
           this.inputs[index].type = '*'
           this.inputs[index].label = `anything_${index + 1}`
         }
+        return r
       }
 
       const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
+      nodeType.prototype.onExecuted = function (data) {
         onExecuted?.apply(this, arguments)
 
         const prefix = 'anything_'
 
         if (this.widgets) {
-          // const pos = this.widgets.findIndex((w) => w.name === "anything_1");
-          // if (pos !== -1) {
           for (let i = 0; i < this.widgets.length; i++) {
             if (this.widgets[i].name !== 'output_to_console') {
               this.widgets[i].onRemoved?.()
@@ -82,29 +95,27 @@ app.registerExtension({
           this.widgets.length = 1
         }
         let widgetI = 1
-
-        if (message.text) {
-          for (const txt of message.text) {
+        // console.log(message)
+        if (data.text) {
+          for (const txt of data.text) {
             const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt))
+              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt)),
             )
             w.parent = this
             widgetI++
           }
         }
-        if (message.b64_images) {
-          for (const img of message.b64_images) {
+        if (data.b64_images) {
+          for (const img of data.b64_images) {
             const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_IMG(`${prefix}_${widgetI}`, img)
+              MtbWidgets.DEBUG_IMG(`${prefix}_${widgetI}`, img),
             )
             w.parent = this
             widgetI++
           }
-          // this.onResize?.(this.size);
-          // this.resize?.(this.size)
         }
 
-        this.setSize(this.computeSize())
+        // this.setSize(this.computeSize())
 
         this.onRemoved = function () {
           // When removing this node we need to remove the input from the DOM

web/imageFeed.js

@@ -11,7 +11,7 @@
 
 import { api } from '../../scripts/api.js'
 import { app } from '../../scripts/app.js'
-
+import { LocalStorageManager } from './comfy_shared.js'
 const styles = {
   lighbox: {
     position: 'fixed',
@@ -53,17 +53,39 @@ let currentImageIndex = 0
 const imageUrls = []
 
 let image_menu = null
-let activated = true
+const storage = new LocalStorageManager('mtb')
+
+let activated = storage.get('image_feed', false)
 
 app.registerExtension({
   name: 'mtb.ImageFeed',
+  setup: () => {
+    app.ui.settings.addSetting({
+      id: 'mtb.imageFeed.enabled',
+      name: '[⚡mtb] Enable image feed',
+      type: 'boolean',
+      defaultValue: true,
+      attrs: {
+        style: {
+          fontFamily: 'monospace',
+        },
+      },
+      async onChange(value) {
+        storage.set('image_feed', value)
+        activated = value
+      },
+    })
+  },
   init: async () => {
+    if (!activated) {
+      return
+    }
     const pythongossFeed = app.extensions.find(
-      (e) => e.name == 'pysssss.ImageFeed'
+      (e) => e.name === 'pysssss.ImageFeed',
     )
     if (pythongossFeed) {
       console.warn(
-        "[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed"
+        "[mtb] - Aborting the loading of mtb's imageFeed in favor of pysssss.ImageFeed",
       )
       activated = false // just in case other methods are added later on
       return
@@ -94,7 +116,7 @@ app.registerExtension({
     const lightboxCloseBtn = document.createElement('button')
     Object.assign(
       lightboxCloseBtn.style,
-      styles.lightboxBtn({ right: '0', top: '0' })
+      styles.lightboxBtn({ right: '0', top: '0' }),
     )
     lightboxCloseBtn.textContent = '❌'
 
@@ -164,7 +186,7 @@ app.registerExtension({
     //- append to DOM
     document.body.append(imageListContainer)
 
-    showBtn.textContent = '🖼️'
+    showBtn.textContent = '🖼'
     showBtn.onclick = () => {
       imageListContainer.style.display = 'block'
       showBtn.style.display = 'none'

web/mtb_widgets.js

@@ -7,6 +7,8 @@
  *
  */
 
+/// <reference path="../types/typedefs.js" />
+
 // TODO: Use the builtin addDOMWidget everywhere appropriate
 
 import { app } from '../../scripts/app.js'
@@ -14,9 +16,16 @@ import { api } from '../../scripts/api.js'
 
 import parseCss from './extern/parse-css.js'
 import * as shared from './comfy_shared.js'
-import { log } from './comfy_shared.js'
+import { infoLogger } from './comfy_shared.js'
+import { NumberInputWidget } from './numberInput.js'
 
-const newTypes = [, /*'BOOL'*/ 'COLOR', 'BBOX']
+// NOTE: new widget types registered by MTB Widgets
+const newTypes = [/*'BOOL'*/ , 'COLOR', 'BBOX']
+
+const deprecated_nodes = {
+  //  'Animation Builder':
+  //    'Kept to avoid breaking older script but replaced by TimeEngine',
+}
 
 const withFont = (ctx, font, cb) => {
   const oldFont = ctx.font
@@ -43,12 +52,255 @@ const calculateTextDimensions = (ctx, value, width, fontSize = 16) => {
   const textHeight = (lines.length + 1) * fontSize
   const maxLineWidth = lines.reduce(
     (maxWidth, line) => Math.max(maxWidth, ctx.measureText(line).width),
-    0
+    0,
   )
   return { textHeight, maxLineWidth }
 }
 
+export function addMultilineWidget(node, name, opts, callback) {
+  const inputEl = document.createElement('textarea')
+  inputEl.className = 'comfy-multiline-input'
+  inputEl.value = opts.defaultVal
+  inputEl.placeholder = opts.placeholder || name
+
+  const widget = node.addDOMWidget(name, 'textmultiline', inputEl, {
+    getValue() {
+      return inputEl.value
+    },
+    setValue(v) {
+      inputEl.value = v
+    },
+  })
+  widget.inputEl = inputEl
+
+  inputEl.addEventListener('input', () => {
+    callback?.(widget.value)
+    widget.callback?.(widget.value)
+  })
+  widget.onRemove = () => {
+    inputEl.remove()
+  }
+
+  return { minWidth: 400, minHeight: 200, widget }
+}
+
+export const VECTOR_AXIS = {
+  0: 'x',
+  1: 'y',
+  2: 'z',
+  3: 'w',
+}
+
+export function addVectorWidgetW(
+  node,
+  name,
+  value,
+  vector_size,
+  callback,
+  app,
+) {
+  // const inputEl = document.createElement('div')
+  // const vecEl = document.createElement('div')
+  //
+  // inputEl.style.background = 'red'
+  //
+  // inputEl.className = 'comfy-vector-container'
+  // vecEl.className = 'comfy-vector-input'
+  //
+  // vecEl.style.display = 'flex'
+  // inputEl.appendChild(vecEl)
+  const inputs = []
+
+  for (let i = 0; i < vector_size; i++) {
+    // const input = document.createElement('input')
+    // input.type = 'number'
+    // input.value = value[VECTOR_AXIS[i]]
+    const input = node.addWidget(
+      'number',
+      `${name}_${VECTOR_AXIS[i]}`,
+      value[VECTOR_AXIS[i]],
+      (val) => {},
+    )
+
+    inputs.push(input)
+    // vecEl.appendChild(input)
+  }
+  //
+  // const widget = node.addDOMWidget(name, 'vector', inputEl, {
+  //   getValue() {
+  //     return JSON.stringify(widget._value)
+  //   },
+  //   setValue(v) {
+  //     widget._value = v
+  //   },
+  //   afterResize(node, widget) {
+  //     console.log('After resize', { that: this, node, widget })
+  //   },
+  // })
+  //
+  // console.log('prev callback', widget.callback)
+  // widget.callback = callback
+  // widget._value = value
+  //
+  // for (let i = 0; i < vector_size; i++) {
+  //   const input = inputs[i]
+  //   input.addEventListener('change', (event) => {
+  //     widget._value[VECTOR_AXIS[i]] = Number.parseFloat(event.target.value)
+  //     widget.callback?.(widget._value)
+  //     node.graph._version++
+  //     node.setDirtyCanvas(true, true)
+  //   })
+  // }
+  // // document.body.append(inputEl)
+  //
+  // widget.inputEl = inputEl
+  // widget.vecEl = vecEl
+  //
+  // inputEl.addEventListener('input', () => {
+  //   widget.callback?.(widget.value)
+  // })
+  //
+  return { minWidth: 400, minHeight: 200, widget }
+}
+export function addVectorWidget(node, name, value, vector_size, callback, app) {
+  const inputEl = document.createElement('div')
+  const vecEl = document.createElement('div')
+
+  inputEl.className = 'comfy-vector-container'
+  vecEl.className = 'comfy-vector-input'
+  vecEl.id = 'vecEl'
+
+  vecEl.style.display = 'flex'
+  vecEl.style.flexDirection = 'column'
+  inputEl.appendChild(vecEl)
+  const inputs = []
+
+  //
+  // for (let i = 0; i < vector_size; i++) {
+  //   const input = document.createElement('input')
+  //   input.type = 'number'
+  //   input.value = value[VECTOR_AXIS[i]]
+  //   inputs.push(input)
+  //   vecEl.appendChild(input)
+  // }
+
+  const widget = node.addDOMWidget(name, 'vector', inputEl, {
+    getValue() {
+      return JSON.stringify(widget._value)
+    },
+    setValue(v) {
+      widget._value = v
+    },
+  })
+  const vec = new NumberInputWidget('vecEl', vector_size, true)
+  vec.setValue(...Object.values(value))
+  vec.onChange = (value) => {
+    for (let i = 0; i < value.length; i++) {
+      const val = value[i]
+      widget._value[VECTOR_AXIS[i]] = Number.parseFloat(val)
+    }
+
+    widget.callback?.(widget._value)
+    // widget._value[VECTOR_AXIS[index]] = Number.parseFloat(value)
+  }
+
+  console.log('prev callback', widget.callback)
+  widget.callback = callback
+  widget._value = value
+
+  // for (let i = 0; i < vector_size; i++) {
+  //   const input = inputs[i]
+  //   input.addEventListener('change', (event) => {
+  //     widget._value[VECTOR_AXIS[i]] = Number.parseFloat(event.target.value)
+  //     widget.callback?.(widget._value)
+  //     node.graph._version++
+  //     node.setDirtyCanvas(true, true)
+  //   })
+  // }
+
+  widget.inputEl = inputEl
+  widget.vecEl = vecEl
+  widget.vec = vec
+
+  return { minWidth: 400, minHeight: 200 * vector_size, widget }
+}
 export const MtbWidgets = {
+  //TODO: complete this properly
+
+  /**
+   * Creates a vector widget.
+   * @param {string} key - The key for the widget.
+   * @param {number[]} [val] - The initial value for the widget.
+   * @param {number} size - The size of the vector.
+   * @returns {VectorWidget} The vector widget.
+   */
+  VECTOR: (key, val, size) => {
+    shared.infoLogger('Adding VECTOR widget', { key, val, size })
+    /** @type {VectorWidget} */
+    const widget = {
+      name: key,
+      type: `vector${size}`,
+      y: 0,
+      options: { default: Array.from({ length: size }, () => 0.0) },
+      _value: val || Array.from({ length: size }, () => 0.0),
+      draw: function (ctx, node, width, widgetY, height) {
+        ctx.textAlign = 'left'
+        ctx.strokeStyle = outline_color
+        ctx.fillStyle = background_color
+        ctx.beginPath()
+        if (show_text)
+          ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5])
+        else ctx.rect(margin, y, widget_width - margin * 2, H)
+        ctx.fill()
+        if (show_text) {
+          if (!w.disabled) ctx.stroke()
+          ctx.fillStyle = text_color
+          if (!w.disabled) {
+            ctx.beginPath()
+            ctx.moveTo(margin + 16, y + 5)
+            ctx.lineTo(margin + 6, y + H * 0.5)
+            ctx.lineTo(margin + 16, y + H - 5)
+            ctx.fill()
+            ctx.beginPath()
+            ctx.moveTo(widget_width - margin - 16, y + 5)
+            ctx.lineTo(widget_width - margin - 6, y + H * 0.5)
+            ctx.lineTo(widget_width - margin - 16, y + H - 5)
+            ctx.fill()
+          }
+          ctx.fillStyle = secondary_text_color
+          ctx.fillText(w.label || w.name, margin * 2 + 5, y + H * 0.7)
+          ctx.fillStyle = text_color
+          ctx.textAlign = 'right'
+          if (w.type === 'number') {
+            ctx.fillText(
+              Number(w.value).toFixed(
+                w.options.precision !== undefined ? w.options.precision : 3,
+              ),
+              widget_width - margin * 2 - 20,
+              y + H * 0.7,
+            )
+          } else {
+            let v = w.value
+            if (w.options.values) {
+              let values = w.options.values
+              if (values.constructor === Function) values = values()
+              if (values && values.constructor !== Array) v = values[w.value]
+            }
+            ctx.fillText(v, widget_width - margin * 2 - 20, y + H * 0.7)
+          }
+        }
+      },
+      get value() {
+        return this._value
+      },
+      set value(val) {
+        this._value = val
+        this.callback?.(this._value)
+      },
+    }
+
+    return widget
+  },
   BBOX: (key, val) => {
     /** @type {import("./types/litegraph").IWidget} */
     const widget = {
@@ -106,7 +358,7 @@ export const MtbWidgets = {
             ctx.fillText(
               this.label || this.name,
               margin * 2 + 5,
-              currentY + H * 0.7
+              currentY + H * 0.7,
             )
             ctx.fillStyle = text_color
             ctx.textAlign = 'right'
@@ -115,10 +367,10 @@ export const MtbWidgets = {
               Number(this.value).toFixed(
                 this.options?.precision !== undefined
                   ? this.options.precision
-                  : 3
+                  : 3,
               ),
               widget_width - margin * 2 - 20,
-              currentY + H * 0.7
+              currentY + H * 0.7,
             )
           }
         }
@@ -200,7 +452,7 @@ export const MtbWidgets = {
                   this.value = Number(v)
                   shared.inner_value_change(this, this.value, event)
                 }.bind(w),
-                event
+                event,
               )
             }
           }
@@ -210,7 +462,7 @@ export const MtbWidgets = {
               function () {
                 shared.inner_value_change(this, this.value, event)
               }.bind(this),
-              20
+              20,
             )
 
           app.canvas.setDirty(true)
@@ -259,7 +511,7 @@ export const MtbWidgets = {
         border,
         widgetY + border,
         widgetWidth - border * 2,
-        height - border * 2
+        height - border * 2,
       )
       const color = parseCss(this.value.default || this.value)
       if (!color) {
@@ -291,6 +543,7 @@ export const MtbWidgets = {
 
             picker.addEventListener('change', () => {
               this.value = picker.value
+              this.callback?.(this.value)
               node.graph._version++
               node.setDirtyCanvas(true, true)
               picker.remove()
@@ -362,7 +615,7 @@ export const MtbWidgets = {
         })
         const widgetWidth = Math.max(
           width || this.width || 32,
-          dimensions.maxLineWidth
+          dimensions.maxLineWidth,
         )
         const widgetHeight = dimensions.textHeight * 1.5
         return [widgetWidth, widgetHeight]
@@ -386,7 +639,7 @@ export const MtbWidgets = {
       text-align: center;
       font-size: ${fontSize}px;
       color: var(--input-text);
-      line-height: 0;
+      line-height: 1em;
       font-family: monospace;
     `
     w.value = val
@@ -403,7 +656,7 @@ const mtb_widgets = {
   name: 'mtb.widgets',
 
   init: async () => {
-    log('Registering mtb.widgets')
+    infoLogger('Registering mtb.widgets')
     try {
       const res = await api.fetchApi('/mtb/debug')
       const msg = await res.json()
@@ -419,7 +672,7 @@ const mtb_widgets = {
   setup: () => {
     app.ui.settings.addSetting({
       id: 'mtb.Debug.enabled',
-      name: '[mtb] Enable Debug (py and js)',
+      name: '[⚡mtb] Enable Debug (py and js)',
       type: 'boolean',
       defaultValue: false,
 
