bump pre-commit hooks and update links to diagrams repo

Author: janosh

.github/workflows/generate-readme.yml

@@ -14,12 +14,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ github.head_ref }}
 
       - name: Set up python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.11"
 

.github/workflows/link-check.yml

@@ -7,15 +7,13 @@ on:
     branches: [main]
 
 jobs:
-  markdown-link-check:
+  link-check:
     runs-on: ubuntu-latest
     steps:
       - name: Check out repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
-      - name: Run markdown link check
-        uses: gaurav-nelson/github-action-markdown-link-check@v1
-        # docs at https://git.io/JBaKu
+      - name: Discover broken links
+        uses: lycheeverse/lychee-action@v2
         with:
-          config-file: .github/workflows/link-check-config.json
-          max-depth: 1 # only check readme.md
+          args: --accept 100..=103,200..=299,403,429,500 -- ./**/*.{md,py,ipynb}

.pre-commit-config.yaml

@@ -1,13 +1,8 @@
-ci:
-  autoupdate_schedule: quarterly
-
-default_stages: [commit]
-
 default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.7
+    rev: v0.15.7
     hooks:
       - id: ruff
         args:
@@ -20,21 +15,20 @@ repos:
           - py312
       - id: ruff-format
 
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+  - repo: builtin
     hooks:
+      - id: check-added-large-files
       - id: check-case-conflict
       - id: check-symlinks
       - id: check-yaml
       - id: destroyed-symlinks
       - id: end-of-file-fixer
         exclude_types: [svg]
-      - id: forbid-new-submodules
       - id: mixed-line-ending
       - id: trailing-whitespace
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.42.0
+    rev: v0.48.0
     hooks:
       - id: markdownlint
         # MD013: line length
@@ -43,14 +37,14 @@ repos:
         args: [--disable, MD013, MD033, MD041, "--"]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.2
     hooks:
       - id: codespell
-        stages: [commit, commit-msg]
+        stages: [pre-commit, commit-msg]
         args: [--ignore-words-list, gool, --check-filenames]
 
   - repo: https://github.com/google/yamlfmt
-    rev: v0.13.0
+    rev: v0.21.0
     hooks:
       - id: yamlfmt
         args: [-formatter, retain_line_breaks=true]

data/publications.yml

@@ -29,10 +29,10 @@
 
        A popular and efficient way to make flows autoregressive is to construct them from MADE nets.
 
-       <a href="https://github.com/janosh/tikz/tree/main/assets/made">
+       <a href="https://github.com/janosh/diagrams/tree/main/assets/made">
          <picture>
-           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/made/made-white.svg">
-           <img alt="Masked Autoencoder for Distribution Estimation" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/made/made.svg">
+           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/made/made-white.svg">
+           <img alt="Masked Autoencoder for Distribution Estimation" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/made/made.svg">
          </picture>
        </a>
 
@@ -54,10 +54,10 @@
   description: |
     They introduce the affine coupling layer (RNVP), a major improvement in terms of flexibility over the additive coupling layer (NICE) with unit Jacobian while keeping a single-pass forward and inverse transformation for fast sampling and density estimation, respectively.
 
-       <a href="https://github.com/janosh/tikz/tree/main/assets/rnvp">
+       <a href="https://github.com/janosh/diagrams/tree/main/assets/rnvp">
          <picture>
-           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/rnvp/rnvp-white.svg">
-           <img alt="Diagram of real-valued non-volume preserving (RNVP) coupling layer" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/rnvp/rnvp.svg">
+           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/rnvp/rnvp-white.svg">
+           <img alt="Diagram of real-valued non-volume preserving (RNVP) coupling layer" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/rnvp/rnvp.svg">
          </picture>
        </a>
 
@@ -92,10 +92,10 @@
   description: |
     Introduces MAF, a stack of autoregressive models forming a normalizing flow suitable for fast density estimation but slow at sampling. Analogous to Inverse Autoregressive Flow (IAF) except the forward and inverse passes are exchanged. Generalization of RNVP.
 
-       <a href="https://github.com/janosh/tikz/tree/main/assets/maf">
+       <a href="https://github.com/janosh/diagrams/tree/main/assets/maf">
          <picture>
-           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/maf/maf-white.svg">
-           <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/maf/maf.svg">
+           <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/maf/maf-white.svg">
+           <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/maf/maf.svg">
          </picture>
        </a>
 
@@ -156,7 +156,7 @@
     - name: Jongyoon Song
     - name: Jaehyeon Kim
     - name: Sungroh Yoon
-  description: A flow-based generative model for raw audo synthesis.
+  description: A flow-based generative model for raw audio synthesis.
   repo: https://github.com/ksw0306/FloWaveNet
 
 - title: Block Neural Autoregressive Flow

readme.md

@@ -13,14 +13,14 @@
 
 A list of awesome resources for understanding and applying normalizing flows (NF): a relatively simple yet powerful new tool in statistics for constructing expressive probability distributions from simple base distributions using a chain (flow) of trainable smooth bijective transformations (diffeomorphisms).
 
