test: update selection toolbox linux snapshot

- groupNode: no-prefix path and non-object config[1] fallback
- useIntWidget: string control_after_generate passed as default type

.claude/skills/backport-management/SKILL.md

@@ -11,10 +11,11 @@ Cherry-pick backport management for Comfy-Org/ComfyUI_frontend stable release br
 
 1. **Discover** — Collect candidates from Slack bot + git log gap (`reference/discovery.md`)
 2. **Analyze** — Categorize MUST/SHOULD/SKIP, check deps (`reference/analysis.md`)
-3. **Plan** — Order by dependency (leaf fixes first), group into waves per branch
-4. **Execute** — Label-driven automation → worktree fallback for conflicts (`reference/execution.md`)
-5. **Verify** — After each wave, verify branch integrity before proceeding
-6. **Log & Report** — Generate session report with mermaid diagram (`reference/logging.md`)
+3. **Human Review** — Present candidates in batches for interactive approval (see Interactive Approval Flow)
+4. **Plan** — Order by dependency (leaf fixes first), group into waves per branch
+5. **Execute** — Label-driven automation → worktree fallback for conflicts (`reference/execution.md`)
+6. **Verify** — After each wave, verify branch integrity before proceeding
+7. **Log & Report** — Generate session report (`reference/logging.md`)
 
 ## System Context
 
@@ -37,16 +38,29 @@ Cherry-pick backport management for Comfy-Org/ComfyUI_frontend stable release br
 
 **Critical: Match PRs to the correct target branches.**
 
-| Branch prefix | Scope                          | Example                                   |
-| ------------- | ------------------------------ | ----------------------------------------- |
-| `cloud/*`     | Cloud-hosted ComfyUI only      | App mode, cloud auth, cloud-specific UI   |
-| `core/*`      | Local/self-hosted ComfyUI only | Core editor, local workflows, node system |
+| Branch prefix | Scope                          | Example                                           |
+| ------------- | ------------------------------ | ------------------------------------------------- |
+| `cloud/*`     | Cloud-hosted ComfyUI only      | Team workspaces, cloud queue, cloud-only login    |
+| `core/*`      | Local/self-hosted ComfyUI only | Core editor, local workflows, node system         |
+| Both          | Shared infrastructure          | App mode, Firebase auth (API nodes), payment URLs |
 
-**⚠️ NEVER backport cloud-only PRs to `core/*` branches.** Cloud-only changes (app mode, cloud auth, cloud billing UI, cloud-specific API calls) are irrelevant to local users and waste effort. Before backporting any PR to a `core/*` branch, check:
+### What Goes Where
 
-- Does the PR title/description mention "app mode", "cloud", or cloud-specific features?
-- Does the PR only touch files like `appModeStore.ts`, cloud auth, or cloud-specific components?
-- If yes → skip for `core/*` branches (may still apply to `cloud/*` branches)
+**Both core + cloud:**
+
+- **App mode** PRs — app mode is NOT cloud-only
+- **Firebase auth** PRs — Firebase auth is on core for API nodes
+- **Payment redirect** PRs — payment infrastructure shared
+- **Bug fixes** touching shared components
+
+**Cloud-only (skip for core):**
+
+- Team workspaces
+- Cloud queue virtualization
+- Hide API key login
+- Cloud-specific UI behind cloud feature flags
+
+**⚠️ NEVER backport cloud-only PRs to `core/*` branches.** But do NOT assume "app mode" or "Firebase" = cloud-only. Check the actual files changed.
 
 ## ⚠️ Gotchas (Learn from Past Sessions)
 
@@ -67,6 +81,32 @@ The `pr-backport.yaml` action reports more conflicts than reality. `git cherry-p
 
 12 or 27 conflicting files can be trivial (snapshots, new files). **Categorize conflicts first**, then decide. See Conflict Triage below.
 
+### Accept-Theirs Can Produce Broken Hybrids
+
+When a PR **rewrites a component** (e.g., PrimeVue → Reka UI), the accept-theirs regex produces a broken mix of old and new code. The template may reference new APIs while the script still has old imports, or vice versa.
+
+**Detection:** Content conflicts with 4+ conflict markers in a single `.vue` file, especially when imports change between component libraries.
+
+**Fix:** Instead of accept-theirs regex, use `git show MERGE_SHA:path/to/file > path/to/file` to get the complete correct version from the merge commit on main. This bypasses the conflict entirely.
+
+### Cherry-Picks Can Reference Missing Dependencies
+
+When PR A on main depends on code introduced by PR B (which was merged before A), cherry-picking A brings in code that references B's additions. The cherry-pick succeeds but the branch is broken.
+
+**Common pattern:** Composables, component files, or type definitions introduced by an earlier PR and used by the cherry-picked PR.
+
+**Detection:** `pnpm typecheck` fails with "Cannot find module" or "is not defined" errors after cherry-pick.
+
+**Fix:** Use `git show MERGE_SHA:path/to/missing/file > path/to/missing/file` to bring the missing files from main. Always verify with typecheck.
+
+### Use `--no-verify` for Worktree Pushes
+
+Husky hooks fail in worktrees (can't find lint-staged config). Always use `git push --no-verify` and `git commit --no-verify` when working in `/tmp/` worktrees.
+
+### Automation Success Varies Wildly by Branch
+
+In the 2026-04-06 session: core/1.42 got 18/26 auto-PRs, cloud/1.42 got only 1/25. The cloud branch has more divergence. **Always plan for manual fallback** — don't assume automation will handle most PRs.
+
 ## Conflict Triage
 
 **Always categorize before deciding to skip. High conflict count ≠ hard conflicts.**
@@ -77,6 +117,8 @@ The `pr-backport.yaml` action reports more conflicts than reality. `git cherry-p
 | **Modify/delete (new file)** | PR introduces files not on target    | `git add $FILE` — keep the new file                             |
 | **Modify/delete (removed)**  | Target removed files the PR modifies | `git rm $FILE` — file no longer relevant                        |
 | **Content conflicts**        | Marker-based (`<<<<<<<`)             | Accept theirs via python regex (see below)                      |
+| **Component rewrites**       | 4+ markers in `.vue`, library change | Use `git show SHA:path > path` — do NOT accept-theirs           |
+| **Import-only conflicts**    | Only import lines differ             | Keep both imports if both used; remove unused after             |
 | **Add/add**                  | Both sides added same file           | Accept theirs, verify no logic conflict                         |
 | **Locale/JSON files**        | i18n key additions                   | Accept theirs, validate JSON after                              |
 
@@ -103,7 +145,7 @@ Skip these without discussion:
 - **Test-only / lint rule changes** — Not user-facing
 - **Revert pairs** — If PR A reverted by PR B, skip both. If fixed version (PR C) exists, backport only C.
 - **Features not on target branch** — e.g., Painter, GLSLShader, appModeStore on core/1.40
-- **Cloud-only PRs on core/\* branches** — App mode, cloud auth, cloud billing. These only affect cloud-hosted ComfyUI.
+- **Cloud-only PRs on core/\* branches** — Team workspaces, cloud queue, cloud-only login. (Note: app mode and Firebase auth are NOT cloud-only — see Branch Scope Rules)
 
 ## Wave Verification
 
@@ -122,6 +164,18 @@ git worktree remove /tmp/verify-TARGET --force
 
 If typecheck or tests fail, stop and investigate before continuing. A broken branch after wave N means all subsequent waves will compound the problem.
 
+### Fix PRs Are Normal
+
+Expect to create 1 fix PR per branch after verification. Common issues:
+
+1. **Component rewrite hybrids** — accept-theirs produced broken `.vue` files. Fix: overwrite with correct version from merge commit via `git show SHA:path > path`
+2. **Missing dependency files** — cherry-pick brought in code referencing composables/components not on the branch. Fix: add missing files from merge commit
+3. **Missing type properties** — cherry-picked code uses interface properties not yet on the branch (e.g., `key` on `ConfirmDialogOptions`). Fix: add the property to the interface
+4. **Unused imports** — conflict resolution kept imports that the branch doesn't use. Fix: remove unused imports
+5. **Wrong types from conflict resolution** — e.g., `{ top: number; right: number }` vs `{ top: number; left: number }`. Fix: match the return type of the actual function
+
+Create a fix PR on a branch from the target, verify typecheck passes, then merge with `--squash --admin`.
+
 ### Never Admin-Merge Without CI
 
 In a previous bulk session, all 69 backport PRs were merged with `gh pr merge --squash --admin`, bypassing required CI checks. This shipped 3 test failures to a release branch. **Lesson: `--admin` skips all branch protection, including required status checks.** Only use `--admin` after confirming CI has passed (e.g., `gh pr checks $PR` shows all green), or rely on auto-merge (`--auto --squash`) which waits for CI by design.
@@ -135,6 +189,43 @@ Large backport sessions (50+ PRs) are expensive and error-prone. Prefer continuo
 - Reserve session-style bulk backporting for catching up after gaps
 - When a release branch is created, immediately start the continuous process
 
+## Interactive Approval Flow
+
+After analysis, present ALL candidates (MUST, SHOULD, and borderline) to the human for interactive review before execution. Do not write a static decisions.md — collect approvals in conversation.
+
+### Batch Presentation
+
+Present PRs in batches of 5-10, grouped by theme (visual bugs, interaction bugs, cloud/auth, data correctness, etc.). Use this table format:
+
+```
+ #  | PR     | Title                                    | Target        | Rec  | Context
+----+--------+------------------------------------------+---------------+------+--------
+ 1  | #12345 | fix: broken thing                        | core+cloud/42 | Y    | Description here. Why it matters. Agent reasoning.
+ 2  | #12346 | fix: another issue                       | core/42       | N    | Only affects removed feature. Not on target branch.
+```
+
+Each row includes:
+
+- PR number and title
+- Target branches
+- Agent recommendation: `Rec: Y` or `Rec: N` with brief reasoning
+- 2-3 sentence context: what the PR does, why it matters (or doesn't)
+
+### Human Response Format
+
+- `Y` — approve for backport
+- `N` — skip
+- `?` — investigate (agent shows PR description, files changed, detailed take, then re-asks)
+- Any freeform question or comment triggers discussion before moving on
+- Bulk responses accepted (e.g. `1 Y, 2 Y, 3 N, 4 ?`)
+
+### Rules
+
+- ALL candidates are reviewed, not just MUST items
+- When human responds `?`, show the PR description, files changed, and agent's detailed analysis, then re-ask for their decision
+- When human asks a question about a PR, answer with context and recommendation, then wait for their decision
+- Do not proceed to execution until all batches are reviewed and every candidate has a Y or N
+
 ## Quick Reference
 
 ### Label-Driven Automation (default path)
@@ -150,13 +241,96 @@ gh api repos/Comfy-Org/ComfyUI_frontend/issues/$PR/labels \
 ```bash
 git worktree add /tmp/backport-$BRANCH origin/$BRANCH
 cd /tmp/backport-$BRANCH
+
+# For each PR:
+git fetch origin $BRANCH
 git checkout -b backport-$PR-to-$BRANCH origin/$BRANCH
 git cherry-pick -m 1 $MERGE_SHA
-# Resolve conflicts, push, create PR, merge
+# Resolve conflicts (see Conflict Triage)
+git push origin backport-$PR-to-$BRANCH --no-verify
+gh pr create --base $BRANCH --head backport-$PR-to-$BRANCH \
+  --title "[backport $BRANCH] $TITLE (#$PR)" \
+  --body "Backport of #$PR. [conflict notes]"
+gh pr merge $NEW_PR --squash --admin
+sleep 25
 ```
 
+### Efficient Batch: Test-Then-Resolve Pattern
+
+When many PRs need manual cherry-pick (e.g., cloud branches), test all first:
+
+```bash
+cd /tmp/backport-$BRANCH
+for pr in "${ORDER[@]}"; do
+  git checkout -b test-$pr origin/$BRANCH
+  if git cherry-pick -m 1 $SHA 2>/dev/null; then
+    echo "CLEAN: $pr"
+  else
+    echo "CONFLICT: $pr"
+    git cherry-pick --abort
+  fi
+  git checkout --detach HEAD
+  git branch -D test-$pr
+done
+```
+
+Then process clean PRs in a batch loop, conflicts individually.
+
 ### PR Title Convention
 
 ```
 [backport TARGET_BRANCH] Original Title (#ORIGINAL_PR)
 ```
+
+## Final Deliverables (Slack-Compatible)
+
+After execution completes, generate two files in `~/temp/backport-session/`. Both must be **Slack-compatible plain text** — no emojis, no markdown tables, no headers (`#`), no bold (`**`), no inline code. Use plain dashes, indentation, and line breaks only.
+
+### 1. Author Accountability Report
+
+File: `backport-author-accountability.md`
+
+Lists all backported PRs grouped by original author (via `gh pr view $PR --json author`). Surfaces who should be self-labeling.
+
+```
+Backport Session YYYY-MM-DD -- PRs that should have been labeled by authors
+
+- author-login
+    - #1234 fix: short title
+    - #5678 fix: another title
+- other-author
+    - #9012 fix: some other fix
+```
+
+Authors sorted alphabetically, 4-space indent for nested items.
+
+### 2. Slack Status Update
+
+File: `slack-status-update.md`
+
+A shareable summary of the session. Structure:
+
+```
+Backport session complete -- YYYY-MM-DD
+
+[1-sentence summary: N PRs backported to which branches. All pass typecheck.]
+
+Branches updated:
+- core/X.XX: N PRs + N fix PRs (N auto, N manual)
+- cloud/X.XX: N PRs + N fix PRs (N auto, N manual)
+- ...
+
+N total PRs created and merged (N backports + N fix PRs).
+
+Notable fixes included:
+- [category]: [list of fixes]
+- ...
+
+Conflict patterns encountered:
+- [pattern and how it was resolved]
+- ...
+
+N authors had PRs backported. See author accountability list for details.
+```
+
+No emojis, no tables, no bold, no headers. Plain text that pastes cleanly into Slack.

.claude/skills/backport-management/reference/analysis.md

@@ -23,10 +23,10 @@ For SHOULD items with conflicts: if conflict resolution requires more than trivi
 
 **Before categorizing, filter by branch scope:**
 
-| Target branch | Skip if PR is...                                                    |
-| ------------- | ------------------------------------------------------------------- |
-| `core/*`      | Cloud-only (app mode, cloud auth, cloud billing, cloud-specific UI) |
-| `cloud/*`     | Local-only features not present on cloud branch                     |
+| Target branch | Skip if PR is...                                                                                                  |
+| ------------- | ----------------------------------------------------------------------------------------------------------------- |
+| `core/*`      | Cloud-only (team workspaces, cloud queue, cloud-only login). Note: app mode and Firebase auth are NOT cloud-only. |
+| `cloud/*`     | Local-only features not present on cloud branch                                                                   |
 
 Cloud-only PRs backported to `core/*` are wasted effort — `core/*` branches serve local/self-hosted users who never see cloud features. Check PR titles, descriptions, and files changed for cloud-specific indicators.
 
@@ -61,8 +61,6 @@ done
 
 ## Human Review Checkpoint
 
-Present decisions.md before execution. Include:
+Use the Interactive Approval Flow (see SKILL.md) to review all candidates interactively. Do not write a static decisions.md for the human to edit — instead, present batches of 5-10 PRs with context and recommendations, and collect Y/N/? responses in conversation.
 
-1. All MUST/SHOULD/SKIP categorizations with rationale
-2. Questions for human (feature existence, scope, deps)
-3. Estimated effort per branch
+All candidates must be reviewed (MUST, SHOULD, and borderline items), not just a subset.

.claude/skills/backport-management/reference/execution.md

@@ -73,14 +73,22 @@ for PR in ${CONFLICT_PRS[@]}; do
   git cherry-pick -m 1 $MERGE_SHA
 
   # If conflict — NEVER skip based on file count alone!
-  # Categorize conflicts first: binary PNGs, modify/delete, content, add/add
+  # Categorize conflicts first: binary PNGs, modify/delete, content, add/add, component rewrites
   # See SKILL.md Conflict Triage table for resolution per type.
 
+  # For component rewrites (4+ markers in a .vue file, library migration):
+  # DO NOT use accept-theirs regex — it produces broken hybrids.
+  # Instead, use the complete file from the merge commit:
+  # git show $MERGE_SHA:path/to/file > path/to/file
+
+  # For simple content conflicts, accept theirs:
+  # python3 -c "import re; ..."
+
   # Resolve all conflicts, then:
   git add .
   GIT_EDITOR=true git cherry-pick --continue
 
-  git push origin backport-$PR-to-TARGET
+  git push origin backport-$PR-to-TARGET --no-verify
   NEW_PR=$(gh pr create --base TARGET_BRANCH --head backport-$PR-to-TARGET \
     --title "[backport TARGET] TITLE (#$PR)" \
     --body "Backport of #$PR..." | grep -oP '\d+$')
@@ -114,7 +122,30 @@ source ~/.nvm/nvm.sh && nvm use 24 && pnpm install && pnpm typecheck && pnpm tes
 git worktree remove /tmp/verify-TARGET --force
 ```
 
-If verification fails, stop and fix before proceeding to the next wave. Do not compound problems across waves.
+If verification fails, **do not skip** — create a fix PR:
+
+```bash
+# Stay in the verify worktree
+git checkout -b fix-backport-TARGET origin/TARGET_BRANCH
+
+# Common fixes:
+# 1. Component rewrite hybrids: overwrite with merge commit version
+git show MERGE_SHA:path/to/Component.vue > path/to/Component.vue
+
+# 2. Missing dependency files
+git show MERGE_SHA:path/to/missing.ts > path/to/missing.ts
+
+# 3. Missing type properties: edit the interface
+# 4. Unused imports: delete the import lines
+
+git add -A
+git commit --no-verify -m "fix: resolve backport typecheck issues on TARGET"
+git push origin fix-backport-TARGET --no-verify
+gh pr create --base TARGET --head fix-backport-TARGET --title "fix: resolve backport typecheck issues on TARGET" --body "..."
+gh pr merge $PR --squash --admin
+```
+
+Do not proceed to the next branch until typecheck passes.
 
 ## Conflict Resolution Patterns
 
@@ -142,7 +173,35 @@ git rm $FILE
 git checkout --theirs $FILE && git add $FILE
 ```
 
-### 4. Locale Files
+### 4. Component Rewrites (DO NOT accept-theirs)
+
+When a PR completely rewrites a component (e.g., PrimeVue → Reka UI), accept-theirs produces
+a broken hybrid with mismatched template/script sections.
+
+```bash
+# Use the complete correct file from the merge commit instead:
+git show $MERGE_SHA:src/components/input/MultiSelect.vue > src/components/input/MultiSelect.vue
+git show $MERGE_SHA:src/components/input/SingleSelect.vue > src/components/input/SingleSelect.vue
+git add src/components/input/MultiSelect.vue src/components/input/SingleSelect.vue
+```
+
+**Detection:** 4+ conflict markers in a single `.vue` file, imports changing between component
+libraries (PrimeVue → Reka UI, etc.), template structure completely different on each side.
+
+### 5. Missing Dependencies After Cherry-Pick
+
+Cherry-picks can succeed but leave the branch broken because the PR's code on main
+references composables/components introduced by an earlier PR.
+
+```bash
+# Add the missing file from the merge commit:
+git show $MERGE_SHA:src/composables/queue/useJobDetailsHover.ts > src/composables/queue/useJobDetailsHover.ts
+git show $MERGE_SHA:src/components/builder/BuilderSaveDialogContent.vue > src/components/builder/BuilderSaveDialogContent.vue
+```
+
+**Detection:** `pnpm typecheck` fails with "Cannot find module" or "X is not defined" after cherry-pick succeeds cleanly.
+
+### 6. Locale Files
 
 Usually adding new i18n keys — accept theirs, validate JSON:
 
@@ -176,8 +235,14 @@ gh pr checks $PR --watch --fail-fast && gh pr merge $PR --squash --admin
 8. **Always validate JSON** after resolving locale file conflicts
 9. **Dep refresh PRs** — skip on stable branches. Risk of transitive dep regressions outweighs audit cleanup. Cherry-pick individual CVE fixes instead.
 10. **Verify after each wave** — run `pnpm typecheck && pnpm test:unit` on the target branch after merging a batch. Catching breakage early prevents compounding errors.
-11. **Cloud-only PRs don't belong on core/\* branches** — app mode, cloud auth, and cloud-specific UI changes are irrelevant to local users. Always check PR scope against branch scope before backporting.
+11. **App mode and Firebase auth are NOT cloud-only** — they go to both core and cloud branches. Only team workspaces, cloud queue, and cloud-specific login are cloud-only.
 12. **Never admin-merge without CI** — `--admin` bypasses all branch protections including required status checks. A bulk session of 69 admin-merges shipped 3 test failures. Always wait for CI to pass first, or use `--auto --squash` which waits by design.
+13. **Accept-theirs regex breaks component rewrites** — when a PR migrates between component libraries (PrimeVue → Reka UI), the regex produces a broken hybrid. Use `git show SHA:path > path` to get the complete correct version instead.
+14. **Cherry-picks can silently bring in missing-dependency code** — if PR A references a composable introduced by PR B, cherry-picking A succeeds but typecheck fails. Always run typecheck after each wave and add missing files from the merge commit.
+15. **Fix PRs are expected** — plan for 1 fix PR per branch to resolve typecheck issues from conflict resolutions. This is normal, not a failure.
+16. **Use `--no-verify` in worktrees** — husky hooks fail in `/tmp/` worktrees. Always push/commit with `--no-verify`.
+17. **Automation success varies by branch** — core/1.42 got 18/26 auto-PRs (69%), cloud/1.42 got 1/25 (4%). Cloud branches diverge more. Plan for manual fallback.
+18. **Test-then-resolve pattern** — for branches with low automation success, run a dry-run loop to classify clean vs conflict PRs before processing. This is much faster than resolving conflicts serially.
 
 ## CI Failure Triage
 

.claude/skills/backport-management/reference/logging.md

@@ -2,26 +2,25 @@
 
 ## During Execution
 
-Maintain `execution-log.md` with per-branch tables:
+Maintain `execution-log.md` with per-branch tables (this is internal, markdown tables are fine here):
 
 ```markdown
-| PR#   | Title | CI Status                      | Status                            | Backport PR | Notes   |
-| ----- | ----- | ------------------------------ | --------------------------------- | ----------- | ------- |
-| #XXXX | Title | ✅ Pass / ❌ Fail / ⏳ Pending | ✅ Merged / ⏭️ Skip / ⏸️ Deferred | #YYYY       | Details |
+| PR#   | Title | Status | Backport PR | Notes   |
+| ----- | ----- | ------ | ----------- | ------- |
+| #XXXX | Title | merged | #YYYY       | Details |
 ```
 
 ## Wave Verification Log
 
-Track verification results per wave:
+Track verification results per wave within execution-log.md:
 
 ```markdown
-## Wave N Verification — TARGET_BRANCH
+Wave N Verification -- TARGET_BRANCH
 
 - PRs merged: #A, #B, #C
-- Typecheck: ✅ Pass / ❌ Fail
-- Unit tests: ✅ Pass / ❌ Fail
+- Typecheck: pass / fail
+- Fix PR: #YYYY (if needed)
 - Issues found: (if any)
-- Human review needed: (list any non-trivial conflict resolutions)
 ```
 