@@ -431,13 +684,14 @@ const mtb_widgets = {
         },
       },
       async onChange(value) {
-        if (value) {
-          console.log('Enabled DEBUG mode')
-        }
         if (!window.MTB) {
           window.MTB = {}
         }
         window.MTB.DEBUG = value
+        if (value) {
+          infoLogger('Enabled DEBUG mode')
+        }
+
         await api
           .fetchApi('/mtb/debug', {
             method: 'POST',
@@ -445,7 +699,7 @@ const mtb_widgets = {
               enabled: value,
             }),
           })
-          .then((response) => {})
+          .then((_response) => {})
           .catch((error) => {
             console.error('Error:', error)
           })
@@ -453,25 +707,25 @@ const mtb_widgets = {
     })
   },
 
-  getCustomWidgets: function () {
+  getCustomWidgets: () => {
     return {
-      BOOL: (node, inputName, inputData, app) => {
+      BOOL: (node, inputName, inputData, _app) => {
         console.debug('Registering bool')
 
         return {
           widget: node.addCustomWidget(
-            MtbWidgets.BOOL(inputName, inputData[1]?.default || false)
+            MtbWidgets.BOOL(inputName, inputData[1]?.default || false),
           ),
           minWidth: 150,
           minHeight: 30,
         }
       },
 
-      COLOR: (node, inputName, inputData, app) => {
+      COLOR: (node, inputName, inputData, _app) => {
         console.debug('Registering color')
         return {
           widget: node.addCustomWidget(
-            MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000')
+            MtbWidgets.COLOR(inputName, inputData[1]?.default || '#ff0000'),
           ),
           minWidth: 150,
           minHeight: 30,
@@ -489,8 +743,8 @@ const mtb_widgets = {
     }
   },
   /**
-   * @param {import("./types/comfy").NodeType} nodeType
-   * @param {import("./types/comfy").NodeDef} nodeData
+   * @param {NodeType} nodeType
+   * @param {NodeData} nodeData
    * @param {import("./types/comfy").App} app
    */
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
@@ -566,34 +820,24 @@ const mtb_widgets = {
     if (!nodeData.name.endsWith('(mtb)')) {
       return
     }
+    // console.log('MTB Node', { description: nodeData.description, nodeType })
 
+    shared.addDocumentation(nodeData, nodeType)
+
+    const deprecation = deprecated_nodes[nodeData.name.replace(' (mtb)', '')]
+
+    if (deprecation) {
+      shared.addDeprecation(nodeType, deprecation)
+    }
     //- Extending Python Nodes
     switch (nodeData.name) {
-      case 'Psd Save (mtb)': {
-        const onConnectionsChange = nodeType.prototype.onConnectionsChange
-        nodeType.prototype.onConnectionsChange = function (
-          type,
-          index,
-          connected,
-          link_info
-        ) {
-          const r = onConnectionsChange
-            ? onConnectionsChange.apply(this, arguments)
-            : undefined
-          shared.dynamic_connection(this, index, connected)
-          return r
-        }
-        break
-      }
       //TODO: remove this non sense
       case 'Get Batch From History (mtb)': {
         const onNodeCreated = nodeType.prototype.onNodeCreated
         nodeType.prototype.onNodeCreated = function () {
-          const r = onNodeCreated
-            ? onNodeCreated.apply(this, arguments)
-            : undefined
+          const r = onNodeCreated ? onNodeCreated.apply(this, []) : undefined
           const internal_count = this.widgets.find(
-            (w) => w.name === 'internal_count'
+            (w) => w.name === 'internal_count',
           )
           shared.hideWidgetForGood(this, internal_count)
           internal_count.afterQueued = function () {
@@ -633,24 +877,24 @@ const mtb_widgets = {
                 imgURLs = imgURLs.concat(
                   message.gif.map((params) => {
                     return api.apiURL(
-                      '/view?' + new URLSearchParams(params).toString()
+                      '/view?' + new URLSearchParams(params).toString(),
                     )
-                  })
+                  }),
                 )
               }
               if (message.apng) {
                 imgURLs = imgURLs.concat(
                   message.apng.map((params) => {
                     return api.apiURL(
-                      '/view?' + new URLSearchParams(params).toString()
+                      '/view?' + new URLSearchParams(params).toString(),
                     )
-                  })
+                  }),
                 )
               }
               let i = 0
               for (const img of imgURLs) {
                 const w = this.addCustomWidget(
-                  MtbWidgets.DEBUG_IMG(`${prefix}_${i}`, img)
+                  MtbWidgets.DEBUG_IMG(`${prefix}_${i}`, img),
                 )
                 w.parent = this
                 i++
@@ -678,12 +922,12 @@ const mtb_widgets = {
           this.changeMode(LiteGraph.ALWAYS)
 
           const raw_iteration = this.widgets.find(
-            (w) => w.name === 'raw_iteration'
+            (w) => w.name === 'raw_iteration',
           )
           const raw_loop = this.widgets.find((w) => w.name === 'raw_loop')
 
           const total_frames = this.widgets.find(
-            (w) => w.name === 'total_frames'
+            (w) => w.name === 'total_frames',
           )
           const loop_count = this.widgets.find((w) => w.name === 'loop_count')
 
@@ -693,12 +937,12 @@ const mtb_widgets = {
           raw_iteration._value = 0
 
           const value_preview = this.addCustomWidget(
-            MtbWidgets['DEBUG_STRING']('value_preview', 'Idle')
+            MtbWidgets['DEBUG_STRING']('value_preview', 'Idle'),
           )
           value_preview.parent = this
 
           const loop_preview = this.addCustomWidget(
-            MtbWidgets['DEBUG_STRING']('loop_preview', 'Iteration: Idle')
+            MtbWidgets['DEBUG_STRING']('loop_preview', 'Iteration: Idle'),
           )
           loop_preview.parent = this
 
@@ -712,21 +956,18 @@ const mtb_widgets = {
             app.canvas.setDirty(true)
           }
 
-          const reset_button = this.addWidget(
-            'button',
-            `Reset`,
-            'reset',
-            onReset
-          )
+          // reset button
+          this.addWidget('button', `Reset`, 'reset', onReset)
 
-          const run_button = this.addWidget('button', `Queue`, 'queue', () => {
+          // run button
+          this.addWidget('button', `Queue`, 'queue', () => {
             onReset() // this could maybe be a setting or checkbox
             app.queuePrompt(0, total_frames.value * loop_count.value)
             window.MTB?.notify?.(
               `Started a queue of ${total_frames.value} frames (for ${
                 loop_count.value
               } loop, so ${total_frames.value * loop_count.value})`,
-              5000
+              5000,
             )
           })
 
@@ -757,23 +998,6 @@ const mtb_widgets = {
 
         break
       }
-      case 'Text Encore Frames (mtb)': {
-        const onConnectionsChange = nodeType.prototype.onConnectionsChange
-        nodeType.prototype.onConnectionsChange = function (
-          type,
-          index,
-          connected,
-          link_info
-        ) {
-          const r = onConnectionsChange
-            ? onConnectionsChange.apply(this, arguments)
-            : undefined
-
-          shared.dynamic_connection(this, index, connected)
-          return r
-        }
-        break
-      }
       case 'Interpolate Clip Sequential (mtb)': {
         const onNodeCreated = nodeType.prototype.onNodeCreated
         nodeType.prototype.onNodeCreated = function () {
@@ -784,7 +1008,7 @@ const mtb_widgets = {
             const input = this.addInput(
               `replacement_${this.widgets.length}`,
               'STRING',
-              ''
+              '',
             )
             console.log(input)
             this.addWidget('STRING', `replacement_${this.widgets.length}`, '')
@@ -801,7 +1025,7 @@ const mtb_widgets = {
             'remove',
             function (value, widget, node) {
               console.log(`Button clicked: ${value}`, widget, node)
-            }
+            },
           )
 
           return r
@@ -842,15 +1066,16 @@ const mtb_widgets = {
                 if (style && style.length >= 1) {
                   if (style[0]) {
                     window.MTB?.notify?.(
-                      `Extracted positive from ${this.widgets[0].value}`
+                      `Extracted positive from ${this.widgets[0].value}`,
                     )
-                    const tn = LiteGraph.createNode('Text box')
+                    // const tn = LiteGraph.createNode('Text box')
+                    const tn = LiteGraph.createNode('CLIPTextEncode')
                     app.graph.add(tn)
                     tn.title = `${this.widgets[0].value} (Positive)`
                     tn.widgets[0].value = style[0]
                   } else {
                     window.MTB?.notify?.(
-                      `No positive to extract for ${this.widgets[0].value}`
+                      `No positive to extract for ${this.widgets[0].value}`,
                     )
                   }
                 }
@@ -863,15 +1088,15 @@ const mtb_widgets = {
                 if (style && style.length >= 2) {
                   if (style[1]) {
                     window.MTB?.notify?.(
-                      `Extracted negative from ${this.widgets[0].value}`
+                      `Extracted negative from ${this.widgets[0].value}`,
                     )
-                    const tn = LiteGraph.createNode('Text box')
+                    const tn = LiteGraph.createNode('CLIPTextEncode')
                     app.graph.add(tn)
                     tn.title = `${this.widgets[0].value} (Negative)`
                     tn.widgets[0].value = style[1]
                   } else {
                     window.MTB.notify(
-                      `No negative to extract for ${this.widgets[0].value}`
+                      `No negative to extract for ${this.widgets[0].value}`,
                     )
                   }
                 }
@@ -883,17 +1108,37 @@ const mtb_widgets = {
 
         break
       }
+
+      //NOTE: dynamic nodes
+      case 'Apply Text Template (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'var', '*')
+        break
+      }
+      case 'Save Data Bundle (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'data', '*') // [MASK,IMAGE]
+        break
+      }
       case 'Add To Playlist (mtb)': {
         shared.setupDynamicConnections(nodeType, 'video', 'VIDEO')
         break
       }
+      case 'Psd Save (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'input_', 'PSDLAYER')
+        break
+      }
+      // case 'Text Encode Frames (mtb)' : {
+      //   shared.setupDynamicConnections(nodeType, 'input_', 'IMAGE')
+      //   break
+      // }
       case 'Stack Images (mtb)':
       case 'Concat Images (mtb)': {
         shared.setupDynamicConnections(nodeType, 'image', 'IMAGE')
 
         break
       }
-      case 'Batch Float Assemble (mtb)': {
+      case 'Batch Float Assemble (mtb)':
+      case 'Batch Float Math (mtb)':
+      case 'Plot Batch Float (mtb)': {
         shared.setupDynamicConnections(nodeType, 'floats', 'FLOATS')
         break
       }
@@ -918,21 +1163,19 @@ const mtb_widgets = {
           type,
           index,
           connected,
-          link_info
+          link_info,
         ) {
           const r = onConnectionsChange
             ? onConnectionsChange.apply(this, arguments)
             : undefined
-          shared.dynamic_connection(this, index, connected, 'var_', '*', [
-            'x',
-            'y',
-            'z',
-          ])
+          shared.dynamic_connection(this, index, connected, 'var_', '*', {
+            nameArray: ['x', 'y', 'z'],
+          })
 
           //- infer type
           if (link_info) {
             const fromNode = this.graph._nodes.find(
-              (otherNode) => otherNode.id == link_info.origin_id
+              (otherNode) => otherNode.id == link_info.origin_id,
             )
             const type = fromNode.outputs[link_info.origin_slot].type
             this.inputs[index].type = type
@@ -947,6 +1190,33 @@ const mtb_widgets = {
 
         break
       }
+
+      case 'Batch Shape (mtb)':
+      case 'Text To Image (mtb)': {
+        shared.addMenuHandler(nodeType, function (_app, options) {
+          /** @type {ContextMenuItem} */
+          const item = {
+            content: 'swap colors',
+            title: 'Swap BG/FG Color ⚡',
+            callback: (_menuItem) => {
+              const color_w = this.widgets.find((w) => w.name === 'color')
+              const bg_w = this.widgets.find(
+                (w) => w.name === 'background' || w.name === 'bg_color',
+              )
+
+              const color = color_w.value
+              const bg = bg_w.value
+
+              color_w.value = bg
+              bg_w.value = color
+            },
+          }
+
+          options.push(item)
+          return [item]
+        })
+        break
+      }
       case 'Save Tensors (mtb)': {
         const onDrawBackground = nodeType.prototype.onDrawBackground
         nodeType.prototype.onDrawBackground = function (ctx, canvas) {

web/note_plus.js

@@ -1,181 +1,693 @@
 import { app } from '../../scripts/app.js'
-import { api } from '../../scripts/api.js'
 import * as shared from './comfy_shared.js'
+import { infoLogger, successLogger, errorLogger } from './comfy_shared.js'
 
