remove-ai-watermarks/docs/release-and-distribution.md

Release and distribution

This page describes the release behavior defined in this repository. External registry state can change independently, so verify it during a release.

Published surfaces

A release is complete only after all four published surfaces are verified:

Surface	Published result	Automation
PyPI	The remove-ai-watermarks wheel and source distribution	publish.yml
Homebrew	The remove-ai-watermarks formula in wiltodelta/homebrew-tap	distribute.yml
Hugging Face Space	The Space demo deployed on the new release through a requirements-pin bump in wiltodelta/raiw-hf-space	distribute.yml dispatches pin-library.yml in that repo, waits for the Hub requirements.txt pin, then factory-rebuilds; sync-to-hf.yml mirrors the pin onto the Space
ComfyUI Registry	A compatible release of wiltodelta/ComfyUI-remove-ai-watermarks with its own node version	distribute.yml and the node repository's workflows

The GitHub Release is the trigger and release record for this flow. Conda is not a supported publishing surface: this repository has no conda recipe or conda publication job.

Release sources of truth

The package version appears in:

• pyproject.toml;
• src/remove_ai_watermarks/__init__.py;
• the root package entry generated in uv.lock.

Update the first two, then refresh the lock file with uv. Do not edit a line-number-specific location in uv.lock; its package order changes.

Before choosing a version, verify it is absent from git tags, GitHub Releases, and PyPI. An unpublished remote tag still reserves that version; never move it, publish the next version instead.

Publish flow

PyPI publishing is triggered by a published GitHub Release, not by a tag push alone.

The expected sequence:

1. update the version sources and lock file;
2. run the complete project gate;
3. commit the release change;
4. create an annotated vX.Y.Z tag;
5. push the commit and tag;
6. publish the GitHub Release.

.github/workflows/publish.yml then:

1. checks that the release tag matches pyproject.toml;
2. builds the package with uv;
3. publishes with uv publish through PyPI trusted publishing.

The workflow uses GitHub OIDC through the pypi environment. It does not read a PyPI API token from the repository.

Post-release distribution

.github/workflows/distribute.yml runs on the same published-release event. It waits for the matching source distribution to appear on PyPI, then:

• updates the Homebrew tap formula URL and SHA-256;
• bumps the Hugging Face Space pin and factory-rebuilds the demo on that pin;
• synchronizes, tests, versions, and publishes the ComfyUI nodes.

Upgrading the Hugging Face Space is dispatched from distribute.yml: it runs pin-library.yml in wiltodelta/raiw-hf-space, which bumps remove-ai-watermarks[visible,heif] in pyproject.toml, refreshes uv.lock, and pushes. That repository's sync-to-hf.yml then mirrors the files onto the Space. distribute.yml waits for the Hub requirements.txt pin to match, then factory-rebuilds. The RAIW_HF_SPACE_TOKEN repository secret is a fine-grained token limited to the Space repository, with Contents write and Actions write. Without the secret the pin bump is skipped and only the factory rebuild runs, which reinstalls whatever version is already pinned. The Space repo's callback smoke tests still run on every push; they are the scenario check for the demo surface.

The Hugging Face model wiltodelta/raiw-photo-classify is not part of that release fan-out. It holds the photo-classify freeze weights. The library extra classify downloads that snapshot on first use, or reads RAIW_CLASSIFY_WEIGHTS. Update the Hub repo only with .github/workflows/publish-photo-classify-hf.yml (workflow_dispatch, type publish). Full mode downloads the four weight files from the GitHub Release tag photo-classify-freeze-2026-08-31 (or the tag you pass), exports the static FP32 vision-only ONNX graph from clip-l-ft.pt, and uploads all five files with the card in docs/photo-classify-hf/. Card mode updates the README, operating-point.json, and the receipt-gate head staged from the package assets; freeze weights are untouched. A library version bump alone does not upload new heads -- bump the pinned Hub revision when a new head must reach installed clients. Pin a Hub revision in production so a later freeze cannot silently change installed clients.

The separate wiltodelta/raiw-source-classify model is published with .github/workflows/publish-source-classify-hf.yml. The workflow downloads the derivative source-pipeline-mlp.npz artifact from the declared GitHub model freeze, verifies its SHA-256, and uploads only that file plus the tracked card and aggregate metrics under docs/source-classify-hf/. The private corpus, catalog, paths, hashes, embeddings, and feature caches are outside this flow.

