Merge branch 'main' into dev/doc-widget

This requires this PR to be merged:
https://github.com/comfyanonymous/ComfyUI/pull/3329

There is a trick to make it work without that PR but it
feels very hacky, the code for it is kept for reference but unused

.editorconfig

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,7 +1,9 @@
 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
@@ -10,6 +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
 
@@ -40,16 +44,30 @@ body:
       label: Expected behavior
       description: A clear description of what you expected to happen.
 
-  - type: textarea
-    id: info
+  - type: dropdown
+    id: os
     attributes:
-      label: Platform and versions
-      description: "informations about the environment you run Comfy in"
-      render: sh
-      placeholder: |
-        - OS: [e.g. Linux]
-        - Comfy Mode [e.g. custom env, standalone, google colab]
+      label: Operating System
+      description: What OS are you using?
+      options:
+        - Windows (Default)
+        - Linux
+        - Mac
+      default: 0
+    validations:
+      required: true
 
+  - type: dropdown
+    id: comfy_mode
+    attributes:
+      label: Comfy Mode
+      description: What flavor of Comfy do you use?
+      options:
+        - Comfy Portable (embed) (Default)
+        - In a custom virtual env (venv, virtualenv, conda...)
+        - Google Colab
+        - Other (online services, containers etc..)
+      default: 0
     validations:
       required: true
 

.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

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
+

INSTALL.md

@@ -4,7 +4,6 @@
     - [ComfyUI Manager](#comfyui-manager)
     - [Virtual Env](#virtual-env)
   - [Models Download](#models-download)
-    - [Web Extensions](#web-extensions)
   - [Old installation method (MANUAL)](#old-installation-method-manual)
     - [Dependencies](#dependencies)
 
@@ -35,11 +34,6 @@ then follow the prompt or just press enter to download every models.
   python scripts/download_models.py -y
   ```
 
-### Web Extensions
-
-On first run the script [tries to symlink](https://github.com/melMass/comfy_mtb/blob/d982b69a58c05ccead9c49370764beaa4549992a/__init__.py#L45-L61) the [web extensions](https://github.com/melMass/comfy_mtb/tree/main/web) to your comfy `web/extensions` folder. In case it fails you can manually copy the mtb folder to `ComfyUI/web/extensions` it only provides a color widget for now shared by a few nodes:  
-
-<img alt="color widget preview" src="https://github.com/melMass/comfy_mtb/assets/7041726/cff7e66a-4cc4-4866-b35b-10af0bb2d110" width=450>
 
 ## Old installation method (MANUAL)
 ### Dependencies

README.md

@@ -1,7 +1,8 @@
 # MTB Nodes
-
 [![embedded test](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml/badge.svg)](https://github.com/melMass/comfy_mtb/actions/workflows/test_embedded.yml)
 
+![home](https://repository-images.githubusercontent.com/649047066/a3eef9a7-20dd-4ef9-b839-884502d4e873)
+
 <!-- omit in toc -->
 
 **Translated Readme (using DeepTranslate, PRs are welcome)**:  
@@ -14,24 +15,60 @@
 
 [**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).
 
+- [Web Extensions](#web-extensions)
 - [Node List](#node-list)
+  - [Animation](#animation)
   - [bbox](#bbox)
   - [colors](#colors)
-  - [face detection / swapping](#face-detection--swapping)
-  - [image interpolation (animation)](#image-interpolation-animation)
   - [image ops](#image-ops)
   - [latent utils](#latent-utils)
-  - [misc utils](#misc-utils)
   - [textures](#textures)
+  - [misc utils](#misc-utils)
+  - [Optional nodes](#optional-nodes)
+    - [face detection / swapping](#face-detection--swapping)
+    - [image interpolation (animation)](#image-interpolation-animation)
 - [Comfy Resources](#comfy-resources)
 
+# Web Extensions
+mtb add a few widgets like `COLOR`
+
+<img alt="color widget preview" src="https://github.com/melMass/comfy_mtb/assets/7041726/cff7e66a-4cc4-4866-b35b-10af0bb2d110" width=450>
+
+A few nodes have the concept of "dynamic" inputs:  
+<img alt="dynamic inputs" width=450  src="https://github.com/melMass/comfy_mtb/assets/7041726/10b3976e-b212-4968-91eb-f34c02bb80c3" />
+
+<!-- NOTE: Here it should just be some examples and warnings, move the rest to the wiki -->
 
 # Node List
 
+## Animation
+- `Animation Builder`: Convenient way to manage basic animation maths at the core of many of my workflows (both worflows for the following GIFs are in the [examples](https://github.com/melMass/comfy_mtb/wiki/Examples))  
+
+  **[Example lerping two conditions (blue car -> yellow car)](https://github.com/melMass/comfy_mtb/blob/main/examples/03-animation_builder-condition-lerp.json)**
+  
+  <img width=300 src="https://user-images.githubusercontent.com/7041726/260258970-d6d66d96-fb34-40d0-9038-cbabf0714c5d.gif"/>  
+  
+
+  **[Example using image transforms a feedback for a fake deforum effect](https://github.com/melMass/comfy_mtb/blob/main/examples/04-animation_builder-deforum.json)**
+  
+  <img width=300 src="https://user-images.githubusercontent.com/7041726/260261504-303a1037-60d3-4b31-a589-b15d549752f6.gif"/>
+  
+- `Batch Float`: Generates a batch of float values with interpolation.
+- `Batch Shape`: Generates a batch of 2D shapes with optional shading (experimental).
+- `Batch Transform`: Transform a batch of images using a batch of keyframes.  
+  <img width=400 src="https://github.com/melMass/comfy_mtb/assets/7041726/3f217de1-79aa-49b0-a66a-35cf29dd8f01"/>
+- `Export With Ffmpeg`: Export with FFmpeg, it used to be export to Proress and is still tailored for YUV
+- `Fit Number` : Fit the input float using a source and target range, you can also control the interpolation curve from a list of presets (default to linear)
+  
 ## bbox
 - `Bounding Box`: BBox constructor (custom type),
 - `BBox From Mask`: From a mask extract the bounding box
@@ -43,21 +80,7 @@ Before proceeding, please be aware of the licenses associated with certain libra
 - `RGB to HSV`: -,
 - `HSV to RGB`: -,
 - `Color Correct`: Basic color correction tools  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/7c20ac83-31ff-40ea-a1a0-06c2acefb2ef" width=345/>
-
-## face detection / swapping
-- `Face Swap`: Face swap using deepinsight/insightface models (this node used to be called `Roop` in early versions, it does the same, roop is *just* an app that uses those model)
-  > **Note**
-  > The face index allow you to choose which face to replace as you can see here:  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/2e9d6066-c466-4a01-bd6c-315f7f1e8b42" width=320/>
-- `Load Face Swap Model`: Load an insightface model for face swapping
-- `Restore Face`: Using [GFPGan](https://github.com/TencentARC/GFPGAN) to restore faces, works great in conjunction with `Face Swap` and supports Comfy native upscalers for the `bg_upscaler`
-  
-## image interpolation (animation)
-- `Load Film Model`: Loads a [FILM](https://github.com/google-research/frame-interpolation) model
-- `Film Interpolation`: Process input frames using [FILM](https://github.com/google-research/frame-interpolation)  
-  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/3afd1647-6634-4b92-a34b-51432e6a9834" width=400/>
-- `Export to Prores (experimental)`: Exports the input frames to a ProRes 4444 mov file. This is using ffmpeg stdin to send raw numpy arrays, used with `Film Interpolation` and very simple for now but could be expanded upon.
+  <img src="https://github.com/melMass/comfy_mtb/assets/7041726/7c20ac83-31ff-40ea-a1a0-06c2acefb2ef" width=400/>
 
 ## image ops
 - `Blur`: Blur an image using a Gaussian filter.
@@ -74,8 +97,16 @@ Before proceeding, please be aware of the licenses associated with certain libra
 ## latent utils
 - `Latent Lerp`: Linear interpolation (blend) between two latent 
 
+## textures
+- `Model Patch Seamless`: Use the [seamless diffusion "hack"](https://gitlab.com/-/snippets/2395088) to patch any model to infere seamless images, check the [examples](https://github.com/melMass/comfy_mtb/wiki/Examples) to see how to use all those textures node together  
+  <img width=500 src="https://user-images.githubusercontent.com/7041726/272970506-9db516b5-45d2-4389-b904-b3a94660f24c.png"/>
+- `DeepBump`: Normal & height maps generation from single pictures  
+  <img width=500 src="https://user-images.githubusercontent.com/7041726/272970715-7e4477f6-8e18-4839-9864-83d07d6690a1.png"/>
+- `Image Tile Offset`: Mimics an old photoshop technique to check for seamless textures by offsetting tiles of the image.  
+  <img width=600 src="https://github.com/melMass/comfy_mtb/assets/7041726/cbcc51fb-922f-433f-acf1-c6c6c2a7ffc4" />
 
 ## misc utils
+- `Any To String`: Tries to take any input and convert it to a string.
 - `Concat Images`: Takes two image stream and merge them as a batch of images supported by other Comfy pipelines.
 - `Image Resize Factor`: **Deprecated**, I since discovered the builtin image resize.
 - `Text To Image`: Utils to convert text to image using a font
@@ -86,13 +117,57 @@ Before proceeding, please be aware of the licenses associated with certain libra
 - `Save Tensors`: Debug node that will probably be removed in the future
 - `Int to Number`: Supplement for WASSuite number nodes
 - `Smart Step`: A very basic tool to control the steps (start/stop) of the `KAdvancedSampler` using percentage
+- `Load Image From Url`: Load an image from the given URL
 
-## textures
 
-- `DeepBump`: Normal & height maps generation from single pictures
+## Optional nodes
+
+These nodes are still bundled in mtb, but moving forward (>0.2.0) they won't
+be setup by the install script and their dependencies won't install either.
+The reason is mostly that they all have a better alternatives available and tensorflow on windows was not a fun experience and since Python 3.11 not an experience at all.
+
+For linux and mac users though these nodes didn't cause any issue and I personally still use them, these are the extra requirements needed:
+
+```console
+.venv/python -m pip install tensorflow facexlib insightface basicsr
+```
+
+### face detection / swapping
+> **Warning**
+> Those nodes were among the first to be implemented they do work, but on windows the installation is still not properly handled for everyone  
+> As alternatives you can use [reactor](https://github.com/Gourieff/comfyui-reactor-node) for face swap and [facerestore](https://github.com/Haidra-Org/hordelib/tree/main/hordelib/nodes/facerestore) for restoration  
+> You can check [this video](https://www.youtube.com/watch?v=FShlpMxbU0E) for a tutorial by Ferniclestix using these alternatives  
+
+- `Face Swap`: Face swap using deepinsight/insightface models (this node used to be called `Roop` in early versions, it does the same, roop is *just* an app that uses those model)  
+  <img  width=320 src="https://user-images.githubusercontent.com/7041726/260261217-54e33446-183f-4dda-88b3-d38a1e6de980.gif"/>
+- `Load Face Swap Model`: Load an insightface model for face swapping
+- `Restore Face`: Using [GFPGan](https://github.com/TencentARC/GFPGAN) to restore faces, works great in conjunction with `Face Swap` and supports Comfy native upscalers for the `bg_upscaler`
+  
+### image interpolation (animation)
+> **Warning**
+> The FILM nodes will be deprecated at some point after 0.2.0, [Fannovel16](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation)'s interpolation nodes implement it and they rely on a pytorch implementation of FILM
+> which solves the issues related to the ones included in mtb. They will probably remain available if your system meet the requirements and ignored otherwise.
+
+<details><summary>Why?</summary>
+  
+> **Windows only issue**: This requires tensorflow-gpu that is unfortunately not a thing anymore on Windows since 2.10.1 (unless you use a complex WSL passthrough setup but it's still not "Windows")  
+> Using this old version is quite clunky and require some patching that install.py does automatically, but the main issue is that no wheels are available for python > 3.10
+> Comfy-nightly is already using Python 11 so installing this old tf version won't work there.  
+> You can in any case install the normal up to date tensorflow but that will run on CPU and is much MUCH slower for FILM inference.
+</details>
+
+- `Load Film Model`: Loads a [FILM](https://github.com/google-research/frame-interpolation) model
+- `Film Interpolation`: Process input frames using [FILM](https://github.com/google-research/frame-interpolation)  
+  <img width=400 src="https://github.com/melMass/comfy_mtb/assets/7041726/3afd1647-6634-4b92-a34b-51432e6a9834"/>  
+  <img width=400 src="https://user-images.githubusercontent.com/7041726/260259079-c0f04a63-960c-43a7-ba78-a45cd5ac7514.gif"/>
+- `Export to Prores (experimental)`: Exports the input frames to a ProRes 4444 mov file. This is using ffmpeg stdin to send raw numpy arrays, used with `Film Interpolation` and very simple for now but could be expanded upon.
 
 # Comfy Resources
 
+**Misc**
+
+- [Slick ComfyUI by NoCrypt](https://colab.research.google.com/drive/1ZMvLWEiYITmBJngtqeIQToeNuiydwI0z#scrollTo=1fWMaexXS188): A colab notebook with batteries included!
+
 **Guides**:
 - [Official Examples (eng)](https://comfyanonymous.github.io/ComfyUI_examples/)
 - [ComfyUI Community Manual (eng)](https://blenderneko.github.io/ComfyUI-docs/) by @BlenderNeko

__init__.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
 ###
 # File: __init__.py
 # Project: comfy_mtb
@@ -13,26 +12,37 @@ import os
 os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
 os.environ["TF_GPU_ALLOCATOR"] = "cuda_malloc_async"
 
-import traceback
-from .log import log, blue_text, cyan_text, get_summary, get_label
-from .utils import here
-from .utils import comfy_dir
-import importlib
-import os
 import ast
+import contextlib
+import importlib
 import json
+import logging
+import os
+import shutil
+import traceback
+from importlib import reload
+
+from aiohttp import web
+from server import PromptServer
+
+import nodes
+
+from .endpoint import endlog
+from .log import blue_text, cyan_text, get_label, get_summary, log
+from .utils import comfy_dir, here
 
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 NODE_CLASS_MAPPINGS_DEBUG = {}
+WEB_DIRECTORY = "./web"
 
-__version__ = "0.1.4"
+__version__ = "0.2.0"
 
 
 def extract_nodes_from_source(filename):
     source_code = ""
 
-    with open(filename, "r") as file:
+    with open(filename, encoding="utf8") as file:
         source_code = file.read()
 
     nodes = []
@@ -70,7 +80,7 @@ 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")
 
@@ -85,8 +95,8 @@ def load_nodes():
                 nodes_failed.extend(extract_nodes_from_source(filename))
 
     if errors:
-        log.info(
-            f"Some nodes failed to load:\n\t"
+        log.debug(
+            "Some nodes failed to load:\n\t"
             + "\n\t".join(errors)
             + "\n\n"
             + "Check that you properly installed the dependencies.\n"
@@ -100,53 +110,26 @@ def load_nodes():
 web_extensions_root = comfy_dir / "web" / "extensions"
 web_mtb = web_extensions_root / "mtb"
 
-if web_mtb.exists():
-    log.debug(f"Web extensions folder found at {web_mtb}")
-    if not os.path.islink(web_mtb.as_posix()):
-        log.warn(
-            f"Web extensions folder at {web_mtb} is not a symlink, if updating please delete it before"
-        )
-
-
-elif web_extensions_root.exists():
-    web_tgt = here / "web"
-    src = web_tgt.as_posix()
-    dst = web_mtb.as_posix()
+if web_mtb.exists() and hasattr(nodes, "EXTENSION_WEB_DIRS"):
     try:
-        if os.name == "nt":
-            import _winapi
-
-            _winapi.CreateJunction(src, dst)
+        if web_mtb.is_symlink():
+            web_mtb.unlink()
         else:
-            os.symlink(web_tgt.as_posix(), web_mtb.as_posix())
-
-    except OSError:
-        log.warn(f"Failed to create symlink to {web_mtb}, trying to copy it")
-        try:
-            import shutil
-
-            shutil.copytree(web_tgt, web_mtb)
-            log.info(f"Successfully copied {web_tgt} to {web_mtb}")
-        except Exception as e:
-            log.warn(
-                f"Failed to symlink and copy {web_tgt} to {web_mtb}. Please copy the folder manually."
-            )
-            log.warn(e)
-
+            shutil.rmtree(web_mtb)
     except Exception as e:
-        log.warn(
-            f"Failed to create symlink to {web_mtb}. Please copy the folder manually."
+        log.warning(
+            f"Failed to remove web mtb directory: {e}\nPlease manually remove it from disk ({web_mtb}) and restart the server."
         )
-        log.warn(e)
-else:
-    log.warn(
-        f"Comfy root probably not found automatically, please copy the folder {web_mtb} manually in the web/extensions folder of ComfyUI"
-    )
+
 
 # - REGISTER NODES
 nodes, failed = load_nodes()
 for node_class in nodes:
     class_name = node_class.__name__
+    # fallback to __doc__
+    if not hasattr(node_class, "DESCRIPTION") and node_class.__doc__:
+        node_class.DESCRIPTION = node_class.__doc__
+
     node_label = f"{get_label(class_name)} (mtb)"
     NODE_CLASS_MAPPINGS[node_label] = node_class
     NODE_DISPLAY_NAME_MAPPINGS[class_name] = node_label
@@ -166,32 +149,70 @@ for node_class in nodes:
                 )
             )
 
-log.info(
-    f"Loaded the following nodes:\n\t"
+log.debug(
+    "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()
     )
 )
 
+log.info(f"loaded {cyan_text(len(nodes))} nodes successfuly")
+if failed:
+    with contextlib.suppress(Exception):
+        base_url, port = utils.get_server_info()
+        log.info(
+            f"Some nodes ({len(failed)}) could not be loaded. This can be ignored, but go to http://{base_url}:{port}/mtb if you want more information."
+        )
+
+
 # - ENDPOINT
-from server import PromptServer
-from .log import log
-from aiohttp import web
-from importlib import reload
-import logging
-from .endpoint import endlog
+
 
 if hasattr(PromptServer, "instance"):
     restore_deps = ["basicsr"]
-    swap_deps = ["insightface", "onnxruntime"]
-
+    onnx_deps = ["onnxruntime"]
+    swap_deps = ["insightface"] + onnx_deps
     node_dependency_mapping = {
+        "QrCode": ["qrcode"],
+        "DeepBump": onnx_deps,
         "FaceSwap": swap_deps,
         "LoadFaceSwapModel": swap_deps,
         "LoadFaceAnalysisModel": restore_deps,
     }
 
+    PromptServer.instance.app.router.add_static(
+        "/mtb-assets/", path=(here / "html").as_posix()
+    )
+
+    # NOTE: we add an extra static path to avoid comfy mechanism
+    # that loads every script in web.