 ## Session Report Template
@@ -63,40 +62,42 @@ Track verification results per wave:
 - Feature branches that need tracking for future sessions?
 ```
 
-## Final Deliverable: Visual Summary
+## Final Deliverables
 
-At session end, generate a **mermaid diagram** showing all backported PRs organized by target branch and category (MUST/SHOULD), plus a summary table. Present this to the user as the final output.
+After all branches are complete and verified, generate these files in `~/temp/backport-session/`:
 
-```mermaid
-graph TD
-    subgraph branch1["☁️ cloud/X.XX — N PRs"]
-        C1["#XXXX title"]
-        C2["#XXXX title"]
-    end
+### 1. execution-log.md (internal)
 
-    subgraph branch2must["🔴 core/X.XX MUST — N PRs"]
-        M1["#XXXX title"]
-    end
+Per-branch tables with PR#, title, status, backport PR#, notes. Markdown tables are fine — this is for internal tracking, not Slack.
 
-    subgraph branch2should["🟡 core/X.XX SHOULD — N PRs"]
-        S1["#XXXX-#XXXX N auto-merged"]
-        S2["#XXXX-#XXXX N manual picks"]
-    end
+### 2. backport-author-accountability.md (Slack-compatible)
 
-    classDef cloudStyle fill:#1a3a5c,stroke:#4da6ff,color:#e0f0ff
-    classDef coreStyle fill:#1a4a2e,stroke:#4dff88,color:#e0ffe8
-    classDef mustStyle fill:#5c1a1a,stroke:#ff4d4d,color:#ffe0e0
-    classDef shouldStyle fill:#4a3a1a,stroke:#ffcc4d,color:#fff5e0
-```
+See SKILL.md "Final Deliverables" section. Plain text, no emojis/tables/headers/bold. Authors sorted alphabetically with PRs nested under each.
 
-Use the `mermaid` tool to render this diagram and present it alongside the summary table as the session's final deliverable.
+### 3. slack-status-update.md (Slack-compatible)
+
+See SKILL.md "Final Deliverables" section. Plain text summary that pastes cleanly into Slack. Includes branch counts, notable fixes, conflict patterns, author count.
+
+## Slack Formatting Rules
+
+Both shareable files (author accountability + status update) must follow these rules:
+
+- No emojis (no checkmarks, no arrows, no icons)
+- No markdown tables (use plain lists with dashes)
+- No headers (no # or ##)
+- No bold (\*_) or italic (_)
+- No inline code backticks
+- Use -- instead of em dash
+- Use plain dashes (-) for lists with 4-space indent for nesting
+- Line breaks between sections for readability
+
+These files should paste directly into a Slack message and look clean.
 
 ## Files to Track
 
-- `candidate_list.md` — all candidates per branch
-- `decisions.md` — MUST/SHOULD/SKIP with rationale
-- `wave-plan.md` — execution order
-- `execution-log.md` — real-time status
-- `backport-session-report.md` — final summary
+All in `~/temp/backport-session/`:
 
-All in `~/temp/backport-session/`.
+- `execution-plan.md` -- approved PRs with merge SHAs (input)
+- `execution-log.md` -- real-time status with per-branch tables (internal)
+- `backport-author-accountability.md` -- PRs grouped by author (Slack-compatible)
+- `slack-status-update.md` -- session summary (Slack-compatible)

.claude/skills/hardening-flaky-e2e-tests/SKILL.md

@@ -0,0 +1,246 @@
+---
+name: hardening-flaky-e2e-tests
+description: 'Diagnoses and fixes flaky Playwright e2e tests by replacing race-prone patterns with retry-safe alternatives. Use when triaging CI flakes, hardening spec files, fixing timing races, or asked to stabilize browser tests. Triggers on: flaky, flake, harden, stabilize, race condition in e2e, intermittent failure.'
+---
+
+# Hardening Flaky E2E Tests
+
+Fix flaky Playwright specs by identifying race-prone patterns and replacing them with retry-safe alternatives. This skill covers diagnosis, pattern matching, and mechanical transforms — not writing new tests (see `writing-playwright-tests` for that).
+
+## Workflow
+
+### 1. Gather CI Evidence
+
+```bash
+gh run list --workflow=ci-test.yaml --limit=5
+gh run download <run-id> -n playwright-report
+```
+
+- Open `report.json` and search for `"status": "flaky"` entries.
+- Collect file paths, test titles, and error messages.
+- Do NOT trust green checks alone — flaky tests that passed on retry still need fixing.
+- Use `error-context.md`, traces, and page snapshots before editing code.
+- Pull the newest run after each push instead of assuming the flaky set is unchanged.
+
+### 2. Classify the Flake
+
+Read the failing assertion and match it against the pattern table. Most flakes fall into one of these categories:
+
+| #   | Pattern                               | Signature in Code                                         | Fix                                                              |
+| --- | ------------------------------------- | --------------------------------------------------------- | ---------------------------------------------------------------- |
+| 1   | **Snapshot-then-assert**              | `expect(await evaluate()).toBe(x)`                        | `await expect.poll(() => evaluate()).toBe(x)`                    |
+| 2   | **Immediate count**                   | `const n = await loc.count(); expect(n).toBe(3)`          | `await expect(loc).toHaveCount(3)`                               |
+| 3   | **nextFrame after menu click**        | `clickMenuItem(x); nextFrame()`                           | `clickMenuItem(x); contextMenu.waitForHidden()`                  |
+| 4   | **Tight poll timeout**                | `expect.poll(..., { timeout: 250 })`                      | ≥2000 ms; prefer default 5000 ms                                 |
+| 5   | **Immediate evaluate after mutation** | `setSetting(k, v); expect(await evaluate()).toBe(x)`      | `await expect.poll(() => evaluate()).toBe(x)`                    |
+| 6   | **Screenshot without readiness**      | `loadWorkflow(); nextFrame(); toHaveScreenshot()`         | `waitForNodes()` or poll state first                             |
+| 7   | **Non-deterministic node order**      | `getNodeRefsByType('X')[0]` with >1 match                 | `getNodeRefById(id)` or guard `toHaveLength(1)`                  |
+| 8   | **Fake readiness helper**             | Helper clicks but doesn't assert state                    | Remove; poll the actual value                                    |
+| 9   | **Immediate graph state after drop**  | `expect(await getLinkCount()).toBe(1)`                    | `await expect.poll(() => getLinkCount()).toBe(1)`                |
+| 10  | **Immediate boundingBox/layout read** | `const box = await loc.boundingBox(); expect(box!.width)` | `await expect.poll(() => loc.boundingBox().then(b => b?.width))` |
+
+### 3. Apply the Transform
+
+#### Rule: Choose the Smallest Correct Assertion
+
+- **Locator state** → use built-in retrying assertions: `toBeVisible()`, `toHaveText()`, `toHaveCount()`, `toHaveClass()`
+- **Single async value** → `expect.poll(() => asyncFn()).toBe(expected)`
+- **Multiple assertions that must settle together** → `expect(async () => { ... }).toPass()`
+- **Never** use `waitForTimeout()` to hide a race.
+
+```typescript
+// ✅ Single value — use expect.poll
+await expect
+  .poll(() => comfyPage.page.evaluate(() => window.app!.graph.links.length))
+  .toBe(3)
+
+// ✅ Locator count — use toHaveCount
+await expect(comfyPage.page.locator('.dom-widget')).toHaveCount(2)
+
+// ✅ Multiple conditions — use toPass
+await expect(async () => {
+  expect(await node1.getValue()).toBe('foo')
+  expect(await node2.getValue()).toBe('bar')
+}).toPass({ timeout: 5000 })
+```
+
+#### Rule: Wait for the Real Readiness Boundary
+
+Visible is not always ready. Prefer user-facing assertions when possible; poll internal state only when there is no UI surface to assert on.
+
+Common readiness boundaries:
+
+| After this action...                   | Wait for...                                                  |
+| -------------------------------------- | ------------------------------------------------------------ |
+| Canvas interaction (drag, click node)  | `await comfyPage.nextFrame()`                                |
+| Menu item click                        | `await contextMenu.waitForHidden()`                          |
+| Workflow load                          | `await comfyPage.workflow.loadWorkflow(...)` (built-in wait) |
+| Settings write                         | Poll the setting value with `expect.poll()`                  |
+| Node pin/bypass/collapse toggle        | `await expect.poll(() => nodeRef.isPinned()).toBe(true)`     |
+| Graph mutation (add/remove node, link) | Poll link/node count                                         |
+| Clipboard write                        | Poll pasted value                                            |
+| Screenshot                             | Ensure nodes are rendered: `waitForNodes()` or poll state    |
+
+#### Rule: Expose Locators for Retrying Assertions
+
+When a helper returns a count via `await loc.count()`, callers can't use `toHaveCount()`. Expose the underlying `Locator` as a getter so callers choose between:
+
+```typescript
+// Helper exposes locator
+get domWidgets(): Locator {
+  return this.page.locator('.dom-widget')
+}
+
+// Caller uses retrying assertion
+await expect(comfyPage.domWidgets).toHaveCount(2)
+```
+
+Replace count methods with locator getters so callers can use retrying assertions directly.
+
+#### Rule: Fix Check-then-Act Races in Helpers
+
+```typescript
+// ❌ Race: count can change between check and waitFor
+const count = await locator.count()
+if (count > 0) {
+  await locator.waitFor({ state: 'hidden' })
+}
+
+// ✅ Direct: waitFor handles both cases
+await locator.waitFor({ state: 'hidden' })
+```
+
+#### Rule: Remove force:true from Clicks
+
+`force: true` bypasses actionability checks, hiding real animation/visibility races. Remove it and fix the underlying timing issue.
+
+```typescript
+// ❌ Hides the race
+await closeButton.click({ force: true })
+
+// ✅ Surfaces the real issue — fix with proper wait
+await closeButton.click()
+await dialog.waitForHidden()
+```
+
+#### Rule: Handle Non-deterministic Element Order
+
+When `getNodeRefsByType` returns multiple nodes, the order is not guaranteed. Don't use index `[0]` blindly.
+
+```typescript
+// ❌ Assumes order
+const node = (await comfyPage.nodeOps.getNodeRefsByType('CLIPTextEncode'))[0]
+
+// ✅ Find by ID or proximity
+const nodes = await comfyPage.nodeOps.getNodeRefsByType('CLIPTextEncode')
+let target = nodes[0]
+for (const n of nodes) {
+  const pos = await n.getPosition()
+  if (Math.abs(pos.y - expectedY) < minDist) target = n
+}
+```
+
+Or guard the assumption:
+
+```typescript
+const nodes = await comfyPage.nodeOps.getNodeRefsByType('CLIPTextEncode')
+expect(nodes).toHaveLength(1)
+const node = nodes[0]
+```
+
+#### Rule: Use toPass for Timing-sensitive Dismiss Guards
+
+Some UI elements (e.g. LiteGraph's graphdialog) have built-in dismiss delays. Retry the entire dismiss action:
+
+```typescript
+// ✅ Retry click+assert together
+await expect(async () => {
+  await comfyPage.canvas.click({ position: { x: 10, y: 10 } })
+  await expect(dialog).toBeHidden({ timeout: 500 })
+}).toPass({ timeout: 5000 })
+```
+
+### 4. Keep Changes Narrow
+
+- Shared helpers should drive setup to a stable boundary.
+- Do not encode one-spec timing assumptions into generic helpers.
+- If a race only matters to one spec, prefer a local wait in that spec.
+- If a helper fails before the real test begins, remove or relax the brittle precondition and let downstream UI interaction prove readiness.
+
+### 5. Verify Narrowly
+
+```bash
+# Targeted rerun with repetition
+pnpm test:browser:local -- browser_tests/tests/myFile.spec.ts --repeat-each 10
+
+# Single test by line number (avoids grep quoting issues on Windows)
+pnpm test:browser:local -- browser_tests/tests/myFile.spec.ts:42
+```
+
+- Use `--repeat-each 10` for targeted flake verification (use 20 for single test cases).
+- Verify with the smallest command that exercises the flaky path.
+
+### 6. Watch CI E2E Runs
+
+After pushing, use `gh` to monitor the E2E workflow:
+
+```bash
+# Find the run for the current branch
+gh run list --workflow="CI: Tests E2E" --branch=$(git branch --show-current) --limit=1
+
+# Watch it live (blocks until complete, streams logs)
+gh run watch <run-id>
+
+# One-liner: find and watch the latest E2E run for the current branch
+gh run list --workflow="CI: Tests E2E" --branch=$(git branch --show-current) --limit=1 --json databaseId --jq ".[0].databaseId" | xargs gh run watch
+```
+
+On Windows (PowerShell):
+
+```powershell
+# One-liner equivalent
+gh run watch (gh run list --workflow="CI: Tests E2E" --branch=$(git branch --show-current) --limit=1 --json databaseId --jq ".[0].databaseId")
+```
+
+After the run completes:
+
+```bash
+# Download the Playwright report artifact
+gh run download <run-id> -n playwright-report
+
+# View the run summary in browser
+gh run view <run-id> --web
+```
+
+Also watch the unit test workflow in parallel if you changed helpers:
+
+```bash
+gh run list --workflow="CI: Tests Unit" --branch=$(git branch --show-current) --limit=1
+```
+
+### 7. Pre-merge Checklist
+
+Before merging a flaky-test fix, confirm:
+
+- [ ] The latest CI artifact was inspected directly
+- [ ] The root cause is stated as a race or readiness mismatch
+- [ ] The fix waits on the real readiness boundary
+- [ ] The assertion primitive matches the job (poll vs toHaveCount vs toPass)
+- [ ] The fix stays local unless a shared helper truly owns the race
+- [ ] Local verification uses a targeted rerun
+- [ ] No behavioral changes to the test — only timing/retry strategy updated
+
+## Local Noise — Do Not Fix
+
+These are local distractions, not CI root causes:
+
+- Missing local input fixture files required by the test path
+- Missing local models directory
+- Teardown `EPERM` while restoring the local browser-test user data directory
+- Local screenshot baseline differences on Windows
+
+Rules:
+
+- First confirm whether it blocks the exact flaky path under investigation.
+- Do not commit temporary local assets used only for verification.
+- Do not commit local screenshot baselines.

.claude/skills/ticket-intake/SKILL.md

@@ -1,361 +0,0 @@
----
-name: ticket-intake
-description: 'Parse ticket URL (Notion or GitHub), extract all data, initialize pipeline run. Use when starting work on a new ticket or when asked to pick up a ticket.'
----
-
-# Ticket Intake
-
-Parses a ticket URL from supported sources (Notion or GitHub), extracts all relevant information, and creates a ticket in the pipeline API.
-
-> **🚨 CRITICAL REQUIREMENT**: This skill MUST register the ticket in the Pipeline API and update the source (Notion/GitHub). If these steps are skipped, the entire pipeline breaks. See [Mandatory API Calls](#mandatory-api-calls-execute-all-three) below.
-
-## Supported Sources
-
-| Source | URL Pattern                                         | Provider File         |
-| ------ | --------------------------------------------------- | --------------------- |
-| Notion | `https://notion.so/...` `https://www.notion.so/...` | `providers/notion.md` |
-| GitHub | `https://github.com/{owner}/{repo}/issues/{n}`      | `providers/github.md` |
-
-## Quick Start
-
-When given a ticket URL:
-
-1. **Detect source type** from URL pattern
-2. **Load provider-specific logic** from `providers/` directory
-3. Fetch ticket content via appropriate API
-4. Extract and normalize properties to common schema
-5. **Register ticket in pipeline API** ← MANDATORY
-6. **Update source** (Notion status / GitHub comment) ← MANDATORY
-7. **Run verification script** to confirm API registration
-8. Output summary and handoff to `research-orchestrator`
-
-## Configuration
-
-Uses the **production API** by default. No configuration needed for read operations.
-
-**Defaults (no setup required):**
-
-- API URL: `https://api-gateway-856475788601.us-central1.run.app`
-- Read-only endpoints at `/public/*` require no authentication
-
-**For write operations** (transitions, creating tickets), set:
-
-```bash
-export PIPELINE_API_KEY="..."  # Get from GCP Secret Manager or ask admin
-```
-
-**Optional (for local working artifacts):**
-
-```bash
-PIPELINE_DIR="${PIPELINE_DIR:-$HOME/repos/ticket-to-pr-pipeline}"
-```
-
-## Mandatory API Calls (Execute ALL Three)
-
-**⚠️ These three API calls are the ENTIRE POINT of this skill. Without them, the ticket is invisible to the pipeline, downstream skills will fail, and Notion status won't update.**
-
-**You MUST make these HTTP requests.** Use `curl` from bash — do not just read this as documentation.
-
-### Call 1: Create Ticket
-
-```bash
-API_URL="${PIPELINE_API_URL:-https://api-gateway-856475788601.us-central1.run.app}"
-API_KEY="${PIPELINE_API_KEY}"
-
-curl -s -X POST "${API_URL}/v1/tickets" \
-  -H "Authorization: Bearer ${API_KEY}" \
-  -H "Content-Type: application/json" \
-  -H "X-Agent-ID: ${AGENT_ID:-amp-agent}" \
-  -d '{
-    "notion_page_id": "NOTION_PAGE_UUID_HERE",
-    "title": "TICKET_TITLE_HERE",
-    "source": "notion",
-    "metadata": {
-      "description": "DESCRIPTION_HERE",
-      "priority": "High",
-      "labels": [],
-      "acceptanceCriteria": []
-    }
-  }'
-```
-
-Save the returned `id` — you need it for the next two calls.
-
-### Call 2: Transition to RESEARCH
-
-```bash
-TICKET_ID="id-from-step-1"
-
-curl -s -X POST "${API_URL}/v1/tickets/${TICKET_ID}/transition" \
-  -H "Authorization: Bearer ${API_KEY}" \
-  -H "Content-Type: application/json" \
-  -H "X-Agent-ID: ${AGENT_ID:-amp-agent}" \
-  -d '{
-    "to_state": "RESEARCH",
-    "reason": "Intake complete, starting research"
-  }'
-```
-
-### Call 3: Queue Source Update
-
-```bash
-curl -s -X POST "${API_URL}/v1/sync/queue" \
-  -H "Authorization: Bearer ${API_KEY}" \
-  -H "Content-Type: application/json" \
-  -H "X-Agent-ID: ${AGENT_ID:-amp-agent}" \
-  -d '{
-    "ticket_id": "TICKET_ID_HERE",
-    "action": "update_status",
-    "payload": { "status": "In Progress" },
-    "priority": "normal"
-  }'
-```
-
-> **Note:** The action MUST be `"update_status"` (not `"UPDATE_NOTION_STATUS"`). Valid actions: `update_status`, `update_pr_url`, `mark_done`.
-
-### TypeScript Equivalent (if using pipeline client)
-
-```typescript
-import { PipelineClient } from '@pipeline/client'
-
-const client = new PipelineClient({
-  apiUrl:
-    process.env.PIPELINE_API_URL ||
-    'https://api-gateway-856475788601.us-central1.run.app',
-  agentId: process.env.AGENT_ID!
-})
-
-const ticket = await client.createTicket({
-  notion_page_id: pageId,
-  title: ticketTitle,
-  source: 'notion',
-  metadata: { description, priority, labels, acceptanceCriteria }
-})
-
-await client.transitionState(
-  ticket.id,
-  'RESEARCH',
-  'Intake complete, starting research'
-)
-
-await client.queueSync(ticket.id, 'update_status', { status: 'In Progress' })
-```
-
-## Workflow
-
-### Step 1: Detect Source Type
-
-Parse the URL to determine source:
-
-```javascript
-if (url.includes('notion.so')) {
-  source = 'notion'
-  // Load providers/notion.md
-} else if (url.match(/github\.com\/[^\/]+\/[^\/]+\/issues\/\d+/)) {
-  source = 'github'
-  // Load providers/github.md
-} else {
-  // Error: Unsupported URL format
-}
-```
-
-### Step 2: Load Provider and Fetch Data
-
-Read the appropriate provider file for source-specific instructions:
-
-- **Notion**: `providers/notion.md` - Uses Notion MCP, handles Slack links
-- **GitHub**: `providers/github.md` - Uses `gh` CLI, handles Dosu comments
-
-Follow the provider's instructions for:
-
-- Fetching content
-- Extracting properties
-- **Updating the source** (Notion status → "In Progress", Assignee → pipeline owner)
-
-### Step 3: Normalize to Common Schema
-
-All providers must extract normalized ticket data following `schema.md`:
-
-```json
-{
-  "id": "abc12345",
-  "url": "https://...",
-  "source": "notion | github",
-  "title": "Ticket title",
-  "description": "Full description",
-  "status": "Not Started",
-  "assignee": "username",
-  "priority": "High",
-  "area": "UI",
-  "labels": ["bug", "frontend"],
-  "acceptanceCriteria": ["Criterion 1", "Criterion 2"],
-  "fetchedAt": "2024-01-15T10:30:00Z"
-}
-```
-
-### Step 4: Register Ticket in Pipeline API (MANDATORY — DO NOT SKIP)
-
-**Execute all three API calls from [Mandatory API Calls](#mandatory-api-calls-execute-all-three) above.**
-
-This is not optional. This is not documentation. You MUST make these HTTP requests right now.
-
-1. `createTicket()` → save the returned ticket ID
-2. `transitionState(id, 'RESEARCH')` → confirm state changed
-3. `queueSync(id, 'update_status', { status: 'In Progress' })` → confirm queued
-
-**If any call fails**, retry once. If it still fails, report the error prominently — do NOT silently continue.
-
-### Step 5: Run Verification Script
-
-After making the API calls, run the verification script to confirm everything worked:
-
-```bash
-bash scripts/verify-intake.sh TICKET_ID_OR_NOTION_PAGE_ID
-```
-
-**If the script is not available locally**, verify manually via the public API:
-
-```bash
-curl -s "${API_URL}/public/tickets/${TICKET_ID}" | jq '{id, state, title, notion_page_id}'
-```
-
-Expected output:
-
-```json
-{
-  "id": "...",
-  "state": "RESEARCH",
-  "title": "...",
-  "notion_page_id": "..."
-}
-```
-
-**If `state` is not `RESEARCH`, go back to Step 4 and complete the missing calls.**
-
-### Step 6: Output Summary and Handoff
-
-Print a clear summary:
-
-```markdown
-## Ticket Intake Complete
-
-**Source:** Notion | GitHub
-**Title:** [Ticket title]
-**ID:** abc12345
-**Status:** In Progress (queued)
-**Priority:** High
-**Area:** UI
-
-### Description
-
-[Brief description or first 200 chars]
-
-### Acceptance Criteria
-
-- [ ] Criterion 1
-- [ ] Criterion 2
-
-### Links
-
-- **Ticket:** [Original URL]
-- **Slack:** [Slack thread content fetched via slackdump] (Notion only)
-
-### Pipeline
-
-- **API Ticket ID:** abc12345
-- **State:** RESEARCH
-- **Verified:** ✅ (via verify-intake.sh or public API)
-```
-
-**After printing the summary, immediately handoff** to continue the pipeline. Use the `handoff` tool with all necessary context (ticket ID, source, title, description, slack context if any):
-
-> **Handoff goal:** "Continue pipeline for ticket {ID} ({title}). Ticket is in RESEARCH state. Load skill: `research-orchestrator` to begin research phase. Ticket data: source={source}, notion_page_id={pageId}, priority={priority}. {slack context summary if available}"
-
-**Do NOT wait for human approval to proceed.** The intake phase is complete — handoff immediately.
-
-## Error Handling
-
-### Unsupported URL
-
-```
-❌ Unsupported ticket URL format.
-
-Supported formats:
-- Notion: https://notion.so/... or https://www.notion.so/...
-- GitHub: https://github.com/{owner}/{repo}/issues/{number}
-
-Received: [provided URL]
-```
-
-### Provider-Specific Errors
-
-See individual provider files for source-specific error handling:
-
-- `providers/notion.md` - Authentication, page not found
-- `providers/github.md` - Auth, rate limits, issue not found
-
-### Missing Properties
-
-Continue with available data and note what's missing:
-
-```
-⚠️ Some properties unavailable:
-- Priority: not found (using default: Medium)
-- Area: not found
-
-Proceeding with available data...
-```
-
-### API Call Failures
-
-```
-❌ Pipeline API call failed: {method} {endpoint}
-   Status: {status}
-   Error: {message}
-
-Retrying once...
-
-❌ Retry also failed. INTAKE IS INCOMPLETE.
-   The ticket was NOT registered in the pipeline.
-   Downstream skills will not work until this is fixed.
-```
-
-## Notes
-
-- This skill focuses ONLY on intake — it does not do research
-- Slack thread content is fetched automatically via the `slackdump` skill — no manual copy-paste needed
-- ALL API calls (createTicket, transitionState, queueSync) are MANDATORY — never skip them
-- The `queueSync` action must be `"update_status"`, NOT `"UPDATE_NOTION_STATUS"`
-- Pipeline state is tracked via the API, not local files
-- Working artifacts (research-report.md, plan.md) can be saved locally to `$PIPELINE_DIR/runs/{ticket-id}/`
-- The `source` field in the ticket determines which research strategies to use
-
-## API Client Reference
-
-### Available Methods
-
-| Method                                                      | Description                                                         |
-| ----------------------------------------------------------- | ------------------------------------------------------------------- |
-| `createTicket({ notion_page_id, title, source, metadata })` | Create a new ticket in the API                                      |
-| `getTicket(id)`                                             | Retrieve a ticket by ID                                             |
-| `findByNotionId(notionPageId)`                              | Look up a ticket by its Notion page ID                              |
-| `listTickets({ state, agent_id, limit, offset })`           | List tickets with optional filters                                  |
-| `transitionState(id, state, reason)`                        | Move ticket to a new state (e.g., `'RESEARCH'`)                     |
-| `setPRCreated(id, prUrl)`                                   | Mark ticket as having a PR created                                  |
-| `queueSync(id, action, payload)`                            | Queue a sync action (`update_status`, `update_pr_url`, `mark_done`) |
-| `registerBranch(id, branch, repo)`                          | Register working branch for automatic PR detection                  |
-
-### Error Handling
-
-```typescript
-import { PipelineClient, PipelineAPIError } from '@pipeline/client';
-
-try {
-  await client.createTicket({ ... });
-} catch (error) {
-  if (error instanceof PipelineAPIError) {
-    console.error(`API Error ${error.status}: ${error.message}`);
-  }
-  throw error;
-}
-```

.claude/skills/ticket-intake/providers/github.md

@@ -1,194 +0,0 @@
-# GitHub Provider - Ticket Intake
-
-Provider-specific logic for ingesting tickets from GitHub Issues.
-
-## URL Pattern
-
-```
-https://github.com/{owner}/{repo}/issues/{number}
-https://www.github.com/{owner}/{repo}/issues/{number}
-```
-
-Extract: `owner`, `repo`, `issue_number` from URL.
-
-## Prerequisites
-
-- `gh` CLI authenticated (`gh auth status`)
-- Access to the repository
-
-## Fetch Issue Content
-
-Use `gh` CLI to fetch issue details:
-
-```bash
-# Get issue details in JSON
-gh issue view {number} --repo {owner}/{repo} --json title,body,state,labels,assignees,milestone,author,createdAt,comments,linkedPRs
-
-# Get comments separately if needed
-gh issue view {number} --repo {owner}/{repo} --comments
-```
-
-## Extract Ticket Data
-
-Map GitHub issue fields to normalized ticket data (stored via API):
-
-| GitHub Field | ticket.json Field | Notes                      |
-| ------------ | ----------------- | -------------------------- |
-| title        | title             | Direct mapping             |
-| body         | description       | Issue body/description     |
-| state        | status            | Map: open → "Not Started"  |
-| labels       | labels            | Array of label names       |
-| assignees    | assignee          | First assignee login       |
-| author       | author            | Issue author login         |
-| milestone    | milestone         | Milestone title if present |
-| comments     | comments          | Array of comment objects   |
-| linkedPRs    | linkedPRs         | PRs linked to this issue   |
-
-### Priority Mapping
-
-Infer priority from labels:
-
-- `priority:critical`, `P0` → "Critical"
-- `priority:high`, `P1` → "High"
-- `priority:medium`, `P2` → "Medium"
-- `priority:low`, `P3` → "Low"
-- No priority label → "Medium" (default)
-
-### Area Mapping
-
-Infer area from labels:
-
-- `area:ui`, `frontend`, `component:*` → "UI"
-- `area:api`, `backend` → "API"
-- `area:docs`, `documentation` → "Docs"
-- `bug`, `fix` → "Bug"
-- `enhancement`, `feature` → "Feature"
-
-## Update Source
-
-**For GitHub issues, update is optional but recommended.**
-
-Add a comment to indicate work has started:
-
-```bash
-gh issue comment {number} --repo {owner}/{repo} --body "🤖 Pipeline started processing this issue."
-```
-
-Optionally assign to self:
-
-```bash
-gh issue edit {number} --repo {owner}/{repo} --add-assignee @me
-```
-
-Log any updates via the Pipeline API:
-
-```typescript
-await client.updateTicket(ticketId, {
-  metadata: {
-    ...ticket.metadata,
-    githubWrites: [
-      ...(ticket.metadata?.githubWrites || []),
-      {
-        action: 'comment',
-        issueNumber: 123,
-        at: new Date().toISOString(),
-        skill: 'ticket-intake',
-        success: true
-      }
-    ]
-  }
-})
-```
-
-## GitHub-Specific Ticket Fields
-
-Store via API using `client.createTicket()`:
-
-```json
-{
-  "source": "github",
-  "githubOwner": "Comfy-Org",
-  "githubRepo": "ComfyUI_frontend",
-  "githubIssueNumber": 123,
-  "githubIssueUrl": "https://github.com/Comfy-Org/ComfyUI_frontend/issues/123",
-  "labels": ["bug", "area:ui", "priority:high"],
-  "linkedPRs": [456, 789],
-  "dosuComment": "..." // Extracted Dosu bot analysis if present
-}
-```
-
-## Dosu Bot Detection
-
-Many repositories use Dosu bot for automated issue analysis. Check comments for Dosu:
-
-```bash
-gh issue view {number} --repo {owner}/{repo} --comments | grep -A 100 "dosu"
-```
-
-Look for comments from:
-
-- `dosu[bot]`
-- `dosu-bot`
-
-Extract Dosu analysis which typically includes:
-
-- Root cause analysis
-- Suggested files to modify
-- Related issues/PRs
-- Potential solutions
-
-Store in ticket data via API:
-
-```json
-{
-  "dosuComment": {
-    "found": true,
-    "analysis": "...",
-    "suggestedFiles": ["src/file1.ts", "src/file2.ts"],
-    "relatedIssues": [100, 101]
-  }
-}
-```
-
-## Extract Linked Issues/PRs
-
-Parse issue body and comments for references:
-
-- `#123` → Issue or PR reference
-- `fixes #123`, `closes #123` → Linked issue
-- `https://github.com/.../issues/123` → Full URL reference
-
-Store in ticket data via API for research phase:
-
-```json
-{
-  "referencedIssues": [100, 101, 102],
-  "referencedPRs": [200, 201]
-}
-```
-
-## Error Handling
-
-### Authentication Error
-
-```
-⚠️ GitHub CLI not authenticated.
-Run: gh auth login
-```
-
-### Issue Not Found
-
-```
-❌ GitHub issue not found or inaccessible.
-- Check the URL is correct
-- Ensure you have access to this repository
-- Run: gh auth status
-```
-
-### Rate Limiting
-
-```
-⚠️ GitHub API rate limited.
-Wait a few minutes and try again.
-Check status: gh api rate_limit
-```

.claude/skills/ticket-intake/providers/notion.md

@@ -1,202 +0,0 @@
-# Notion Provider - Ticket Intake
-
-Provider-specific logic for ingesting tickets from Notion.
-
-## URL Pattern
-
-```
-https://www.notion.so/workspace/Page-Title-abc123def456...
-https://notion.so/Page-Title-abc123def456...
-https://www.notion.so/abc123def456...
-```
-
-Page ID is the 32-character hex string (with or without hyphens).
-
-## Prerequisites
-
-- Notion MCP connected and authenticated
-- If not setup: `claude mcp add --transport http notion https://mcp.notion.com/mcp`
-- Authenticate via `/mcp` command if prompted
-
-## Fetch Ticket Content
-
-Use `Notion:notion-fetch` with the page URL or ID:
-
-```
-Fetch the full page content including all properties
-```
-
-## Extract Ticket Data
-
-Extract these properties (names may vary):
-
-| Property      | Expected Name             | Type         |
-| ------------- | ------------------------- | ------------ |
-| Title         | Name / Title              | Title        |
-| Status        | Status                    | Select       |
-| Assignee      | Assignee / Assigned To    | Person       |
-| Description   | -                         | Page content |
-| Slack Link    | Slack Link / Slack Thread | URL          |
-| GitHub PR     | GitHub PR / PR Link       | URL          |
-| Priority      | Priority                  | Select       |
-| Area          | Area / Category           | Select       |
-| Related Tasks | Related Tasks             | Relation     |
-
-**If properties are missing**: Note what's unavailable and continue with available data.
-
-## Update Source (REQUIRED)
-
-**⚠️ DO NOT SKIP THIS STEP. This is a required action, not optional.**
-
-**⚠️ Notion Write Safety rules apply (see `$PIPELINE_DIR/docs/notion-write-safety.md` for full reference):**
-
-- **Whitelist**: Only `Status`, `GitHub PR`, and `Assignee` fields may be written
-- **Valid transitions**: Not Started → In Progress, In Progress → In Review, In Review → Done
-- **Logging**: Every write attempt MUST be logged with timestamp, field, value, previous value, skill name, and success status
-
-Use `Notion:notion-update-page` to update the ticket:
-
-1. **Status**: Set to "In Progress" (only valid from "Not Started")
-2. **Assignee**: Assign to pipeline owner (Notion ID: `175d872b-594c-81d4-ba5a-0002911c5966`)
-
-```json
-{
-  "page_id": "{page_id_from_ticket}",
-  "command": "update_properties",
-  "properties": {
-    "Status": "In Progress",
-    "Assignee": "175d872b-594c-81d4-ba5a-0002911c5966"
-  }
-}
-```
-
-**After the update succeeds**, log the write via the Pipeline API:
-
-```typescript
-await client.updateTicket(ticketId, {
-  metadata: {
-    ...ticket.metadata,
-    notionWrites: [
-      ...(ticket.metadata?.notionWrites || []),
-      {
-        field: 'Status',
-        value: 'In Progress',
-        previousValue: 'Not Started',
-        at: new Date().toISOString(),
-        skill: 'ticket-intake',
-        success: true
-      }
-    ]
-  }
-})
-```
-
-If update fails, log with `success: false` and continue.
-
-## Notion-Specific Ticket Fields
-
-Store via API using `client.createTicket()`:
-
-```json
-{
-  "source": "notion",
-  "notionPageId": "abc123def456...",
-  "slackLink": "https://slack.com/...",
-  "relatedTasks": ["page-id-1", "page-id-2"]
-}
-```
-
