Compare commits
@@ -0,0 +1,22 @@
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name: Publish to Comfy Registry
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on:
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workflow_dispatch:
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push:
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branches:
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- main
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paths:
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- "pyproject.toml"
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jobs:
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publish-node:
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name: Publish Custom Node to registry
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runs-on: ubuntu-latest
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steps:
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- name: Check out code
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uses: actions/checkout@v4
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- name: Publish Custom Node
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uses: Comfy-Org/publish-node-action@main
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with:
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personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
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@@ -185,3 +185,17 @@ os.environ["HF_HOME"] = "D:\\hf_cache"
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5. `INPUT_TYPES` 変更時は `widgets_values` も同時更新
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6. 削除した依存への参照が残っていないか grep で確認
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7. ComfyUI完全再起動 → ワークフロー読み込み → エンドツーエンド実行確認
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---
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## リリース手順
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コミット・プッシュ・GitHub リリース（`gh release create`）を行う際は、**必ず以下の順序**で実施する。
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1. `pyproject.toml` の `version` を新バージョン番号に更新してステージングに含める
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2. `README.md` のバージョンバッジを更新（``）
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3. `DEVLOG.md` の先頭に変更詳細エントリを追加
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4. 上記すべてを同一コミットにまとめてプッシュ
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5. `gh release create vX.Y.Z` でリリースを作成（リリースノート本文に変更点を記載する — v0.3.90でREADME.mdのChangelogセクションは廃止したため、以後のバージョン履歴はGitHub Releaseのリリースノートのみで管理する）
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`pyproject.toml` のバージョン変更が GitHub Actions のトリガー（`paths: pyproject.toml`）になっており、ComfyUI Registry への自動公開もここで行われる。バージョン更新を忘れると Registry に反映されない。
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@@ -1,65 +1,173 @@
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# ComfyUI-Workflow-Studio
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A comprehensive workflow management and generation UI plugin for [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
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A comprehensive workflow, asset management, and generation UI plugin for [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
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Browse, organize, and execute workflows directly from a dedicated studio interface — without switching between windows or manually editing JSON.
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**📁 Management** — organize all your assets in one studio
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- Workflow, model, image, and prompt management
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- AI-powered prompt writing assistance, translation, and tag generation
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- File drop & Gallery-linked metadata viewing — inspect and reuse generation settings instantly
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**⚡ GenerateUI** — a productivity-focused generation tab connected to every other tab
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- Batch generation across models, samplers, prompts, and workflows
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- Image Feeder for continuous folder-based generation
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- Lab: experimental I2I batch generation with independent per-iteration keyframes for Checkpoint/VAE/Prompt/KSampler
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**📚 Workflow Studio Library** — a multi-function side panel for smooth ComfyUI integration
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- Drag & drop models, nodes, prompts, and workflows straight onto the canvas
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- **Send to Canvas**: click "Send to Canvas" (Workflow tab) or "Copy & Send Canvas" (Gallery tab) to send the workflow directly to the ComfyUI canvas — UI and API formats both supported
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- View metadata from images / JSON, then drop the detected models and prompts onto the canvas
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- Built-in AI tools (translation and more)
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## Screenshots
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| Workflow Tab | GenerateUI Tab |
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|:---:|:---:|
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|  |  |
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| Workflow Tab | Models Tab |
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| :-------------------------------------: | :---------------------------------: |
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|  |  |
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| Prompt Tab | Settings Tab |
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|:---:|:---:|
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|  |  |
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| Prompt Input Assistance | Gen UI Feeder |
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| :----------------------------------------------------------: | :---------------------------------------: |
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|  |  |
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| Nodes Tab | Models Tab |
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|:---:|:---:|
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|  |  |
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| GenUI LoRA Stack | GenUI Batch |
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| :---------------------------------------------: | :-------------------------------------: |
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|  |  |
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| Help & Support Tab | |
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|:---:|:---:|
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|  | |
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| Models Multi-select Menu | Top Bar |
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| :-----------------------------------------------------------: | :---------------------------: |
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|  |  |
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| Workflow Studio Library (ComfyUI) | ComfyUI Integration |
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|:---:|:---:|
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|  |  |
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| WS Library | Library Information |
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| :----------------------------------: | :---------------------------------------------------: |
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|  |  |
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| Tagger Tab | Image Edit Tab |
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| :-------------------------------: | :--------------------------------------: |
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|  |  |
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| Customize | |
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| :--------------------------------: | :-: |
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|  | |
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---
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## Features
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### Workflow Tab
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- **Thumbnail / Card / Table views** — switch between view modes to browse your workflow library
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<details>
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<summary><h3>Workflow Tab</h3></summary>
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- **Thumbnail / Table views** — switch between view modes to browse your workflow library
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- **Thumbnail side panel** — preview workflow canvas snapshots in the side panel
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- **Tag-based filtering** — filter by base model (SD1.5, SDXL, etc.) and custom groups
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- **Badge filtering** — filter by user-defined badges (free labels you assign to each workflow)
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- **Search** — full-text search across workflow names and metadata
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- **Side panel tabs** — Thumbnail preview, JSON viewer with syntax highlighting, and Group management
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- **Batch analysis** — auto-detect checkpoint, model type, prompt, and I/O node counts
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- **Badge management** — add, rename, delete badges with custom colors shared with the Models tab (⚙ Badge button)
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- **AI summary** — generate workflow descriptions using Ollama
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- **Import / Export** — import workflows from files or clipboard, open in ComfyUI directly
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- **Default view setting** — persist your preferred view mode (Thumbnail / Card / Table)
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- **Import / Export** — import workflows from files or clipboard; **Send to Canvas** button sends the selected workflow directly to the ComfyUI canvas (UI and API formats both supported)
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- **Default view setting** — persist your preferred view mode (Thumbnail / Table)
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- **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
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- **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, group filter, and badge filter (ALL) in one click
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</details>
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<details>
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<summary><h3>Canvas Snapshot (v0.1.2)</h3></summary>
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### Canvas Snapshot (v0.1.2)
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- **One-click capture** — click the camera button in ComfyUI's top bar to snapshot the current workflow canvas
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- **Auto-save as thumbnail** — the snapshot is saved directly to the workflow data folder as a PNG thumbnail
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- **Embedded workflow metadata** — workflow JSON is embedded in the PNG (tEXt chunk), compatible with ComfyUI's drag-and-drop import
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- **Auto-import** — the captured workflow is automatically imported and appears in the Workflow tab
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### GenerateUI Tab
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- **Auto-generated parameter UI** — prompt, model, sampler, image, and other settings extracted from the workflow
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</details>
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<details>
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<summary><h3>GenerateUI Tab (v0.3.5)</h3></summary>
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- **6-tab layout** — Input / Model / Settings / Feeder / Batch / **Lab** tabs; Input, Model, and Settings each include a Raw JSON column on the right for instant preview and direct editing
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- **Save button** — located at the right end of the subtab row; opens a filename dialog (default: current workflow name) and saves the current workflow as a `.json` file to the Workflow tab via the import API
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- **Input tab** — Prompt and Image inner tabs (drag-and-drop upload); Prompt tab shows Positive Prompt and Negative Prompt textareas, plus an **Embeddings selector** at the bottom (Filter + Select + Weight input + Paste button); Paste inserts `(embedding:Name:weight)` at the cursor position of the last focused textarea (defaults to Positive when neither is focused); Raw JSON (540px) in the right column
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- **Revert button** (v0.3.70) — a ↺ button next to each Apply button reverts the textarea to the node's currently applied value (the reverse of Apply)
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- **Emphasis weight editing** (v0.3.70) — select text (or just place the cursor on a word/parenthesis block) and press Ctrl+↑/↓ to adjust `(text:weight)` by ±0.05, A1111/native-ComfyUI style; parentheses are removed automatically when the weight returns to 1.0
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- **Mask slot for inpainting** (v0.3.71) — LoadImage slots whose MASK output feeds an inpaint node (e.g. `VAEEncodeForInpaint`) show an additional Mask drop zone; when a mask is set, image + mask are composited into a single RGBA upload (mask baked into the alpha channel) matching ComfyUI's native alpha→MASK extraction — no extra nodes required
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- **Model tab** — Checkpoint, VAE, LoRA, ControlNet, Diffusion Model (UNETLoader / UnetLoaderGGUF / LoaderGGUF), Text Encoder (CLIPLoader / DualCLIPLoader / ClipLoaderGGUF / DualClipLoaderGGUF — single clip: type + device; dual clip: two clip selectors + type + device), **Hypernetwork** (with Strength field) selectors with filter; Raw JSON on the right
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- **LoRA Stack Group toggle** (v0.3.92) — for `Lora Loader (LoraManager)` nodes, a **Use Stack Group** checkbox next to the Single/Stack tabs switches what the Stack tab shows and edits: OFF (the default whenever a workflow is loaded) displays and edits the LoRAs actually configured in the workflow's own Lora Manager node — correctly showing every LoRA it holds, not just the first one; ON switches to the LoRA group registered in the Models tab instead; toggling either direction clears the node's LoRA list first, then loads it from the newly selected source, and switching back to OFF restores exactly the list that was present when the workflow was loaded
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- **Settings tab** — KSampler and Latent Image side by side at 50% width each; Raw JSON on the right
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- **Always-visible Raw JSON** — edit the API-format JSON directly from any tab with syntax highlighting; Apply button reloads the workflow; built-in **search bar** (always shown) finds all matches as you type with count display (`3/12`); navigate with ↑/↓ buttons or Enter / Shift+Enter; Escape or ✕ clears; current match highlighted in orange, other matches in yellow
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- **Bypass/Mute node handling** (v0.3.75) — loading a UI-format workflow with Bypass-mode nodes now reroutes their wires to the upstream source (matching same-type input/output slots, recursively through chained bypasses) instead of leaving a dangling reference that failed validation; Mute-mode nodes are excluded without rerouting, same as ComfyUI itself; when either is present, a note listing the affected nodes appears above the Raw JSON search bar in the Input/Model/Settings tabs
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- **One-click generation** — queue prompts to ComfyUI without leaving the studio
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- **Seed control** — randomize, lock, or manually set seeds
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- **Raw JSON editor** — view and edit the API-format JSON with syntax highlighting
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- **UI-to-API conversion** — automatic conversion supporting subgraphs (nested workflows), COMBO types, and display-only node exclusion
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- **Seed control** — randomize, lock, or manually set seeds; seed input and mode selector stacked vertically for readability
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- **Style selector** — checkbox and dropdown next to the Reset Workflow button; enable to apply a Fooocus-style JSON to positive and negative prompts at generation time; style files (`*.json`) are loaded from `user/default/Workflow-Studio/style/`; the style's `{prompt}` placeholder is replaced with the original prompt text, or the style is appended if no placeholder exists; `negative_prompt` is appended to the existing negative prompt; styles are applied to a per-generation copy and do not modify the loaded workflow
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- **Create Catalog / Catalog buttons** (v0.3.95) — next to the Style dropdown. **Create Catalog** generates one preview image per Style checked in the Batch tab's Style sub-tab (using the currently loaded workflow, typically T2I), then saves each result into a chosen `ws_style_catalog` subfolder named after the Style (existing files with the same name are overwritten); a modal lets you create a new destination folder or pick an existing one — separate folders are useful for grouping catalogs by checkpoint or by purpose. **Catalog** jumps straight to the Gallery tab's Style_Catalog sub-tab to browse the results (see below)
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- **Batch type selector** — check one of the column header checkboxes in the Batch Queue pane (Checkpoint / Lora / Prompt / Workflow / Sampler / Scheduler / Style) to activate that batch type; only one type can be active at a time; the Batch panel below Generate shows the active type, progress, and Pause/Resume/Stop controls
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- **Batch tab** (v0.3.18) — dedicated 3-pane layout for assembling the batch queue:
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- **Left pane** — 4 tabs: **Checkpoints** (file-tree; Filter / All / None), **Sampler** (KSampler sampler list), **Scheduler** (KSampler scheduler list), **Style** (flat list of all styles from `Workflow-Studio/style/`; All / None buttons)
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- **Center pane** — group-based selection with inner tabs (Checkpoint | Lora | Prompt | Workflow); Checkpoint/Lora groups come from the Models tab, Prompt groups from the Prompt tab, Workflow groups from the Workflow tab — check a group to add all its members, expand ▶ to select individually
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- **Right pane (Batch Queue)** — 7 columns: Checkpoint / Lora / Prompt / Workflow / Sampler / Scheduler / **Style**; each column header has an enable checkbox (radio behavior: only one at a time); count shown per column; Style batch applies each selected style sequentially to a workflow copy
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- **Lora batch prompt sync** (v0.3.69) — for each selected LoRA, its syntax (`<lora:name:strength_model:strength_clip>`) and CivitAI trained words are automatically appended to the Positive prompt (rebuilt from the original prompt each time, not accumulated), then restored to the pre-batch state when the batch completes or is stopped; if a selected LoRA is not yet recognized by ComfyUI's model list, a warning toast suggests a Refresh or restart
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- **UI-to-API conversion** — automatic conversion supporting subgraphs (nested workflows), COMBO types, and display-only node exclusion; improved analysis covers SDXL multi-hop CONDITIONING chains, CLIPTextEncodeSDXL, SDXLPromptStyler, KSamplerAdvanced, and Guider-based "Advanced Sampling" workflows (`SamplerCustomAdvanced`/`SamplerCustom` + `CFGGuider`/`DualCFGGuider`/`BasicGuider` + `RandomNoise` + `KSamplerSelect` + `*Scheduler` — Flux.2 Klein, LongCat, Boogu, HiDream E1, etc.)