+const DEFAULT_CSS = ''
+const DEFAULT_HTML = `<p style='color:red;font-family:monospace'>
+    Note+
+</p>`
+const DEFAULT_MD = '## Note+'
+const DEFAULT_MODE = 'markdown'
+const DEFAULT_THEME = 'one_dark'
+
+const CSS_RESET = `
+* {
+  font-family: monospace;
+  line-height: 1.25em;
+}
+
+h1, h2, h3, h4, h5, h6 {
+  margin: 0;
+  padding: 0;
+  font-weight: normal;
+}
+
+p, ul, ol, dl, blockquote {
+  margin: 0.3em;
+  padding: 0;
+}
+
+
+ul, ol {
+  
+  padding-left: 1em;
+  
+}
+
+a {
+  color: inherit;
+  text-decoration: none;
+  pointer-events: all;
+  color: cyan;
+}
+
+img {
+  padding: 1em 0;
+  max-width: 100%;
+}
+
+iframe {
+  width: 100%;
+  height: auto;
+  border:none;
+  pointer-events:all;
+}
+
+blockquote {
+  border-left: 4px solid #ccc;
+  padding-left: 1em;
+  margin-left: 0;
+  font-style: italic;
+}
+
+pre, code {
+  font-family: monospace;
+}
+
+table {
+  border-collapse: collapse;
+  width: 100%;
+  border-bottom: 1px solid #000;
+  margin: 1em 0;
+}
+
+th, td {
+  border-left: 1px solid #000;
+  border-right: 1px solid #000;
+  padding: 8px;
+  text-align: left;
+}
+
+th {
+  border: 1px solid #000;
+
+  background-color: rgba(0,0,0,0.5);
+}
+
+input[type="checkbox"] {
+  margin-right: 10px;
+}
+
+`
+
+const themes = [
+  'ambiance',
+  'chaos',
+  'chrome',
+  'cloud9_day',
+  'cloud9_night',
+  'cloud9_night_low_color',
+  'cloud_editor',
+  'cloud_editor_dark',
+  'clouds',
+  'clouds_midnight',
+  'cobalt',
+  'crimson_editor',
+  'dawn',
+  'dracula',
+  'dreamweaver',
+  'eclipse',
+  'github',
+  'github_dark',
+  'gob',
+  'gruvbox',
+  'gruvbox_dark_hard',
+  'gruvbox_light_hard',
+  'idle_fingers',
+  'iplastic',
+  'katzenmilch',
+  'kr_theme',
+  'kuroir',
+  'merbivore',
+  'merbivore_soft',
+  'mono_industrial',
+  'monokai',
+  'nord_dark',
+  'one_dark',
+  'pastel_on_dark',
+  'solarized_dark',
+  'solarized_light',
+  'sqlserver',
+  'terminal',
+  'textmate',
+  'tomorrow',
+  'tomorrow_night',
+  'tomorrow_night_blue',
+  'tomorrow_night_bright',
+  'tomorrow_night_eighties',
+  'twilight',
+  'vibrant_ink',
+  'vscode',
+]
 class NotePlus extends LiteGraph.LGraphNode {
-  title = 'Note+ (mtb)'
-  category = 'mtb/utils'
+  // same values as the comfy note
+  color = LGraphCanvas.node_colors.yellow.color
+  bgcolor = LGraphCanvas.node_colors.yellow.bgcolor
+  groupcolor = LGraphCanvas.node_colors.yellow.groupcolor
 
   constructor() {
     super()
+    this.uuid = shared.makeUUID()
 
+    infoLogger('Constructing Note+ instance')
+    // - litegraph settings
+    this.collapsable = true
     this.isVirtualNode = true
+    this.shape = LiteGraph.BOX_SHAPE
     this.serialize_widgets = true
 
-    this.editing = false
+    // - default values, serialization is done through widgets
+    this._raw_html = DEFAULT_MODE === 'html' ? DEFAULT_HTML : DEFAULT_MD
+
+    // - mardown converter
+    this.markdownConverter = new showdown.Converter({
+      tables: true,
+      strikethrough: true,
+      emoji: true,
+      ghCodeBlocks: true,
+      tasklists: true,
+      ghMentions: true,
+      smoothLivePreview: true,
+      simplifiedAutoLink: true,
+      parseImgDimensions: true,
+      openLinksInNewWindow: true,
+    })
+
+    // - state
     this.live = true
-    this.rawVal = "<p style='color:red;font-family:monospace'\n>    Note+\n</p>"
-
-    this.calculated_height = 36
+    this.calculated_height = 0
 
+    // - add widgets
     const inner = document.createElement('div')
     inner.style.margin = '0'
     inner.style.padding = '0'
+    inner.style.pointerEvents = 'none'
     this.html_widget = this.addDOMWidget('HTML', 'html', inner, {
-      setValue: (v) => {
-        // update our widget preview
-        this.html_widget.element.innerHTML = v
-        // calculate height
-        this.calculated_height = this.html_widget.element.scrollHeight + 36
+      setValue: (val) => {
+        this._raw_html = val
       },
-      getValue: () => this.rawVal,
+      getValue: () => this._raw_html,
       getMinHeight: () => this.calculated_height, // (the edit button),
+      hideOnZoom: false,
     })
 
-    // console.log(`Value of HTML: ${this.html_widget.value}`)
-    this.html_widget.element.innerHTML = this.html_widget.value
+    this.setupSerializationWidgets()
+    this.setupDialog()
+    this.loadAceEditor()
+  }
 
-    //- ace based editor
-    this.addWidget('button', 'Edit', 'Edit', () => {
-      const container = document.createElement('div')
-      Object.assign(container.style, {
-        display: 'flex',
-        gap: '10px',
-      })
+  /**
+   *
+   * @param {CanvasRenderingContext2D} ctx
+   * @param {LGraphCanvas} graphcanvas
+   * @returns
+   */
 
-      dialog.show('')
-      dialog.textElement.append(container)
+  onDrawForeground(ctx, _graphcanvas) {
+    if (this.flags.collapsed) return
 
-      const value = document.createElement('div')
-      value.id = 'noteplus-editor'
-      Object.assign(value.style, {
-        width: '300px',
-        height: '200px',
-        backgroundColor: 'rgb(30,30,30)',
-        color: 'whitesmoke',
-      })
+    // Define the size and position of the icon
+    const iconSize = 14 // Size of the icon
+    const iconMargin = 8 // Margin from the edges
+    const x = this.size[0] - iconSize - iconMargin
+    const y = iconMargin * 1.5
 
-      container.append(value)
+    // Create a new Path2D object from SVG path data
+    const pencilPath = new Path2D(
+      'M21.28 6.4l-9.54 9.54c-.95.95-3.77 1.39-4.4.76-.63-.63-.2-3.45.75-4.4l9.55-9.55a2.58 2.58 0 1 1 3.64 3.65z',
+    )
+    const folderPath = new Path2D(
+      'M11 4H6a4 4 0 0 0-4 4v10a4 4 0 0 0 4 4h11c2.21 0 3-1.8 3-4v-5',
+    )
 
-      const live_edit = document.createElement('input')
-      live_edit.type = 'checkbox'
-      live_edit.checked = this.live
-      live_edit.onchange = () => {
-        this.live = live_edit.checked
-      }
+    // Draw the paths
+    ctx.save()
+    ctx.translate(x, y) // Position the icon on the canvas
+    ctx.scale(iconSize / 32, iconSize / 32) // Scale the icon to the desired size
+    ctx.strokeStyle = 'rgba(255,255,255,0.3)'
 
-      const live_edit_label = document.createElement('label')
-      live_edit_label.textContent = 'Live Edit'
-      live_edit_label.append(live_edit)
+    ctx.lineCap = 'round'
+    ctx.lineJoin = 'round'
 
-      value.after(live_edit_label)
+    ctx.lineWidth = 2.4
+    ctx.stroke(pencilPath)
+    ctx.stroke(folderPath)
+    ctx.restore()
+  }
+  onMouseDown(_e, localPos, _graphcanvas) {
+    // Check if the click is within the pencil icon bounds
+    const iconSize = 14
+    const iconMargin = 8
+    const iconX = this.size[0] - iconSize - iconMargin
+    const iconY = iconMargin * 1.5
 
-      this.setupEditor()
-      this.editor.setValue(this.html_widget.element.innerHTML)
+    if (
+      localPos[0] > iconX &&
+      localPos[0] < iconX + iconSize &&
+      localPos[1] > iconY &&
+      localPos[1] < iconY + iconSize
+    ) {
+      // Pencil icon was clicked, open the editor
+      this.openEditorDialog()
+      return true // Return true to indicate the event was handled
+    }
+
+    return false // Return false to let the event propagate
+  }
+
+  setupSerializationWidgets() {
+    infoLogger('Setup Serializing widgets')
+
+    this.edit_mode_widget = this.addWidget(
+      'combo',
+      'Mode',
+      DEFAULT_MODE,
+      (me) => successLogger('Updating edit_mode', me),
+      {
+        values: ['html', 'markdown', 'raw'],
+      },
+    )
+    this.css_widget = this.addWidget('text', 'CSS', DEFAULT_CSS, (val) => {
+      successLogger(`Updating css ${val}`)
     })
+    this.theme_widget = this.addWidget(
+      'text',
+      'Theme',
+      DEFAULT_THEME,
+      (val) => {
+        successLogger(`Setting theme ${val}`)
+      },
+    )
 
-    const dialog = new app.ui.dialog.constructor()
-    dialog.element.classList.add('comfy-settings')
+    shared.hideWidgetForGood(this, this.edit_mode_widget)
+    shared.hideWidgetForGood(this, this.css_widget)
+    shared.hideWidgetForGood(this, this.theme_widget)
+  }
+  setupDialog() {
+    infoLogger('Setup dialog')
+    // this.addWidget('button', 'Edit', 'Edit', this.openEditorDialog.bind(this))
 
-    const closeButton = dialog.element.querySelector('button')
+    this.dialog = new app.ui.dialog.constructor()
+    this.dialog.element.classList.add('comfy-settings')
+
+    const closeButton = this.dialog.element.querySelector('button')
     closeButton.textContent = 'CANCEL'
     const saveButton = document.createElement('button')
     saveButton.textContent = 'SAVE'
     saveButton.onclick = () => {
-      this.updateHTML(this.editor.getValue())
-
-      this.editor.destroy()
-      this.editor.container.remove()
-
-      dialog.close()
+      this.closeEditorDialog(true)
+    }
+    closeButton.onclick = () => {
+      this.closeEditorDialog(false)
     }
-
     closeButton.before(saveButton)
-
-    shared
-      .loadScript(
-        'https://cdn.jsdelivr.net/npm/ace-builds@1.16.0/src-min-noconflict/ace.min.js'
-      )
-      .catch((e) => {
-        console.error(e)
-      })
   }
 
-  setupEditor() {
-    this.editor = ace.edit('noteplus-editor')
-    this.editor.setTheme('ace/theme/dracula')
-    this.editor.session.setMode('ace/mode/html')
+  teardownEditors() {
+    this.css_editor.destroy()
+    this.css_editor.container.remove()
 
-    this.editor.setShowPrintMargin(false)
-    this.editor.session.setUseWrapMode(true)
-    this.editor.renderer.setShowGutter(false)
-    this.editor.session.setTabSize(4)
-    this.editor.session.setUseSoftTabs(true)
-    this.editor.setFontSize(14)
-    this.editor.setReadOnly(false)
-    this.editor.setHighlightActiveLine(false)
-    this.editor.setShowFoldWidgets(true)
+    this.html_editor.destroy()
+    this.html_editor.container.remove()
+  }
 