-## Slack Thread Handling
-
-If a Slack link exists, use the `slackdump` skill to fetch the thread content programmatically.
-
-### Slack URL Conversion
-
-Notion stores Slack links in `slackMessage://` format:
-
-```
-slackMessage://comfy-organization.slack.com/CHANNEL_ID/THREAD_TS/MESSAGE_TS
-```
-
-Convert to browser-clickable format:
-
-```
-https://comfy-organization.slack.com/archives/CHANNEL_ID/pMESSAGE_TS_NO_DOT
-```
-
-**Example:**
-
-- Input: `slackMessage://comfy-organization.slack.com/C075ANWQ8KS/1766022478.450909/1764772881.854829`
-- Output: `https://comfy-organization.slack.com/archives/C075ANWQ8KS/p1764772881854829`
-
-(Remove the dot from the last timestamp and prefix with `p`)
-
-### Fetching Thread Content
-
-Load the `slackdump` skill and use the **export-thread** workflow:
-
-```bash
-# Export thread by URL
-slackdump dump "https://comfy-organization.slack.com/archives/CHANNEL_ID/pMESSAGE_TS"
-
-# Or by colon notation (channel_id:thread_ts)
-slackdump dump CHANNEL_ID:THREAD_TS
-```
-
-Save the thread content to `$RUN_DIR/slack-context.md` and include it in the ticket metadata.
-
-> **No manual action required.** The slackdump CLI handles authentication via stored credentials at `~/.cache/slackdump/comfy-organization.bin`.
-
-## Database Reference: Comfy Tasks
-
-The "Comfy Tasks" database has these properties (verify via `notion-search`):
-
-- **Status values**: Not Started, In Progress, In Review, Done
-- **Team assignment**: "Frontend Team" for unassigned tickets
-- **Filtering note**: Team filtering in Notion may have quirks - handle gracefully
-
-### Pipeline Owner Details
-
-When assigning tickets, use these identifiers:
-
-| Platform        | Identifier                             |
-| --------------- | -------------------------------------- |
-| Notion User ID  | `175d872b-594c-81d4-ba5a-0002911c5966` |
-| Notion Name     | Christian Byrne                        |
-| Notion Email    | cbyrne@comfy.org                       |
-| Slack User ID   | U087MJCDHHC                            |
-| GitHub Username | christian-byrne                        |
-
-**To update Assignee**, use the Notion User ID (not name):
-
-```
-properties: {"Assignee": "175d872b-594c-81d4-ba5a-0002911c5966"}
-```
-
-### Finding Active Tickets
-
-To list your active tickets:
-
-```
-Use Notion:notion-search for "Comfy Tasks"
-Filter by Assignee = current user OR Team = "Frontend Team"
-```
-
-## Error Handling
-
-### Authentication Error
-
-```
-⚠️ Notion authentication required.
-Run: claude mcp add --transport http notion https://mcp.notion.com/mcp
-Then authenticate via /mcp command.
-```
-
-### Page Not Found
-
-```
-❌ Notion page not found or inaccessible.
-- Check the URL is correct
-- Ensure you have access to this page
-- Try re-authenticating via /mcp
-```

.claude/skills/ticket-intake/schema.md

@@ -1,81 +0,0 @@
-# Ticket Schema
-
-Common schema for normalized ticket data across all sources. This data is stored and retrieved via the Pipeline API, not local files.
-
-## Ticket Data Schema
-
-```json
-{
-  // Required fields (all sources)
-  "id": "string", // Unique identifier (short form)
-  "url": "string", // Original URL
-  "source": "notion | github", // Source type
-  "title": "string", // Ticket title
-  "description": "string", // Full description/body
-  "fetchedAt": "ISO8601", // When ticket was fetched
-
-  // Common optional fields
-  "status": "string", // Current status
-  "assignee": "string", // Assigned user
-  "priority": "string", // Priority level
-  "area": "string", // Category/area
-  "labels": ["string"], // Tags/labels
-  "acceptanceCriteria": ["string"] // List of AC items
-
-  // Source-specific fields (see providers)
-  // Notion: notionPageId, slackLink, relatedTasks, notionWrites
-  // GitHub: githubOwner, githubRepo, githubIssueNumber, linkedPRs, dosuComment, referencedIssues
-}
-```
-
-## Ticket State Schema (via API)
-
-State is managed via the Pipeline API using `client.transitionState()`:
-
-```json
-{
-  "ticketId": "string",
-  "state": "intake | research | planning | implementation | pr_created | done | failed",
-  "stateChangedAt": "ISO8601",
-
-  // Timestamps tracked by API
-  "createdAt": "ISO8601",
-  "updatedAt": "ISO8601"
-}
-```
-
-## Priority Normalization
-
-All sources should normalize to these values:
-
-| Normalized | Description               |
-| ---------- | ------------------------- |
-| Critical   | Production down, security |
-| High       | Blocking work, urgent     |
-| Medium     | Normal priority (default) |
-| Low        | Nice to have, backlog     |
-
-## Status Normalization
-
-Pipeline tracks these statuses internally:
-
-| Status         | Description                  |
-| -------------- | ---------------------------- |
-| research       | Gathering context            |
-| planning       | Creating implementation plan |
-| implementation | Writing code                 |
-| review         | Code review in progress      |
-| qa             | Quality assurance            |
-| done           | PR merged or completed       |
-
-## ID Generation
-
-IDs are generated by the API when creating tickets. For reference:
-
-- **Notion**: First 8 characters of page ID
-- **GitHub**: `gh-{owner}-{repo}-{issue_number}` (sanitized)
-
-Examples:
-
-- Notion: `abc12345`
-- GitHub: `gh-comfy-org-frontend-123`

.github/actions/lint-format-verify/action.yml

@@ -0,0 +1,31 @@
+name: 'Lint and format verify'
+description: >
+  Runs the lint/format/knip verification suite plus a conditional
+  browser-tests typecheck. Shared by ci-lint-format.yaml (PR) and
+  ci-lint-format-queue.yaml (merge queue) so both paths run the exact
+  same checks. The caller is responsible for checkout and frontend setup
+  before invoking this action.
+
+runs:
+  using: composite
+  steps:
+    - name: Detect browser_tests changes
+      id: changed-paths
+      uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3.0.2
+      with:
+        filters: |
+          browser_tests:
+            - 'browser_tests/**'
+
+    - name: Verify lint and format
+      shell: bash
+      run: |
+        pnpm lint
+        pnpm stylelint
+        pnpm format:check
+        pnpm knip
+
+    - name: Typecheck browser tests
+      if: steps.changed-paths.outputs.browser_tests == 'true'
+      shell: bash
+      run: pnpm typecheck:browser

.github/workflows/api-update-registry-api-types.yaml

@@ -1,107 +0,0 @@
-# Description: When upstream comfy-api is updated, click dispatch to update the TypeScript type definitions in this repo
-name: 'Api: Update Registry API Types'
-
-on:
-  # Manual trigger
-  workflow_dispatch:
-
-  # Triggered from comfy-api repo
-  repository_dispatch:
-    types: [comfy-api-updated]
-
-jobs:
-  update-registry-types:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@fc06bc1257f339d1d5d8b3a19a8cae5388b55320 # v4.4.0
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version-file: '.nvmrc'
-          cache: 'pnpm'
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Checkout comfy-api repository
-        uses: actions/checkout@v6
-        with:
-          repository: Comfy-Org/comfy-api
-          path: comfy-api
-          token: ${{ secrets.COMFY_API_PAT }}
-          clean: true
-
-      - name: Get API commit information
-        id: api-info
-        run: |
-          cd comfy-api
-          API_COMMIT=$(git rev-parse --short HEAD)
-          echo "commit=${API_COMMIT}" >> $GITHUB_OUTPUT
-          cd ..
-
-      - name: Generate API types
-        run: |
-          echo "Generating TypeScript types from comfy-api@${{ steps.api-info.outputs.commit }}..."
-          mkdir -p ./packages/registry-types/src
-          pnpm dlx openapi-typescript ./comfy-api/openapi.yml --output ./packages/registry-types/src/comfyRegistryTypes.ts
-
-      - name: Validate generated types
-        run: |
-          if [ ! -f ./packages/registry-types/src/comfyRegistryTypes.ts ]; then
-            echo "Error: Types file was not generated."
-            exit 1
-          fi
-
-          # Check if file is not empty
-          if [ ! -s ./packages/registry-types/src/comfyRegistryTypes.ts ]; then
-            echo "Error: Generated types file is empty."
-            exit 1
-          fi
-
-      - name: Lint generated types
-        run: |
-          echo "Linting generated Comfy Registry API types..."
-          pnpm lint:fix:no-cache -- ./packages/registry-types/src/comfyRegistryTypes.ts
-
-      - name: Check for changes
-        id: check-changes
-        run: |
-          if [[ -z $(git status --porcelain ./packages/registry-types/src/comfyRegistryTypes.ts) ]]; then
-            echo "No changes to Comfy Registry API types detected."
-            echo "changed=false" >> $GITHUB_OUTPUT
-            exit 0
-          else
-            echo "Changes detected in Comfy Registry API types."
-            echo "changed=true" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Create Pull Request
-        if: steps.check-changes.outputs.changed == 'true'
-        uses: peter-evans/create-pull-request@c0f553fe549906ede9cf27b5156039d195d2ece0 # v8.1.0
-        with:
-          token: ${{ secrets.PR_GH_TOKEN }}
-          commit-message: '[chore] Update Comfy Registry API types from comfy-api@${{ steps.api-info.outputs.commit }}'
-          title: '[chore] Update Comfy Registry API types from comfy-api@${{ steps.api-info.outputs.commit }}'
-          body: |
-            ## Automated API Type Update
-
-            This PR updates the Comfy Registry API types from the latest comfy-api OpenAPI specification.
-
-            - API commit: ${{ steps.api-info.outputs.commit }}
-            - Generated on: ${{ github.event.repository.updated_at }}
-
-            These types are automatically generated using openapi-typescript.
-          branch: update-registry-types-${{ steps.api-info.outputs.commit }}
-          base: main
-          labels: CNR
-          delete-branch: true
-          add-paths: |
-            packages/registry-types/src/comfyRegistryTypes.ts

.github/workflows/ci-lint-format-queue.yaml

@@ -0,0 +1,29 @@
+# Description: Lint and format verification for GitHub merge queue runs.
+# Paired with ci-lint-format.yaml — workflow name and job name must match
+# so branch protection resolves a single required check in both the
+# pull_request and merge_group contexts. This file runs verify-only steps
+# with a read-only token; auto-fix and PR comments live in the PR workflow.
+name: 'CI: Lint Format'
+
+on:
+  merge_group:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  lint-and-format:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout merge group ref
+        uses: actions/checkout@v6
+
+      - name: Setup frontend
+        uses: ./.github/actions/setup-frontend
+
+      - name: Verify lint and format
+        uses: ./.github/actions/lint-format-verify

.github/workflows/ci-lint-format.yaml

@@ -1,4 +1,7 @@
-# Description: Linting and code formatting validation for pull requests
+# Description: Linting and code formatting validation for pull requests.
+# Paired with ci-lint-format-queue.yaml - workflow name and job name must
+# match so branch protection resolves a single required check in both the
+# pull_request and merge_group contexts.
 name: 'CI: Lint Format'
 
 on:
@@ -26,14 +29,6 @@ jobs:
       - name: Setup frontend
         uses: ./.github/actions/setup-frontend
 
-      - name: Detect browser_tests changes
-        id: changed-paths
-        uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3.0.2
-        with:
-          filters: |
-            browser_tests:
-              - 'browser_tests/**'
-
       - name: Run ESLint with auto-fix
         run: pnpm lint:fix
 
@@ -77,16 +72,8 @@ jobs:
           echo "See CONTRIBUTING.md for more details."
           exit 1
 
-      - name: Final validation
-        run: |
-          pnpm lint
-          pnpm stylelint
-          pnpm format:check
-          pnpm knip
-
-      - name: Typecheck browser tests
-        if: steps.changed-paths.outputs.browser_tests == 'true'
-        run: pnpm typecheck:browser
+      - name: Verify lint and format
+        uses: ./.github/actions/lint-format-verify
 
       - name: Comment on PR about auto-fix
         if: steps.verify-changed-files.outputs.changed == 'true' && github.event.pull_request.head.repo.full_name == github.repository

.github/workflows/ci-tests-e2e.yaml

@@ -8,6 +8,7 @@ on:
   pull_request:
     branches-ignore: [wip/*, draft/*, temp/*]
     paths-ignore: ['**/*.md']
+  merge_group:
   workflow_dispatch:
 
 concurrency:

.github/workflows/ci-tests-unit.yaml

@@ -8,6 +8,7 @@ on:
   pull_request:
     branches-ignore: [wip/*, draft/*, temp/*]
     paths-ignore: ['**/*.md']
+  merge_group:
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -31,4 +32,5 @@ jobs:
         uses: codecov/codecov-action@1af58845a975a7985b0beb0cbe6fbbb71a41dbad # v5.5.3
         with:
           files: coverage/lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}
           fail_ci_if_error: false

.github/workflows/ci-website-build.yaml

@@ -0,0 +1,33 @@
+# Description: Build and validate the marketing website (apps/website)
+name: 'CI: Website Build'
+
+on:
+  push:
+    branches: [main, master, website/*]
+    paths:
+      - 'apps/website/**'
+      - 'packages/design-system/**'
+      - 'pnpm-lock.yaml'
+  pull_request:
+    branches-ignore: [wip/*, draft/*, temp/*]
+    paths:
+      - 'apps/website/**'
+      - 'packages/design-system/**'
+      - 'pnpm-lock.yaml'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup frontend
+        uses: ./.github/actions/setup-frontend
+
+      - name: Build website
+        run: pnpm --filter @comfyorg/website build

.github/workflows/hub-ci.yaml

@@ -1,182 +0,0 @@
-name: Hub CI
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - 'apps/hub/**'
-      - '.github/workflows/hub-ci.yaml'
-  pull_request:
-    branches: [main]
-    paths:
-      - 'apps/hub/**'
-      - '.github/workflows/hub-ci.yaml'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  lint:
-    name: Lint & Check
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: apps/hub
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Astro Check
-        run: pnpm run check
-
-      - name: Unit Tests
-        run: pnpm test
-
-      - name: Validate Templates
-        run: pnpm run validate:templates
-        continue-on-error: true
-
-  build:
-    name: Build Hub
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: apps/hub
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Build site
-        run: pnpm run build
-        env:
-          HUB_SKIP_SYNC: 'true'
-          SKIP_AI_GENERATION: 'true'
-
-      - name: Upload build artifact
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: hub-build
-          path: apps/hub/dist
-          retention-days: 1
-
-  seo-audit:
-    name: SEO Audit
-    needs: build
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: apps/hub
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Download build artifact
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          name: hub-build
-          path: apps/hub/dist
-
-      - name: Validate sitemap
-        id: sitemap
-        continue-on-error: true
-        run: |
-          echo "## Sitemap Validation" >> $GITHUB_STEP_SUMMARY
-          if pnpm run validate:sitemap 2>&1 | tee sitemap-output.txt; then
-            echo "✅ Sitemap validation passed" >> $GITHUB_STEP_SUMMARY
-            echo "status=passed" >> $GITHUB_OUTPUT
-          else
-            echo "❌ Sitemap validation failed" >> $GITHUB_STEP_SUMMARY
-            echo "status=failed" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Run SEO audit
-        id: seo
-        continue-on-error: true
-        run: |
-          echo "## SEO Audit" >> $GITHUB_STEP_SUMMARY
-          if pnpm run audit:seo 2>&1 | tee seo-output.txt; then
-            echo "✅ SEO audit passed" >> $GITHUB_STEP_SUMMARY
-            echo "status=passed" >> $GITHUB_OUTPUT
-          else
-            echo "⚠️ SEO audit found issues" >> $GITHUB_STEP_SUMMARY
-            echo "status=issues" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Check internal links
-        id: links
-        continue-on-error: true
-        run: |
-          echo "## Link Check" >> $GITHUB_STEP_SUMMARY
-          DIST_DIR="dist"
-          if [ ! -d "$DIST_DIR" ]; then
-            echo "⚠️ No build output found at $DIST_DIR" >> $GITHUB_STEP_SUMMARY
-            echo "status=skipped" >> $GITHUB_OUTPUT
-            exit 0
-          fi
-
-          BROKEN_FILE="broken-links.txt"
-          : > "$BROKEN_FILE"
-          BROKEN_COUNT=0
-          TOTAL_COUNT=0
-
-          for htmlfile in $(find "$DIST_DIR" -name '*.html' \
-            -not -path "$DIST_DIR/ar/*" -not -path "$DIST_DIR/es/*" -not -path "$DIST_DIR/fr/*" \
-            -not -path "$DIST_DIR/ja/*" -not -path "$DIST_DIR/ko/*" -not -path "$DIST_DIR/pt-BR/*" \
-            -not -path "$DIST_DIR/ru/*" -not -path "$DIST_DIR/tr/*" -not -path "$DIST_DIR/zh/*" \
-            -not -path "$DIST_DIR/zh-TW/*" | head -500); do
-            hrefs=$(grep -oP 'href="(/[^"]*)"' "$htmlfile" | sed 's/href="//;s/"$//' || true)
-            for href in $hrefs; do
-              TOTAL_COUNT=$((TOTAL_COUNT + 1))
-              clean="${href%%#*}"
-              clean="${clean%%\?*}"
-              if [ -z "$clean" ] || [ "$clean" = "/" ]; then continue; fi
-              found=false
-              if [[ "$clean" =~ \.[a-zA-Z0-9]+$ ]]; then
-                [ -f "${DIST_DIR}${clean}" ] && found=true
-              else
-                base="${clean%/}"
-                [ -f "${DIST_DIR}${base}/index.html" ] && found=true
-                [ "$found" = false ] && [ -f "${DIST_DIR}${base}.html" ] && found=true
-                [ "$found" = false ] && [ -f "${DIST_DIR}${clean}" ] && found=true
-                [ "$found" = false ] && [ -d "${DIST_DIR}${base}" ] && found=true
-              fi
-              if [ "$found" = false ]; then
-                BROKEN_COUNT=$((BROKEN_COUNT + 1))
-                echo "- \`${href}\` (in ${htmlfile#${DIST_DIR}/})" >> "$BROKEN_FILE"
-              fi
-            done
-          done
-
-          if [ "$BROKEN_COUNT" -eq 0 ]; then
-            echo "✅ All internal links valid ($TOTAL_COUNT checked)" >> $GITHUB_STEP_SUMMARY
-            echo "status=passed" >> $GITHUB_OUTPUT
-          else
-            echo "❌ Found $BROKEN_COUNT broken internal links out of $TOTAL_COUNT" >> $GITHUB_STEP_SUMMARY
-            head -n 50 "$BROKEN_FILE" >> $GITHUB_STEP_SUMMARY
-            echo "status=failed" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Upload SEO reports
-        if: always()
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: hub-seo-reports
-          path: |
-            apps/hub/seo-output.txt
-            apps/hub/seo-summary.json
-            apps/hub/broken-links.txt
-          if-no-files-found: ignore

.github/workflows/hub-cron-rebuild.yaml

@@ -1,68 +0,0 @@
-name: Hub Cron Rebuild
-
-on:
-  schedule:
-    # Every 15 minutes — rebuilds the site to pick up new UGC workflows
-    # for search index, sitemap, filter pages, and pre-rendered detail pages.
-    - cron: '*/15 * * * *'
-  workflow_dispatch:
-
-concurrency:
-  group: hub-deploy-prod
-  cancel-in-progress: false
-
-permissions:
-  contents: read
-
-jobs:
-  rebuild:
-    runs-on: ubuntu-latest
-    env:
-      SKIP_AI_GENERATION: 'true'
-      PUBLIC_POSTHOG_KEY: ${{ secrets.HUB_POSTHOG_KEY }}
-      PUBLIC_GA_MEASUREMENT_ID: ${{ secrets.HUB_GA_MEASUREMENT_ID }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Checkout templates data
-        uses: actions/checkout@v6
-        with:
-          repository: Comfy-Org/workflow_templates
-          path: _workflow_templates
-          sparse-checkout: templates
-          token: ${{ secrets.GH_TOKEN }}
-
-      - name: Restore content cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: apps/hub/.content-cache
-          key: hub-content-cache-cron-prod-${{ hashFiles('_workflow_templates/templates/**', 'apps/hub/src/**') }}
-          restore-keys: |
-            hub-content-cache-cron-prod-
-
-      - name: Sync templates
-        run: pnpm run sync
-        working-directory: apps/hub
-        env:
-          HUB_TEMPLATES_DIR: ${{ github.workspace }}/_workflow_templates/templates
-
-      - name: Build Astro site
-        run: pnpm run build
-        working-directory: apps/hub
-        env:
-          PUBLIC_HUB_API_URL: ${{ secrets.HUB_API_URL_PRODUCTION }}
-          PUBLIC_COMFY_CLOUD_URL: ${{ secrets.COMFY_CLOUD_URL_PRODUCTION }}
-          PUBLIC_APPROVED_ONLY: 'true'
-
-      - name: Deploy to Vercel
-        uses: amondnet/vercel-action@16e87c0a08142b0d0d33b76aeaf20823c381b9b9 # v25.2.0
-        with:
-          vercel-token: ${{ secrets.VERCEL_TOKEN }}
-          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
-          vercel-project-id: ${{ secrets.HUB_VERCEL_PROJECT_ID }}
-          working-directory: apps/hub
-          vercel-args: '--prebuilt --prod'

.github/workflows/hub-deploy.yaml

@@ -1,80 +0,0 @@
-name: Deploy Hub
-
-on:
-  workflow_dispatch:
-    inputs:
-      skip_ai:
-        description: 'Skip AI content generation'
-        type: boolean
-        default: false
-      force_regenerate:
-        description: 'Force regenerate all content (ignore cache)'
-        type: boolean
-        default: false
-      template_filter:
-        description: 'Regenerate specific template only (e.g. "flux_schnell")'
-        type: string
-        default: ''
-
-concurrency:
-  group: hub-deploy-prod
-  cancel-in-progress: false
-
-permissions:
-  contents: read
-
-jobs:
-  build-deploy:
-    runs-on: ubuntu-latest
-    env:
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      PUBLIC_POSTHOG_KEY: ${{ secrets.HUB_POSTHOG_KEY }}
-      PUBLIC_GA_MEASUREMENT_ID: ${{ secrets.HUB_GA_MEASUREMENT_ID }}
-      SKIP_AI_GENERATION: ${{ inputs.skip_ai && 'true' || '' }}
-      FORCE_AI_REGENERATE: ${{ inputs.force_regenerate && 'true' || '' }}
-      AI_TEMPLATE_FILTER: ${{ inputs.template_filter }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Checkout templates data
-        uses: actions/checkout@v6
-        with:
-          repository: Comfy-Org/workflow_templates
-          path: _workflow_templates
-          sparse-checkout: templates
-          token: ${{ secrets.GH_TOKEN }}
-
-      - name: Restore content cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: apps/hub/.content-cache
-          key: hub-content-cache-${{ hashFiles('_workflow_templates/templates/**', 'apps/hub/src/**') }}
-          restore-keys: |
-            hub-content-cache-
-
-      - name: Sync templates
-        run: pnpm run sync
-        working-directory: apps/hub
-        env:
-          HUB_TEMPLATES_DIR: ${{ github.workspace }}/_workflow_templates/templates
-
-      - name: Build Astro site
-        run: pnpm run build
-        working-directory: apps/hub
-        env:
-          PUBLIC_HUB_API_URL: ${{ secrets.HUB_API_URL_PRODUCTION }}
-          PUBLIC_COMFY_CLOUD_URL: ${{ secrets.COMFY_CLOUD_URL_PRODUCTION }}
-          PUBLIC_APPROVED_ONLY: 'true'
-
-      - name: Deploy to Vercel
-        uses: amondnet/vercel-action@16e87c0a08142b0d0d33b76aeaf20823c381b9b9 # v25.2.0
-        with:
-          vercel-token: ${{ secrets.VERCEL_TOKEN }}
-          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
-          vercel-project-id: ${{ secrets.HUB_VERCEL_PROJECT_ID }}
-          working-directory: apps/hub
-          vercel-args: '--prebuilt --prod'

.github/workflows/hub-preview-cron.yaml

@@ -1,134 +0,0 @@
-name: Hub Preview Cron
-
-on:
-  schedule:
-    - cron: '*/15 * * * *'
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  discover:
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.targets.outputs.matrix }}
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: Build rebuild targets
-        id: targets
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          targets='[]'
-
-          # Main with production API (all workflows, no approved filter)
-          targets=$(echo "$targets" | jq -c '. + [{"ref": "main", "is_main": true, "pr": 0, "api_env": "production"}]')
-
-          # Main with test API
-          targets=$(echo "$targets" | jq -c '. + [{"ref": "main", "is_main": true, "pr": 0, "api_env": "test"}]')
-
-          # Find open PRs with the "preview-cron" label
-          prs=$(gh pr list --label "preview-cron" --state open --json number,headRefName)
-          for row in $(echo "$prs" | jq -c '.[]'); do
-            ref=$(echo "$row" | jq -r '.headRefName')
-            num=$(echo "$row" | jq -r '.number')
-            targets=$(echo "$targets" | jq -c \
-              --arg ref "$ref" --argjson num "$num" \
-              '. + [{"ref": $ref, "is_main": false, "pr": $num, "api_env": "test"}]')
-          done
-
-          echo "matrix={\"include\":$targets}" >> "$GITHUB_OUTPUT"
-          echo "### Rebuild targets" >> "$GITHUB_STEP_SUMMARY"
-          echo "$targets" | jq '.' >> "$GITHUB_STEP_SUMMARY"
-
-  rebuild:
-    needs: discover
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix: ${{ fromJson(needs.discover.outputs.matrix) }}
-    concurrency:
-      group: hub-preview-cron-${{ matrix.ref }}-${{ matrix.api_env }}
-      cancel-in-progress: true
-    env:
-      SKIP_AI_GENERATION: 'true'
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          ref: ${{ matrix.ref }}
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Checkout templates data
-        uses: actions/checkout@v6
-        with:
-          repository: Comfy-Org/workflow_templates
-          path: _workflow_templates
-          sparse-checkout: templates
-          token: ${{ secrets.GH_TOKEN }}
-
-      - name: Restore content cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: apps/hub/.content-cache
-          key: hub-content-cache-cron-${{ matrix.ref }}-${{ matrix.api_env }}-${{ hashFiles('_workflow_templates/templates/**', 'apps/hub/src/**') }}
-          restore-keys: |
-            hub-content-cache-cron-${{ matrix.ref }}-${{ matrix.api_env }}-
-
-      - name: Sync templates
-        run: pnpm run sync:en-only
-        working-directory: apps/hub
-        env:
-          HUB_TEMPLATES_DIR: ${{ github.workspace }}/_workflow_templates/templates
-
-      - name: Build Astro site
-        run: pnpm run build
-        working-directory: apps/hub
-        env:
-          PUBLIC_HUB_API_URL: ${{ matrix.api_env == 'test' && secrets.HUB_API_URL_PREVIEW || secrets.HUB_API_URL_PRODUCTION }}
-          PUBLIC_COMFY_CLOUD_URL: ${{ matrix.api_env == 'test' && secrets.COMFY_CLOUD_URL_PREVIEW || secrets.COMFY_CLOUD_URL_PRODUCTION }}
-
-      - name: Deploy to Vercel
-        id: deploy
-        uses: amondnet/vercel-action@16e87c0a08142b0d0d33b76aeaf20823c381b9b9 # v25.2.0
-        with:
-          vercel-token: ${{ secrets.VERCEL_TOKEN }}
-          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
-          vercel-project-id: ${{ secrets.HUB_VERCEL_PROJECT_ID }}
-          working-directory: apps/hub
-          vercel-args: '--prebuilt'
-
-      - name: Alias main preview (prod API)
-        if: matrix.is_main && matrix.api_env == 'production' && secrets.HUB_PREVIEW_ALIAS
-        env:
-          PREVIEW_URL: ${{ steps.deploy.outputs.preview-url }}
-          ALIAS: ${{ secrets.HUB_PREVIEW_ALIAS }}
-          VERCEL_TOKEN_VAL: ${{ secrets.VERCEL_TOKEN }}
-          VERCEL_SCOPE: ${{ secrets.VERCEL_ORG_ID }}
-        run: |
-          npx vercel alias "$PREVIEW_URL" "$ALIAS" --token="$VERCEL_TOKEN_VAL" --scope="$VERCEL_SCOPE"
-
-      - name: Alias main preview (test API)
-        if: matrix.is_main && matrix.api_env == 'test' && secrets.HUB_PREVIEW_TEST_ALIAS
-        env:
-          PREVIEW_URL: ${{ steps.deploy.outputs.preview-url }}
-          ALIAS: ${{ secrets.HUB_PREVIEW_TEST_ALIAS }}
-          VERCEL_TOKEN_VAL: ${{ secrets.VERCEL_TOKEN }}
-          VERCEL_SCOPE: ${{ secrets.VERCEL_ORG_ID }}
-        run: |
-          npx vercel alias "$PREVIEW_URL" "$ALIAS" --token="$VERCEL_TOKEN_VAL" --scope="$VERCEL_SCOPE"
-
-      - name: Comment preview URL on PR
-        if: matrix.pr > 0
-        uses: marocchino/sticky-pull-request-comment@773744901bac0e8cbb5a0dc842800d45e9b2b405 # v2.9.4
-        with:
-          number: ${{ matrix.pr }}
-          header: hub-preview-cron
-          message: |
-            🔄 **Hub preview cron rebuilt:** ${{ steps.deploy.outputs.preview-url }}
-            _Last rebuild: ${{ github.event.head_commit.timestamp || 'manual trigger' }}_

.github/workflows/hub-preview.yaml

@@ -1,74 +0,0 @@
-name: Hub Preview
-
-on:
-  pull_request:
-    paths:
-      - 'apps/hub/**'
-  workflow_dispatch:
-
-concurrency:
-  group: hub-preview-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  preview:
-    runs-on: ubuntu-latest
-    env:
-      SKIP_AI_GENERATION: 'true'
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup frontend
-        uses: ./.github/actions/setup-frontend
-
-      - name: Checkout templates data
-        uses: actions/checkout@v6
-        with:
-          repository: Comfy-Org/workflow_templates
-          path: _workflow_templates
-          sparse-checkout: templates
-          token: ${{ secrets.GH_TOKEN }}
-
-      - name: Restore content cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: apps/hub/.content-cache
-          key: hub-content-cache-preview-${{ hashFiles('_workflow_templates/templates/**', 'apps/hub/src/**') }}
-          restore-keys: |
-            hub-content-cache-preview-
-
-      - name: Sync templates
-        run: pnpm run sync:en-only
-        working-directory: apps/hub
-        env:
-          HUB_TEMPLATES_DIR: ${{ github.workspace }}/_workflow_templates/templates
-
-      - name: Build Astro site
-        run: pnpm run build
-        working-directory: apps/hub
-        env:
-          PUBLIC_HUB_API_URL: ${{ secrets.HUB_API_URL_PREVIEW }}
-          PUBLIC_COMFY_CLOUD_URL: ${{ secrets.COMFY_CLOUD_URL_PREVIEW }}
-
-      - name: Deploy preview to Vercel
-        id: deploy
-        uses: amondnet/vercel-action@16e87c0a08142b0d0d33b76aeaf20823c381b9b9 # v25.2.0
-        with:
-          vercel-token: ${{ secrets.VERCEL_TOKEN }}
-          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
-          vercel-project-id: ${{ secrets.HUB_VERCEL_PROJECT_ID }}
-          working-directory: apps/hub
-          vercel-args: '--prebuilt'
-
-      - name: Comment preview URL
-        if: github.event_name == 'pull_request'
-        uses: marocchino/sticky-pull-request-comment@773744901bac0e8cbb5a0dc842800d45e9b2b405 # v2.9.4
-        with:
-          header: hub-vercel-preview
-          message: |
-            🚀 **Hub preview deployed:** ${{ steps.deploy.outputs.preview-url }}

.github/workflows/release-version-bump.yaml

@@ -142,6 +142,20 @@ jobs:
           fi
           echo "✅ Branch '$BRANCH' exists"
 
+      - name: Ensure packageManager field exists
+        run: |
+          if ! grep -q '"packageManager"' package.json; then
+            # Old branches (e.g. core/1.42) predate the packageManager field.
+            # Inject it so pnpm/action-setup can resolve the version.
+            node -e "
+              const fs = require('fs');
+              const pkg = JSON.parse(fs.readFileSync('package.json','utf8'));
+              pkg.packageManager = 'pnpm@10.33.0';
+              fs.writeFileSync('package.json', JSON.stringify(pkg, null, 2) + '\n');
+            "
+            echo "Injected packageManager into package.json for legacy branch"
+          fi
+
       - name: Install pnpm
         uses: pnpm/action-setup@fc06bc1257f339d1d5d8b3a19a8cae5388b55320 # v4.4.0
 

.oxlintrc.json

@@ -12,8 +12,6 @@
     "playwright-report/*",
     "src/extensions/core/*",
     "src/scripts/*",
-    "apps/hub/scripts/**/*",
-    "apps/hub/src/scripts/*",
     "src/types/generatedManagerTypes.ts",
     "src/types/vue-shim.d.ts",
     "test-results/*",
@@ -66,6 +64,7 @@
         ]
       }
     ],
+    "no-unsafe-optional-chaining": "error",
     "no-self-assign": "allow",
     "no-unused-expressions": "off",
     "no-unused-private-class-members": "off",
@@ -85,6 +84,7 @@
     "typescript/no-unsafe-declaration-merging": "off",
     "typescript/no-unused-vars": "off",
     "unicorn/no-empty-file": "off",
+    "vitest/require-mock-type-parameters": "off",
     "unicorn/no-new-array": "off",
     "unicorn/no-single-promise-in-promise-methods": "off",
     "unicorn/no-useless-fallback-in-spread": "off",
@@ -106,8 +106,7 @@
         "allowInterfaces": "always"
       }
     ],
-    "vue/no-import-compiler-macros": "error",
-    "vue/no-dupe-keys": "error"
+    "vue/no-import-compiler-macros": "error"
   },
   "overrides": [
     {
@@ -118,13 +117,60 @@
     },
     {
       "files": ["browser_tests/**/*.ts"],
+      "jsPlugins": ["eslint-plugin-playwright"],
       "rules": {
         "typescript/no-explicit-any": "error",
         "no-async-promise-executor": "error",
         "no-control-regex": "error",
         "no-useless-rename": "error",
         "no-unused-private-class-members": "error",
-        "unicorn/no-empty-file": "error"
+        "unicorn/no-empty-file": "error",
+        "playwright/consistent-spacing-between-blocks": "error",
+        "playwright/expect-expect": [
+          "error",
+          {
+            "assertFunctionNames": [
+              "recordMeasurement",
+              "logMeasurement",
+              "builderSaveAs"
+            ],
+            "assertFunctionPatterns": [
+              "^expect",
+              "^assert",
+              "^verify",
+              "^searchAndExpect",
+              "waitForOpen",
+              "waitForClosed",
+              "waitForRequest"
+            ]
+          }
+        ],