+    PromptServer.instance.app.add_routes(
+        [web.static("/mtb_async", (here / "web_async").as_posix())]
+    )
+
+    @PromptServer.instance.routes.get("/mtb/manage")
+    async def manage(request):
+        from . import endpoint
+
+        reload(endpoint)
+
+        endlog.debug("Initializing Manager")
+        if "text/html" in request.headers.get("Accept", ""):
+            csv_editor = endpoint.csv_editor()
+
+            tabview = endpoint.render_tab_view(Styles=csv_editor)
+            return web.Response(
+                text=endpoint.render_base_template("MTB", tabview),
+                content_type="text/html",
+            )
+
+        return web.json_response(
+            {
+                "message": "manage only has a POST api for now",
+            }
+        )
+
     @PromptServer.instance.routes.get("/mtb/status")
     async def get_full_library(request):
         from . import endpoint
@@ -255,6 +276,7 @@ if hasattr(PromptServer, "instance"):
             # # Return an HTML page
             html_response = """
             <div class="flex-container menu">
+                <a href="/mtb/manage">manage</a>
                 <a href="/mtb/debug">debug</a>
                 <a href="/mtb/status">status</a>
             </div>            

biome.json

@@ -0,0 +1,19 @@
+{
+    "$schema": "https://biomejs.dev/schemas/1.6.1/schema.json",
+    "organizeImports": {
+        "enabled": true
+    },
+    "linter": {
+        "enabled": true,
+        "rules": {
+            "recommended": true
+        }
+    },
+    "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

@@ -1,34 +1,46 @@
-from .utils import here, run_command, comfy_mode
+import csv
+
 from aiohttp import web
+
 from .log import mklog
-import sys
+from .utils import backup_file, here, import_install, reqs_map, run_command, styles_dir
 
 endlog = mklog("mtb endpoint")
 
 # - ACTIONS
-import requirements
+import platform
+import sys
+from pathlib import Path
 
+import_install("requirements")
 
 
 def ACTIONS_installDependency(dependency_names=None):
     if dependency_names is None:
         return {"error": "No dependency name provided"}
     endlog.debug(f"Received Install Dependency request for {dependency_names}")
-    reqs = []
-    if comfy_mode == "embeded":
-        reqs = list(requirements.parse((here / "reqs_portable.txt").read_text()))
-    else:
-        reqs = list(requirements.parse((here / "reqs.txt").read_text()))
-    print([x.specs for x in reqs])
-    print(
-        "\n".join([f"{x.line} {''.join(x.specs[0] if x.specs else '')}" for x in reqs])
-    )
-    for dependency_name in dependency_names:
-        for req in reqs:
-            if req.name == dependency_name:
-                endlog.debug(f"Dependency {dependency_name} installed")
-                break
-    return {"success": True}
+    # reqs = []
+    resolved_names = [reqs_map.get(name, name) for name in dependency_names]
+    try:
+        run_command([Path(sys.executable), "-m", "pip", "install"] + resolved_names)
+        return {"success": True}
+
+    except Exception as e:
+        return {"error": f"Failed to install dependencies: {e}"}
+
+    # if platform.system() == "Windows":
+    #     reqs = list(requirements.parse((here / "reqs_windows.txt").read_text()))
+    # else:
+    #     reqs = list(requirements.parse((here / "reqs.txt").read_text()))
+    # print([x.specs for x in reqs])
+    # print(
+    #     "\n".join([f"{x.line} {''.join(x.specs[0] if x.specs else '')}" for x in reqs])
+    # )
+    # for dependency_name in dependency_names:
+    #     for req in reqs:
+    #         if req.name == dependency_name:
+    #             endlog.debug(f"Dependency {dependency_name} installed")
+    #             break
 
 
 def ACTIONS_getStyles(style_name=None):
@@ -48,6 +60,32 @@ def ACTIONS_getStyles(style_name=None):
     return {"error": "No styles found"}
 
 
+def ACTIONS_saveStyle(data):
+    # endlog.debug(f"Received Save Styles for {data.keys()}")
+    # endlog.debug(data)
+
+    styles = [f.name for f in styles_dir.iterdir() if f.suffix == ".csv"]
+    target = None
+    rows = []
+    for fp, content in data.items():
+        if fp in styles:
+            endlog.debug(f"Overwriting {fp}")
+            target = styles_dir / fp
+            rows = content
+            break
+
+    if not target:
+        endlog.warning(f"Could not determine the target file for {data.keys()}")
+        return {"error": "Could not determine the target file for the style"}
+
+    backup_file(target)
+
+    with target.open("w", newline="", encoding="utf-8") as file:
+        csv_writer = csv.writer(file, quoting=csv.QUOTE_ALL)
+        for row in rows:
+            csv_writer.writerow(row)
+
+
 async def do_action(request) -> web.Response:
     endlog.debug("Init action request")
     request_data = await request.json()
@@ -83,6 +121,129 @@ def dependencies_button(name, dependencies):
         """
 
 
+def csv_editor():
+    inputs = [f for f in styles_dir.iterdir() if f.suffix == ".csv"]
+    # rows = {f.stem: list(csv.reader(f.read_text("utf8"))) for f in styles}
+
+    style_files = {}
+    for file in inputs:
+        with open(file, "r", encoding="utf8") as f:
+            parsed = csv.reader(f)
+            style_files[file.name] = []
+            for row in parsed:
+                endlog.debug(f"Adding style {row[0]}")
+                style_files[file.name].append((row[0], row[1], row[2]))
+
+    html_out = """
+            <div id="style-editor">
+             <h1>Style Editor</h1>
+            
+            """
+    for current, styles in style_files.items():
+        current_out = f"<h3>{current}</h3>"
+        table_rows = []
+        for index, style in enumerate(styles):
+            table_rows += (
+                (["<tr>"] + [f"<th>{cell}</th>" for cell in style] + ["</tr>"])
+                if index == 0
+                else (
+                    ["<tr>"]
+                    + [
+                        f"<td><input type='text' value='{cell}'></td>"
+                        if i == 0
+                        else f"<td><textarea name='Text1' cols='40' rows='5'>{cell}</textarea></td>"
+                        for i, cell in enumerate(style)
+                    ]
+                    + ["</tr>"]
+                )
+            )
+        current_out += (
+            f"<table data-id='{current}' data-filename='{current}'>"
+            + "".join(table_rows)
+            + "</table>"
+        )
+        current_out += f"<button data-id='{current}' onclick='saveTableData(this.getAttribute(\"data-id\"))'>Save {current}</button>"
+
+        html_out += add_foldable_region(current, current_out)
+
+    html_out += "</div>"
+    html_out += """<script src='/mtb-assets/js/saveTableData.js'></script>"""
+
+    return html_out
+
+
+def render_tab_view(**kwargs):
+    tab_headers = []
+    tab_contents = []
+
+    for idx, (tab_name, content) in enumerate(kwargs.items()):
+        active_class = "active" if idx == 0 else ""
+        tab_headers.append(
+            f"<button class='tablinks {active_class}' onclick=\"openTab(event, '{tab_name}')\">{tab_name}</button>"
+        )
+        tab_contents.append(
+            f"<div id='{tab_name}' class='tabcontent {active_class}'>{content}</div>"
+        )
+
+    headers_str = "\n".join(tab_headers)
+    contents_str = "\n".join(tab_contents)
+
+    return f"""
+<div class='tab-container'>
+    <div class='tab'>
+        {headers_str}
+    </div>
+    {contents_str}
+    </div>
+    <script src='/mtb-assets/js/tabSwitch.js'></script>
+    """
+
+
+def add_foldable_region(title, content):
+    symbol_id = f"{title}-symbol"
+    return f"""
+    <div class='foldable'>
+        <div class='foldable-title' onclick="toggleFoldable('{title}', '{symbol_id}')">
+            <span id='{symbol_id}' class='foldable-symbol'>&#9655;</span>
+            {title}
+        </div>
+        <div id='{title}' class='foldable-content'>
+            {content}
+        </div>
+    </div>
+    <script src='/mtb-assets/js/foldable.js'></script>
+    """
+
+
+def add_split_pane(left_content, right_content, vertical=True):
+    orientation = "vertical" if vertical else "horizontal"
+    return f"""
+    <div class="split-pane {orientation}">
+        <div id="leftPane">
+            {left_content}
+        </div>
+        <div id="resizer"></div>
+        <div id="rightPane">
+            {right_content}
+        </div>
+    </div>
+    <script>
+        initSplitPane({str(vertical).lower()});
+    </script>
+    <script src='/mtb-assets/js/splitPane.js'></script>
+    """
+
+
+def add_dropdown(title, options):
+    option_str = "\n".join([f"<option value='{opt}'>{opt}</option>" for opt in options])
+    return f"""
+    <select>
+        <option disabled selected>{title}</option>
+        {option_str}
+    </select>
+    """
+
+
 def render_table(table_dict, sort=True, title=None):
     table_dict = sorted(
         table_dict.items(), key=lambda item: item[0]
@@ -122,21 +283,13 @@ def render_table(table_dict, sort=True, title=None):
 
 
 def render_base_template(title, content):
-    css_content = ""
-    css_path = here / "html" / "style.css"
-    if css_path:
-        with open(css_path, "r") as css_file:
-            css_content = css_file.read()
-
     github_icon_svg = """<svg xmlns="http://www.w3.org/2000/svg" fill="whitesmoke" height="3em" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>"""
     return f"""
     <!DOCTYPE html>
     <html>
     <head>
         <title>{title}</title>
-        <style>
-            {css_content}
-        </style>
+        <link rel="stylesheet" href="/mtb-assets/style.css"/>
     </head>
     <script type="module">
         import {{ api }} from '/scripts/api.js'

errors.py

@@ -0,0 +1,7 @@
+class ModelNotFound(Exception):
+    def __init__(self, model_name, *args, **kwargs):
+        super().__init__(
+            f"The model {model_name} could not be found, make sure to download it using ComfyManager first.\nrepository: https://github.com/ltdrdata/ComfyUI-Manager",
+            *args,
+            **kwargs,
+        )

examples/05-seamless_texture.json

@@ -0,0 +1,905 @@
+{
+  "last_node_id": 97,
+  "last_link_id": 179,
+  "nodes": [
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -1165.8749246009997,
+        30
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4,
+            158
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "Closeup texture of rocks"
+      ],
+      "color": "#432",
+      "bgcolor": "#653",
+      "shape": 1
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -1175.8749246009997,
+        250
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "((drawing, cartoon, painting, sketch, blur, depth of field, dof))"
+      ],
+      "color": "#432",
+      "bgcolor": "#653",
+      "shape": 1
+    },
+    {
+      "id": 89,
+      "type": "Reroute",
+      "pos": [
+        350,
+        803
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 176
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "IMAGE",
+          "links": [
+            167
+          ]
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -1740,
+        236
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            170
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "revAnimated_v122.safetensors"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 63,
+      "type": "SaveImage",
+      "pos": [
+        1315,
+        18
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 115
+        }
+      ],
+      "title": "Normal",
+      "properties": {},
+      "widgets_values": [
+        "Normal"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 67,
+      "type": "SaveImage",
+      "pos": [
+        2095,
+        22
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 119
+        }
+      ],
+      "title": "Curvature",
+      "properties": {},
+      "widgets_values": [
+        "Curvature"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 69,
+      "type": "SaveImage",
+      "pos": [
+        1560,
+        1290
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 17,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 121
+        }
+      ],
+      "title": "Depth",
+      "properties": {},
+      "widgets_values": [
+        "Height"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 91,
+      "type": "Model Patch Seamless (mtb)",
+      "pos": [
+        -1150,
+        -146
+      ],
+      "size": [
+        430.8000183105469,
+        78
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 170
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Original Model (passthrough)",
+          "type": "MODEL",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "Patched Model",
+          "type": "MODEL",
+          "links": [
+            169
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Model Patch Seamless (mtb)"
+      },
+      "widgets_values": [
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 93,
+      "type": "PreviewImage",
+      "pos": [
+        1115,
+        -597
+      ],
+      "size": [
+        451.3526306152344,
+        478.3444519042969
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 179
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 43,
+      "type": "VAELoader",
+      "pos": [
+        -598.2757622278747,
+        577.3595309932109
+      ],
+      "size": [
+        387.48089599609375,
+        70.60645294189453
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            174
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "vae-ft-mse-840000-ema-pruned.safetensors"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 97,
+      "type": "Image Tile Offset (mtb)",
+      "pos": [
+        617,
+        -598
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 178
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            179
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Image Tile Offset (mtb)"
+      },
+      "widgets_values": [
+        2
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 96,
+      "type": "Vae Decode (mtb)",
+      "pos": [
+        -52,
+        40
+      ],
+      "size": [
+        315,
+        126
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 173
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 174
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            175,
+            176,
+            178
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Vae Decode (mtb)"
+      },
+      "widgets_values": [
+        true,
+        false,
+        512
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 46,
+      "type": "SaveImage",
+      "pos": [
+        533,
+        25
+      ],
+      "size": [
+        539.2050170898438,
+        617.2159423828125
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 175
+        }
+      ],
+      "title": "Albedo",
+      "properties": {},
+      "widgets_values": [
+        "Albedo"
+      ],
+      "shape": 1
+    },
+    {
+      "id": 74,
+      "type": "EmptyLatentImage",
+      "pos": [
+        -1075.8749246009997,
+        480
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            132
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        768,
+        768,
+        1
+      ],
+      "color": "#323",
+      "bgcolor": "#535",
+      "shape": 1
+    },
+    {
+      "id": 62,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        727,
+        801
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 167
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            115,
+            118,
+            122
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Color to Normals",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 66,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        1626,
+        808
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 118
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            119
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Normals to Curvature",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 68,
+      "type": "Deep Bump (mtb)",
+      "pos": [
+        1185,
+        1288
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 122
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            121
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Deep Bump (mtb)"
+      },
+      "widgets_values": [
+        "Normals to Height",
+        "SMALL",
+        "SMALLEST",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353",
+      "shape": 1
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        -518.2757622278748,
+        47.359530993211024
+      ],
+      "size": [
+        315,
+        474
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 169
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 132
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            173
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        1001,
+        "fixed",
+        28,
+        8,
+        "dpmpp_2m",
+        "normal",
+        1
+      ],
+      "color": "#222",
+      "bgcolor": "#000",
+      "shape": 1
+    }
+  ],
+  "links": [
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      115,
+      62,
+      0,
+      63,
+      0,
+      "IMAGE"
+    ],
+    [
+      118,
+      62,
+      0,
+      66,
+      0,
+      "IMAGE"
+    ],
+    [
+      119,
+      66,
+      0,
+      67,
+      0,
+      "IMAGE"
+    ],
+    [
+      121,
+      68,
+      0,
+      69,
+      0,
+      "IMAGE"
+    ],
+    [
+      122,
+      62,
+      0,
+      68,
+      0,
+      "IMAGE"
+    ],
+    [
+      132,
+      74,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      158,
+      6,
+      0,
+      86,
+      0,
+      "*"
+    ],
+    [
+      167,
+      89,
+      0,
+      62,
+      0,
+      "IMAGE"
+    ],
+    [
+      169,
+      91,
+      1,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      170,
+      4,
+      0,
+      91,
+      0,
+      "MODEL"
+    ],
+    [
+      173,
+      3,
+      0,
+      96,
+      0,
+      "LATENT"
+    ],
+    [
+      174,
+      43,
+      0,
+      96,
+      1,
+      "VAE"
+    ],
+    [
+      175,
+      96,
+      0,
+      46,
+      0,
+      "IMAGE"
+    ],
+    [
+      176,
+      96,
+      0,
+      89,
+      0,
+      "*"
+    ],
+    [
+      178,
+      96,
+      0,
+      97,
+      0,
+      "IMAGE"
+    ],
+    [
+      179,
+      97,
+      0,
+      93,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "title": "Seamless Diffusion",
+      "bounding": [
+        -1752,
+        -392,
+        1658,
+        1102
+      ],
+      "color": "#3f789e",
+      "font_size": 76,
+      "locked": false
+    },
+    {
+      "title": "Seamless Check",
+      "bounding": [
+        421,
+        -795,
+        1374,
+        763
+      ],
+      "color": "#3f789e",
+      "font_size": 76,
+      "locked": false
+    }
+  ],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

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}

html/js/foldable.js

@@ -0,0 +1,20 @@
+/**
+ * File: foldable.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function toggleFoldable(elementId, symbolId) {
+  const content = document.getElementById(elementId)
+  const symbol = document.getElementById(symbolId)
+  if (content.style.display === 'none' || content.style.display === '') {
+    content.style.display = 'flex'
+    symbol.innerHTML = '▽' // Down arrow
+  } else {
+    content.style.display = 'none'
+    symbol.innerHTML = '▷' // Right arrow
+  }
+}

html/js/saveTableData.js

@@ -0,0 +1,54 @@
+/**
+ * File: saveTableData.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function saveTableData(identifier) {
+  const table = document.querySelector(
+    `#style-editor table[data-id='${identifier}']`
+  )
+
+  let currentData = []
+  const rows = table.querySelectorAll('tr')
+  const filename = table.getAttribute('data-id')
+
+  rows.forEach((row, rowIndex) => {
+    const rowData = []
+    const cells =
+      rowIndex === 0
+        ? row.querySelectorAll('th')
+        : row.querySelectorAll('td input, td textarea')
+
+    cells.forEach((cell) => {
+      rowData.push(rowIndex === 0 ? cell.textContent : cell.value)
+    })
+
+    currentData.push(rowData)
+  })
+
+  let tablesData = {}
+  tablesData[filename] = currentData
+
+  console.debug('Sending styles to manage endpoint:', tablesData)
+  fetch('/mtb/actions', {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify({
+      name: 'saveStyle',
+      args: tablesData,
+    }),
+  })
+    .then((response) => response.json())
+    .then((data) => {
+      console.debug('Success:', data)
+    })
+    .catch((error) => {
+      console.error('Error:', error)
+    })
+}

html/js/splitPane.js

@@ -0,0 +1,34 @@
+/**
+ * File: splitPane.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function initSplitPane(vertical) {
+  let resizer = document.getElementById('resizer')
+  let left = document.getElementById('leftPane')
+  let right = document.getElementById('rightPane')
+  resizer.addEventListener('mousedown', function (e) {
+    document.addEventListener('mousemove', onMouseMove)
+    document.addEventListener('mouseup', function () {
+      document.removeEventListener('mousemove', onMouseMove)
+    })
+  })
+
+  const onMouseMove = (e) => {
+    if (vertical) {
+      let leftWidth = e.clientX
+      let rightWidth = window.innerWidth - e.clientX
+      left.style.width = leftWidth + 'px'
+      right.style.width = rightWidth + 'px'
+    } else {
+      let topHeight = e.clientY
+      let bottomHeight = window.innerHeight - e.clientY
+      left.style.height = topHeight + 'px'
+      right.style.height = bottomHeight + 'px'
+    }
+  }
+}

html/js/tabSwitch.js

@@ -0,0 +1,22 @@
+/**
+ * File: tabSwitch.js
+ * Project: comfy_mtb
+ * Author: Mel Massadian
+ *
+ * Copyright (c) 2023 Mel Massadian
+ *
+ */
+
+function openTab(evt, tabName) {
+  var i, tabcontent, tablinks
+  tabcontent = document.getElementsByClassName('tabcontent')
+  for (i = 0; i < tabcontent.length; i++) {
+    tabcontent[i].style.display = 'none'
+  }
+  tablinks = document.getElementsByClassName('tablinks')
+  for (i = 0; i < tablinks.length; i++) {
+    tablinks[i].className = tablinks[i].className.replace(' active', '')
+  }
+  document.getElementById(tabName).style.display = 'block'
+  evt.currentTarget.className += ' active'
+}

html/style.css

@@ -18,7 +18,7 @@ a {
 }
 
 table {
-
+    width: 100%;
     border-collapse: collapse;
 }
 
@@ -119,7 +119,7 @@ main {
     justify-content: center;
     padding: 1em;
     margin: 0;
-    height: 80%;
+    /* height: 80%; */
 }
 
 .flex-container {
@@ -130,4 +130,99 @@ main {
 .menu {
     font-size: 3em;
     text-align: center;
-}
+}
+
+input, button, textarea {
+    background-color: rgba(0,0,0,0.5);
+    color: white;
+    border: none;
+}
+
+button:hover {
+    background-color: rgba(0,0,0,0.3);
+
+}
+button {
+    padding: 14px 16px;
+
+}
+/* -STYLES EDITOR */
+
+#style-editor {
+    display: flex;
+    flex-direction: column;
+    width:100%;
+    
+}
+
+#style-editor > table {
+    /* background-color: red; */
+    width:100%;
+}
+#style-editor input, #style-editor textarea {
+    /* background-color: blue; */
+    width:100%;
+}
+
+#style-editor td{
+    width: 33.33%;
+}
+
+/* -TABS */
+
+.tab {
+    overflow: hidden;
+    width: 100%;
+    display: flex;
+    flex-direction: row;
+
+}
+
+.tab-container{
+    width: 100%;
+    display: flex;
+    flex-direction: column;
+}
+
+.tab button {
+    background-color: transparent;
+    color:white;
+    float: left;
+    border: none;
+    outline: none;
+    cursor: pointer;
+    padding: 14px 16px;
+    transition: 0.3s;
+    width:100%;
+    font-size: 1.5em;
+}
+
+.tab button.active {
+    background-color: #2e2e2e;
+}
+
+.tabcontent {
+    display: none;
+}
+
+.tabcontent.active {
+    display: block;
+}
+
+
+
+.foldable-title {
+    cursor: pointer;
+    font-weight: bold;
+    user-select: none;
+}
+
+.foldable-symbol {
+    margin-right: 10px;
+}
+
+.foldable-content {
+    display: none;
+    flex-direction: column;
+    margin-left: 20px;
+}

install.py

@@ -1,37 +1,34 @@
-import requests
-import os
-import ast
 import argparse
-import sys
-import subprocess
-from importlib import import_module
+import ast
+import os
 import platform
-from pathlib import Path
-import sys
+import shlex
 import stat
-import threading
-import signal
-from contextlib import suppress
-from queue import Queue, Empty
+import subprocess
+import sys
 from contextlib import contextmanager
+from importlib import import_module
+from pathlib import Path
 
+import requests
+
+# region constants
 here = Path(__file__).parent
-executable = sys.executable
+executable = Path(sys.executable)
 
 # - detect mode
 mode = None
 if os.environ.get("COLAB_GPU"):
     mode = "colab"
-elif "python_embeded" in executable:
+elif "python_embeded" in str(executable):
     mode = "embeded"
-elif ".venv" in executable:
+elif ".venv" in str(executable):
     mode = "venv"
 
 
 if mode is None:
     mode = "unknown"
 
-# - Constants
 repo_url = "https://github.com/melmass/comfy_mtb.git"
 repo_owner = "melmass"
 repo_name = "comfy_mtb"
@@ -40,6 +37,17 @@ short_platform = {
     "linux": "linux_x86_64",
 }
 current_platform = platform.system().lower()
+pip_map = {
+    "onnxruntime-gpu": "onnxruntime",
+    "opencv-contrib": "cv2",
+    "tb-nightly": "tensorboard",
+    "protobuf": "google.protobuf",
+    "qrcode[pil]": "qrcode",
+    "requirements-parser": "requirements"
+    # Add more mappings as needed
+}
+
+# endregion
 
 # region ansi
 # ANSI escape sequences for text styling
@@ -136,12 +144,6 @@ def print_formatted(text, *formats, color=None, background=None, **kwargs):
 
 
 # region utils
-def enqueue_output(out, queue):
-    for char in iter(lambda: out.read(1), b""):
-        queue.put(char)
-    out.close()
-
-
 def run_command(cmd, ignored_lines_start=None):
     if ignored_lines_start is None:
         ignored_lines_start = []
@@ -149,113 +151,49 @@ def run_command(cmd, ignored_lines_start=None):
     if isinstance(cmd, str):
         shell_cmd = cmd
     elif isinstance(cmd, list):
-        shell_cmd = ""
-        for arg in cmd:
-            if isinstance(arg, Path):
-                arg = arg.as_posix()
-            shell_cmd += f"{arg} "
+        shell_cmd = " ".join(
+            arg.as_posix() if isinstance(arg, Path) else shlex.quote(str(arg))
+            for arg in cmd
+        )
     else:
         raise ValueError(
             "Invalid 'cmd' argument. It must be a string or a list of arguments."
         )
 
-    process = subprocess.Popen(
+    try:
+        _run_command(shell_cmd, ignored_lines_start)
+    except subprocess.CalledProcessError as e:
+        print(f"Command failed with return code: {e.returncode}", file=sys.stderr)
+        print(e.stderr.strip(), file=sys.stderr)
+
+    except KeyboardInterrupt:
+        print("Command execution interrupted.")
+
+
+def _run_command(shell_cmd, ignored_lines_start):
+    print_formatted(f"Running {shell_cmd}", "bold")
+    result = subprocess.run(
         shell_cmd,
         stdout=subprocess.PIPE,
         stderr=subprocess.PIPE,
-        universal_newlines=True,
+        text=True,
         shell=True,
+        check=True,
     )
 
-    # Create separate threads to read standard output and standard error streams
-    stdout_queue = Queue()
-    stderr_queue = Queue()
-    stdout_thread = threading.Thread(
-        target=enqueue_output, args=(process.stdout, stdout_queue)
-    )
-    stderr_thread = threading.Thread(
-        target=enqueue_output, args=(process.stderr, stderr_queue)
-    )
-    stdout_thread.daemon = True
-    stderr_thread.daemon = True
-    stdout_thread.start()
-    stderr_thread.start()
+    stdout_lines = result.stdout.strip().split("\n")
+    stderr_lines = result.stderr.strip().split("\n")
 
-    interrupted = False
+    # Print stdout, skipping ignored lines
+    for line in stdout_lines:
+        if not any(line.startswith(ign) for ign in ignored_lines_start):
+            print(line)
 
-    def signal_handler(signum, frame):
-        nonlocal interrupted
-        interrupted = True
-        print("Command execution interrupted.")
+    # Print stderr
+    for line in stderr_lines:
+        print(line, file=sys.stderr)
 
-    # Register the signal handler for keyboard interrupts (SIGINT)
-    signal.signal(signal.SIGINT, signal_handler)
-
-    stdout_buffer = ""
-    stderr_buffer = ""
-
-    # Process output from both streams until the process completes or interrupted
-    while not interrupted and (
-        process.poll() is None or not stdout_queue.empty() or not stderr_queue.empty()
-    ):
-        with suppress(Empty):
-            stdout_char = stdout_queue.get_nowait()
-            stdout_buffer += stdout_char
-            if stdout_char == "\n":
-                if not any(
-                    stdout_buffer.startswith(ign) for ign in ignored_lines_start
-                ):
-                    print(stdout_buffer.strip())
-                stdout_buffer = ""
-        with suppress(Empty):
-            stderr_char = stderr_queue.get_nowait()
-            stderr_buffer += stderr_char
-            if stderr_char == "\n":
-                print(stderr_buffer.strip())
-                stderr_buffer = ""
-
-    # Print any remaining content in buffers
-    if stdout_buffer and not any(
-        stdout_buffer.startswith(ign) for ign in ignored_lines_start
-    ):
-        print(stdout_buffer.strip())
-    if stderr_buffer:
-        print(stderr_buffer.strip())
-
-    return_code = process.returncode
-
-    if return_code == 0 and not interrupted:
-        print("Command executed successfully!")
-    else:
-        if not interrupted:
-            print(f"Command failed with return code: {return_code}")
-
-
-# endregion
-
-try:
-    import requirements
-except ImportError:
-    print_formatted("Installing requirements-parser...", "italic", color="yellow")
-    run_command([sys.executable, "-m", "pip", "install", "requirements-parser"])
-    import requirements
-
-    print_formatted("Done.", "italic", color="green")
-
-try:
-    from tqdm import tqdm
-except ImportError:
-    print_formatted("Installing tqdm...", "italic", color="yellow")
-    run_command([sys.executable, "-m", "pip", "install", "--upgrade", "tqdm"])
-    from tqdm import tqdm
-
-pip_map = {
-    "onnxruntime-gpu": "onnxruntime",
-    "opencv-contrib": "cv2",
-    "tb-nightly": "tensorboard",
-    "protobuf": "google.protobuf",
-    # Add more mappings as needed
-}
+    print("Command executed successfully!")
 
 
 def is_pipe():
@@ -330,24 +268,6 @@ def download_file(url, file_name):
                 progress_bar.update(len(chunk))
 
 
-def get_requirements(path: Path):
-    with open(path.resolve(), "r") as requirements_file:
-        requirements_txt = requirements_file.read()
-
-    try:
-        parsed_requirements = requirements.parse(requirements_txt)
-    except AttributeError:
-        print_formatted(
-            f"Failed to parse {path}. Please make sure the file is correctly formatted.",
-            "bold",
-            color="red",
-        )
-
-        return
-
-    return parsed_requirements
-
-
 def try_import(requirement):
     dependency = requirement.name.strip()
     import_name = pip_map.get(dependency, dependency)
@@ -390,7 +310,7 @@ def import_or_install(requirement, dry=False):
             )
         else:
             try:
-                run_command([sys.executable, "-m", "pip", "install", pip_install_name])
+                run_command([executable, "-m", "pip", "install", pip_install_name])
                 print_formatted(
                     f"Package {pip_install_name} installed successfully using pip package name  (import name: '{import_name}')",
                     "bold",
@@ -427,29 +347,29 @@ def get_github_assets(tag=None):
     return tag_data, tag_name
 
 
-# Install dependencies from requirements.txt
-def install_dependencies(dry=False):
-    parsed_requirements = get_requirements(here / "reqs.txt")
-    if not parsed_requirements:
-        return
-    print_formatted(
-        "Installing dependencies from reqs.txt...", "italic", color="yellow"
-    )
-
-    for requirement in parsed_requirements:
-        import_or_install(requirement, dry=dry)
+# endregion
 
 
-if __name__ == "__main__":
-    full = False
+try:
+    from tqdm import tqdm
+except ImportError:
+    print_formatted("Installing tqdm...", "italic", color="yellow")
+    run_command([executable, "-m", "pip", "install", "--upgrade", "tqdm"])
+    from tqdm import tqdm
+
+
+def main():
     if len(sys.argv) == 1:
         print_formatted(
-            "No arguments provided, doing a full install/update...",
-            "italic",
-            color="yellow",
+            "mtb doesn't need an install script anymore.", "italic", color="yellow"
         )
-
-        full = True
+        return
+    if all(arg not in ("-p", "--path") for arg in sys.argv):
+        print(
+            "This script is only used for and edge case of remote installs on some cloud providers, unrecognized arguments:",
+            sys.argv[1:],
+        )
+        return
 
     # Parse command-line arguments
     parser = argparse.ArgumentParser(description="Comfy_mtb install script")
@@ -459,29 +379,11 @@ if __name__ == "__main__":
         type=str,
         help="Path to clone the repository to (i.e the absolute path to ComfyUI/custom_nodes)",
     )
-    parser.add_argument(
-        "--wheels", "-w", action="store_true", help="Install wheel dependencies"
-    )
-    parser.add_argument(
-        "--requirements", "-r", action="store_true", help="Install requirements.txt"
-    )
-    parser.add_argument(
-        "--dry",
-        action="store_true",
-        help="Print what will happen without doing it (still making requests to the GH Api)",
-    )
 
-    # - keep
-    # parser.add_argument(
-    #     "--version",
-    #     default=get_local_version(),
-    #     help="Version to check against the GitHub API",
-    # )
     print_formatted("mtb install", "bold", color="yellow")
 
     args = parser.parse_args()
 
-    # wheels_directory = here / "wheels"
     print_formatted(f"Detected environment: {apply_color(mode,'cyan')}")
 
     if args.path:
@@ -502,131 +404,18 @@ if __name__ == "__main__":
                     f"Directory {repo_dir} already exists, we will update it..."