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- **Eagle integration** — auto-save generated images to [Eagle](https://eagle.cool/) with metadata
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### Prompt Tab
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- **3-column layout** — AI Assistant, Presets editor, and Preset Manager displayed side-by-side
|
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</details>
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<details>
|
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<summary><h3>Feeder subtab (v0.3.5 / v0.3.42)</h3></summary>
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|
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Two independent modes selectable via **[Image Loop] / [Gallery]** toggle buttons at the top of the left pane (persisted in `localStorage`).
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|
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**Image Loop mode** — requires the **[comfyui-image-feeder](https://github.com/ketle-man/comfyui-image-feeder)** custom node.
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- **ImageFeeder node control** — select the target node from a dropdown auto-populated from the loaded workflow; edit all node parameters (Directory, Sort Mode, Index, Start/End Index, Batch Size, Seed, Use Selection) and Apply to the workflow
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- **Image library** — 3-pane layout: folder tree (left) browsing `user/default/image-loop-data/`, image grid with checkbox selection (center), preview panel with resolution and file size (right)
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- **Selection management** — check individual images; All / None buttons for the current folder; selected files are reflected in `selected_files` on Apply
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- **Presets** — save the current directory + selection as a named preset; load or delete presets (server-side persistence via `image-feeder-presets.json`)
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- **Continuous Run loop** — Run / Stop buttons below the presets; **After gen** mode controls index behavior after each generation:
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- **Loop** — advance index and wrap back to 0 when all images are exhausted (runs indefinitely)
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- **Increment** — advance index and auto-stop when all images are consumed
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- **Fixed** — always use the same index
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- **Index sync** — after each generation the node returns `next_index` via WebSocket (`image_loop_node_sync`); the Index field updates automatically
|
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- **Seed** — Run loop uses the right-pane seed setting (Random / Fixed / Increment / Decrement); the node's own Seed field only affects random sort order
|
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|
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**Gallery mode** (v0.3.42) — no external plugin required; uses the built-in **WFS_GalleryFeeder** custom node.
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|
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- **WFS_GalleryFeeder node** — reads images directly from a Gallery group and outputs them one at a time; inputs: `group_name`, `index`, `sort_mode` (filename_asc / filename_desc / random), `seed`
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- **Node & group selector** — pick the WFS_GalleryFeeder node from the loaded workflow and choose which Gallery group to feed images from; `__Feeder__` group is auto-created on first open
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- **Image grid** — center pane shows all images in the selected group; click any image to update the Index to that position
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- **After Gen modes** — Loop (wrap at end and continue indefinitely), Increment (stop automatically when last image is reached), Fixed (always use the same index)
|
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- **Run / Stop controls** — left pane Run / Stop buttons manage the generation loop; After Gen combo (loop / increment / fixed), ▶ Run, and ■ Stop widgets are also available directly on the WFS_GalleryFeeder node in the ComfyUI canvas (`gallery_feeder_extension.js`)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Lab subtab (v0.3.87 – v0.3.93)</h3></summary>
|
||||
|
||||
An experimental batch generator: runs the workflow currently loaded in GenerateUI N times, letting Checkpoint / VAE / Prompt / KSampler each change independently starting at a chosen iteration — unlike the Batch tab's single-axis queue, every column keeps its own list of change points. Never mutates the loaded workflow; every iteration runs on a fresh clone. Works with both I2I and T2I workflows (v0.3.90).
|
||||
|
||||
- **Setting / Results / Plan JSON sub-panels** — Setting: image drop zone + 4 keyframe columns + run controls; Results: source image and up to 9 generated thumbnails (click to enlarge); Plan JSON: the current plan as raw editable JSON (v0.3.88)
|
||||
- **Per-column keyframes** — Checkpoint, VAE, Prompt, KSampler each hold an independent list of `{iteration, value}` keyframes; +/− per column adds/removes the last keyframe (iteration #1 is fixed and can't be removed); any keyframe other than #1 can also be deleted individually from inside its own edit modal (v0.3.93), not just the last one; click a cell to edit its value, the iteration it starts applying from, and a "revert to #1's setting" checkbox; the effective value at iteration N is the latest keyframe with iteration ≤ N, carried forward until the next one changes it; lowering the Batch count below an existing keyframe's iteration doesn't delete or renumber it — a warning toast appears and it just stays dormant until Batch count is raised again (v0.3.93)
|
||||
- **Wildcards** (v0.3.93) — Prompt keyframes' Positive/Negative text supports the same A1111-style `__name__` syntax as the main GenerateUI prompt fields; each keyframe is expanded fresh right before that iteration runs, so reusing the same keyframe across iterations can still draw a different random line each time
|
||||
- **Checkpoint / VAE cell filtering** (v0.3.88) — a Filter... box above the value dropdown narrows the list live, same as GenerateUI's Model tab; overlay ✕ clear button matches the Models/Nodes/Workflow/Gallery tab search boxes
|
||||
- **Prompt cell extras** (v0.3.88, refined in v0.3.89 and v0.3.93) — **Get from GenerateUI** fills Positive/Negative from the workflow currently loaded in GenerateUI; **Get from Image** extracts a prompt from the image currently loaded in Lab's own Image drop zone (same detection as the Metadata tab; no separate file picker); **Style** row — an **Apply** button merges whichever style is currently selected in GenerateUI's own top-bar Style dropdown into the Positive/Negative text below, comma-separated after any existing text (never clears it) and works regardless of that top-bar's own Style enable checkbox; a checkbox next to it tracks on/off and shows the applied style's name beside itself (not on the button) — unchecking only clears that name label, the already-merged text stays and is yours to edit/delete by hand
|
||||
- **T2I workflow support** (v0.3.90) — a **T2I workflow (no source image)** checkbox in the Image pane skips the source-image requirement and never touches a LoadImage node, so workflows with no image input (e.g. plain `EmptyLatentImage` txt2img) can also use the Checkpoint/VAE/Prompt/KSampler keyframes; the Image drop zone and "Use generated image for next" are greyed out (not cleared) while it's on; saved with the plan as `t2i_mode`
|
||||
- **Use generated image for next** — optional checkbox that chains iterations: from iteration 2 onward the previous iteration's first output image is fed back in as the I2I source instead of the original dropped image, using ComfyUI's `"name [type]"` annotated-filename reference (no re-upload needed); disabled together with the Image drop zone in T2I mode
|
||||
- **Workflow recall** (v0.3.90) — every saved plan records the filename of the workflow that was loaded in GenerateUI at save time (`workflow_filename` in the Plan JSON); after loading a plan, an **Open Workflow** button appears next to it — click to load that exact workflow file back into GenerateUI (asks for confirmation first, since it replaces whatever is currently loaded)
|
||||
- **Plan files** — Plan Save (silently overwrites the loaded plan) / Save As (always asks for a new name) / Plan Clear; plans are stored as `ws_labplan_<name>.json` in `user/default/Workflow-Studio/lab_plan/` (the `ws_labplan_` prefix is added automatically, v0.3.90 — type just the plain name when prompted), alongside an auto-generated `ws_labplan_<name>.png` index image (a 3-per-row contact sheet of up to 9 result thumbnails)
|
||||
- **Plan Load** — same drag-and-drop / click-to-browse drop zone as the Image slot; drop a plan's `.json` (read directly in the browser) or any of its index-image `.png`s — since v0.3.91 every index image carries the full plan embedded in it directly (see below), so it's read straight from the file with no server round-trip; older index images without that embedding fall back to fetching a same-named `.json` from the server
|
||||
- **Plan JSON tab** (v0.3.88) — view/edit the current plan as raw JSON (same shape saved to disk); Refresh re-reads it from the Setting tab, Apply to Setting parses your edits and writes them back into the Setting tab's columns/note/batch/chain-image/source-image/results — the same effect as loading that JSON as a plan file, without saving it first
|
||||
- **Save index image to Output on Run** (v0.3.90, on by default since v0.3.93) — checkbox in the run controls; when checked, every completed Run also builds the same contact-sheet index image used for Plan files and saves it directly into ComfyUI's own Output folder as `Lab_index_<counter>_.png` (auto-numbered like a normal generated image, right alongside the images that Run just generated); independent of Plan Save, which always writes its own copy next to the plan file regardless of this checkbox; saved with the plan as `save_index_on_run`
|
||||
- **Index images are self-contained plan files** (v0.3.91) — every index-image PNG, whether from Plan Save/Save As or from Save index image to Output on Run, carries the entire plan embedded directly in the file as a PNG `wfm_lab_plan` chunk; this is what lets the Output-folder copy above work as a Plan Load source even though it has no matching `.json` anywhere
|
||||
- **Eagle integration** — every image Lab generates is auto-saved to Eagle the same way as GenerateUI's own Generate button, if enabled in the Settings tab; the Output-folder index image above is included too (v0.3.90) once saved
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Prompt Tab</h3></summary>
|
||||
|
||||
- **3-column layout** — AI Assistant (left), Presets/Preset Manager tab-panel (center), Wildcard support (right)
|
||||
- **AI chat assistant** — powered by [Ollama](https://ollama.com/), generate and refine prompts interactively
|
||||
- **Image attachment** — attach reference images for vision-capable models
|
||||
- **Translation** — JA/EN/ZH translation buttons for multilingual prompt creation
|
||||
@@ -68,52 +176,245 @@ Browse, organize, and execute workflows directly from a dedicated studio interfa
|
||||
- **Group management** — create groups, assign/remove presets, delete groups from the Presets panel
|
||||
- **Clipboard copy** — copy positive/negative prompts individually (PP Copy / NP Copy)
|
||||
- **GenUI Set** — apply preset prompts directly to the GenerateUI interface
|
||||
- **Wildcard input toolbar** — one-click buttons to insert `{|}`, `{n$|}`, `__|__`, `<lora::1:LBW=;>` and other wildcard syntax; wraps selected text when applicable
|
||||
- **Wildcard file manager** — create, view, and edit `.txt` / `.yaml` wildcard files stored in `user/default/Workflow-Studio/wildcard/`; click a filename in the file picker to insert `__filename__` at cursor
|
||||
|
||||
### Settings Tab
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Metadata Tab (v0.3.8)</h3></summary>
|
||||
|
||||
- **3-column layout** — Drop zone (left) | Model info (center) | LoRA + Prompt (right)
|
||||
- **File drop** — drop a ComfyUI-generated PNG / WebP or workflow JSON onto the drop zone (or click to open a file picker); PNG/WebP images are shown as a preview
|
||||
- **Model extraction** — automatically extracts Checkpoint, VAE, Diffusion Model, and Text Encoder names from the workflow; supports both standard and subgraph-based workflows (Flux.2 Dev/Klein, Qwen-Image-Edit/2511/Layered, Z-Image Base/Turbo, Ernie Image, WAN2.2, Mage-Flow, LongCat, Boogu, HiDream E1, FireRed); node types covered: UNETLoader, UnetLoaderGGUF, UNETLoaderGGUF (e.g. HiDream GGUF), CLIPLoader, DualCLIPLoader, TripleCLIPLoader, QuadrupleCLIPLoader (e.g. HiDream 4-CLIP)
|
||||
- **LoRA extraction** — lists all LoRA models with `strength_model / strength_clip` values
|
||||
- **Prompt extraction** — lists prompts with POS / NEG badges when positive/negative can be determined; when distinction is not possible (e.g. `SamplerCustomAdvanced`, intermediate nodes, cross-level connections), prompts are shown without a badge as plain **Text**; click any entry to view the full text below
|
||||