+        "playwright/max-nested-describe": "error",
+        "playwright/no-duplicate-hooks": "error",
+        "playwright/no-element-handle": "error",
+        "playwright/no-eval": "error",
+        "playwright/no-focused-test": "error",
+        "playwright/no-force-option": "off",
+        "playwright/no-networkidle": "error",
+        "playwright/no-page-pause": "error",
+        "playwright/no-skipped-test": "error",
+        "playwright/no-unsafe-references": "error",
+        "playwright/no-unused-locators": "error",
+        "playwright/no-useless-await": "error",
+        "playwright/no-useless-not": "error",
+        "playwright/no-wait-for-navigation": "error",
+        "playwright/no-wait-for-selector": "error",
+        "playwright/no-wait-for-timeout": "error",
+        "playwright/prefer-hooks-on-top": "error",
+        "playwright/prefer-locator": "error",
+        "playwright/prefer-to-have-count": "error",
+        "playwright/prefer-to-have-length": "error",
+        "playwright/prefer-web-first-assertions": "error",
+        "playwright/prefer-native-locators": "error",
+        "playwright/require-to-pass-timeout": "error",
+        "playwright/valid-expect": "error",
+        "playwright/valid-expect-in-promise": "error",
+        "playwright/valid-title": "error"
       }
     }
   ]

AGENTS.md

@@ -179,6 +179,12 @@ This project uses **pnpm**. Always prefer scripts defined in `package.json` (e.g
 24. Do not use function expressions if it's possible to use function declarations instead
 25. Watch out for [Code Smells](https://wiki.c2.com/?CodeSmell) and refactor to avoid them
 
+## Design Standards
+
+Before implementing any user-facing feature, consult the [Comfy Design Standards](https://www.figma.com/design/QreIv5htUaSICNuO2VBHw0/Comfy-Design-Standards) Figma file. Use the Figma MCP to fetch it live — the file is the single source of truth and may be updated by designers at any time.
+
+See `docs/guidance/design-standards.md` for Figma file keys, section node IDs, and component references.
+
 ## Testing Guidelines
 
 See @docs/testing/\*.md for detailed patterns.
@@ -226,6 +232,7 @@ See @docs/testing/\*.md for detailed patterns.
 - shadcn/vue: <https://www.shadcn-vue.com/>
 - Reka UI: <https://reka-ui.com/>
 - PrimeVue: <https://primevue.org>
+- Comfy Design Standards: <https://www.figma.com/design/QreIv5htUaSICNuO2VBHw0/Comfy-Design-Standards>
 - ComfyUI: <https://docs.comfy.org>
 - Electron: <https://www.electronjs.org/docs/latest/>
 - Wiki: <https://deepwiki.com/Comfy-Org/ComfyUI_frontend/1-overview>
@@ -311,6 +318,9 @@ When referencing Comfy-Org repos:
   - Find existing `!important` classes that are interfering with the styling and propose corrections of those instead.
 - NEVER use arbitrary percentage values like `w-[80%]` when a Tailwind fraction utility exists
   - Use `w-4/5` instead of `w-[80%]`, `w-1/2` instead of `w-[50%]`, etc.
+- NEVER use font-size classes (`text-xs`, `text-sm`, etc.) to size `icon-[...]` (iconify) icons
+  - Iconify icons size via `width`/`height: 1.2em`, so font-size produces unpredictable results
+  - Use `size-*` classes for explicit sizing, or set font-size on the **parent** container and let `1.2em` scale naturally
 
 ## Agent-only rules
 

CONTRIBUTING.md

@@ -62,6 +62,37 @@ python main.py --port 8188 --cpu
 - Run `pnpm dev:electron` to start the dev server with electron API mocked
 - Run `pnpm dev:cloud` to start the dev server against the cloud backend (instead of local ComfyUI server)
 
+#### Testing with Cloud & Staging Environments
+
+Some features — particularly **partner/API nodes** (e.g. BFL, OpenAI, Stability AI) — require a cloud backend for authentication and billing. Running these against a local ComfyUI instance will result in permission errors or logged-out states. There are two ways to connect to a cloud/staging backend:
+
+**Option 1: Frontend — `pnpm dev:cloud`**
+
+The simplest approach. This proxies all API requests to the test cloud environment:
+
+```bash
+pnpm dev:cloud
+```
+
+This sets `DEV_SERVER_COMFYUI_URL` to `https://testcloud.comfy.org/` automatically. You can also set this variable manually in your `.env` file to target a different environment:
+
+```bash
+# .env
+DEV_SERVER_COMFYUI_URL=https://stagingcloud.comfy.org/
+```
+
+Any `*.comfy.org` URL automatically enables cloud mode, which includes the GCS media proxy needed for viewing generated images and videos. See [.env_example](.env_example) for all available cloud URLs.
+
+**Option 2: Backend — `--comfy-api-base`**
+
+Alternatively, launch the ComfyUI backend pointed at the staging API:
+
+```bash
+python main.py --comfy-api-base https://stagingapi.comfy.org --verbose
+```
+
+Then run `pnpm dev` as usual. This keeps the frontend in local mode but routes backend API calls through staging.
+
 #### Access dev server on touch devices
 
 Enable remote access to the dev server by setting `VITE_REMOTE_DEV` in `.env` to `true`.

apps/hub/.gitignore

@@ -1,9 +0,0 @@
-dist/
-.astro/
-.content-cache/
-src/content/templates/
-public/workflows/thumbnails/
-public/workflows/avatars/
-public/previews/
-public/search-index.json
-knowledge/tutorials/

apps/hub/astro.config.mjs

@@ -1,254 +0,0 @@
-import { defineConfig } from 'astro/config'
-import sitemap from '@astrojs/sitemap'
-import vercel from '@astrojs/vercel'
-import tailwindcss from '@tailwindcss/vite'
-import fs from 'node:fs'
-import path from 'node:path'
-import os from 'node:os'
-
-import vue from '@astrojs/vue'
-
-// Build template date lookup at config time
-const templatesDir = path.join(process.cwd(), 'src/content/templates')
-const templateDates = new Map()
-
-if (fs.existsSync(templatesDir)) {
-  const files = fs.readdirSync(templatesDir).filter((f) => f.endsWith('.json'))
-  for (const file of files) {
-    try {
-      const content = JSON.parse(
-        fs.readFileSync(path.join(templatesDir, file), 'utf-8')
-      )
-      if (content.name && content.date) {
-        templateDates.set(content.name, content.date)
-      }
-    } catch {
-      // Skip invalid JSON files
-    }
-  }
-}
-
-// Build timestamp used as lastmod fallback for pages without a specific date
-const buildDate = new Date().toISOString()
-
-// Supported locales (matches src/i18n/config.ts)
-const locales = [
-  'en',
-  'zh',
-  'zh-TW',
-  'ja',
-  'ko',
-  'es',
-  'fr',
-  'ru',
-  'tr',
-  'ar',
-  'pt-BR'
-]
-const nonDefaultLocales = locales.filter((l) => l !== 'en')
-
-// Custom sitemap pages for ISR routes not discovered at build time
-const siteOrigin = (
-  process.env.PUBLIC_SITE_ORIGIN || 'https://www.comfy.org'
-).replace(/\/$/, '')
-
-// Creator profile pages — extract unique usernames from synced templates
-const creatorUsernames = new Set()
-if (fs.existsSync(templatesDir)) {
-  const files = fs.readdirSync(templatesDir).filter((f) => f.endsWith('.json'))
-  for (const file of files) {
-    try {
-      const content = JSON.parse(
-        fs.readFileSync(path.join(templatesDir, file), 'utf-8')
-      )
-      if (content.username) creatorUsernames.add(content.username)
-    } catch {
-      // Skip invalid JSON
-    }
-  }
-}
-
-const creatorPages = [...creatorUsernames].map(
-  (u) => `${siteOrigin}/workflows/${u}/`
-)
-const localeCustomPages = nonDefaultLocales.map(
-  (locale) => `${siteOrigin}/${locale}/workflows/`
-)
-const customPages = [...creatorPages, ...localeCustomPages]
-
-// https://astro.build/config
-export default defineConfig({
-  site: (process.env.PUBLIC_SITE_ORIGIN || 'https://www.comfy.org').replace(
-    /\/$/,
-    ''
-  ),
-  prefetch: {
-    prefetchAll: false,
-    defaultStrategy: 'hover'
-  },
-  i18n: {
-    defaultLocale: 'en',
-    locales: locales,
-    routing: {
-      prefixDefaultLocale: false // English at root, others prefixed (/zh/, /ja/, etc.)
-    }
-  },
-  integrations: [
-    sitemap({
-      // Use custom filename to avoid collision with Framer's /sitemap.xml
-      filenameBase: 'sitemap-workflows',
-      // Include Framer's marketing sitemap in the index
-      customSitemaps: ['https://www.comfy.org/sitemap.xml'],
-      // Include on-demand locale pages that aren't discovered at build time
-      customPages: customPages,
-      serialize(item) {
-        const url = new URL(item.url)
-        const pathname = url.pathname
-
-        // Template detail pages: /workflows/{slug}/ or /{locale}/workflows/{slug}/
-        const templateMatch = pathname.match(
-          /^(?:\/([a-z]{2}(?:-[A-Z]{2})?))?\/workflows\/([^/]+)\/?$/
-        )
-        if (templateMatch) {
-          const slug = templateMatch[2]
-          const date = templateDates.get(slug)
-          item.lastmod = date ? new Date(date).toISOString() : buildDate
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'monthly'
-          item.priority = 0.8
-          return item
-        }
-
-        // Homepage
-        if (pathname === '/' || pathname === '') {
-          item.lastmod = buildDate
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'daily'
-          item.priority = 1.0
-          return item
-        }
-
-        // Workflows index (including localized versions)
-        if (pathname.match(/^(?:\/[a-z]{2}(?:-[A-Z]{2})?)?\/workflows\/?$/)) {
-          item.lastmod = buildDate
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'daily'
-          item.priority = 0.9
-          return item
-        }
-
-        // Category pages: /workflows/category/{type}/ or /{locale}/workflows/category/{type}/
-        if (
-          pathname.match(
-            /^(?:\/[a-z]{2}(?:-[A-Z]{2})?)?\/workflows\/category\//
-          )
-        ) {
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'weekly'
-          item.priority = 0.7
-          return item
-        }
-
-        // Model pages: /workflows/model/{model}/ or /{locale}/workflows/model/{model}/
-        if (
-          pathname.match(/^(?:\/[a-z]{2}(?:-[A-Z]{2})?)?\/workflows\/model\//)
-        ) {
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'weekly'
-          item.priority = 0.6
-          return item
-        }
-
-        // Tag pages: /workflows/tag/{tag}/ or /{locale}/workflows/tag/{tag}/
-        if (
-          pathname.match(/^(?:\/[a-z]{2}(?:-[A-Z]{2})?)?\/workflows\/tag\//)
-        ) {
-          // @ts-expect-error - sitemap types are stricter than actual API
-          item.changefreq = 'weekly'
-          item.priority = 0.6
-          return item
-        }
-
-        // Default for other pages
-        // @ts-expect-error - sitemap types are stricter than actual API
-        item.changefreq = 'weekly'
-        item.priority = 0.5
-        return item
-      },
-      // Exclude OG image routes and legacy redirect pages from sitemap.
-      // Legacy redirects are /workflows/{slug}/ without a 12-char hex share_id suffix.
-      // Canonical detail pages are /workflows/{slug}-{shareId}/ (shareId = 12 hex chars).
-      filter: (page) => {
-        if (
-          page.includes('/workflows/og/') ||
-          page.includes('/workflows/og.png')
-        )
-          return false
-        // Check if this is a workflow detail path (not category/tag/model/creators)
-        const match = page.match(/\/workflows\/([^/]+)\/$/)
-        if (match) {
-          const segment = match[1]
-          // Skip known sub-paths
-          if (
-            ['category', 'tag', 'model', 'creators'].some((p) =>
-              page.includes(`/workflows/${p}/`)
-            )
-          )
-            return true
-          // Include if it has a share_id suffix (12 hex chars after last hyphen)
-          const lastHyphen = segment.lastIndexOf('-')
-          if (lastHyphen === -1) return false // No hyphen = legacy redirect
-          const candidate = segment.slice(lastHyphen + 1)
-          if (candidate.length === 12 && /^[0-9a-f]+$/.test(candidate))
-            return true
-          return false // Has hyphen but not a valid share_id = legacy redirect
-        }
-        return true
-      }
-    }),
-    vue()
-  ],
-  output: 'static',
-  adapter: vercel({
-    webAnalytics: { enabled: true },
-    skewProtection: true
-  }),
-
-  // Build performance optimizations
-  build: {
-    // Increase concurrency for faster builds on multi-core systems
-    concurrency: Math.max(1, os.cpus().length),
-    // Inline small stylesheets automatically
-    inlineStylesheets: 'auto'
-  },
-
-  // HTML compression
-  compressHTML: true,
-
-  // Image optimization settings
-  image: {
-    service: {
-      entrypoint: 'astro/assets/services/sharp',
-      config: {
-        // Limit input pixels to prevent memory issues with large images
-        limitInputPixels: 268402689 // ~16384x16384
-      }
-    }
-  },
-
-  // Responsive images for automatic srcset generation (now stable in Astro 5)
-  // Note: responsiveImages was moved from experimental to stable in Astro 5.x
-
-  vite: {
-    plugins: [tailwindcss()],
-    build: {
-      chunkSizeWarningLimit: 1000
-    },
-    optimizeDeps: {
-      include: ['web-vitals']
-    },
-    css: {
-      devSourcemap: false
-    }
-  }
-})

apps/hub/knowledge/concepts/3d-generation.md

@@ -1,22 +0,0 @@
-# 3D Generation
-
-3D generation creates three-dimensional models — meshes, point clouds, or multi-view images — from text or image inputs. This enables rapid prototyping of 3D assets without manual modeling. In ComfyUI, several approaches exist: image-to-3D (lifting a single photo into a mesh), text-to-3D (generating a 3D object from a description), and multi-view generation (producing consistent views of an object that can be reconstructed into 3D).
-
-## How It Works in ComfyUI
-
-- Key nodes involved: Model-specific loaders (`TripoSR`, `InstantMesh`, `StableZero123`), `LoadImage`, `Save3D` / `Preview3D`, `CRM` nodes
-- Typical workflow pattern: Load image → Load 3D model → Run inference → Preview 3D result → Export mesh
-
-## Key Settings
-
-- **Inference steps**: Number of denoising/reconstruction steps. More steps generally improve quality but increase generation time.
-- **Elevation angle**: Camera elevation for multi-view generation, controlling the vertical viewing angle of the generated views.
-- **Guidance scale**: How closely the model follows the input image or text. Higher values increase fidelity to the input but may reduce diversity.
-- **Output format**: Export format for the 3D mesh — OBJ, GLB, and PLY are common options, each suited to different downstream tools.
-
-## Tips
-
-- Clean single-object images on white or simple backgrounds work best for image-to-3D conversion.
-- Multi-view approaches (like Zero123) often produce better geometry than single-view methods.
-- Post-process generated meshes in Blender for cleanup, retopology, or texturing before production use.
-- Start with TripoSR for quick results — it generates meshes in seconds and is a good baseline to compare against other methods.

apps/hub/knowledge/concepts/_template-index.json

@@ -1,374 +0,0 @@
-{
-  "text-to-image": [
-    "01_get_started_text_to_image",
-    "api_bfl_flux2_max_sofa_swap",
-    "api_bfl_flux_1_kontext_max_image",
-    "api_bfl_flux_1_kontext_multiple_images_input",
-    "api_bfl_flux_1_kontext_pro_image",
-    "api_bfl_flux_pro_t2i",
-    "api_bytedance_seedream4",
-    "api_flux2",
-    "api_from_photo_2_miniature",
-    "api_google_gemini_image",
-    "api_grok_text_to_image",
-    "api_ideogram_v3_t2i",
-    "api_kling_omni_image",
-    "api_luma_photon_i2i",
-    "api_luma_photon_style_ref",
-    "api_nano_banana_pro",
-    "api_openai_dall_e_2_inpaint",
-    "api_openai_dall_e_2_t2i",
-    "api_openai_dall_e_3_t2i",
-    "api_openai_fashion_billboard_generator",
-    "api_openai_image_1_i2i",
-    "api_openai_image_1_inpaint",
-    "api_openai_image_1_multi_inputs",
-    "api_openai_image_1_t2i",
-    "api_recraft_image_gen_with_color_control",
-    "api_recraft_image_gen_with_style_control",
-    "api_recraft_style_reference",
-    "api_recraft_vector_gen",
-    "api_runway_reference_to_image",
-    "api_runway_text_to_image",
-    "api_stability_ai_i2i",
-    "api_stability_ai_sd3.5_i2i",
-    "api_stability_ai_sd3.5_t2i",
-    "api_stability_ai_stable_image_ultra_t2i",
-    "api_wan_text_to_image",
-    "default",
-    "flux1_dev_uso_reference_image_gen",
-    "flux1_krea_dev",
-    "flux_canny_model_example",
-    "flux_depth_lora_example",
-    "flux_dev_checkpoint_example",
-    "flux_dev_full_text_to_image",
-    "flux_fill_inpaint_example",
-    "flux_fill_outpaint_example",
-    "flux_redux_model_example",
-    "flux_schnell",
-    "flux_schnell_full_text_to_image",
-    "hidream_e1_1",
-    "hidream_e1_full",
-    "hidream_i1_dev",
-    "hidream_i1_fast",
-    "hidream_i1_full",
-    "image-qwen_image_edit_2511_lora_inflation",
-    "image_anima_preview",
-    "image_chroma1_radiance_text_to_image",
-    "image_chroma_text_to_image",
-    "image_flux2",
-    "image_flux2_fp8",
-    "image_flux2_klein_image_edit_4b_base",
-    "image_flux2_klein_image_edit_4b_distilled",
-    "image_flux2_klein_image_edit_9b_base",
-    "image_flux2_klein_image_edit_9b_distilled",
-    "image_flux2_klein_text_to_image",
-    "image_flux2_text_to_image",
-    "image_flux2_text_to_image_9b",
-    "image_kandinsky5_t2i",
-    "image_lotus_depth_v1_1",
-    "image_netayume_lumina_t2i",
-    "image_newbieimage_exp0_1-t2i",
-    "image_omnigen2_image_edit",
-    "image_omnigen2_t2i",
-    "image_ovis_text_to_image",
-    "image_qwen_Image_2512",
-    "image_qwen_image",
-    "image_qwen_image_2512_with_2steps_lora",
-    "image_qwen_image_controlnet_patch",
-    "image_qwen_image_instantx_controlnet",
-    "image_qwen_image_instantx_inpainting_controlnet",
-    "image_qwen_image_union_control_lora",
-    "image_z_image",
-    "image_z_image_turbo",
-    "image_z_image_turbo_fun_union_controlnet",
-    "sd3.5_large_blur",
-    "sd3.5_large_canny_controlnet_example",
-    "sd3.5_large_depth",
-    "sd3.5_simple_example",
-    "sdxl_refiner_prompt_example",
-    "sdxl_revision_text_prompts",
-    "sdxl_simple_example",
-    "sdxlturbo_example",
-    "templates-9grid_social_media-v2.0"
-  ],
-  "img2img": [
-    "02_qwen_Image_edit_subgraphed",
-    "api_luma_photon_i2i",
-    "api_meshy_multi_image_to_model",
-    "api_openai_image_1_i2i",
-    "api_runway_reference_to_image",
-    "api_stability_ai_i2i",
-    "api_stability_ai_sd3.5_i2i",
-    "flux1_dev_uso_reference_image_gen",
-    "flux_canny_model_example",
-    "flux_depth_lora_example",
-    "flux_fill_inpaint_example",
-    "flux_fill_outpaint_example",
-    "flux_kontext_dev_basic",
-    "flux_redux_model_example",
-    "image_chrono_edit_14B",
-    "image_qwen_image_edit",
-    "image_qwen_image_edit_2509",
-    "image_qwen_image_instantx_controlnet",
-    "image_qwen_image_instantx_inpainting_controlnet",
-    "sd3.5_large_blur",
-    "sd3.5_large_canny_controlnet_example",
-    "sd3.5_large_depth"
-  ],
-  "inpainting": [
-    "api_openai_dall_e_2_inpaint",
-    "api_openai_image_1_inpaint",
-    "api_stability_ai_audio_inpaint",
-    "flux_fill_inpaint_example",
-    "flux_fill_outpaint_example",
-    "image_flux.1_fill_dev_OneReward",
-    "image_qwen_image_instantx_inpainting_controlnet",
-    "video_wan2_2_14B_fun_inpaint",
-    "video_wan2_2_5B_fun_inpaint",
-    "video_wan_vace_inpainting",
-    "wan2.1_fun_inp"
-  ],
-  "outpainting": [
-    "api_bria_image_outpainting",
-    "flux_fill_outpaint_example",
-    "image_flux.1_fill_dev_OneReward",
-    "video_wan_vace_outpainting"
-  ],
-  "controlnet": [
-    "02_qwen_Image_edit_subgraphed",
-    "flux_canny_model_example",
-    "flux_depth_lora_example",
-    "flux_redux_model_example",
-    "image_lotus_depth_v1_1",
-    "image_qwen_image_controlnet_patch",
-    "image_qwen_image_edit_2509",
-    "image_qwen_image_instantx_controlnet",
-    "image_qwen_image_instantx_inpainting_controlnet",
-    "image_qwen_image_union_control_lora",
-    "image_z_image_turbo_fun_union_controlnet",
-    "sd3.5_large_canny_controlnet_example",
-    "sd3.5_large_depth",
-    "utility-depthAnything-v2-relative-video",
-    "utility-frame_interpolation-film",
-    "utility-lineart-video",
-    "utility-normal_crafter-video",
-    "utility-openpose-video",
-    "video_ltx2_canny_to_video",
-    "video_ltx2_depth_to_video",
-    "video_ltx2_pose_to_video",
-    "wan2.1_fun_control"
-  ],
-  "upscaling": [
-    "api_topaz_image_enhance",
-    "api_topaz_video_enhance",
-    "api_wavespeed_flshvsr_video_upscale",
-    "api_wavespped_image_upscale",
-    "api_wavespped_seedvr2_ai_image_fix",
-    "ultility_hitpaw_general_image_enhance",
-    "ultility_hitpaw_video_enhance",
-    "utility-gan_upscaler",
-    "utility-topaz_landscape_upscaler",
-    "utility_interpolation_image_upscale",
-    "utility_nanobanana_pro_ai_image_fix",
-    "utility_nanobanana_pro_illustration_upscale",
-    "utility_nanobanana_pro_product_upscale",
-    "utility_recraft_creative_image_upscale",
-    "utility_recraft_crisp_image_upscale",
-    "utility_seedvr2_image_upscale",
-    "utility_seedvr2_video_upscale",
-    "utility_topaz_illustration_upscale",
-    "utility_video_upscale"
-  ],
-  "video-generation": [
-    "03_video_wan2_2_14B_i2v_subgraphed",
-    "api_bytedace_seedance1_5_flf2v",
-    "api_bytedace_seedance1_5_image_to_video",
-    "api_bytedace_seedance1_5_text_to_video",
-    "api_bytedance_flf2v",
-    "api_bytedance_image_to_video",
-    "api_bytedance_text_to_video",
-    "api_grok_video",
-    "api_grok_video_edit",
-    "api_hailuo_minimax_i2v",
-    "api_hailuo_minimax_t2v",
-    "api_hailuo_minimax_video",
-    "api_kling2_6_i2v",
-    "api_kling2_6_t2v",
-    "api_kling_effects",
-    "api_kling_flf",
-    "api_kling_i2v",
-    "api_kling_motion_control",
-    "api_kling_omni_edit_video",
-    "api_kling_omni_i2v",
-    "api_kling_omni_t2v",
-    "api_kling_omni_v2v",
-    "api_ltxv_image_to_video",
-    "api_ltxv_text_to_video",
-    "api_luma_i2v",
-    "api_luma_t2v",
-    "api_moonvalley_image_to_video",
-    "api_moonvalley_text_to_video",
-    "api_moonvalley_video_to_video_motion_transfer",
-    "api_moonvalley_video_to_video_pose_control",
-    "api_openai_sora_video",
-    "api_pixverse_i2v",
-    "api_pixverse_t2v",
-    "api_pixverse_template_i2v",
-    "api_runway_first_last_frame",
-    "api_runway_gen3a_turbo_image_to_video",
-    "api_runway_gen4_turo_image_to_video",
-    "api_topaz_video_enhance",
-    "api_veo2_i2v",
-    "api_veo3",
-    "api_vidu_image_to_video",
-    "api_vidu_q2_flf2v",
-    "api_vidu_q2_i2v",
-    "api_vidu_q2_r2v",
-    "api_vidu_q2_t2v",
-    "api_vidu_q3_image_to_video",
-    "api_vidu_q3_text_to_video",
-    "api_vidu_reference_to_video",
-    "api_vidu_start_end_to_video",
-    "api_vidu_text_to_video",
-    "api_vidu_video_extension",
-    "api_wan2_6_i2v",
-    "api_wan2_6_t2v",
-    "api_wan_image_to_video",
-    "api_wan_r2v",
-    "api_wan_text_to_video",
-    "api_wavespeed_flshvsr_video_upscale",
-    "gsc_starter_2",
-    "hunyuan_video_text_to_video",
-    "image_to_video_wan",
-    "ltxv_image_to_video",
-    "ltxv_text_to_video",
-    "template-Animation_Trajectory_Control_Wan_ATI",
-    "templates-3D_logo_texture_animation",
-    "templates-6-key-frames",
-    "templates-car_product",
-    "templates-photo_to_product_vid",
-    "templates-sprite_sheet",
-    "templates-stitched_vid_contact_sheet",
-    "templates-textured_logo_elements",
-    "templates-textured_logotype-v2.1",
-    "text_to_video_wan",
-    "txt_to_image_to_video",
-    "ultility_hitpaw_video_enhance",
-    "utility-depthAnything-v2-relative-video",
-    "utility-frame_interpolation-film",
-    "utility-gan_upscaler",
-    "utility-lineart-video",
-    "utility-normal_crafter-video",
-    "utility-openpose-video",
-    "utility_seedvr2_video_upscale",
-    "utility_video_upscale",
-    "video-wan21_scail",
-    "video_humo",
-    "video_hunyuan_video_1.5_720p_i2v",
-    "video_hunyuan_video_1.5_720p_t2v",
-    "video_kandinsky5_i2v",
-    "video_kandinsky5_t2v",
-    "video_ltx2_canny_to_video",
-    "video_ltx2_depth_to_video",
-    "video_ltx2_i2v",
-    "video_ltx2_i2v_distilled",
-    "video_ltx2_pose_to_video",
-    "video_ltx2_t2v",
-    "video_ltx2_t2v_distilled",
-    "video_wan2.1_alpha_t2v_14B",
-    "video_wan2.1_fun_camera_v1.1_1.3B",
-    "video_wan2.1_fun_camera_v1.1_14B",
-    "video_wan2_1_infinitetalk",
-    "video_wan2_2_14B_animate",
-    "video_wan2_2_14B_flf2v",
-    "video_wan2_2_14B_fun_camera",
-    "video_wan2_2_14B_fun_control",
-    "video_wan2_2_14B_fun_inpaint",
-    "video_wan2_2_14B_i2v",
-    "video_wan2_2_14B_s2v",
-    "video_wan2_2_14B_t2v",
-    "video_wan2_2_5B_fun_control",
-    "video_wan2_2_5B_fun_inpaint",
-    "video_wan2_2_5B_ti2v",
-    "video_wan_ati",
-    "video_wan_vace_14B_ref2v",
-    "video_wan_vace_14B_t2v",
-    "video_wan_vace_14B_v2v",
-    "video_wan_vace_flf2v",
-    "video_wan_vace_inpainting",
-    "video_wan_vace_outpainting",
-    "video_wanmove_480p",
-    "video_wanmove_480p_hallucination",
-    "wan2.1_flf2v_720_f16",
-    "wan2.1_fun_control",
-    "wan2.1_fun_inp"
-  ],
-  "audio-generation": [
-    "05_audio_ace_step_1_t2a_song_subgraphed",
-    "api_kling2_6_i2v",
-    "api_kling2_6_t2v",
-    "api_stability_ai_audio_inpaint",
-    "api_stability_ai_audio_to_audio",
-    "api_stability_ai_text_to_audio",
-    "api_vidu_q3_image_to_video",
-    "api_vidu_q3_text_to_video",
-    "audio-chatterbox_tts",
-    "audio-chatterbox_tts_dialog",
-    "audio-chatterbox_tts_multilingual",
-    "audio-chatterbox_vc",
-    "audio_ace_step_1_5_checkpoint",
-    "audio_ace_step_1_5_split",
-    "audio_ace_step_1_5_split_4b",
-    "audio_ace_step_1_m2m_editing",
-    "audio_ace_step_1_t2a_instrumentals",
-    "audio_ace_step_1_t2a_song",
-    "audio_stable_audio_example",
-    "utility-audioseparation",
-    "video_wan2_1_infinitetalk",
-    "video_wan2_2_14B_s2v"
-  ],
-  "3d-generation": [
-    "04_hunyuan_3d_2.1_subgraphed",
-    "3d_hunyuan3d-v2.1",
-    "3d_hunyuan3d_image_to_model",
-    "3d_hunyuan3d_multiview_to_model",
-    "3d_hunyuan3d_multiview_to_model_turbo",
-    "api_from_photo_2_miniature",
-    "api_hunyuan3d_image_to_model",
-    "api_hunyuan3d_text_to_model",
-    "api_meshy_image_to_model",
-    "api_meshy_multi_image_to_model",
-    "api_meshy_text_to_model",
-    "api_rodin_gen2",
-    "api_rodin_image_to_model",
-    "api_rodin_multiview_to_model",
-    "api_tripo3_0_image_to_model",
-    "api_tripo3_0_text_to_model",
-    "api_tripo_image_to_model",
-    "api_tripo_multiview_to_model",
-    "api_tripo_text_to_model",
-    "templates-3D_logo_texture_animation",
-    "templates-qwen_multiangle"
-  ],
-  "lora": [
-    "flux_depth_lora_example",
-    "image-qwen_image_edit_2511_lora_inflation",
-    "image_qwen_image_2512_with_2steps_lora",
-    "image_qwen_image_union_control_lora"
-  ],
-  "embeddings": [],
-  "ip-adapter": [
-    "api_kling_omni_i2v",
-    "api_kling_omni_image",
-    "api_kling_omni_v2v",
-    "api_magnific_image_style_transfer",
-    "api_recraft_style_reference",
-    "api_vidu_q2_r2v",
-    "api_wan_r2v",
-    "templates-product_ad-v2.0"
-  ],
-  "samplers": [],
-  "cfg": [],
-  "vae": []
-}

apps/hub/knowledge/concepts/audio-generation.md

@@ -1,22 +0,0 @@
-# Audio Generation
-
-Audio generation in ComfyUI covers creating speech (text-to-speech), music, and sound effects from text prompts or reference audio. Dedicated audio models run within ComfyUI's node graph, letting you integrate audio creation into larger multimedia workflows — for example, generating a video and its soundtrack in a single pipeline.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: Model-specific nodes (`CosyVoice` nodes for TTS, `StableAudio` nodes for music/SFX), audio preview and save nodes, `AudioScheduler`
-- Typical workflow pattern: Load audio model → Provide text/reference input → Generate audio → Preview/save audio
-
-## Key Settings
-
-- **Sample rate**: Output audio quality, typically 24000–48000 Hz. Higher rates capture more detail but produce larger files.
-- **Duration**: Length of generated audio in seconds. Longer durations may reduce quality or coherence depending on the model.
-- **Voice reference**: For voice cloning, a short audio clip of the target voice (3–10 seconds of clean speech works best).
-- **Text input**: The text to be spoken (TTS) or the description of the desired audio (music/SFX generation).
-
-## Tips
-
-- CosyVoice and F5-TTS are popular choices for text-to-speech in ComfyUI, each with dedicated custom nodes.
-- Stable Audio Open handles music and sound effect generation from text descriptions.
-- Use clean, noise-free reference audio clips for voice cloning to get the best results.
-- Keep text inputs short and well-punctuated for the highest quality speech output — long paragraphs may degrade in naturalness.

apps/hub/knowledge/concepts/cfg.md

@@ -1,23 +0,0 @@
-# CFG / Guidance Scale
-
-Classifier-Free Guidance (CFG) controls how strongly the model follows your text prompt versus generating freely. Higher CFG values produce outputs that adhere more closely to the prompt but can cause oversaturation and artifacts, while lower values yield more natural-looking images at the cost of reduced prompt control. Finding the right balance is essential for every workflow.
-
-## How It Works in ComfyUI
-
-- Key nodes: `KSampler` (the `cfg` parameter), `ModelSamplingDiscrete` (for advanced noise schedule configurations)
-- During each sampling step, the model generates both a conditioned prediction (with your prompt) and an unconditioned prediction (without it). CFG scales the difference between the two — higher values push the output further toward the conditioned prediction, amplifying prompt influence.
-
-## Key Settings
-
-- **cfg** (1.0–30.0): The guidance scale value. Recommended ranges vary by model architecture:
-  - SD 1.5 / SDXL: 7–8 is the standard starting point
-  - Flux: 1.0–4.0 (Flux uses much lower guidance)
-  - Video models (e.g., Wan, HunyuanVideo): 3.5–5.0
-
-## Tips
-
-- Start at 7 for SD-based models and 3.5 for Flux, then adjust based on results
-- Values above ~12 for SD models typically cause color oversaturation, harsh contrast, and visible artifacts
-- Values below ~3 for SD models tend to produce blurry or incoherent results
-- Some models like Flux Schnell use a guidance embedding baked into the model rather than traditional CFG — for these, the `cfg` parameter may have little or no effect
-- When experimenting, change CFG in increments of 0.5–1.0 to see its impact clearly

apps/hub/knowledge/concepts/controlnet.md

@@ -1,28 +0,0 @@
-# ControlNet
-
-ControlNet guides image generation using structural conditions extracted from reference images — such as edge maps, depth information, or human poses. Instead of relying solely on text prompts for composition, ControlNet lets you specify the spatial layout precisely. This bridges the gap between text-to-image flexibility and the structural precision needed for professional workflows.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `ControlNetLoader`, `ControlNetApplyAdvanced`, preprocessor nodes (`CannyEdgePreprocessor`, `DepthAnythingPreprocessor`, `DWPosePreprocessor`, `LineartPreprocessor`)
-- Typical workflow pattern: Load reference image → preprocess to extract condition (edges/depth/pose) → load ControlNet model → apply condition to sampling → generate image with structural guidance
-
-## ControlNet Types
-
-- **Canny**: Detects edges to preserve outlines and shapes
-- **Depth**: Captures spatial depth for accurate foreground/background placement
-- **OpenPose**: Extracts human body and hand poses for character positioning
-- **Normal Map**: Encodes surface orientation for consistent lighting and geometry
-- **Lineart**: Follows line drawings and illustrations as generation guides
-- **Scribble**: Uses rough sketches as loose compositional guides
-
-## Key Settings
-
-- **Strength**: Controls how strongly the condition guides generation (0.0–1.0). Values of 0.5–1.0 are typical. Higher values enforce the structure more rigidly; lower values allow the model more creative freedom.
-- **start_percent / end_percent**: Controls when the ControlNet activates during the sampling process. Starting at 0.0 and ending at 1.0 applies guidance throughout. Ending earlier (e.g., 0.8) lets the model refine fine details freely in final steps.
-
-## Tips
-
-- Always preprocess your input image with the appropriate preprocessor node before feeding it to ControlNet. Raw images will not produce correct conditioning.
-- Combine multiple ControlNets for precise control — for example, Depth for spatial layout plus OpenPose for character positioning. Stack them by chaining `ControlNetApplyAdvanced` nodes.
-- If your generation looks distorted or overcooked, lower the ControlNet strength. Values above 0.8 can fight with the text prompt and produce artifacts.

apps/hub/knowledge/concepts/embeddings.md

@@ -1,19 +0,0 @@
-# Textual Embeddings
-
-Textual embeddings are learned text representations that encode specific concepts, styles, or objects into the CLIP text encoder's vocabulary. These tiny files (~10–100 KB) effectively add new "words" to your prompt vocabulary, letting you reference complex visual concepts — a particular art style, a specific character, or a set of undesirable artifacts — with a single token. Because they operate at the text-encoding level, embeddings integrate seamlessly with your existing prompts and require no changes to the model itself.
-
-## How It Works in ComfyUI
-
-- Key nodes: `CLIPTextEncode` — reference embeddings directly in your prompt text using the syntax `embedding:name_of_embedding`
-- Typical workflow pattern: Place embedding files in `ComfyUI/models/embeddings/` → type `embedding:name_of_embedding` inside your positive or negative prompt in a `CLIPTextEncode` node → connect to sampler as usual