                 )
                 run_command(["git", "pull", "-C", repo_dir])
-        # os.chdir(clone_dir)
         here = clone_dir
         full = True
 
-    # Install dependencies from requirements.txt
-    # if args.requirements or mode == "venv":
-
-    # if (not args.wheels and mode not in ["colab", "embeded"]) and not full:
-    #     print_formatted(
-    #         "Skipping wheel installation. Use --wheels to install wheel dependencies. (only needed for Comfy embed)",
-    #         "italic",
-    #         color="yellow",
-    #     )
-
-    #     install_dependencies(dry=args.dry)
-    #     sys.exit()
-
-    # if mode in ["colab", "embeded"]:
-    #     print_formatted(
-    #         f"Downloading and installing release wheels since we are in a Comfy {apply_color(mode,'cyan')} environment",
-    #         "italic",
-    #         color="yellow",
-    #     )
-    # if full:
-    #     print_formatted(
-    #         f"Downloading and installing release wheels since no arguments where provided",
-    #         "italic",
-    #         color="yellow",
-    #     )
-
     print_formatted("Checking environment...", "italic", color="yellow")
     missing_deps = []
-    if parsed_requirements := get_requirements(here / "reqs.txt"):
-        for requirement in parsed_requirements:
-            installed, pip_name, pip_spec, import_name = try_import(requirement)
-            if not installed:
-                missing_deps.append(pip_name.split("-")[0])
+    install_cmd = [executable, "-m", "pip", "install", "-r", "requirements.txt"]
+    run_command(install_cmd)
 
-    if not missing_deps:
-        print_formatted(
-            "All requirements are already installed. Enjoy 🚀",
-            "italic",
-            color="green",
-        )
-        sys.exit()
+    print_formatted(
+        "✅ Successfully installed all dependencies.", "italic", color="green"
+    )
 
-    # # - Get the tag version from the GitHub API
-    # tag_data, tag_name = get_github_assets(tag=None)
 
-    # # - keep
-    # version = args.version
-    # # Compare the local and tag versions
-    # if version and tag_name:
-    #     if re.match(r"v?(\d+(\.\d+)+)", version) and re.match(
-    #         r"v?(\d+(\.\d+)+)", tag_name
-    #     ):
-    #         version_parts = [int(part) for part in version.lstrip("v").split(".")]
-    #         tag_version_parts = [int(part) for part in tag_name.lstrip("v").split(".")]
-
-    #         if version_parts > tag_version_parts:
-    #             print_formatted(
-    #                 f"Local version ({version}) is greater than the release version ({tag_name}).",
-    #                 "bold",
-    #                 "yellow",
-    #             )
-    #             sys.exit()
-
-    # matching_assets = [
-    #     asset
-    #     for asset in tag_data["assets"]
-    #     if asset["name"].endswith(".whl")
-    #     and (
-    #         "any" in asset["name"] or short_platform[current_platform] in asset["name"]
-    #     )
-    # ]
-    # if not matching_assets:
-    #     print_formatted(
-    #         f"Unsupported operating system: {current_platform}", color="yellow"
-    #     )
-    # wheel_order_asset = next(
-    #     (asset for asset in tag_data["assets"] if asset["name"] == "wheel_order.txt"),
-    #     None,
-    # )
-    # if wheel_order_asset is not None:
-    #     print_formatted(
-    #         "⚙️ Sorting the release wheels using wheels order", "italic", color="yellow"
-    #     )
-    #     response = requests.get(wheel_order_asset["browser_download_url"])
-    #     if response.status_code == 200:
-    #         wheel_order = [line.strip() for line in response.text.splitlines()]
-
-    #         def get_order_index(val):
-    #             try:
-    #                 return wheel_order.index(val)
-    #             except ValueError:
-    #                 return len(wheel_order)
-
-    #         matching_assets = sorted(
-    #             matching_assets,
-    #             key=lambda x: get_order_index(x["name"].split("-")[0]),
-    #         )
-    #     else:
-    #         print("Failed to fetch wheel_order.txt. Status code:", response.status_code)
-
-    # missing_deps_urls = []
-    # for whl_file in matching_assets:
-    #     # check if installed
-    #     missing_deps_urls.append(whl_file["browser_download_url"])
-
-    install_cmd = [sys.executable, "-m", "pip", "install"]
-
-    # - Install all deps
-    if not args.dry:
-        if platform.system() == "Windows":
-            wheel_cmd = install_cmd + ["-r", (here / "reqs_windows.txt")]
-        else:
-            wheel_cmd = install_cmd + ["-r", (here / "reqs.txt")]
-
-        run_command(wheel_cmd)
-        print_formatted(
-            "✅ Successfully installed all dependencies.", "italic", color="green"
-        )
-    else:
-        print_formatted(
-            f"Would have run the following command:\n\t{apply_color(' '.join(install_cmd),'cyan')}",
-            "italic",
-            color="yellow",
-        )
+if __name__ == "__main__":
+    main()

log.py

@@ -1,6 +1,6 @@
 import logging
-import re
 import os
+import re
 
 base_log_level = logging.DEBUG if os.environ.get("MTB_DEBUG") else logging.INFO
 
@@ -75,5 +75,7 @@ def cyan_text(text):
 
 
 def get_label(label):
+    if label.startswith("MTB_"):
+        label = label[4:]
     words = re.findall(r"(?:^|[A-Z])[a-z]*", label)
     return " ".join(words).strip()

node_list.json

@@ -1,46 +1,62 @@
-{
-    "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
-    "Any To String (mtb)": "Tries to take any input and convert it to a string",
-    "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
-    "Bbox From Mask (mtb)": "From a mask extract the bounding box",
-    "Blur (mtb)": "Blur an image using a Gaussian filter.",
-    "Color Correct (mtb)": "Various color correction methods",
-    "Colored Image (mtb)": "Constant color image of given size",
-    "Concat Images (mtb)": "Add images to batch",
-    "Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input\n    ",
-    "Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
-    "Deep Bump (mtb)": "Normal & height maps generation from single pictures",
-    "Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)",
-    "Face Swap (mtb)": "Face swap using deepinsight/insightface models",
-    "Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
-    "Fit Number (mtb)": "Fit the input float using a source and target range",
-    "Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
-    "Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n    Queue items without output are ignored in the count.",
-    "Image Compare (mtb)": "Compare two images and return a difference image",
-    "Image Premultiply (mtb)": "Premultiply image with mask",
-    "Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
-    "Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
-    "Int To Bool (mtb)": "Basic int to bool conversion",
-    "Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
-    "Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
-    "Load Face Analysis Model (mtb)": "Loads a face analysis model",
-    "Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
-    "Load Face Swap Model (mtb)": "Loads a faceswap model",
-    "Load Film Model (mtb)": "Loads a FILM model",
-    "Load Image From Url (mtb)": "Load an image from the given URL",
-    "Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n    Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n    Use -1 to load all matching frames as a batch.\n    ",
-    "Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
-    "Qr Code (mtb)": "Basic QR Code generator",
-    "Restore Face (mtb)": "Uses GFPGan to restore faces",
-    "Save Gif (mtb)": "Save the images from the batch as a GIF",
-    "Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
-    "Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n    This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n    ",
-    "Save Tensors (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy",
-    "Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
-    "String Replace (mtb)": "Basic string replacement",
-    "Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
-    "Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n    The tool looks for any .ttf file in the Comfy folder hierarchy.\n    ",
-    "Transform Image (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy\n\n\n    it return a tensor representing the transformed images with the same shape as the input tensor\n    ",
-    "Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input",
-    "Unsplash Image (mtb)": "Unsplash Image given a keyword and a size"
-}
+{
+    "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
+    "Any To String (mtb)": "Tries to take any input and convert it to a string",
+    "Batch Float (mtb)": "Generates a batch of float values with interpolation",
+    "Batch Float Assemble (mtb)": "Assembles mutiple batches of floats into a single stream (batch)",
+    "Batch Float Fill (mtb)": "Fills a batch float with a single value until it reaches the target length",
+    "Batch Make (mtb)": "Simply duplicates the input frame as a batch",
+    "Batch Merge (mtb)": "Merges multiple image batches with different frame counts",
+    "Batch Shake (mtb)": "Applies a shaking effect to batches of images.",
+    "Batch Shape (mtb)": "Generates a batch of 2D shapes with optional shading (experimental)",
+    "Batch Transform (mtb)": "Transform a batch of images using a batch of keyframes",
+    "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
+    "Bbox From Mask (mtb)": "From a mask extract the bounding box",
+    "Blur (mtb)": "Blur an image using a Gaussian filter.",
+    "Color Correct (mtb)": "Various color correction methods",
+    "Colored Image (mtb)": "Constant color image of given size",
+    "Concat Images (mtb)": "Add images to batch",
+    "Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input\n    ",
+    "Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
+    "Deep Bump (mtb)": "Normal & height maps generation from single pictures",
+    "Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)",
+    "Face Swap (mtb)": "Face swap using deepinsight/insightface models",
+    "Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
+    "Fit Number (mtb)": "Fit the input float using a source and target range",
+    "Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
+    "Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n    Queue items without output are ignored in the count.",
+    "Image Compare (mtb)": "Compare two images and return a difference image",
+    "Image Premultiply (mtb)": "Premultiply image with mask",
+    "Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
+    "Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
+    "Image Tile Offset (mtb)": "Mimics an old photoshop technique to check for seamless textures",
+    "Int To Bool (mtb)": "Basic int to bool conversion",
+    "Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
+    "Interpolate Clip Sequential (mtb)": null,
+    "Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
+    "Load Face Analysis Model (mtb)": "Loads a face analysis model",
+    "Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
+    "Load Face Swap Model (mtb)": "Loads a faceswap model",
+    "Load Film Model (mtb)": "Loads a FILM model",
+    "Load Image From Url (mtb)": "Load an image from the given URL",
+    "Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n    Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n    Use -1 to load all matching frames as a batch.\n    ",
+    "Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
+    "Math Expression (mtb)": "Node to evaluate a simple math expression string",
+    "Model Patch Seamless (mtb)": "Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)",
+    "Pick From Batch (mtb)": "Pick a specific number of images from a batch, either from the start or end.",
+    "Qr Code (mtb)": "Basic QR Code generator",
+    "Restore Face (mtb)": "Uses GFPGan to restore faces",
+    "Save Gif (mtb)": "Save the images from the batch as a GIF",
+    "Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
+    "Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n    This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n    ",
+    "Save Tensors (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy",
+    "Sharpen (mtb)": "Sharpens an image using a Gaussian kernel.",
+    "Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
+    "Stack Images (mtb)": "Stack the input images horizontally or vertically",
+    "String Replace (mtb)": "Basic string replacement",
+    "Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
+    "Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n    The tool looks for any .ttf file in the Comfy folder hierarchy.\n    ",
+    "Transform Image (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy\n\n\n    it return a tensor representing the transformed images with the same shape as the input tensor\n    ",
+    "Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n    The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n    The BBOX input takes precedence over the tuple input",
+    "Unsplash Image (mtb)": "Unsplash Image given a keyword and a size",
+    "Vae Decode (mtb)": "Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"
+}

nodes/__init__.py

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

nodes/animation.py

@@ -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

@@ -0,0 +1,804 @@
+from io import BytesIO
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+from ..log import log
+from ..utils import apply_easing, pil2tensor
+from .transform import TransformImage
+
+
+def hex_to_rgb(hex_color, bgr=False):
+    hex_color = hex_color.lstrip("#")
+    if bgr:
+        return tuple(int(hex_color[i : i + 2], 16) for i in (4, 2, 0))
+
+    return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
+
+
+class MTB_BatchMake:
+    """Simply duplicates the input frame as a batch"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_batch"
+    CATEGORY = "mtb/batch"
+
+    def generate_batch(self, image: torch.Tensor, count):
+        if len(image.shape) == 3:
+            image = image.unsqueeze(0)
+
+        return (image.repeat(count, 1, 1, 1),)
+
+
+class MTB_BatchShape:
+    """Generates a batch of 2D shapes with optional shading (experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "count": ("INT", {"default": 1}),
+                "shape": (
+                    ["Box", "Circle", "Diamond", "Tube"],
+                    {"default": "Circle"},
+                ),
+                "image_width": ("INT", {"default": 512}),
+                "image_height": ("INT", {"default": 512}),
+                "shape_size": ("INT", {"default": 100}),
+                "color": ("COLOR", {"default": "#ffffff"}),
+                "bg_color": ("COLOR", {"default": "#000000"}),
+                "shade_color": ("COLOR", {"default": "#000000"}),
+                "thickness": ("INT", {"default": 5}),
+                "shadex": ("FLOAT", {"default": 0.0}),
+                "shadey": ("FLOAT", {"default": 0.0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_shapes"
+    CATEGORY = "mtb/batch"
+
+    def generate_shapes(
+        self,
+        count,
+        shape,
+        image_width,
+        image_height,
+        shape_size,
+        color,
+        bg_color,
+        shade_color,
+        thickness,
+        shadex,
+        shadey,
+    ):
+        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)
+        bg_color = hex_to_rgb(bg_color)
+        shade_color = hex_to_rgb(shade_color)
+        res = []
+        for x in range(count):
+            # Initialize an image canvas
+            canvas = np.full(
+                (image_height, image_width, 3), bg_color, dtype=np.uint8
+            )
+            mask = np.zeros((image_height, image_width), dtype=np.uint8)
+
+            # Compute the center point of the shape
+            center = (image_width // 2, image_height // 2)
+
+            if shape == "Box":
+                half_size = shape_size // 2
+                top_left = (center[0] - half_size, center[1] - half_size)
+                bottom_right = (center[0] + half_size, center[1] + half_size)
+                cv2.rectangle(mask, top_left, bottom_right, 255, -1)
+            elif shape == "Circle":
+                cv2.circle(mask, center, shape_size // 2, 255, -1)
+            elif shape == "Diamond":
+                pts = np.array(
+                    [
+                        [center[0], center[1] - shape_size // 2],
+                        [center[0] + shape_size // 2, center[1]],
+                        [center[0], center[1] + shape_size // 2],
+                        [center[0] - shape_size // 2, center[1]],
+                    ]
+                )
+                cv2.fillPoly(mask, [pts], 255)
+
+            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_canvas = cv2.addWeighted(
+                canvas.astype(np.float32), 1, shading, 1, 0
+            ).astype(np.uint8)
+
+            # Apply shading only to the shape area using the mask
+            canvas[mask == 255] = shading_canvas[mask == 255]
+            res.append(canvas)
+
+        return (pil2tensor(res),)
+
+
+class MTB_BatchFloatFill:
+    """Fills a batch float with a single value until it reaches the target length"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "floats": ("FLOATS",),
+                "direction": (["head", "tail"], {"default": "tail"}),
+                "value": ("FLOAT", {"default": 0.0}),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    FUNCTION = "fill_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def fill_floats(self, floats, direction, value, count):
+        size = len(floats)
+        if size > count:
+            raise ValueError(
+                f"Size ({size}) is less then target count ({count})"
+            )
+
+        rem = count - size
+        if direction == "tail":
+            floats = floats + [value] * rem
+        else:
+            floats = [value] * rem + floats
+        return (floats,)
+
+
+class 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"
+
+    def assemble_floats(self, reverse, **kwargs):
+        res = []
+        if reverse:
+            for x in reversed(kwargs.values()):
+                res += x
+        else:
+            for x in kwargs.values():
+                res += x
+
+        return (res,)
+
+
+class MTB_BatchFloat:
+    """Generates a batch of float values with interpolation"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mode": (
+                    ["Single", "Steps"],
+                    {"default": "Steps"},
+                ),
+                "count": ("INT", {"default": 1}),
+                "min": ("FLOAT", {"default": 0.0, "step": 0.001}),
+                "max": ("FLOAT", {"default": 1.0, "step": 0.001}),
+                "easing": (
+                    [
+                        "Linear",
+                        "Sine In",
+                        "Sine Out",
+                        "Sine In/Out",
+                        "Quart In",
+                        "Quart Out",
+                        "Quart In/Out",
+                        "Cubic In",
+                        "Cubic Out",
+                        "Cubic In/Out",
+                        "Circ In",
+                        "Circ Out",
+                        "Circ In/Out",
+                        "Back In",
+                        "Back Out",
+                        "Back In/Out",
+                        "Elastic In",
+                        "Elastic Out",
+                        "Elastic In/Out",
+                        "Bounce In",
+                        "Bounce Out",
+                        "Bounce In/Out",
+                    ],
+                    {"default": "Linear"},
+                ),
+            }
+        }
+
+    FUNCTION = "set_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def set_floats(self, mode, count, min, max, easing):
+        keyframes = []
+        if mode == "Single":
+            keyframes = [min] * count
+            return (keyframes,)
+
+        for i in range(count):
+            normalized_step = i / (count - 1)
+            eased_step = apply_easing(normalized_step, easing)
+            eased_value = min + (max - min) * eased_step
+            keyframes.append(eased_value)
+
+        return (keyframes,)
+
+
+class MTB_BatchMerge:
+    """Merges multiple image batches with different frame counts"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "fusion_mode": (
+                    ["add", "multiply", "average"],
+                    {"default": "average"},
+                ),
+                "fill": (["head", "tail"], {"default": "tail"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "merge_batches"
+    CATEGORY = "mtb/batch"
+
+    def merge_batches(self, fusion_mode: str, fill: str, **kwargs):
+        images = kwargs.values()
+        max_frames = max(img.shape[0] for img in images)
+
+        adjusted_images = []
+        for img in images:
+            frame_count = img.shape[0]
+            if frame_count < max_frames:
+                fill_frame = img[0] if fill == "head" else img[-1]
+                fill_frames = fill_frame.repeat(
+                    max_frames - frame_count, 1, 1, 1
+                )
+                adjusted_batch = (
+                    torch.cat((fill_frames, img), dim=0)
+                    if fill == "head"
+                    else torch.cat((img, fill_frames), dim=0)
+                )
+            else:
+                adjusted_batch = img
+            adjusted_images.append(adjusted_batch)
+
+        # Merge the adjusted batches
+        merged_image = None
+        for img in adjusted_images:
+            if merged_image is None:
+                merged_image = img
+            else:
+                if fusion_mode == "add":
+                    merged_image += img
+                elif fusion_mode == "multiply":
+                    merged_image *= img
+                elif fusion_mode == "average":
+                    merged_image = (merged_image + img) / 2
+
+        return (merged_image,)
+
+
+class MTB_Batch2dTransform:
+    """Transform a batch of images using a batch of keyframes"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "border_handling": (
+                    ["edge", "constant", "reflect", "symmetric"],
+                    {"default": "edge"},
+                ),
+                "constant_color": ("COLOR", {"default": "#000000"}),
+            },
+            "optional": {
+                "x": ("FLOATS",),
+                "y": ("FLOATS",),
+                "zoom": ("FLOATS",),
+                "angle": ("FLOATS",),
+                "shear": ("FLOATS",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "transform_batch"
+    CATEGORY = "mtb/batch"
+
+    def get_num_elements(
+        self, param: 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)
+
+        return 0
+
+    def transform_batch(
+        self,
+        image: torch.Tensor,
+        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]
+        ):
+            raise ValueError(
+                "At least one transform parameter must be provided"
+            )
+
+        keyframes: dict[str, list[float]] = {
+            "x": [],
+            "y": [],
+            "zoom": [],
+            "angle": [],
+            "shear": [],
+        }
+
+        default_vals = {"x": 0, "y": 0, "zoom": 1.0, "angle": 0, "shear": 0}
+
+        if x and self.get_num_elements(x) > 0:
+            keyframes["x"] = x
+        if y and self.get_num_elements(y) > 0:
+            keyframes["y"] = y
+        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 shear and self.get_num_elements(shear) > 0:
+            keyframes["shear"] = shear
+
+        for name, values in keyframes.items():
+            count = len(values)
+            if count > 0 and count != image.shape[0]:
+                raise ValueError(
+                    f"Length of {name} values ({count}) must match number of images ({image.shape[0]})"
+                )
+            if count == 0:
+                keyframes[name] = [default_vals[name]] * image.shape[0]
+
+        transformer = TransformImage()
+        res = [
+            transformer.transform(
+                image[i].unsqueeze(0),
+                keyframes["x"][i],
+                keyframes["y"][i],
+                keyframes["zoom"][i],
+                keyframes["angle"][i],
+                keyframes["shear"][i],
+                border_handling,
+                constant_color,
+            )[0]
+            for i in range(image.shape[0])
+        ]
+        return (torch.cat(res, dim=0),)
+
+
+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}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("plot",)
+    FUNCTION = "plot"
+    CATEGORY = "mtb/batch"
+
+    def plot(self, width, height, point_size, seed, **kwargs):
+        import matplotlib
+
+        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
+        global_min = min(min(values) for values in kwargs.values())
+        global_max = max(max(values) for values in kwargs.values())
+
+        # Color cycle to ensure each plot has a distinct color
+        colormap = plt.cm.get_cmap("viridis", len(kwargs))
+        color_normalization_factor = (
+            0.5 if len(kwargs) == 1 else (len(kwargs) - 1)
+        )
+
+        # Plotting each list with a unique color
+        for i, (label, values) in enumerate(kwargs.items()):
+            color_value = i / color_normalization_factor
+            ax.plot(values, label=label, color=colormap(color_value))
+
+        ax.set_ylim(global_min, global_max)  # Scaling the y-axis
+        ax.legend(
+            title="Legend",
+            title_fontsize="large",
+            fontsize="medium",
+            edgecolor="black",
+        )
+
+        # 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 MTB_BatchShake:
+    """Applies a shaking effect to batches of images."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "position_amount_x": ("FLOAT", {"default": 1.0}),
+                "position_amount_y": ("FLOAT", {"default": 1.0}),
+                "rotation_amount": ("FLOAT", {"default": 10.0}),
+                "frequency": ("FLOAT", {"default": 1.0, "min": 0.005}),
+                "frequency_divider": ("FLOAT", {"default": 1.0, "min": 0.005}),
+                "octaves": ("INT", {"default": 1, "min": 1}),
+                "seed": ("INT", {"default": 0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "FLOATS", "FLOATS", "FLOATS")
+    RETURN_NAMES = ("image", "pos_x", "pos_y", "rot")
+    FUNCTION = "apply_shake"
+    CATEGORY = "mtb/batch"
+
+    # def interpolant(self, t):
+    # return t * t * t * (t * (t * 6 - 15) + 10)
+
+    def generate_perlin_noise_2d(
+        self, shape, res, tileable=(False, False), interpolant=None
+    ):
+        """Generate a 2D numpy array of perlin noise.
+
+        Args:
+            shape: The shape of the generated array (tuple of two ints).
+                This must be a multple of res.
+            res: The number of periods of noise to generate along each
+                axis (tuple of two ints). Note shape must be a multiple of
+                res.
+            tileable: If the noise should be tileable along each axis
+                (tuple of two bools). Defaults to (False, False).
+            interpolant: The interpolation function, defaults to
+                t*t*t*(t*(t*6 - 15) + 10).
+
+        Returns
+        -------
+            A numpy array of shape shape with the generated noise.
+
+        Raises
+        ------
+            ValueError: If shape is not a multiple of res.
+        """
+        interpolant = interpolant or DEFAULT_INTERPOLANT
+        delta = (res[0] / shape[0], res[1] / shape[1])
+        d = (shape[0] // res[0], shape[1] // res[1])
+        grid = (
+            np.mgrid[0 : res[0] : delta[0], 0 : res[1] : delta[1]].transpose(
+                1, 2, 0
+            )
+            % 1
+        )
+        # Gradients
+        angles = 2 * np.pi * np.random.rand(res[0] + 1, res[1] + 1)
+        gradients = np.dstack((np.cos(angles), np.sin(angles)))
+        if tileable[0]:
+            gradients[-1, :] = gradients[0, :]
+        if tileable[1]:
+            gradients[:, -1] = gradients[:, 0]
+        gradients = gradients.repeat(d[0], 0).repeat(d[1], 1)
+        g00 = gradients[: -d[0], : -d[1]]
+        g10 = gradients[d[0] :, : -d[1]]
+        g01 = gradients[: -d[0], d[1] :]
+        g11 = gradients[d[0] :, d[1] :]
+        # Ramps
+        n00 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1])) * g00, 2)
+        n10 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1])) * g10, 2)
+        n01 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1] - 1)) * g01, 2)
+        n11 = np.sum(
+            np.dstack((grid[:, :, 0] - 1, grid[:, :, 1] - 1)) * g11, 2
+        )
+        # Interpolation
+        t = interpolant(grid)
+        n0 = n00 * (1 - t[:, :, 0]) + t[:, :, 0] * n10
+        n1 = n01 * (1 - t[:, :, 0]) + t[:, :, 0] * n11
+        return np.sqrt(2) * ((1 - t[:, :, 1]) * n0 + t[:, :, 1] * n1)
+
+    def generate_fractal_noise_2d(
+        self,
+        shape,
+        res,
+        octaves=1,
+        persistence=0.5,
+        lacunarity=2,
+        tileable=(True, True),
+        interpolant=None,
+    ):
+        """Generate a 2D numpy array of fractal noise.
+
+        Args:
+            shape: The shape of the generated array (tuple of two ints).
+                This must be a multiple of lacunarity**(octaves-1)*res.
+            res: The number of periods of noise to generate along each
+                axis (tuple of two ints). Note shape must be a multiple of
+                (lacunarity**(octaves-1)*res).
+            octaves: The number of octaves in the noise. Defaults to 1.
+            persistence: The scaling factor between two octaves.
+            lacunarity: The frequency factor between two octaves.
+            tileable: If the noise should be tileable along each axis
+                (tuple of two bools). Defaults to (True,True).
+            interpolant: The, interpolation function, defaults to
+                t*t*t*(t*(t*6 - 15) + 10).
+
+        Returns
+        -------
+            A numpy array of fractal noise and of shape shape generated by
+            combining several octaves of perlin noise.
+
+        Raises
+        ------
+            ValueError: If shape is not a multiple of
+                (lacunarity**(octaves-1)*res).