The workflow is manual and requires the model-freeze release tag plus an explicit publish confirmation. Keep model-freeze GitHub releases marked as pre-releases so a model artifact cannot replace the latest package release in GitHub's release UI. Package publication and distribution jobs run only for non-prerelease v* tags, so model-only prereleases cannot enter PyPI or the package fan-out. A draft release keeps its target commit when published; target the commit containing those guards before creating a model-only release, not merely the then-current main after the draft already exists.

The newer wiltodelta/openai-google-image-source-classifier uses the separate publish-image-source-hf.yml workflow. Its release asset is only the hash-verified derivative openai-google-source-v1.npz; the workflow uploads that artifact plus docs/image-source-hf/README.md and metrics.json. The training corpus does not enter the library checkout or this release.

If a distribution job fails because a repository or Hugging Face credential is invalid, rotate the corresponding GitHub secret and rerun the failed job. A manual Homebrew formula update is the fallback when its automation is blocked.

Source distribution boundary

The wheel includes the package under src/.

The source distribution uses an explicit allowlist for /src, /LICENSE, /README.md, and /pyproject.toml through [tool.hatch.build.targets.sdist] in pyproject.toml. It also defensively excludes /data, /tmp, and /.sc. Keep both controls: calibration captures, test corpora, generated research outputs, and local session state do not belong in the published package archive. Hatchling always adds the root .gitignore to the sdist, so keep its comments generic and free of local operational context. Ignore rules are not the build boundary: data/ is deliberately tracked, while the sdist configuration keeps it and the other excluded paths out of the archive.

Build backend

The package uses hatchling through the unpinned hatchling build requirement in pyproject.toml. Uploading uses uv rather than the older twine-based action.

Other channels

The ComfyUI nodes are maintained and versioned in their own repository. After the matching source distribution appears on PyPI, distribute.yml dispatches that repository's sync workflow with the exact library version and waits for it to finish. The sync updates the dependency floor, runs compatibility tests, bumps the node patch version, and publishes to the ComfyUI Registry only when those tests pass. Its daily schedule remains as a recovery path if a release dispatch is interrupted. The COMFYUI_RELEASE_TOKEN repository secret is a fine-grained token limited to the ComfyUI node repository, with Actions read and write access.

Agent skill

The Agent Skill and Claude plugin live in this repository:

• skills/remove-ai-watermarks/ is the portable Agent Skills package;
• skills/.claude-plugin/plugin.json is the Claude plugin manifest, with source ./skills so an install copies only the skill tree;
• .claude-plugin/marketplace.json makes the repository a Claude Code marketplace.

They are not a fifth required release surface. A CLI change that affects command routing, extras, exit codes, mark keys, or the intended-use boundary must update the skill in the same commit. User-facing install steps live in docs/agent-skill.md.

After the skill files reach the default branch:

• npx skills add wiltodelta/remove-ai-watermarks installs from GitHub;
• SkillsMP can index the public SKILL.md;
• Claude Code users can run /plugin marketplace add wiltodelta/remove-ai-watermarks.

ClawHub publishes automatically: the clawhub job in distribute.yml compares the SKILL.md version with the published one on every GitHub Release and submits an update when they differ. After its local npm installation, the job invokes ./node_modules/.bin/clawhub explicitly. It needs the CLAWHUB_TOKEN secret (create a dedicated token on clawhub.ai, not a device-flow one); without the secret the job skips with a notice. A submission waits for ClawHub security scans before the listing goes public, so a green job means "submitted", not "listed": verify the live catalog before calling the surface complete. The Claude community marketplace remains a manual, authenticated publish. Do not treat any listing as complete until the live catalog shows the skill.

When metadata.version in SKILL.md changes, finish the catalog follow-up as part of the release that carries that version:

1. Verify the automatic ClawHub submission and then the live version. Do not publish it a second time by hand.
2. If the Claude community directory listing is live, publish the updated plugin through its authenticated submission surface and verify the listing. If the original submission is still under review, leave it pending rather than opening a duplicate.
3. If the AI Skills Catalog listing is live, send the new skill version through the update route supplied with its acceptance. The catalog stores compatible packages as well as source metadata, so a GitHub push alone is not a verified update. If no update control was supplied, contact the catalog or resubmit and verify the resulting listing. Do not duplicate a pending initial review.
4. If the OpenAI plugin directory listing has been approved, upload the new plugin archive through the Platform portal and verify the published version. Until the initial product-specific review is approved, there is no release action beyond tracking that review.
5. SkillsMP, skills.sh, AgenticSkills, and the awesome lists point at GitHub or refresh from it. Do not resubmit a routine version bump. Contact them or open a new PR only when their public description, category, tags, or repository URL must change.