-
-## Key Settings
-
-- **Prompt weighting**: Embeddings have no dedicated strength slider, but you can adjust their influence with prompt weighting syntax, e.g., `(embedding:name_of_embedding:1.2)` to increase strength or `(embedding:name_of_embedding:0.6)` to soften it
-- **Placement**: Add embeddings to the negative prompt to suppress unwanted features, or to the positive prompt to invoke a learned concept
-
-## Tips
-
-- Embeddings are commonly used in negative prompts (e.g., `embedding:EasyNegative`, `embedding:bad-hands-5`) to reduce common artifacts like malformed hands or distorted faces
-- Make sure the embedding matches your base model version — an SD 1.5 embedding will not work correctly with an SDXL checkpoint
-- You can combine multiple embeddings with regular text in the same prompt for fine-grained control

apps/hub/knowledge/concepts/img2img.md

@@ -1,20 +0,0 @@
-# Image-to-Image
-
-Image-to-image (img2img) transforms an existing image using a text prompt while preserving the original structure and composition. Instead of starting from pure noise, the source image is encoded into latent space and partially noised, then the sampler denoises it guided by your prompt. This lets you restyle photos, refine AI-generated images, or apply creative modifications while keeping the overall layout intact.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `LoadImage`, `VAEEncode`, `CLIPTextEncode` (positive + negative), `KSampler`, `VAEDecode`, `SaveImage`
-- Typical workflow pattern: Load source image → encode to latent with VAE → encode text prompts → sample with partial denoise → decode latent to image → save
-
-## Key Settings
-
-- **Denoise Strength**: The most important setting, ranging from 0.0 to 1.0. Lower values (0.2–0.4) preserve more of the original image with subtle changes. Higher values (0.6–0.8) allow more creative freedom but deviate further from the source. A value of 1.0 effectively ignores the input image entirely.
-- **Steps**: Number of sampling steps. 20–30 is typical. Fewer steps may be sufficient at low denoise values since less transformation is needed.
-- **CFG Scale**: Controls prompt adherence, same as text-to-image. 7–8 is a standard starting point.
-
-## Tips
-
-- Start with a denoise strength of 0.5 and adjust up or down based on how much change you want. This gives a balanced mix of original structure and new content.
-- Your input image resolution should match the model's training resolution. Resize or crop your source image to 512×512 (SD 1.5) or 1024×1024 (SDXL) before loading to avoid quality issues.
-- Use img2img iteratively: generate an initial text-to-image result, then refine it with img2img at low denoise to fix details without losing the overall composition.

apps/hub/knowledge/concepts/inpainting.md

@@ -1,21 +0,0 @@
-# Inpainting
-
-Inpainting selectively regenerates parts of an image using a mask while leaving the rest untouched. You paint a mask over the area you want to change, provide a text prompt describing the desired replacement, and the model fills in only the masked region. This is essential for fixing defects, replacing objects, or refining specific details in an otherwise finished image.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `LoadImage`, `VAEEncodeForInpainting`, `CLIPTextEncode` (positive + negative), `KSampler`, `VAEDecode`, `SaveImage`
-- Typical workflow pattern: Load image + mask → encode with inpainting-aware VAE node → encode text prompts → sample → decode → save
-- The mask can be created using ComfyUI's built-in mask editor or loaded from an external image
-
-## Key Settings
-
-- **grow_mask_by**: Expands the mask by a number of pixels, helping the regenerated area blend smoothly with the surrounding image. 6–8 pixels is typical. Too little causes visible seams; too much affects areas you wanted to keep.
-- **Denoise Strength**: For inpainting, higher values (0.7–1.0) generally work best since you want the masked region to be fully regenerated. Lower values may produce inconsistent blending.
-- **Checkpoint**: Dedicated inpainting models like `512-inpainting-ema` produce significantly better edge blending than standard checkpoints.
-
-## Tips
-
-- Always expand your mask slightly beyond the target area. Tight masks create hard edges that look unnatural against the surrounding image.
-- Describe what you want to appear in the masked region, not what you want to remove. For example, prompt "a clear blue sky" rather than "remove the bird."
-- Use inpainting-specific checkpoints whenever possible. Standard models can inpaint but often struggle with seamless blending at mask boundaries.

apps/hub/knowledge/concepts/ip-adapter.md

@@ -1,21 +0,0 @@
-# IP-Adapter
-
-IP-Adapter (Image Prompt Adapter) uses reference images to guide generation style, composition, or subject instead of — or alongside — text prompts. Rather than describing what you want in words, you show the model an image, enabling "image prompting." This is especially powerful for transferring artistic style, maintaining character consistency across generations, or conveying visual concepts that are difficult to express in text.
-
-## How It Works in ComfyUI
-
-- Key nodes: `IPAdapterModelLoader`, `IPAdapterApply` (or `IPAdapterAdvanced`), `CLIPVisionLoader`, `CLIPVisionEncode`, `PrepImageForClipVision`
-- Typical workflow pattern: Load IP-Adapter model + CLIP Vision model → prepare and encode reference image → apply adapter to the main model → connect to sampler → decode
-
-## Key Settings
-
-- **weight** (0.0–1.0): Controls the influence of the reference image on the output. A range of 0.5–0.8 is typical; higher values make the output closer to the reference
-- **weight_type**: Determines how the reference is interpreted — `standard` for general use, `style transfer` for artistic style without copying content, `composition` for layout guidance
-- **start_at / end_at** (0.0–1.0): Controls when the adapter is active during sampling. Limiting the range (e.g., 0.0–0.8) can improve prompt responsiveness while retaining reference influence
-
-## Tips
-
-- Use the `style_transfer` weight type when you want to borrow an artistic style without reproducing the reference image's content
-- Combine IP-Adapter with a text prompt for the best results — the text adds detail and specificity on top of the visual guidance
-- Face-specific IP-Adapter models (e.g., `ip-adapter-faceid`) exist for portrait consistency across multiple generations
-- Lower the weight if your output looks too similar to the reference image

apps/hub/knowledge/concepts/lora.md

@@ -1,20 +0,0 @@
-# LoRA
-
-LoRA (Low-Rank Adaptation) is a technique for fine-tuning a base model's behavior using a small add-on file rather than retraining the entire model. LoRAs adjust a model's style, teach it specific subjects, or introduce new concepts — all in a file typically just 10–200 MB, compared to multi-gigabyte full checkpoints. This makes them easy to share, swap, and combine. In ComfyUI, you load LoRAs on top of a checkpoint and control how strongly they influence the output.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `LoraLoader` (loads one LoRA and applies it to both MODEL and CLIP), `LoraLoaderModelOnly` (applies to MODEL only, skipping CLIP for faster loading)
-- Typical workflow pattern: Load checkpoint → LoraLoader (attach LoRA) → CLIP Text Encode → KSampler → VAE Decode. Chain multiple `LoraLoader` nodes to stack LoRAs.
-
-## Key Settings
-
-- **strength_model**: Controls how much the LoRA affects the diffusion model. Range 0.0–1.0; typical values are 0.6–1.0. Higher values apply the LoRA effect more strongly.
-- **strength_clip**: Controls how much the LoRA affects text encoding. Usually set to the same value as strength_model, but can be adjusted independently for fine control.
-
-## Tips
-
-- Start with strength 0.7 and adjust up or down based on results — too high can cause oversaturation or artifacts.
-- Stacking too many LoRAs simultaneously can cause visual artifacts or conflicting styles; two or three is usually a safe limit.
-- Ensure the LoRA matches your base model architecture — SD 1.5 LoRAs will not work with SDXL checkpoints, and vice versa.
-- Many LoRAs require specific trigger words in your prompt to activate; always check the LoRA's documentation or model card.

apps/hub/knowledge/concepts/outpainting.md

@@ -1,20 +0,0 @@
-# Outpainting
-
-Outpainting extends an image beyond its original borders, generating new content that seamlessly continues the existing scene. Unlike inpainting which replaces content within an image, outpainting adds content outside the frame — expanding the canvas in any direction. This is useful for changing aspect ratios, adding environmental context, or creating panoramic compositions from a single image.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `LoadImage`, `ImagePadForOutpaint`, `VAEEncodeForInpainting`, `CLIPTextEncode` (positive + negative), `KSampler`, `VAEDecode`, `SaveImage`
-- Typical workflow pattern: Load image → pad image with transparent/noised borders → encode with inpainting VAE node (padded area becomes the mask) → encode text prompts → sample → decode → save
-
-## Key Settings
-
-- **Padding Pixels**: The number of pixels to extend on each side, typically 64–256. Smaller increments produce more coherent results since the model has more context relative to the new area.
-- **Denoise Strength**: Use high values (0.8–1.0) for outpainted regions since the padded area is essentially blank and needs full generation.
-- **Feathering**: Controls the gradient blend between the original image and the new content. Higher feathering values create smoother transitions and reduce visible seams.
-
-## Tips
-
-- Outpaint in stages rather than all at once. Extending by 128 pixels at a time and iterating produces far more coherent results than trying to add 512 pixels in a single pass.
-- Use a lower CFG scale (5–6) for outpainting. This allows the model to generate more natural, context-aware extensions rather than forcing strict prompt adherence that may clash with the existing image.
-- Include scene context in your prompt that matches the original image. If the source shows an indoor room, describe the room's style and lighting so the extension feels continuous.

apps/hub/knowledge/concepts/samplers.md

@@ -1,21 +0,0 @@
-# Samplers & Schedulers
-
-Samplers are the algorithms that iteratively denoise a random latent into a coherent image, while schedulers control the noise schedule — how much noise is removed at each step. Together they determine the image's quality, speed, and visual character. Choosing the right combination is one of the most impactful decisions in any generation workflow.
-
-## How It Works in ComfyUI
-
-- Key nodes: `KSampler` (main sampling node), `KSamplerAdvanced` (provides control over start/end steps for multi-pass workflows)
-- Typical workflow pattern: Load model → connect conditioning → configure sampler/scheduler/steps → sample → decode
-
-## Key Settings
-
-- **sampler_name**: The denoising algorithm. Common choices include `euler` (fast, good baseline), `euler_ancestral` (more creative variation), `dpmpp_2m` (balanced quality and speed), `dpmpp_2m_sde` (high quality, slightly slower), `dpmpp_3m_sde` (very high quality), and `uni_pc` (fast convergence)
-- **scheduler**: Controls the noise reduction curve. `normal` is linear, `karras` front-loads noise reduction for better detail, `exponential` and `sgm_uniform` (recommended for SDXL) are also available
-- **steps** (1–100): Number of denoising iterations. 20–30 is typical; more steps give diminishing returns. Flux and LCM models need far fewer (4–8 steps)
-
-## Tips
-
-- `euler` + `normal` is the safest starting combination for any model
-- `dpmpp_2m` + `karras` is a popular choice when you want higher quality with minimal speed cost
-- Ancestral samplers (`euler_ancestral`, any `_sde` variant) produce different results each run even with the same seed — useful for exploration, but not for reproducibility
-- Flux and LCM models converge much faster; using 20+ steps with them wastes time without improving quality

apps/hub/knowledge/concepts/text-to-image.md

@@ -1,21 +0,0 @@
-# Text-to-Image Generation
-
-Text-to-image is the foundational workflow in ComfyUI: you provide a text description (prompt) and the system generates an image from scratch. This is the starting point for most generative AI art. A diffusion model iteratively denoises a random latent image, guided by your text prompt encoded through CLIP, to produce a coherent image matching your description.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `CheckpointLoaderSimple`, `CLIPTextEncode` (positive + negative), `EmptyLatentImage`, `KSampler`, `VAEDecode`, `SaveImage`
-- Typical workflow pattern: Load checkpoint → encode text prompts → create empty latent → sample → decode latent to image → save
-
-## Key Settings
-
-- **Resolution**: Must match the model's training resolution. Use 512×512 for SD 1.5, 1024×1024 for SDXL and Flux models. Mismatched resolutions produce artifacts like duplicated limbs or distorted compositions.
-- **Steps**: Number of denoising iterations. 20–30 steps is a good balance between quality and speed. More steps refine details but with diminishing returns beyond 30.
-- **CFG Scale**: Controls how strongly the sampler follows your prompt. 7–8 is the typical range. Higher values increase prompt adherence but can introduce oversaturation or artifacts.
-- **Seed**: Determines the initial random noise. A fixed seed produces reproducible results, which is useful for iterating on prompts while keeping composition consistent.
-
-## Tips
-
-- Start with simple, descriptive prompts before adding stylistic modifiers. Complex prompts can conflict and produce muddy results.
-- Use the negative prompt `CLIPTextEncode` to specify what you want to avoid (e.g., "blurry, low quality, deformed hands") — this significantly improves output quality.
-- Always match your `EmptyLatentImage` resolution to the model you loaded. A 768×768 image on an SD 1.5 checkpoint will produce noticeably worse results than 512×512.

apps/hub/knowledge/concepts/upscaling.md

@@ -1,21 +0,0 @@
-# Upscaling
-
-Upscaling increases image resolution while adding detail, turning a small generated image into a large, sharp result. In ComfyUI, there are two main approaches: model-based upscaling, which uses trained AI models (like RealESRGAN or 4x-UltraSharp) to intelligently enlarge an image in one pass, and latent-based upscaling, which works in latent space with a KSampler to add new detail during the enlargement process. Model-based is faster, while latent-based offers more creative control.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: `UpscaleModelLoader`, `ImageUpscaleWithModel`, `ImageScaleBy`, `LatentUpscale`, `VAEDecodeTiled`
-- Typical workflow pattern: Generate image → Upscale model loader → ImageUpscaleWithModel → Save image (model-based), or Generate latent → LatentUpscale → KSampler (lower denoise) → VAEDecode → Save image (latent-based)
-
-## Key Settings
-
-- **Upscale model**: The AI model used for model-based upscaling. `RealESRGAN_x4plus` is a reliable general-purpose choice; `4x-UltraSharp` excels at photo-realistic detail.
-- **Scale factor**: How much to enlarge — 2x and 4x are typical. Higher factors increase VRAM usage significantly.
-- **tile_size**: For tiled decoding/encoding of very large images. Range 512–1024; smaller tiles use less VRAM but take longer.
-
-## Tips
-
-- Model-based upscaling is faster but less creative; latent upscaling paired with a KSampler adds genuinely new detail.
-- Use `VAEDecodeTiled` for very large images to avoid out-of-memory errors.
-- Chain two 2x upscales instead of one 4x for better overall quality.
-- When using latent upscaling, set KSampler denoise to 0.3–0.5 to add detail without changing the composition.

apps/hub/knowledge/concepts/vae.md

@@ -1,20 +0,0 @@
-# VAE (Variational Autoencoder)
-
-The VAE encodes pixel images into a compact latent representation and decodes latents back into pixel images. All diffusion in Stable Diffusion and Flux happens in latent space — the VAE is the bridge between the images you see and the mathematical space where the model actually works. Every generation workflow ends with a VAE decode step to produce a viewable image.
-
-## How It Works in ComfyUI
-
-- Key nodes: `VAEDecode` (latent → image), `VAEEncode` (image → latent), `VAEDecodeTiled` (for large images to avoid out-of-memory errors), `VAELoader` (load a standalone VAE file)
-- Typical workflow pattern: Most checkpoints include a built-in VAE, so the `VAEDecode` node can pull directly from the loaded checkpoint. To use a different VAE, add a `VAELoader` node and connect it to `VAEDecode` instead.
-
-## Key Settings
-
-- **tile_size** (for `VAEDecodeTiled`): Size of each tile when decoding in chunks. Default is 512; reduce if you still encounter memory issues
-- **VAE choice**: VAE files are model-specific. Use `sdxl_vae.safetensors` for SDXL, `ae.safetensors` for Flux. Place files in `ComfyUI/models/vae/`
-
-## Tips
-
-- If colors look washed out or slightly off, try loading an external VAE — the VAE baked into a checkpoint is not always optimal, especially for community fine-tunes
-- Use `VAEDecodeTiled` for images larger than ~2048 px on either side to prevent out-of-memory crashes
-- SDXL and Flux each have their own VAE architecture — using the wrong one will produce corrupted output
-- When doing img2img or inpainting, the `VAEEncode` node converts your input image into the latent space the model expects

apps/hub/knowledge/concepts/video-generation.md

@@ -1,22 +0,0 @@
-# Video Generation
-
-Video generation creates video content from text prompts (T2V), reference images (I2V), or existing video (V2V) using specialized video diffusion models. Unlike image generation, video models must maintain temporal coherence across frames, ensuring smooth motion and consistent subjects. ComfyUI supports several leading open-source video models including WAN 2.1 and HunyuanVideo, each with their own loader and latent nodes.
-
-## How It Works in ComfyUI
-
-- Key nodes involved: Model-specific loaders (e.g. `WAN` video nodes, `HunyuanVideo` nodes, `LTXVLoader`), `EmptyHunyuanLatentVideo` / `EmptyLTXVLatentVideo`, `KSampler`, `VHS_VideoCombine` (from Video Helper Suite)
-- Typical workflow pattern: Load video model → Create empty video latent → KSampler (with video-aware scheduling) → VAE decode → VHS_VideoCombine → Save video
-
-## Key Settings
-
-- **Frame count**: Number of frames to generate. Typically 16–81 frames depending on the model; more frames require more VRAM and time.
-- **Resolution**: Often 512×320 or 848×480 for T2V. Higher resolutions need significantly more resources.
-- **FPS**: Frames per second for output, typically 8–24. Higher FPS gives smoother motion but requires more frames for the same duration.
-- **Motion scale/strength**: Controls the amount of movement in the generated video. Lower values produce subtle motion; higher values produce more dynamic scenes.
-
-## Tips
-
-- Start with fewer frames and lower resolution to test your prompt and settings before committing to a full-quality render.
-- Image-to-video (I2V) typically gives better coherence than text-to-video (T2V) because the model has a visual anchor.
-- Video Helper Suite (VHS) nodes are essential for loading, previewing, and saving video — install this custom node pack first.
-- WAN 2.1 and HunyuanVideo are currently the leading open models for quality video generation in ComfyUI.

apps/hub/knowledge/models/_aliases.json

@@ -1,88 +0,0 @@
-{
-  "Wan": "wan",
-  "Wan2.1": "wan",
-  "Wan2.2": "wan",
-  "Wan2.5": "wan",
-  "Wan2.6": "wan",
-  "Wan-Move": "wan",
-  "Motion Control": "wan",
-  "Flux": "flux",
-  "Flux.2": "flux",
-  "Flux.2 Dev": "flux",
-  "Flux.2 Klein": "flux",
-  "Kontext": "flux",
-  "BFL": "flux",
-  "SDXL": "sdxl",
-  "SD1.5": "sdxl",
-  "Stability": "sdxl",
-  "Reimagine": "sdxl",
-  "SD3.5": "sd3-5",
-  "SVD": "svd",
-  "Stable Audio": "stable-audio",
-  "Google": "gemini",
-  "Google Gemini": "gemini",
-  "Google Gemini Image": "gemini",
-  "Gemini3 Pro Image Preview": "gemini",
-  "Gemini-2.5-Flash": "gemini",
-  "Veo": "veo",
-  "Nano Banana Pro": "nano-banana-pro",
-  "nano-banana": "nano-banana-pro",
-  "OpenAI": "gpt-image-1",
-  "GPT-Image-1": "gpt-image-1",
-  "GPT-Image-1.5": "gpt-image-1",
-  "Qwen": "qwen",
-  "Qwen-Image": "qwen",
-  "Qwen-Image-Edit": "qwen",
-  "Qwen-Image-Layered": "qwen",
-  "Qwen-Image 2512": "qwen",
-  "Hunyuan Video": "hunyuan",
-  "Hunyuan3D": "hunyuan",
-  "Tencent": "hunyuan",
-  "LTX-2": "ltx-video",
-  "LTXV": "ltx-video",
-  "Lightricks": "ltx-video",
-  "ByteDance": "seedance",
-  "Seedance": "seedance",
-  "Seedream": "seedream",
-  "Seedream 4.0": "seedream",
-  "SeedVR2": "seedvr2",
-  "Vidu": "vidu",
-  "Vidu Q2": "vidu",
-  "Vidu Q3": "vidu",
-  "Kling": "kling",
-  "Kling O1": "kling",
-  "Kling2.6": "kling",
-  "ACE-Step": "ace-step",
-  "Chatter Box": "chatterbox",
-  "Recraft": "recraft",
-  "Runway": "runway",
-  "Luma": "luma",
-  "HiDream": "hidream",
-  "Tripo": "tripo",
-  "MiniMax": "minimax",
-  "Z-Image-Turbo": "z-image",
-  "Z-Image": "z-image",
-  "Grok": "grok",
-  "Moonvalley": "moonvalley",
-  "Topaz": "topaz",
-  "Kandinsky": "kandinsky",
-  "OmniGen": "omnigen",
-  "Magnific": "magnific",
-  "PixVerse": "pixverse",
-  "Meshy": "meshy",
-  "Rodin": "rodin",
-  "WaveSpeed": "wavespeed",
-  "Chroma": "chroma",
-  "BRIA": "bria",
-  "HitPaw": "hitpaw",
-  "NewBie": "newbie",
-  "Ovis-Image": "ovis-image",
-  "Ideogram": "ideogram",
-  "Anima": "anima",
-  "ChronoEdit": "chronoedit",
-  "Nvidia": "chronoedit",
-  "HuMo": "humo",
-  "FlashVSR": "flashvsr",
-  "Real-ESRGAN": "real-esrgan",
-  "Depth Anything\u00a0v2": "depth-anything-v2"
-}

apps/hub/knowledge/models/ace-step.md

@@ -1,47 +0,0 @@
-# ACE-Step
-
-ACE-Step is a foundation model for music generation developed by ACE Studio and StepFun. It uses diffusion-based generation with a Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer to achieve state-of-the-art speed and musical coherence.
-
-## Model Variants
-
-### ACE-Step (3.5B)
-
-- 3.5B parameter diffusion model
-- DCAE encoder with linear transformer conditioning
-- 27 or 60 inference steps recommended
-- Apache 2.0 license
-
-## Key Features
-
-- 15x faster than LLM-based baselines (20 seconds for a 4-minute song on A100)
-- Full-song generation with lyrics and structure
-- Duration control for variable-length output
-- Music remixing and style transfer
-- Lyrics editing and vocal synthesis
-- Supports 16+ languages including English, Chinese, Japanese, Korean, French, German, Spanish, and more
-- Text-to-music from natural language descriptions
-
-## Hardware Requirements
-
-- RTX 3090: 12.76x real-time factor at 27 steps
-- RTX 4090: 34.48x real-time factor at 27 steps
-- NVIDIA A100: 27.27x real-time factor at 27 steps
-- Apple M2 Max: 2.27x real-time factor at 27 steps
-- Higher step counts (60) reduce speed by roughly half
-
-## Common Use Cases
-
-- Original music generation from text descriptions
-- Song remixing and style transfer
-- Lyrics-based music creation
-- Multi-language vocal music generation
-- Rapid music prototyping for content creators
-- Background music and soundtrack generation
-
-## Key Parameters
-
-- **steps**: Inference steps (27 for speed, 60 for quality)
-- **duration**: Target audio length in seconds (up to ~5 minutes)
-- **lyrics**: Song lyrics text input for vocal generation
-- **prompt**: Natural language description of desired music style and mood
-- **seed**: Random seed for reproducible generation (results are seed-sensitive)

apps/hub/knowledge/models/anima.md

@@ -1,46 +0,0 @@
-# Anima
-
-Anima is an API-based AI video generation platform that creates animated video content from text prompts, supporting character consistency and storyboard-driven workflows.
-
-## Model Variants
-
-### Anima Video Generation
-
-- Cloud-based video generation service
-- Supports multiple underlying AI models (Runway, Kling, Minimax, Luma)
-- Integrated text, image, and audio generation pipeline
-
-## Key Features
-
-- AI character generation with persistent identity across scenes
-- Storyboard-based workflow: script to visual scenes with narration
-- Multi-model integration (GPT-4, Claude, Gemini for text; FLUX, MidJourney for images)
-- Voice generation via ElevenLabs integration
-- Music composition via Suno integration
-- Autopilot mode for fully automated video creation
-- Prompt enhancement for optimized output quality
-- Template library for rapid content creation
-- Scene-by-scene generation with character consistency
-
-## Hardware Requirements
-
-- No local hardware required (cloud-based service)
-- Runs entirely through web API
-- Browser-based interface for interactive use
-
-## Common Use Cases
-
-- Animated story series production
-- Movie trailer and concept video creation
-- Kids bedtime story animation
-- Lofi music video generation
-- Marketing and explainer video content
-- Storyboard visualization
-
-## Key Parameters
-
-- **prompt**: Text description of the scene or story
-- **character**: Selected or generated character for identity consistency
-- **style**: Visual style preset (animation, cinematic, etc.)
-- **duration**: Target video length
-- **resolution**: Output video resolution

apps/hub/knowledge/models/bria.md

@@ -1,48 +0,0 @@
-# BRIA AI
-
-BRIA AI is an enterprise-focused visual generative AI platform that trains its models exclusively on licensed, ethically sourced data, ensuring commercially safe outputs with full IP indemnification.
-
-## Model Variants
-
-### BRIA Fibo
-
-- Flagship hyper-controllable text-to-image model
-- JSON-based control framework with 100+ disentangled visual attributes
-- Supports lighting, depth, color, composition, and camera control
-- Ideal for agentic workflows and enterprise-scale creative automation
-
-### BRIA Text-to-Image Lite
-
-- Fully private, self-hosted deployment of the Fibo pipeline
-- Designed for regulated industries requiring total data control
-- Runs on-premises with no external data transfer
-
-## Key Features
-
-- Trained on 100% licensed data from 20+ partners including Getty Images
-- Full IP indemnification for commercial use
-- Tri-layer content moderation for brand-safe outputs
-- Patented attribution engine compensating data owners by usage
-- ControlNet support for canny, depth, recoloring, and IP Adapter
-- Multilingual prompt support
-- Fine-tuning API for brand-specific customization
-
-## Hardware Requirements
-
-- Cloud-hosted API available (no local GPU required)
-- Self-hosted Lite version supports deployment on AWS and Azure
-- Open-source weights available on Hugging Face for local inference
-
-## Common Use Cases
-
-- Enterprise marketing and advertising content
-- E-commerce product photography
-- Brand-consistent visual asset generation
-- Storyboarding and concept art for media production
-
-## Key Parameters
-
-- **prompt**: Text description of desired image
-- **style**: Photorealistic, illustrative, or custom styles
-- **guidance_methods**: ControlNet canny, depth, recoloring, IP Adapter
-- **resolution**: Multiple aspect ratios supported

apps/hub/knowledge/models/chatterbox.md

@@ -1,52 +0,0 @@
-# Chatterbox
-
-Chatterbox is a family of state-of-the-art open-source text-to-speech models developed by Resemble AI, featuring zero-shot voice cloning and emotion control.
-
-## Model Variants
-
-### Chatterbox Turbo
-
-- 350M parameters, single-step mel decoding for low latency
-- Paralinguistic tags for non-speech sounds ([laugh], [cough], [chuckle])
-- English only, optimized for voice agents and production use
-
-### Chatterbox (Original)
-
-- 500M parameter Llama backbone, English only
-- CFG and exaggeration control for emotion intensity
-
-### Chatterbox Multilingual
-
-- 500M parameters, 23 languages (Arabic, Chinese, French, German, Hindi, Japanese, Korean, Spanish, and more)
-- Zero-shot voice cloning across languages
-
-## Key Features
-
-- Zero-shot voice cloning from a few seconds of reference audio
-- Emotion exaggeration control (first open-source model with this feature)
-- Built-in PerTh neural watermarking for responsible AI
-- Sub-200ms latency for real-time applications
-- Trained on 500K hours of cleaned speech data
-- MIT license (free for commercial use)
-- Outperforms ElevenLabs in subjective evaluations
-
-## Hardware Requirements
-
-- Minimum: NVIDIA GPU with CUDA support
-- Turbo model requires less VRAM than original due to smaller architecture
-- Runs on consumer GPUs (RTX 3060 and above)
-- CPU inference possible but significantly slower
-
-## Common Use Cases
-
-- Voice cloning for content creation
-- AI voice agents and assistants
-- Audiobook narration
-- Game and media dialogue generation
-
-## Key Parameters
-
-- **exaggeration**: Emotion intensity control (0.0 to 1.0, default 0.5)
-- **cfg_weight**: Classifier-free guidance weight (0.0 to 1.0, default 0.5)
-- **audio_prompt_path**: Path to reference audio clip for voice cloning
-- **language_id**: Language code for multilingual model (e.g., "fr", "zh", "ja")

apps/hub/knowledge/models/chroma.md

@@ -1,50 +0,0 @@
-# Chroma
-
-Chroma is an open-source 8.9 billion parameter text-to-image model based on the FLUX.1-schnell architecture, developed by Lodestone Rock and the community. It is fully Apache 2.0 licensed.
-
-## Model Variants
-
-### Chroma
-
-- 8.9B parameter model based on FLUX.1-schnell
-- Trained on a curated 5M sample dataset (from 20M candidates)
-- Apache 2.0 license for unrestricted use
-- Supports both tag-based and natural language prompting
-
-### Chroma XL
-
-- Experimental merge and fine-tune based on NoobAI-XL (SDXL architecture)
-- Low CFG (2.5-3.0) and low step count (8-12 steps)
-- Optimized for fast generation on consumer hardware
-
-## Key Features
-
-- Fully open-source with Apache 2.0 licensing
-- Diverse training data spanning anime, artistic, and photographic styles
-- Community-driven development with public training logs
-- Compatible with FLUX ecosystem (VAE, T5 text encoder)
-- ComfyUI workflow support
-- LoRA and fine-tuning compatible
-- GGUF quantized versions available for lower VRAM
-
-## Hardware Requirements
-
-- Base model: 24GB VRAM recommended (BF16)
-- Q8_0 quantized: ~13GB VRAM
-- Q4_0 quantized: ~7GB VRAM
-- Requires FLUX.1 VAE and T5 text encoder
-
-## Common Use Cases
-
-- Open-source text-to-image generation
-- Artistic and stylized image creation
-- Community model fine-tuning and experimentation
-- LoRA training for custom styles and characters
-
-## Key Parameters
-
-- **prompt**: Text description or tag-based prompt
-- **steps**: Inference steps (15-30 recommended)
-- **cfg_scale**: Guidance scale (1-4, model uses low CFG)
-- **resolution**: Output resolution (1024x1024 default)
-- **guidance**: Flux-style guidance parameter (around 4)

apps/hub/knowledge/models/chronoedit.md

@@ -1,58 +0,0 @@
-# ChronoEdit
-
-ChronoEdit is an image editing framework by NVIDIA that reframes editing as a video generation task, using temporal reasoning to ensure physically plausible and consistent edits.
-
-## Model Variants
-
-### ChronoEdit-14B
-
-- Full 14 billion parameter model for maximum quality
-- Built on pretrained video diffusion model architecture
-- Requires ~34GB VRAM (38GB with temporal reasoning enabled)
-
-### ChronoEdit-2B
-
-- Compact 2 billion parameter variant for efficiency
-- Maintains core temporal reasoning capabilities
-- Lower VRAM requirements for broader hardware compatibility
-
-### ChronoEdit-14B 8-Step Distilled LoRA
-
-- Distilled variant requiring only 8 inference steps
-- Faster generation with minimal quality loss
-- Uses flow-shift 2.0 and guidance-scale 1.0
-
-## Key Features
-
-- Treats image editing as a video generation task for temporal consistency
-- Temporal reasoning tokens simulate intermediate editing trajectories
-- Ensures physically plausible edits (object interactions, lighting, shadows)
-- Two-stage pipeline: temporal reasoning stage followed by editing frame generation
-- Prompt enhancer integration for improved editing instructions
-- LoRA fine-tuning support via DiffSynth-Studio
-- Upscaler LoRA available for super-resolution editing
-- PaintBrush LoRA for sketch-to-object editing
-- Apache-2.0 license
-
-## Hardware Requirements
-
-- 14B model: 34GB VRAM minimum (38GB with temporal reasoning)
-- 2B model: 12GB+ VRAM estimated
-- Supports model offloading to reduce peak VRAM
-- Linux only (not supported on Windows/macOS)
-
-## Common Use Cases
-
-- Physically consistent image editing (add/remove/modify objects)
-- World simulation for autonomous driving and robotics
-- Visualizing editing trajectories and reasoning
-- Image super-resolution via upscaler LoRA
-- Sketch-to-object conversion via PaintBrush LoRA
-
-## Key Parameters
-
-- **prompt**: Text description of the desired edit
-- **num_inference_steps**: Denoising steps (default ~50, or 8 with distilled LoRA)
-- **guidance_scale**: Prompt adherence strength (default ~7.5, or 1.0 with distilled LoRA)
-- **flow_shift**: Flow matching shift parameter (2.0 for distilled LoRA)
-- **enable_temporal_reasoning**: Toggle temporal reasoning stage for better consistency

apps/hub/knowledge/models/depth-anything-v2.md

@@ -1,60 +0,0 @@
-# Depth Anything V2
-
-Depth Anything V2 is a monocular depth estimation model trained on 595K synthetic labeled images and 62M+ real unlabeled images, providing robust relative depth maps from single images.
-
-## Model Variants
-
-### Depth-Anything-V2-Small
-
-- Lightweight variant for fast inference