+        """
+        interpolant = interpolant or DEFAULT_INTERPOLANT
+
+        noise = np.zeros(shape)
+        frequency = 1
+        amplitude = 1
+        for _ in range(octaves):
+            noise += amplitude * self.generate_perlin_noise_2d(
+                shape,
+                (frequency * res[0], frequency * res[1]),
+                tileable,
+                interpolant,
+            )
+            frequency *= lacunarity
+            amplitude *= persistence
+        return noise
+
+    def fbm(self, x, y, octaves):
+        # noise_2d = self.generate_fractal_noise_2d((256, 256), (8, 8), octaves)
+        # Now, extract a single noise value based on x and y, wrapping indices if necessary
+        x_idx = int(x) % 256
+        y_idx = int(y) % 256
+        return self.noise_pattern[x_idx, y_idx]
+
+    def apply_shake(
+        self,
+        images,
+        position_amount_x,
+        position_amount_y,
+        rotation_amount,
+        frequency,
+        frequency_divider,
+        octaves,
+        seed,
+    ):
+        # Rehash
+        np.random.seed(seed)
+        self.position_offset = np.random.uniform(-1e3, 1e3, 3)
+        self.rotation_offset = np.random.uniform(-1e3, 1e3, 3)
+        self.noise_pattern = self.generate_perlin_noise_2d(
+            (512, 512), (32, 32), (True, True)
+        )
+
+        # Assuming frame count is derived from the first dimension of images tensor
+        frame_count = images.shape[0]
+
+        frequency = frequency / frequency_divider
+
+        # Generate shaking parameters for each frame
+        x_translations = []
+        y_translations = []
+        rotations = []
+
+        for frame_num in range(frame_count):
+            time = frame_num * frequency
+            x_idx = (self.position_offset[0] + frame_num) % 256
+            y_idx = (self.position_offset[1] + frame_num) % 256
+
+            np_position = np.array(
+                [
+                    self.fbm(x_idx, time, octaves),
+                    self.fbm(y_idx, time, octaves),
+                ]
+            )
+
+            # np_position = np.array(
+            #     [
+            #         self.fbm(self.position_offset[0] + frame_num, time, octaves),
+            #         self.fbm(self.position_offset[1] + frame_num, time, octaves),
+            #     ]
+            # )
+            # np_rotation = self.fbm(self.rotation_offset[2] + frame_num, time, octaves)
+
+            rot_idx = (self.rotation_offset[2] + frame_num) % 256
+            np_rotation = self.fbm(rot_idx, time, octaves)
+
+            x_translations.append(np_position[0] * position_amount_x)
+            y_translations.append(np_position[1] * position_amount_y)
+            rotations.append(np_rotation * rotation_amount)
+
+        # Convert lists to tensors
+        # x_translations = torch.tensor(x_translations, dtype=torch.float32)
+        # y_translations = torch.tensor(y_translations, dtype=torch.float32)
+        # rotations = torch.tensor(rotations, dtype=torch.float32)
+
+        # Create an instance of Batch2dTransform
+        transform = MTB_Batch2dTransform()
+
+        log.debug(
+            f"Applying shaking with parameters: \nposition {position_amount_x}, {position_amount_y}\nrotation {rotation_amount}\nfrequency {frequency}\noctaves {octaves}"
+        )
+
+        # Apply shaking transformations to images
+        shaken_images = transform.transform_batch(
+            images,
+            border_handling="edge",  # Assuming edge handling as default
+            constant_color="#000000",  # Assuming black as default constant color
+            x=x_translations,
+            y=y_translations,
+            angle=rotations,
+        )[0]
+
+        return (shaken_images, x_translations, y_translations, rotations)
+
+
+__nodes__ = [
+    MTB_BatchFloat,
+    MTB_Batch2dTransform,
+    MTB_BatchShape,
+    MTB_BatchMake,
+    MTB_BatchFloatAssemble,
+    MTB_BatchFloatFill,
+    MTB_BatchMerge,
+    MTB_BatchShake,
+    MTB_PlotBatchFloat,
+]

nodes/conditions.py

@@ -1,9 +1,10 @@
-from ..utils import here
-from ..log import log
-import folder_paths
+import csv, shutil
 from pathlib import Path
-import shutil
-import csv
+
+import folder_paths
+
+from ..log import log
+from ..utils import here
 
 
 class InterpolateClipSequential:
@@ -155,9 +156,23 @@ class StylesLoader:
             for file in files:
                 with open(file, "r", encoding="utf8") as f:
                     parsed = csv.reader(f)
-                    for row in parsed:
+                    for i, row in enumerate(parsed):
                         log.debug(f"Adding style {row[0]}")
-                        cls.options[row[0]] = (row[1], row[2])
+                        try:
+                            name, positive, negative = (row + [None] * 3)[:3]
+                            positive = positive or ""
+                            negative = negative or ""
+                            if name is not None:
+                                cls.options[name] = (positive, negative)
+                            else:
+                                # Handle the case where 'name' is None
+                                log.warning(f"Missing 'name' in row {i}.")
+
+                        except Exception as e:
+                            log.warning(
+                                f"There was an error while parsing {file}, make sure it respects A1111 format, i.e 3 columns name, positive, negative:\n{e}"
+                            )
+                            continue
 
         else:
             log.debug(f"Using cached styles (count: {len(cls.options)})")

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

@@ -1,9 +1,9 @@
-import torch
-from ..utils import tensor2pil, pil2tensor, tensor2np, np2tensor
-from PIL import Image, ImageFilter, ImageDraw, ImageChops
 import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFilter
 
 from ..log import log
+from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
 
 
 class Bbox:
@@ -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},
@@ -31,9 +37,8 @@ class Bbox:
     FUNCTION = "do_crop"
     CATEGORY = "mtb/crop"
 
-    def do_crop(self, x, y, width, height):  # bbox
-        return (x, y, width, height)
-        # return bbox
+    def do_crop(self, x: int, y: int, width: int, height: int):  # bbox
+        return ((x, y, width, height),)
 
 
 class BboxFromMask:
@@ -44,6 +49,7 @@ class BboxFromMask:
         return {
             "required": {
                 "mask": ("MASK",),
+                "invert": ("BOOLEAN", {"default": False}),
             },
             "optional": {
                 "image": ("IMAGE",),
@@ -61,7 +67,9 @@ class BboxFromMask:
     FUNCTION = "extract_bounding_box"
     CATEGORY = "mtb/crop"
 
-    def extract_bounding_box(self, mask: torch.Tensor, image=None):
+    def extract_bounding_box(
+        self, mask: torch.Tensor, invert: bool, image=None
+    ):
         # if image != None:
         #     if mask.size(0) != image.size(0):
         #         if mask.size(0) != 1:
@@ -73,9 +81,8 @@ class BboxFromMask:
         #                 f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
         #             )
 
-        _mask = tensor2pil(1.0 - mask)[0]
-
         # we invert it
+        _mask = tensor2pil(1.0 - mask)[0] if invert else tensor2pil(mask)[0]
         alpha_channel = np.array(_mask)
 
         non_zero_indices = np.nonzero(alpha_channel)
@@ -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},
@@ -138,22 +151,37 @@ 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:
+        if mask is not None:
             mask = mask.numpy()
 
-        if bbox != None:
+        if bbox is not None:
             x, y, width, height = bbox
 
         cropped_image = image[:, y : y + height, x : x + width, :]
-        cropped_mask = mask[y : y + height, x : x + width] if mask != None else None
+        cropped_mask = None
+        if mask is not None:
+            cropped_mask = (
+                mask[:, y : y + height, x : x + width]
+                if mask is not None
+                else None
+            )
         crop_data = (x, y, width, height)
 
         return (
             torch.from_numpy(cropped_image),
-            torch.from_numpy(cropped_mask) if mask != None else None,
+            torch.from_numpy(cropped_mask)
+            if cropped_mask is not None
+            else None,
             crop_data,
         )
 
@@ -194,7 +222,8 @@ class 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):
@@ -218,11 +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
 
@@ -245,7 +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}")
@@ -271,12 +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)

nodes/curve.py

@@ -0,0 +1,36 @@
+import json
+
+
+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):
+        return (curve,)
+
+
+__nodes__ = [MTB_Curve]

nodes/debug.py

@@ -1,64 +1,128 @@
-from ..utils import tensor2pil
-from ..log import log
-import io, base64
-import torch
-import folder_paths
-from typing import Optional
+import base64
+import io
 from pathlib import Path
+from typing import Optional
+
+import folder_paths
+import torch
+
+from ..log import log
+from ..utils import tensor2pil
 
 
-class Debug:
-    """Experimental node to debug any Comfy values, support for more types and widgets is planned"""
+# region processors
+def process_tensor(tensor):
+    log.debug(f"Tensor: {tensor.shape}")
+
+    image = tensor2pil(tensor)
+    b64_imgs = []
+    for im in image:
+        buffered = io.BytesIO()
+        im.save(buffered, format="PNG")
+        b64_imgs.append(
+            "data:image/png;base64,"
+            + base64.b64encode(buffered.getvalue()).decode("utf-8")
+        )
+
+    return {"b64_images": b64_imgs}
+
+
+def process_list(anything):
+    text = []
+    if not anything:
+        return {"text": []}
+
+    first_element = anything[0]
+    if (
+        isinstance(first_element, list)
+        and first_element
+        and isinstance(first_element[0], torch.Tensor)
+    ):
+        text.append(
+            "List of List of Tensors: "
+            f"{first_element[0].shape} (x{len(anything)})"
+        )
+
+    elif isinstance(first_element, torch.Tensor):
+        text.append(
+            f"List of Tensors: {first_element.shape} (x{len(anything)})"
+        )
+    else:
+        text.append(f"Array: {anything}")
+
+    return {"text": text}
+
+
+def process_dict(anything):
+    text = []
+    if "samples" in anything:
+        is_empty = (
+            "(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
+        )
+        text.append(f"Latent Samples: {anything['samples'].shape} {is_empty}")
+
+    return {"text": text}
+
+
+def process_bool(anything):
+    return {"text": ["True" if anything else "False"]}
+
+
+def process_text(anything):
+    return {"text": [str(anything)]}
+
+
+# endregion
+
+
+class MTB_Debug:
+    """Experimental node to debug any Comfy values.
+
+    support for more types and widgets is planned.
+    """
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
-            "required": {"anything_1": ("*")},
+            "required": {"output_to_console": ("BOOLEAN", {"default": False})},
         }
 
-    RETURN_TYPES = ("STRING",)
+    RETURN_TYPES = ()
     FUNCTION = "do_debug"
     CATEGORY = "mtb/debug"
     OUTPUT_NODE = True
 
-    def do_debug(self, **kwargs):
+    def do_debug(self, output_to_console: bool, **kwargs):
         output = {
             "ui": {"b64_images": [], "text": []},
-            "result": ("A"),
+            # "result": ("A"),
         }
-        for k, v in kwargs.items():
-            anything = v
-            text = ""
-            if isinstance(anything, torch.Tensor):
-                log.debug(f"Tensor: {anything.shape}")
 
-                # write the images to temp
+        processors = {
+            torch.Tensor: process_tensor,
+            list: process_list,
+            dict: process_dict,
+            bool: process_bool,
+        }
+        if output_to_console:
+            for k, v in kwargs.items():
+                print(f"{k}: {v}")
 
-                image = tensor2pil(anything)
-                b64_imgs = []
-                for im in image:
-                    buffered = io.BytesIO()
-                    im.save(buffered, format="PNG")
-                    b64_imgs.append(
-                        "data:image/png;base64,"
-                        + base64.b64encode(buffered.getvalue()).decode("utf-8")
-                    )
+        for anything in kwargs.values():
+            processor = processors.get(type(anything), process_text)
+            processed_data = processor(anything)
 
-                output["ui"]["b64_images"] += b64_imgs
-                log.debug(f"Input {k} contains {len(b64_imgs)} images")
-            elif isinstance(anything, bool):
-                log.debug(f"Input {k} contains boolean: {anything}")
-                output["ui"]["text"] += ["True" if anything else "False"]
-            else:
-                text = str(anything)
-                log.debug(f"Input {k} contains text: {text}")
-                output["ui"]["text"] += [text]
+            for ui_key, ui_value in processed_data.items():
+                output["ui"][ui_key].extend(ui_value)
 
         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()
@@ -113,9 +177,10 @@ 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

@@ -1,23 +1,49 @@
-import onnxruntime as ort
+import tempfile
+from pathlib import Path
+
 import numpy as np
-import pathlib
 import onnxruntime as ort
-import numpy as np
-from .. import utils as utils_inference
-from ..log import log
+import torch
+from PIL import Image
+
+from ..errors import ModelNotFound
+from ..log import mklog
+from ..utils import (
+    get_model_path,
+    tensor2pil,
+    tiles_infer,
+    tiles_merge,
+    tiles_split,
+)
 
 # Disable MS telemetry
 ort.disable_telemetry_events()
+log = mklog(__name__)
 
 
 # - COLOR to NORMALS
-def color_to_normals(color_img, overlap, progress_callback):
-    """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
 
     # Remove alpha & convert to grayscale
-    img = np.mean(color_img[:3], axis=0, keepdimss=True)
+    img = np.mean(color_img[:3], axis=0, keepdims=True)
+
+    if temp_dir:
+        Image.fromarray((img[0] * 255).astype(np.uint8)).save(
+            temp_dir / "grayscale_img.png"
+        )
+
+    log.debug(
+        "Converting color image to grayscale by taking "
+        f"the mean over color channels: {img.shape}"
+    )
 
     # Split image in tiles
     log.debug("DeepBump Color → Normals : tilling")
@@ -28,72 +54,105 @@ def color_to_normals(color_img, overlap, progress_callback):
         "LARGE": tile_size // 2,
     }
     stride_size = tile_size - overlaps[overlap]
-    tiles, paddings = utils_inference.tiles_split(
+    tiles, paddings = tiles_split(
         img, (tile_size, tile_size), (stride_size, stride_size)
     )
+    if temp_dir:
+        for i, tile in enumerate(tiles):
+            Image.fromarray((tile[0] * 255).astype(np.uint8)).save(
+                temp_dir / f"tile_{i}.png"
+            )
 
     # Load model
     log.debug("DeepBump Color → Normals : loading model")
-    addon_path = str(pathlib.Path(__file__).parent.absolute())
-    ort_session = ort.InferenceSession(f"{addon_path}/models/deepbump256.onnx")
+    model = get_model_path("deepbump", "deepbump256.onnx")
+    if not model or not model.exists():
+        raise ModelNotFound(f"deepbump ({model})")
+
+    ort_session = ort.InferenceSession(model)
 
     # Predict normal map for each tile
     log.debug("DeepBump Color → Normals : generating")
-    pred_tiles = utils_inference.tiles_infer(
+    pred_tiles = tiles_infer(
         tiles, ort_session, progress_callback=progress_callback
     )
 
+    if temp_dir:
+        for i, pred_tile in enumerate(pred_tiles):
+            Image.fromarray(
+                (pred_tile.transpose(1, 2, 0) * 255).astype(np.uint8)
+            ).save(temp_dir / f"pred_tile_{i}.png")
+
     # Merge tiles
     log.debug("DeepBump Color → Normals : merging")
-    pred_img = utils_inference.tiles_merge(
+    pred_img = tiles_merge(
         pred_tiles,
         (stride_size, stride_size),
         (3, img.shape[1], img.shape[2]),
         paddings,
     )
 
+    if temp_dir:
+        Image.fromarray(
+            (pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)
+        ).save(temp_dir / "merged_img.png")
+
     # Normalize each pixel to unit vector
-    pred_img = utils_inference.normalize(pred_img)
+    pred_img = normalize(pred_img)
+
+    if temp_dir:
+        Image.fromarray(
+            (pred_img.transpose(1, 2, 0) * 255).astype(np.uint8)
+        ).save(temp_dir / "final_img.png")
+
+        log.debug(f"Debug images saved in {temp_dir}")
 
     return pred_img
 
 
 # - NORMALS to CURVATURE
 def conv_1d(array, kernel_1d):
-    """Performs row by row 1D convolutions of the given 2D image with the given 1D kernel."""
+    """Perform row by row 1D convolutions.
 
+    of the given 2D image with the given 1D kernel.
+    """
     # Input kernel length must be odd
     k_l = len(kernel_1d)
+
     assert k_l % 2 != 0
     # Convolution is repeat-padded
     extended = np.pad(array, k_l // 2, mode="wrap")
     # Output has same size as input (padded, valid-mode convolution)
     output = np.empty(array.shape)
     for i in range(array.shape[0]):
-        output[i] = np.convolve(extended[i + (k_l // 2)], kernel_1d, mode="valid")
+        output[i] = np.convolve(
+            extended[i + (k_l // 2)], kernel_1d, mode="valid"
+        )
 
     return output * -1
 
 
 def gaussian_kernel(length, sigma):
-    """Returns a 1D gaussian kernel of size 'length'."""
-
+    """Return a 1D gaussian kernel of size 'length'."""
     space = np.linspace(-(length - 1) / 2, (length - 1) / 2, length)
     kernel = np.exp(-0.5 * np.square(space) / np.square(sigma))
     return kernel / np.sum(kernel)
 
 
 def normalize(np_array):
-    """Normalize all elements of the given numpy array to [0,1]"""
-
-    return (np_array - np.min(np_array)) / (np.max(np_array) - np.min(np_array))
+    """Normalize all elements of the given numpy array to [0,1]."""
+    return (np_array - np.min(np_array)) / (
+        np.max(np_array) - np.min(np_array)
+    )
 
 
 def normals_to_curvature(normals_img, blur_radius, progress_callback):
-    """Computes a curvature map from the given normal map. 'normals_img' must be a numpy array
-    in C,H,W format (with C as RGB). 'blur_radius' must be one of 'SMALLEST', 'SMALLER', 'SMALL',
-    'MEDIUM', 'LARGE', 'LARGER', 'LARGEST'."""
+    """Compute a curvature map from the given normal map.
 
+    'normals_img' must be a numpy array in C,H,W format (with C as RGB).
+    'blur_radius' must be one of:
+        'SMALLEST', 'SMALLER', 'SMALL', 'MEDIUM', 'LARGE', 'LARGER', 'LARGEST'.
+    """
     # Convolutions on normal map red & green channels
     if progress_callback is not None:
         progress_callback(0, 4)
@@ -118,8 +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:
@@ -156,8 +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])
@@ -171,7 +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)
 
@@ -211,8 +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)
 
@@ -226,7 +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:
@@ -247,7 +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": (
@@ -261,7 +330,7 @@ class DeepBump:
                         "LARGEST",
                     ],
                 ),
-                "normals_to_height_seamless": ("BOOLEAN", {"default": False}),
+                "normals_to_height_seamless": ("BOOLEAN", {"default": True}),
             },
         }
 
@@ -272,31 +341,49 @@ class DeepBump:
 
     def apply(
         self,
+        *,
         image,
         mode="Color to Normals",
         color_to_normals_overlap="SMALL",
         normals_to_curvature_blur_radius="SMALL",
         normals_to_height_seamless=True,
     ):
-        image = utils_inference.tensor2pil(image)
+        images = tensor2pil(image)
+        out_images = []
 
-        in_img = np.transpose(image, (2, 0, 1)) / 255
+        for image in images:
+            log.debug(f"Input image shape: {image}")
 
-        log.debug(f"Input image shape: {in_img.shape}")
+            in_img = np.transpose(image, (2, 0, 1)) / 255
+            log.debug(f"transposed for deep image shape: {in_img.shape}")
+            out_img = None
 
-        # Apply processing
-        if mode == "Color to Normals":
-            out_img = color_to_normals(in_img, color_to_normals_overlap, None)
-        if mode == "Normals to Curvature":
-            out_img = normals_to_curvature(
-                in_img, normals_to_curvature_blur_radius, None
-            )
-        if mode == "Normals to Height":
-            out_img = normals_to_height(in_img, normals_to_height_seamless, None)
+            # Apply processing
+            if mode == "Color to Normals":
+                out_img = color_to_normals(
+                    in_img, color_to_normals_overlap, None
+                )
+            if mode == "Normals to Curvature":
+                out_img = normals_to_curvature(
+                    in_img, normals_to_curvature_blur_radius, None
+                )
+            if mode == "Normals to Height":
+                out_img = normals_to_height(
+                    in_img, normals_to_height_seamless, None
+                )
 
-        out_img = (np.transpose(out_img, (1, 2, 0)) * 255).astype(np.uint8)
-
-        return (utils_inference.pil2tensor(out_img),)
+            if out_img is not None:
+                log.debug(f"Output image shape: {out_img.shape}")
+                out_images.append(
+                    torch.from_numpy(
+                        np.transpose(out_img, (1, 2, 0)).astype(np.float32)
+                    ).unsqueeze(0)
+                )
+            else:
+                log.error("No out img... This should not happen")
+        for outi in out_images:
+            log.debug(f"Shape fed to utils: {outi.shape}")
+        return (torch.cat(out_images, dim=0),)
 
 
 __nodes__ = [DeepBump]

nodes/faceenhance.py

@@ -1,21 +1,19 @@
-from gfpgan import GFPGANer
-import cv2
-import numpy as np
 import os
 from pathlib import Path
-import folder_paths
-from ..utils import pil2tensor, np2tensor, tensor2np
+from typing import Tuple
 
-from basicsr.utils import imwrite
-
-
-from PIL import Image
-import torch
-from ..log import NullWriter, log
-from comfy import model_management
 import comfy
 import comfy.utils
-from typing import Tuple
+import cv2
+import folder_paths
+import numpy as np
+import torch
+from comfy import model_management
+from gfpgan import GFPGANer
+from PIL import Image
+
+from ..log import NullWriter, log
+from ..utils import get_model_path, np2tensor, pil2tensor, tensor2np
 
 
 class LoadFaceEnhanceModel:
@@ -26,11 +24,12 @@ class LoadFaceEnhanceModel:
 
     @classmethod
     def get_models_root(cls):
-        fr = Path(folder_paths.models_dir) / "face_restore"
+        fr = get_model_path("face_restore")
+        # fr = Path(folder_paths.models_dir) / "face_restore"
         if fr.exists():
             return (fr, None)
 
-        um = Path(folder_paths.models_dir) / "upscale_models"
+        um = get_model_path("upscale_models")
         return (fr, um) if um.exists() else (None, None)
 
     @classmethod
@@ -41,12 +40,12 @@ class LoadFaceEnhanceModel:
             log.warning("Face restoration models not found.")