-<a href="https://github.com/janosh/tikz/tree/main/assets/normalizing-flow">
+<a href="https://github.com/janosh/diagrams/tree/main/assets/normalizing-flow">
    <picture>
-      <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/normalizing-flow/normalizing-flow-white.svg">
-      <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/normalizing-flow/normalizing-flow.svg">
+      <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/normalizing-flow/normalizing-flow-white.svg">
+      <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/normalizing-flow/normalizing-flow.svg">
    </picture>
 </a>
 
-<sup>_Figure inspired by [Lilian Weng](https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models). Created in TikZ. [View source](https://github.com/janosh/tikz/tree/main/assets/normalizing-flow)._</sup>
+<sup>_Figure inspired by [Lilian Weng](https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models). Created in [CeTZ](https://cetz-package.github.io). [View source](https://github.com/janosh/diagrams/blob/main/assets/normalizing-flow/normalizing-flow.typ)._</sup>
 
 <br>
 
@@ -183,7 +183,7 @@ A list of awesome resources for understanding and applying normalizing flows (NF
    Introduces autoregressive-like convolutional layers that operate on the channel **and** spatial axes. This improved upon the performance of image datasets compared to the standard 1x1 Convolutions. The trade-off is that the inverse operator is quite expensive however the authors provide a fast C++ implementation. [[Code](https://github.com/ehoogeboom/emerging)]
 
 1. 2018-11-06 - [FloWaveNet : A Generative Flow for Raw Audio](https://arxiv.org/abs/1811.02155) by Kim, Lee et al.<br>
-   A flow-based generative model for raw audo synthesis. [[Code](https://github.com/ksw0306/FloWaveNet)]
+   A flow-based generative model for raw audio synthesis. [[Code](https://github.com/ksw0306/FloWaveNet)]
 
 1. 2018-10-02 - [FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models](https://arxiv.org/abs/1810.01367) by Grathwohl, Chen et al.<br>
    Uses Neural ODEs as a solver to produce continuous-time normalizing flows (CNF).
@@ -206,10 +206,10 @@ A list of awesome resources for understanding and applying normalizing flows (NF
 1. 2017-05-19 - [Masked Autoregressive Flow for Density Estimation](https://arxiv.org/abs/1705.07057) by Papamakarios, Pavlakou et al.<br>
    Introduces MAF, a stack of autoregressive models forming a normalizing flow suitable for fast density estimation but slow at sampling. Analogous to Inverse Autoregressive Flow (IAF) except the forward and inverse passes are exchanged. Generalization of RNVP.
 
-   <a href="https://github.com/janosh/tikz/tree/main/assets/maf">
+   <a href="https://github.com/janosh/diagrams/tree/main/assets/maf">
      <picture>
-       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/maf/maf-white.svg">
-       <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/maf/maf.svg">
+       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/maf/maf-white.svg">
+       <img alt="Diagram of the slow (sequential) forward pass of a Masked Autoregressive Flow (MAF) layer" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/maf/maf.svg">
      </picture>
    </a>
 
@@ -222,10 +222,10 @@ A list of awesome resources for understanding and applying normalizing flows (NF
 1. 2016-05-27 - [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803) by Dinh, Sohl-Dickstein et al.<br>
    They introduce the affine coupling layer (RNVP), a major improvement in terms of flexibility over the additive coupling layer (NICE) with unit Jacobian while keeping a single-pass forward and inverse transformation for fast sampling and density estimation, respectively.
 
-   <a href="https://github.com/janosh/tikz/tree/main/assets/rnvp">
+   <a href="https://github.com/janosh/diagrams/tree/main/assets/rnvp">
      <picture>
-       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/rnvp/rnvp-white.svg">
-       <img alt="Diagram of real-valued non-volume preserving (RNVP) coupling layer" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/rnvp/rnvp.svg">
+       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/rnvp/rnvp-white.svg">
+       <img alt="Diagram of real-valued non-volume preserving (RNVP) coupling layer" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/rnvp/rnvp.svg">
      </picture>
    </a>
 
@@ -237,10 +237,10 @@ A list of awesome resources for understanding and applying normalizing flows (NF
 
    A popular and efficient way to make flows autoregressive is to construct them from MADE nets.
 
-   <a href="https://github.com/janosh/tikz/tree/main/assets/made">
+   <a href="https://github.com/janosh/diagrams/tree/main/assets/made">
      <picture>
-       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/tikz/main/assets/made/made-white.svg">
-       <img alt="Masked Autoencoder for Distribution Estimation" src="https://raw.githubusercontent.com/janosh/tikz/main/assets/made/made.svg">
+       <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/janosh/diagrams/main/assets/made/made-white.svg">
+       <img alt="Masked Autoencoder for Distribution Estimation" src="https://raw.githubusercontent.com/janosh/diagrams/main/assets/made/made.svg">
      </picture>
    </a>