-    this.editor.session.on('change', (delta) => {
+  closeEditorDialog(accept) {
+    infoLogger('Closing editor dialog', accept)
+    if (accept) {
+      this.updateHTML(this.html_editor.getValue())
+      this.updateCSS(this.css_editor.getValue())
+    }
+    this.teardownEditors()
+    this.dialog.close()
+  }
+
+  openEditorDialog() {
+    infoLogger(`Current edit mode ${this.edit_mode_widget.value}`)
+    const container = document.createElement('div')
+
+    Object.assign(container.style, {
+      display: 'flex',
+      gap: '10px',
+      flexDirection: 'column',
+    })
+
+    const editorsContainer = document.createElement('div')
+    Object.assign(editorsContainer.style, {
+      display: 'flex',
+      gap: '10px',
+      flexDirection: 'row',
+    })
+
+    container.append(editorsContainer)
+
+    this.dialog.show('')
+    this.dialog.textElement.append(container)
+
+    const aceHTML = document.createElement('div')
+    aceHTML.id = 'noteplus-html-editor'
+    Object.assign(aceHTML.style, {
+      width: '300px',
+      height: '300px',
+      // backgroundColor: 'rgb(30,30,30)',
+      // color: 'whitesmoke',
+    })
+
+    editorsContainer.append(aceHTML)
+
+    const aceCSS = document.createElement('div')
+    aceCSS.id = 'noteplus-css-editor'
+    Object.assign(aceCSS.style, {
+      width: '300px',
+      height: '300px',
+      // backgroundColor: 'rgb(30,30,30)',
+      // color: 'whitesmoke',
+    })
+
+    editorsContainer.append(aceCSS)
+
+    const live_edit = document.createElement('input')
+    live_edit.type = 'checkbox'
+    live_edit.checked = this.live
+    live_edit.onchange = () => {
+      this.live = live_edit.checked
+    }
+
+    //- "Dynamic" elements
+    const firstButton = this.dialog.element.querySelector('button')
+    const syncUI = () => {
+      let convert_to_html =
+        this.dialog.element.querySelector('#convert-to-html')
+      if (this.edit_mode_widget.value === 'markdown') {
+        if (convert_to_html == null) {
+          convert_to_html = document.createElement('button')
+          convert_to_html.textContent = 'Convert to HTML (NO UNDO!)'
+          convert_to_html.id = 'convert-to-html'
+          convert_to_html.onclick = () => {
+            const select_mode = this.dialog.element.querySelector('#edit_mode')
+
+            const md = this.html_editor.getValue()
+            this.edit_mode_widget.value = 'html'
+            select_mode.value = 'html'
+            const html = this.markdownConverter.makeHtml(md)
+            this.html_widget.value = html
+            this.html_editor.setValue(html)
+            this.html_editor.session.setMode('ace/mode/html')
+            this.updateHTML(this.html_widget.value)
+
+            convert_to_html.remove()
+          }
+
+          firstButton.before(convert_to_html)
+        }
+      } else {
+        if (convert_to_html != null) {
+          convert_to_html.remove()
+          convert_to_html = null
+        }
+      }
+      select_mode.value = this.edit_mode_widget.value
+    }
+    //- combobox
+    let theme_select = this.dialog.element.querySelector('#theme_select')
+    if (!theme_select) {
+      infoLogger('Creating combobox for select')
+      theme_select = document.createElement('select')
+      theme_select.name = 'theme'
+      theme_select.id = 'theme_select'
+
+      const addOption = (label) => {
+        const option = document.createElement('option')
+        option.value = label
+        option.textContent = label
+        theme_select.append(option)
+      }
+      for (const t of themes) {
+        addOption(t)
+      }
+
+      theme_select.addEventListener('change', (event) => {
+        const val = event.target.value
+        this.setTheme(val)
+      })
+
+      container.prepend(theme_select)
+    }
+
+    theme_select.value = this.theme_widget.value
+
+    let select_mode = this.dialog.element.querySelector('#edit_mode')
+
+    if (!select_mode) {
+      infoLogger('Creating combobox for select')
+      select_mode = document.createElement('select')
+      select_mode.name = 'mode'
+      select_mode.id = 'edit_mode'
+
+      const addOption = (label) => {
+        const option = document.createElement('option')
+        option.value = label
+        option.textContent = label
+        select_mode.append(option)
+      }
+      addOption('markdown')
+      addOption('html')
+
+      select_mode.addEventListener('change', (event) => {
+        const val = event.target.value
+        this.edit_mode_widget.value = val
+        if (this.html_editor) {
+          this.html_editor.session.setMode(`ace/mode/${val}`)
+          this.updateHTML(this.html_editor.getValue())
+
+          syncUI()
+        }
+      })
+
+      container.append(select_mode)
+    }
+    select_mode.value = this.edit_mode_widget.value
+
+    syncUI()
+
+    const live_edit_label = document.createElement('label')
+    live_edit_label.textContent = 'Live Edit'
+
+    // add a tooltip
+    live_edit_label.title =
+      'When this is on, the editor will update the note+ whenever you change the text.'
+
+    live_edit_label.append(live_edit)
+
+    // select_mode.before(live_edit_label)
+    container.append(live_edit_label)
+
+    this.setupEditors()
+  }
+  loadAceEditor() {
+    shared.loadScript('/mtb_async/ace/ace.js').catch((e) => {
+      errorLogger(e)
+    })
+  }
+  onCreate() {
+    errorLogger('NotePlus onCreate')
+  }
+  configure(info) {
+    super.configure(info)
+    infoLogger('Restoring serialized values', info)
+    // - update view from serialzed data
+    this.html_widget.element.id = `note-plus-${this.uuid}`
+    this.setMode(this.edit_mode_widget.value)
+    this.setTheme(this.theme_widget.value)
+    this.updateHTML(this.html_widget.value)
+    this.updateCSS(this.css_widget.value)
+    this.setSize(info.size)
+  }
+  onNodeCreated() {
+    infoLogger('Node created', this.uuid)
+    this.html_widget.element.id = `note-plus-${this.uuid}`
+    this.setMode(this.edit_mode_widget.value)
+    this.setTheme(this.theme_widget.value)
+    this.updateHTML(this.html_widget.value) // widget is populated here since we called super
+    this.updateCSS(this.css_widget.value)
+  }
+  onRemoved() {
+    infoLogger('Node removed', this.uuid)
+  }
+  getExtraMenuOptions() {
+    const options = []
+    // {
+    //       content: string;
+    //       callback?: ContextMenuEventListener;
+    //       /** Used as innerHTML for extra child element */
+    //       title?: string;
+    //       disabled?: boolean;
+    //       has_submenu?: boolean;
+    //       submenu?: {
+    //           options: ContextMenuItem[];
+    //       } & IContextMenuOptions;
+    //       className?: string;
+    //   }
+    options.push({
+      content: `Set to ${
+        this.edit_mode_widget.value === 'html' ? 'markdown' : 'html'
+      }`,
+      callback: () => {
+        this.edit_mode_widget.value =
+          this.edit_mode_widget.value === 'html' ? 'markdown' : 'html'
+        this.updateHTML(this.html_widget.value)
+      },
+    })
+
+    return options
+  }
+
+  _setupEditor(editor) {
+    this.setTheme(this.theme_widget.value)
+
+    editor.setShowPrintMargin(false)
+    editor.session.setUseWrapMode(true)
+    editor.renderer.setShowGutter(false)
+    editor.session.setTabSize(4)
+    editor.session.setUseSoftTabs(true)
+    editor.setFontSize(14)
+    editor.setReadOnly(false)
+    editor.setHighlightActiveLine(false)
+    editor.setShowFoldWidgets(true)
+    return editor
+  }
+
+  setTheme(theme) {
+    this.theme_widget.value = theme
+    if (this.html_editor) {
+      this.html_editor.setTheme(`ace/theme/${theme}`)
+    }
+    if (this.css_editor) {
+      this.css_editor.setTheme(`ace/theme/${theme}`)
+    }
+  }
+
+  setMode(mode) {
+    this.edit_mode_widget.value = mode
+    if (this.html_editor) {
+      this.html_editor.session.setMode(`ace/mode/${mode}`)
+    }
+
+    this.updateHTML(this.html_widget.value)
+  }
+  setupEditors() {
+    infoLogger('NotePlus setupEditor')
+
+    this.html_editor = ace.edit('noteplus-html-editor')
+
+    this.css_editor = ace.edit('noteplus-css-editor')
+    this.css_editor.session.setMode('ace/mode/css')
+
+    this.setMode(DEFAULT_MODE)
+
+    this._setupEditor(this.html_editor)
+    this._setupEditor(this.css_editor)
+
+    this.css_editor.session.on('change', (_delta) => {
       // delta.start, delta.end, delta.lines, delta.action
       if (this.live) {
-        this.updateHTML(this.editor.getValue())
+        this.updateCSS(this.css_editor.getValue())
       }
     })
+
+    this.html_editor.session.on('change', (_delta) => {
+      // delta.start, delta.end, delta.lines, delta.action
+      if (this.live) {
+        this.updateHTML(this.html_editor.getValue())
+      }
+    })
+
+    this.html_editor.setValue(this.html_widget.value)
+    this.css_editor.setValue(this.css_widget.value)
+  }
+
+  scopeCss(css, scopeId) {
+    return css
+      .split('}')
+      .map((rule) => {
+        if (rule.trim() === '') {
+          return ''
+        }
+        const scopedRule = rule
+          .split('{')
+          .map((segment, index) => {
+            if (index === 0) {
+              return `#${scopeId} ${segment.trim()}`
+            }
+            return `{${segment.trim()}`
+          })
+          .join(' ')
+        return `${scopedRule}}`
+      })
+      .join('\n')
+  }
+  getCssDom() {
+    const styleTagId = `note-plus-stylesheet-${this.uuid}`
+
+    let styleTag = document.head.querySelector(`#${styleTagId}`)
+
+    if (!styleTag) {
+      styleTag = document.createElement('style')
+      styleTag.type = 'text/css'
+      styleTag.id = styleTagId
+      document.head.appendChild(styleTag)
+      infoLogger(`Creating note-plus-stylesheet-${this.uuid}`, styleTag)
+    }
+
+    return styleTag
+  }
+  calculateHeight() {
+    this.calculated_height = shared.calculateTotalChildrenHeight(
+      this.html_widget.element,
+    )
+    this.setDirtyCanvas(true, true)
+  }
+  updateCSS(css) {
+    infoLogger('NotePlus updateCSS')
+    // this.html_widget.element.style = css
+    const scopedCss = this.scopeCss(
+      `${CSS_RESET}\n${css}`,
+      `note-plus-${this.uuid}`,
+    )
+
+    const cssDom = this.getCssDom()
+    cssDom.innerHTML = scopedCss
+
+    this.css_widget.value = css
+    this.calculateHeight()
+    infoLogger('NotePlus updateCSS', this.calculated_height)
+    // this.setSize(this.computeSize())
   }
 
   updateHTML(val) {
-    // if (CONTAINER_HTML.includes('${html}')) {
-    //   console.log('found template')
-    //   val = CONTAINER_HTML.replace('${html}', val)
-    // }
+    const cleanHTML = DOMPurify.sanitize(val, { ADD_TAGS: ['iframe'] })
+    this.html_widget.value = cleanHTML
 
-    this.html_widget.value = val
-    this.rawVal = val
-
-    this.calculated_height = this.html_widget.element.scrollHeight
-
-    this.setSize(this.computeSize())
+    // update our widget preview
+    if (this.edit_mode_widget.value === 'html') {
+      this.html_widget.element.innerHTML = cleanHTML
+    } else if (this.edit_mode_widget.value === 'markdown') {
+      this.html_widget.element.innerHTML =
+        this.markdownConverter.makeHtml(cleanHTML)
+    }
+    this.calculateHeight()
+    // this.setSize(this.computeSize())
   }
-
-  // //   onRemoved() {
-  // //     console.log('Removing', this)
-  // //     for (const w of this.widgets) {
-  // //       console.log('Removing', w)
-  // //       w.onRemove?.()
-  // //       w.onRemoved?.()
-  // //     }
-  // //   }
 }
 
 app.registerExtension({
   name: 'mtb.noteplus',
 
-  setup() {
-    // app.ui.settings.addSetting({
-    //     id: "mtb.noteplus.Container",
-    //     name: "📦 HTML container",
-    //     type: "text",
-    //     defaultValue: "<div>${html}</div>",
-    //     tooltip:
-    //         "This defines the wrapper for the noteplus html content, use '${html}' to define the location of the placeholder",
-    //     attrs: {
-    //         style: {
-    //             fontFamily: "monospace",
-    //         },
-    //     },
-    //     onChange(value) {
-    //         if (!value) {
-    //             CONTAINER_HTML = null;
-    //             return;
-    //         }
-    //         console.log(`NOTEPLUS| value changed: ${value}`)
-    //         CONTAINER_HTML = value
-    //     },
-    // });
-  },
-
   registerCustomNodes() {
     LiteGraph.registerNodeType('Note Plus (mtb)', NotePlus)
+
+    NotePlus.category = 'mtb/utils'
+    NotePlus.title = 'Note+ (mtb)'
+
+    NotePlus.title_mode = LiteGraph.NO_TITLE
   },
 })

web/numberInput.js

@@ -0,0 +1,334 @@
+// This is a vanillajs implementation of Houdini's number input widgets.
+// It basically popup a visual sensitivity slider of steps to use as incr/decr
+// TODO: Convert it to IWidget
+
+// import styles from "./style.module.css";
+
+function getValidNumber(numberInput) {
+  let num =
+    isNaN(numberInput.value) || numberInput.value === ''
+      ? 0
+      : parseFloat(numberInput.value)
+  return num
+}
+/**
+ * Number input widgets
+ */
+export class NumberInputWidget {
+  constructor(containerId, numberOfInputs = 1, isDebug = false) {
+    this.container = document.getElementById(containerId)
+    this.numberOfInputs = numberOfInputs
+    this.currentInput = null // Store the currently active input
+
+    this.threshold = 30
+    this.mouseSensitivityMultiplier = 0.05
+    this.debug = isDebug
+
+    //- states
+    this.initialMouseX
+    this.lastMouseX
+    this.activeStep = 1
+    this.accumulatedDelta = 0
+    this.stepLocked = false
+    this.thresholdExceeded = false
+    this.isDragging = false
+
+    const styleTagId = 'mtb-constant-style'
+
+    let styleTag = document.head.querySelector(`#${styleTagId}`)
+
+    if (!styleTag) {
+      styleTag = document.createElement('style')
+      styleTag.type = 'text/css'
+      styleTag.id = styleTagId
+
+      styleTag.innerHTML = `
+
+.${containerId}{
+  margin-top: 20px;
+  margin-bottom: 20px;
+}
+.sensitivity-menu {
+  display: none;
+  position: absolute;
+  /* Additional styling */
+}
+
+.sensitivity-menu .step {
+  cursor: pointer;
+  padding: 0.5em;
+  /* Add more styling as needed */
+}
+
+.sensitivity-menu {
+  font-family: monospace;
+
+  background: var(--bg-color);
+  border: 1px solid var(--fg-color); 
+  /* Highlight for the active step */
+}
+.number-input {
+  background: var(--bg-color);
+  color: var(--fg-color)
+}
+
+.sensitivity-menu .step.active {
+  background-color:var(--drag-text); 
+  /* Highlight for the active step */
+}
+
+.sensitivity-menu .step.locked {
+  background-color: #f00;
+  /* Change to your preferred color for the locked state */
+}
+#debug-container {
+  transform: translateX(50%);
+  width: 50%;
+  text-align: center;
+  font-family: monospace;
+}
+`
+      document.head.appendChild(styleTag)
+    }
+
+    this.createWidgetElements()
+    this.initializeEventListeners()
+  }
+
+  setLabel(str) {
+    this.label.textContent = str
+  }
+  setValue(...values) {
+    if (values.length !== this.numberInputs.length) {
+      console.error('Number of values does not match the number of inputs.')
+      console.error(
+        `You provided ${values.length} but the input want ${this.numberInputs.length}`,
+        { values },
+      )
+      return
+    }
+    // Set each input value
+    this.numberInputs.forEach((input, index) => {
+      input.value = values[index]
+    })
+  }
+  getValue() {
+    const value = []
+    this.numberInputs.forEach((input, index) => {
+      value.push(Number.parseFloat(input.value) || 0.0)
+    })
+    return value
+  }
+  resetValues() {
+    for (const input of numberInputs) {
+      input.value = 0
+    }
+    this.onChange?.(this.getValue())
+  }
+
+  createWidgetElements() {
+    this.label = document.createElement('label')
+    this.label.textContent = 'Control All:'
+    this.label.className = 'widget-label'
+    this.container.appendChild(this.label)
+
+    this.label.addEventListener('mousedown', (event) => {
+      if (event.button === 1) {
+        this.currentInput = null
+        this.handleMouseDown(event)
+      }
+    })
+
+    this.label.addEventListener('contextmenu', (event) => {
+      event.preventDefault()
+      this.resetValues()
+    })
+
+    this.numberInputs = []
+
+    // create linked inputs
+    for (let i = 0; i < this.numberOfInputs; i++) {
+      const numberInput = document.createElement('input')
+      numberInput.type = 'number'
+      numberInput.className = 'number-input' //styles.numberInput; //"number-input";
+      numberInput.step = 'any'
+      this.container.appendChild(numberInput)
+      this.numberInputs.push(numberInput)
+
+      numberInput.addEventListener('mousedown', (event) => {
+        if (event.button === 1) {
+          this.currentInput = numberInput
+          this.handleMouseDown(event)
+        }
+      })
+    }
+    this.sensitivityMenu = document.createElement('div')
+    this.sensitivityMenu.className = 'sensitivity-menu' //styles.sensitivityMenu; //"sensitivity-menu";
+    this.container.appendChild(this.sensitivityMenu)
+
+    // create steps
+    const stepsValues = [0.001, 0.01, 0.1, 1, 10, 100]
+    stepsValues.forEach((value) => {
+      const step = document.createElement('div')
+      step.className = 'step' //styles.step //"step";
+      step.dataset.step = value
+      step.textContent = value.toString()
+      this.sensitivityMenu.appendChild(step)
+    })
+
+    this.steps = this.sensitivityMenu.getElementsByClassName('step') //styles.step)
+
+    if (this.debug) {
+      this.debugContainer = document.createElement('div')
+      this.debugContainer.id = 'debug-container' //styles.debugContainer //"debugContainer";
+      document.body.appendChild(this.debugContainer)
+    }
+  }
+  showSensitivityMenu(pageX, pageY) {
+    this.sensitivityMenu.style.display = 'block'
+    this.sensitivityMenu.style.left = `${pageX}px`
+    this.sensitivityMenu.style.top = `${pageY}px`
+    this.initialMouseX = pageX
+    this.lastMouseX = pageX
+    this.isDragging = true
+    this.thresholdExceeded = false
+    this.stepLocked = false
+    this.updateDebugInfo()
+  }
+  updateDebugInfo() {
+    if (this.debug) {
+      this.debugContainer.innerHTML = `
+        <div>Active Step: ${this.activeStep}</div>
+        <div>Initial Mouse X: ${this.initialMouseX}</div>
+        <div>Last Mouse X: ${this.lastMouseX}</div>
+        <div>Accumulated Delta: ${this.accumulatedDelta}</div>
+        <div>Threshold Exceeded: ${this.thresholdExceeded}</div>
+        <div>Step Locked: ${this.stepLocked}</div>
+        <div>Number Input Value: ${this.currentInput?.value}</div>
+    `
+    }
+  }
+  handleMouseDown(event) {
+    if (event.button === 1) {
+      this.showSensitivityMenu(
+        event.target.offsetWidth,
+        event.target.offsetHeight,
+      )
+      event.preventDefault()
+    }
+  }
+  handleMouseUp(event) {
+    if (event.button === 1) {
+      this.resetWidgetState()
+    }
+  }
+  handleClickOutside(event) {
+    if (event.target !== this.numberInput) {
+      this.resetWidgetState()
+    }
+  }
+  handleMouseMove(event) {
+    if (this.sensitivityMenu.style.display === 'block') {
+      const relativeY = event.pageY - 300 // this.sensitivityMenu.offsetTop
+
+      const horizontalDistanceFromInitial = Math.abs(
+        event.target.offsetWidth - this.initialMouseX,
+      )
+
+      // Unlock if the mouse moves back towards the initial position
+      if (horizontalDistanceFromInitial < this.threshold) {
+        this.thresholdExceeded = false
+        this.stepLocked = false
+        this.accumulatedDelta = 0
+      }
+
+      // Update step only if it is not locked
+      if (!this.stepLocked) {
+        for (let step of this.steps) {
+          step.classList.remove('active') //styles.active)
+          step.classList.remove('locked') //styles.locked)
+          if (
+            relativeY >= step.offsetTop &&
+            relativeY <= step.offsetTop + step.offsetHeight
+          ) {
+            step.classList.add('active') //styles.active)
+            this.setActiveStep(parseFloat(step.dataset.step))
+          }
+        }
+      }
+
+      if (this.stepLocked) {
+        this.sensitivityMenu
+          .querySelector('.step.active')
+          ?.classList.add('locked')
+      }
+
+      this.updateStepValue(event.pageX)
+    }
+  }
+
+  initializeEventListeners() {
+    document.addEventListener('mousemove', (event) =>
+      this.handleMouseMove(event),
+    )
+    document.addEventListener('mouseup', (event) => this.handleMouseUp(event))
+
+    document.addEventListener('click', (event) =>
+      this.handleClickOutside(event),
+    )
+  }
+
+  setActiveStep(val) {
+    if (this.activeStep !== val) {
+      this.activeStep = val
+      this.stepLocked = false
+      this.accumulatedDelta = 0
+      this.thresholdExceeded = false
+    }
+  }
+  resetWidgetState() {
+    this.sensitivityMenu.style.display = 'none'
+    this.isDragging = false
+    this.lastMouseX = undefined
+    this.thresholdExceeded = false
+    this.stepLocked = false
+    this.updateDebugInfo()
+  }
+  updateStepValue(mouseX) {
+    if (this.isDragging && this.lastMouseX !== undefined) {
+      const deltaX = mouseX - this.lastMouseX
+      this.accumulatedDelta += deltaX
+
+      if (
+        !this.thresholdExceeded &&
+        Math.abs(this.accumulatedDelta) > this.threshold
+      ) {
+        this.thresholdExceeded = true
+        this.stepLocked = true
+      }
+
+      if (this.thresholdExceeded && this.stepLocked) {
+        // frequency of value changes
+        if (
+          Math.abs(this.accumulatedDelta) * this.mouseSensitivityMultiplier >=
+          1
+        ) {
+          const valueChange = Math.sign(this.accumulatedDelta) * this.activeStep
+          if (this.currentInput) {
+            this.currentInput.value =
+              getValidNumber(this.currentInput) + valueChange
+            this.onChange?.(this.getValue())
+          } else {
+            this.numberInputs.forEach((input) => {
+              input.value = getValidNumber(input) + valueChange
+            })
+          }
+          this.accumulatedDelta = 0
+        }
+      }
+
+      this.lastMouseX = mouseX
+    }
+    this.updateDebugInfo()
+  }
+}