             return []
         if not fr_models_path.exists():
-            log.warning(
-                f"No Face Restore checkpoints found at {fr_models_path} (if you've used mtb before these checkpoints were saved in upscale_models before)"
-            )
-            log.warning(
-                "For now we fallback to upscale_models but this will be removed in a future version"
-            )
+            # 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
@@ -99,7 +98,9 @@ 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,
         )
@@ -123,7 +124,11 @@ 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}")
@@ -200,10 +205,14 @@ 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)
@@ -219,7 +228,12 @@ class RestoreFace:
     ) -> 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))
         ]
@@ -241,22 +255,24 @@ 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)
         ):
             face_id = idx + 1
             file = self.get_step_image_path("cropped_faces", face_id)
-            imwrite(cropped_face, file)
+            cv2.imwrite(file, cropped_face)
 
             file = self.get_step_image_path("cropped_faces_restored", face_id)
-            imwrite(restored_face, file)
+            cv2.imwrite(file, restored_face)
 
             file = self.get_step_image_path("cropped_faces_compare", face_id)
 
             # save comparison image
             cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
-            imwrite(cmp_img, file)
+            cv2.imwrite(file, cmp_img)
 
 
 __nodes__ = [RestoreFace, LoadFaceEnhanceModel]

nodes/faceswap.py

@@ -1,21 +1,21 @@
+# Optional face enhance nodes
 # region imports
-import onnxruntime
+import sys
 from pathlib import Path
-from PIL import Image
-from typing import List, Set, Union, Optional
+from typing import List, Optional, Set, Union
+
+import comfy.model_management as model_management
 import cv2
-import folder_paths
-import glob
 import insightface
 import numpy as np
-import os
+import onnxruntime
 import torch
 from insightface.model_zoo.inswapper import INSwapper
-from ..utils import pil2tensor, tensor2pil, download_antelopev2
-from ..log import mklog, NullWriter
-import sys
-import comfy.model_management as model_management
+from PIL import Image
 
+from ..errors import ModelNotFound
+from ..log import NullWriter, mklog
+from ..utils import download_antelopev2, get_model_path, pil2tensor, tensor2pil
 
 # endregion
 
@@ -27,15 +27,6 @@ class LoadFaceAnalysisModel:
 
     models = []
 
-    @staticmethod
-    def get_models() -> List[str]:
-        models_path = os.path.join(folder_paths.models_dir, "insightface/*")
-        models = glob.glob(models_path)
-        models = [
-            Path(x).name for x in models if x.endswith(".onnx") or x.endswith(".pth")
-        ]
-        return models
-
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -57,7 +48,7 @@ class LoadFaceAnalysisModel:
 
         face_analyser = insightface.app.FaceAnalysis(
             name=faceswap_model,
-            root=os.path.join(folder_paths.models_dir, "insightface"),
+            root=get_model_path("insightface").as_posix(),
         )
         return (face_analyser,)
 
@@ -66,11 +57,12 @@ class LoadFaceSwapModel:
     """Loads a faceswap model"""
 
     @staticmethod
-    def get_models() -> List[Path]:
-        models_path = os.path.join(folder_paths.models_dir, "insightface/*")
-        models = glob.glob(models_path)
-        models = [Path(x) for x in models if x.endswith(".onnx") or x.endswith(".pth")]
-        return models
+    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):
@@ -88,9 +80,10 @@ class LoadFaceSwapModel:
     CATEGORY = "mtb/facetools"
 
     def load_model(self, faceswap_model: str):
-        model_path = os.path.join(
-            folder_paths.models_dir, "insightface", faceswap_model
-        )
+        model_path = get_model_path("insightface", faceswap_model)
+        if not model_path or not model_path.exists():
+            raise ModelNotFound(f"{faceswap_model} ({model_path})")
+
         log.info(f"Loading model {model_path}")
         return (
             INSwapper(
@@ -120,7 +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": {},
@@ -143,10 +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)
 
@@ -180,7 +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:
@@ -204,7 +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
 
@@ -214,12 +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:

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

@@ -1,9 +1,8 @@
 import qrcode
-from ..utils import pil2tensor
-from ..utils import comfy_dir
-from typing import cast
 from PIL import Image
+
 from ..log import log
+from ..utils import comfy_dir, font_path, pil2tensor
 
 # class MtbExamples:
 #     """MTB Example Images"""
@@ -60,9 +59,18 @@ class UnsplashImage:
     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"}),
@@ -74,9 +82,10 @@ class UnsplashImage:
     CATEGORY = "mtb/generate"
 
     def do_unsplash_image(self, width, height, random_seed, keyword=None):
-        import requests
         import io
 
+        import requests
+
         base_url = "https://source.unsplash.com/random/"
 
         if width and height:
@@ -121,8 +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}),
             }
         }
@@ -131,7 +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"
         )
@@ -156,7 +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)
@@ -171,37 +190,46 @@ 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}")
-            cls.fonts[font.stem] = font.as_posix()
+            MTB_TextToImage.fonts[font.stem] = font.as_posix()
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -216,13 +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",
@@ -232,7 +262,6 @@ class TextToImage:
                     "INT",
                     {"default": 512, "min": 1, "max": 8096, "step": 1},
                 ),
-                # "position": (["INT"], {"default": 0, "min": 0, "max": 100, "step": 1}),
                 "color": (
                     "COLOR",
                     {"default": "black"},
@@ -241,6 +270,20 @@ class TextToImage:
                     "COLOR",
                     {"default": "white"},
                 ),
+                "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},
+                ),
             }
         }
 
@@ -250,37 +293,79 @@ class TextToImage:
     CATEGORY = "mtb/generate"
 
     def text_to_image(
-        self, text, font, wrap, font_size, width, height, color, background
+        self,
+        text: str,
+        font,
+        wrap,
+        trim,
+        line_height,
+        font_size,
+        width,
+        height,
+        color,
+        background,
+        h_align="left",
+        v_align="top",
+        h_offset=0,
+        v_offset=0,
+        h_coverage=100,
     ):
-        from PIL import Image, ImageDraw, ImageFont
         import textwrap
 
-        font = self.fonts[font]
-        font = cast(ImageFont.FreeTypeFont, ImageFont.truetype(font, font_size))
-        if wrap == 0:
-            wrap = width / font_size
-        lines = textwrap.wrap(text, width=wrap)
+        from PIL import Image, ImageDraw, ImageFont
+
+        font_path = self.fonts[font]
+
+        text = (
+            text.encode("ascii", "ignore").decode().strip() if trim else text
+        )
+        # Handle word wrapping
+        if 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, size=font_size)
         log.debug(f"Lines: {lines}")
-        line_height = bbox_dim(font.getbbox("hg"))[1]
-        img_height = height  # line_height * len(lines)
-        img_width = width  # max(font.getsize(line)[0] for line in lines)
-
-        img = Image.new("RGBA", (img_width, img_height), background)
+        img = Image.new("RGBA", (width, height), background)
         draw = ImageDraw.Draw(img)
-        y_text = 0
-        # - bbox is [left, upper, right, lower]
-        for line in lines:
-            width, height = bbox_dim(font.getbbox(line))
-            draw.text((0, y_text), line, color, font=font)
-            y_text += height
 
-        # img.save(os.path.join(folder_paths.base_path, f'{str(uuid.uuid4())}.png'))
+        line_height_px = line_height * font_size
+
+        # Vertical alignment
+        if v_align == "top":
+            y_text = v_offset
+        elif v_align == "center":
+            y_text = ((height - (line_height_px * len(lines))) // 2) + v_offset
+        else:  # bottom
+            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 = get_width(line)
+            # Horizontal alignment
+            if h_align == "left":
+                x_text = h_offset
+            elif h_align == "center":
+                x_text = ((width - line_width) // 2) + h_offset
+            else:  # right
+                x_text = (width - line_width) - h_offset
+
+            draw.text((x_text, y_text), line, fill=color, font=font)
+            y_text += line_height_px
+
         return (pil2tensor(img),)
 
 
 __nodes__ = [
     QrCode,
     UnsplashImage,
-    TextToImage
+    MTB_TextToImage,
     #  MtbExamples,
 ]

nodes/graph_utils.py

@@ -1,29 +1,294 @@
-from ..log import log
-from PIL import Image
-import urllib.request
-import urllib.parse
-import torch
-import json
-from comfy.cli_args import args
-from ..utils import pil2tensor, apply_easing
 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,
+    numpy_NFOV,
+    pil2tensor,
+    tensor2np,
+)
 
 
 def get_image(filename, subfolder, folder_type):
-    log.debug(f"Getting image {filename} from {subfolder} of {folder_type}")
+    log.debug(
+        f"Getting image {filename} from foldertype {folder_type} {f'in subfolder: {subfolder}' if subfolder else ''}"
+    )
     data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    base_url, port = get_server_info()
+
     url_values = urllib.parse.urlencode(data)
-    with urllib.request.urlopen(
-        f"http://{args.listen}:{args.port}/view?{url_values}"
-    ) as response:
+    url = f"http://{base_url}:{port}/view?{url_values}"
+    log.debug(f"Fetching image from {url}")
+    with urllib.request.urlopen(url) as response:
         return io.BytesIO(response.read())
 
 
-class GetBatchFromHistory:
+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_FloatsToFloat:
+    """AD, IPA, Fitz etc have commonly choose to mistype float lists as FLOAT.
+
+    This is just a hack to be compatible with these
+    """
+
+    @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):
@@ -46,6 +311,7 @@ class GetBatchFromHistory:
 
     def load_from_history(
         self,
+        *,
         enable=True,
         count=0,
         offset=0,
@@ -60,10 +326,18 @@ class GetBatchFromHistory:
             return (torch.zeros(0),)
         frames = []
 
-        with urllib.request.urlopen(
-            f"http://{args.listen}:{args.port}/history"
-        ) as response:
-            return self.load_batch_frames(response, offset, count, frames)
+        base_url, port = get_server_info()
+
+        history_url = f"http://{base_url}:{port}/history"
+        log.debug(f"Fetching history from {history_url}")
+        output = torch.zeros(0)
+        with urllib.request.urlopen(history_url) as response:
+            output = self.load_batch_frames(response, offset, count, frames)
+
+        if output.size(0) == 0:
+            log.warn("No output found in history")
+
+        return (output,)
 
     def load_batch_frames(self, response, offset, count, frames):
         history = json.loads(response.read())
@@ -75,12 +349,14 @@ 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)
 
         if not output_images:
-            return (torch.zeros(0),)
+            return torch.zeros(0)
 
         # Directly get desired range of images
         start_index = max(len(output_images) - offset - count, 0)
@@ -90,17 +366,15 @@ class GetBatchFromHistory:
         frames = [Image.open(image) for image in selected_images]
 
         if not frames:
-            return (torch.zeros(0),)
+            return torch.zeros(0)
         elif len(frames) != count:
             log.warning(f"Expected {count} images, got {len(frames)} instead")
 
-        output = pil2tensor(frames)
-
-        return (output,)
+        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):
@@ -120,18 +394,22 @@ 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):
@@ -159,7 +437,54 @@ class StringReplace:
         return (string,)
 
 
-class FitNumber:
+class MTB_MathExpression:
+    """Node to evaluate a simple math expression string"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "expression": ("STRING", {"default": "", "multiline": True}),
+            }
+        }
+
+    FUNCTION = "eval_expression"
+    RETURN_TYPES = ("FLOAT", "INT")
+    RETURN_NAMES = ("result (float)", "result (int)")
+    CATEGORY = "mtb/math"
+    DESCRIPTION = (
+        "evaluate a simple math expression string (!! Fallsback to eval)"
+    )
+
+    def eval_expression(self, expression, **kwargs):
+        from ast import literal_eval
+
+        for key, value in kwargs.items():
+            print(f"Replacing placeholder <{key}> with value {value}")
+            expression = expression.replace(f"<{key}>", str(value))
+
+        result = -1
+        try:
+            result = literal_eval(expression)
+        except SyntaxError as e:
+            raise ValueError(
+                f"The expression syntax is wrong '{expression}': {e}"
+            ) from e
+
+        except ValueError:
+            try:
+                expression = expression.replace("^", "**")
+                result = eval(expression)
+            except Exception as e:
+                # Handle any other exceptions and provide a meaningful error message
+                raise ValueError(
+                    f"Error evaluating expression '{expression}': {e}"
+                ) from e
+
+        return (result, int(result))
+
+
+class MTB_FitNumber:
     """Fit the input float using a source and target range"""
 
     @classmethod
@@ -168,10 +493,10 @@ class FitNumber:
             "required": {
                 "value": ("FLOAT", {"default": 0, "forceInput": True}),
                 "clamp": ("BOOLEAN", {"default": False}),
-                "source_min": ("FLOAT", {"default": 0.0}),
-                "source_max": ("FLOAT", {"default": 1.0}),
-                "target_min": ("FLOAT", {"default": 0.0}),
-                "target_max": ("FLOAT", {"default": 1.0}),
+                "source_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "source_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
+                "target_min": ("FLOAT", {"default": 0.0, "step": 0.01}),
+                "target_max": ("FLOAT", {"default": 1.0, "step": 0.01}),
                 "easing": (
                     [
                         "Linear",
@@ -205,6 +530,7 @@ class FitNumber:
     FUNCTION = "set_range"
     RETURN_TYPES = ("FLOAT",)
     CATEGORY = "mtb/math"
+    DESCRIPTION = "Fit the input float using a source and target range"
 
     def set_range(
         self,
@@ -216,20 +542,58 @@ class FitNumber:
         target_max: float,
         easing: str,
     ):
-        normalized_value = (value - source_min) / (source_max - source_min)
+        if source_min == source_max:
+            normalized_value = 0
+        else:
+            normalized_value = (value - source_min) / (source_max - source_min)
+        if clamp:
+            normalized_value = max(min(normalized_value, 1), 0)
 
         eased_value = apply_easing(normalized_value, easing)
 
         # - Convert the eased value to the target range
         res = target_min + (target_max - target_min) * eased_value
 
-        if clamp:
-            if target_min > target_max:
-                res = max(min(res, target_min), target_max)
-            else:
-                res = max(min(res, target_max), target_min)
-
         return (res,)
 
 
-__nodes__ = [StringReplace, FitNumber, GetBatchFromHistory, AnyToString]
+class MTB_ConcatImages:
+    """Add images to batch."""
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "concatenate_tensors"
+    CATEGORY = "mtb/image"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"reverse": ("BOOLEAN", {"default": False})},
+        }
+
+    def concatenate_tensors(self, reverse, **kwargs):
+        tensors = tuple(kwargs.values())
+        batch_sizes = [tensor.size(0) for tensor in tensors]
+
+        concatenated = torch.cat(tensors, dim=0)
+
+        # Update the batch size in the concatenated tensor
+        concatenated_size = list(concatenated.size())
+        concatenated_size[0] = sum(batch_sizes)
+        concatenated = concatenated.view(*concatenated_size)
+
+        return (concatenated,)
+
+
+__nodes__ = [
+    MTB_StringReplace,
+    MTB_FitNumber,
+    MTB_GetBatchFromHistory,
+    MTB_AnyToString,
+    MTB_ConcatImages,
+    MTB_MathExpression,
+    MTB_ToDevice,
+    MTB_ApplyTextTemplate,
+    MTB_MatchDimensions,
+    MTB_AutoPanEquilateral,
+    MTB_FloatsToFloat,
+]

nodes/image_interpolation.py

@@ -1,16 +1,20 @@
-from typing import List
-from pathlib import Path
-import os
 import glob
-import folder_paths
-from ..log import log
-import torch
-from frame_interpolation.eval import util, interpolator
-import numpy as np
+import os
+from pathlib import Path
+from typing import List
+
 import comfy
-import comfy.utils
-import tensorflow as tf
 import comfy.model_management as model_management
+import comfy.utils
+import folder_paths
+import numpy as np
+import tensorflow as tf
+import torch
+from frame_interpolation.eval import interpolator, util
+
+from ..errors import ModelNotFound
+from ..log import log
+from ..utils import get_model_path
 
 
 class LoadFilmModel:
@@ -18,10 +22,9 @@ class LoadFilmModel:
 
     @staticmethod
     def get_models() -> List[Path]:
-        models_path = os.path.join(folder_paths.models_dir, "FILM/*")
-        models = glob.glob(models_path)
-        models = [Path(x) for x in models if x.endswith(".onnx") or x.endswith(".pth")]
-        return models
+        models_paths = get_model_path("FILM").iterdir()
+
+        return [x for x in models_paths if x.suffix in [".onnx", ".pth"]]
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -39,7 +42,10 @@ class LoadFilmModel:
     CATEGORY = "mtb/frame iterpolation"
 
     def load_model(self, film_model: str):
-        model_path = Path(folder_paths.models_dir) / "FILM" / film_model
+        model_path = get_model_path("FILM", film_model)
+        if not model_path or not model_path.exists():
+            raise ModelNotFound(f"FILM ({model_path})")
+
         if not (model_path / "saved_model.pb").exists():
             model_path = model_path / "saved_model"
 
@@ -114,41 +120,4 @@ class FilmInterpolation:
         return (out_tensors,)
 
 
-class ConcatImages:
-    """Add images to batch"""
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "concat_images"
-    CATEGORY = "mtb/image"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "imageA": ("IMAGE",),
-                "imageB": ("IMAGE",),
-            },
-        }
-
-    @classmethod
-    def concatenate_tensors(cls, A: torch.Tensor, B: torch.Tensor):
-        # Get the batch sizes of A and B
-        batch_size_A = A.size(0)
-        batch_size_B = B.size(0)
-
-        # Concatenate the tensors along the batch dimension
-        concatenated = torch.cat((A, B), dim=0)
-
-        # Update the batch size in the concatenated tensor
-        concatenated_size = list(concatenated.size())
-        concatenated_size[0] = batch_size_A + batch_size_B
-        concatenated = concatenated.view(*concatenated_size)
-
-        return concatenated
-
-    def concat_images(self, imageA: torch.Tensor, imageB: torch.Tensor):
-        log.debug(f"Concatenating A ({imageA.shape}) and B ({imageB.shape})")
-        return (self.concatenate_tensors(imageA, imageB),)
-
-
-__nodes__ = [LoadFilmModel, FilmInterpolation, ConcatImages]
+__nodes__ = [LoadFilmModel, FilmInterpolation]

nodes/image_processing.py

@@ -1,17 +1,19 @@
+import itertools
+import json
+import math
+import os
+
+import folder_paths
+import numpy as np
 import torch
+import torch.nn.functional as F
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
 from skimage.filters import gaussian
 from skimage.util import compare_images
-import numpy as np
-import torch.nn.functional as F
-from PIL import Image
-from ..utils import tensor2pil, pil2tensor, tensor2np
-import torch
-import folder_paths
-from PIL.PngImagePlugin import PngInfo
-import json
-import os
-import math
 
+from ..log import log
+from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
 
 # try:
 #     from cv2.ximgproc import guidedFilter
@@ -19,7 +21,21 @@ import math
 #     log.warning("cv2.ximgproc.guidedFilter not found, use opencv-contrib-python")
 
 
-class ColorCorrect:
+def gaussian_kernel(
+    kernel_size: int, sigma_x: float, sigma_y: float, device=None
+):
+    x, y = torch.meshgrid(
+        torch.linspace(-1, 1, kernel_size, device=device),
+        torch.linspace(-1, 1, kernel_size, device=device),
+        indexing="ij",
+    )
+    d_x = x * x / (2.0 * sigma_x * sigma_x)
+    d_y = y * y / (2.0 * sigma_y * sigma_y)
+    g = torch.exp(-(d_x + d_y))
+    return g / g.sum()
+
+
+class MTB_ColorCorrect:
     """Various color correction methods"""
 
     @classmethod
@@ -100,7 +116,9 @@ 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)
 
@@ -177,7 +195,7 @@ class ColorCorrect:
         return (image,)
 
 
-class ImageCompare:
+class MTB_ImageCompare:
     """Compare two images and return a difference image"""
 
     @classmethod
@@ -213,7 +231,7 @@ class ImageCompare:
 import requests
 
 
-class LoadImageFromUrl:
+class MTB_LoadImageFromUrl:
     """Load an image from the given URL"""
 
     @classmethod
@@ -236,10 +254,11 @@ 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
@@ -249,25 +268,129 @@ 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 MTB_Sharpen:
+    """Sharpens an image using a Gaussian kernel."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "sharpen_radius": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": 31, "step": 1},
+                ),
+                "sigma_x": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1},
+                ),
+                "sigma_y": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1},
+                ),
+                "alpha": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "do_sharp"
+    CATEGORY = "mtb/image processing"
+
+    def do_sharp(
+        self,
+        image: torch.Tensor,
+        sharpen_radius: int,
+        sigma_x: float,
+        sigma_y: float,
+        alpha: float,
+    ):
+        if sharpen_radius == 0:
+            return (image,)
+
+        channels = image.shape[3]
+
+        kernel_size = 2 * sharpen_radius + 1
+        kernel = gaussian_kernel(kernel_size, sigma_x, sigma_y) * -(alpha * 10)
+
+        # Modify center of kernel to make it a sharpening kernel
+        center = kernel_size // 2
+        kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+
+        kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
+        tensor_image = image.permute(0, 3, 1, 2)
+
+        tensor_image = F.pad(
+            tensor_image,
+            (sharpen_radius, sharpen_radius, sharpen_radius, sharpen_radius),
+            "reflect",
+        )
+        sharpened = F.conv2d(
+            tensor_image, kernel, padding=center, groups=channels
+        )
+
+        # Remove padding
+        sharpened = sharpened[
+            :,
+            :,
+            sharpen_radius:-sharpen_radius,
+            sharpen_radius:-sharpen_radius,
+        ]
+
+        sharpened = sharpened.permute(0, 2, 3, 1)
+        result = torch.clamp(sharpened, 0, 1)
+
+        return (result,)
 
 
 # https://github.com/lllyasviel/AdverseCleaner/blob/main/clean.py
@@ -296,7 +419,7 @@ class Blur:
 #         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
@@ -316,26 +439,32 @@ class MaskToImage:
     FUNCTION = "render_mask"
 
     def render_mask(self, mask, color, background):
-        mask = tensor2np(mask)
-        mask = Image.fromarray(mask).convert("L")
+        masks = tensor2np(mask)[0]
+        images = []
+        for m in masks:
+            _mask = Image.fromarray(m).convert("L")
 
-        image = Image.new("RGBA", mask.size, color=color)
-        # apply the mask
-        image = Image.composite(
-            image, Image.new("RGBA", mask.size, color=background), mask
-        )
+            log.debug(
+                f"Converted mask to PIL Image format, size: {_mask.size}"
+            )
 
-        # image = ImageChops.multiply(image, mask)
-        # apply over background
-        # image = Image.alpha_composite(Image.new("RGBA", image.size, color=background), image)
+            image = Image.new("RGBA", _mask.size, color=color)
+            # apply the mask
+            image = Image.composite(
+                image, Image.new("RGBA", _mask.size, color=background), _mask
+            )
 
-        image = pil2tensor(image.convert("RGB"))
+            # image = ImageChops.multiply(image, mask)
+            # apply over background
+            # image = Image.alpha_composite(Image.new("RGBA", image.size, color=background), image)
 
-        return (image,)
+            images.append(image.convert("RGB"))
+
+        return (pil2tensor(images),)
 
 
-class ColoredImage:
-    """Constant color image of given size"""
+class MTB_ColoredImage:
+    """Constant color image of given size."""
 
     def __init__(self) -> None:
         pass
@@ -347,7 +476,11 @@ class ColoredImage:
                 "color": ("COLOR",),
                 "width": ("INT", {"default": 512, "min": 16, "max": 8160}),
                 "height": ("INT", {"default": 512, "min": 16, "max": 8160}),
-            }
+            },
+            "optional": {
+                "foreground_image": ("IMAGE",),
+                "foreground_mask": ("MASK",),
+            },
         }
 
     CATEGORY = "mtb/generate"
@@ -356,15 +489,101 @@ class ColoredImage:
 
     FUNCTION = "render_img"
 
-    def render_img(self, color, width, height):
-        image = Image.new("RGB", (width, height), color=color)
+    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
 
-        image = pil2tensor(image)
+        # Use the smaller scaling factor to maintain aspect ratio
+        scale = max(scale_x, scale_y)
 
-        return (image,)
+        # 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: 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:
+            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(
+                            "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")
+                    )
+
+        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
@@ -379,19 +598,13 @@ class ImagePremultiply:
 
     CATEGORY = "mtb/image"
     RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("RGBA",)
     FUNCTION = "premultiply"
 
     def premultiply(self, image, mask, invert):
         images = tensor2pil(image)
-        if invert:
-            masks = tensor2pil(mask)  # .convert("L")
-        else:
-            masks = tensor2pil(1.0 - mask)
-
-        single = False
-        if len(mask) == 1:
-            single = True
-
+        masks = tensor2pil(mask) if invert else tensor2pil(1.0 - mask)
+        single = len(mask) == 1
         masks = [x.convert("L") for x in masks]
 
         out = []
@@ -409,7 +622,7 @@ 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
@@ -454,7 +667,9 @@ 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:
@@ -501,7 +716,7 @@ 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):
@@ -566,7 +781,10 @@ 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
@@ -596,22 +814,79 @@ 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 MTB_ImageTileOffset:
+    """Mimics an old photoshop technique to check for seamless textures"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "tilesX": ("INT", {"default": 2, "min": 1}),
+                "tilesY": ("INT", {"default": 2, "min": 1}),
+            }
+        }
+
+    CATEGORY = "mtb/generate"
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "tile_image"
+
+    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 // tilesY
+        tile_width = width // tilesX
+
+        output_image = torch.zeros_like(image)
+
+        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
+            end_w = start_w + tile_width
+
+            tile = image[:, start_h:end_h, start_w:end_w, :]
+
+            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
+
+            output_image[
+                :, output_start_h:output_end_h, output_start_w:output_end_w, :
+            ] = tile
+
+        return (output_image,)
+
+
 __nodes__ = [
-    ColorCorrect,
-    ImageCompare,
-    Blur,
+    MTB_ColorCorrect,
+    MTB_ImageCompare,
+    MTB_ImageTileOffset,
+    MTB_Blur,
     # DeglazeImage,
-    MaskToImage,
-    ColoredImage,
-    ImagePremultiply,
-    ImageResizeFactor,
-    SaveImageGrid,
-    LoadImageFromUrl,
+    MTB_MaskToImage,
+    MTB_ColoredImage,
+    MTB_ImagePremultiply,
+    MTB_ImageResizeFactor,
+    MTB_SaveImageGrid,
+    MTB_LoadImageFromUrl,
+    MTB_Sharpen,
 ]

nodes/image_utils.py

@@ -0,0 +1,110 @@
+import torch
+
+from ..log import log
+
+
+class MTB_StackImages:
+    """Stack the input images horizontally or vertically."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"vertical": ("BOOLEAN", {"default": False})}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stack"
+    CATEGORY = "mtb/image utils"
+
+    def stack(self, vertical, **kwargs):
+        if not kwargs:
+            raise ValueError("At least one tensor must be provided.")
+
+        tensors = list(kwargs.values())
+        log.debug(
+            f"Stacking {len(tensors)} tensors "
+            f"{'vertically' if vertical else 'horizontally'}"
+        )
+
+        normalized_tensors = [
+            self.normalize_to_rgba(tensor) for tensor in tensors
+        ]
+
+        if vertical:
+            width = normalized_tensors[0].shape[2]
+            if any(tensor.shape[2] != width for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same width "
+                    "for vertical stacking."
+                )
+            dim = 1
+        else:
+            height = normalized_tensors[0].shape[1]
+            if any(tensor.shape[1] != height for tensor in normalized_tensors):
+                raise ValueError(
+                    "All tensors must have the same height "
+                    "for horizontal stacking."
+                )
+            dim = 2
+
+        stacked_tensor = torch.cat(normalized_tensors, dim=dim)
+
+        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 MTB_PickFromBatch:
+    """Pick a specific number of images from a batch.
+
+    either from the start or end.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "from_direction": (["end", "start"], {"default": "start"}),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "pick_from_batch"
+    CATEGORY = "mtb/image utils"
+
+    def pick_from_batch(self, image, from_direction, count):
+        batch_size = image.size(0)
+
+        # Limit count to the available number of images in the batch
+        count = min(count, batch_size)
+        if count < batch_size:
+            log.warning(
+                f"Requested {count} images, "
+                f"but only {batch_size} are available."