-- ViT-S (Small) encoder backbone
-- Suitable for real-time applications
-
-### Depth-Anything-V2-Base
-
-- Mid-range variant balancing speed and accuracy
-- ViT-B (Base) encoder backbone
-
-### Depth-Anything-V2-Large
-
-- High-accuracy variant for detailed depth maps
-- ViT-L (Large) encoder backbone with 256 output features
-- Recommended for most production use cases
-
-### Depth-Anything-V2-Giant
-
-- Maximum accuracy variant
-- ViT-G (Giant) encoder backbone
-- Highest computational requirements
-
-## Key Features
-
-- More fine-grained depth detail than Depth Anything V1
-- More robust than V1 and Stable Diffusion-based alternatives (Marigold, Geowizard)
-- 10× faster than SD-based depth estimation models
-- Trained on large-scale synthetic + real data mixture
-- Produces relative (not metric) depth maps by default
-- DPT (Dense Prediction Transformer) decoder architecture
-
-## Hardware Requirements
-
-- Small: 2GB VRAM minimum
-- Base: 4GB VRAM minimum
-- Large: 6GB VRAM recommended
-- Giant: 12GB+ VRAM recommended
-- CPU inference supported for smaller variants
-
-## Common Use Cases
-
-- Depth map generation for compositing and VFX
-- ControlNet depth conditioning for image generation
-- 3D scene understanding and reconstruction
-- Foreground/background separation
-- Augmented reality occlusion
-- Video depth estimation for parallax effects
-
-## Key Parameters
-
-- **encoder**: Model size variant (vits, vitb, vitl, vitg)
-- **input_size**: Processing resolution (higher = more detail, more VRAM)
-- **output_type**: Raw depth array or normalized visualization

apps/hub/knowledge/models/flashvsr.md

@@ -1,50 +0,0 @@
-# FlashVSR
-
-FlashVSR is a diffusion-based streaming video super-resolution framework that achieves near real-time 4× upscaling through one-step inference with locality-constrained sparse attention.
-
-## Model Variants
-
-### FlashVSR v1
-
-- Initial release of the one-step streaming VSR model
-- Built on Wan2.1 1.3B video diffusion backbone
-- 4× super-resolution optimized
-
-### FlashVSR v1.1
-
-- Enhanced stability and fidelity over v1
-- Improved artifact handling across different aspect ratios
-- Recommended for production use
-
-## Key Features
-
-- One-step diffusion inference (no multi-step denoising required)
-- Streaming architecture with KV cache for sequential frame processing
-- Locality-Constrained Sparse Attention (LCSA) prevents artifacts at high resolutions
-- Tiny Conditional Decoder (TC Decoder) achieves 7× faster decoding than standard WanVAE
-- Three-stage distillation pipeline from multi-step to single-step inference
-- Runs at ~17 FPS for 768×1408 videos on a single A100 GPU
-- Up to 12× speedup over prior one-step diffusion VSR models
-- Scales reliably to ultra-high resolutions
-
-## Hardware Requirements
-
-- Minimum: 24GB VRAM (A100 or similar recommended)
-- Optimized for NVIDIA A100 GPUs
-- Significant VRAM required for high-resolution video processing
-- Multi-GPU inference not required but beneficial for throughput
-
-## Common Use Cases
-
-- Real-world video upscaling to 4K
-- AI-generated video enhancement and artifact removal
-- Long video super-resolution with temporal consistency
-- Streaming video quality improvement
-- Restoring compressed or low-resolution video footage
-
-## Key Parameters
-
-- **scale**: Upscaling factor (4× recommended for best results)
-- **tile_size**: Spatial tiling for memory management (0 = auto)
-- **input_resolution**: Source video resolution (outputs 4× larger)
-- **model_version**: v1 or v1.1 checkpoint selection

apps/hub/knowledge/models/flux.md

@@ -1,98 +0,0 @@
-# Flux
-
-Flux is a family of state-of-the-art text-to-image and image editing models developed by Black Forest Labs (BFL).
-
-## Model Variants
-
-### Flux.1 Schnell
-
-- Ultra-fast inference (1-4 steps)
-- 12B parameter rectified flow transformer
-- Apache 2.0 license (open source)
-- Best for rapid prototyping and real-time applications
-
-### Flux.1 Dev
-
-- High-quality 12B parameter development model
-- 20-50 steps for best results
-- Non-commercial license for research
-- Guidance-distilled for efficient generation
-
-### Flux.1 Pro
-
-- Highest quality Flux.1 outputs via commercial API
-- Best prompt adherence and detail
-
-### Flux.2 Dev
-
-- 32B parameter rectified flow transformer
-- Unified text-to-image, single-reference editing, and multi-reference editing
-- No fine-tuning needed for character/object/style reference
-- Up to 4MP photorealistic output with improved autoencoder
-- Non-commercial license; quantized versions available for consumer GPUs
-
-### Flux.2 Klein
-
-- Fastest Flux model family — sub-second inference on modern hardware
-- **Klein 4B**: ~8GB VRAM, Apache 2.0 license, ideal for edge deployment
-- **Klein 9B**: Best quality-to-latency ratio, non-commercial license
-- Base (undistilled) variants available for fine-tuning and LoRA training
-- Supports text-to-image, single-reference editing, and multi-reference editing
-
-### Flux.1 Kontext
-
-- In-context image generation and editing via text instructions
-- Available as Kontext Max (premium), Pro (API), and Dev (open-weights, 12B)
-- Character consistency across multiple scenes without fine-tuning
-- Typography manipulation and local editing within images
-
-### Flux.1 Fill
-
-- Dedicated inpainting and outpainting model
-- Maintains consistency with surrounding image context
-- Available as Fill Pro (API) and Fill Dev (open-weights)
-
-### Flux Redux / Canny / Depth
-
-- **Redux**: Image variation generation from reference images
-- **Canny**: Edge-detection-based structural conditioning
-- **Depth**: Depth-map-based structural conditioning for pose/layout control
-
-## Key Features
-
-- Excellent text rendering in images
-- Strong prompt following and instruction adherence
-- High resolution output (up to 4MP with Flux.2)
-- Multi-reference editing: combine up to 6 reference images
-- Consistent style and quality across generations
-
-## Hardware Requirements
-
-- Flux.2 Klein 4B: ~8GB VRAM (consumer GPUs like RTX 4070)
-- Flux.2 Klein 9B: ~20GB VRAM
-- Flux.1 models: 12GB VRAM minimum (fp16), 24GB recommended
-- Flux.2 Dev: 64GB+ VRAM native, FP8 quantized ~40GB
-- Quantized and weight-streaming options available for lower VRAM cards
-
-## Common Use Cases
-
-- Text-to-image generation
-- Iterative image editing via text instructions
-- Character-consistent multi-scene generation
-- Inpainting and outpainting
-- Style transfer and image variation
-- Structural conditioning (canny, depth)
-
-## Key Parameters
-
-- **steps**: 1-4 (Schnell/Klein distilled), 20-50 (Dev/Base)
-- **guidance_scale**: 3.5-4.0 typical for Flux.2, 3.5 for Flux.1
-- **resolution**: Up to 2048x2048 (Flux.1), up to 4MP (Flux.2)
-- **seed**: For reproducible generation
-- **prompt_upsampling**: Optional LLM-based prompt enhancement (Flux.2)
-
-## Blog References
-
-- [FLUX.2 Day-0 Support in ComfyUI](../blog/flux2-day-0-support.md) — FLUX.2 with 4MP output, multi-reference consistency, professional text rendering
-- [FLUX.2 [klein] 4B & 9B](../blog/flux2-klein-4b.md) — Fastest Flux models, sub-second inference, unified generation and editing
-- [The Complete AI Upscaling Handbook](../blog/upscaling-handbook.md) — Benchmarks for upscaling workflows

apps/hub/knowledge/models/flux.summary.md

@@ -1 +0,0 @@
-Flux is Black Forest Labs' family of text-to-image and image editing models. The lineup includes Flux.1 Schnell (ultra-fast, 1-4 steps, Apache 2.0), Flux.1 Dev (high-quality, 20-50 steps, non-commercial), Flux.1 Pro (commercial API), and the newer Flux.2 Dev (32B parameters, up to 4MP output, multi-reference editing without fine-tuning). Flux.2 Klein offers sub-second inference in 4B (~8GB VRAM, Apache 2.0) and 9B variants. Specialized models include Kontext (in-context editing, character consistency), Fill (inpainting/outpainting), Redux (image variations), and Canny/Depth (structural conditioning). Flux excels at text rendering in images, strong prompt adherence, and consistent multi-scene generation. VRAM ranges from ~8GB (Klein 4B) to 64GB+ (Flux.2 Dev native), with quantized options available. Key parameters: guidance_scale 3.5-4.0, resolution up to 4MP for Flux.2. Primary uses include text-to-image, iterative editing, style transfer, and structural conditioning.

apps/hub/knowledge/models/gemini.md

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-# Gemini
-
-Gemini is Google DeepMind's multimodal AI model family with native image generation, editing, and video generation capabilities, accessible in ComfyUI through API nodes.
-
-## Model Variants
-
-### Gemini 3 Pro Image Preview
-
-- Most capable Gemini image model with advanced reasoning
-- Complex multi-turn image generation and editing
-- Up to 14 input images, native 4K output
-- Also known as Nano Banana Pro
-- Model ID: `gemini-3-pro-image-preview`
-
-### Gemini 2.5 Flash Image
-
-- Cost-effective image generation optimized for speed and low latency
-- Character consistency, multi-image fusion, and prompt-based editing
-- $0.039 per image (1290 output tokens per image)
-- Model ID: `gemini-2.5-flash-image`
-
-### Google Gemini (General)
-
-- Multimodal model for text, image understanding, and generation
-- Interleaved text-and-image output in conversational context
-- Supports image input for analysis and editing tasks
-
-### Veo 2
-
-- Text-to-video and image-to-video generation
-- 8-second video clips at 720p resolution
-- Realistic physics simulation and cinematic styles
-- Supports 16:9 and 9:16 aspect ratios
-- Model ID: `veo-2.0-generate-001`
-
-### Veo 3 / 3.1
-
-- Latest video generation with native audio (dialogue, SFX, ambient)
-- Up to 1080p and 4K resolution (Veo 3.1)
-- Style reference images for aesthetic control
-- 4, 6, or 8-second video duration options
-
-## Key Features
-
-- Native multimodal generation: text, images, and video in one model family
-- World knowledge from Google Search for factually accurate image generation
-- SynthID invisible watermarking on all generated content
-- Multi-image fusion and character consistency across generations
-- Clean text rendering across multiple languages
-- Prompt-based image editing without masks or complex workflows
-
-## Hardware Requirements
-
-- No local GPU required — all models accessed via cloud API
-- Available through ComfyUI API nodes, Google AI Studio, and Vertex AI
-- Requires API key and network access
-
-## Common Use Cases
-
-- Text-to-image and image editing via API nodes
-- Multi-turn conversational image generation
-- Video generation from text prompts or reference images
-- Product animation and social media video content
-- Style-consistent character and brand asset generation
-- Text rendering and translation in images
-
-## Key Parameters
-
-- **prompt**: Text description for generation or editing
-- **aspect_ratio**: 1:1, 3:4, 4:3, 9:16, 16:9, 21:9 (images); 16:9, 9:16 (video)
-- **temperature**: 0.0-2.0 (default 1.0 for image models)
-- **durationSeconds**: 4-8 seconds for Veo models
-- **sampleCount**: 1-4 output videos per request
-- **seed**: Integer for reproducible generation
-- **personGeneration**: Safety control — `allow_adult`, `dont_allow`, or `allow_all`

apps/hub/knowledge/models/gpt-image-1.md

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-# GPT-Image-1
-
-GPT-Image-1 is OpenAI's natively multimodal image generation model, capable of generating and editing images from text and image inputs. It is accessed in ComfyUI through API nodes.
-
-## Model Variants
-
-### GPT-Image-1.5
-
-- Latest and most advanced GPT Image model
-- Best overall quality with superior instruction following
-- High input fidelity for the first 5 input images
-- Supports generate vs. edit action control
-- Multi-turn editing via the Responses API
-
-### GPT-Image-1
-
-- Production-grade image generation and editing
-- High input fidelity for the first input image
-- Supports up to 16 input images for editing
-- Up to 10 images per generation request
-
-### GPT-Image-1-Mini
-
-- Cost-effective variant for lower quality requirements
-- Same API surface as GPT-Image-1
-- Suitable for rapid prototyping and high-volume workloads
-
-## Key Features
-
-- Superior text rendering in generated images
-- Real-world knowledge for accurate depictions
-- Transparent background support (PNG and WebP)
-- Mask-based inpainting with prompt guidance
-- Multi-image editing: combine up to 16 reference images
-- Streaming partial image output during generation
-- Content moderation with adjustable strictness
-
-## Hardware Requirements
-
-- No local GPU required — cloud API service via OpenAI
-- Accessed through ComfyUI API nodes
-- Requires OpenAI API key and organization verification
-
-## Common Use Cases
-
-- Text-to-image generation with detailed prompts
-- Image editing and compositing from multiple references
-- Product photography and mockup generation
-- Inpainting with mask-guided editing
-- Transparent asset generation (stickers, logos, icons)
-- Multi-turn iterative image refinement
-
-## Key Parameters
-
-- **prompt**: Text description up to 32,000 characters
-- **size**: `1024x1024`, `1536x1024` (landscape), `1024x1536` (portrait), or `auto`
-- **quality**: `low`, `medium`, `high`, or `auto` (affects cost and detail)
-- **n**: Number of images to generate (1-10)
-- **background**: `transparent`, `opaque`, or `auto`
-- **output_format**: `png`, `jpeg`, or `webp`
-- **moderation**: `auto` (default) or `low` (less restrictive)
-- **input_fidelity**: `low` (default) or `high` for preserving input image details

apps/hub/knowledge/models/grok.md

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-# Grok (Aurora)
-
-Aurora is xAI's autoregressive image generation model integrated into Grok, excelling at photorealistic rendering and precise text instruction following.
-
-## Model Variants
-
-### grok-2-image-1212
-
-- API-accessible image generation model
-- Generates multiple images from text prompts
-- $0.07 per generated image
-- OpenAI and Anthropic SDK compatible
-
-### Aurora (Consumer)
-
-- Autoregressive mixture-of-experts network
-- Trained on billions of text and image examples
-- Available via Grok on X platform, web, iOS, and Android
-
-### Grok Imagine
-
-- Video and image generation model
-- State-of-the-art quality across cost and latency
-- API available since January 2026
-
-## Key Features
-
-- Photorealistic image generation from text prompts
-- Precise text rendering within images
-- Accurate rendering of real-world entities, logos, and text
-- Image editing via uploaded photos with text instructions
-- Multi-image generation per request
-- Native multimodal input support
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- All generation runs on xAI infrastructure
-- API access via console.x.ai
-
-## Common Use Cases
-
-- Photorealistic image generation
-- Text and logo rendering in images
-- Image editing and style transfer
-- Meme and social media content creation
-- Product visualization
-- Character and portrait generation
-
-## Key Parameters
-
-- **prompt**: Text description of desired image
-- **model**: Model identifier (grok-2-image-1212)
-- **n**: Number of images to generate
-- **response_format**: Output format (url or b64_json)
-- **size**: Image dimensions

apps/hub/knowledge/models/hidream.md

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-# HiDream-I1
-
-HiDream-I1 is a 17B parameter image generation foundation model by HiDream.ai that achieves state-of-the-art quality using a sparse diffusion transformer architecture.
-
-## Model Variants
-
-### HiDream-I1 Full
-
-- Full 17B parameter sparse diffusion transformer
-- Uses Llama-3.1-8B-Instruct and T5-XXL as text encoders
-- VAE from FLUX.1 Schnell, MIT license
-
-### HiDream-I1 Dev
-
-- Distilled variant, faster inference with minor quality tradeoff
-
-### HiDream-I1 Fast
-
-- Further distilled for maximum speed, best for rapid prototyping
-
-### HiDream-E1
-
-- Instruction-based image editing model
-
-## Key Features
-
-- State-of-the-art HPS v2.1 score (33.82), surpassing Flux.1-dev, DALL-E 3, and Midjourney V6
-- Best-in-class prompt following on GenEval (0.83) and DPG-Bench (85.89)
-- Multiple output styles: photorealistic, cartoon, artistic, and more
-- Dual text encoding with Llama-3.1-8B-Instruct and T5-XXL for strong prompt adherence
-- MIT license for commercial use
-- Requires Flash Attention for optimal performance
-
-## Hardware Requirements
-
-- Minimum: 24GB VRAM (Full model), Dev and Fast variants run on lower VRAM
-- Recommended: 40GB+ VRAM for Full model at high resolution
-- CUDA 12.4+ recommended for Flash Attention
-- Llama-3.1-8B-Instruct weights downloaded automatically
-
-## Common Use Cases
-
-- High-fidelity text-to-image generation
-- Photorealistic image creation
-- Artistic and stylized illustrations
-- Instruction-based image editing (E1 variant)
-- Commercial image generation
-
-## Key Parameters
-
-- **model_type**: Variant selection (full, dev, fast)
-- **steps**: Inference steps (varies by variant; fewer for fast/dev)
-- **cfg_scale**: Guidance scale for prompt adherence
-- **resolution**: Output image dimensions
-- **prompt**: Detailed text description of desired image

apps/hub/knowledge/models/hitpaw.md

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-# HitPaw
-
-HitPaw is an AI-powered visual enhancement platform providing image and video upscaling, restoration, and denoising through dedicated API services and desktop applications.
-
-## Model Variants
-
-### HitPaw Image Enhancer
-
-- AI-powered photo enhancement with super-resolution up to 8x
-- Face Clear Model: dual-model portrait upscaling (2x and 4x)
-- Face Natural Model: texture-preserving portrait enhancement
-- General Enhance Model: super-resolution for scenes and objects
-- High Fidelity Model: premium upscaling for DSLR and AIGC images
-- Generative Portrait/Enhance Models: diffusion-based restoration for heavily compressed images
-
-### HitPaw Video Enhancer (VikPea)
-
-- Frame-aware video restoration and ultra HD upscaling
-- Face Soft Model: face-optimized noise and blur reduction
-- Portrait Restore Model: multi-frame fusion for facial detail
-- General Restore Model: GAN-based restoration for broad scenarios
-- Ultra HD Model: premium upscaling from HD to ultra HD
-- Generative Model: diffusion-driven repair for low-resolution video
-
-## Key Features
-
-- One-click portrait and scene enhancement
-- Dual-model face and background processing pipelines
-- Batch processing and API access for automated workflows
-- Support for 30+ video input formats and 5 export formats
-- Multi-frame face restoration for temporal consistency in video
-- Denoising models for mobile and camera images
-
-## Hardware Requirements
-
-- Cloud API available (no local GPU required)
-- Desktop apps for Windows, Mac, Android, and iOS
-- API integration via HTTP-based interface
-
-## Common Use Cases
-
-- Upscaling AI-generated images to publication quality
-- Restoring old or low-resolution photos and videos
-- Enhancing portrait and landscape photography
-- Video quality improvement for content creators
-
-## Key Parameters
-
-- **model**: Select enhancement model per content type
-- **scale**: 2x or 4x super-resolution options
-- **format**: Output format selection (mp4, mov, mkv, m4v, avi for video)

apps/hub/knowledge/models/humo.md

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-# HuMo
-
-HuMo is a human-centric video generation model by ByteDance that produces videos from collaborative multi-modal conditioning using text, image, and audio inputs.
-
-## Model Variants
-
-### HuMo (Wan2.1-T2V-1.3B based)
-
-- Built on the Wan2.1-T2V-1.3B video foundation model
-- Supports Text+Image (TI), Text+Audio (TA), and Text+Image+Audio (TIA) modes
-- Two-stage training: subject preservation then audio-visual sync
-
-## Key Features
-
-- Multi-modal conditioning: text, reference images, and audio simultaneously
-- Subject identity preservation from reference images across frames
-- Audio-driven lip synchronization with facial expression alignment
-- Focus-by-predicting strategy for facial region attention during audio sync
-- Time-adaptive guidance dynamically adjusts input weights across denoising steps
-- Minimal-invasive image injection maintains base model prompt understanding
-- Progressive two-stage training separates identity learning from audio sync
-- Supports text-controlled appearance editing while preserving identity
-
-## Hardware Requirements
-
-- Minimum: 24GB VRAM (RTX 3090/4090 or similar)
-- Multi-GPU inference supported via FSDP and sequence parallelism
-- Whisper-large-v3 audio encoder required for audio modes
-- Optional audio separator for cleaner speech input
-
-## Common Use Cases
-
-- Digital avatar and virtual presenter creation
-- Audio-driven talking head generation
-- Character-consistent video clips from reference photos
-- Lip-synced dialogue video from audio tracks
-- Prompted reenactment with identity preservation
-- Text-controlled outfit and style changes on consistent subjects
-
-## Key Parameters
-
-- **mode**: Generation mode (TI, TA, or TIA)
-- **scale_t**: Text guidance strength (default: 7.5)
-- **scale_a**: Audio guidance strength (default: 2.0)
-- **frames**: Number of output frames (97 at 25 FPS = ~4 seconds)
-- **height/width**: Output resolution (480p or 720p supported)
-- **steps**: Denoising steps (30-50 recommended)

apps/hub/knowledge/models/hunyuan.md

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-# Hunyuan
-
-Hunyuan is Tencent's family of open-source generative models spanning text-to-image, text-to-video, and 3D asset generation.
-
-## Model Variants
-
-### Hunyuan-DiT
-
-- Text-to-image diffusion transformer with native Chinese and English support
-- 1.5B parameter DiT architecture, native 1024x1024 resolution
-- Bilingual text encoder for strong CJK text rendering in images
-- v1.2 is the latest version with improved quality
-
-### HunyuanVideo
-
-- Large-scale text-to-video and image-to-video generation model
-- 13B+ parameters, the largest open-source video generation model
-- Dual-stream to single-stream transformer architecture with full attention
-- MLLM text encoder (decoder-only LLM) for better instruction following
-- Causal 3D VAE with 4x temporal, 8x spatial, 16x channel compression
-- Generates 720p video (1280x720) at up to 129 frames (~5s at 24fps)
-- FP8 quantized weights available to reduce memory by ~10GB
-- Outperforms Runway Gen-3, Luma 1.6 in professional evaluations
-- 3 workflow templates available
-
-### Hunyuan3D 2.0
-
-- Image-to-3D and text-to-3D asset generation system
-- Two-stage pipeline: Hunyuan3D-DiT (shape) + Hunyuan3D-Paint (texture)
-- Flow-based diffusion transformer for geometry generation
-- High-resolution texture synthesis with geometric and diffusion priors
-- Outputs textured meshes in GLB/OBJ format
-- Outperforms both open and closed-source 3D generation models
-- 7 workflow templates available
-
-## Key Features
-
-- Native bilingual support (Chinese and English) across the family
-- Strong text rendering in generated images (Hunyuan-DiT)
-- State-of-the-art video generation quality (HunyuanVideo)
-- End-to-end 3D asset creation with texturing (Hunyuan3D)
-- Multi-resolution generation across all model types
-- Prompt rewrite system for improved generation quality (HunyuanVideo)
-
-## Hardware Requirements
-
-- Hunyuan-DiT: 11GB VRAM minimum (fp16), 16GB recommended
-- HunyuanVideo 540p (544x960): 45GB VRAM minimum
-- HunyuanVideo 720p (720x1280): 60GB VRAM minimum, 80GB recommended
-- HunyuanVideo FP8: Saves ~10GB compared to fp16 weights
-- Hunyuan3D 2.0: 16-24GB VRAM for shape + texture pipeline
-
-## Common Use Cases
-
-- Bilingual content creation and marketing materials
-- Asian-style artwork and illustrations
-- Text-in-image generation (Chinese/English)
-- High-quality video generation from text or image prompts
-- 3D asset creation for games, design, and prototyping
-- Textured mesh generation from reference images
-
-## Key Parameters
-
-- **steps**: 25-50 for Hunyuan-DiT (default 40), 50 for HunyuanVideo
-- **cfg_scale**: 5-8 for DiT (6 typical), 6.0 embedded for HunyuanVideo
-- **flow_shift**: 7.0 for HunyuanVideo flow matching scheduler
-- **video_length**: 129 frames for HunyuanVideo (~5s at 24fps)
-- **resolution**: 1024x1024 for DiT, 720x1280 or 544x960 for video
-- **negative_prompt**: Recommended for Hunyuan-DiT quality control
-
-## Blog References
-
-- [HunyuanVideo Native Support](../blog/hunyuanvideo-native-support.md) — 13B parameter video model, dual-stream transformer, MLLM text encoder
-- [HunyuanVideo 1.5 Native Support](../blog/hunyuanvideo-15-native-support.md) — Lightweight 8.3B model, 720p output, runs on 24GB consumer GPUs
-- [Hunyuan3D 2.0 and MultiView Native Support](../blog/hunyuan3d-20-native-support.md) — 3D model generation with PBR materials, 1.1B parameter multi-view model

apps/hub/knowledge/models/hunyuan.summary.md

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-Hunyuan is Tencent's open-source generative model family spanning text-to-image, text-to-video, and 3D generation. Hunyuan-DiT is a 1.5B parameter text-to-image model with native Chinese and English support and strong CJK text rendering at 1024x1024 (11-16GB VRAM). HunyuanVideo is the largest open-source video model at 13B+ parameters, generating 720p video up to 129 frames (~5s at 24fps) using a dual-stream transformer with MLLM text encoder; it requires 45-80GB VRAM depending on resolution (FP8 saves ~10GB). Hunyuan3D 2.0 handles image-to-3D and text-to-3D generation via a two-stage pipeline producing textured GLB/OBJ meshes (16-24GB VRAM). Key strengths: bilingual content creation, state-of-the-art video quality surpassing Runway Gen-3, and end-to-end 3D asset creation. Typical parameters: 25-50 steps for DiT, 50 steps for video, cfg_scale 5-8.

apps/hub/knowledge/models/ideogram.md

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-# Ideogram
-
-Ideogram is an AI image generation platform founded by former Google Brain researchers, known for industry-leading text rendering accuracy in generated images. It achieves approximately 90% text rendering accuracy compared to roughly 30% for competing tools.
-
-## Model Variants
-
-### Ideogram 3.0
-
-- Latest generation released March 2025
-- Highest ELO rating in human evaluations across diverse prompts
-- Style References support with up to 3 reference images
-- Random style feature with 4.3 billion style presets
-- Batch generation for scaled content production
-
-### Ideogram 2.0
-
-- Previous generation model
-- Available as alternative option in the platform
-- Solid text rendering and general image quality
-
-## Key Features
-
-- Best-in-class text rendering with accurate typography and spelling
-- Handles complex, multi-line text compositions and curved surfaces
-- Style modes: Realistic, Anime, 3D, Watercolor, Typography
-- Magic Prompt for automatic prompt enhancement
-- Canvas editing for post-generation refinement
-- Upscaler up to 8K resolution in 2x increments
-- Color palette control for brand consistency
-- API available for programmatic integration
-
-## Hardware Requirements
-
-- Cloud API only (no local GPU required)
-- API pricing at approximately $0.06 per image
-- Web interface with credit-based subscription plans
-
-## Common Use Cases
-
-- Marketing materials with branded text and logos
-- Social media graphics with text overlays
-- Product packaging and label design
-- Event posters, flyers, and invitations
-- Book covers and editorial design
-
-## Key Parameters
-
-- **prompt**: Text description with quoted text for typography
-- **model**: Version selection (2.0 or 3.0)
-- **style**: Realistic, Anime, 3D, Watercolor, Typography
-- **aspect_ratio**: 16 aspect ratio options available
-- **magic_prompt**: Toggle for automatic prompt enhancement

apps/hub/knowledge/models/kandinsky.md

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-# Kandinsky
-
-Kandinsky is a family of open-source diffusion models for video and image generation, developed by Kandinsky Lab (Sber AI, Russia). The models support both English and Russian text prompts.
-
-## Model Variants
-
-### Kandinsky 5.0 Video Pro (19B)
-
-- HD video at 1280x768, 24fps (5 or 10 seconds)
-- Controllable camera motion via LoRA
-- Top-1 open-source T2V model on LMArena
-
-### Kandinsky 5.0 Video Lite (2B)
-
-- Lightweight model, #1 among open-source in its class
-- CFG-distilled (2x faster) and diffusion-distilled (6x faster) variants
-- Best Russian concept understanding in open source
-
-### Kandinsky 5.0 Image Lite (6B)
-
-- HD image output (1280x768, 1024x1024)
-- Strong text rendering; image editing variant available
-
-## Key Features
-
-- Bilingual support (English and Russian prompts)
-- Flow Matching architecture with MIT license
-- Camera control via trained LoRAs
-- ComfyUI and Diffusers integration
-- MagCache acceleration for faster inference
-
-## Hardware Requirements
-
-- Video Lite: 12GB VRAM minimum with optimizations
-- Video Pro: 24GB+ VRAM recommended
-- NF4 quantization and FlashAttention 2/3 or SDPA supported
-
-## Common Use Cases
-
-- Open-source video generation research
-- Russian and English bilingual content creation
-- Camera-controlled video synthesis
-- Image generation with text rendering
-- Fine-tuning with custom LoRAs
-
-## Key Parameters
-
-- **prompt**: Text description in English or Russian
-- **num_frames**: Number of output frames (5s or 10s)
-- **resolution**: Output resolution (up to 1280x768)
-- **steps**: Inference steps (varies by distillation level)

apps/hub/knowledge/models/kling.md

@@ -1,64 +0,0 @@
-# Kling
-
-Kling is a video and image generation platform developed by Kuaishou Technology. It offers text-to-video, image-to-video, video editing, audio generation, and virtual try-on capabilities through both a creative studio and a developer API.
-
-## Model Variants
-
-### Kling O1
-
-- First unified multimodal video model combining generation and editing
-- Built on Multimodal Visual Language (MVL) framework
-- Accepts text, image, video, and subject inputs in a single prompt
-- Supports video inpainting, outpainting, style re-rendering, and shot extension
-- Character and scene consistency via Element Library with director-like memory
-- Generates 3-10 second videos at up to 2K resolution
-
-### Kling 2.6
-
-- Simultaneous audio-visual generation in a single pass
-- Produces video with speech, sound effects, and ambient sounds together
-- Supports Chinese and English voice generation
-- Video content up to 10 seconds with synchronized audio
-- Deep semantic alignment between audio and visual dynamics
-
-### Kling (Base Models)
-
-- Text-to-video and image-to-video with Standard and Professional modes
-- Multi-image-to-video with multiple reference inputs
-- Camera control with 6 basic movements and 4 master shots
-- Video extension, lip-sync, and avatar generation
-- Start and end frame generation for controlled transitions
-
-## Key Features
-
-- Unified generation and editing in a single model (O1)
-- Simultaneous audio-visual generation (2.6)
-- Multi-subject consistency across shots and angles
-- Conversational editing via natural language prompts
-- Video effects center for special effects and transformations
-- Virtual try-on and image recognition capabilities
-- DeepSeek integration for prompt optimization
-
-## Hardware Requirements
-
-- Cloud API only; no local hardware required
-- Accessed via klingai.com creative studio or API platform
-- Standard and Professional generation modes (speed vs. quality tradeoff)
-
-## Common Use Cases
-
-- Film and television pre-production and shot generation
-- Social media content creation with audio
-- E-commerce product videos and virtual try-on
-- Advertising with one-click ad generation
-- Video post-production editing via text prompts
-- Multi-character narrative video creation
-
-## Key Parameters
-
-- **prompt**: Text description with positive and negative prompts
-- **mode**: Standard (fast) or Professional (high quality)
-- **duration**: Video length (3-10 seconds for O1, up to 10s for 2.6)
-- **aspect_ratio**: Width-to-height ratio for output
-- **camera_control**: Predefined camera movements and master shots
-- **creativity_strength**: Balance between reference fidelity and creative variation

apps/hub/knowledge/models/ltx-video.md

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-# LTX-Video
-
-LTX-Video is Lightricks' open-source DiT-based video generation model, the first capable of generating high-quality videos in real-time.
-
-## Model Variants
-
-### LTX-Video 2 (v0.9.7/v0.9.8)
-
-- Major quality upgrade over the original release
-- Available in 2B and 13B parameter sizes
-- 13B dev: highest quality, requires more VRAM
-- 13B distilled: faster inference, fewer steps needed, slight quality trade-off
-- 2B distilled: lightweight option for lower VRAM usage
-- FP8 quantized versions available for all sizes (13B-dev, 13B-distilled, 2B-distilled)
-- Multi-condition generation: condition on multiple images or video segments at specific frames
-- Spatial and temporal upscaler models for enhanced resolution and frame rate
-- ICLoRA adapters for depth, pose, and canny edge conditioning
-- 9 workflow templates available
-
-### LTX-Video 0.9.1/0.9.6
-
-- Original public releases with 2B parameter DiT architecture
-- Text-to-video and image-to-video modes
-- 768x512 native resolution at 24fps
-- 0.9.6 distilled variant: 15x faster, real-time capable, no CFG required
-- Foundation for community fine-tunes
-
-## Key Features
-
-- Real-time video generation on high-end GPUs (first DiT model to achieve this)
-- Generates 30 FPS video at 1216x704 resolution faster than playback speed