web_async/ace/ext-beautify.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/beautify",["require","exports","module","ace/token_iterator"],function(e,t,n){"use strict";function i(e,t){return e.type.lastIndexOf(t+".xml")>-1}var r=e("../token_iterator").TokenIterator;t.singletonTags=["area","base","br","col","command","embed","hr","html","img","input","keygen","link","meta","param","source","track","wbr"],t.blockTags=["article","aside","blockquote","body","div","dl","fieldset","footer","form","head","header","html","nav","ol","p","script","section","style","table","tbody","tfoot","thead","ul"],t.formatOptions={lineBreaksAfterCommasInCurlyBlock:!0},t.beautify=function(e){var n=new r(e,0,0),s=n.getCurrentToken(),o=e.getTabString(),u=t.singletonTags,a=t.blockTags,f=t.formatOptions||{},l,c=!1,h=!1,p=!1,d="",v="",m="",g=0,y=0,b=0,w=0,E=0,S=0,x=0,T,N=0,C=0,k=[],L=!1,A,O=!1,M=!1,_=!1,D=!1,P={0:0},H=[],B=!1,j=function(){l&&l.value&&l.type!=="string.regexp"&&(l.value=l.value.replace(/^\s*/,""))},F=function(){var e=d.length-1;for(;;){if(e==0)break;if(d[e]!==" ")break;e-=1}d=d.slice(0,e+1)},I=function(){d=d.trimRight(),c=!1};while(s!==null){N=n.getCurrentTokenRow(),k=n.$rowTokens,l=n.stepForward();if(typeof s!="undefined"){v=s.value,E=0,_=m==="style"||e.$modeId==="ace/mode/css",i(s,"tag-open")?(M=!0,l&&(D=a.indexOf(l.value)!==-1),v==="</"&&(D&&!c&&C<1&&C++,_&&(C=1),E=1,D=!1)):i(s,"tag-close")?M=!1:i(s,"comment.start")?D=!0:i(s,"comment.end")&&(D=!1),!M&&!C&&s.type==="paren.rparen"&&s.value.substr(0,1)==="}"&&C++,N!==T&&(C=N,T&&(C-=T));if(C){I();for(;C>0;C--)d+="\n";c=!0,!i(s,"comment")&&!s.type.match(/^(comment|string)$/)&&(v=v.trimLeft())}if(v){s.type==="keyword"&&v.match(/^(if|else|elseif|for|foreach|while|switch)$/)?(H[g]=v,j(),p=!0,v.match(/^(else|elseif)$/)&&d.match(/\}[\s]*$/)&&(I(),h=!0)):s.type==="paren.lparen"?(j(),v.substr(-1)==="{"&&(p=!0,O=!1,M||(C=1)),v.substr(0,1)==="{"&&(h=!0,d.substr(-1)!=="["&&d.trimRight().substr(-1)==="["?(I(),h=!1):d.trimRight().substr(-1)===")"?I():F())):s.type==="paren.rparen"?(E=1,v.substr(0,1)==="}"&&(H[g-1]==="case"&&E++,d.trimRight().substr(-1)==="{"?I():(h=!0,_&&(C+=2))),v.substr(0,1)==="]"&&d.substr(-1)!=="}"&&d.trimRight().substr(-1)==="}"&&(h=!1,w++,I()),v.substr(0,1)===")"&&d.substr(-1)!=="("&&d.trimRight().substr(-1)==="("&&(h=!1,w++,I()),F()):s.type!=="keyword.operator"&&s.type!=="keyword"||!v.match(/^(=|==|===|!=|!==|&&|\|\||and|or|xor|\+=|.=|>|>=|<|<=|=>)$/)?s.type==="punctuation.operator"&&v===";"?(I(),j(),p=!0,_&&C++):s.type==="punctuation.operator"&&v.match(/^(:|,)$/)?(I(),j(),v.match(/^(,)$/)&&x>0&&S===0&&f.lineBreaksAfterCommasInCurlyBlock?C++:(p=!0,c=!1)):s.type==="support.php_tag"&&v==="?>"&&!c?(I(),h=!0):i(s,"attribute-name")&&d.substr(-1).match(/^\s$/)?h=!0:i(s,"attribute-equals")?(F(),j()):i(s,"tag-close")?(F(),v==="/>"&&(h=!0)):s.type==="keyword"&&v.match(/^(case|default)$/)&&B&&(E=1):(I(),j(),h=!0,p=!0);if(c&&(!s.type.match(/^(comment)$/)||!!v.substr(0,1).match(/^[/#]$/))&&(!s.type.match(/^(string)$/)||!!v.substr(0,1).match(/^['"@]$/))){w=b;if(g>y){w++;for(A=g;A>y;A--)P[A]=w}else g<y&&(w=P[g]);y=g,b=w,E&&(w-=E),O&&!S&&(w++,O=!1);for(A=0;A<w;A++)d+=o}s.type==="keyword"&&v.match(/^(case|default)$/)?B===!1&&(H[g]=v,g++,B=!0):s.type==="keyword"&&v.match(/^(break)$/)&&H[g-1]&&H[g-1].match(/^(case|default)$/)&&(g--,B=!1),s.type==="paren.lparen"&&(S+=(v.match(/\(/g)||[]).length,x+=(v.match(/\{/g)||[]).length,g+=v.length),s.type==="keyword"&&v.match(/^(if|else|elseif|for|while)$/)?(O=!0,S=0):!S&&v.trim()&&s.type!=="comment"&&(O=!1);if(s.type==="paren.rparen"){S-=(v.match(/\)/g)||[]).length,x-=(v.match(/\}/g)||[]).length;for(A=0;A<v.length;A++)g--,v.substr(A,1)==="}"&&H[g]==="case"&&g--}s.type=="text"&&(v=v.replace(/\s+$/," ")),h&&!c&&(F(),d.substr(-1)!=="\n"&&(d+=" ")),d+=v,p&&(d+=" "),c=!1,h=!1,p=!1;if(i(s,"tag-close")&&(D||a.indexOf(m)!==-1)||i(s,"doctype")&&v===">")D&&l&&l.value==="</"?C=-1:C=1;l&&u.indexOf(l.value)===-1&&(i(s,"tag-open")&&v==="</"?g--:i(s,"tag-open")&&v==="<"?g++:i(s,"tag-close")&&v==="/>"&&g--),i(s,"tag-name")&&(m=v),T=N}}s=l}d=d.trim(),e.doc.setValue(d)},t.commands=[{name:"beautify",description:"Format selection (Beautify)",exec:function(e){t.beautify(e.session)},bindKey:"Ctrl-Shift-B"}]});                (function() {
+                    ace.require(["ace/ext/beautify"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-code_lens.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/code_lens",["require","exports","module","ace/line_widgets","ace/lib/event","ace/lib/lang","ace/lib/dom","ace/editor","ace/config"],function(e,t,n){"use strict";function u(e){var t=e.$textLayer,n=t.$lenses;n&&n.forEach(function(e){e.remove()}),t.$lenses=null}function a(e,t){var n=e&t.CHANGE_LINES||e&t.CHANGE_FULL||e&t.CHANGE_SCROLL||e&t.CHANGE_TEXT;if(!n)return;var r=t.session,i=t.session.lineWidgets,s=t.$textLayer,a=s.$lenses;if(!i){a&&u(t);return}var f=t.$textLayer.$lines.cells,l=t.layerConfig,c=t.$padding;a||(a=s.$lenses=[]);var h=0;for(var p=0;p<f.length;p++){var d=f[p].row,v=i[d],m=v&&v.lenses;if(!m||!m.length)continue;var g=a[h];g||(g=a[h]=o.buildDom(["div",{"class":"ace_codeLens"}],t.container)),g.style.height=l.lineHeight+"px",h++;for(var y=0;y<m.length;y++){var b=g.childNodes[2*y];b||(y!=0&&g.appendChild(o.createTextNode("\u00a0|\u00a0")),b=o.buildDom(["a"],g)),b.textContent=m[y].title,b.lensCommand=m[y]}while(g.childNodes.length>2*y-1)g.lastChild.remove();var w=t.$cursorLayer.getPixelPosition({row:d,column:0},!0).top-l.lineHeight*v.rowsAbove-l.offset;g.style.top=w+"px";var E=t.gutterWidth,S=r.getLine(d).search(/\S|$/);S==-1&&(S=0),E+=S*l.characterWidth,g.style.paddingLeft=c+E+"px"}while(h<a.length)a.pop().remove()}function f(e){if(!e.lineWidgets)return;var t=e.widgetManager;e.lineWidgets.forEach(function(e){e&&e.lenses&&t.removeLineWidget(e)})}function l(e){e.codeLensProviders=[],e.renderer.on("afterRender",a),e.$codeLensClickHandler||(e.$codeLensClickHandler=function(t){var n=t.target.lensCommand;if(!n)return;e.execCommand(n.id,n.arguments),e._emit("codeLensClick",t)},i.addListener(e.container,"click",e.$codeLensClickHandler,e)),e.$updateLenses=function(){function o(){var r=n.selection.cursor,i=n.documentToScreenRow(r),o=n.getScrollTop(),u=t.setLenses(n,s),a=n.$undoManager&&n.$undoManager.$lastDelta;if(a&&a.action=="remove"&&a.lines.length>1)return;var f=n.documentToScreenRow(r),l=e.renderer.layerConfig.lineHeight,c=n.getScrollTop()+(f-i)*l;u==0&&o<l/4&&o>-l/4&&(c=-l),n.setScrollTop(c)}var n=e.session;if(!n)return;n.widgetManager||(n.widgetManager=new r(n),n.widgetManager.attach(e));var i=e.codeLensProviders.length,s=[];e.codeLensProviders.forEach(function(e){e.provideCodeLenses(n,function(e,t){if(e)return;t.forEach(function(e){s.push(e)}),i--,i==0&&o()})})};var n=s.delayedCall(e.$updateLenses);e.$updateLensesOnInput=function(){n.delay(250)},e.on("input",e.$updateLensesOnInput)}function c(e){e.off("input",e.$updateLensesOnInput),e.renderer.off("afterRender",a),e.$codeLensClickHandler&&e.container.removeEventListener("click",e.$codeLensClickHandler)}var r=e("../line_widgets").LineWidgets,i=e("../lib/event"),s=e("../lib/lang"),o=e("../lib/dom");t.setLenses=function(e,t){var n=Number.MAX_VALUE;return f(e),t&&t.forEach(function(t){var r=t.start.row,i=t.start.column,s=e.lineWidgets&&e.lineWidgets[r];if(!s||!s.lenses)s=e.widgetManager.$registerLineWidget({rowCount:1,rowsAbove:1,row:r,column:i,lenses:[]});s.lenses.push(t.command),r<n&&(n=r)}),e._emit("changeFold",{data:{start:{row:n}}}),n},t.registerCodeLensProvider=function(e,t){e.setOption("enableCodeLens",!0),e.codeLensProviders.push(t),e.$updateLensesOnInput()},t.clear=function(e){t.setLenses(e,null)};var h=e("../editor").Editor;e("../config").defineOptions(h.prototype,"editor",{enableCodeLens:{set:function(e){e?l(this):c(this)}}}),o.importCssString("\n.ace_codeLens {\n    position: absolute;\n    color: #aaa;\n    font-size: 88%;\n    background: inherit;\n    width: 100%;\n    display: flex;\n    align-items: flex-end;\n    pointer-events: none;\n}\n.ace_codeLens > a {\n    cursor: pointer;\n    pointer-events: auto;\n}\n.ace_codeLens > a:hover {\n    color: #0000ff;\n    text-decoration: underline;\n}\n.ace_dark > .ace_codeLens > a:hover {\n    color: #4e94ce;\n}\n","codelense.css",!1)});                (function() {
+                    ace.require(["ace/ext/code_lens"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-elastic_tabstops_lite.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/elastic_tabstops_lite",["require","exports","module","ace/editor","ace/config"],function(e,t,n){"use strict";var r=function(){function e(e){this.$editor=e;var t=this,n=[],r=!1;this.onAfterExec=function(){r=!1,t.processRows(n),n=[]},this.onExec=function(){r=!0},this.onChange=function(e){r&&(n.indexOf(e.start.row)==-1&&n.push(e.start.row),e.end.row!=e.start.row&&n.push(e.end.row))}}return e.prototype.processRows=function(e){this.$inChange=!0;var t=[];for(var n=0,r=e.length;n<r;n++){var i=e[n];if(t.indexOf(i)>-1)continue;var s=this.$findCellWidthsForBlock(i),o=this.$setBlockCellWidthsToMax(s.cellWidths),u=s.firstRow;for(var a=0,f=o.length;a<f;a++){var l=o[a];t.push(u),this.$adjustRow(u,l),u++}}this.$inChange=!1},e.prototype.$findCellWidthsForBlock=function(e){var t=[],n,r=e;while(r>=0){n=this.$cellWidthsForRow(r);if(n.length==0)break;t.unshift(n),r--}var i=r+1;r=e;var s=this.$editor.session.getLength();while(r<s-1){r++,n=this.$cellWidthsForRow(r);if(n.length==0)break;t.push(n)}return{cellWidths:t,firstRow:i}},e.prototype.$cellWidthsForRow=function(e){var t=this.$selectionColumnsForRow(e),n=[-1].concat(this.$tabsForRow(e)),r=n.map(function(e){return 0}).slice(1),i=this.$editor.session.getLine(e);for(var s=0,o=n.length-1;s<o;s++){var u=n[s]+1,a=n[s+1],f=this.$rightmostSelectionInCell(t,a),l=i.substring(u,a);r[s]=Math.max(l.replace(/\s+$/g,"").length,f-u)}return r},e.prototype.$selectionColumnsForRow=function(e){var t=[],n=this.$editor.getCursorPosition();return this.$editor.session.getSelection().isEmpty()&&e==n.row&&t.push(n.column),t},e.prototype.$setBlockCellWidthsToMax=function(e){var t=!0,n,r,i,s=this.$izip_longest(e);for(var o=0,u=s.length;o<u;o++){var a=s[o];if(!a.push){console.error(a);continue}a.push(NaN);for(var f=0,l=a.length;f<l;f++){var c=a[f];t&&(n=f,i=0,t=!1);if(isNaN(c)){r=f;for(var h=n;h<r;h++)e[h][o]=i;t=!0}i=Math.max(i,c)}}return e},e.prototype.$rightmostSelectionInCell=function(e,t){var n=0;if(e.length){var r=[];for(var i=0,s=e.length;i<s;i++)e[i]<=t?r.push(i):r.push(0);n=Math.max.apply(Math,r)}return n},e.prototype.$tabsForRow=function(e){var t=[],n=this.$editor.session.getLine(e),r=/\t/g,i;while((i=r.exec(n))!=null)t.push(i.index);return t},e.prototype.$adjustRow=function(e,t){var n=this.$tabsForRow(e);if(n.length==0)return;var r=0,i=-1,s=this.$izip(t,n);for(var o=0,u=s.length;o<u;o++){var a=s[o][0],f=s[o][1];i+=1+a,f+=r;var l=i-f;if(l==0)continue;var c=this.$editor.session.getLine(e).substr(0,f),h=c.replace(/\s*$/g,""),p=c.length-h.length;l>0&&(this.$editor.session.getDocument().insertInLine({row:e,column:f+1},Array(l+1).join(" ")+"	"),this.$editor.session.getDocument().removeInLine(e,f,f+1),r+=l),l<0&&p>=-l&&(this.$editor.session.getDocument().removeInLine(e,f+l,f),r+=l)}},e.prototype.$izip_longest=function(e){if(!e[0])return[];var t=e[0].length,n=e.length;for(var r=1;r<n;r++){var i=e[r].length;i>t&&(t=i)}var s=[];for(var o=0;o<t;o++){var u=[];for(var r=0;r<n;r++)e[r][o]===""?u.push(NaN):u.push(e[r][o]);s.push(u)}return s},e.prototype.$izip=function(e,t){var n=e.length>=t.length?t.length:e.length,r=[];for(var i=0;i<n;i++){var s=[e[i],t[i]];r.push(s)}return r},e}();t.ElasticTabstopsLite=r;var i=e("../editor").Editor;e("../config").defineOptions(i.prototype,"editor",{useElasticTabstops:{set:function(e){e?(this.elasticTabstops||(this.elasticTabstops=new r(this)),this.commands.on("afterExec",this.elasticTabstops.onAfterExec),this.commands.on("exec",this.elasticTabstops.onExec),this.on("change",this.elasticTabstops.onChange)):this.elasticTabstops&&(this.commands.removeListener("afterExec",this.elasticTabstops.onAfterExec),this.commands.removeListener("exec",this.elasticTabstops.onExec),this.removeListener("change",this.elasticTabstops.onChange))}}})});                (function() {
+                    ace.require(["ace/ext/elastic_tabstops_lite"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-error_marker.js