+            )
+
+        if from_direction == "end":
+            selected_tensors = image[-count:]
+        else:
+            selected_tensors = image[:count]
+
+        return (selected_tensors,)
+
+
+__nodes__ = [MTB_StackImages, MTB_PickFromBatch]

nodes/io.py

@@ -1,33 +1,143 @@
-from ..utils import tensor2np, PIL_FILTER_MAP
-import uuid
-import folder_paths
-from ..log import log
-import comfy.model_management as model_management
+import json
 import subprocess
-import torch
+import uuid
 from pathlib import Path
+from typing import List, Optional
+
+import comfy.model_management as model_management
+import folder_paths
 import numpy as np
+import torch
 from PIL import Image
-from typing import Optional, List
+
+from ..log import log
+from ..utils import PIL_FILTER_MAP, output_dir, session_id, tensor2np
 
 
-class ExportWithFfmpeg:
-    """Export with FFmpeg (Experimental)"""
+def get_playlist_path(playlist_name: str, persistant_playlist=False):
+    if persistant_playlist:
+        return output_dir / "playlists" / f"{playlist_name}.json"
+
+    return output_dir / "playlists" / session_id / f"{playlist_name}.json"
+
+
+class ReadPlaylist:
+    """Read a playlist"""
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
+                "enable": ("BOOLEAN", {"default": True}),
+                "persistant_playlist": ("BOOLEAN", {"default": False}),
+                "playlist_name": (
+                    "STRING",
+                    {"default": "playlist_{index:04d}"},
+                ),
+                "index": ("INT", {"default": 0, "min": 0}),
+            }
+        }
+
+    RETURN_TYPES = ("PLAYLIST",)
+    FUNCTION = "read_playlist"
+    CATEGORY = "mtb/IO"
+
+    def read_playlist(
+        self,
+        enable: bool,
+        persistant_playlist: bool,
+        playlist_name: str,
+        index: int,
+    ):
+        playlist_name = playlist_name.format(index=index)
+        playlist_path = get_playlist_path(playlist_name, persistant_playlist)
+        if not enable:
+            return (None,)
+
+        if not playlist_path.exists():
+            log.warning(f"Playlist {playlist_path} does not exist, skipping")
+            return (None,)
+
+        log.debug(f"Reading playlist {playlist_path}")
+        return (json.loads(playlist_path.read_text(encoding="utf-8")),)
+
+
+class MTB_AddToPlaylist:
+    """Add a video to the playlist"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "relative_paths": ("BOOLEAN", {"default": False}),
+                "persistant_playlist": ("BOOLEAN", {"default": False}),
+                "playlist_name": (
+                    "STRING",
+                    {"default": "playlist_{index:04d}"},
+                ),
+                "index": ("INT", {"default": 0, "min": 0}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "add_to_playlist"
+    CATEGORY = "mtb/IO"
+
+    def add_to_playlist(
+        self,
+        relative_paths: bool,
+        persistant_playlist: bool,
+        playlist_name: str,
+        index: int,
+        **kwargs,
+    ):
+        playlist_name = playlist_name.format(index=index)
+        playlist_path = get_playlist_path(playlist_name, persistant_playlist)
+
+        if not playlist_path.parent.exists():
+            playlist_path.parent.mkdir(parents=True, exist_ok=True)
+
+        playlist = []
+        if not playlist_path.exists():
+            playlist_path.write_text("[]")
+        else:
+            playlist = json.loads(playlist_path.read_text())
+        log.debug(f"Playlist {playlist_path} has {len(playlist)} items")
+        for video in kwargs.values():
+            if relative_paths:
+                video = Path(video).relative_to(output_dir).as_posix()
+
+            log.debug(f"Adding {video} to playlist")
+            playlist.append(video)
+
+        log.debug(f"Writing playlist {playlist_path}")
+        playlist_path.write_text(json.dumps(playlist), encoding="utf-8")
+        return ()
+
+
+class MTB_ExportWithFfmpeg:
+    """Export with FFmpeg (Experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
                 "images": ("IMAGE",),
-                # "frames": ("FRAMES",),
+                "playlist": ("PLAYLIST",),
+            },
+            "required": {
                 "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"},
                 ),
-            }
+            },
         }
 
     RETURN_TYPES = ("VIDEO",)
@@ -37,26 +147,94 @@ class ExportWithFfmpeg:
 
     def export_prores(
         self,
-        images: torch.Tensor,
         fps: float,
         prefix: str,
         format: str,
         codec: str,
+        images: Optional[torch.Tensor] = None,
+        playlist: Optional[List[str]] = None,
     ):
-        if images.size(0) == 0:
-            return ("",)
-        output_dir = Path(folder_paths.get_output_directory())
         pix_fmt = "rgb48le" if codec == "prores_ks" else "yuv420p"
         file_ext = format
         file_id = f"{prefix}_{uuid.uuid4()}.{file_ext}"
 
-        log.debug(f"Exporting to {output_dir / file_id}")
+        if playlist is not None and images is not None:
+            log.info(f"Exporting to {output_dir / file_id}")
+
+        if playlist is not None:
+            if len(playlist) == 0:
+                log.debug("Playlist is empty, skipping")
+                return ("",)
+
+            temp_playlist_path = (
+                output_dir / f"temp_playlist_{uuid.uuid4()}.txt"
+            )
+            log.debug(
+                f"Create a temporary file to list the videos for concatenation to {temp_playlist_path}"
+            )
+
+            with open(temp_playlist_path, "w") as f:
+                for video_path in playlist:
+                    f.write(f"file '{video_path}'\n")
+
+            out_path = (output_dir / file_id).as_posix()
+
+            # Prepare the FFmpeg command for concatenating videos from the playlist
+            command = [
+                "ffmpeg",
+                "-f",
+                "concat",
+                "-safe",
+                "0",
+                "-i",
+                temp_playlist_path.as_posix(),
+                "-c",
+                "copy",
+                "-y",
+                out_path,
+            ]
+            log.debug(f"Executing {command}")
+            subprocess.run(command)
+
+            temp_playlist_path.unlink()
+
+            return (out_path,)
+
+        if (
+            images is None or images.size(0) == 0
+        ):  # the is None check is just for the type checker
+            return ("",)
 
         frames = tensor2np(images)
         log.debug(f"Frames type {type(frames[0])}")
         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
 
@@ -129,7 +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
@@ -141,9 +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}),
             },
         }
 
@@ -160,6 +337,7 @@ class SaveGif:
         optimize=False,
         pingpong=False,
         resample_filter=None,
+        use_ffmpeg=False,
     ):
         if image.size(0) == 0:
             return ("",)
@@ -178,18 +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]
+__nodes__ = [
+    MTB_SaveGif,
+    MTB_ExportWithFfmpeg,
+    MTB_AddToPlaylist,
+    ReadPlaylist,
+]

nodes/mask.py

@@ -1,10 +1,11 @@
-from rembg import remove
-from ..utils import pil2tensor, tensor2pil
-from PIL import Image
 import comfy.utils
+from PIL import Image
+from rembg import remove
+
+from ..utils import pil2tensor, tensor2pil
 
 
-class ImageRemoveBackgroundRembg:
+class MTB_ImageRemoveBackgroundRembg:
     """Removes the background from the input using Rembg."""
 
     @classmethod
@@ -91,15 +92,21 @@ class ImageRemoveBackgroundRembg:
 
             image_on_bg.paste(img_rm, mask=mask)
 
+            image_on_bg = image_on_bg.convert("RGB")
+
             out_img.append(img_rm)
             out_mask.append(mask)
             out_img_on_bg.append(image_on_bg)
 
             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

@@ -0,0 +1,155 @@
+import copy
+
+import torch
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair
+
+from ..log import log
+
+
+class MTB_VaeDecode:
+    """Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "vae": ("VAE",),
+                "seamless_model": ("BOOLEAN", {"default": False}),
+                "use_tiling_decoder": ("BOOLEAN", {"default": True}),
+                "tile_size": (
+                    "INT",
+                    {"default": 512, "min": 320, "max": 4096, "step": 64},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    CATEGORY = "mtb/decode"
+
+    def decode(
+        self,
+        vae,
+        samples,
+        seamless_model,
+        use_tiling_decoder=True,
+        tile_size=512,
+    ):
+        if seamless_model:
+            if use_tiling_decoder:
+                log.error(
+                    "You cannot use seamless mode with tiling decoder together, skipping tiling."
+                )
+                use_tiling_decoder = False
+            for layer in [
+                layer
+                for layer in vae.first_stage_model.modules()
+                if isinstance(layer, torch.nn.Conv2d)
+            ]:
+                layer.padding_mode = "circular"
+        if use_tiling_decoder:
+            return (
+                vae.decode_tiled(
+                    samples["samples"],
+                    tile_x=tile_size // 8,
+                    tile_y=tile_size // 8,
+                ),
+            )
+        else:
+            return (vae.decode(samples["samples"]),)
+
+
+def conv_forward(lyr, tensor, weight, bias):
+    step = lyr.timestep
+    if (lyr.paddingStartStep < 0 or step >= lyr.paddingStartStep) and (
+        lyr.paddingStopStep < 0 or step <= lyr.paddingStopStep
+    ):
+        working = F.pad(tensor, lyr.paddingX, mode=lyr.padding_modeX)
+        working = F.pad(working, lyr.paddingY, mode=lyr.padding_modeY)
+    else:
+        working = F.pad(tensor, lyr.paddingX, mode="constant")
+        working = F.pad(working, lyr.paddingY, mode="constant")
+
+    lyr.timestep += 1
+
+    return F.conv2d(
+        working, weight, bias, lyr.stride, _pair(0), lyr.dilation, lyr.groups
+    )
+
+
+class ModelPatchSeamless:
+    """Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "startStep": ("INT", {"default": 0}),
+                "stopStep": ("INT", {"default": 999}),
+                "tilingX": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+                "tilingY": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "MODEL")
+    RETURN_NAMES = (
+        "Original Model (passthrough)",
+        "Patched Model",
+    )
+    FUNCTION = "hack"
+
+    CATEGORY = "mtb/textures"
+
+    def apply_circular(self, model, startStep, stopStep, x, y):
+        for layer in [
+            layer
+            for layer in model.modules()
+            if isinstance(layer, torch.nn.Conv2d)
+        ]:
+            layer.padding_modeX = "circular" if x else "constant"
+            layer.padding_modeY = "circular" if y else "constant"
+            layer.paddingX = (
+                layer._reversed_padding_repeated_twice[0],
+                layer._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            layer.paddingY = (
+                0,
+                0,
+                layer._reversed_padding_repeated_twice[2],
+                layer._reversed_padding_repeated_twice[3],
+            )
+            layer.paddingStartStep = startStep
+            layer.paddingStopStep = stopStep
+            layer.timestep = 0
+            layer._conv_forward = conv_forward.__get__(layer, torch.nn.Conv2d)
+
+        return model
+
+    def hack(
+        self,
+        model,
+        startStep,
+        stopStep,
+        tilingX,
+        tilingY,
+    ):
+        hacked_model = copy.deepcopy(model)
+        self.apply_circular(
+            hacked_model.model, startStep, stopStep, tilingX, tilingY
+        )
+        return (model, hacked_model)
+
+
+__nodes__ = [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
@@ -81,7 +81,7 @@ class FloatToNumber:
 
 
 __nodes__ = [
-    FloatToNumber,
-    IntToBool,
-    IntToNumber,
+    MTB_FloatToNumber,
+    MTB_IntToBool,
+    MTB_IntToNumber,
 ]

nodes/video.py

@@ -1,13 +1,11 @@
-import os
-import re
-import torch
+import hashlib, json, os, re
+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
 
@@ -173,7 +171,10 @@ 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 +221,14 @@ 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))

pyproject.toml

@@ -0,0 +1,114 @@
+[tool.poetry]
+name = "comfy-mtb"
+version = "0.4.0"
+description = "Animation oriented nodes pack for ComfyUI."
+license = "MIT"
+readme = "README.md"
+repository = "https://github.com/melMass/comfy_mtb"
+authors = ["Mel Massadian"]
+packages = [{ include = "comfy-mtb" }]
+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",
+]
+
+[tool.poetry.urls]
+"Bug Tracker" = "https://github.com/melMass/comfy_mtb/issues"
+"Changelog" = "https://github.com/melMass/comfy_mtb/releases"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+
+[tool.poetry.group.dev.dependencies]
+black = { extras = ["jupyter"], version = "^23.7.0" }
+codespell = "^2.2.5"
+mypy = "^1.5.1"
+pre-commit = "^3.3.3"
+pytest = "^7.4.0"
+pytest-cov = "^4.1.0"
+pytest-random-order = "^1.1.0"
+ruff = "^0.0.285"
+
+[tool.poetry.group.docs]
+optional = true
+
+[tool.poetry.group.docs.dependencies]
+docutils = "0.17.1"
+jupyter-book = "^0.15.1"
+sphinx-autobuild = "^2021.3.14"
+
+[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
+
+[tool.poetry-version-plugin]
+source = "git-tag"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

reqs.txt

@@ -1,7 +0,0 @@
-onnxruntime-gpu==1.15.1
-qrcode[pil]
-rembg==2.0.50
-tensorflow
-facexlib==0.3.0
-insightface==0.7.3
-basicsr==1.4.2

reqs_windows.txt

@@ -1,18 +0,0 @@
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/pycocotools-2.0.6-cp310-cp310-win_amd64.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/future-0.18.3-py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/filterpy-1.4.5-py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/easydict-1.10-py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/gdown-4.7.1-py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/basicsr-1.4.2-py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/mmcv-2.0.0-py2.py3-none-any.whl
-https://github.com/melMass/comfy_mtb/releases/download/v0.1.3/insightface-0.7.3-cp310-cp310-win_amd64.whl
-
-onnxruntime-gpu==1.15.1
-qrcode[pil]
-rembg==2.0.50
-# on windows non WSL 2.10 is the last version with GPU support
-tensorflow==2.10.1;
-tb-nightly==2.12.0a20230126; platform_system == "Windows"
-facexlib==0.3.0
-# the old tf version on windows comes with a breaking protobuf version
-protobuf==3.19.6

requirements.txt

@@ -0,0 +1,10 @@
+qrcode[pil]
+onnxruntime-gpu
+requirements-parser
+# opencv-contrib
+rembg
+imageio_ffmpeg
+rich
+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 +1,52 @@
+// Some manual types I use to facilitate developing on top of
+// Comfy's Litegraph implementation.
+
+import type { ContextMenuItem, LGraphNode } from '../web/types/litegraph'
+
+export type { ContextMenuItem } from '../web/types/litegraph'
+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 ExtendedLGraphNode {
+  onNodeCreated: () => void
+  getExtraMenuOptions: () => ContextMenuItem[]
+}
+
+export interface NodeType /*extends LGraphNode*/ {
+  category: str
+  comfyClass: str
+  length: 0
+  name: str
+  nodeData: NodeData
+  prototype: LGraphNode & ExtendedLGraphNode
+  title: str
+  type: str
+}
+
+export interface NodeInput {
+  required: object
+}
+
+// NOTE: for prototype overriding
+export type OnDrawForegroundParams = Parameters<LGraphNode['onDrawForeground']>
+export type OnMouseDownParams = Parameters<LGraphNode['onMouseDown']>
+export type OnConnectionsChangeParams = Parameters<
+  LGraphNode['onConnectionsChange']
+>
+export type OnNodeCreatedParams = Parameters<
+  ExtendedLGraphNode['onNodeCreated']
+>
+
+export interface DocumentationOptions {
+  icon_size?: number
+  icon_margin?: number
+}

types/typedefs.js

@@ -0,0 +1,9 @@
+/**
+ * @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").getExtraMenuOptionsParams} getExtraMenuOptionsParams
+ */

utils.py

@@ -1,16 +1,23 @@
-from PIL import Image
-import numpy as np
-import torch
-from pathlib import Path
-import sys
-from typing import List
-import signal
-from contextlib import suppress
-from queue import Queue, Empty
-import subprocess
-import threading
-import os
+import contextlib
+import functools
 import math
+import os
+import shlex
+import shutil
+import socket
+import subprocess
+import sys
+import uuid
+from pathlib import Path
+from typing import List, Optional, Union
+
+import folder_paths
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+from .install import pip_map
 
 try:
     from .log import log
@@ -26,7 +33,208 @@ except ImportError:
         log.warn("[comfy mtb] You probably called the file outside a module.")
 
 
+# region SANITY_CHECK Utilities
+
+
+def make_report():
+    pass
+
+
+# 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):
+        self.ips = list(self.get_local_ips())
+        log.debug(f"Found {len(self.ips)} local ips")
+        self.checked_ips = set()
+
+    def get_working_ip(self, test_url_template):
+        for ip in self.ips:
+            if ip not in self.checked_ips:
+                self.checked_ips.add(ip)
+                test_url = test_url_template.format(ip)
+                if self._test_url(test_url):
+                    return ip
+        return None
+
+    @staticmethod
+    def get_local_ips(prefix="192.168."):
+        hostname = socket.gethostname()
+        log.debug(f"Getting local ips for {hostname}")
+        for info in socket.getaddrinfo(hostname, None):
+            # Filter out IPv6 addresses if you only want IPv4
+            log.debug(info)
+            # if info[1] == socket.SOCK_STREAM and
+            if info[0] == socket.AF_INET and info[4][0].startswith(prefix):
+                yield info[4][0]
+
+    def _test_url(self, url):
+        try:
+            response = requests.get(url)
+            return response.status_code == 200
+        except Exception:
+            return False
+
+
+@functools.lru_cache(maxsize=1)
+def get_server_info():
+    from comfy.cli_args import args
+
+    ip_checker = IPChecker()
+    base_url = args.listen
+    if base_url == "0.0.0.0":
+        log.debug("Server set to 0.0.0.0, we will try to resolve the host IP")
+        base_url = ip_checker.get_working_ip(
+            f"http://{{}}:{args.port}/history"
+        )
+        log.debug(f"Setting ip to {base_url}")
+    return (base_url, args.port)
+
+
+# endregion
+
+
 # region MISC Utilities
+def backup_file(
+    fp: Path,
+    target: Optional[Path] = None,
+    backup_dir: str = ".bak",
+    suffix: Optional[str] = None,
+    prefix: Optional[str] = None,
+):
+    if not fp.exists():
+        raise FileNotFoundError(f"No file found at {fp}")
+
+    backup_directory = target or fp.parent / backup_dir
+    backup_directory.mkdir(parents=True, exist_ok=True)
+
+    stem = fp.stem
+
+    if suffix or prefix:
+        new_stem = f"{prefix or ''}{stem}{suffix or ''}"
+    else:
+        new_stem = f"{stem}_{uuid.uuid4()}"
+
+    backup_file_path = backup_directory / f"{new_stem}{fp.suffix}"
+
+    # Perform the backup
+    shutil.copy(fp, backup_file_path)
+    log.debug(f"File backed up to {backup_file_path}")
+
+
 def hex_to_rgb(hex_color):
     try:
         hex_color = hex_color.lstrip("#")
@@ -52,101 +260,84 @@ def add_path(path, prepend=False):
             sys.path.append(path)
 
 
-def enqueue_output(out, queue):
-    for line in iter(out.readline, b""):
-        queue.put(line)
-    out.close()
+def run_command(cmd, ignored_lines_start=None):
+    if ignored_lines_start is None:
+        ignored_lines_start = []
 
-
-def run_command(cmd):
     if isinstance(cmd, str):
         shell_cmd = cmd
     elif isinstance(cmd, list):
-        shell_cmd = ""
-        for arg in cmd:
-            if isinstance(arg, Path):
-                arg = arg.as_posix()
-            shell_cmd += f"{arg} "
+        shell_cmd = " ".join(
+            arg.as_posix() if isinstance(arg, Path) else shlex.quote(str(arg))
+            for arg in cmd
+        )
     else:
         raise ValueError(
             "Invalid 'cmd' argument. It must be a string or a list of arguments."
         )
 
-    process = subprocess.Popen(
+    try:
+        _run_command(shell_cmd, ignored_lines_start)
+    except subprocess.CalledProcessError as e:
+        print(
+            f"Command failed with return code: {e.returncode}", file=sys.stderr
+        )
+        print(e.stderr.strip(), file=sys.stderr)
+
+    except KeyboardInterrupt:
+        print("Command execution interrupted.")
+
+
+def _run_command(shell_cmd, ignored_lines_start):
+    log.debug(f"Running {shell_cmd}")
+
+    result = subprocess.run(
         shell_cmd,
         stdout=subprocess.PIPE,
         stderr=subprocess.PIPE,
-        universal_newlines=True,
+        text=True,
         shell=True,
+        check=True,
     )
 
-    # Create separate threads to read standard output and standard error streams
-    stdout_queue = Queue()
-    stderr_queue = Queue()
-    stdout_thread = threading.Thread(
-        target=enqueue_output, args=(process.stdout, stdout_queue)
-    )
-    stderr_thread = threading.Thread(
-        target=enqueue_output, args=(process.stderr, stderr_queue)
-    )
-    stdout_thread.daemon = True
-    stderr_thread.daemon = True
-    stdout_thread.start()
-    stderr_thread.start()
+    stdout_lines = result.stdout.strip().split("\n")
+    stderr_lines = result.stderr.strip().split("\n")
 
-    interrupted = False
+    # Print stdout, skipping ignored lines
+    for line in stdout_lines:
+        if not any(line.startswith(ign) for ign in ignored_lines_start):
+            print(line)
 
-    def signal_handler(signum, frame):
-        nonlocal interrupted
-        interrupted = True
-        print("Command execution interrupted.")
+    # Print stderr
+    for line in stderr_lines:
+        print(line, file=sys.stderr)
 
-    # Register the signal handler for keyboard interrupts (SIGINT)
-    signal.signal(signal.SIGINT, signal_handler)
-
-    # Process output from both streams until the process completes or interrupted
-    while not interrupted and (
-        process.poll() is None or not stdout_queue.empty() or not stderr_queue.empty()
-    ):
-        with suppress(Empty):
-            stdout_line = stdout_queue.get_nowait()
-            if stdout_line.strip() != "":
-                print(stdout_line.strip())
-        with suppress(Empty):
-            stderr_line = stderr_queue.get_nowait()
-            if stderr_line.strip() != "":
-                print(stderr_line.strip())
-    return_code = process.returncode
-
-    if return_code == 0 and not interrupted:
-        print("Command executed successfully!")
-    else:
-        if not interrupted:
-            print(f"Command failed with return code: {return_code}")
+    print("Command executed successfully!")
 
 
 # todo use the requirements library
-reqs_map = {
-    "onnxruntime": "onnxruntime-gpu==1.15.1",
-    "basicsr": "basicsr==1.4.2",
-    "rembg": "rembg==2.0.50",
-    "qrcode": "qrcode[pil]",
-}
+reqs_map = {value: key for key, value in pip_map.items()}
+
+import importlib
 
 
 def import_install(package_name):
-    from pip._internal import main as pip_main
+    package_spec = reqs_map.get(package_name, package_name)
 
     try:
-        __import__(package_name)
-    except ImportError:
-        package_spec = reqs_map.get(package_name)
-        if package_spec is None:
-            print(f"Installing {package_name}")
-            package_spec = package_name
+        importlib.import_module(package_name)
 
-        pip_main(["install", package_spec])
-        __import__(package_name)
+    except Exception:  # (ImportError, ModuleNotFoundError):
+        run_command(
+            [
+                Path(sys.executable).as_posix(),
+                "-m",
+                "pip",
+                "install",
+                package_spec,
+            ]
+        )
+        importlib.import_module(package_name)
 
 
 # endregion
@@ -163,13 +354,16 @@ elif ".venv" in sys.executable:
     comfy_mode = "venv"
 
 # - Get the absolute path of the parent directory of the current script
-here = Path(__file__).parent.resolve()
+here = Path(__file__).parent.absolute()
 
 # - Construct the absolute path to the ComfyUI directory
-comfy_dir = here.parent.parent
-
+comfy_dir = Path(folder_paths.base_path)
+models_dir = Path(folder_paths.models_dir)
+output_dir = Path(folder_paths.output_directory)
+styles_dir = comfy_dir / "styles"
+session_id = str(uuid.uuid4())
 # - Construct the path to the font file
-font_path = here / "font.ttf"
+font_path = here / "data" / "font.ttf"
 
 # - Add extern folder to path
 extern_root = here / "extern"
@@ -180,7 +374,7 @@ for pth in extern_root.iterdir():
 
 # - 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")
 
 PIL_FILTER_MAP = {
     "nearest": Image.Resampling.NEAREST,
@@ -193,7 +387,7 @@ PIL_FILTER_MAP = {
 # endregion
 
 
-# region TENSOR UTILITIES
+# region TENSOR Utilities
 def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
     batch_count = image.size(0) if len(image.shape) > 3 else 1
     if batch_count > 1:
@@ -204,19 +398,23 @@ 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: Image.Image | List[Image.Image]) -> torch.Tensor:
+def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
     if isinstance(image, list):
         return torch.cat([pil2tensor(img) for img in image], dim=0)
 
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+    return torch.from_numpy(
+        np.array(image).astype(np.float32) / 255.0
+    ).unsqueeze(0)
 
 
-def np2tensor(img_np: np.ndarray | List[np.ndarray]) -> torch.Tensor:
+def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
     if isinstance(img_np, list):
         return torch.cat([np2tensor(img) for img in img_np], dim=0)
 
@@ -231,7 +429,204 @@ 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
+    return np.pad(img, pad_width, mode="wrap")
+
+
+def tiles_infer(tiles, ort_session, progress_callback=None):
+    """Infer each tile with the given model. progress_callback will be called with
+    arguments : current tile idx and total tiles amount (used to show progress on
+    cursor in Blender).
+    """
+    out_channels = 3  # normal map RGB channels
+    tiles_nb = tiles.shape[0]
+    pred_tiles = np.empty(
+        (tiles_nb, out_channels, tiles.shape[2], tiles.shape[3])
+    )
+
+    for i in range(tiles_nb):
+        if progress_callback != None:
+            progress_callback(i + 1, tiles_nb)
+        pred_tiles[i] = ort_session.run(
+            None, {"input": tiles[i : i + 1].astype(np.float32)}
+        )[0]
+
+    return pred_tiles
+
+
+def generate_mask(tile_size, stride_size):
+    """Generates a pyramidal-like mask. Used for mixing overlapping predicted tiles."""
+    tile_h, tile_w = tile_size
+    stride_h, stride_w = stride_size
+    ramp_h = tile_h - stride_h
+    ramp_w = tile_w - stride_w
+
+    mask = np.ones((tile_h, tile_w))
+
+    # ramps in width direction
+    mask[ramp_h:-ramp_h, :ramp_w] = np.linspace(0, 1, num=ramp_w)
+    mask[ramp_h:-ramp_h, -ramp_w:] = np.linspace(1, 0, num=ramp_w)
+    # ramps in height direction
+    mask[:ramp_h, ramp_w:-ramp_w] = np.transpose(
+        np.linspace(0, 1, num=ramp_h)[None], (1, 0)
+    )
+    mask[-ramp_h:, ramp_w:-ramp_w] = np.transpose(
+        np.linspace(1, 0, num=ramp_h)[None], (1, 0)
+    )
+
+    # Assume tiles are squared
+    assert ramp_h == ramp_w
+    # top left corner
+    corner = np.rot90(corner_mask(ramp_h), 2)
+    mask[:ramp_h, :ramp_w] = corner
+    # top right corner
+    corner = np.flip(corner, 1)
+    mask[:ramp_h, -ramp_w:] = corner
+    # bottom right corner
+    corner = np.flip(corner, 0)
+    mask[-ramp_h:, -ramp_w:] = corner
+    # bottom right corner
+    corner = np.flip(corner, 1)
+    mask[-ramp_h:, :ramp_w] = corner
+
+    return mask
+
+
+def corner_mask(side_length):
+    """Generates the corner part of the pyramidal-like mask.