-- Multi-condition generation with per-frame image/video conditioning and strength control
-- Temporal VAE for smooth, consistent motion
-- Multi-scale rendering pipeline mixing dev and distilled models for speed-quality balance
-- Latent upsampling pipeline for progressive resolution enhancement
-
-## Hardware Requirements
-
-- 2B model: 12GB VRAM minimum, 16GB recommended
-- 2B distilled FP8: 8-10GB VRAM
-- 13B model: 24-32GB VRAM (fp16)
-- 13B FP8: 16-20GB VRAM
-- 13B distilled: less VRAM than 13B dev, ideal for rapid iterations
-- 32GB+ system RAM recommended for all variants
-
-## Common Use Cases
-
-- Short-form video content and social media clips
-- Image-to-video animation from reference frames
-- Video-to-video transformation and extension
-- Multi-condition video generation (start/end frame, keyframes)
-- Depth, pose, and edge-conditioned video generation via ICLoRA
-- Rapid video prototyping and creative experimentation
-
-## Key Parameters
-
-- **num_frames**: Output frame count (divisible by 8 + 1, e.g. 97, 161, 257)
-- **steps**: 30-50 for dev models, 8-15 for distilled variants
-- **cfg_scale**: 3-5 typical for dev, not required for distilled
-- **width/height**: Divisible by 32, best under 720x1280 for 13B
-- **denoise_strength**: 0.3-0.5 when using latent upsampler refinement pass
-- **conditioning_strength**: Per-condition strength for multi-condition generation (default 1.0)
-- **seed**: For reproducible generation
-
-## Blog References
-
-- [LTX-Video 0.9.5 Day-1 Support](../blog/ltx-video-095-support.md) — Commercial license (OpenRail-M), multi-frame control, improved quality
-- [LTX-2: Open Source Audio-Video AI](../blog/ltx-2-open-source-audio-video.md) — Synchronized audio-video generation, NVFP4 for 3x speed / 60% less VRAM

apps/hub/knowledge/models/ltx-video.summary.md

@@ -1 +0,0 @@
-LTX-Video is Lightricks' open-source DiT-based video generation model, the first to achieve real-time video generation. LTX-Video 2 (v0.9.7/0.9.8) is available in 2B and 13B parameter sizes, with dev, distilled, and FP8 quantized variants. It supports multi-condition generation with per-frame image/video conditioning, spatial and temporal upscalers, and ICLoRA adapters for depth, pose, and canny conditioning. The 2B model needs 12-16GB VRAM (8-10GB FP8), while the 13B model requires 24-32GB (16-20GB FP8). It generates 30fps video at 1216x704 faster than playback speed. Earlier versions (0.9.1/0.9.6) established the 2B foundation with a 15x faster distilled variant. Primary uses: short-form video, image-to-video animation, video extension, and multi-condition keyframe generation. Key parameters: 30-50 steps for dev, 8-15 for distilled, cfg_scale 3-5, frames divisible by 8+1.

apps/hub/knowledge/models/luma.md

@@ -1,50 +0,0 @@
-# Luma
-
-Luma AI develops video and image generation models through its Dream Machine platform, powered by the Ray model family and Photon image model.
-
-## Model Variants
-
-### Ray3 / Ray3.14
-
-- Native 1080p video with reasoning-driven generation
-- World's first native 16-bit HDR video generation
-- Character reference, Modify Video, and Draft Mode (5x faster)
-
-### Ray2
-
-- Production-ready text-to-video and image-to-video
-- 5-9 second output at 24fps with coherent motion
-
-### Photon
-
-- Image generation with strong prompt following
-- Character and visual reference support
-- 1080p output at $0.016 per image
-
-## Key Features
-
-- Reasoning capability for understanding creative intent
-- Visual annotation for precise layout and motion control
-- HDR generation with 16-bit EXR export for pro workflows
-- Keyframe control, video extension, looping, and camera control
-
-## Hardware Requirements
-
-- API-only access via Luma AI API
-- No local hardware requirements
-- Available through Dream Machine web and iOS app
-
-## Common Use Cases
-
-- Cinematic video production and storytelling
-- Commercial advertising and product videos
-- Visual effects with Modify Video workflows
-- HDR content for professional post-production
-
-## Key Parameters
-
-- **prompt**: Text description for video generation
-- **keyframes**: Start and/or end frame images
-- **aspect_ratio**: Output dimensions and ratio
-- **loop**: Enable seamless looping
-- **camera_control**: Camera movement via text instructions

apps/hub/knowledge/models/magnific.md

@@ -1,47 +0,0 @@
-# Magnific
-
-Magnific is an AI-powered image upscaler and enhancer that uses generative AI to hallucinate new details and textures during the upscaling process.
-
-## Model Variants
-
-### Magnific Creative Upscaler
-
-- Generative upscaling up to 16x (max 10,000px per dimension)
-- AI engines: Illusio (illustration), Sharpy (photography), Sparkle (balanced)
-- Adds hallucinated details guided by text prompts
-
-### Magnific Precision Upscaler
-
-- Faithful high-fidelity upscaling without creative reinterpretation
-- Clean enlargement that stays true to the source image
-
-### Mystic Image Generator
-
-- Photorealistic text-to-image/image-to-image with LoRA styles at up to 4K
-
-## Key Features
-
-- Creativity slider controls AI-hallucinated detail level
-- HDR control for micro-contrast and crispness
-- Resemblance slider to balance fidelity vs. creative enhancement
-- Optimized modes for portraits, illustrations, video games, and film
-- API hosted on Freepik with Skin Enhancer endpoint
-
-## Hardware Requirements
-
-- Cloud-only service with no local hardware requirements
-- API available through Freepik's developer platform
-- Subscription-based with credit system
-
-## Common Use Cases
-
-- Upscaling AI-generated images for print and production
-- Enhancing low-resolution concept art and illustrations
-- Restoring old or compressed photographs
-
-## Key Parameters
-
-- Creativity: level of new detail hallucination (0-10)
-- HDR: micro-contrast and sharpness (-10 to 10)
-- Resemblance: fidelity to source image (-10 to 10)
-- Scale Factor: 2x, 4x, 8x, or 16x magnification

apps/hub/knowledge/models/meshy.md

@@ -1,49 +0,0 @@
-# Meshy
-
-Meshy is a popular AI 3D model generator enabling text-to-3D and image-to-3D creation with PBR textures and production-ready exports.
-
-## Model Variants
-
-### Meshy-6
-
-- Latest generation with highest quality geometry
-- Supports symmetry and pose control (A-pose, T-pose)
-- Configurable polygon counts up to 300,000
-
-### Meshy-5
-
-- Previous generation with art style support
-- Realistic and sculpture style options
-
-## Key Features
-
-- Text-to-3D with two-stage workflow (preview mesh, then refine textures)
-- Image-to-3D from photos, sketches, or illustrations
-- Multi-image input for multi-view reconstruction
-- AI texturing with PBR maps (diffuse, roughness, metallic, normal)
-- Automatic rigging and 500+ animation motion library
-- Smart remesh with quad or triangle topology control
-- Export in FBX, GLB, OBJ, STL, 3MF, USDZ, BLEND formats
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- All generation runs on Meshy servers
-- API available on Pro tier and above
-
-## Common Use Cases
-
-- Game development asset creation
-- 3D printing and prototyping
-- Film and VFX previsualization
-- VR/AR content development
-- Product design and e-commerce
-
-## Key Parameters
-
-- **prompt**: Text description up to 600 characters
-- **ai_model**: Model version (meshy-5, meshy-6, latest)
-- **topology**: Mesh type (quad or triangle)
-- **target_polycount**: 100 to 300,000 polygons
-- **enable_pbr**: Generate PBR material maps
-- **pose_mode**: Character pose (a-pose, t-pose, or none)

apps/hub/knowledge/models/minimax.md

@@ -1,58 +0,0 @@
-# MiniMax
-
-MiniMax is a multi-modal AI company known for the Hailuo video generation models and Image-01, offering API-based video and image creation.
-
-## Model Variants
-
-### Hailuo 2.3
-
-- Latest video model with improved body movement and facial expressions
-- Supports anime, illustration, ink-wash, and game-CG styles
-- 768p or 1080p resolution, 6 or 10 second clips
-- Available in Quality and Fast variants
-
-### Hailuo 2.0 (Hailuo 02)
-
-- Native 1080p with Noise-aware Compute Redistribution (NCR)
-- 2.5x efficiency improvement over predecessors
-- Last-frame conditioning support
-
-### Image-01
-
-- Text-to-image generation with multiple output sizes
-
-### T2V-01-Director
-
-- Enhanced camera control with natural language commands
-- Pan, zoom, tracking shot, and shake directives
-
-## Key Features
-
-- Text-to-video and image-to-video generation
-- Up to 1080p resolution at 25fps
-- Video clips up to 10 seconds
-- Camera control with natural language commands
-- Subject consistency with reference images
-- Text-to-image generation with Image-01
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- All generation runs on MiniMax servers
-- API access via platform.minimax.io
-
-## Common Use Cases
-
-- Social media video content creation
-- Cinematic short film production
-- Product advertising and e-commerce videos
-- Anime and illustrated content
-- Character-driven narrative scenes
-
-## Key Parameters
-
-- **prompt**: Text description for generation
-- **model**: Model selection (hailuo-2.3, hailuo-02, image-01)
-- **resolution**: Output resolution (768p or 1080p)
-- **duration**: Clip length (6 or 10 seconds for video)
-- **first_frame_image**: Reference image for image-to-video

apps/hub/knowledge/models/model-triage.json

@@ -1,762 +0,0 @@
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-      },
-      {
-        "name": "Hunyuan Video",
-        "category": "variant",
-        "templateCount": 3,
-        "priority": 0,
-        "mapsTo": "hunyuan",
-        "hasExistingDoc": true
-      },
-      {
-        "name": "Vidu Q3",
-        "category": "variant",
-        "templateCount": 2,
-        "priority": 0,
-        "mapsTo": "vidu",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "LTXV",
-        "category": "variant",
-        "templateCount": 2,
-        "priority": 0,
-        "mapsTo": "ltx-video",
-        "hasExistingDoc": true
-      },
-      {
-        "name": "Qwen-Image-Layered",
-        "category": "variant",
-        "templateCount": 2,
-        "priority": 0,
-        "mapsTo": "qwen",
-        "hasExistingDoc": true
-      },
-      {
-        "name": "SD1.5",
-        "category": "variant",
-        "templateCount": 2,
-        "priority": 0,
-        "mapsTo": "sdxl",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "Gemini-2.5-Flash",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "gemini",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "Qwen-Image 2512",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "qwen",
-        "hasExistingDoc": true
-      },
-      {
-        "name": "Seedream 4.0",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "seedream",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "GPT-Image-1.5",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "gpt-image-1",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "Kling2.6",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "kling",
-        "hasExistingDoc": false
-      },
-      {
-        "name": "Wan-Move",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "wan",
-        "hasExistingDoc": true
-      },
-      {
-        "name": "Motion Control",
-        "category": "variant",
-        "templateCount": 1,
-        "priority": 0,
-        "mapsTo": "wan",
-        "hasExistingDoc": false
-      }
-    ],
-    "skip": [
-      {
-        "name": "None",
-        "category": "skip",
-        "templateCount": 1,
-        "priority": 0,
-        "hasExistingDoc": false
-      },
-      {
-        "name": "nano-banana",
-        "category": "skip",
-        "templateCount": 1,
-        "priority": 0,
-        "note": "Duplicate of Nano Banana Pro",
-        "hasExistingDoc": false
-      }
-    ]
-  },
-  "priorityOrder": [
-    "wan",
-    "nano-banana-pro",
-    "flux",
-    "gemini",
-    "ace-step",
-    "vidu",
-    "kling",
-    "seedance",
-    "seedream",
-    "hidream",
-    "sdxl",
-    "stable-audio",
-    "chatterbox",
-    "z-image",
-    "recraft",
-    "runway",
-    "tripo",
-    "kandinsky",
-    "omnigen",
-    "seedvr2",
-    "gpt-image-1",
-    "minimax",
-    "grok",
-    "luma",
-    "moonvalley",
-    "topaz",
-    "chroma",
-    "pixverse",
-    "meshy",
-    "rodin",
-    "magnific",
-    "wavespeed",
-    "bria",
-    "veo",
-    "hitpaw",
-    "newbie",
-    "ovis-image",
-    "chronoedit",
-    "humo",
-    "anima",
-    "reimagine",
-    "ideogram",
-    "svd",
-    "real-esrgan",
-    "depth-anything-v2",
-    "flashvsr"
-  ]
-}

apps/hub/knowledge/models/moonvalley.md

@@ -1,53 +0,0 @@
-# Moonvalley (Marey)
-
-Marey is Moonvalley's AI video generation model for professional filmmakers, delivering studio-grade quality and trained exclusively on licensed footage.
-
-## Model Variants
-
-### Marey Realism v1.5
-
-- Latest production model with cinematic detail
-- 1080p resolution at 24fps, up to 5-second clips
-- Available via ComfyUI native nodes and fal.ai
-
-### Marey Director Controls
-
-- 3D-aware camera control from single images
-- Motion transfer from reference videos
-- Trajectory control for object path definition
-- Pose transfer and keyframing with multi-image timeline
-
-## Key Features
-
-- Text-to-video and image-to-video generation
-- Camera control with 3D scene understanding
-- Motion transfer from reference video clips
-- Trajectory control via drawn paths
-- Pose transfer for expressive character animation
-- Shot extension for seamless duration increase
-- Commercially safe (trained on licensed data only)
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- Available via Moonvalley platform, ComfyUI, and fal.ai
-- Subscription tiers starting at $14.99/month
-
-## Common Use Cases
-
-- Professional film and commercial production
-- Cinematic B-roll generation
-- Previsualization and storyboarding
-- Music video and social media content
-- Product advertising with dynamic camera
-- Animation and character-driven storytelling
-
-## Key Parameters
-
-- **prompt**: Text description of desired video scene
-- **image**: Reference image for image-to-video mode
-- **camera_control**: Camera movement specification
-- **motion_reference**: Video reference for motion transfer
-- **trajectory**: Drawn path for object movement
-- **duration**: Clip length (up to 5 seconds)
-- **resolution**: Output resolution (up to 1080p at 24fps)

apps/hub/knowledge/models/nano-banana-pro.md

@@ -1,53 +0,0 @@
-# Nano Banana Pro
-
-Nano Banana Pro is Google DeepMind's flagship image generation and editing model, accessed through ComfyUI's API nodes. Internally it is the Gemini 3 Pro Image model, designed for production-ready high-fidelity visuals.
-
-## Model Variants
-
-### Nano Banana Pro (Gemini 3 Pro Image)
-
-- State-of-the-art reasoning-powered image generation
-- Supports up to 14 reference image inputs
-- Native 4K output resolution (up to 4096x4096)
-- Complex multi-turn image generation and editing
-- Model ID: `gemini-3-pro-image-preview`
-
-### Gemini 2.5 Flash Image (Nano Banana)
-
-- Cost-effective alternative optimized for speed
-- Balanced price-to-performance for interactive workflows
-- Character consistency and prompt-based editing
-- Model ID: `gemini-2.5-flash-image`
-
-## Key Features
-
-- **World knowledge**: Generates accurate real-world images using Google Search's knowledge base
-- **Text rendering**: Clean text generation with detection and translation across 10 languages
-- **Multi-image fusion**: Blend up to 14 input images into a single coherent output
-- **Studio controls**: Adjust angles, focus, color grading in generated images
-- **Character consistency**: Maintain subject identity across multiple generations
-- **Prompt-based editing**: Targeted transformations via natural language instructions
-
-## Hardware Requirements
-
-- No local GPU required — runs as a cloud API service
-- Accessed via ComfyUI API nodes (requires ComfyUI login and network access)
-- Available on Comfy Cloud or local ComfyUI with API node support
-
-## Common Use Cases
-
-- High-fidelity text-to-image generation
-- Multi-reference style transfer and image blending
-- Product visualization and mockups
-- Sketch-to-image and blueprint-to-3D visualization
-- Text rendering and translation in images
-- Iterative prompt-based image editing
-
-## Key Parameters
-
-- **prompt**: Text description of desired image or edit
-- **aspect_ratio**: Supported ratios include 1:1, 3:2, 4:3, 9:16, 16:9, 21:9
-- **temperature**: 0.0-2.0 (default 1.0)
-- **topP**: 0.0-1.0 (default 0.95)
-- **max_output_tokens**: Up to 32,768 tokens per response
-- **input images**: Up to 14 reference images per prompt

apps/hub/knowledge/models/newbie.md

@@ -1,43 +0,0 @@
-# NewBie
-
-NewBie image Exp0.1 is a 3.5B parameter open-source text-to-image model built on the Next-DiT architecture, developed by the NewBie-AI community. It is specifically pretrained on high-quality anime data for detailed and visually striking anime-style image generation.
-
-## Model Variants
-
-### NewBie image Exp0.1
-
-- 3.5B parameter DiT model based on Next-DiT architecture
-- Uses Gemma3-4B-it as primary text encoder with Jina CLIP v2 for pooled features
-- FLUX.1-dev 16-channel VAE for rich color rendering and fine texture detail
-- Supports natural language, tags, and XML structured prompts
-- Non-commercial community license (Newbie-NC-1.0) for model weights
-
-## Key Features
-
-- Exceptional anime and ACG (Anime, Comics, Games) style generation
-- XML structured prompting for improved attribute binding and element disentanglement
-- Strong multi-character scene generation with accurate attribute assignment
-- ComfyUI integration via dedicated custom nodes
-- LoRA training support with community trainer
-- Built on research from the Lumina architecture family
-
-## Hardware Requirements
-
-- Minimum: 12GB VRAM (bfloat16 or float16)
-- Recommended: 24GB VRAM for comfortable generation
-- Requires Gemma3-4B-it and Jina CLIP v2 text encoders
-- Python 3.10, PyTorch 2.6.0+, Transformers 4.57.1+
-
-## Common Use Cases
-
-- Anime and illustration generation
-- Character design with precise attribute control
-- Multi-character scene composition
-- Fan art and creative anime artwork
-
-## Key Parameters
-
-- **num_inference_steps**: 28 recommended
-- **height/width**: 1024x1024 native resolution
-- **prompt_format**: Natural language, tags, or XML structured
-- **torch_dtype**: bfloat16 recommended (float16 fallback)

apps/hub/knowledge/models/omnigen.md

@@ -1,53 +0,0 @@
-# OmniGen2
-
-OmniGen2 is a multimodal generation model with dual decoding pathways for text and image, built on the Qwen-VL-2.5 foundation by VectorSpaceLab.
-
-## Model Variants
-
-### OmniGen2
-
-- 3B vision-language encoder (Qwen-VL-2.5) + 4B image decoder
-- Dual decoding with unshared parameters for text and image
-- Decoupled image tokenizer
-- Apache 2.0 license
-
-### OmniGen v1
-
-- Earlier single-pathway architecture
-- Fewer capabilities than OmniGen2
-- Superseded by OmniGen2
-
-## Key Features
-
-- Text-to-image generation with high fidelity and aesthetics
-- Instruction-guided image editing (state-of-the-art among open-source models)
-- In-context generation combining multiple reference inputs (humans, objects, scenes)
-- Visual understanding inherited from Qwen-VL-2.5
-- CPU offload support reduces VRAM usage by nearly 50%
-- Sequential CPU offload available for under 3GB VRAM (slower inference)
-- Supports negative prompts and configurable guidance scales
-
-## Hardware Requirements
-
-- Minimum: NVIDIA RTX 3090 or equivalent (~17GB VRAM)
-- With CPU offload: ~9GB VRAM
-- With sequential CPU offload: under 3GB VRAM (significantly slower)
-- Flash Attention optional but recommended for best performance
-- CUDA 12.4+ recommended
-- Default output resolution: 1024x1024
-
-## Common Use Cases
-
-- Text-to-image generation
-- Instruction-based photo editing
-- Subject-driven image generation from reference photos
-- Multi-image composition and in-context editing
-
-## Key Parameters
-
-- **text_guidance_scale**: Controls adherence to text prompt (CFG)
-- **image_guidance_scale**: Controls similarity to reference image (1.2-2.0 for editing, 2.5-3.0 for in-context)
-- **num_inference_step**: Diffusion steps (default 50)
-- **max_pixels**: Maximum total pixel count for input images (default 1024x1024)
-- **negative_prompt**: Text describing undesired qualities (e.g., "blurry, low quality, watermark")
-- **scheduler**: ODE solver choice (euler or dpmsolver++)

apps/hub/knowledge/models/ovis-image.md

@@ -1,43 +0,0 @@
-# Ovis-Image
-
-Ovis-Image is a 7B text-to-image model by AIDC-AI, built on Ovis-U1, optimized for high-quality text rendering in generated images. It achieves state-of-the-art results on the CVTG-2K text rendering benchmark while remaining compact enough for single-GPU deployment.
-
-## Model Variants
-
-### Ovis-Image-7B
-
-- 2B (Ovis2.5-2B) + 7B parameter architecture
-- State-of-the-art on CVTG-2K benchmark for text rendering accuracy
-- Competitive with 20B+ models (Qwen-Image) and GPT-4o on text-centric tasks
-- Uses FLUX-based autoencoder for latent encoding
-- Apache 2.0 license
-
-## Key Features
-
-- Excellent text rendering with correct spelling and consistent typography
-- High fidelity on text-heavy, layout-sensitive prompts
-- Handles posters, banners, logos, UI mockups, and infographics
-- Supports diverse fonts, sizes, and aspect ratios
-- Strong performance on both English and Chinese text generation
-- Available via Diffusers library with OvisImagePipeline
-
-## Hardware Requirements
-
-- Minimum: 16GB VRAM (bfloat16)
-- Recommended: 24GB VRAM for comfortable use
-- Fits on a single high-end GPU
-- Tested with Python 3.10, PyTorch 2.6.0, Transformers 4.57.1
-
-## Common Use Cases
-
-- Generating posters and banners with accurate text
-- Logo and brand asset creation
-- UI mockup and infographic generation
-- Marketing materials with embedded typography
-
-## Key Parameters
-
-- **num_inference_steps**: 50 recommended
-- **guidance_scale**: 5.0
-- **resolution**: 1024x1024 native
-- **negative_prompt**: Supported for quality control

apps/hub/knowledge/models/pixverse.md

@@ -1,46 +0,0 @@
-# PixVerse
-
-PixVerse is an AI video generation platform founded in 2023 and backed by Alibaba, offering text-to-video and image-to-video capabilities with over 100 million registered users.
-
-## Model Variants
-
-### PixVerse V5.5
-
-- Latest model with improved fidelity, text-to-video, image-to-video, and modification
-
-### PixVerse R1
-
-- Real-time AI video generation model
-- Interactive control where users direct character actions as video unfolds
-
-### PixVerse V4.5 / V5
-
-- Previous generation models with strong cinematic quality and trending effects
-
-## Key Features
-
-- Text-to-video generation from natural language prompts
-- Image-to-video animation with realistic physics simulation
-- Fusion mode combining up to 3 images into one video
-- Key frame control and video extension with AI continuity
-- AI Video Modify for text-prompt-based editing
-
-## Hardware Requirements
-
-- Cloud-based platform with no local GPU required
-- Web app at app.pixverse.ai and mobile apps (iOS/Android)
-- API at platform.pixverse.ai for developer integration
-
-## Common Use Cases
-
-- Social media content creation (AI Kiss, Hug, Dance effects)
-- Marketing and promotional video production
-- Old photo revival and animation
-- Cinematic narrative and stylistic art generation
-
-## Key Parameters
-
-- prompt: text description of the desired video content
-- duration: video length (typically 5s clips)
-- resolution: output quality (360p to 720p+)
-- aspect_ratio: 16:9, 9:16, 1:1, and other ratios

apps/hub/knowledge/models/qwen.md

@@ -1,77 +0,0 @@
-# Qwen
-
-Qwen is Alibaba's family of vision-language and image generation models, spanning visual understanding, image editing, and image generation.
-
-## Model Variants
-
-### Qwen2.5-VL
-
-- Multimodal vision-language model from the Qwen team
-- Available in 3B, 7B, and 72B parameter sizes
-- Image understanding, video comprehension (1+ hour videos), and visual localization
-- Visual agent capabilities: computer use, phone use, dynamic tool calling
-- Structured output generation for invoices, forms, and tables
-- Dynamic resolution and frame rate training for video understanding
-- Optimized ViT encoder with window attention, SwiGLU, and RMSNorm
-
-### Qwen-Image-Edit
-
-- Specialized image editing model with instruction-following
-- Supports inpainting, outpainting, style transfer, and content-aware edits
-- 11 workflow templates available
-
-### Qwen-Image
-
-- Text-to-image generation model from the Qwen family
-- 7 workflow templates available
-
-### Qwen-Image-Layered
-
-- Layered image generation for composable outputs
-- Generates images with separate foreground/background layers
-- 2 workflow templates available
-
-### Qwen-Image 2512
-
-- Specific variant optimized for particular generation tasks
-- 1 workflow template available
-
-## Key Features
-
-- Strong visual understanding with state-of-the-art benchmark results
-- Native multi-language support including Chinese and English
-- Visual agent capabilities for computer and phone interaction
-- Video event capture with temporal segment pinpointing
-- Bounding box and point-based visual localization
-- Structured JSON output for document and table extraction
-- Instruction-based image editing with precise control
-
-## Hardware Requirements
-
-- 3B model: 6-8GB VRAM
-- 7B model: 16GB VRAM, flash_attention_2 recommended for multi-image/video
-- 72B model: Multi-GPU setup required (80GB+ per GPU)
-- Context length: 32,768 tokens default, extendable to 64K+ with YaRN
-- Dynamic pixel budget: 256-1280 tokens per image (configurable min/max pixels)
-
-## Common Use Cases
-
-- Image editing based on text instructions
-- Visual question answering and image description
-- Long video comprehension and event extraction
-- Document OCR and structured data extraction
-- Visual agent tasks (screen interaction, UI navigation)
-- Layered image generation for design workflows
-- Text-to-image generation with strong prompt following
-
-## Key Parameters
-
-- **max_new_tokens**: Controls output length for VL model responses
-- **min_pixels / max_pixels**: Control image token budget (e.g. 256x28x28 to 1280x28x28)
-- **temperature**: Generation diversity for text outputs
-- **resized_height / resized_width**: Direct image dimension control (rounded to nearest 28)
-- **fps**: Frame rate for video input processing in Qwen2.5-VL
-
-## Blog References
-
-- [Qwen Image Edit 2511 & Qwen Image Layered](../blog/qwen-image-edit-2511.md) — Better character consistency, RGBA layer decomposition, built-in LoRA support

apps/hub/knowledge/models/qwen.summary.md

@@ -1 +0,0 @@
-Qwen is Alibaba's family of vision-language and image generation models. Qwen2.5-VL is a multimodal vision-language model available in 3B (6-8GB VRAM), 7B (16GB), and 72B (multi-GPU 80GB+) sizes, capable of image understanding, hour-long video comprehension, visual localization, visual agent tasks (computer/phone use), and structured JSON output for document extraction. Qwen-Image-Edit provides instruction-based image editing with inpainting, outpainting, and style transfer. Qwen-Image handles text-to-image generation, while Qwen-Image-Layered produces composable foreground/background layer outputs. The family features native Chinese/English support, strong prompt following, and state-of-the-art visual understanding benchmarks. Key parameters include dynamic pixel budgets (256-1280 tokens per image), configurable frame rates for video input, and temperature for text diversity. Primary uses: image editing, visual QA, video comprehension, document OCR, and layered image generation.

apps/hub/knowledge/models/real-esrgan.md

@@ -1,61 +0,0 @@
-# Real-ESRGAN
-
-Real-ESRGAN is a practical image and video super-resolution model that extends ESRGAN with improved training on pure synthetic data for real-world restoration.
-
-## Model Variants
-
-### RealESRGAN_x4plus
-
-- General-purpose 4× upscaling model for real-world images
-- RRDB (Residual-in-Residual Dense Block) architecture
-- Handles noise, blur, JPEG compression artifacts
-
-### RealESRGAN_x4plus_anime_6B
-
-- Optimized for anime and illustration images
-- Smaller 6-block model for faster inference
-- Better edge preservation for line art
-
-### RealESRGAN_x2plus
-
-- 2× upscaling variant for moderate enlargement
-- Lower risk of hallucinated details
-
-### realesr-animevideov3
-
-- Lightweight model designed for anime video frames
-- Temporal consistency for video processing
-
-## Key Features
-
-- Trained entirely on synthetic degradation data (no paired real-world data needed)
-- Second-order degradation modeling simulates real-world compression chains
-- GFPGAN integration for face enhancement during upscaling
-- Tiling support for processing large images with limited VRAM
-- FP16 (half precision) inference for faster processing
-- NCNN Vulkan portable executables for cross-platform GPU support (Intel/AMD/NVIDIA)
-- Supports 2×, 3×, and 4× upscaling with arbitrary output scale via LANCZOS4 resize
-
-## Hardware Requirements
-
-- Minimum: 2GB VRAM with tiling enabled
-- Recommended: 4GB+ VRAM for comfortable use
-- NCNN Vulkan build runs on any GPU with Vulkan support
-- CPU inference supported but significantly slower
-
-## Common Use Cases
-
-- Upscaling old or low-resolution photographs
-- Enhancing compressed web images
-- Anime and manga image upscaling
-- Video frame super-resolution
-- Restoring degraded historical images
-- Pre-processing for print from low-resolution sources
-
-## Key Parameters
-
-- **outscale**: Final upsampling scale factor (default: 4)
-- **tile**: Tile size for memory management (0 = no tiling)
-- **face_enhance**: Enable GFPGAN face enhancement (default: false)
-- **model_name**: Select model variant (RealESRGAN_x4plus, anime_6B, etc.)
-- **denoise_strength**: Balance noise removal vs detail preservation (realesr-general-x4v3)

apps/hub/knowledge/models/recraft.md

@@ -1,50 +0,0 @@
-# Recraft
-
-Recraft is an AI image generation platform known for its V3 model and unique ability to produce both raster and vector (SVG) images from text prompts.
-
-## Model Variants
-
-### Recraft V3
-
-- Top-ranked model on Artificial Analysis Text-to-Image Leaderboard
-- Supports raster image generation at $0.04 per image
-- Supports vector SVG generation at $0.08 per image
-- Accurate text rendering at any size in generated images
-
-### Recraft 20B
-
-- More cost-effective variant at $0.022 per raster image
-- Vector generation at $0.044 per image
-- Suitable for high-volume production workflows
-
-## Key Features
-
-- Native vector SVG image generation from text prompts
-- Accurate text rendering (headlines, labels, signs) in images
-- Custom brand style creation from reference images
-- Generation in exact brand colors for brand consistency
-- AI-powered image vectorization (PNG/JPG to SVG)
-- Background removal, creative upscaling, and crisp upscaling
-- Multiple style presets: photorealism, clay, retro-pop, hand-drawn, 80s
-
-## Hardware Requirements
-
-- API-only access via Recraft API
-- No local hardware requirements
-- Available through Recraft Studio web interface
-
-## Common Use Cases
-
-- Logo and icon design (SVG output)
-- Brand-consistent marketing asset generation
-- Poster and advertisement creation with text
-- Scalable vector illustrations for web and print
-- Product mockup generation
-- SEO blog imagery at scale
-
-## Key Parameters
-
-- **prompt**: Text description of the desired image
-- **style**: Visual style (realistic_image, digital_illustration, vector_illustration, icon)
-- **colors**: Brand color palette for consistent output
-- **format**: Output format (raster PNG/JPG or vector SVG)

apps/hub/knowledge/models/rodin.md

@@ -1,57 +0,0 @@
-# Rodin
-
-Rodin is a 3D generation API by Hyper3D (DeemosTech) that creates production-ready 3D models from text or images with PBR materials.
-
-## Model Variants
-
-### Rodin Gen-2
-
-- Most advanced model with 10 billion parameters
-- Built on the BANG architecture
-- 4x improved geometric mesh quality over Gen-1
-- Generation time approximately 90 seconds
-
-### Rodin Gen-1.5 Regular
-
-- Detailed 3D assets with customizable quality
-- Adjustable polygon counts and 2K textures
-- Generation time approximately 70 seconds
-
-### Rodin Sketch
-
-- Fast prototyping with basic geometry and 1K textures
-- GLB format only, generation in approximately 20 seconds
-
-## Key Features
-
-- Text-to-3D and image-to-3D generation
-- Multi-view image input (up to 5 images) with fuse and concat modes
-- PBR and Shaded material options
-- Quad and triangle mesh modes
-- HighPack add-on for 4K textures and high-poly models
-- Bounding box ControlNet for dimension constraints
-- T/A pose control for humanoid models
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- All generation runs on Hyper3D servers
-- API key required via hyper3d.ai dashboard
-
-## Common Use Cases
-
-- Game asset production
-- VR/AR content creation
-- Product visualization
-- Character modeling with pose control
-- Rapid 3D prototyping
-
-## Key Parameters
-
-- **prompt**: Text description for text-to-3D mode
-- **images**: Up to 5 reference images for image-to-3D
-- **quality**: Detail level (high, medium, low, extra-low)
-- **mesh_mode**: Face type (Quad or Raw triangles)
-- **material**: Material type (PBR, Shaded, or All)
-- **geometry_file_format**: Output format (glb, fbx, obj, stl, usdz)
-- **seed**: Randomization seed (0-65535)

apps/hub/knowledge/models/runway.md

@@ -1,50 +0,0 @@
-# Runway
-
-Runway is a generative AI company producing state-of-the-art video generation models, accessible via API and web interface.