@@ -0,0 +1,8 @@
+;                (function() {
+                    ace.require(["ace/ext/error_marker"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-hardwrap.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/hardwrap",["require","exports","module","ace/range","ace/editor","ace/config"],function(e,t,n){"use strict";function i(e,t){function m(e,t,n){if(e.length<t)return;var r=e.slice(0,t),i=e.slice(t),s=/^(?:(\s+)|(\S+)(\s+))/.exec(i),o=/(?:(\s+)|(\s+)(\S+))$/.exec(r),u=0,a=0;o&&!o[2]&&(u=t-o[1].length,a=t),s&&!s[2]&&(u||(u=t),a=t+s[1].length);if(u)return{start:u,end:a};if(o&&o[2]&&o.index>n)return{start:o.index,end:o.index+o[2].length};if(s&&s[2])return u=t+s[2].length,{start:u,end:u+s[3].length}}var n=t.column||e.getOption("printMarginColumn"),i=t.allowMerge!=0,s=Math.min(t.startRow,t.endRow),o=Math.max(t.startRow,t.endRow),u=e.session;while(s<=o){var a=u.getLine(s);if(a.length>n){var f=m(a,n,5);if(f){var l=/^\s*/.exec(a)[0];u.replace(new r(s,f.start,s,f.end),"\n"+l)}o++}else if(i&&/\S/.test(a)&&s!=o){var c=u.getLine(s+1);if(c&&/\S/.test(c)){var h=a.replace(/\s+$/,""),p=c.replace(/^\s+/,""),d=h+" "+p,f=m(d,n,5);if(f&&f.start>h.length||d.length<n){var v=new r(s,h.length,s+1,c.length-p.length);u.replace(v," "),s--,o--}else h.length<a.length&&u.remove(new r(s,h.length,s,a.length))}}s++}}function s(e){if(e.command.name=="insertstring"&&/\S/.test(e.args)){var t=e.editor,n=t.selection.cursor;if(n.column<=t.renderer.$printMarginColumn)return;var r=t.session.$undoManager.$lastDelta;i(t,{startRow:n.row,endRow:n.row,allowMerge:!1}),r!=t.session.$undoManager.$lastDelta&&t.session.markUndoGroup()}}var r=e("../range").Range,o=e("../editor").Editor;e("../config").defineOptions(o.prototype,"editor",{hardWrap:{set:function(e){e?this.commands.on("afterExec",s):this.commands.off("afterExec",s)},value:!1}}),t.hardWrap=i});                (function() {
+                    ace.require(["ace/ext/hardwrap"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-keybinding_menu.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/menu_tools/settings_menu.css",["require","exports","module"],function(e,t,n){n.exports="#ace_settingsmenu, #kbshortcutmenu {\n    background-color: #F7F7F7;\n    color: black;\n    box-shadow: -5px 4px 5px rgba(126, 126, 126, 0.55);\n    padding: 1em 0.5em 2em 1em;\n    overflow: auto;\n    position: absolute;\n    margin: 0;\n    bottom: 0;\n    right: 0;\n    top: 0;\n    z-index: 9991;\n    cursor: default;\n}\n\n.ace_dark #ace_settingsmenu, .ace_dark #kbshortcutmenu {\n    box-shadow: -20px 10px 25px rgba(126, 126, 126, 0.25);\n    background-color: rgba(255, 255, 255, 0.6);\n    color: black;\n}\n\n.ace_optionsMenuEntry:hover {\n    background-color: rgba(100, 100, 100, 0.1);\n    transition: all 0.3s\n}\n\n.ace_closeButton {\n    background: rgba(245, 146, 146, 0.5);\n    border: 1px solid #F48A8A;\n    border-radius: 50%;\n    padding: 7px;\n    position: absolute;\n    right: -8px;\n    top: -8px;\n    z-index: 100000;\n}\n.ace_closeButton{\n    background: rgba(245, 146, 146, 0.9);\n}\n.ace_optionsMenuKey {\n    color: darkslateblue;\n    font-weight: bold;\n}\n.ace_optionsMenuCommand {\n    color: darkcyan;\n    font-weight: normal;\n}\n.ace_optionsMenuEntry input, .ace_optionsMenuEntry button {\n    vertical-align: middle;\n}\n\n.ace_optionsMenuEntry button[ace_selected_button=true] {\n    background: #e7e7e7;\n    box-shadow: 1px 0px 2px 0px #adadad inset;\n    border-color: #adadad;\n}\n.ace_optionsMenuEntry button {\n    background: white;\n    border: 1px solid lightgray;\n    margin: 0px;\n}\n.ace_optionsMenuEntry button:hover{\n    background: #f0f0f0;\n}"}),ace.define("ace/ext/menu_tools/overlay_page",["require","exports","module","ace/lib/dom","ace/ext/menu_tools/settings_menu.css"],function(e,t,n){"use strict";var r=e("../../lib/dom"),i=e("./settings_menu.css");r.importCssString(i,"settings_menu.css",!1),n.exports.overlayPage=function(t,n,r){function o(e){e.keyCode===27&&u()}function u(){if(!i)return;document.removeEventListener("keydown",o),i.parentNode.removeChild(i),t&&t.focus(),i=null,r&&r()}function a(e){s=e,e&&(i.style.pointerEvents="none",n.style.pointerEvents="auto")}var i=document.createElement("div"),s=!1;return i.style.cssText="margin: 0; padding: 0; position: fixed; top:0; bottom:0; left:0; right:0;z-index: 9990; "+(t?"background-color: rgba(0, 0, 0, 0.3);":""),i.addEventListener("click",function(e){s||u()}),document.addEventListener("keydown",o),n.addEventListener("click",function(e){e.stopPropagation()}),i.appendChild(n),document.body.appendChild(i),t&&t.blur(),{close:u,setIgnoreFocusOut:a}}}),ace.define("ace/ext/menu_tools/get_editor_keyboard_shortcuts",["require","exports","module","ace/lib/keys"],function(e,t,n){"use strict";var r=e("../../lib/keys");n.exports.getEditorKeybordShortcuts=function(e){var t=r.KEY_MODS,n=[],i={};return e.keyBinding.$handlers.forEach(function(e){var t=e.commandKeyBinding;for(var r in t){var s=r.replace(/(^|-)\w/g,function(e){return e.toUpperCase()}),o=t[r];Array.isArray(o)||(o=[o]),o.forEach(function(e){typeof e!="string"&&(e=e.name),i[e]?i[e].key+="|"+s:(i[e]={key:s,command:e},n.push(i[e]))})}}),n}}),ace.define("ace/ext/keybinding_menu",["require","exports","module","ace/editor","ace/ext/menu_tools/overlay_page","ace/ext/menu_tools/get_editor_keyboard_shortcuts"],function(e,t,n){"use strict";function i(t){if(!document.getElementById("kbshortcutmenu")){var n=e("./menu_tools/overlay_page").overlayPage,r=e("./menu_tools/get_editor_keyboard_shortcuts").getEditorKeybordShortcuts,i=r(t),s=document.createElement("div"),o=i.reduce(function(e,t){return e+'<div class="ace_optionsMenuEntry"><span class="ace_optionsMenuCommand">'+t.command+"</span> : "+'<span class="ace_optionsMenuKey">'+t.key+"</span></div>"},"");s.id="kbshortcutmenu",s.innerHTML="<h1>Keyboard Shortcuts</h1>"+o+"</div>",n(t,s)}}var r=e("../editor").Editor;n.exports.init=function(e){r.prototype.showKeyboardShortcuts=function(){i(this)},e.commands.addCommands([{name:"showKeyboardShortcuts",bindKey:{win:"Ctrl-Alt-h",mac:"Command-Alt-h"},exec:function(e,t){e.showKeyboardShortcuts()}}])}});                (function() {
+                    ace.require(["ace/ext/keybinding_menu"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-linking.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/linking",["require","exports","module","ace/editor","ace/config"],function(e,t,n){function i(e){var n=e.editor,r=e.getAccelKey();if(r){var n=e.editor,i=e.getDocumentPosition(),s=n.session,o=s.getTokenAt(i.row,i.column);t.previousLinkingHover&&t.previousLinkingHover!=o&&n._emit("linkHoverOut"),n._emit("linkHover",{position:i,token:o}),t.previousLinkingHover=o}else t.previousLinkingHover&&(n._emit("linkHoverOut"),t.previousLinkingHover=!1)}function s(e){var t=e.getAccelKey(),n=e.getButton();if(n==0&&t){var r=e.editor,i=e.getDocumentPosition(),s=r.session,o=s.getTokenAt(i.row,i.column);r._emit("linkClick",{position:i,token:o})}}var r=e("../editor").Editor;e("../config").defineOptions(r.prototype,"editor",{enableLinking:{set:function(e){e?(this.on("click",s),this.on("mousemove",i)):(this.off("click",s),this.off("mousemove",i))},value:!1}}),t.previousLinkingHover=!1});                (function() {
+                    ace.require(["ace/ext/linking"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-modelist.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/modelist",["require","exports","module"],function(e,t,n){"use strict";function i(e){var t=a.text,n=e.split(/[\/\\]/).pop();for(var i=0;i<r.length;i++)if(r[i].supportsFile(n)){t=r[i];break}return t}var r=[],s=function(){function e(e,t,n){this.name=e,this.caption=t,this.mode="ace/mode/"+e,this.extensions=n;var r;/\^/.test(n)?r=n.replace(/\|(\^)?/g,function(e,t){return"$|"+(t?"^":"^.*\\.")})+"$":r="^.*\\.("+n+")$",this.extRe=new RegExp(r,"gi")}return e.prototype.supportsFile=function(e){return e.match(this.extRe)},e}(),o={ABAP:["abap"],ABC:["abc"],ActionScript:["as"],ADA:["ada|adb"],Alda:["alda"],Apache_Conf:["^htaccess|^htgroups|^htpasswd|^conf|htaccess|htgroups|htpasswd"],Apex:["apex|cls|trigger|tgr"],AQL:["aql"],AsciiDoc:["asciidoc|adoc"],ASL:["dsl|asl|asl.json"],Assembly_ARM32:["s"],Assembly_x86:["asm|a"],Astro:["astro"],AutoHotKey:["ahk"],BatchFile:["bat|cmd"],BibTeX:["bib"],C_Cpp:["cpp|c|cc|cxx|h|hh|hpp|ino"],C9Search:["c9search_results"],Cirru:["cirru|cr"],Clojure:["clj|cljs"],Cobol:["CBL|COB"],coffee:["coffee|cf|cson|^Cakefile"],ColdFusion:["cfm|cfc"],Crystal:["cr"],CSharp:["cs"],Csound_Document:["csd"],Csound_Orchestra:["orc"],Csound_Score:["sco"],CSS:["css"],Curly:["curly"],Cuttlefish:["conf"],D:["d|di"],Dart:["dart"],Diff:["diff|patch"],Django:["djt|html.djt|dj.html|djhtml"],Dockerfile:["^Dockerfile"],Dot:["dot"],Drools:["drl"],Edifact:["edi"],Eiffel:["e|ge"],EJS:["ejs"],Elixir:["ex|exs"],Elm:["elm"],Erlang:["erl|hrl"],Flix:["flix"],Forth:["frt|fs|ldr|fth|4th"],Fortran:["f|f90"],FSharp:["fsi|fs|ml|mli|fsx|fsscript"],FSL:["fsl"],FTL:["ftl"],Gcode:["gcode"],Gherkin:["feature"],Gitignore:["^.gitignore"],Glsl:["glsl|frag|vert"],Gobstones:["gbs"],golang:["go"],GraphQLSchema:["gql"],Groovy:["groovy"],HAML:["haml"],Handlebars:["hbs|handlebars|tpl|mustache"],Haskell:["hs"],Haskell_Cabal:["cabal"],haXe:["hx"],Hjson:["hjson"],HTML:["html|htm|xhtml|we|wpy"],HTML_Elixir:["eex|html.eex"],HTML_Ruby:["erb|rhtml|html.erb"],INI:["ini|conf|cfg|prefs"],Io:["io"],Ion:["ion"],Jack:["jack"],Jade:["jade|pug"],Java:["java"],JavaScript:["js|jsm|cjs|mjs"],JEXL:["jexl"],JSON:["json"],JSON5:["json5"],JSONiq:["jq"],JSP:["jsp"],JSSM:["jssm|jssm_state"],JSX:["jsx"],Julia:["jl"],Kotlin:["kt|kts"],LaTeX:["tex|latex|ltx|bib"],Latte:["latte"],LESS:["less"],Liquid:["liquid"],Lisp:["lisp"],LiveScript:["ls"],Log:["log"],LogiQL:["logic|lql"],Logtalk:["lgt"],LSL:["lsl"],Lua:["lua"],LuaPage:["lp"],Lucene:["lucene"],Makefile:["^Makefile|^GNUmakefile|^makefile|^OCamlMakefile|make"],Markdown:["md|markdown"],Mask:["mask"],MATLAB:["matlab"],Maze:["mz"],MediaWiki:["wiki|mediawiki"],MEL:["mel"],MIPS:["s|asm"],MIXAL:["mixal"],MUSHCode:["mc|mush"],MySQL:["mysql"],Nasal:["nas"],Nginx:["nginx|conf"],Nim:["nim"],Nix:["nix"],NSIS:["nsi|nsh"],Nunjucks:["nunjucks|nunjs|nj|njk"],ObjectiveC:["m|mm"],OCaml:["ml|mli"],Odin:["odin"],PartiQL:["partiql|pql"],Pascal:["pas|p"],Perl:["pl|pm"],pgSQL:["pgsql"],PHP:["php|inc|phtml|shtml|php3|php4|php5|phps|phpt|aw|ctp|module"],PHP_Laravel_blade:["blade.php"],Pig:["pig"],PLSQL:["plsql"],Powershell:["ps1"],Praat:["praat|praatscript|psc|proc"],Prisma:["prisma"],Prolog:["plg|prolog"],Properties:["properties"],Protobuf:["proto"],PRQL:["prql"],Puppet:["epp|pp"],Python:["py"],QML:["qml"],R:["r"],Raku:["raku|rakumod|rakutest|p6|pl6|pm6"],Razor:["cshtml|asp"],RDoc:["Rd"],Red:["red|reds"],RHTML:["Rhtml"],Robot:["robot|resource"],RST:["rst"],Ruby:["rb|ru|gemspec|rake|^Guardfile|^Rakefile|^Gemfile"],Rust:["rs"],SaC:["sac"],SASS:["sass"],SCAD:["scad"],Scala:["scala|sbt"],Scheme:["scm|sm|rkt|oak|scheme"],Scrypt:["scrypt"],SCSS:["scss"],SH:["sh|bash|^.bashrc"],SJS:["sjs"],Slim:["slim|skim"],Smarty:["smarty|tpl"],Smithy:["smithy"],snippets:["snippets"],Soy_Template:["soy"],Space:["space"],SPARQL:["rq"],SQL:["sql"],SQLServer:["sqlserver"],Stylus:["styl|stylus"],SVG:["svg"],Swift:["swift"],Tcl:["tcl"],Terraform:["tf","tfvars","terragrunt"],Tex:["tex"],Text:["txt"],Textile:["textile"],Toml:["toml"],TSX:["tsx"],Turtle:["ttl"],Twig:["twig|swig"],Typescript:["ts|mts|cts|typescript|str"],Vala:["vala"],VBScript:["vbs|vb"],Velocity:["vm"],Verilog:["v|vh|sv|svh"],VHDL:["vhd|vhdl"],Visualforce:["vfp|component|page"],Vue:["vue"],Wollok:["wlk|wpgm|wtest"],XML:["xml|rdf|rss|wsdl|xslt|atom|mathml|mml|xul|xbl|xaml"],XQuery:["xq"],YAML:["yaml|yml"],Zeek:["zeek|bro"],Zig:["zig"]},u={ObjectiveC:"Objective-C",CSharp:"C#",golang:"Go",C_Cpp:"C and C++",Csound_Document:"Csound Document",Csound_Orchestra:"Csound",Csound_Score:"Csound Score",coffee:"CoffeeScript",HTML_Ruby:"HTML (Ruby)",HTML_Elixir:"HTML (Elixir)",FTL:"FreeMarker",PHP_Laravel_blade:"PHP (Blade Template)",Perl6:"Perl 6",AutoHotKey:"AutoHotkey / AutoIt"},a={};for(var f in o){var l=o[f],c=(u[f]||f).replace(/_/g," "),h=f.toLowerCase(),p=new s(h,c,l[0]);a[h]=p,r.push(p)}n.exports={getModeForPath:i,modes:r,modesByName:a}});                (function() {
+                    ace.require(["ace/ext/modelist"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-rtl.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/rtl",["require","exports","module","ace/editor","ace/config"],function(e,t,n){"use strict";function s(e,t){var n=t.getSelection().lead;t.session.$bidiHandler.isRtlLine(n.row)&&n.column===0&&(t.session.$bidiHandler.isMoveLeftOperation&&n.row>0?t.getSelection().moveCursorTo(n.row-1,t.session.getLine(n.row-1).length):t.getSelection().isEmpty()?n.column+=1:n.setPosition(n.row,n.column+1))}function o(e){e.editor.session.$bidiHandler.isMoveLeftOperation=/gotoleft|selectleft|backspace|removewordleft/.test(e.command.name)}function u(e,t){var n=t.session;n.$bidiHandler.currentRow=null;if(n.$bidiHandler.isRtlLine(e.start.row)&&e.action==="insert"&&e.lines.length>1)for(var r=e.start.row;r<e.end.row;r++)n.getLine(r+1).charAt(0)!==n.$bidiHandler.RLE&&(n.doc.$lines[r+1]=n.$bidiHandler.RLE+n.getLine(r+1))}function a(e,t){var n=t.session,r=n.$bidiHandler,i=t.$textLayer.$lines.cells,s=t.layerConfig.width-t.layerConfig.padding+"px";i.forEach(function(e){var t=e.element.style;r&&r.isRtlLine(e.row)?(t.direction="rtl",t.textAlign="right",t.width=s):(t.direction="",t.textAlign="",t.width="")})}function f(e){function n(e){var t=e.element.style;t.direction=t.textAlign=t.width=""}var t=e.$textLayer.$lines;t.cells.forEach(n),t.cellCache.forEach(n)}var r=[{name:"leftToRight",bindKey:{win:"Ctrl-Alt-Shift-L",mac:"Command-Alt-Shift-L"},exec:function(e){e.session.$bidiHandler.setRtlDirection(e,!1)},readOnly:!0},{name:"rightToLeft",bindKey:{win:"Ctrl-Alt-Shift-R",mac:"Command-Alt-Shift-R"},exec:function(e){e.session.$bidiHandler.setRtlDirection(e,!0)},readOnly:!0}],i=e("../editor").Editor;e("../config").defineOptions(i.prototype,"editor",{rtlText:{set:function(e){e?(this.on("change",u),this.on("changeSelection",s),this.renderer.on("afterRender",a),this.commands.on("exec",o),this.commands.addCommands(r)):(this.off("change",u),this.off("changeSelection",s),this.renderer.off("afterRender",a),this.commands.off("exec",o),this.commands.removeCommands(r),f(this.renderer)),this.renderer.updateFull()}},rtl:{set:function(e){this.session.$bidiHandler.$isRtl=e,e?(this.setOption("rtlText",!1),this.renderer.on("afterRender",a),this.session.$bidiHandler.seenBidi=!0):(this.renderer.off("afterRender",a),f(this.renderer)),this.renderer.updateFull()}}})});                (function() {
+                    ace.require(["ace/ext/rtl"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-simple_tokenizer.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/simple_tokenizer",["require","exports","module","ace/tokenizer","ace/layer/text_util"],function(e,t,n){"use strict";function o(e,t){var n=new s(e,new r(t.getRules())),o=[];for(var u=0;u<n.getLength();u++){var a=n.getTokens(u);o.push(a.map(function(e){return{className:i(e.type)?undefined:"ace_"+e.type.replace(/\./g," ace_"),value:e.value}}))}return o}var r=e("../tokenizer").Tokenizer,i=e("../layer/text_util").isTextToken,s=function(){function e(e,t){this._lines=e.split(/\r\n|\r|\n/),this._states=[],this._tokenizer=t}return e.prototype.getTokens=function(e){var t=this._lines[e],n=this._states[e-1],r=this._tokenizer.getLineTokens(t,n);return this._states[e]=r.state,r.tokens},e.prototype.getLength=function(){return this._lines.length},e}();n.exports={tokenize:o}});                (function() {
+                    ace.require(["ace/ext/simple_tokenizer"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-spellcheck.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/spellcheck",["require","exports","module","ace/lib/event","ace/editor","ace/config"],function(e,t,n){"use strict";var r=e("../lib/event");t.contextMenuHandler=function(e){var t=e.target,n=t.textInput.getElement();if(!t.selection.isEmpty())return;var i=t.getCursorPosition(),s=t.session.getWordRange(i.row,i.column),o=t.session.getTextRange(s);t.session.tokenRe.lastIndex=0;if(!t.session.tokenRe.test(o))return;var u="\x01\x01",a=o+" "+u;n.value=a,n.setSelectionRange(o.length,o.length+1),n.setSelectionRange(0,0),n.setSelectionRange(0,o.length);var f=!1;r.addListener(n,"keydown",function l(){r.removeListener(n,"keydown",l),f=!0}),t.textInput.setInputHandler(function(e){if(e==a)return"";if(e.lastIndexOf(a,0)===0)return e.slice(a.length);if(e.substr(n.selectionEnd)==a)return e.slice(0,-a.length);if(e.slice(-2)==u){var r=e.slice(0,-2);if(r.slice(-1)==" ")return f?r.substring(0,n.selectionEnd):(r=r.slice(0,-1),t.session.replace(s,r),"")}return e})};var i=e("../editor").Editor;e("../config").defineOptions(i.prototype,"editor",{spellcheck:{set:function(e){var n=this.textInput.getElement();n.spellcheck=!!e,e?this.on("nativecontextmenu",t.contextMenuHandler):this.removeListener("nativecontextmenu",t.contextMenuHandler)},value:!0}})});                (function() {
+                    ace.require(["ace/ext/spellcheck"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-split.js