+    Currently, only for square shapes.
+    """
+    corner = np.zeros([side_length, side_length])
+
+    for h in range(0, side_length):
+        for w in range(0, side_length):
+            if h >= w:
+                sh = h / (side_length - 1)
+                corner[h, w] = 1 - sh
+            if h <= w:
+                sw = w / (side_length - 1)
+                corner[h, w] = 1 - sw
+
+    return corner - 0.25 * scaling_mask(side_length)
+
+
+def scaling_mask(side_length):
+    scaling = np.zeros([side_length, side_length])
+
+    for h in range(0, side_length):
+        for w in range(0, side_length):
+            sh = h / (side_length - 1)
+            sw = w / (side_length - 1)
+            if h >= w and h <= side_length - w:
+                scaling[h, w] = sw
+            if h <= w and h <= side_length - w:
+                scaling[h, w] = sh
+            if h >= w and h >= side_length - w:
+                scaling[h, w] = 1 - sh
+            if h <= w and h >= side_length - w:
+                scaling[h, w] = 1 - sw
+
+    return 2 * scaling
+
+
+def tiles_merge(tiles, stride_size, img_size, paddings):
+    """Merges the list of tiles into one image. img_size is the original size, before
+    padding.
+    """
+    _, tile_h, tile_w = tiles[0].shape
+    pad_left, pad_right, pad_top, pad_bottom = paddings
+    height = img_size[1] + pad_top + pad_bottom
+    width = img_size[2] + pad_left + pad_right
+    stride_h, stride_w = stride_size
+
+    # stride must be even
+    assert (stride_h % 2 == 0) and (stride_w % 2 == 0)
+    # stride must be greater or equal than half tile
+    assert (stride_h >= tile_h / 2) and (stride_w >= tile_w / 2)
+    # stride must be smaller or equal tile size
+    assert (stride_h <= tile_h) and (stride_w <= tile_w)
+
+    merged = np.zeros((img_size[0], height, width))
+    mask = generate_mask((tile_h, tile_w), stride_size)
+
+    h_range = ((height - tile_h) // stride_h) + 1
+    w_range = ((width - tile_w) // stride_w) + 1
+
+    idx = 0
+    for h in range(0, h_range):
+        for w in range(0, w_range):
+            h_from, h_to = h * stride_h, h * stride_h + tile_h
+            w_from, w_to = w * stride_w, w * stride_w + tile_w
+            merged[:, h_from:h_to, w_from:w_to] += tiles[idx] * mask
+            idx += 1
+
+    return merged[:, pad_top:-pad_bottom, pad_left:-pad_right]
+
+
+def tiles_split(img, tile_size, stride_size):
+    """Returns list of tiles from the given image and the padding used to fit the tiles
+    in it. Input image must have dimension C,H,W.
+    """
+    log.debug(f"Splitting img: tile {tile_size}, stride {stride_size} ")
+    tile_h, tile_w = tile_size
+    stride_h, stride_w = stride_size
+    img_h, img_w = img.shape[0], img.shape[1]
+
+    # stride must be even
+    assert (stride_h % 2 == 0) and (stride_w % 2 == 0)
+    # stride must be greater or equal than half tile
+    assert (stride_h >= tile_h / 2) and (stride_w >= tile_w / 2)
+    # stride must be smaller or equal tile size
+    assert (stride_h <= tile_h) and (stride_w <= tile_w)
+
+    # find total height & width padding sizes
+    pad_h, pad_w = 0, 0
+    remainer_h = (img_h - tile_h) % stride_h
+    remainer_w = (img_w - tile_w) % stride_w
+    if remainer_h != 0:
+        pad_h = stride_h - remainer_h
+    if remainer_w != 0:
+        pad_w = stride_w - remainer_w
+
+    # if tile bigger than image, pad image to tile size
+    if tile_h > img_h:
+        pad_h = tile_h - img_h
+    if tile_w > img_w:
+        pad_w = tile_w - img_w
+
+    # pad image, add extra stride to padding to avoid pyramid
+    # weighting leaking onto the valid part of the picture
+    pad_left = pad_w // 2 + stride_w
+    pad_right = pad_left if pad_w % 2 == 0 else pad_left + 1
+    pad_top = pad_h // 2 + stride_h
+    pad_bottom = pad_top if pad_h % 2 == 0 else pad_top + 1
+    img = pad(img, pad_left, pad_right, pad_top, pad_bottom)
+    img_h, img_w = img.shape[1], img.shape[2]
+
+    # extract tiles
+    h_range = ((img_h - tile_h) // stride_h) + 1
+    w_range = ((img_w - tile_w) // stride_w) + 1
+    tiles = np.empty([h_range * w_range, img.shape[0], tile_h, tile_w])
+    idx = 0
+    for h in range(0, h_range):
+        for w in range(0, w_range):
+            h_from, h_to = h * stride_h, h * stride_h + tile_h
+            w_from, w_to = w * stride_w, w * stride_w + tile_w
+            tiles[idx] = img[:, h_from:h_to, w_from:w_to]
+            idx += 1
+
+    return tiles, (pad_left, pad_right, pad_top, pad_bottom)
 
 
 # endregion
@@ -239,16 +634,16 @@ def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
 
 # 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
 
-        import folder_paths
-
         log.debug("Loading antelopev2 model")
 
-        dest = Path(folder_paths.models_dir) / "insightface"
+        dest = get_model_path("insightface")
         archive = dest / "antelopev2.zip"
         final_path = dest / "models" / "antelopev2"
         if not final_path.exists():
@@ -275,6 +670,33 @@ def download_antelopev2():
         raise e
 
 
+def get_model_path(fam, model=None):
+    log.debug(f"Requesting {fam} with model {model}")
+    res = None
+    if model:
+        res = folder_paths.get_full_path(fam, model)
+    else:
+        # this one can raise errors...
+        with contextlib.suppress(KeyError):
+            res = folder_paths.get_folder_paths(fam)
+
+    if res:
+        if isinstance(res, list):
+            if len(res) > 1:
+                log.warning(
+                    f"Found multiple match, we will pick the first {res[0]}\n{res}"
+                )
+            res = res[0]
+        res = Path(res)
+        log.debug(f"Resolved model path from folder_paths: {res}")
+    else:
+        res = models_dir / fam
+        if model:
+            res /= model
+
+    return res
+
+
 # endregion
 
 
@@ -299,9 +721,6 @@ def create_uv_map_tensor(width=512, height=512):
 
 # region ANIMATION Utilities
 def apply_easing(value, easing_type):
-    if value < 0 or value > 1:
-        raise ValueError("The value should be between 0 and 1.")
-
     if easing_type == "Linear":
         return value
 
@@ -328,7 +747,10 @@ 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:
@@ -349,10 +771,13 @@ 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/README.md

@@ -13,6 +13,10 @@ data otherwise:
 ![debug](https://github.com/melMass/comfy_mtb/assets/7041726/1f4393e4-1c3d-4807-9501-fe8888bfae25)
 
 
+**note +**
+A basic HTML note mainly to add better looking notes/instructions for workflow makers:
+![image](https://github.com/melMass/comfy_mtb/assets/7041726/2ba1f832-0044-4bad-974c-e6387981af57)
+
 
 ## Standalone
 These scripts can be taken and placed independently of `comfy_mtb` or any other files, mimicking what pythongosss did for their 

web/comfy_shared.js

@@ -7,7 +7,73 @@
  *
  */
 
-import { app } from '/scripts/app.js'
+// Reference the shared typedefs file
+/// <reference path="../types/typedefs.js" />
+
+import { app } from '../../scripts/app.js'
+
+// - crude uuid
+export function makeUUID() {
+  let dt = new Date().getTime()
+  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
+    const r = ((dt + Math.random() * 16) % 16) | 0
+    dt = Math.floor(dt / 16)
+    return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16)
+  })
+  return uuid
+}
+
+//- local storage manager
+export class LocalStorageManager {
+  constructor(namespace) {
+    this.namespace = namespace
+  }
+
+  _namespacedKey(key) {
+    return `${this.namespace}:${key}`
+  }
+
+  set(key, value) {
+    const serializedValue = JSON.stringify(value)
+    localStorage.setItem(this._namespacedKey(key), serializedValue)
+  }
+
+  get(key, default_val = null) {
+    const value = localStorage.getItem(this._namespacedKey(key))
+    return value ? JSON.parse(value) : default_val
+  }
+
+  remove(key) {
+    localStorage.removeItem(this._namespacedKey(key))
+  }
+
+  clear() {
+    for (const key of Object.keys(localStorage).filter((k) =>
+      k.startsWith(`${this.namespace}:`),
+    )) {
+      localStorage.removeItem(key)
+    }
+  }
+}
+
+// - log utilities
+
+function createLogger(emoji, color, consoleMethod = 'log') {
+  return (message, ...args) => {
+    if (window.MTB?.DEBUG) {
+      console[consoleMethod](
+        `%c${emoji} ${message}`,
+        `color: ${color};`,
+        ...args,
+      )
+    }
+  }
+}
+
+export const infoLogger = createLogger('i', 'yellow')
+export const warnLogger = createLogger('!', 'orange', 'warn')
+export const errorLogger = createLogger('🔥', 'red', 'error')
+export const successLogger = createLogger('✅', 'green')
 
 export const log = (...args) => {
   if (window.MTB?.DEBUG) {
@@ -18,20 +84,44 @@ export const log = (...args) => {
 //- WIDGET UTILS
 export const CONVERTED_TYPE = 'converted-widget'
 
+export const hasWidgets = (node) => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false
+  }
+  return true
+}
+
+export const cleanupNode = (node) => {
+  if (!hasWidgets(node)) {
+    return
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove()
+    }
+    if (w.inputEl) {
+      w.inputEl.remove()
+    }
+    // calls the widget remove callback
+    w.onRemoved?.()
+  }
+}
+
 export function offsetDOMWidget(
   widget,
   ctx,
   node,
   widgetWidth,
   widgetY,
-  height
+  height,
 ) {
   const margin = 10
   const elRect = ctx.canvas.getBoundingClientRect()
   const transform = new DOMMatrix()
     .scaleSelf(
       elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
+      elRect.height / ctx.canvas.height,
     )
     .multiplySelf(ctx.getTransform())
     .translateSelf(margin, margin + widgetY)
@@ -62,20 +152,49 @@ export function getWidgetType(config) {
   // Special handling for COMBO so we restrict links based on the entries
   let type = config?.[0]
   let linkType = type
-  if (type instanceof Array) {
+  if (Array.isArray(type)) {
     type = 'COMBO'
     linkType = linkType.join(',')
   }
   return { type, linkType }
 }
+export const setupDynamicConnections = (nodeType, prefix, inputType) => {
+  const onNodeCreated = nodeType.prototype.onNodeCreated
+  // check if it's a list
+  const inputList = typeof inputType === 'object'
+  nodeType.prototype.onNodeCreated = function () {
+    const r = onNodeCreated ? onNodeCreated.apply(this, []) : undefined
+    this.addInput(`${prefix}_1`, inputList ? '*' : inputType)
+    return r
+  }
 
+  const onConnectionsChange = nodeType.prototype.onConnectionsChange
+
+  /**
+   * @param {OnConnectionsChangeParams} args
+   */
+  nodeType.prototype.onConnectionsChange = function (...args) {
+    const [_type, index, connected, _link_info] = args
+    const r = onConnectionsChange
+      ? onConnectionsChange.apply(this, args)
+      : undefined
+    dynamic_connection(this, index, connected, `${prefix}_`, inputList)
+    return r
+  }
+}
 export const dynamic_connection = (
   node,
   index,
   connected,
   connectionPrefix = 'input_',
-  connectionType = 'PSDLAYER'
+  connectionType = 'PSDLAYER',
+  nameArray = [],
 ) => {
+  if (!node.inputs[index].name.startsWith(connectionPrefix)) {
+    return
+  }
+  const listConnection = typeof connectionType === 'object'
+
   // remove all non connected inputs
   if (!connected && node.inputs.length > 1) {
     log(`Removing input ${index} (${node.inputs[index].name})`)
@@ -90,25 +209,46 @@ export const dynamic_connection = (
 
     // make inputs sequential again
     for (let i = 0; i < node.inputs.length; i++) {
-      node.inputs[i].label = `${connectionPrefix}${i + 1}`
+      const name =
+        i < nameArray.length ? nameArray[i] : `${connectionPrefix}${i + 1}`
+      node.inputs[i].label = name
+      node.inputs[i].name = name
     }
   }
 
   // add an extra input
-  if (node.inputs[node.inputs.length - 1].link != undefined) {
-    log(
-      `Adding input ${node.inputs.length + 1} (${connectionPrefix}${
-        node.inputs.length + 1
-      })`
-    )
+  if (node.inputs[node.inputs.length - 1].link !== undefined) {
+    const nextIndex = node.inputs.length
+    const name =
+      nextIndex < nameArray.length
+        ? nameArray[nextIndex]
+        : `${connectionPrefix}${nextIndex + 1}`
 
-    node.addInput(
-      `${connectionPrefix}${node.inputs.length + 1}`,
-      connectionType
-    )
+    log(`Adding input ${nextIndex + 1} (${name})`)
+
+    node.addInput(name, listConnection ? '*' : connectionType)
   }
 }
 
+export function calculateTotalChildrenHeight(parentElement) {
+  let totalHeight = 0
+
+  for (const child of parentElement.children) {
+    const style = window.getComputedStyle(child)
+
+    // Get height as an integer (without 'px')
+    const height = Number.parseInt(style.height, 10)
+
+    // Get vertical margin as integers
+    const marginTop = Number.parseInt(style.marginTop, 10)
+    const marginBottom = Number.parseInt(style.marginBottom, 10)
+
+    // Sum up height and vertical margins
+    totalHeight += height + marginTop + marginBottom
+  }
+
+  return totalHeight
+}
 /**
  * Appends a callback to the extra menu options of a given node type.
  * @param {*} nodeType
@@ -116,9 +256,12 @@ export const dynamic_connection = (
  */
 export function addMenuHandler(nodeType, cb) {
   const getOpts = nodeType.prototype.getExtraMenuOptions
+  /**
+   * @returns {ContextMenuItem[]} items
+   */
   nodeType.prototype.getExtraMenuOptions = function () {
-    const r = getOpts.apply(this, arguments)
-    cb.apply(this, arguments)
+    const r = getOpts.apply(this, [])
+    cb.apply(this, [])
     return r
   }
 }
@@ -144,7 +287,7 @@ export function hideWidget(node, widget, suffix = '') {
   // Hide any linked widgets, e.g. seed+seedControl
   if (widget.linkedWidgets) {
     for (const w of widget.linkedWidgets) {
-      hideWidget(node, w, ':' + widget.name)
+      hideWidget(node, w, `:${widget.name}`)
     }
   }
 }
@@ -216,7 +359,7 @@ export function hideWidgetForGood(node, widget, suffix = '') {
   // Hide any linked widgets, e.g. seed+seedControl
   if (widget.linkedWidgets) {
     for (const w of widget.linkedWidgets) {
-      hideWidgetForGood(node, w, ':' + widget.name)
+      hideWidgetForGood(node, w, `:${widget.name}`)
     }
   }
 }
@@ -234,7 +377,7 @@ export function fixWidgets(node) {
           //     continue
           // }
           const w = node.widgets.find((w) => w.name === matching_widget.name)
-          if (w && w.type != CONVERTED_TYPE) {
+          if (w && w.type !== CONVERTED_TYPE) {
             log(w)
             log(`hidding ${w.name}(${w.type}) from ${node.type}`)
             log(node)
@@ -249,16 +392,16 @@ export function fixWidgets(node) {
     }
   }
 }
-export function inner_value_change(widget, value, event = undefined) {
-  if (widget.type == 'number' || widget.type == 'BBOX') {
+export function inner_value_change(widget, val, event = undefined) {
+  let value = val
+  if (widget.type === 'number' || widget.type === 'BBOX') {
     value = Number(value)
-  } else if (widget.type == 'BOOL') {
+  } else if (widget.type === 'BOOL') {
     value = Boolean(value)
   }
   widget.value = value
   if (
-    widget.options &&
-    widget.options.property &&
+    widget.options?.property &&
     node.properties[widget.options.property] !== undefined
   ) {
     node.setProperty(widget.options.property, value)
@@ -276,14 +419,51 @@ export function isColorBright(rgb, threshold = 240) {
 
 function getBrightness(rgbObj) {
   return Math.round(
-    (parseInt(rgbObj[0]) * 299 +
-      parseInt(rgbObj[1]) * 587 +
-      parseInt(rgbObj[2]) * 114) /
-      1000
+    (Number.parseInt(rgbObj[0]) * 299 +
+      Number.parseInt(rgbObj[1]) * 587 +
+      Number.parseInt(rgbObj[2]) * 114) /
+      1000,
   )
 }
 
 //- HTML / CSS UTILS
+export const loadScript = (
+  FILE_URL,
+  async = true,
+  type = 'text/javascript',
+) => {
+  return new Promise((resolve, reject) => {
+    try {
+      // Check if the script already exists
+      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
+      if (existingScript) {
+        resolve({ status: true, message: 'Script already loaded' })
+        return
+      }
+
+      const scriptEle = document.createElement('script')
+      scriptEle.type = type
+      scriptEle.async = async
+      scriptEle.src = FILE_URL
+
+      scriptEle.addEventListener('load', (_ev) => {
+        resolve({ status: true })
+      })
+
+      scriptEle.addEventListener('error', (_ev) => {
+        reject({
+          status: false,
+          message: `Failed to load the script ${FILE_URL}`,
+        })
+      })
+
+      document.body.appendChild(scriptEle)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
 export function defineClass(className, classStyles) {
   const styleSheets = document.styleSheets
 
@@ -316,3 +496,315 @@ export function defineClass(className, classStyles) {
     }
   }
 }
+
+/** Prefixes the node title with '[DEPRECATED]' and log the deprecation reason to the console.*/
+export const addDeprecation = (nodeType, reason) => {
+  const title = nodeType.title
+  nodeType.title = `[DEPRECATED] ${title}`
+  // console.log(nodeType)
+
+  const styles = {
+    title: 'font-size:1.3em;font-weight:900;color:yellow; background: black',
+    reason: 'font-size:1.2em',
+  }
+  console.log(
+    `%c!  ${title} is deprecated:%c ${reason}`,
+    styles.title,
+    styles.reason,
+  )
+}
+
+const create_documentation_stylesheet = () => {
+  const tag = 'mtb-documentation-stylesheet'
+
+  let styleTag = document.head.querySelector(tag)
+
+  if (!styleTag) {
+    styleTag = document.createElement('style')
+    styleTag.type = 'text/css'
+    styleTag.id = tag
+
+    styleTag.innerHTML = `
+.documentation-popup {
+    background: var(--comfy-menu-bg);
+    position: absolute;
+    color: var(--fg-color);
+    font: 12px monospace;
+    line-height: 1.5em;
+    padding: 10px;
+    border-radius: 6px;
+    pointer-events: "inherit";
+    z-index: 5;
+    overflow: hidden;
+}
+.documentation-wrapper {
+    padding: 0 2em;
+    overflow: auto;
+    max-height: 100%;
+    /* Scrollbar styling for Chrome */
+    &::-webkit-scrollbar {
+       width: 6px;
+    }
+    &::-webkit-scrollbar-track {
+       background: var(--bg-color);
+    }
+    &::-webkit-scrollbar-thumb {
+       background-color: var(--fg-color);
+       border-radius: 6px;
+       border: 3px solid var(--bg-color);
+    }
+   
+    /* Scrollbar styling for Firefox */
+    scrollbar-width: thin;
+    scrollbar-color: var(--fg-color) var(--bg-color);
+    a {
+      color: yellow;
+    }
+    a:visited {
+      color: orange;
+    }
+    a:hover {
+      color: red;
+    }
+}
+
+.documentation-popup img {
+  max-width: 100%;
+}
+.documentation-popup table {
+  border-collapse: collapse;
+  border: 1px var(--border-color) solid;
+}
+.documentation-popup th, 
+.documentation-popup td {
+  border: 1px var(--border-color) solid;
+}
+.documentation-popup th {
+  background-color: var(--comfy-input-bg);
+}`
+    document.head.appendChild(styleTag)
+  }
+}
+let documentationConverter
+
+/**
+ * Add documentation widget to the selected node
+ * @param {NodeData} nodeData
+ * @param {NodeType}  nodeType
+ * @param {DocumentationOptions} opts
+ */
+export const addDocumentation = (
+  nodeData,
+  nodeType,
+  opts = { icon_size: 14, icon_margin: 4 },
+) => {
+  if (!nodeData.description) {
+    infoLogger(
+      `Skipping ${nodeData.name} doesn't have a description, skipping...`,
+    )
+    return
+  }
+
+  if (!documentationConverter) {
+    infoLogger('Initializing our mardown converter')
+    documentationConverter = new showdown.Converter({
+      tables: true,
+      strikethrough: true,
+      emoji: true,
+      ghCodeBlocks: true,
+      tasklists: true,
+      ghMentions: true,
+      smoothLivePreview: true,
+      simplifiedAutoLink: true,
+      parseImgDimensions: true,
+      openLinksInNewWindow: true,
+    })
+  }
+
+  const options = opts || {}
+  const iconSize = options.icon_size || 14
+  const iconMargin = options.icon_margin || 4
+  let docElement = null
+  let wrapper = null
+  const drawFg = nodeType.prototype.onDrawForeground
+
+  /**
+   * @param {OnDrawForegroundParams} args
+   */
+  nodeType.prototype.onDrawForeground = function (...args) {
+    const [ctx, _canvas] = args
+    const r = drawFg ? drawFg.apply(this, args) : undefined
+
+    if (this.flags.collapsed) return r
+
+    // icon position
+    const x = this.size[0] - iconSize - iconMargin
+
+    if (this.show_doc && docElement === null) {
+      create_documentation_stylesheet()
+
+      docElement = document.createElement('div')
+      docElement.classList.add('documentation-popup')
+      document.body.appendChild(docElement)
+      // docElement.innerHTML = documentationConverter.makeHtml(
+      //   nodeData.description,
+      // )
+
+      wrapper = document.createElement('div')
+      wrapper.classList.add('documentation-wrapper')
+      wrapper.innerHTML = documentationConverter.makeHtml(nodeData.description)
+      docElement.appendChild(wrapper)
+
+      // resize handle
+      const resizeHandle = document.createElement('div')
+      resizeHandle.style.width = '10px'
+      resizeHandle.style.height = '10px'
+      // resizeHandle.style.background = 'gray'
+      resizeHandle.style.position = 'absolute'
+      resizeHandle.style.bottom = '0'
+      resizeHandle.style.right = '0'
+      // resizeHandle.style.left = '95%'
+      resizeHandle.style.cursor = 'se-resize'
+      resizeHandle.style.userSelect = 'none'
+
+      const borderColor = getComputedStyle(document.documentElement)
+        .getPropertyValue('--border-color')
+        .trim()
+      resizeHandle.style.borderTop = '10px solid transparent'
+      resizeHandle.style.borderLeft = '10px solid transparent'
+      resizeHandle.style.borderBottom = `10px solid ${borderColor}`
+      resizeHandle.style.borderRight = `10px solid ${borderColor}`
+
+      wrapper.appendChild(resizeHandle)
+      let isResizing = false
+
+      let startX
+      let startY
+      let startWidth
+      let startHeight
+
+      resizeHandle.addEventListener('mousedown', (e) => {
+        e.stopPropagation()
+        isResizing = true
+        startX = e.clientX
+        startY = e.clientY
+        startWidth = Number.parseInt(
+          document.defaultView.getComputedStyle(docElement).width,
+          10,
+        )
+        startHeight = Number.parseInt(
+          document.defaultView.getComputedStyle(docElement).height,
+          10,
+        )
+      })
+
+      document.addEventListener('mousemove', (e) => {
+        console.log('Moving mouse')
+        if (!isResizing) return
+        const newWidth = startWidth + e.clientX - startX
+        const newHeight = startHeight + e.clientY - startY
+
+        docElement.style.width = `${newWidth}px`
+        docElement.style.height = `${newHeight}px`
+
+        this.docPos = {
+          width: `${newWidth}px`,
+          height: `${newHeight}px`,
+        }
+      })
+
+      document.addEventListener('mouseup', () => {
+        isResizing = false
+      })
+    } else if (!this.show_doc && docElement !== null) {
+      docElement.parentNode.removeChild(docElement)
+      docElement = null
+    }
+
+    // reposition
+    if (this.show_doc && docElement !== null) {
+      const rect = ctx.canvas.getBoundingClientRect()
+
+      const scaleX = rect.width / ctx.canvas.width
+      const scaleY = rect.height / ctx.canvas.height
+      const transform = new DOMMatrix()
+        .scaleSelf(scaleX, scaleY)
+        .multiplySelf(ctx.getTransform())
+        .translateSelf(this.size[0] * scaleX, 0)
+        .translateSelf(10, -32)
+
+      const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
+
+      Object.assign(docElement.style, {
+        transformOrigin: '0 0',
+        transform: scale,
+        left: `${transform.a + transform.e}px`,
+        top: `${transform.d + transform.f}px`,
+        width: this.docPos ? this.docPos.width : `${this.size[0] * 1.5}px`,
+        height: this.docPos?.height,
+        // width: `${this.size[0] * 2}px`,
+        // height: `${(widget.parent?.inputHeight || 32) - (margin * 2)}px`,
+        // height: `${this.size[1] || this.parent?.inputHeight || 32}px`,
+
+        // background: !node.color ? "" : node.color,
+        // color: "blue", //!node.color ? "" : "white",
+      })
+
+      if (this.docPos === undefined) {
+        this.docPos = {
+          width: docElement.style.width,
+          height: docElement.style.height,
+        }
+      }
+
+      // docElement.style.left = 140 - rect.right + "px";
+      // docElement.style.top = rect.top + "px";
+    }
+
+    ctx.save()
+    ctx.translate(x, iconSize - 34) // 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)'
+
+    ctx.lineCap = 'round'
+    ctx.lineJoin = 'round'
+
+    ctx.lineWidth = 2.4
+    // ctx.stroke(questionMark);
+    ctx.font = 'bold 36px monospace'
+    ctx.fillText('?', 0, 24)
+    ctx.restore()
+
+    return r
+  }
+  const mouseDown = nodeType.prototype.onMouseDown
+
+  /**
+   * @param {OnMouseDownParams} args
+   */
+  nodeType.prototype.onMouseDown = function (...args) {
+    const [_event, localPos, _graphCanvas] = args
+    const r = mouseDown ? mouseDown.apply(this, args) : undefined
+    const iconX = this.size[0] - iconSize - iconMargin
+    const iconY = iconSize - 34
+    if (
+      localPos[0] > iconX &&
+      localPos[0] < iconX + iconSize &&
+      localPos[1] > iconY &&
+      localPos[1] < iconY + iconSize
+    ) {
+      // Pencil icon was clicked, open the editor
+      // this.openEditorDialog();
+      if (this.show_doc === undefined) {
+        this.show_doc = true
+      } else {
+        this.show_doc = !this.show_doc
+      }
+      return true // Return true to indicate the event was handled
+    }
+
+    return r // Return false to let the event propagate
+
+    // return r;
+  }
+}

web/constant.js

@@ -0,0 +1,262 @@
+import { app } from '../../scripts/app.js'
+import * as shared from './comfy_shared.js'