- **CLIP Text Encode edit+ support** (v0.3.92) — `CLIPTextEncodeEditPlus` nodes are resolved using the same RAW / EDIT / front / back combination rule as the node itself, correctly merging the linked `text1` (e.g. a Lora Manager's trigger words) with the editable `text_edit` field instead of showing only one side
|
||||
- **Prompt actions** — Copy to clipboard, **GenUI:P/N** (set GenerateUI positive/negative prompt), **Prompt:P/N** (set Prompt tab preset positive/negative)
|
||||
- **Format support** — ComfyUI PNG/WebP/JSON (standard + Flux.2 / Qwen-Image / Z-Image / Ernie Image / WAN2.2 / Mage-Flow / LongCat / Boogu / HiDream E1 / FireRed subgraph workflows), SD WebUI, SD Forge, Fooocus
|
||||
- **Format note** — supported formats and covered model types are always shown in the left column
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Settings Tab</h3></summary>
|
||||
|
||||
- **2-column layout** — left column for all settings; right column shows the Theme panel fixed in place (sticky)
|
||||
- **Collapsible sections** — all settings organized in accordion panels for a clean layout
|
||||
- **Theme selection** — 13 built-in themes with visual swatch preview (Dark, Pop, Minimalist, Cyberpunk, Glassmorphism, Neumorphism, Retro Pixel, Pastel, Brutalism, Earthy, Material, Monotone, Corporate)
|
||||
- **Theme customization** — override colors (background, surface, text, primary, accent), add background patterns (horizontal/vertical/diagonal stripes, polka dot, checkerboard, custom SVG tiling with color/opacity/scale/gap controls), and select from 16 fonts including Japanese display fonts (Google Fonts)
|
||||
- **Workflows directory** — configure which folder to scan for workflows
|
||||
- **Gallery output directory** — configure which output folder the Gallery tab scans for images
|
||||
- **Eagle connection** — set Eagle API endpoint for auto-save
|
||||
- **Ollama connection** — configure Ollama server URL
|
||||
- **CivitAI Host** — choose which site opens when clicking a model link: `civitai.com` (SFW only) or `civitai.red` (unrestricted); saved to `settings.json` and synced to `localStorage` for the Models tab to use without extra fetches
|
||||
- **CivitAI API Key** — optional Bearer token for authenticated CivitAI access; stored in `settings.json` (excluded from data exports); environment variable `CIVITAI_API_KEY` takes priority if set
|
||||
- **Default workflow** — set a workflow to auto-load on startup
|
||||
- **Default Checkpoint** (v0.3.73) — optional checkbox to force every checkpoint loader node's model to a chosen default whenever a workflow is loaded into GenerateUI; a safeguard against generation errors or garbled images from a stale/mismatched checkpoint saved in the workflow file
|
||||
- **Data Management** — export all plugin data (settings, metadata, prompts, etc.) to a single JSON file; import to restore data (useful when migrating or reinstalling); API keys are excluded from exports for security
|
||||
- **Text Size** — one slider (10–28 px) adjusts font size for all prompt and chat textareas at once: Generate UI positive/negative prompts, AI Assistant chat input, Preset prompts, Wildcard prompt and file editor, and Metadata prompt full preview; takes effect immediately and saved with Save Settings
|
||||
- **RAW JSON Colors** — customize the 6 syntax highlight colors for the Raw JSON editor in Generate UI: Default Text (base), Name/Scheduler (yellow), Title (pink), Width/Height (green), Prompt/Text (cyan), Image/File (red); changes apply immediately on color pick; Reset Defaults restores the original scheme; saved to `localStorage` under `wfm_settings.jsonColors` and applied on startup
|
||||
- **Wildcard Integration** — link the WFS wildcard directory to ComfyUI-Impact-Pack's `wildcards/` directory (directory junction on Windows, symlink on other OS); existing WFS wildcard files are automatically migrated; requires [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
|
||||
- **G'MIC-Qt Integration** — set the path to `gmic_qt.exe` for the Image Edit Filter tool; download G'MIC-Qt Standalone from `gmic.eu/download.html` (Windows: `gmic_qt-win64.zip`), extract anywhere, then enter the full path to `gmic_qt.exe` and click Save
|
||||
- **Language** — English / Japanese / Chinese
|
||||
|
||||
### Nodes Tab (v0.1.7)
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Gallery Tab (v0.3.44)</h3></summary>
|
||||
|
||||
- **Output / Style-Prompt sub-tabs** (v0.3.94) — the Gallery tab has two sub-tabs at the top: **Output** (the image browser described below) and **Style/Prompt** (a separate visual prompt-building library — see its own section below)
|
||||
- **Image browser** — browse ComfyUI output images (Thumbnail / Table views) with server-side scanning optimized for 6,000+ image libraries
|
||||
- **Folder tree root label** (v0.3.90) — the root entry in the folder tree is labeled simply **[root]**, no longer suffixed with the scanned folder's own directory name (which could be a misleading, environment-specific name — e.g. StabilityMatrix installs where the output folder is a symlink named `Text2Img` — even though the folder holds every kind of generated output, not just text-to-image results)
|
||||
- **Thumbnail / Table views** — switch view modes; Favorites column shown leftmost in Table view
|
||||
- **Folder management** — create subfolders ("+ New") or delete the selected folder with all contents ("Del") from the folder tree header
|
||||
- **File operations** — move or delete individual images from the detail panel's Info tab; bulk Move To..., Export, and Delete File from the multi-select bar
|
||||
- **Download** — hover over the image preview in the detail panel to reveal a download icon (⬇); click to download the single image
|
||||
- **Multi-select** — Ctrl+click to select multiple images; Bulk Bar appears for batch operations: select group → "Add to Group" / "Remove from Group"; Favorite All / Unfavorite All; Compare (2–4 images); Move To...; **Export** (downloads all selected images as a ZIP file); Delete File
|
||||
- **Image Compare** — select 2–4 images with Ctrl+click, then click "Compare" in the Bulk Bar to open a side-by-side lightbox; grid adapts to the number of selected images
|
||||
- **Prompt search** — text search covers filename, tags, memo, and prompt text (A1111 `parameters` field cached on first detail panel open)
|
||||
- **Server-side filtering** — filter by group, favorites, or tags with fast server-side set lookup (no full rescan)
|
||||
- **Group management** — create, rename, delete groups and assign/remove images using the same 4-section panel as Models tab; **`__Feeder__`** is a reserved group (🔒 prefix) that cannot be renamed or deleted; used by the Feeder Gallery mode to define the generation queue; **FC** button in the toolbar clears all members without deleting the group
|
||||
- **Thumbnail F button** — cyan overlay button (top-left of each image card) toggles the image's membership in the `__Feeder__` group; cyan and always-visible when the image is in the group; visible on hover only when inactive
|
||||
- **Favorites** — star images inline without reopening the detail panel
|
||||
- **Detail panel** — view filename, path, tags, groups, and metadata in a slide-out panel
|
||||
- **Workflow viewer** — Metadata tab displays workflow JSON from PNG embedded data (`prompt` / `workflow` keys) or from workflow saved by the Generate UI tab; **Copy & Send Canvas** button copies the JSON to the clipboard and sends the workflow directly to the ComfyUI canvas (UI and API formats both supported)
|
||||
- **Load GenUI button** — loads the embedded ComfyUI workflow from the selected image directly into the GenerateUI tab; shows a warning toast if no workflow is embedded or the format is unsupported; Metadata button is styled green, Load GenUI button uses the primary accent color
|
||||
- **Image Edit button** — toolbar button; sends the selected image directly to the Image Edit tab as the base layer
|
||||
- **Send GenUI Image button** — toolbar button (next to Image Edit); uploads the selected image to ComfyUI and sets it as the input for the first LoadImage node in the GenerateUI tab; automatically switches to GenerateUI → Input → Image; requires a workflow with at least one LoadImage node to be loaded
|
||||
- **Send CC button** (v0.3.69) — toolbar button, shown only when this Gallery tab is embedded as an iframe inside ComfyUI Comic Creator (a separate custom node); sends the selected image directly into Comic Creator's currently selected panel/overlay
|
||||
- **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
|
||||
- **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, tag filter, group filter, and favorites filter in one click (sort order is preserved)
|
||||
- **Workflow auto-save** — images generated from the Generate UI tab have their workflow automatically saved to gallery metadata
|
||||
- **Output folder configurable** — set the scanned output folder from Settings tab
|
||||
- **SVG file support** (v0.3.77) — `.svg` files are browsable, previewable (detail panel and lightbox), and included in favorites/tags/groups/move/delete/export alongside raster formats; served as-is with no rasterized thumbnail, since `<img>` scales vector graphics natively at any size
|
||||
- **Performance** — server-side 256px JPEG thumbnail generation with disk cache (`data/thumb_cache/`); infinite-scroll paging (50 images per page, IntersectionObserver); folder-level mtime cache (60s TTL); bulk operations use single-request API endpoints
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>ImagePrompt Gallery subtab (v0.3.94, renamed from Style/Prompt in v0.3.95)</h3></summary>
|
||||
|
||||
- **Visual prompt library** — a Gallery sub-tab for browsing reference images and building prompts from them visually, separate from the Output browser; manages a dedicated `ws_image_prompt` folder, created automatically inside the ComfyUI output directory
|
||||
- **3-column layout** — folder tree (left) | thumbnail grid (center) | prompt builder (right); selecting any folder (including a parent category folder) recursively shows every image in its subfolders too, both in the grid and in the tree's count badges
|
||||
- **Prompt builder (right pane)** — clicking a thumbnail previews it and its prompt text without adding it yet; **+ Add** appends it as a removable chip under "Selected Prompts" (**Clear All** to reset); **Save** edits and persists the prompt text for that image; **Final Prompt** combines all chips into one comma-separated, freely-editable text; **Copy** sends it to the clipboard
|
||||
- **Plain-text prompt storage** — each image's prompt lives in a `.txt` sidecar file next to it (same base name, no database) — anyone can add their own entries by dropping an image + matching `.txt` file into a subfolder; the Output Gallery's own detail panel reads and displays the same file when browsing into `ws_image_prompt`
|
||||
- **ponyxlWildcardsVault format support** — if no `.txt` sidecar exists, prompts are resolved on the fly from a `*.yaml` + `thumbnails/` (also `thumbnails_option2/`) pack placed directly under the folder, using the same key/tag structure as Navimixu's [PonyXL Wildcards Vault](https://civitai.com/models/615967/ponyxl-wildcards-vault) packs — just drop the downloaded pack folder in, no import step required
|
||||
- **Seed data importer** — `tools/import_style_prompt_seed.py` (dev tool, ships no image data) pre-processes a wildcards-vault-style source folder into portable image + `.txt` pairs under `ws_image_prompt`, for archiving a stable, portable copy instead of relying on the on-the-fly YAML fallback
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Style_Catalog Gallery subtab (v0.3.95)</h3></summary>
|
||||
|
||||
- **Visual Style picker** — a third Gallery sub-tab for picking a registered GenerateUI Style (`user/default/Workflow-Studio/style/*.json`) by its preview image instead of its name in a dropdown; manages a dedicated `ws_style_catalog` folder, created automatically inside the ComfyUI output directory; populated by GenerateUI's **Create Catalog** button (see GenerateUI Tab above)
|
||||
- **3-column layout** — folder tree (left) | thumbnail grid (center) | Positive/Negative prompt panel (right); like ImagePrompt, selecting a parent folder recursively shows every image in its subfolders
|
||||
- **Positive/Negative panel** — clicking a thumbnail reads the Positive/Negative prompt actually embedded in that generated image (reusing the Metadata tab's extraction logic — no separate metadata store), each with its own **Copy** button
|
||||
- **Select as Style button** — matches the selected image's filename (without extension) against a registered Style name and switches the GenerateUI tab's Style dropdown to it; a visual shortcut into the existing named-Style system, not a one-off apply of the embedded prompt — if the Style was renamed or deleted since the catalog image was created, a "no matching style" toast is shown instead
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Nodes Tab (v0.1.7)</h3></summary>
|
||||
|
||||
- **Node Browser** — browse all installed ComfyUI nodes from `/object_info` API with Card/Table views
|
||||
- **Search & Filter** — full-text search, filter by category, package, tags, groups, and favorites
|
||||
- **Package badges** — color-coded badges generated from package names
|
||||
- **Node detail panel** — view I/O specifications, edit tags, manage groups
|
||||
- **Node Sets** — save multiple nodes + connections as reusable sets from the ComfyUI canvas
|
||||
- **Right-click context menu** — "Save as Node Set" option on any node in ComfyUI
|
||||