The automated release workflow cannot perform the authenticated directory steps. A release with a changed skill version is not fully distributed until each applicable manual step is verified or explicitly marked pending because the catalog has not accepted the initial submission. The catalog-specific submission state and links live in docs/agent-skill.md.

Release verification

Forensic transports are versioned independently from the package. Before publishing a change to provenance metadata, provenance reports, broad forensic metadata, or pixel evidence, run their schema 1 contract tests. Additive fields are compatible; renaming a field, changing its type or meaning, changing a signal name or watermark label, or removing a field requires a new output schema. Add the new serializer without removing schema 1 so long-lived consumers can update separately. A package release must never silently substitute its latest schema when a caller explicitly requests an older supported one.

After publication, the verify-release.yml workflow runs automatically once distribute.yml completes and checks every surface below. The automatic run downloads distributed-release-* from the exact triggering distribution run, verifies the run ID and artifact attempt, and selects the highest recorded attempt no later than the triggering attempt. This also handles reruns of failed jobs that reuse an earlier successful version-resolution job. A newer PyPI release cannot silently change the recorded target. Manual dispatch accepts an explicit version, or uses PyPI latest when omitted. Every check fails the job; none of them warns and continues, because a surface nobody can confirm is not a verified surface, with one deliberate exception noted below. The ComfyUI check reads the registry's JSON API and asserts that some published node version declares this library release. Dependencies are parsed as packaging requirements: the canonical package name must be remove-ai-watermarks, an exact >= floor must name the target release, and the full constraint must admit it. URL and environment-conditional requirements do not satisfy that contract. It deliberately does NOT proxy through the sync workflow's run status: the node repository also runs that workflow on a daily schedule, so a green nightly run says nothing about whether this release was synced.

Read /nodes/remove-ai-watermarks/versions, not latest_version on the node object. The registry reviews an upload before activating it, and latest_version reports only the active version, so during that review window a correctly published version is indistinguishable from one that was never uploaded. A new version therefore sits in NodeVersionStatusPending for a while after a release; the check reports that state in the run summary and passes, because it is the registry's queue rather than an incomplete release. A missing version, or any other status, fails. The full checklist for completeness:

• both wheel and source distribution exist on PyPI;
• the package version matches the tag;
• the Homebrew formula points to the new source distribution;
• the distribution workflow completed successfully;
• the ComfyUI Registry node requires the new library version;
• the Hugging Face Space serves the new release: its requirements.txt pin matches (raw file via the HF API), the runtime stage is RUNNING after the rebuild, and one live identify and one live visible API call succeed against the running Space;
• a clean install can run remove-ai-watermarks --version.
• when the Agent Skill version changed, every applicable manual catalog update above is verified, or its initial submission remains pending without a duplicate submission.

A clean-install check run immediately after publication can fail with "no version of remove-ai-watermarks==X.Y.Z" even though the simple index already lists it: uv serves its cached pre-release view of the index. Re-run with a fresh cache (UV_CACHE_DIR=$(mktemp -d)) before treating it as a release failure.

The ComfyUI sync resolves the version from the PyPI JSON API but installs its test dependency through pip's simple index, whose CDN edges can lag by minutes. distribute.yml gates every downstream job on both APIs listing the release, so it cannot dispatch that race again. The ComfyUI waiter itself has a clock: 0.40.0's node suite finished after the 15-minute poll, so it now waits 30 minutes and then accepts a registry row that already requires this library version. For an older distribution that started before the simple index caught up, rerun its failed comfyui job only when the registry still has no such row.

The automatic verify-release.yml run after distribute.yml checks the Hugging Face Space pin. When RAIW_HF_SPACE_TOKEN is set, that pin lands during distribution. Without the secret, bump the pin by dispatching pin-library.yml in wiltodelta/raiw-hf-space and then dispatch verify-release.yml with the release version. Do not read a missing Space pin as a broken PyPI or Homebrew publish.

If the release changed CLI routing, extras, exit codes, mark keys, or the intended-use boundary, confirm skills/remove-ai-watermarks/ still matches.