-
-## Model Variants
-
-### Gen-3 Alpha
-
-- Text-to-video and image-to-video at 1280x768, 24fps
-- 5 or 10 second output, extendable up to 40 seconds
-- Photorealistic human character generation
-
-### Gen-3 Alpha Turbo
-
-- Faster, lower-cost variant (5 credits/sec vs 10)
-- Requires input image; supports first, middle, and last keyframes
-- Video extension up to 34 seconds total
-
-### Gen-4 Turbo
-
-- Latest generation with improved motion and prompt adherence
-- Image reference support and text-to-image (gen4_image)
-
-## Key Features
-
-- Advanced camera controls (Motion Brush, Director Mode)
-- C2PA provenance metadata for content authenticity
-- Expressive human characters with gestures and emotions
-- Wide range of cinematic styles and terminology support
-
-## Hardware Requirements
-
-- API-only access via Runway developer portal
-- No local hardware requirements
-- Enterprise tier available for higher rate limits
-
-## Common Use Cases
-
-- Film pre-visualization and storyboarding
-- Commercial advertisement production
-- Social media video content
-- Visual effects and motion graphics
-- Music video and artistic video creation
-
-## Key Parameters
-
-- **prompt**: Text description guiding video generation
-- **duration**: Output length (5 or 10 seconds)
-- **ratio**: Aspect ratio (1280:768 or 768:1280)
-- **keyframes**: Start, middle, and/or end frame images

apps/hub/knowledge/models/sd3-5.md

@@ -1,63 +0,0 @@
-# Stable Diffusion 3.5
-
-Stable Diffusion 3.5 is Stability AI's text-to-image model family based on the Multimodal Diffusion Transformer (MMDiT) architecture with rectified flow matching.
-
-## Model Variants
-
-### Stable Diffusion 3.5 Large
-
-- 8.1 billion parameter MMDiT model
-- Highest quality and prompt adherence in the SD family
-- 1 megapixel native resolution (1024×1024)
-- 28-50 inference steps recommended
-
-### Stable Diffusion 3.5 Large Turbo
-
-- Distilled version of SD 3.5 Large
-- 4-step inference for fast generation
-- Guidance scale of 0 (classifier-free guidance disabled)
-- Comparable quality to full model at fraction of the time
-
-### Stable Diffusion 3.5 Medium
-
-- 2.5 billion parameter MMDiT-X architecture
-- Designed for consumer hardware (9.9GB VRAM for transformer)
-- Dual attention blocks in first 12 transformer layers
-- Multi-resolution generation from 0.25 to 2 megapixels
-- Skip Layer Guidance recommended for better coherency
-
-## Key Features
-
-- Three text encoders: CLIP ViT-L, OpenCLIP ViT-bigG (77 tokens each), T5-XXL (256 tokens)
-- QK-normalization for stable training and easier fine-tuning
-- Rectified flow matching replaces traditional DDPM/DDIM sampling
-- Strong text rendering and typography in generated images
-- Diverse output styles (photography, 3D, painting, line art)
-- Highly customizable base for fine-tuning and LoRA training
-- T5-XXL encoder optional (can be removed to save memory with minimal quality loss)
-- Supports negative prompts for excluding unwanted elements
-
-## Hardware Requirements
-
-- Large: 24GB+ VRAM recommended (fp16), quantizable to fit smaller GPUs
-- Large Turbo: 16GB+ VRAM recommended
-- Medium: 10GB VRAM minimum (excluding text encoders)
-- NF4 quantization available via bitsandbytes for low-VRAM GPUs
-- CPU offloading supported via diffusers pipeline
-
-## Common Use Cases
-
-- Photorealistic image generation
-- Artistic illustration and concept art
-- Typography and text-heavy designs
-- Product visualization
-- Fine-tuning and LoRA development
-- ControlNet-guided generation
-
-## Key Parameters
-
-- **steps**: 28-50 for Large, 4 for Large Turbo, 20-40 for Medium
-- **guidance_scale**: 4.5-7.5 for Large/Medium, 0 for Large Turbo
-- **max_sequence_length**: T5 token limit (77 or 256, higher = better prompt understanding)
-- **resolution**: 1024×1024 native, flexible aspect ratios around 1MP
-- **negative_prompt**: Text describing elements to exclude (not supported by Turbo)

apps/hub/knowledge/models/sdxl.md

@@ -1,75 +0,0 @@
-# Stable Diffusion
-
-Stable Diffusion is Stability AI's family of open-source image and video generation models, spanning multiple architectures from U-Net to diffusion transformers.
-
-## Model Variants
-
-### SDXL (Stable Diffusion XL)
-
-- Stability AI's flagship text-to-image model (6.6B parameter U-Net)
-- Native 1024x1024 resolution with flexible aspect ratios around 1MP
-- Two text encoders (CLIP ViT-L + OpenCLIP ViT-bigG)
-- Optional refiner model for second-stage detail enhancement
-- Turbo and Lightning distilled variants for 1-4 step generation
-- Largest ecosystem of LoRAs, fine-tunes, and community models
-
-### SD3.5 (Stable Diffusion 3.5)
-
-- Diffusion transformer (DiT) architecture, successor to SDXL
-- Three text encoders (CLIP ViT-L, OpenCLIP ViT-bigG, T5-XXL) for stronger prompt following
-- Available in Large (8B) and Medium (2B) parameter sizes
-- Improved text rendering and compositional accuracy over SDXL
-- 4 workflow templates available
-
-### SD1.5 (Stable Diffusion 1.5)
-
-- The classic 512x512 latent diffusion model
-- Single CLIP ViT-L text encoder, 860M parameter U-Net
-- Still widely used for its massive LoRA and checkpoint ecosystem
-- Lower VRAM requirements make it accessible on consumer hardware
-- 2 workflow templates available
-
-### SVD (Stable Video Diffusion)
-
-- Image-to-video generation model based on Stable Diffusion
-- Generates short video clips (14 or 25 frames) from a single image
-- Related model for motion generation from static inputs
-
-### Stability API Products
-
-- Reimagine: Stability's API-based image variation and transformation service
-
-## Key Features
-
-- Excellent composition, layout, and photorealism (SDXL/SD3.5)
-- Large open-source ecosystem with thousands of community fine-tunes
-- Flexible aspect ratios and multi-resolution support
-- Dual/triple CLIP text encoding for nuanced prompt interpretation
-- Strong text rendering in SD3.5 via T5-XXL encoder
-
-## Hardware Requirements
-
-- SD1.5: 4-6GB VRAM (fp16), runs on most consumer GPUs
-- SDXL Base: 8GB VRAM minimum (fp16), 12GB recommended
-- SDXL Base + Refiner: 16GB+ VRAM
-- SD3.5 Medium: 8-12GB VRAM
-- SD3.5 Large: 16-24GB VRAM (fp16), quantized versions for 12GB cards
-
-## Common Use Cases
-
-- Photorealistic image generation
-- Artistic illustrations and concept art
-- Product photography and design
-- Character and portrait generation
-- LoRA-based custom style and subject training
-- Image-to-video with SVD
-
-## Key Parameters
-
-- **steps**: 20-40 for SDXL base, 15-25 for refiner, 28+ for SD3.5
-- **cfg_scale**: 5-10 (7 default for SDXL), 3.5-7 for SD3.5
-- **sampler**: DPM++ 2M Karras and Euler are popular for SDXL; Euler for SD3.5
-- **resolution**: 1024x1024 native for SDXL/SD3.5, 512x512 for SD1.5
-- **clip_skip**: Often set to 1-2; important for SD1.5 LoRA compatibility
-- **denoise_strength**: 0.7-0.8 when using the SDXL refiner (img2img)
-- **negative_prompt**: Supported in SDXL/SD1.5; not used in SD3.5 by default

apps/hub/knowledge/models/sdxl.summary.md

@@ -1 +0,0 @@
-Stable Diffusion is Stability AI's open-source image and video generation family. SDXL is the flagship text-to-image model (6.6B U-Net, dual CLIP encoders) generating 1024x1024 images with the largest ecosystem of LoRAs and community fine-tunes; it requires 8-12GB VRAM with Turbo/Lightning variants for 1-4 step generation. SD3.5 is the DiT-based successor with triple text encoders (including T5-XXL) in Large (8B, 16-24GB) and Medium (2B, 8-12GB) sizes, offering improved text rendering and compositional accuracy. SD1.5 remains popular for its massive ecosystem at just 4-6GB VRAM (512x512). SVD handles image-to-video generation (14 or 25 frames). Key parameters: 20-40 steps for SDXL, cfg_scale 5-10 (7 default), DPM++ 2M Karras sampler. Primary uses: photorealistic generation, artistic illustration, product photography, character generation, and LoRA-based custom training.

apps/hub/knowledge/models/seedance.md

@@ -1,64 +0,0 @@
-# Seedance
-
-Seedance is ByteDance's video generation model family, designed for cinematic, high-fidelity video creation from text and images. The 1.0 series established a standard for fluid motion and multi-shot consistency, while the 1.5 series adds native joint audio-visual generation.
-
-## Model Variants
-
-### Seedance 1.5 Pro
-
-- Native audio-visual generation producing video and audio in a single pass
-- Multilingual lip-sync supporting English, Mandarin, Japanese, Korean, and Spanish
-- 1080p output with 5-12 second duration
-- Advanced directorial camera controls (dolly zoom, tracking shots, whip pans)
-- Captures micro-expressions, non-verbal cues, and emotional transitions
-
-### Seedance 1.0 Pro
-
-- Production-quality 1080p video generation
-- Text-to-video and image-to-video with first and last frame control
-- Native multi-shot storytelling with subject and style consistency across cuts
-- Cinematic camera grammar interpretation (35mm film, noir lighting, drone shots)
-- 2-12 second video duration at 24-30fps
-
-### Seedance 1.0 Pro Fast
-
-- Faster, more cost-effective version of 1.0 Pro
-- Same capabilities with reduced generation time
-
-### Seedance 1.0 Lite
-
-- Optimized for speed and iteration at 720p or 1080p
-- Lower cost per generation for rapid prototyping
-
-## Key Features
-
-- Smooth, stable motion with wide dynamic range for large-scale movements
-- Native multi-shot storytelling maintaining consistency across transitions
-- Diverse stylistic expression (photorealism, cyberpunk, illustration, pixel art)
-- Precise prompt following for complex actions, multi-agent interactions, and camera work
-- Joint audio-visual synthesis with environmental sounds and dialogue (1.5)
-- Supports multiple aspect ratios (16:9, 9:16, 1:1, 4:3, 21:9, and more)
-
-## Hardware Requirements
-
-- Cloud API only; no local weights publicly available
-- Accessed via seed.bytedance.com, Scenario, fal.ai, and other API providers
-- 1080p 5-second video costs approximately $0.62 via fal.ai (Pro)
-- Lite version available at lower cost ($0.18 per 720p 5-second video)
-
-## Common Use Cases
-
-- Cinematic shorts and scene previsualization
-- Music video concept development
-- Product demonstration and marketing videos
-- Character-focused animation sequences
-- Social media content with audio (1.5)
-- Moodboard and style exploration for creative teams
-
-## Key Parameters
-
-- **prompt**: Text description of desired scene, action, and camera work
-- **image_url**: Source image for image-to-video generation (first frame)
-- **duration**: Video length (2-12 seconds for 1.0, 5-12 seconds for 1.5)
-- **resolution**: 480p, 720p, or 1080p output
-- **aspect_ratio**: 16:9, 9:16, 1:1, 4:3, 21:9, 9:21

apps/hub/knowledge/models/seedream.md

@@ -1,50 +0,0 @@
-# Seedream
-
-Seedream is ByteDance's text-to-image generation model, capable of producing high-quality images with strong text rendering, bilingual support (English and Chinese), and native high-resolution output.
-
-## Model Variants
-
-### Seedream 3.0
-
-- Native 2K resolution output without post-processing
-- Bilingual image generation (English and Chinese)
-- 3-second end-to-end generation for 1K images
-- Improved text rendering for small fonts and long text layouts
-
-### Seedream 4.0
-
-- Unified architecture for text-to-image and image editing
-- Native output up to 4K resolution
-- Multi-image reference input (up to 6 source images)
-- 1.8-second inference for 2K images
-- Batch input and output for multiple generations
-- Natural language image editing capabilities
-
-## Key Features
-
-- Accurate text rendering in both English and Chinese
-- Knowledge-driven generation for educational illustrations and charts
-- Strong character consistency across multiple angles
-- Prompt-based image editing without separate tools
-- Versatile style support from photorealism to anime
-- Leading scores on Artificial Analysis Image Arena
-
-## Hardware Requirements
-
-- API-only access via ByteDance Volcano Engine
-- No local hardware requirements for end users
-- Third-party API providers available (e.g., EvoLink)
-
-## Common Use Cases
-
-- Poster and advertisement design with embedded text
-- E-commerce product photography
-- Character design with multi-angle consistency
-- Educational illustration and infographic generation
-- Brand-consistent marketing materials
-
-## Key Parameters
-
-- **prompt**: Text description of the desired image
-- **resolution**: Output resolution (up to 4K supported)
-- **aspect_ratio**: Supports 16:9, 4:3, 1:1, and custom ratios

apps/hub/knowledge/models/seedvr2.md

@@ -1,47 +0,0 @@
-# SeedVR2
-
-SeedVR2 is a one-step diffusion-based video restoration model developed by ByteDance Seed and NTU S-Lab, published at ICLR 2026.
-
-## Model Variants
-
-### SeedVR2-3B
-
-- 3B parameter DiT with one-step inference for video and image upscaling
-- Available in FP16, FP8, and GGUF quantized formats
-
-### SeedVR2-7B
-
-- 7B parameter model with Sharp variant for maximum detail
-- Multi-GPU inference; supports 1080p and 2K on 4x H100-80GB
-
-### SeedVR (Original)
-
-- Multi-step diffusion model (CVPR 2025 Highlight)
-- Arbitrary-resolution restoration without pretrained diffusion prior
-
-## Key Features
-
-- One-step inference achieving 10x speedup over multi-step methods
-- Adaptive window attention with dynamic sizing for high-resolution inputs
-- Adversarial post-training against real data for faithful detail recovery
-- ComfyUI integration via official SeedVR2 Video Upscaler nodes
-- Apache 2.0 open-source license
-
-## Hardware Requirements
-
-- Minimum: 8-12GB VRAM with GGUF quantization and tiled VAE
-- Recommended: 24GB+ VRAM (RTX 4090) for 3B model at 1080p
-- High-end: 4x H100-80GB for 7B model at 2K resolution
-
-## Common Use Cases
-
-- Upscaling AI-generated video to 1080p or 4K
-- Restoring degraded or compressed video footage
-- Image super-resolution and detail recovery
-
-## Key Parameters
-
-- resolution: target shortest-edge resolution (720, 1080, 2160)
-- batch_size: frames per batch, must follow 4n+1 formula (5, 9, 13, 17, 21)
-- seed: random seed for reproducible generation
-- color_fix_type: wavelet, adain, hsv, or none

apps/hub/knowledge/models/stable-audio.md

@@ -1,53 +0,0 @@
-# Stable Audio Open
-
-Stable Audio Open 1.0 is Stability AI's open-source text-to-audio model for generating sound effects, production elements, and short musical clips.
-
-## Model Variants
-
-### Stable Audio Open 1.0
-
-- 1.2B parameter latent diffusion model
-- Transformer-based diffusion (DiT) architecture
-- T5-base text encoder for conditioning
-- Variational autoencoder for audio compression
-- Stability AI Community License (non-commercial)
-
-### Stable Audio (Commercial)
-
-- Full-length music generation up to 3 minutes with audio-to-audio and inpainting
-- Available via Stability AI platform API, commercial license
-
-## Key Features
-
-- Generates up to 47 seconds of stereo audio at 44.1kHz
-- Text-prompted sound effects, drum beats, ambient sounds, and foley
-- Variable-length output with timing control
-- Fine-tunable on custom audio datasets
-- Trained exclusively on Creative Commons licensed audio (CC0, CC BY, CC Sampling+)
-- Strong performance for sound effects and field recordings
-- Compatible with both stable-audio-tools and diffusers libraries
-
-## Hardware Requirements
-
-- Minimum: 8GB VRAM (fp16)
-- Recommended: 12GB+ VRAM for comfortable inference
-- Half-precision (fp16) supported for reduced memory
-- Chunked decoding available for memory-constrained setups
-- Inference speed: 8-20 diffusion steps per second depending on GPU
-
-## Common Use Cases
-
-- Sound effect and foley generation
-- Drum beats and instrument riff creation
-- Ambient soundscapes and background audio
-- Music production elements and samples
-- Audio prototyping for film and game sound design
-
-## Key Parameters
-
-- **steps**: Number of inference steps (100-200 recommended)
-- **cfg_scale**: Classifier-free guidance scale (typically 7)
-- **seconds_total**: Target audio duration (up to 47 seconds)
-- **seconds_start**: Start time offset for timing control
-- **negative_prompt**: Text describing undesired audio qualities
-- **sampler_type**: Diffusion sampler (dpmpp-3m-sde recommended)

apps/hub/knowledge/models/svd.md

@@ -1,55 +0,0 @@
-# Stable Video Diffusion
-
-Stable Video Diffusion (SVD) is Stability AI's image-to-video diffusion model that generates short video clips from a single conditioning image. In user studies, SVD was preferred over GEN-2 and PikaLabs for video quality.
-
-## Model Variants
-
-### SVD-XT (25 frames)
-
-- Generates 25 frames at 576x1024 resolution
-- Finetuned from the 14-frame SVD base model
-- Includes temporally consistent f8-decoder
-- Standard frame-wise decoder also available
-
-### SVD (14 frames)
-
-- Original release generating 14 frames
-- Foundation for community fine-tunes and extensions
-- Same 576x1024 native resolution
-
-## Key Features
-
-- Image-to-video generation from a single still image
-- Temporally consistent video output with finetuned decoder
-- Preferred over GEN-2 and PikaLabs in human evaluation studies
-- SynthID-compatible watermarking enabled by default
-- Latent diffusion architecture for efficient generation
-
-## Hardware Requirements
-
-- Minimum: 16GB VRAM
-- Recommended: A100 80GB for full quality (tested configuration)
-- SVD generation ~100s, SVD-XT ~180s on A100 80GB
-- Optimizations available for lower VRAM cards with quality tradeoffs
-
-## Common Use Cases
-
-- Animating still images into short video clips
-- Product visualization and motion graphics
-- Creative video experiments and art
-- Research on generative video models
-
-## Key Parameters
-
-- **num_frames**: 14 (SVD) or 25 (SVD-XT)
-- **resolution**: 576x1024 native
-- **conditioning_frame**: Input image at same resolution
-- **duration**: Up to ~4 seconds (25 frames)
-
-## Limitations
-
-- Short videos only (4 seconds maximum)
-- No text-based control (image conditioning only)
-- Cannot render legible text in output
-- Faces and people may not generate properly
-- May produce videos without motion or with very slow camera pans

apps/hub/knowledge/models/topaz.md

@@ -1,45 +0,0 @@
-# Topaz
-
-Topaz Labs provides AI-powered image and video enhancement software for upscaling, denoising, sharpening, and restoration.
-
-## Model Variants
-
-### Topaz Photo AI
-
-- All-in-one image enhancement with 11 AI tools including Sharpen, Denoise, Recover Faces, and Upscale
-- RAW image support and plugin integration with Photoshop and Lightroom
-
-### Topaz Video AI
-
-- 19 AI models: Proteus, Artemis, Gaia, Iris, Nyx, Starlight, and more
-- Upscale video from SD to 4K/8K/16K with frame interpolation and stabilization
-
-### Bloom
-
-- Creative upscaler that removes the artificial look from AI-generated images
-- Realism mode for natural skin, hair, and eyes on AI faces
-
-## Key Features
-
-- Multiple specialized AI models optimized for different content types
-- Enterprise API with Face Realism, Colorization, and Video Colorization
-- Local and cloud rendering with After Effects and DaVinci Resolve plugins
-
-## Hardware Requirements
-
-- Minimum: 8GB VRAM for GPU-accelerated processing
-- Recommended: NVIDIA RTX 3080+ with 32GB+ RAM for video
-- Available on Mac and Windows as standalone or plugin
-
-## Common Use Cases
-
-- Upscaling old or low-resolution footage to 4K+
-- Denoising low-light photography and restoring archival video
-- Enhancing AI-generated images for photorealism
-
-## Key Parameters
-
-- AI Model Selection: choose specialized models per content type
-- Scale Factor: 2x, 4x, or higher depending on tool
-- Denoise Strength: adjustable noise reduction level
-- Sharpen Amount: controls detail enhancement intensity

apps/hub/knowledge/models/tripo.md

@@ -1,50 +0,0 @@
-# Tripo
-
-Tripo is an AI-powered 3D generation platform that creates production-ready 3D models from text or images in seconds, developed by VAST AI Research.
-
-## Model Variants
-
-### Tripo v3.0
-
-- Sculpture-level geometry precision with sharp edges
-- Best for high-fidelity production assets
-
-### Tripo v2.0
-
-- Industry-leading geometry with PBR material support
-- High accuracy for detailed models
-
-### Tripo v1.4
-
-- Fast generation with realistic texture effects
-- Best for rapid prototyping
-
-## Key Features
-
-- Text-to-3D and image-to-3D generation
-- Multi-image input for high-fidelity reconstruction
-- 4K PBR-ready texture generation
-- Automatic rigging and animation
-- Model segmentation for part-based editing
-- Export in STL, OBJ, FBX, GLB, and USDZ formats
-
-## Hardware Requirements
-
-- Cloud API-based (no local GPU required)
-- TripoSR open-source variant requires 8GB+ VRAM
-
-## Common Use Cases
-
-- Game asset creation
-- 3D printing prototyping
-- AR/VR content development
-- Product visualization and e-commerce
-- Character design and animation
-
-## Key Parameters
-
-- **prompt**: Text description of desired 3D model
-- **image**: Reference image (JPG, PNG, WEBP, up to 5MB)
-- **texture_resolution**: Up to 4K with PBR maps
-- **format**: Output format (GLB, FBX, OBJ, STL, USDZ)
-- **style**: Optional stylization (Lego, Voxel, Voronoi)

apps/hub/knowledge/models/veo.md

@@ -1,49 +0,0 @@
-# Google Veo
-
-Veo is Google DeepMind's state-of-the-art video generation model family, designed for high-quality cinematic video creation with strong prompt adherence and realistic physics simulation.
-
-## Model Variants
-
-### Veo 2
-
-- Text-to-video and image-to-video generation
-- Up to 4K resolution output (720p default in VideoFX)
-- 8-second clips, extendable to minutes
-- State-of-the-art in human preference evaluations against Sora, Kling, and Minimax
-
-### Veo 3 / 3.1
-
-- Latest generation with native audio generation
-- Generates sound effects, ambient noise, and dialogue alongside video
-- Improved prompt adherence and real-world physics simulation
-- 1080p and 4K output support
-- Scene extension, first/last frame, and object insertion capabilities
-
-## Key Features
-
-- Cinematic camera control (lens types, angles, depth of field)
-- Realistic physics and natural human motion
-- SynthID invisible watermarking on all outputs
-- Style reference image support for consistent aesthetics
-- Reduced hallucination artifacts compared to prior models
-- Available via Gemini API and Google AI Studio
-
-## Hardware Requirements
-
-- Cloud API only (Google-hosted infrastructure)
-- No local GPU required
-- Available through VideoFX, Vertex AI, and Gemini API
-
-## Common Use Cases
-
-- Short-form video content and social media clips
-- Product demos and promotional videos
-- Cinematic storytelling and filmmaking
-- Marketing and advertising video production
-
-## Key Parameters
-
-- **prompt**: Detailed text description with cinematic language
-- **aspect_ratio**: 16:9, 9:16, and other formats
-- **person_generation**: Control for human figure generation
-- **duration**: Up to 8 seconds per clip (Veo 2), extendable in Veo 3.1

apps/hub/knowledge/models/vidu.md

@@ -1,63 +0,0 @@
-# Vidu
-
-Vidu is a video generation API developed by ShengShu Technology. It supports text-to-video, image-to-video, reference-to-video with multi-entity consistency, and start-end frame interpolation. Vidu is known for fast generation speeds (as low as 10 seconds) and strong anime-style output.
-
-## Model Variants
-
-### Vidu 2.0
-
-- Extended 8-second video generation at up to 1080p
-- Text-to-video and image-to-video modes
-- First and last frame control for transitions
-- Available via the Vidu API and third-party platforms
-
-### Vidu Q1
-
-- Reference-to-video with multi-entity consistency
-- Supports up to 7 reference images with semantic understanding
-- Infers missing elements from text prompts and reference context
-- Generates coherent scenes combining multiple characters, objects, and environments
-
-### Vidu Q2
-
-- Optimized for quality and speed balance
-- Supports 6-8 second generation at up to 1080p
-- 1080p image generation included in higher tiers
-
-### Vidu Q3
-
-- Latest generation model with improved output quality
-- Available through the Vidu platform and API
-
-## Key Features
-
-- Ultra-fast inference (videos generated in as few as 10 seconds)
-- Multi-entity consistency across characters, objects, and scenes
-- First and last frame control for precise transitions
-- Superior anime and 2D animation quality
-- Up to 1080p resolution output with multiple aspect ratios
-- Optimized scene templates for interactive effects and e-commerce
-
-## Hardware Requirements
-
-- Cloud API only; no local hardware required
-- Accessed via platform.vidu.com or third-party API providers
-- Credit-based pricing with free tier available
-
-## Common Use Cases
-
-- Anime and 2D animation series production
-- E-commerce product video creation
-- Social media content (TikTok, Reels, Shorts)
-- Reference-based multi-character storytelling
-- Marketing and advertising videos
-- Start-end frame transitions and morphing effects
-
-## Key Parameters
-
-- **prompt**: Text description of desired video content
-- **image_url**: Source image for image-to-video generation
-- **duration**: Video length (4-8 seconds depending on model)
-- **resolution**: Output resolution (720p or 1080p)
-- **style**: Visual style selection (realistic or animated)
-- **movement_amplitude**: Controls intensity of motion in output

apps/hub/knowledge/models/wan.md

@@ -1,93 +0,0 @@
-# Wan
-
-Wan is a family of open-source video generation models from Alibaba's Tongyi Lab, spanning text-to-video, image-to-video, speech-to-video, motion control, and video editing. All models are released under the Apache 2.0 license.
-
-## Model Variants
-
-### Wan 2.1 T2V / I2V
-
-- Text-to-video and image-to-video generation
-- Available in 1.3B and 14B parameter sizes
-- Supports 480p and 720p output, variable aspect ratios
-- Chinese and English visual text generation
-
-### Wan 2.1 Fun (Control / InPaint / Camera)
-
-- Camera control with predefined or custom camera movements
-- Video inpainting for targeted frame-level editing
-- Depth, pose, and canny edge control for guided generation
-
-### Wan 2.1 VACE (Video Any-Condition Editing)
-
-- All-in-one model for video creation and editing (ICCV 2025)
-- Reference-to-video (R2V), video-to-video (V2V), and masked editing (MV2V)
-- Supports inpainting, outpainting, first-last-frame interpolation, and animate-anything
-- Available in 1.3B and 14B sizes, built on Wan 2.1 base models
-
-### Wan 2.2 T2V / I2V / TI2V
-
-- Mixture-of-Experts (MoE) architecture with high-noise and low-noise expert models
-- T2V-A14B and I2V-A14B (14B MoE), TI2V-5B (hybrid text+image-to-video)
-- Cinematic-level aesthetic control with lighting, composition, and color tone guidance
-- TI2V-5B uses a high-compression 16×16×4 VAE, runs on consumer GPUs like 4090
-
-### Wan 2.2 S2V (Speech-to-Video)
-
-- Audio-driven cinematic video generation from image + speech + text
-- Supports lip-sync, facial expressions, and pose-driven generation
-- Generates variable-length videos matching input audio duration
-
-### Wan 2.2 Animate
-
-- Character animation and subject replacement from video + reference image
-- Animate mode: transfers motion from reference video onto a still character
-- Replace mode: swaps subjects while preserving background, lighting, and camera motion
-- Includes relighting LoRA for scene-matched lighting adaptation
-
-### Wan Move
-
-- Point-level motion control for image-to-video generation (NeurIPS 2025)
-- Dense trajectory-based guidance for fine-grained object motion
-- Latent trajectory propagation without extra motion modules
-- 14B model generating 5-second 480p videos
-
-## Key Features
-
-- High temporal consistency and natural physics simulation
-- Multiple aspect ratios (16:9, 9:16, 1:1) at 24fps
-- MoE architecture in 2.2 for higher quality at same compute cost
-- Bilingual prompt support (Chinese and English)
-- ComfyUI and Diffusers integration across all variants
-
-## Hardware Requirements
-
-- 1.3B models: 8GB VRAM minimum
-- 14B models: 24GB+ VRAM recommended (80GB for full precision)
-- TI2V-5B: runs on consumer 4090 GPUs at 720p
-- FP8 quantization available for lower VRAM configurations
-- Multi-GPU inference supported via FSDP + DeepSpeed Ulysses
-
-## Common Use Cases
-
-- Social media and short-form video content
-- Character animation and motion transfer
-- Video inpainting and scene editing
-- Product animation and marketing videos
-- Speech-driven talking head generation
-- Storyboard-to-video conversion
-
-## Key Parameters
-
-- **frames**: Number of output frames (typically 81 for ~3.4s at 24fps)
-- **steps**: Inference steps (20-50 recommended)
-- **cfg_scale**: Guidance scale for prompt adherence (3-7 typical)
-- **size**: Output resolution (480p or 720p)
-- **model_name**: Selects variant (e.g., vace-14B, ti2v-5B, s2v-14B)
-
-## Blog References
-
-- [Wan 2.1 Video Model Native Support](../blog/wan21-video-model-native-support.md) — Initial release with 4 model variants, 8.19GB VRAM minimum
-- [Wan 2.1 VACE Native Support](../blog/wan21-vace-native-support.md) — Unified video editing: Move/Swap/Reference/Expand/Animate Anything
-- [Wan 2.2 Day-0 Support](../blog/wan22-day-0-support.md) — MoE architecture, Apache 2.0 license, cinematic controls
-- [WAN 2.6 Reference-to-Video](../blog/wan26-reference-to-video.md) — Generate videos from reference clips at up to 1080p
-- [The Complete AI Upscaling Handbook](../blog/upscaling-handbook.md) — Wan 2.2 used for creative video upscaling

apps/hub/knowledge/models/wan.summary.md

@@ -1 +0,0 @@
-Wan is Alibaba Tongyi Lab's open-source video generation family (Apache 2.0 license) covering text-to-video, image-to-video, speech-to-video, motion control, and video editing. Wan 2.1 offers T2V/I2V in 1.3B (8GB VRAM) and 14B (24GB+) sizes at 480p/720p, plus Fun variants for camera control, inpainting, and depth/pose/canny conditioning. VACE provides unified video editing with reference-to-video, video-to-video, and masked editing. Wan 2.2 introduces MoE architecture for higher quality at same compute cost, TI2V-5B hybrid generation (runs on 4090), and S2V for speech-driven video with lip-sync. Wan 2.2 Animate handles character animation and subject replacement with relighting LoRA. Wan Move offers point-level motion control via dense trajectory guidance. Key strengths: high temporal consistency, bilingual prompts, multiple aspect ratios at 24fps. Parameters: 20-50 steps, cfg_scale 3-7, typically 81 frames (~3.4s).

apps/hub/knowledge/models/wavespeed.md

@@ -1,46 +0,0 @@
-# WaveSpeed
-
-WaveSpeed is a high-performance AI inference platform providing accelerated access to 700+ models for image, video, audio, and text generation via unified REST APIs.
-
-## Model Variants
-
-### WaveSpeed Image Models
-
-- FLUX family (dev, schnell, pro) with sub-2-second generation
-- Seedream, Ideogram, Z-Image, and Qwen-Image integrations
-
-### WaveSpeed Video Models
-
-- WAN 2.x, Kling, Veo, Seedance, PixVerse, and LTX-2 19B access
-
-### WaveSpeed Audio Models
-
-- ACE-Step 1.5 for music generation in 50+ languages
-- Inworld 1.5 text-to-speech with 56+ multilingual voices
-
-## Key Features
-
-- Ultra-fast inference: images in under 2 seconds, video in under 2 minutes
-- Unified API access to 700+ curated, production-ready models
-- Pay-as-you-go credits starting at ~$0.006 per image
-- Exclusive access to ByteDance Seedream V3, Kling, and Alibaba WAN
-
-## Hardware Requirements
-
-- Cloud API platform with no local hardware required
-- RESTful API with SDKs for Python, JavaScript, and other languages
-- Web UI (Studio) and desktop app available for interactive use
-
-## Common Use Cases
-
-- Rapid prototyping and bulk content generation
-- Developer integration for AI-powered media applications
-- High-volume marketing and e-commerce asset creation
-- Video upscaling and enhancement pipelines
-
-## Key Parameters
-
-- model: specific model identifier (e.g., wavespeed-ai/z-image/turbo)
-- prompt: text description for generation
-- resolution: output dimensions
-- num_images: batch generation count

apps/hub/knowledge/models/z-image.md

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-# Z-Image
-
-Z-Image is Zhipu AI's image generation model family, built on the CogView architecture with a hybrid autoregressive and diffusion decoder design.
-
-## Model Variants
-
-### GLM-Image (Z-Image)
-
-- 9B autoregressive + 7B DiT diffusion decoder hybrid architecture
-- First open-source industrial-grade discrete autoregressive image generator
-- State-of-the-art bilingual text rendering (English and Chinese)
-
-### Z-Image-Turbo
-
-- Optimized variant for faster inference with reduced latency
-- Suitable for real-time and batch generation workflows
-
-### CogView-4
-
-- 6B parameter DiT diffusion model, foundation for the Z-Image decoder
-
-## Key Features
-
-- Industry-leading text rendering accuracy for posters and infographics
-- Custom resolution from 512px to 2048px (multiples of 32)
-- Image editing, style transfer, and identity-preserving generation
-- LoRA training support; open weights on HuggingFace
-
-## Hardware Requirements
-
-- Cloud API: no local hardware required ($0.015 per image via Z.ai)
-- Self-hosted: 24GB+ VRAM for the combined 9B+7B architecture
-
-## Common Use Cases
-
-- Text-to-image generation with accurate text rendering
-- Commercial poster and graphic design
-- Social media content creation
-- Multi-subject consistency and identity-preserving generation
-
-## Key Parameters
-
-- prompt: text description of the desired image
-- size: output resolution (e.g., 1280x1280, 1568x1056, 960x1728)
-- model: glm-image or cogview-4