- **Multi-select** — Ctrl+click to toggle individual nodes, Shift+click for range selection; bulk action bar appears with: add to group / remove from group / create & add group / bulk favorite / unfavorite
|
||||
- **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
|
||||
- **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, category, package, tag, group, and favorites filter in one click
|
||||
|
||||
### Models Tab (v0.2.2)
|
||||
- **Model Browser** — browse all installed ComfyUI models (Checkpoint, LoRA, VAE, ControlNet, UNET, TextEncoder) with sub-tab switching
|
||||
- **Thumbnail / Card / Table views** — switch between view modes with pagination (24 items per page)
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Models Tab (v0.2.3)</h3></summary>
|
||||
|
||||
- **Model Browser** — browse all installed ComfyUI models (Checkpoint, LoRA, VAE, ControlNet, UNET, TextEncoder, Hypernetwork, Embedding) with sub-tab switching
|
||||
- **Thumbnail / Table views** — switch between view modes with pagination (24 items per page); Table view includes **Type** and **Base Model** columns sourced from cached CivitAI data, displayed between the Subdirectory and Extension columns; Enable/Disable column labeled **E/D**
|
||||
- **Table column sort** — click any column header to sort ascending (▲), click again for descending (▼), click a third time to clear; active column highlighted in accent color
|
||||
- **Search & Filter** — full-text search, filter by tags, groups, and favorites
|
||||
- **Side panel tabs** — Info (file path display with click-to-copy, tags, memo), Groups management, CivitAI integration
|
||||
- **CivitAI integration** — fetch model metadata by SHA256 hash, view base model, trained words, tags, and model page link
|
||||
- **Batch CivitAI fetch** — one-click batch fetch for all models of a type with SSE progress streaming
|
||||
- **Detail modal** — preview image, CivitAI info, thumbnail change via file upload
|
||||
- **Group management** — create, rename, delete groups and assign/remove models
|
||||
- **User-defined badges** — assign free-label badges to models; badge colors shared with the Workflow tab palette
|
||||
- **Side panel tabs** — opens to CivitAI tab by default when a model is selected; Info (file path display with click-to-copy, tags, memo), Groups management, CivitAI integration
|
||||
- **CivitAI integration** — fetch model metadata by SHA256 hash; side panel shows **Type** (badge), **Base Model**, **Hash** (BLAKE3/SHA256, click to copy), trained words, tags, and model page link; model link opens in the site selected in Settings (civitai.com or civitai.red)
|
||||
- **CivitAI panel — Info / Sample sub-tabs** — Info tab shows model details (name, type, base model, hash, trigger words, tags, description); Sample tab shows all sample images (count shown in tab label); clicking any image opens the full-size version in a new tab
|
||||
- **CivitAI panel states** — three distinct states: not yet checked (fetch button), checked but not found on CivitAI (re-check button with notice), and found (full info display); clicking the CivitAI tab always refreshes to the latest state
|
||||
- **Batch CivitAI fetch** — one-click batch fetch using `POST /model-versions/by-hash` (up to 100 models per request) with SSE progress streaming; previews are auto-saved for models without one
|
||||
- **Detail modal** — preview image, CivitAI info, thumbnail change via file upload; **Delete** button permanently removes the model file and all associated sidecar files (preview images, `.json`, `.civitai.info`, `.metadata.json`, `.cm-info.json`)
|
||||
- **GenUI Model button** — apply the selected model directly to the corresponding node in GenerateUI's current workflow (Checkpoint, LoRA, VAE, ControlNet, UNET, TextEncoder, Hypernetwork); **Embedding** type shows **GenUI PP** / **GenUI NP** buttons instead — appends `(embedding:Name:1.0)` to the Positive or Negative prompt of the loaded workflow; also accessible from the side panel nav bar and the detail modal
|
||||
- **Group management** — create, rename, delete groups and assign/remove models; groups are scoped per model type (checkpoint groups only appear in the Checkpoint tab); Checkpoint and LoRA tabs include **B (Batch)** and **S (Stack)** quick-assign buttons per card/row for one-click group membership toggle without grid re-render
|
||||
- **Table view memo** — memo column displayed in table view for quick reference
|
||||
- **Preview images** — auto-detect `{model_stem}.preview.png` next to model files
|
||||
- **Enable / Disable models** — hide models from ComfyUI by renaming the file extension (`.disabled` suffix); toggle per card (⏸ button), per group (Enable All / Disable All), or filter by status (All / Enabled / Disabled)
|
||||
- **Multi-select & bulk operations** — click the green **Select** button to enter selection mode and check multiple models; the bulk action bar is organized in three rows:
|
||||
- **Group row** — select an existing group and **Add** / **Remove**, or type a new name and **Create & Add**
|
||||
- **Badge row** — select a badge and **+Badge** / **−Badge** to apply or remove from all selected
|
||||
- **File row** — select a destination subfolder (or type a new folder name and create it) then **Move** to relocate the model file with all sidecar files; **Delete Files** (right end) permanently removes selected models and all associated files
|
||||
- **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
|
||||
- **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, tag filter, folder filter, group filter, favorites filter, and status filter in one click
|
||||
|
||||
### Workflow Studio Library (ComfyUI Side Panel) (v0.1.8)
|
||||
- **Workflows tab** — browse favorite workflows, filter by model type, filter by group
|
||||
- **Nodes tab** — browse favorite nodes, node sets, and node groups
|
||||
- **Prompts tab** — browse prompt presets with All / Favorites / Categories sub-tabs
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Tagger Tab (v0.3.38)</h3></summary>
|
||||
|
||||
- **3 sub-tabs** — Single / Batch / DB for single-image tagging, folder batch processing, and tag database management
|
||||
- **Model support** — WD Tagger (ONNX, NCHW/NHWC auto-detect), SwinV2 (ONNX), DeepDanbooru (.h5, requires TensorFlow); place each model in its own subfolder under `ComfyUI/models/tagger/<model-name>/` containing the `.onnx` + `selected_tags.csv` (or `.h5` + label file)
|
||||
- **Threshold sliders** — General threshold (default 0.35) and Character threshold for `character:` prefix tags (default 0.85); sliders update the display value in real time
|
||||
- **Ollama VLM** — optional vision model alongside WD Tagger; set API URL and select model (↻ to refresh list); results from both models are merged into one comma-separated tag string
|
||||
- **Single tab** — drag & drop an image onto the preview area or click Upload; Gallery detail panel **Tagger** button opens the selected image directly here
|
||||
- **Single output options** — (1) **GenUI:P** — appends tags to the GenerateUI tab's positive prompt and immediately applies to the loaded workflow; (2) **Send to Prompt** — appends tags to the Prompt tab's positive textarea; (3) **Save to Gallery** — saves tags to Gallery image metadata (requires image opened from Gallery); (4) **Write to File** — embeds tags into JPEG EXIF (`ImageDescription`) or PNG `tEXt` chunk (`Tags` key), other formats get a `.tags.json` sidecar; (5) **Save to DB** — stores in internal SQLite database
|
||||
- **Batch tab** — enter a folder path, configure WD Tagger and Ollama settings; output options: **Save to DB** (default on), **Write to File** (EXIF/PNG metadata), **Write .txt** (creates `<filename>.txt` alongside each image with all tags); real-time progress bar and log; Stop cancels after the current image
|
||||
- **DB tab** — searchable SQLite database of all tagged images; click a row to open the edit panel and modify WD Tags / VLM Tags; Save updates the record, Delete removes it; Export CSV downloads all records
|
||||
- **Dependencies** — install into ComfyUI's embedded Python: `python_embedded\python.exe -m pip install -r requirements.txt`; for GPU inference use `onnxruntime-gpu`; TensorFlow (DeepDanbooru) is optional and commented out in `requirements.txt`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Image Edit Tab (v0.3.65)</h3></summary>
|
||||
|
||||
- **Layer-based image editor** — compose images with multiple layers (Image, Text, Draw, Mask types) and export the composite as PNG
|
||||
- **Loading images** — drag & drop onto the canvas, Upload button, or send from the Gallery tab via the **Image Edit** toolbar button; first image becomes Layer 1 (auto-locked), subsequent images are added as new layers scaled to fit the canvas
|
||||
- **New button** — create a blank canvas with custom dimensions (WxH prompt); clears all existing layers and automatically adds an empty Layer 1 (v0.3.70)
|
||||
- **Tools** — Select (V), Draw (B), Text (T), Shape (S), Mask (🎭), Blur (≈), BG Remove (⬚), Filter (★), Inpaint (🩹); tool options bar updates per active tool
|
||||
- **Select** — click to select; drag to move; drag corner handles to resize; drag circle handle to rotate; Flip H / Flip V / Rotate angle in the options bar; double-click a text layer to re-edit its content
|
||||
- **Draw** — freehand brush; options: color, brush size, blend mode; paints directly onto the active draw layer while all other layers remain visible; brush cursor shown as a size-accurate circle; strokes can start from outside the canvas margin and continue past the edge
|
||||
- **Text** — click to place; configure font, size, bold/italic, align, and color; placed as an exact-size text layer sized to the measured bounding box; double-click to re-edit
|
||||
- **Shape** — drag to draw geometric shapes (Rect / Ellipse / Line / FreeLine); options: shape type, Rounded toggle (Rect/Ellipse), Fill color, Stroke color + width, Opacity; each committed shape becomes an independent draw layer; Stroke None hidden for Line/FreeLine (stroke always active)
|
||||
- **Mask** — 7 sub-tools for painting masks onto a dedicated mask layer; click **M** in the Layers header to add a mask layer (Mask tool activates automatically); clicking a mask layer in the list also switches to the Mask tool; **Tool Options bar**: sub-tool buttons (Paint / Color / Alpha / Text / Vector / Shape / SAM3), Invert checkbox, Overlay color picker (default red), Blur slider (0–50 px); the overlay is only shown for the currently active mask layer, other mask layers stay hidden until selected (v0.3.70); SAM3 requires Mask Editor One (disabled when not installed); Color / Alpha / Text / Vector / Shape work without Mask Editor One; **Properties pane — Paint**: Mode (Add / Erase), Size (1–200 px), Hardness (0–100%); **MASK EDITOR ONE section** (Paint sub-tool, visible when installed): Select opens the ABR stamp-brush library; ✕ clears back to Circle; when an image brush is active, Hardness is disabled and Spacing (5–100%), Angle (0–359°), Sz Jitter (0–100%), Rot. Jitter (on/off) appear; **Properties pane — Color**: click canvas to flood-select pixels by color similarity; Mode (Add / Subtract), Tolerance (0–255), Feather % (0–100); **Properties pane — Alpha**: extract the active layer's transparency as a mask; Threshold, Invert checkbox, Extract Alpha button; **Properties pane — Text**: click canvas to open a stamp overlay — enter text, set Font / Size / Bold / Italic / Align / Mode (Add / Erase), Stamp or Ctrl+Enter to apply; multi-line supported; **Properties pane — Vector**: click to add Catmull-Rom spline points; Enter (or click first point) closes and fills the polygon; Backspace removes the last point; Esc resets; Mode (Add / Erase); **Properties pane — Shape**: drag to draw Rect or Ellipse; hold Shift for square/circle; blue dashed outline preview while dragging; Mode (Add / Erase); **Properties pane — SAM3**: enter a text prompt, set Max candidates (3/6/9/12), click Segment; select candidate thumbnails (multi-select), Apply Selected (N) applies with current Mode; **Mask layer A/S operation**: A/S toggle — Add (green) composites normally, Subtract (red) cuts the mask from below; **✂ clipping mask button** clips the layer below to the painted area; brush cursor shown as size-accurate circle; strokes can start from outside the canvas margin and continue past the edge
|
||||
- **Blur** — **Whole Blur**: Gaussian blur to the entire active layer with intensity slider (1–50 px); **Whole Mosaic**: pixelation mosaic with block-size slider (5–100 px); **Rect Blur / Rect Mosaic**: enable the toggle then drag a rectangle on the canvas to apply blur or mosaic to that region only (blue preview for blur, orange for mosaic); Rect Blur and Rect Mosaic are mutually exclusive; all operations support Undo
|
||||
- **BG Remove** — remove the background from the active layer; **Lightweight (@imgly)**: runs entirely in the browser via CDN (no server required; ~40 MB model downloaded on first use); **BiRefNet**: high-quality removal via Mask Editor One's Python backend (requires Mask Editor One installed and `birefnet.safetensors` in `ComfyUI/models/background_removal/`); **New Layer** option (default on) adds the result as a new layer above the original; when off, replaces the active layer in-place
|
||||
- **Filter (G'MIC)** — apply G'MIC-Qt filter effects to the active image layer; click **Edit with G'MIC** to launch G'MIC-Qt GUI; select a filter and click OK — a "G'MIC-Qt filter output" window appears; close it to save the result back as the active layer; requires G'MIC-Qt Standalone installed and path configured in Settings → G'MIC-Qt Executable Path