@@ -0,0 +1,8 @@
+ace.define("ace/split",["require","exports","module","ace/lib/oop","ace/lib/lang","ace/lib/event_emitter","ace/editor","ace/virtual_renderer","ace/edit_session"],function(e,t,n){"use strict";var r=e("./lib/oop"),i=e("./lib/lang"),s=e("./lib/event_emitter").EventEmitter,o=e("./editor").Editor,u=e("./virtual_renderer").VirtualRenderer,a=e("./edit_session").EditSession,f;f=function(e,t,n){this.BELOW=1,this.BESIDE=0,this.$container=e,this.$theme=t,this.$splits=0,this.$editorCSS="",this.$editors=[],this.$orientation=this.BESIDE,this.setSplits(n||1),this.$cEditor=this.$editors[0],this.on("focus",function(e){this.$cEditor=e}.bind(this))},function(){r.implement(this,s),this.$createEditor=function(){var e=document.createElement("div");e.className=this.$editorCSS,e.style.cssText="position: absolute; top:0px; bottom:0px",this.$container.appendChild(e);var t=new o(new u(e,this.$theme));return t.on("focus",function(){this._emit("focus",t)}.bind(this)),this.$editors.push(t),t.setFontSize(this.$fontSize),t},this.setSplits=function(e){var t;if(e<1)throw"The number of splits have to be > 0!";if(e==this.$splits)return;if(e>this.$splits){while(this.$splits<this.$editors.length&&this.$splits<e)t=this.$editors[this.$splits],this.$container.appendChild(t.container),t.setFontSize(this.$fontSize),this.$splits++;while(this.$splits<e)this.$createEditor(),this.$splits++}else while(this.$splits>e)t=this.$editors[this.$splits-1],this.$container.removeChild(t.container),this.$splits--;this.resize()},this.getSplits=function(){return this.$splits},this.getEditor=function(e){return this.$editors[e]},this.getCurrentEditor=function(){return this.$cEditor},this.focus=function(){this.$cEditor.focus()},this.blur=function(){this.$cEditor.blur()},this.setTheme=function(e){this.$editors.forEach(function(t){t.setTheme(e)})},this.setKeyboardHandler=function(e){this.$editors.forEach(function(t){t.setKeyboardHandler(e)})},this.forEach=function(e,t){this.$editors.forEach(e,t)},this.$fontSize="",this.setFontSize=function(e){this.$fontSize=e,this.forEach(function(t){t.setFontSize(e)})},this.$cloneSession=function(e){var t=new a(e.getDocument(),e.getMode()),n=e.getUndoManager();return t.setUndoManager(n),t.setTabSize(e.getTabSize()),t.setUseSoftTabs(e.getUseSoftTabs()),t.setOverwrite(e.getOverwrite()),t.setBreakpoints(e.getBreakpoints()),t.setUseWrapMode(e.getUseWrapMode()),t.setUseWorker(e.getUseWorker()),t.setWrapLimitRange(e.$wrapLimitRange.min,e.$wrapLimitRange.max),t.$foldData=e.$cloneFoldData(),t},this.setSession=function(e,t){var n;t==null?n=this.$cEditor:n=this.$editors[t];var r=this.$editors.some(function(t){return t.session===e});return r&&(e=this.$cloneSession(e)),n.setSession(e),e},this.getOrientation=function(){return this.$orientation},this.setOrientation=function(e){if(this.$orientation==e)return;this.$orientation=e,this.resize()},this.resize=function(){var e=this.$container.clientWidth,t=this.$container.clientHeight,n;if(this.$orientation==this.BESIDE){var r=e/this.$splits;for(var i=0;i<this.$splits;i++)n=this.$editors[i],n.container.style.width=r+"px",n.container.style.top="0px",n.container.style.left=i*r+"px",n.container.style.height=t+"px",n.resize()}else{var s=t/this.$splits;for(var i=0;i<this.$splits;i++)n=this.$editors[i],n.container.style.width=e+"px",n.container.style.top=i*s+"px",n.container.style.left="0px",n.container.style.height=s+"px",n.resize()}}}.call(f.prototype),t.Split=f}),ace.define("ace/ext/split",["require","exports","module","ace/split"],function(e,t,n){"use strict";n.exports=e("../split")});                (function() {
+                    ace.require(["ace/ext/split"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-static_highlight.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/static-css",["require","exports","module"],function(e,t,n){n.exports=".ace_static_highlight {\n    font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', 'Consolas', 'Source Code Pro', 'source-code-pro', 'Droid Sans Mono', monospace;\n    font-size: 12px;\n    white-space: pre-wrap\n}\n\n.ace_static_highlight .ace_gutter {\n    width: 2em;\n    text-align: right;\n    padding: 0 3px 0 0;\n    margin-right: 3px;\n    contain: none;\n}\n\n.ace_static_highlight.ace_show_gutter .ace_line {\n    padding-left: 2.6em;\n}\n\n.ace_static_highlight .ace_line { position: relative; }\n\n.ace_static_highlight .ace_gutter-cell {\n    -moz-user-select: -moz-none;\n    -khtml-user-select: none;\n    -webkit-user-select: none;\n    user-select: none;\n    top: 0;\n    bottom: 0;\n    left: 0;\n    position: absolute;\n}\n\n\n.ace_static_highlight .ace_gutter-cell:before {\n    content: counter(ace_line, decimal);\n    counter-increment: ace_line;\n}\n.ace_static_highlight {\n    counter-reset: ace_line;\n}\n"}),ace.define("ace/ext/static_highlight",["require","exports","module","ace/edit_session","ace/layer/text","ace/ext/static-css","ace/config","ace/lib/dom","ace/lib/lang"],function(e,t,n){"use strict";var r=e("../edit_session").EditSession,i=e("../layer/text").Text,s=e("./static-css"),o=e("../config"),u=e("../lib/dom"),a=e("../lib/lang").escapeHTML,f=function(){function e(e){this.className,this.type=e,this.style={},this.textContent=""}return e.prototype.cloneNode=function(){return this},e.prototype.appendChild=function(e){this.textContent+=e.toString()},e.prototype.toString=function(){var e=[];if(this.type!="fragment"){e.push("<",this.type),this.className&&e.push(" class='",this.className,"'");var t=[];for(var n in this.style)t.push(n,":",this.style[n]);t.length&&e.push(" style='",t.join(""),"'"),e.push(">")}return this.textContent&&e.push(this.textContent),this.type!="fragment"&&e.push("</",this.type,">"),e.join("")},e}(),l={createTextNode:function(e,t){return a(e)},createElement:function(e){return new f(e)},createFragment:function(){return new f("fragment")}},c=function(){this.config={},this.dom=l};c.prototype=i.prototype;var h=function(e,t,n){var r=e.className.match(/lang-(\w+)/),i=t.mode||r&&"ace/mode/"+r[1];if(!i)return!1;var s=t.theme||"ace/theme/textmate",o="",a=[];if(e.firstElementChild){var f=0;for(var l=0;l<e.childNodes.length;l++){var c=e.childNodes[l];c.nodeType==3?(f+=c.data.length,o+=c.data):a.push(f,c)}}else o=e.textContent,t.trim&&(o=o.trim());h.render(o,i,s,t.firstLineNumber,!t.showGutter,function(t){u.importCssString(t.css,"ace_highlight",!0),e.innerHTML=t.html;var r=e.firstChild.firstChild;for(var i=0;i<a.length;i+=2){var s=t.session.doc.indexToPosition(a[i]),o=a[i+1],f=r.children[s.row];f&&f.appendChild(o)}n&&n()})};h.render=function(e,t,n,i,s,u){function c(){var r=h.renderSync(e,t,n,i,s);return u?u(r):r}var a=1,f=r.prototype.$modes;typeof n=="string"&&(a++,o.loadModule(["theme",n],function(e){n=e,--a||c()}));var l;return t&&typeof t=="object"&&!t.getTokenizer&&(l=t,t=l.path),typeof t=="string"&&(a++,o.loadModule(["mode",t],function(e){if(!f[t]||l)f[t]=new e.Mode(l);t=f[t],--a||c()})),--a||c()},h.renderSync=function(e,t,n,i,o){i=parseInt(i||1,10);var u=new r("");u.setUseWorker(!1),u.setMode(t);var a=new c;a.setSession(u),Object.keys(a.$tabStrings).forEach(function(e){if(typeof a.$tabStrings[e]=="string"){var t=l.createFragment();t.textContent=a.$tabStrings[e],a.$tabStrings[e]=t}}),u.setValue(e);var f=u.getLength(),h=l.createElement("div");h.className=n.cssClass;var p=l.createElement("div");p.className="ace_static_highlight"+(o?"":" ace_show_gutter"),p.style["counter-reset"]="ace_line "+(i-1);for(var d=0;d<f;d++){var v=l.createElement("div");v.className="ace_line";if(!o){var m=l.createElement("span");m.className="ace_gutter ace_gutter-cell",m.textContent="",v.appendChild(m)}a.$renderLine(v,d,!1),v.textContent+="\n",p.appendChild(v)}return h.appendChild(p),{css:s+n.cssText,html:h.toString(),session:u}},n.exports=h,n.exports.highlight=h});                (function() {
+                    ace.require(["ace/ext/static_highlight"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-statusbar.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/statusbar",["require","exports","module","ace/lib/dom","ace/lib/lang"],function(e,t,n){"use strict";var r=e("../lib/dom"),i=e("../lib/lang"),s=function(){function e(e,t){this.element=r.createElement("div"),this.element.className="ace_status-indicator",this.element.style.cssText="display: inline-block;",t.appendChild(this.element);var n=i.delayedCall(function(){this.updateStatus(e)}.bind(this)).schedule.bind(null,100);e.on("changeStatus",n),e.on("changeSelection",n),e.on("keyboardActivity",n)}return e.prototype.updateStatus=function(e){function n(e,n){e&&t.push(e,n||"|")}var t=[];n(e.keyBinding.getStatusText(e)),e.commands.recording&&n("REC");var r=e.selection,i=r.lead;if(!r.isEmpty()){var s=e.getSelectionRange();n("("+(s.end.row-s.start.row)+":"+(s.end.column-s.start.column)+")"," ")}n(i.row+":"+i.column," "),r.rangeCount&&n("["+r.rangeCount+"]"," "),t.pop(),this.element.textContent=t.join("")},e}();t.StatusBar=s});                (function() {
+                    ace.require(["ace/ext/statusbar"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-themelist.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/themelist",["require","exports","module"],function(e,t,n){"use strict";var r=[["Chrome"],["Clouds"],["Crimson Editor"],["Dawn"],["Dreamweaver"],["Eclipse"],["GitHub"],["IPlastic"],["Solarized Light"],["TextMate"],["Tomorrow"],["XCode"],["Kuroir"],["KatzenMilch"],["SQL Server","sqlserver","light"],["CloudEditor","cloud_editor","light"],["Ambiance","ambiance","dark"],["Chaos","chaos","dark"],["Clouds Midnight","clouds_midnight","dark"],["Dracula","","dark"],["Cobalt","cobalt","dark"],["Gruvbox","gruvbox","dark"],["Green on Black","gob","dark"],["idle Fingers","idle_fingers","dark"],["krTheme","kr_theme","dark"],["Merbivore","merbivore","dark"],["Merbivore Soft","merbivore_soft","dark"],["Mono Industrial","mono_industrial","dark"],["Monokai","monokai","dark"],["Nord Dark","nord_dark","dark"],["One Dark","one_dark","dark"],["Pastel on dark","pastel_on_dark","dark"],["Solarized Dark","solarized_dark","dark"],["Terminal","terminal","dark"],["Tomorrow Night","tomorrow_night","dark"],["Tomorrow Night Blue","tomorrow_night_blue","dark"],["Tomorrow Night Bright","tomorrow_night_bright","dark"],["Tomorrow Night 80s","tomorrow_night_eighties","dark"],["Twilight","twilight","dark"],["Vibrant Ink","vibrant_ink","dark"],["GitHub Dark","github_dark","dark"],["CloudEditor Dark","cloud_editor_dark","dark"]];t.themesByName={},t.themes=r.map(function(e){var n=e[1]||e[0].replace(/ /g,"_").toLowerCase(),r={caption:e[0],theme:"ace/theme/"+n,isDark:e[2]=="dark",name:n};return t.themesByName[n]=r,r})});                (function() {
+                    ace.require(["ace/ext/themelist"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+            