+import { MtbWidgets } from './mtb_widgets.js'
+
+export class Constant extends LiteGraph.LGraphNode {
+  constructor() {
+    super()
+    this.uuid = shared.makeUUID()
+    this.collapsable = true
+
+    // this avoid serializing the node when converting to prompt
+    this.isVirtualNode = true
+
+    this.shape = LiteGraph.BOX_SHAPE
+    this.serialize_widgets = true
+
+    // Properties
+    this.addProperty('type', 'number')
+    this.addProperty('value', 0)
+
+    // Inputs and outputs
+    this.addOutput('Output', '*')
+
+    // Widget for selecting the type
+    this.addWidget(
+      'combo',
+      'Type',
+      this.properties.type,
+      (value) => {
+        this.properties.type = value
+        this.updateWidgets()
+        this.updateOutputType()
+      },
+      {
+        values: ['number', 'string', 'vector2', 'vector3', 'vector4', 'color'],
+      },
+    )
+
+    // Initialize the node
+    this.updateWidgets()
+    this.updateOutputType()
+  }
+  // NOTE: this is called onPrompt
+  applyToGraph() {
+    this.updateTargetWidgets()
+  }
+
+  configure(info) {
+    super.configure(info)
+    this.properties.type = info.properties.type
+    this.properties.value = info.properties.value
+
+    shared.infoLogger('Configure Constant', { info, node: this })
+    this.updateWidgets()
+    this.updateOutputType()
+  }
+
+  updateWidgets() {
+    // 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)
+    switch (this.properties.type) {
+      case 'color': {
+        if (typeof this.properties.value !== 'string') {
+          this.properties.value = '#ffffff'
+        }
+        const col_widget = this.addCustomWidget(
+          MtbWidgets.COLOR('Value', this.properties.value || '#ff0000'),
+        )
+        col_widget.callback = (col) => {
+          this.properties.value = col
+          this.updateOutput()
+        }
+        break
+      }
+      case 'number':
+        if (typeof this.properties.value !== 'number') {
+          this.properties.value = 0.0
+        }
+        this.addWidget('number', 'Value', this.properties.value, (value) => {
+          this.properties.value = value
+          this.updateOutput()
+        })
+        this.addWidget(
+          'toggle',
+          'Convert to Integer',
+          this.properties.force_int,
+          (value) => {
+            this.properties.force_int = value
+            this.updateOutputType()
+          },
+        )
+        break
+      case 'string': {
+        if (typeof this.properties.value !== 'string') {
+          this.properties.value = `${this.properties.value}`
+        }
+        shared.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))
+
+        if (['string', 'number'].includes(typeof this.properties.value)) {
+          this.properties.value = Array.from({ length: numInputs }, () => 0.0)
+        } else if (this.properties.value.length !== numInputs) {
+          if (this.properties.value.length > numInputs) {
+            this.properties.value = this.properties.value.slice(0, numInputs)
+          } else {
+            this.properties.value = this.properties.value.concat(
+              new Array(numInputs - this.properties.value.length).fill(0.0),
+            )
+          }
+        }
+        for (let i = 0; i < numInputs; i++) {
+          this.addWidget(
+            'number',
+            `Value ${i + 1}`,
+            this.properties.value[i] || 0,
+            (value) => {
+              this.properties.value[i] = value
+              this.updateOutput()
+            },
+          )
+        }
+        break
+      }
+      default:
+        break
+    }
+  }
+  onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) {
+    // super.onConnectionsChange(type, slotIndex, isConnected, link, ioSlot)
+    if (isConnected) {
+      this.updateTargetWidgets([link.id])
+    }
+  }
+  updateOutputType() {
+    const cur_type = this.outputs[0].type
+    const rm_if_mismatch = (type) => {
+      if (cur_type !== type) {
+        for (let i = 0; i < this.outputs.length; i++) {
+          this.removeOutput(i)
+        }
+        this.addOutput('output', type)
+      }
+    }
+    switch (this.properties.type) {
+      case 'color':
+        rm_if_mismatch('COLOR')
+        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':
+        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) {
+    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,
+      )
+      if (!tgt_widget) return
+      tgt_widget[0].value = this.properties.value
+    }
+  }
+
+  updateOutput() {
+    const value = this.properties.value
+
+    switch (this.properties.type) {
+      case 'color':
+        this.setOutputData(0, value)
+        break
+      case 'number':
+        this.setOutputData(0, Number.parseFloat(value))
+        break
+      case 'string':
+        this.setOutputData(0, value.toString())
+        break
+      case 'vector2':
+        if (value.length >= 2) {
+          this.setOutputData(0, value.slice(0, 2))
+        }
+        break
+      case 'vector3':
+        if (value.length >= 3) {
+          this.setOutputData(0, value.slice(0, 3))
+        }
+        break
+      case 'vector4':
+        if (value.length >= 4) {
+          this.setOutputData(0, value.slice(0, 4))
+        }
+        break
+      default:
+        break
+    }
+
+    this.updateTargetWidgets()
+  }
+}
+
+// app.registerExtension({
+//   name: 'mtb.constant',
+//   registerCustomNodes() {
+//     LiteGraph.registerNodeType('Constant (mtb)', Constant)
+//
+//     Constant.category = 'mtb/utils'
+//     Constant.title = 'Constant (mtb)'
+//   },
+// })

web/curve_widget.js

@@ -0,0 +1,187 @@
+
+import { app } from '../../scripts/app.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(inputName, defaultValue) {
+        this.name = inputName || "Curve";
+        this._value = defaultValue || [{ x: 0, y: 0 }, { x: 1, y: 1 }];
+        this.type = "FLOAT_CURVE";
+        this.selectedPointIndex = null;
+        this.resize
+    }
+
+    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 < 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 };
+    }
+
+    draw(ctx, node, width, posY, height) {
+        const [cw, ch] = this.computeSize(width)
+
+        ctx.beginPath();
+        ctx.fillStyle = "#000";
+        //ctx.fillRect(0, posY, cw, ch);
+        ctx.strokeStyle = "#fff";
+        ctx.lineWidth = 2;
+
+        // normalized coordinates -> canvas coordinates
+        for (let i = 0; i < 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();
+        // this.drawBSpline(ctx, width, height, posY);
+
+        // points
+        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) {
+        // console.debug(event.type, pos, node)
+        let x = pos[0] - node.pos[0]
+        let y = pos[1] - node.pos[1]
+        let 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;
+    }
+
+
+    detectPoint(localPos, width, height) {
+        const threshold = 20; // TODO: extract
+        for (let i = 0; i < this._value.length; i++) {
+            const p = this._value[i];
+            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 i;
+            }
+        }
+        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 };
+        this._value.push(normalizedPoint);
+        this._value.sort((a, b) => a.x - b.x);
+        this.value = JSON.stringify(this._value);
+    }
+
+    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;
+        this.value = JSON.stringify(this._value);
+    }
+
+    computeSize(width) {
+        return [width, 300];
+    }
+
+    configure(data) {
+        console.log(data)
+    }
+
+    value() {
+        console.debug('Returning value', this._value)
+        return this._value
+    }
+    setValue(value) {
+        console.debug('Setting value', value)
+        this._value = value
+    }
+}
+
+app.registerExtension({
+    name: 'mtb.curves',
+    getCustomWidgets: function () {
+        return {
+            FLOAT_CURVE: (node, inputName, inputData, app) => {
+                console.debug('Registering float curve widget');
+
+                return {
+                    widget: node.addCustomWidget(
+                        new CurveWidget(inputName, inputData[1]?.default)
+                    ),
+                    minWidth: 150,
+                    minHeight: 30,
+                }
+            },
+
+
+        }
+    },
+
+})

web/debug.js

@@ -7,23 +7,41 @@
  *
  */
 
-import { app } from '/scripts/app.js'
-import * as shared from '/extensions/mtb/comfy_shared.js'
-import { log } from '/extensions/mtb/comfy_shared.js'
-import { MtbWidgets } from '/extensions/mtb/mtb_widgets.js'
+import { app } from '../../scripts/app.js'
+
+import * as shared from './comfy_shared.js'
+import { log } from './comfy_shared.js'
+import { MtbWidgets } from './mtb_widgets.js'
 
 // TODO: respect inputs order...
 
+function escapeHtml(unsafe) {
+  return unsafe
+    .replace(/&/g, '&')
+    .replace(/</g, '&lt;')
+    .replace(/>/g, '&gt;')
+    .replace(/"/g, '&quot;')
+    .replace(/'/g, '&#039;')
+}
 app.registerExtension({
   name: 'mtb.Debug',
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     if (nodeData.name === 'Debug (mtb)') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        this.addInput(`anything_1`, '*')
+        return r
+      }
+
       const onConnectionsChange = nodeType.prototype.onConnectionsChange
       nodeType.prototype.onConnectionsChange = function (
         type,
         index,
         connected,
-        link_info
+        link_info,
       ) {
         const r = onConnectionsChange
           ? onConnectionsChange.apply(this, arguments)
@@ -34,7 +52,7 @@ app.registerExtension({
         //- 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
@@ -45,6 +63,7 @@ app.registerExtension({
           this.inputs[index].type = '*'
           this.inputs[index].label = `anything_${index + 1}`
         }
+        return r
       }
 
       const onExecuted = nodeType.prototype.onExecuted
@@ -57,15 +76,17 @@ app.registerExtension({
           // const pos = this.widgets.findIndex((w) => w.name === "anything_1");
           // if (pos !== -1) {
           for (let i = 0; i < this.widgets.length; i++) {
-            this.widgets[i].onRemoved?.()
+            if (this.widgets[i].name !== 'output_to_console') {
+              this.widgets[i].onRemoved?.()
+            }
           }
-          this.widgets.length = 0
+          this.widgets.length = 1
         }
         let widgetI = 1
         if (message.text) {
           for (const txt of message.text) {
             const w = this.addCustomWidget(
-              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, txt)
+              MtbWidgets.DEBUG_STRING(`${prefix}_${widgetI}`, escapeHtml(txt)),
             )
             w.parent = this
             widgetI++
@@ -74,22 +95,24 @@ app.registerExtension({
         if (message.b64_images) {
           for (const img of message.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
           for (let y in this.widgets) {
             if (this.widgets[y].canvas) {
               this.widgets[y].canvas.remove()
             }
+            shared.cleanupNode(this)
             this.widgets[y].onRemoved?.()
           }
         }

web/imageFeed.js

@@ -9,9 +9,9 @@
 
 // forked from pysssss's imageFeed.js
 
-import { api } from '/scripts/api.js'
-import { app } from '/scripts/app.js'
-
+import { api } from '../../scripts/api.js'
+import { app } from '../../scripts/app.js'
+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

@@ -7,13 +7,53 @@
  *
  */
 
-import { app } from '/scripts/app.js'
-import parseCss from '/extensions/mtb/extern/parse-css.js'
-import * as shared from '/extensions/mtb/comfy_shared.js'
-import { log } from '/extensions/mtb/comfy_shared.js'
-import { api } from '/scripts/api.js'
+// TODO: Use the builtin addDOMWidget everywhere appropriate
 
-const newTypes = [, /*'BOOL'*/ 'COLOR', 'BBOX']
+import { app } from '../../scripts/app.js'
+import { api } from '../../scripts/api.js'
+
+import parseCss from './extern/parse-css.js'
+import * as shared from './comfy_shared.js'
+import { log } from './comfy_shared.js'
+import { Constant } from './constant.js'
+
+// 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
+  ctx.font = font
+  cb()
+  ctx.font = oldFont
+}
+
+const calculateTextDimensions = (ctx, value, width, fontSize = 16) => {
+  const words = value.split(' ')
+  const lines = []
+  let currentLine = ''
+  for (const word of words) {
+    const testLine = currentLine.length === 0 ? word : `${currentLine} ${word}`
+    const testWidth = ctx.measureText(testLine).width
+    if (testWidth > width) {
+      lines.push(currentLine)
+      currentLine = word
+    } else {
+      currentLine = testLine
+    }
+  }
+  if (lines.length === 0) lines.push(value)
+  const textHeight = (lines.length + 1) * fontSize
+  const maxLineWidth = lines.reduce(
+    (maxWidth, line) => Math.max(maxWidth, ctx.measureText(line).width),
+    0,
+  )
+  return { textHeight, maxLineWidth }
+}
 
 export const MtbWidgets = {
   BBOX: (key, val) => {
@@ -73,7 +113,7 @@ 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'
@@ -82,10 +122,10 @@ 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,
             )
           }
         }
@@ -167,7 +207,7 @@ export const MtbWidgets = {
                   this.value = Number(v)
                   shared.inner_value_change(this, this.value, event)
                 }.bind(w),
-                event
+                event,
               )
             }
           }
@@ -177,7 +217,7 @@ export const MtbWidgets = {
               function () {
                 shared.inner_value_change(this, this.value, event)
               }.bind(this),
-              20
+              20,
             )
 
           app.canvas.setDirty(true)
@@ -226,7 +266,7 @@ 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) {
@@ -258,6 +298,7 @@ export const MtbWidgets = {
 
             picker.addEventListener('change', () => {
               this.value = picker.value
+              this.callback?.(this.value)
               node.graph._version++
               node.setDirtyCanvas(true, true)
               picker.remove()
@@ -316,46 +357,22 @@ export const MtbWidgets = {
         // const [cw, ch] = this.computeSize(widgetWidth)
         shared.offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, height)
       },
-      computeSize: function (width) {
-        const value = this.inputEl.innerHTML
-        if (!value) {
+      computeSize(width) {
+        if (!this.value) {
           return [32, 32]
         }
         if (!width) {
-          log(`No width ${this.parent.size}`)
+          console.debug(`No width ${this.parent.size}`)
         }
-
-        const oldFont = app.ctx.font
-        app.ctx.font = `${fontSize}px monospace`
-
-        const words = value.split(' ')
-        const lines = []
-        let currentLine = ''
-        for (const word of words) {
-          const testLine =
-            currentLine.length === 0 ? word : `${currentLine} ${word}`
-
-          const testWidth = app.ctx.measureText(testLine).width
-
-          if (testWidth > width) {
-            lines.push(currentLine)
-            currentLine = word
-          } else {
-            currentLine = testLine
-          }
-        }
-        app.ctx.font = oldFont
-        if (lines.length === 0) lines.push(currentLine)
-
-        const textHeight = (lines.length + 1) * fontSize
-
-        const maxLineWidth = lines.reduce(
-          (maxWidth, line) =>
-            Math.max(maxWidth, app.ctx.measureText(line).width),
-          0
+        let dimensions
+        withFont(app.ctx, `${fontSize}px monospace`, () => {
+          dimensions = calculateTextDimensions(app.ctx, this.value, width)
+        })
+        const widgetWidth = Math.max(
+          width || this.width || 32,
+          dimensions.maxLineWidth,
         )
-        const widgetWidth = Math.max(width || this.width || 32, maxLineWidth)
-        const widgetHeight = textHeight * 1.5
+        const widgetHeight = dimensions.textHeight * 1.5
         return [widgetWidth, widgetHeight]
       },
       onRemoved: function () {
@@ -363,25 +380,23 @@ export const MtbWidgets = {
           this.inputEl.remove()
         }
       },
-    }
-
-    Object.defineProperty(w, 'value', {
-      get() {
+      get value() {
         return this.inputEl.innerHTML
       },
-      set(value) {
-        this.inputEl.innerHTML = value
+      set value(val) {
+        this.inputEl.innerHTML = val
         this.parent?.setSize?.(this.parent?.computeSize())
       },
-    })
+    }
 
     w.inputEl = document.createElement('p')
-    w.inputEl.style.textAlign = 'center'
-    w.inputEl.style.fontSize = `${fontSize}px`
-    w.inputEl.style.color = 'var(--input-text)'
-    w.inputEl.style.lineHeight = 0
-
-    w.inputEl.style.fontFamily = 'monospace'
+    w.inputEl.style = `
+      text-align: center;
+      font-size: ${fontSize}px;
+      color: var(--input-text);
+      line-height: 1em;
+      font-family: monospace;
+    `
     w.value = val
     document.body.appendChild(w.inputEl)
 
@@ -412,7 +427,7 @@ 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,
 
@@ -445,6 +460,12 @@ const mtb_widgets = {
       },
     })
   },
+  registerCustomNodes() {
+    LiteGraph.registerNodeType('Constant (mtb)', Constant)
+
+    Constant.category = 'mtb/utils'
+    Constant.title = 'Constant (mtb)'
+  },
 
   getCustomWidgets: function () {
     return {
@@ -453,7 +474,7 @@ const mtb_widgets = {
 
         return {
           widget: node.addCustomWidget(
-            MtbWidgets.BOOL(inputName, inputData[1]?.default || false)
+            MtbWidgets.BOOL(inputName, inputData[1]?.default || false),
           ),
           minWidth: 150,
           minHeight: 30,
@@ -464,7 +485,7 @@ const mtb_widgets = {
         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,
@@ -512,12 +533,7 @@ const mtb_widgets = {
 
         this.onRemoved = function () {
           // When removing this node we need to remove the input from the DOM
-          for (const w of this.widgets) {
-            if (w.canvas) {
-              w.canvas.remove()
-            }
-            w.onRemoved?.()
-          }
+          shared.cleanupNode(this)
         }
         return r
       }
@@ -561,24 +577,20 @@ 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
@@ -587,7 +599,7 @@ const mtb_widgets = {
             ? onNodeCreated.apply(this, arguments)
             : 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 () {
@@ -627,45 +639,37 @@ 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++
               }
             }
-            this.setSize?.(this.computeSize())
-            return r
-          }
-
-          const onRemoved = nodeType.prototype.onRemoved
-          nodeType.prototype.onRemoved = function (message) {
-            const r = onRemoved ? onRemoved.apply(this, message) : undefined
-            if (!this.widgets) return r
-            for (const w of this.widgets) {
-              if (w.canvas) {
-                w.canvas.remove()
-              }
-              w.onRemoved?.()
+            const onRemoved = this.onRemoved
+            this.onRemoved = () => {
+              shared.cleanupNode(this)
+              return onRemoved?.()
             }
-            return r
           }
+          this.setSize?.(this.computeSize())
+          return r
         }
 
         break
@@ -680,12 +684,12 @@ 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')
 
@@ -695,12 +699,12 @@ 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
 
@@ -718,7 +722,7 @@ const mtb_widgets = {
             'button',
             `Reset`,
             'reset',
-            onReset
+            onReset,
           )
 
           const run_button = this.addWidget('button', `Queue`, 'queue', () => {
@@ -728,17 +732,12 @@ const mtb_widgets = {
               `Started a queue of ${total_frames.value} frames (for ${
                 loop_count.value
               } loop, so ${total_frames.value * loop_count.value})`,
-              5000
+              5000,
             )
           })
 
           this.onRemoved = () => {
-            for (const w of this.widgets) {
-              if (w.canvas) {
-                w.canvas.remove()
-              }
-              w.onRemoved?.()
-            }
+            shared.cleanupNode(this)
             app.canvas.setDirty(true)
           }
 
@@ -764,23 +763,6 @@ 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 () {
@@ -791,7 +773,7 @@ const mtb_widgets = {
             const input = this.addInput(
               `replacement_${this.widgets.length}`,
               'STRING',
-              ''
+              '',
             )
             console.log(input)
             this.addWidget('STRING', `replacement_${this.widgets.length}`, '')
@@ -808,7 +790,7 @@ const mtb_widgets = {
             'remove',
             function (value, widget, node) {
               console.log(`Button clicked: ${value}`, widget, node)
-            }
+            },
           )
 
           return r
@@ -849,15 +831,16 @@ 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}`,
                     )
                   }
                 }
@@ -870,15 +853,15 @@ 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}`,
                     )
                   }
                 }
@@ -890,6 +873,89 @@ 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 'Plot Batch Float (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'floats', 'FLOATS')
+        break
+      }
+      case 'Batch Merge (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'batches', 'IMAGE')
+
+        break
+      }
+      // TODO: remove this, recommend pythongoss's version that is much better
+      case 'Math Expression (mtb)': {
+        const onNodeCreated = nodeType.prototype.onNodeCreated
+        nodeType.prototype.onNodeCreated = function () {
+          const r = onNodeCreated
+            ? onNodeCreated.apply(this, arguments)
+            : undefined
+          this.addInput(`x`, '*')
+          return r
+        }
+
+        const onConnectionsChange = nodeType.prototype.onConnectionsChange
+        nodeType.prototype.onConnectionsChange = function (
+          type,
+          index,
+          connected,
+          link_info,
+        ) {
+          const r = onConnectionsChange
+            ? onConnectionsChange.apply(this, arguments)
+            : undefined
+          shared.dynamic_connection(this, index, connected, 'var_', '*', [
+            'x',
+            'y',
+            'z',
+          ])
+
+          //- infer type
+          if (link_info) {
+            const fromNode = this.graph._nodes.find(
+              (otherNode) => otherNode.id == link_info.origin_id,
+            )
+            const type = fromNode.outputs[link_info.origin_slot].type
+            this.inputs[index].type = type
+            // this.inputs[index].label = type.toLowerCase()
+          }
+          //- restore dynamic input
+          if (!connected) {
+            this.inputs[index].type = '*'
+            this.inputs[index].label = `number_${index + 1}`
+          }
+        }
+
+        break
+      }
       case 'Save Tensors (mtb)': {
         const onDrawBackground = nodeType.prototype.onDrawBackground
         nodeType.prototype.onDrawBackground = function (ctx, canvas) {

web/note_plus.js

@@ -0,0 +1,693 @@
+import { app } from '../../scripts/app.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 {
+  // 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
+
+    // - 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.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: (val) => {
+        this._raw_html = val
+      },
+      getValue: () => this._raw_html,
+      getMinHeight: () => this.calculated_height, // (the edit button),
+      hideOnZoom: false,
+    })
+
+    this.setupSerializationWidgets()
+    this.setupDialog()
+    this.loadAceEditor()
+  }
+
+  /**
+   *
+   * @param {CanvasRenderingContext2D} ctx
+   * @param {LGraphCanvas} graphcanvas
+   * @returns
+   */
+
+  onDrawForeground(ctx, _graphcanvas) {
+    if (this.flags.collapsed) return
+
+    // 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
+
+    // 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',
+    )
+
+    // 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)'
+
+    ctx.lineCap = 'round'
+    ctx.lineJoin = 'round'
+
+    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
+
+    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}`)
+      },
+    )
+
+    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))
+
+    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.closeEditorDialog(true)
+    }
+    closeButton.onclick = () => {
+      this.closeEditorDialog(false)
+    }
+    closeButton.before(saveButton)
+  }
+
+  teardownEditors() {
+    this.css_editor.destroy()
+    this.css_editor.container.remove()
+
+    this.html_editor.destroy()
+    this.html_editor.container.remove()
+  }
+
+  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.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) {
+    const cleanHTML = DOMPurify.sanitize(val, { ADD_TAGS: ['iframe'] })
+    this.html_widget.value = cleanHTML
+
+    // 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())
+  }
+}
+
+app.registerExtension({
+  name: 'mtb.noteplus',
+
+  registerCustomNodes() {
+    LiteGraph.registerNodeType('Note Plus (mtb)', NotePlus)
+
+    NotePlus.category = 'mtb/utils'
+    NotePlus.title = 'Note+ (mtb)'
+
+    NotePlus.title_mode = LiteGraph.NO_TITLE
+  },
+})

web/notify.js

@@ -7,7 +7,7 @@
  *
  */
 
-import { app } from '/scripts/app.js'
+import { app } from '../../scripts/app.js'
 
 const log = (...args) => {
   if (window.MTB?.TRACE) {

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;
+                        }
+                    });
+                })();
+