|
||||
- **Inpaint** (v0.3.71) — composites the visible layers plus a Mask layer into one RGBA image (mask baked into the alpha channel) and applies it to the first inpaint-capable LoadImage node in the workflow currently loaded in GenerateUI (a node whose MASK output feeds something like `VAEEncodeForInpaint`); also supports workflows built around the **Mask Editor One** custom node (`comfyui-mask-editor-one`) instead of LoadImage — image/mask are pushed via its node_id-keyed server cache and `layer_data` widget; **Properties pane**: Positive/Negative prompts, Grow Mask By (applied to the VAE Encode (for Inpainting) node), Denoise (applied to KSampler), Run button; **Use dedicated workflow** checkbox — when off, generates against the workflow currently loaded in GenerateUI; when on, pick a saved workflow from the dropdown to load, edit, and generate entirely in the background without disturbing what's shown in GenerateUI; the result image is shown back in the Properties pane when generation completes; requires a Mask layer painted first (Mask tool)
|
||||
- **Layer panel** — layer list with visibility (👁/🚫), lock (🔒/🔓), and clipping-mask (✂) toggles; opacity slider; type icons (🖼 image / T text / ✏ draw / ⬚ mask); Layer 1 is automatically locked on first image load
|
||||
- **Layer lock** — locked layers show an orange bounding box and 🔒 icon on the canvas; move/resize/rotate are disabled while locked; click the 🔓 button in the layer row to unlock
|
||||
- **Text quality** — text layers are rendered at their measured bounding-box size; resizing with the Select tool regenerates the canvas at the new display resolution so text stays sharp
|
||||
- **Export** — Save PNG (download composite locally); **Save to Gallery** (saves to Gallery root folder with a timestamped default name `wfs-image-YYYYMMDDHHmmss`); **Send to ComfyUI** (uploads to ComfyUI input folder for use in Load Image nodes)
|
||||
- **Canvas navigation** — scroll-wheel zoom, Space + drag to pan; zoom indicator in the bottom bar
|
||||
- **Undo / Redo** — Ctrl+Z / Ctrl+Y (or toolbar buttons); keyboard shortcuts: V (Select), B (Draw), T (Text), S (Shape), Delete (remove selected layer when 2+ exist)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>AI TOOL Tab (v0.3.14)</h3></summary>
|
||||
|
||||
- **4-pane layout** — Translation | Chat | TOOLS | Settings; all panes always visible simultaneously; no sub-tab switching required
|
||||
- **Translation pane** — translate text between Japanese, English, Chinese, or a custom Free language using Ollama, LM Studio, or Lemonade; language selectors with ⇄ swap button (swaps both language selectors and text content); selections saved automatically
|
||||
- **Chat pane** (v0.3.40) — multi-turn conversation with the LLM; full conversation history sent each turn for context; Enter to send, Shift+Enter for a newline; Clear button resets history; Ollama uses `/api/chat`, LM Studio and Lemonade use `/v1/chat/completions`
|
||||
- **Chat pane — image generation via Tool Calling** (v0.3.73) — if the selected model supports tool calling and the user asks for an image, a `generate_image` tool is invoked and the request runs through the same generation pipeline as the GenerateUI Generate button, with the result shown inline in the chat; an "Allow image generation" checkbox in the input row toggles this on/off (default on); the workflow used is the one currently loaded in GenerateUI, or a dedicated saved workflow configured in the Settings pane; models verified to work well for tool calling and vision: **Gemma 4:e4b**, **Qwen 3.5:9b**, **Ministral 3:3b** (Ollama)
|
||||
- **TOOLS pane (VLM)** — drop an image into the 110px drop zone, select a task (Describe Image / Create Prompt / Create Tags), and click Run to analyze with a vision model; result shown in the output area with a Copy button
|
||||
- **TOOLS pane — shared Chat attachment** (v0.3.74) — an image dropped in the TOOLS drop zone is also usable as the Chat pane's attachment: it's sent to the LLM as vision input, and used as the base image when `generate_image` runs image-to-image; a ✕ button clears it (from either the TOOLS preview or the Chat attachment indicator); it stays attached across turns until explicitly cleared
|
||||
- **TOOLS pane (Wildcards)** (v0.3.40) — select "Create wildcards" from the task dropdown; enter a category name and count; click Run to generate plain-text wildcard entries one per line (no markdown, no numbering); result can be copied directly into wildcard `.txt` files
|
||||
- **Chat pane — image-to-image (I2I)** (v0.3.74) — when an image is attached and `generate_image` is invoked, the attachment is uploaded and swapped into the target workflow's LoadImage node instead of running text-to-image; the target workflow is the one currently loaded in GenerateUI, or a dedicated I2I workflow configured in the Settings pane
|
||||
- **Chat pane — SVG generation, no ComfyUI workflow involved** (v0.3.77) — the LLM can also produce SVG graphics directly as text; if a reply contains SVG code (wrapped in a markdown code fence or written directly as a bare `<svg>...</svg>`), it's rendered inline as a preview and a "Save to Gallery" button writes it as a `.svg` file to the Gallery tab's output folder; unlike the Tool Calling image generation flow above, this is plain text pattern matching, so it works with any model regardless of tool-calling support
|
||||
- **Chat pane — Skills** (v0.3.79) — select a `.md` skill file from a dropdown above the message list to prepend its instructions to the conversation as a system prompt (not shown in the visible chat log); a pencil button opens an in-pane manager (list + inline editor, same pattern as the Prompt tab's wildcard file manager) to create, edit, or delete skill files stored in `user/default/Workflow-Studio/ai_skills/`; a file's `---`-delimited frontmatter (`name`/`description`) is shown in the dropdown; two starter skills are bundled — **SVG Icon Generator** (explicitly decomposes the subject into parts before drawing, which markedly improves recognizability from small local models) and **Skill Creator** (interviews you conversationally and drafts a new skill as a ```skill code block; a "Save as new skill" button appears under such replies and opens the manager pre-filled with the draft)
|
||||
- **Settings pane** — choose backend (Ollama / LM Studio / Lemonade), set the API URL, test connection, select a model (with refresh button), and configure Free language names for translation source and destination
|
||||
- **Settings pane — Chat Image Generation** (v0.3.73) — "Use dedicated workflow" checkbox lets Chat-triggered image generation use a saved workflow of your choice instead of the one currently loaded in GenerateUI, without disturbing what's shown there (same pattern as Image Edit's Inpaint dedicated workflow)
|
||||
- **Settings pane — Chat I2I Generation** (v0.3.74) — same "Use dedicated workflow" pattern as above, but for image-to-image generation when an image is attached; independent of the text-to-image dedicated workflow setting
|
||||
- **Settings shared** — settings saved to `localStorage` under `wfm_ai_settings`; shared with the Library panel's AI TOOL tab so configuration is consistent across both interfaces
|
||||
- **Backend support** — Ollama (`/api/generate` for text, `/api/chat` for conversations, `/api/tags` for model list); LM Studio and Lemonade via the OpenAI-compatible API (`/v1/chat/completions`, `/v1/models`); VLM images sent as base64 (`images:[]` for Ollama, `image_url` content block for LM Studio/Lemonade)
|
||||
- **URL security** — backend URL validated via `new URL()` to enforce `http://` or `https://` scheme
|
||||
- **Lemonade's other endpoints (not integrated)** — Lemonade Server also exposes OpenAI-compatible image generation (`/v1/images/generations`), text-to-speech (`/v1/audio/speech`), and transcription (`/v1/audio/transcriptions`) endpoints; these are not wired up — image generation would duplicate the existing ComfyUI-workflow-based `generate_image` tool call, and audio/TTS has no corresponding UI yet, so both are future considerations rather than a natural extension of the current backend switch
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Workflow Studio Library (ComfyUI Side Panel) (v0.3.9)</h3></summary>
|
||||
|
||||
- **Tab layout (W / N / P / M / I / A)** — compact single-letter tabs with full name shown on hover
|
||||
- **W — Workflows tab** — browse favorite workflows (All / ★ Favorites / Groups / By Badge sub-tabs), ★ star shown for favorites in All view
|
||||
- **N — Nodes tab** — browse favorite nodes (All / ★ Favorites / Groups / Sets / 📂 Category / 🧩 Package sub-tabs), ★ star shown for favorites in All view
|
||||
- **Category sub-tab** — dropdown to filter nodes by top-level category
|
||||
- **Package sub-tab** — dropdown to filter nodes by custom node package name
|
||||
- **M — Models tab** — browse installed models (All / ★ Favorites / Groups / By Type sub-tabs); LoRA groups show an **All N LoRAs** item — drag to canvas to place a `Lora Loader (LoraManager)` node with all LoRAs pre-loaded
|
||||
- **P — Prompts tab** — browse prompt presets with All / ★ Favorites / Categories sub-tabs; **Groups sub-tab** (row 2) — view presets by group (shared with the Batch tab's `wfm_prompt_preset_groups`)
|
||||
- **I — Information tab** — drop a ComfyUI-generated PNG/WebP or workflow JSON in the side panel to view its metadata; detects LoRAs from `LoraLoader`, `LoraLoaderModelOnly`, and `Lora Loader (LoraManager)` nodes (API format supported); supports `UnetLoaderGGUF` and `QuadrupleCLIPLoader` node types; preview area fixed at 110px
|
||||
- **A — AI TOOL tab** — Translation, Chat, TOOLS, and Settings sub-tabs powered by Ollama, LM Studio, or Lemonade; Chat supports multi-turn conversations (full history sent each turn); TOOLS includes VLM image analysis and wildcard generation; settings (backend, URL, model) shared with the SPA AI TOOL tab via `localStorage`
|
||||
- **model sub-tab** — Checkpoint, VAE, Diffusion Model, and Text Encoder; drag items to canvas to place the corresponding loader node (Checkpoint → `CheckpointLoaderSimple`, VAE → `VAELoader`, Diffusion Model → `UNETLoader`, Text Encoder → `CLIPLoader`); double-click also places at canvas center
|
||||
- **lora sub-tab** — detects LoRAs from `LoraLoader`, `LoraLoaderModelOnly`, and `Lora Loader (LoraManager)` nodes (API format `inputs.loras.__value__` supported); shows `strength_model / strength_clip` values; drag individual LoRA to place `LoraLoader`; **Multiple LORA** section (appears for 1+ LoRAs) drags all LoRAs into a single `Lora Loader (LoraManager)` node with LoRA syntax pre-filled
|
||||
- **Prompts sub-tab** — POS / NEG badge list; drag a prompt to place `CLIPTextEncode` with text pre-filled; click any entry to view full text + Copy button
|
||||
- **Drag & drop workflows** — drag a workflow onto the canvas to load it
|
||||
- **Drag & drop nodes** — drag nodes/node sets onto the canvas to place them
|
||||
- **Drag & drop prompts** — drag a preset onto the canvas to create a WFS_PromptText node with positive/negative prompts
|
||||
- **Send to Canvas** — clicking "Send to Canvas" (Workflow tab toolbar / detail modal) or "Copy & Send Canvas" (Gallery tab JSON panel) sends the workflow directly to the ComfyUI canvas via `window.opener` (UI and API formats both supported); if Workflow Studio was opened from a bookmark instead of the ComfyUI toolbar, UI-format workflows fall back to title drag (panel title highlights blue with a green ● — drag it onto the canvas to load)
|
||||
- **Copy prompts** — copy individual positive (P) or negative (N) prompts from sidebar items
|
||||
- **Double-click** — load workflows or place nodes without dragging
|
||||
- **Search** — search within each sub-tab to quickly find items
|
||||
- **⚙ Theme settings** — customize panel background, sub-header background, text, border, and secondary text colors; saved to localStorage and applied on every open
|
||||
|
||||
### Help & Support Tab (v0.1.3)
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><h3>Help & Support Tab (v0.1.3)</h3></summary>
|
||||
|
||||
- **Sidebar navigation** — 2-column layout: left sidebar (15 topics) + right content pane; click any topic to switch the displayed content
|
||||
- **Support** — fixed at the bottom of the sidebar; shows GitHub, Ko-fi, **ComfyUI Image Feeder**, and **Mask Editor One** repository links in the right pane
|
||||
- **Feature list** — overview of all features organized by tab
|
||||
- **Tips** — quick tips for drag & drop import, favorites, and default workflow
|
||||
- **Support links** — GitHub repository and Ko-fi donation page
|
||||
- **Keyboard Shortcuts** and **Troubleshooting** sections included
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
@@ -134,6 +435,22 @@ Restart ComfyUI after installation.
|
||||
|
||||
---
|
||||
|
||||
## Sample Workflows
|
||||
|
||||
Sample workflows are included in the `workflows/` folder. You can open them directly in ComfyUI via drag & drop, or load them from the Workflow tab.
|
||||
|
||||
> **Note:** Some sample workflows require additional custom nodes.
|
||||
> If a node is shown as missing (red/unknown) after loading, install the required custom nodes via **ComfyUI Manager** or by cloning the repository from GitHub into your `ComfyUI/custom_nodes/` directory.
|
||||
>
|
||||
> ```bash
|
||||
> cd ComfyUI/custom_nodes
|
||||
> git clone <repository-url>
|
||||
> ```
|
||||
>
|
||||
> After installation, restart ComfyUI to activate the new nodes.