web_async/ace/ext-whitespace.js

@@ -0,0 +1,8 @@
+ace.define("ace/ext/whitespace",["require","exports","module","ace/lib/lang"],function(e,t,n){"use strict";var r=e("../lib/lang");t.$detectIndentation=function(e,t){function c(e){var t=0;for(var r=e;r<n.length;r+=e)t+=n[r]||0;return t}var n=[],r=[],i=0,s=0,o=Math.min(e.length,1e3);for(var u=0;u<o;u++){var a=e[u];if(!/^\s*[^*+\-\s]/.test(a))continue;if(a[0]=="	")i++,s=-Number.MAX_VALUE;else{var f=a.match(/^ */)[0].length;if(f&&a[f]!="	"){var l=f-s;l>0&&!(s%l)&&!(f%l)&&(r[l]=(r[l]||0)+1),n[f]=(n[f]||0)+1}s=f}while(u<o&&a[a.length-1]=="\\")a=e[u++]}var h=r.reduce(function(e,t){return e+t},0),p={score:0,length:0},d=0;for(var u=1;u<12;u++){var v=c(u);u==1?(d=v,v=n[1]?.9:.8,n.length||(v=0)):v/=d,r[u]&&(v+=r[u]/h),v>p.score&&(p={score:v,length:u})}if(p.score&&p.score>1.4)var m=p.length;if(i>d+1){if(m==1||d<i/4||p.score<1.8)m=undefined;return{ch:"	",length:m}}if(d>i+1)return{ch:" ",length:m}},t.detectIndentation=function(e){var n=e.getLines(0,1e3),r=t.$detectIndentation(n)||{};return r.ch&&e.setUseSoftTabs(r.ch==" "),r.length&&e.setTabSize(r.length),r},t.trimTrailingSpace=function(e,t){var n=e.getDocument(),r=n.getAllLines(),i=t&&t.trimEmpty?-1:0,s=[],o=-1;t&&t.keepCursorPosition&&(e.selection.rangeCount?e.selection.rangeList.ranges.forEach(function(e,t,n){var r=n[t+1];if(r&&r.cursor.row==e.cursor.row)return;s.push(e.cursor)}):s.push(e.selection.getCursor()),o=0);var u=s[o]&&s[o].row;for(var a=0,f=r.length;a<f;a++){var l=r[a],c=l.search(/\s+$/);a==u&&(c<s[o].column&&c>i&&(c=s[o].column),o++,u=s[o]?s[o].row:-1),c>i&&n.removeInLine(a,c,l.length)}},t.convertIndentation=function(e,t,n){var i=e.getTabString()[0],s=e.getTabSize();n||(n=s),t||(t=i);var o=t=="	"?t:r.stringRepeat(t,n),u=e.doc,a=u.getAllLines(),f={},l={};for(var c=0,h=a.length;c<h;c++){var p=a[c],d=p.match(/^\s*/)[0];if(d){var v=e.$getStringScreenWidth(d)[0],m=Math.floor(v/s),g=v%s,y=f[m]||(f[m]=r.stringRepeat(o,m));y+=l[g]||(l[g]=r.stringRepeat(" ",g)),y!=d&&(u.removeInLine(c,0,d.length),u.insertInLine({row:c,column:0},y))}}e.setTabSize(n),e.setUseSoftTabs(t==" ")},t.$parseStringArg=function(e){var t={};/t/.test(e)?t.ch="	":/s/.test(e)&&(t.ch=" ");var n=e.match(/\d+/);return n&&(t.length=parseInt(n[0],10)),t},t.$parseArg=function(e){return e?typeof e=="string"?t.$parseStringArg(e):typeof e.text=="string"?t.$parseStringArg(e.text):e:{}},t.commands=[{name:"detectIndentation",description:"Detect indentation from content",exec:function(e){t.detectIndentation(e.session)}},{name:"trimTrailingSpace",description:"Trim trailing whitespace",exec:function(e,n){t.trimTrailingSpace(e.session,n)}},{name:"convertIndentation",description:"Convert indentation to ...",exec:function(e,n){var r=t.$parseArg(n);t.convertIndentation(e.session,r.ch,r.length)}},{name:"setIndentation",description:"Set indentation",exec:function(e,n){var r=t.$parseArg(n);r.length&&e.session.setTabSize(r.length),r.ch&&e.session.setUseSoftTabs(r.ch==" ")}}]});                (function() {
+                    ace.require(["ace/ext/whitespace"], function(m) {
+                        if (typeof module == "object" && typeof exports == "object" && module) {
+                            module.exports = m;
+                        }
+                    });
+                })();
+