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
### Launch
|
||||
@@ -168,92 +485,24 @@ Click the **camera icon** (next to the W button) in ComfyUI's top bar to capture
|
||||
|
||||
### Optional
|
||||
|
||||
- **[Ollama](https://ollama.com/)** — for AI chat assistant and translation features
|
||||
- **[Ollama](https://ollama.com/)** — for AI chat assistant, translation, and VLM features
|
||||
- **[LM Studio](https://lmstudio.ai/)** — alternative backend for translation and VLM (OpenAI-compatible API)
|
||||
- **[Lemonade Server](https://lemonade-server.ai/)** — alternative backend for translation, chat, and VLM (OpenAI-compatible API)
|
||||
- **[Eagle](https://eagle.cool/)** — for auto-saving generated images with metadata
|
||||
- **[comfyui-mask-editor-one](https://github.com/ketle-man/comfyui-mask-editor-one) (v0.1.9+)** — enables BiRefNet background removal, SAM3 text-prompt segmentation, and ABR stamp-brush library in the Image Edit Mask tool; `birefnet.safetensors` must be placed in `ComfyUI/models/background_removal/` for BiRefNet
|
||||
|
||||
---
|
||||
|
||||
## Supported Languages
|
||||
|
||||
| Language | Status |
|
||||
|----------|--------|
|
||||
| -------- | ------ |
|
||||
| English | Full |
|
||||
| Japanese | Full |
|
||||
| Chinese | Full |
|
||||
|
||||
---
|
||||
|
||||
## Changelog
|
||||
|
||||
### v0.2.2
|
||||
- **Models tab** — browse, search, and manage all installed ComfyUI models (Checkpoint, LoRA, VAE, ControlNet, UNET, TextEncoder) with Thumbnail/Card/Table views
|
||||
- **CivitAI integration** — fetch model metadata by SHA256 hash (individual or batch per model type) with SSE progress streaming
|
||||
- **Model groups** — create, rename, delete groups and assign/remove models for organization
|
||||
- **Model metadata** — persistent favorites, tags, memo, and SHA256 hash per model
|
||||
- **Preview images** — auto-detect and display preview images, change via file upload in detail modal
|
||||
- **Table view memo column** — memo displayed in table view with compact subdir/extension columns
|
||||
- **Side panel file path** — display full file path with click-to-copy in model info panel
|
||||
- **Node card redesign** — removed package badge, added left border color-coding by package
|
||||
- **Toolbar pagination** — pagination controls moved to toolbar for all tabs (Models, Workflows, Nodes)
|
||||
- **Help tab updated** — Models Tab section added to feature list
|
||||
|
||||
### v0.2.0
|
||||
- **Prompt presets in sidebar** — added Prompts tab to Workflow Studio Library (ComfyUI side panel) with All / Favorites / Categories sub-tabs
|
||||
- **WFS_PromptText custom node** — drag prompt presets onto the canvas to create nodes with positive/negative prompt outputs
|
||||
- **Preset Manager** — 3-column layout in Prompt tab: AI Assistant | Presets editor | Preset Manager (All / Favorites / Groups)
|
||||
- **Group management** — create groups, assign/remove presets, delete groups from the Presets panel
|
||||
- **GenUI Set** — renamed "Apply" button to clarify its purpose (applies presets to GenerateUI)
|
||||
- **Sidebar P/N copy buttons** — copy positive or negative prompts individually from sidebar items
|
||||
- **Panel renamed** — "WF & Node Library" renamed to "Workflow Studio Library"
|
||||
- **Backend API for presets** — presets migrated from localStorage to server-side API with one-time migration
|
||||
- **Help tab updated** — Prompt Tab and Workflow Studio Library sections reflect new features
|
||||
|
||||
### v0.1.9
|
||||
- **Side panel 2-row sub-tabs** — Workflows: row 1 (Workflows / Favorites / Groups), row 2 (Model Type); Nodes: row 1 (Nodes / Favorites / Groups), row 2 (Sets)
|
||||
- **Save dialog** — canvas snapshot now shows a filename edit dialog instead of auto-naming
|
||||
- **API/App format badge** — workflow items in side panel show red (API) or orange (APP) badge
|
||||
- **Group auto-cleanup** — deleted workflows are automatically removed from groups on refresh
|
||||
|
||||
### v0.1.8
|
||||
- **WF & Node Library side panel** — renamed from "Node Library", added two top-level tabs (Workflows / Nodes) accessible from ComfyUI top bar
|
||||
- **Workflows tab** — browse favorite workflows, filter by model type, filter by group with collapsible sections
|
||||
- **Workflow drag & drop** — drag workflows from the side panel onto the canvas to load them instantly
|
||||
- **Help tab update** — added Nodes Tab and WF & Node Library sections to the feature list
|
||||
|
||||
### v0.1.7
|
||||
- **Nodes tab** — browse, search, and filter all installed ComfyUI nodes with Card/Table views, pagination (50 nodes/page), package color badges, and node detail side panel with I/O specs
|
||||
- **Node Sets** — save selected nodes + connections from ComfyUI canvas as reusable sets via right-click context menu
|
||||
- **Node Library side panel** — accessible from ComfyUI top bar with Favorites, Sets, and Groups tabs, drag & drop placement
|
||||
- **3 top bar buttons** — Workflow Studio, Canvas Snapshot, and Node Library buttons in ComfyUI's action bar
|
||||
|
||||
### v0.1.6
|
||||
- **Security fix** — path traversal vulnerability in `workflow_service.py` (reported via ComfyUI-Manager PR review)
|
||||
|
||||
### v0.1.5
|
||||
- **Theme system** — 13 built-in themes selectable from Settings tab with instant preview (Deep Ocean Dark, Pop & Vibrant, Light Minimalist, Cyberpunk, Glassmorphism, Neumorphism, Retro 8-bit, Pastel Dream, Brutalism, Earthy, Material UI, Monotone + Accent, Corporate Trust)
|
||||
- Theme preference persisted in localStorage and restored on page load (no flash)
|
||||
- Special CSS effects per theme: neon glow (Cyberpunk), backdrop blur (Glassmorphism), dual shadow (Neumorphism), pixel borders (Retro/Brutalism)
|
||||
|
||||
### v0.1.4
|
||||
- **App format support** — detect `.app.json` workflows (ComfyUI App mode), show "App Format" badge, block loading in GenerateUI with guidance message
|
||||
- **Preset clipboard copy** — added PP Copy / NP Copy buttons to copy positive/negative prompts to clipboard
|
||||
- **Analysis bugfix** — fixed workflow analysis crash when `widgets_values` contains non-string values (e.g. integers)
|
||||
|
||||
### v0.1.3
|
||||
- Added **Help & Support tab** — feature list, tips, and support links (GitHub, Ko-fi)
|
||||
- Multi-language support (EN/JA/ZH) for all help content
|
||||
|
||||
### v0.1.2
|
||||
- Added **Canvas Snapshot** button to ComfyUI top bar — capture workflow canvas as PNG thumbnail with embedded workflow metadata
|
||||
- Added **Thumbnail tab** to the workflow side panel for quick visual preview
|
||||
- Added **Thumbnail section** to the workflow detail modal
|
||||
- Snapshot images are auto-imported as workflows with thumbnails
|
||||
|
||||
### v0.1.1
|
||||
- Initial feature set: Workflow management, GenerateUI, Prompt assistant, Settings
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
@@ -263,7 +512,8 @@ ComfyUI-Workflow-Studio/
|
||||
│ ├── wfm.py # Main class & route registration
|
||||
│ ├── config.py # Path configuration
|
||||
│ ├── nodes/
|
||||
│ │ └── prompt_text.py # WFS_PromptText custom node (positive/negative prompt)
|
||||
│ │ ├── prompt_text.py # WFS_PromptText custom node (positive/negative prompt)
|
||||
│ │ └── gallery_feeder_node.py # WFS_GalleryFeeder custom node (gallery group → IMAGE)
|
||||
│ ├── routes/
|
||||
│ │ ├── workflow_routes.py # Workflow CRUD & analysis API
|
||||
│ │ ├── nodes_routes.py # Nodes metadata & node sets API
|
||||
@@ -271,7 +521,8 @@ ComfyUI-Workflow-Studio/
|
||||
│ │ ├── prompts_routes.py # Prompt presets CRUD API
|
||||
│ │ ├── settings_routes.py # Settings API
|
||||
│ │ ├── ollama_routes.py # Ollama proxy API
|
||||
│ │ └── eagle_routes.py # Eagle integration API
|
||||
│ │ ├── eagle_routes.py # Eagle integration API
|
||||
│ │ └── gallery_routes.py # Gallery tab API (Output + ImagePrompt + Style_Catalog sub-tabs)
|
||||
│ └── services/
|
||||
│ ├── workflow_service.py # Workflow file operations
|
||||
│ ├── nodes_service.py # Node metadata & node sets
|
||||
@@ -280,9 +531,11 @@ ComfyUI-Workflow-Studio/
|
||||
│ ├── prompts_service.py # Prompt presets persistence
|
||||
│ ├── workflow_analyzer.py # Model/node detection
|
||||
│ ├── settings_service.py # Settings persistence
|
||||
│ └── png_extractor.py # PNG metadata extraction
|
||||
│ ├── png_extractor.py # PNG metadata extraction
|
||||
│ ├── gallery_service.py # Gallery image scanning, .txt sidecar & ponyxlWildcardsVault prompt resolution
|
||||
│ └── gallery_metadata.py # Gallery per-image metadata store (favorites/tags/memo/groups)
|
||||
├── templates/
|
||||
│ └── index.html # SPA template (Workflow/GenerateUI/Prompt/Settings/Nodes/Models/Help)
|
||||
│ └── index.html # SPA template (Workflow/GenerateUI/Prompt/Metadata/Gallery/Nodes/Models/Settings/Help/AI)
|
||||
├── static/
|
||||
│ ├── favicon.svg # Browser tab icon (W+S Wave)
|
||||
│ ├── css/main.css # Styles
|
||||
@@ -290,19 +543,30 @@ ComfyUI-Workflow-Studio/
|
||||
│ ├── app.js # App initialization & routing
|
||||
│ ├── workflow-tab.js # Workflow browser
|
||||
│ ├── generate-tab.js # Generation UI
|
||||
│ ├── feeder-tab.js # Feeder subtab (Image Loop + Gallery modes)
|
||||
│ ├── gallery-tab.js # Gallery tab: Output sub-tab (image browser, groups, metadata) + sub-tab switching
|
||||
│ ├── image-prompt-tab.js # Gallery tab: ImagePrompt sub-tab (visual prompt builder)
|
||||
│ ├── style-catalog-tab.js # Gallery tab: Style_Catalog sub-tab (visual Style picker)
|
||||
│ ├── tagger-tab.js # Tagger tab (WD Tagger, DeepDanbooru, Ollama VLM)
|
||||
│ ├── prompt-tab.js # AI assistant & presets
|
||||
│ ├── metadata-tab.js # Metadata extraction & display (PNG/WebP/JSON)
|
||||
│ ├── settings-tab.js # Settings panel
|
||||
│ ├── comfyui-client.js # ComfyUI WebSocket/API client
|
||||
│ ├── nodes-tab.js # Node browser & node sets
|
||||
│ ├── models-tab.js # Model browser & CivitAI integration
|
||||
│ ├── ai-tab.js # AI TOOL tab (Translation | Chat | TOOLS/VLM | Settings)
|
||||
│ ├── comfyui-workflow.js # UI-to-API format conversion
|
||||
│ ├── comfyui-editor.js # Dynamic parameter editor
|
||||
│ ├── json-highlight.js # JSON syntax highlighting
|
||||
│ ├── util.js # Shared utilities (escapeHtml, readJsonStorage, getSettings)
|
||||
│ └── i18n.js # Internationalization
|
||||
├── web/comfyui/
|
||||
│ ├── top_menu_extension.js # ComfyUI menu bar integration
|
||||
│ └── node_sets_menu.js # Workflow Studio Library side panel
|
||||
└── data/ # Metadata & settings storage (incl. prompts.json, model_metadata.json, civitai_cache.json)
|
||||
│ ├── node_sets_menu.js # Workflow Studio Library side panel
|
||||
│ └── gallery_feeder_extension.js # WFS_GalleryFeeder canvas widgets (After Gen / Run / Stop)
|
||||
├── tools/
|
||||
│ └── import_style_prompt_seed.py # Dev tool: pre-process a wildcards-vault source into ws_image_prompt (not run by the plugin itself)
|
||||
└── data/ # Fallback data dir (used when ComfyUI user/default/ is not found)
|
||||
```
|
||||
|
||||
---
|
||||
@@ -317,6 +581,11 @@ MIT License
|
||||
|
||||
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) by comfyanonymous
|
||||
- [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by pythongosssss — Canvas snapshot and PNG workflow embedding implementation reference
|
||||
- [ComfyUI-Lora-Manager](https://github.com/willchil/ComfyUI-Lora-Manager) — Plugin architecture and UI pattern reference
|
||||
- [ComfyUI-Lora-Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) — Plugin architecture and UI pattern reference
|
||||
- [Ollama](https://ollama.com/) for local LLM inference
|
||||
- [Eagle](https://eagle.cool/) for image management
|
||||
- [Pillow (PIL Fork)](https://python-pillow.org/) — server-side thumbnail generation (`data/thumb_cache/`)
|
||||
- [ComfyUI-Gallery](https://github.com/PanicTitan/ComfyUI-Gallery) by PanicTitan — thumbnail grid UX reference
|
||||
- [infinite-image-browsing](https://github.com/zanllp/sd-webui-infinite-image-browsing) by zanllp — thumbnail caching and index strategy reference
|
||||
- [Fooocus](https://github.com/lllyasviel/Fooocus) by lllyasviel — style preset JSON format reference
|
||||
|
||||
|
||||
+4
-3
@@ -9,15 +9,16 @@ WorkflowStudio.add_routes()
|
||||
|
||||
# Register custom nodes (isolated loading)
|
||||
_NODE_MODULES = {
|
||||
"WFS_PromptText": (".py.nodes.prompt_text", "WFS_PromptText"),
|
||||
"WFS_PromptText": (".py.nodes.prompt_text", "WFS_PromptText", "Prompt Text (WFS)"),
|
||||
"WFS_GalleryFeeder": (".py.nodes.gallery_feeder_node", "WFS_GalleryFeeder", "Gallery Feeder (WFS)"),
|
||||
}
|
||||
|
||||
for _name, (_mod_path, _cls_name) in _NODE_MODULES.items():
|
||||
for _name, (_mod_path, _cls_name, _display_name) in _NODE_MODULES.items():
|
||||
try:
|
||||
import importlib
|
||||
_mod = importlib.import_module(_mod_path, package=__name__)
|
||||
NODE_CLASS_MAPPINGS[_name] = getattr(_mod, _cls_name)
|
||||
NODE_DISPLAY_NAME_MAPPINGS[_name] = "Prompt Text (WFS)"
|
||||
NODE_DISPLAY_NAME_MAPPINGS[_name] = _display_name
|
||||
except Exception as _e:
|
||||
print(f"[WARNING] Workflow Studio: Failed to load '{_name}': {_e}")
|
||||
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 2100 900" fill="none">
|
||||
<defs>
|
||||
<linearGradient id="bg" x1="0" y1="0" x2="2100" y2="900" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#1a1a2e"/>
|
||||
<stop offset="50%" stop-color="#16213e"/>
|
||||
<stop offset="100%" stop-color="#1a1a2e"/>
|
||||
</linearGradient>
|
||||
<linearGradient id="wStroke" x1="0" y1="0" x2="600" y2="600" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#4a9eff"/>
|
||||
<stop offset="100%" stop-color="#7b68ee"/>
|
||||
</linearGradient>
|
||||
<linearGradient id="sWave" x1="0" y1="0" x2="600" y2="600" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#7b68ee"/>
|
||||
<stop offset="100%" stop-color="#4a9eff"/>
|
||||
</linearGradient>
|
||||
<linearGradient id="textGrad" x1="0" y1="0" x2="1200" y2="0" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#e0e0ff"/>
|
||||
<stop offset="100%" stop-color="#b0b0d0"/>
|
||||
</linearGradient>
|
||||
</defs>
|
||||
<!-- Background -->
|
||||
<rect width="2100" height="900" fill="url(#bg)"/>
|
||||
<!-- Subtle grid -->
|
||||
<line x1="0" y1="225" x2="2100" y2="225" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="0" y1="450" x2="2100" y2="450" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="0" y1="675" x2="2100" y2="675" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<!-- Decorative wave lines in background -->
|
||||
<path d="M0 750 Q262 700 525 750 Q787 800 1050 750 Q1312 700 1575 750 Q1837 800 2100 750" stroke="#4a9eff" stroke-opacity="0.08" stroke-width="2" fill="none"/>
|
||||
<path d="M0 780 Q262 730 525 780 Q787 830 1050 780 Q1312 730 1575 780 Q1837 830 2100 780" stroke="#7b68ee" stroke-opacity="0.06" stroke-width="2" fill="none"/>
|
||||
<!-- W+S Logo (left side) -->
|
||||
<g transform="translate(180, 180)">
|
||||
<!-- W -->
|
||||
<path d="M60 80 L120 280 L180 130 L240 280 L300 80" stroke="url(#wStroke)" stroke-width="26" stroke-linecap="round" stroke-linejoin="round" fill="none"/>
|
||||
<!-- S wave -->
|
||||
<path d="M80 340 Q140 300 200 340 Q260 380 320 340" stroke="url(#sWave)" stroke-width="18" stroke-linecap="round" fill="none"/>
|
||||
</g>
|
||||
<!-- Title text -->
|
||||
<text x="720" y="380" font-family="system-ui, -apple-system, 'Segoe UI', sans-serif" font-size="120" font-weight="700" fill="url(#textGrad)" letter-spacing="-2">Workflow Studio</text>
|
||||
<!-- Subtitle -->
|
||||
<text x="720" y="470" font-family="system-ui, -apple-system, 'Segoe UI', sans-serif" font-size="42" fill="#8888aa" letter-spacing="1">Workflow Management & Generation UI for ComfyUI</text>
|
||||
<!-- Feature pills -->
|
||||
<g font-family="system-ui, -apple-system, 'Segoe UI', sans-serif" font-size="28" fill="#a0a0c0">
|
||||
<rect x="720" y="530" width="200" height="44" rx="22" fill="#4a9eff" fill-opacity="0.12" stroke="#4a9eff" stroke-opacity="0.3" stroke-width="1"/>
|
||||
<text x="820" y="559" text-anchor="middle" fill="#4a9eff">Workflows</text>
|
||||
<rect x="940" y="530" width="200" height="44" rx="22" fill="#7b68ee" fill-opacity="0.12" stroke="#7b68ee" stroke-opacity="0.3" stroke-width="1"/>
|
||||
<text x="1040" y="559" text-anchor="middle" fill="#7b68ee">GenerateUI</text>
|
||||
<rect x="1160" y="530" width="200" height="44" rx="22" fill="#4a9eff" fill-opacity="0.12" stroke="#4a9eff" stroke-opacity="0.3" stroke-width="1"/>
|
||||
<text x="1260" y="559" text-anchor="middle" fill="#4a9eff">AI Prompts</text>
|
||||
<rect x="1380" y="530" width="180" height="44" rx="22" fill="#7b68ee" fill-opacity="0.12" stroke="#7b68ee" stroke-opacity="0.3" stroke-width="1"/>
|
||||
<text x="1470" y="559" text-anchor="middle" fill="#7b68ee">Themes</text>
|
||||
</g>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 3.7 KiB |
@@ -0,0 +1,29 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 400 400" fill="none">
|
||||
<defs>
|
||||
<linearGradient id="bg" x1="0" y1="0" x2="400" y2="400" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#1a1a2e"/>
|
||||
<stop offset="100%" stop-color="#16213e"/>
|
||||
</linearGradient>
|
||||
<linearGradient id="wStroke" x1="0" y1="0" x2="400" y2="400" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#4a9eff"/>
|
||||
<stop offset="100%" stop-color="#7b68ee"/>
|
||||
</linearGradient>
|
||||
<linearGradient id="sWave" x1="0" y1="0" x2="400" y2="400" gradientUnits="userSpaceOnUse">
|
||||
<stop offset="0%" stop-color="#7b68ee"/>
|
||||
<stop offset="100%" stop-color="#4a9eff"/>
|
||||
</linearGradient>
|
||||
</defs>
|
||||
<!-- Background -->
|
||||
<rect width="400" height="400" rx="48" fill="url(#bg)"/>
|
||||
<!-- Subtle grid pattern -->
|
||||
<line x1="0" y1="100" x2="400" y2="100" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="0" y1="200" x2="400" y2="200" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="0" y1="300" x2="400" y2="300" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="100" y1="0" x2="100" y2="400" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="200" y1="0" x2="200" y2="400" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<line x1="300" y1="0" x2="300" y2="400" stroke="#ffffff" stroke-opacity="0.03" stroke-width="1"/>
|
||||
<!-- W letterform -->
|
||||
<path d="M80 110 L140 290 L200 150 L260 290 L320 110" stroke="url(#wStroke)" stroke-width="28" stroke-linecap="round" stroke-linejoin="round" fill="none"/>
|
||||
<!-- S wave -->
|
||||
<path d="M100 330 Q150 295 200 330 Q250 365 300 330" stroke="url(#sWave)" stroke-width="20" stroke-linecap="round" fill="none"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.8 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 193 KiB |
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|
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|
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|
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|
After Width: | Height: | Size: 330 KiB |
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|
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|
After Width: | Height: | Size: 72 KiB |
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|
After Width: | Height: | Size: 278 KiB |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
After Width: | Height: | Size: 1.7 MiB |
+21
-4
@@ -10,13 +10,22 @@ PLUGIN_DIR = Path(__file__).resolve().parent.parent
|
||||
# Directory paths
|
||||
TEMPLATES_DIR = PLUGIN_DIR / "templates"
|
||||
STATIC_DIR = PLUGIN_DIR / "static"
|
||||
DATA_DIR = PLUGIN_DIR / "data"
|
||||
|
||||
# Default workflows directory: ComfyUI's user/default/workflows
|
||||
# PLUGIN_DIR is custom_nodes/ComfyUI-Workflow-Studio
|
||||
# ComfyUI root is custom_nodes/../../ = two levels up
|
||||
_COMFYUI_ROOT = PLUGIN_DIR.parent.parent
|
||||
_COMFYUI_WORKFLOWS = _COMFYUI_ROOT / "user" / "default" / "workflows"
|
||||
COMFYUI_ROOT = _COMFYUI_ROOT # exposed for external use
|
||||
|
||||
# Data directory: prefer ComfyUI's user/default/Workflow-Studio/, fallback to plugin data/
|
||||
_COMFYUI_USER_DEFAULT = _COMFYUI_ROOT / "user" / "default"
|
||||
if _COMFYUI_USER_DEFAULT.is_dir():
|
||||
DATA_DIR = _COMFYUI_USER_DEFAULT / "Workflow-Studio"
|
||||
logger.info("Workflow Studio: Using user data dir: %s", DATA_DIR)
|
||||
else:
|
||||
DATA_DIR = PLUGIN_DIR / "data"
|
||||
logger.info("Workflow Studio: user/default not found, using plugin data dir: %s", DATA_DIR)
|
||||
|
||||
# Default workflows directory: ComfyUI's user/default/workflows
|
||||
_COMFYUI_WORKFLOWS = _COMFYUI_USER_DEFAULT / "workflows"
|
||||
|
||||
# Use ComfyUI workflows dir if it exists, otherwise fallback to plugin data dir
|
||||
if _COMFYUI_WORKFLOWS.is_dir():
|
||||
@@ -59,3 +68,11 @@ NODE_METADATA_FILE = DATA_DIR / "node_metadata.json"
|
||||
NODE_SETS_FILE = DATA_DIR / "node_sets.json"
|
||||
PROMPTS_FILE = DATA_DIR / "prompts.json"
|
||||
MODEL_METADATA_FILE = DATA_DIR / "model_metadata.json"
|
||||
WILDCARD_DIR = DATA_DIR / "wildcard"
|
||||
AI_SKILLS_DIR = DATA_DIR / "ai_skills"
|
||||
LAB_PLAN_DIR = DATA_DIR / "lab_plan"
|
||||
|
||||
# Tagger
|
||||
TAGGER_DB_FILE = DATA_DIR / "tagger.db"
|
||||
TAGGER_SETTINGS_FILE = DATA_DIR / "tagger_settings.json"
|
||||
TAGGER_MODELS_DIR = _COMFYUI_ROOT / "models" / "tagger"
|
||||
|
||||
@@ -0,0 +1,95 @@
|
||||
"""WFS_GalleryFeeder – Feeds images from a gallery group into a workflow."""
|
||||
import json
|
||||
import random
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Feeder専用グループ名（gallery_metadata.json 内で予約される）
|
||||
FEEDER_GROUP = "__Feeder__"
|
||||
|
||||
|
||||
class WFS_GalleryFeeder:
|
||||
"""Gallery グループ内の画像をインデックス順に1枚ずつ出力するノード。"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"group_name": ("STRING", {"default": FEEDER_GROUP}),
|
||||
"index": ("INT", {"default": 0, "min": 0, "max": 99999}),
|
||||
"sort_mode": (["filename_asc", "filename_desc", "random"],),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "Workflow Studio"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
@classmethod
|
||||
def IS_CHANGED(cls, **kwargs):
|
||||
# 毎回実行させる（インデックスが変わるため）
|
||||
return float("NaN")
|
||||
|
||||
def execute(self, group_name: str, index: int, sort_mode: str, seed: int):
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image as PILImage
|
||||
|
||||
paths = self._get_sorted_paths(group_name, sort_mode, seed)
|
||||
if not paths:
|
||||
logger.warning("WFS_GalleryFeeder: group '%s' is empty or not found", group_name)
|
||||
return (torch.zeros(1, 64, 64, 3),)
|
||||
|
||||
img_path = paths[index % len(paths)]
|
||||
try:
|
||||
img = PILImage.open(img_path).convert("RGB")
|
||||
except Exception as e:
|
||||
logger.error("WFS_GalleryFeeder: failed to load '%s': %s", img_path, e)
|
||||
return (torch.zeros(1, 64, 64, 3),)
|
||||
|
||||
arr = np.array(img, dtype=np.float32) / 255.0
|
||||
tensor = torch.from_numpy(arr).unsqueeze(0)
|
||||
return (tensor,)
|
||||
|
||||
# ── 内部ヘルパー ──────────────────────────────────────────────
|
||||
|
||||
def _get_sorted_paths(self, group_name: str, sort_mode: str, seed: int) -> list[str]:
|
||||
metadata_file = self._find_metadata_file()
|
||||
if not metadata_file.exists():
|
||||
return []
|
||||
try:
|
||||
with open(metadata_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
except Exception as e:
|
||||
logger.error("WFS_GalleryFeeder: metadata read error: %s", e)
|
||||
return []
|
||||
|
||||
paths = [
|
||||
key for key, meta in data.get("images", {}).items()
|
||||
if group_name in meta.get("groups", []) and Path(key).exists()
|
||||
]
|
||||
|
||||
if sort_mode == "filename_asc":
|
||||
paths.sort(key=lambda p: Path(p).name.lower())
|
||||
elif sort_mode == "filename_desc":
|
||||
paths.sort(key=lambda p: Path(p).name.lower(), reverse=True)
|
||||
elif sort_mode == "random":
|
||||
rng = random.Random(seed)
|
||||
rng.shuffle(paths)
|
||||
|
||||
return paths
|
||||
|
||||
@staticmethod
|
||||
def _find_metadata_file() -> Path:
|
||||
# py/nodes/ -> py/ -> plugin root -> ComfyUI/custom_nodes/ -> ComfyUI root
|
||||
plugin_root = Path(__file__).resolve().parent.parent.parent
|
||||
comfyui_root = plugin_root.parent.parent
|
||||
user_default = comfyui_root / "user" / "default"
|
||||
if user_default.is_dir():
|
||||
return user_default / "Workflow-Studio" / "gallery_metadata.json"
|
||||
return plugin_root / "data" / "gallery_metadata.json"
|
||||
+103
-2
@@ -3,6 +3,7 @@
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
|
||||
@@ -17,6 +18,84 @@ def setup_routes(app: web.Application):
|
||||
app.router.add_post("/api/wfm/eagle/test", handle_test)
|
||||
|
||||
|
||||
def _resolve_local_path(filename, subfolder, type_name):
|
||||
"""filename/subfolder/type を ComfyUI 出力ディレクトリ上の実パスに解決する。
|
||||
ComfyUI と Eagle が同一マシン上で動作している場合のみ有効なパスが返る
|
||||
(別マシンの場合は None を返し、呼び出し側は addFromURL にフォールバックする)。"""
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
base_dir = folder_paths.get_directory_by_type(type_name or "output")
|
||||
if not base_dir:
|
||||
return None
|
||||
base_dir = os.path.abspath(base_dir)
|
||||
|
||||
filename = os.path.basename(filename or "")
|
||||
if not filename:
|
||||
return None
|
||||
|
||||
full_dir = os.path.abspath(os.path.join(base_dir, subfolder or ""))
|
||||
# Windowsはパス比較で大文字小文字を区別しないため normcase で正規化してから比較する
|
||||
if os.path.commonpath((os.path.normcase(full_dir), os.path.normcase(base_dir))) != os.path.normcase(base_dir):
|
||||
# ディレクトリトラバーサル対策: base_dir 配下以外は拒否
|
||||
return None
|
||||
|
||||
full_path = os.path.join(full_dir, filename)
|
||||
if not os.path.isfile(full_path):
|
||||
return None
|
||||
return full_path
|
||||
|
||||
|
||||
def _resolve_absolute_svg_path(abs_path):
|
||||
"""comfyui-tosvg の Save SVG String ノードなどが返す絶対パスを検証する。
|
||||
ComfyUI管理下(output/input/temp)ディレクトリ配下の実在する .svg ファイルのみ許可する
|
||||
(任意の絶対パスをそのまま信頼すると、外部からこのAPIを叩かれた際に
|
||||
ローカルの任意ファイルを送信させられてしまうため)。"""
|
||||
if not abs_path or not abs_path.lower().endswith(".svg"):
|
||||
return None
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
abs_path = os.path.abspath(abs_path)
|
||||
if not os.path.isfile(abs_path):
|
||||
return None
|
||||
|
||||
for type_name in ("output", "temp", "input"):
|
||||
base_dir = folder_paths.get_directory_by_type(type_name)
|
||||
if not base_dir:
|
||||
continue
|
||||
base_dir = os.path.abspath(base_dir)
|
||||
try:
|
||||
# Windowsはパス比較で大文字小文字を区別しないため normcase で正規化してから比較する
|
||||
if os.path.commonpath((os.path.normcase(abs_path), os.path.normcase(base_dir))) == os.path.normcase(base_dir):
|
||||
return abs_path
|
||||
except ValueError:
|
||||
# Windowsで異なるドライブレターの場合 commonpath が例外を投げる
|
||||
continue
|
||||
return None
|
||||
|
||||
|
||||
def _eagle_add_from_path(eagle_url, file_path, name, tags):
|
||||
"""Proxy request to Eagle's addFromPath API (runs in thread).
|
||||
SVG は ComfyUI の /view がセキュリティ上 application/octet-stream +
|
||||
attachment で返すため、addFromURL 経由だと正しい種別で保存されないことがある。
|
||||
ComfyUI と Eagle が同一マシン上にある場合はローカルパスを直接渡す方が確実。"""
|
||||
eagle_url = eagle_url.rstrip("/")
|
||||
payload = json.dumps({"path": file_path, "name": name, "tags": tags}).encode("utf-8")
|
||||
req = urllib.request.Request(
|
||||
f"{eagle_url}/api/item/addFromPath",
|
||||
data=payload,
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||||
return json.loads(resp.read().decode("utf-8"))
|
||||
|
||||
|
||||
def _eagle_add(eagle_url, image_url, name, tags):
|
||||
"""Proxy request to Eagle API (runs in thread)."""
|
||||
eagle_url = eagle_url.rstrip("/")
|
||||
@@ -71,11 +150,33 @@ async def handle_add(request: web.Request) -> web.Response:
|
||||
image_url = body.get("url", "")
|
||||
name = body.get("name", "image.png")
|
||||
tags = body.get("tags", [])
|
||||
filename = body.get("filename", "")
|
||||
subfolder = body.get("subfolder", "")
|
||||
file_type = body.get("type", "output")
|
||||
local_path_hint = body.get("localPath", "")
|
||||
|
||||
if not image_url:
|
||||
if not image_url and not local_path_hint:
|
||||
return web.json_response({"status": "error", "message": "No URL provided"}, status=400)
|
||||
|
||||
result = await asyncio.to_thread(_eagle_add, eagle_url, image_url, name, tags)
|
||||
# SVG は /view が application/octet-stream + attachment で返すため、
|
||||
# ローカルファイルが解決できる場合は addFromPath で直接渡す
|
||||
local_path = None
|
||||
if local_path_hint:
|
||||
# comfyui-tosvg の Save SVG String ノードなど、絶対パスが既知の場合
|
||||
local_path = _resolve_absolute_svg_path(local_path_hint)
|
||||
elif (filename or name or "").lower().endswith(".svg"):
|
||||
local_path = _resolve_local_path(filename or name, subfolder, file_type)
|
||||
|
||||
if not local_path and not image_url:
|
||||
return web.json_response(
|
||||
{"status": "error", "message": f"Could not resolve local SVG path: {local_path_hint}"},
|
||||
status=400,
|
||||
)
|
||||
|
||||
if local_path:
|
||||
result = await asyncio.to_thread(_eagle_add_from_path, eagle_url, local_path, name, tags)
|
||||
else:
|
||||
result = await asyncio.to_thread(_eagle_add, eagle_url, image_url, name, tags)
|
||||
return web.json_response(result)
|
||||
except urllib.error.URLError as e:
|
||||
logger.error("Eagle URL error: %s", e)
|
||||
|
||||
@@ -0,0 +1,543 @@
|
||||
"""
|
||||
Gallery Routes - ギャラリータブ用APIエンドポイント
|
||||
"""
|
||||
import asyncio
|
||||
import json
|
||||
import mimetypes
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import DATA_DIR
|
||||
from ..services.gallery_service import GalleryService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = GalleryService(DATA_DIR)
|
||||
|
||||
|
||||
def _init_allowed_root() -> None:
|
||||
"""起動時に保存済みの出力ディレクトリで _allowed_root を初期化する。
|
||||
Gallery タブを開く前に Feeder タブの Gallery モードが serve_image を呼んでも
|
||||
404 にならないようにするために必要。"""
|
||||
try:
|
||||
settings_file = DATA_DIR / "settings.json"
|
||||
if settings_file.exists():
|
||||
with open(settings_file, "r", encoding="utf-8") as f:
|
||||
settings = json.load(f)
|
||||
saved_root = settings.get("gallery_output_dir", "").strip()
|
||||
if saved_root and Path(saved_root).is_dir():
|
||||
_service.update_output_root(saved_root)
|
||||
return
|
||||
except Exception as e:
|
||||
logger.warning("GalleryRoutes: failed to load saved gallery root: %s", e)
|
||||
|
||||
# フォールバック: ComfyUI のデフォルト output フォルダ
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
if output_dir and Path(output_dir).is_dir():
|
||||
_service.update_output_root(output_dir)
|
||||
except Exception as e:
|
||||
logger.warning("GalleryRoutes: failed to get ComfyUI output dir: %s", e)
|
||||
|
||||
# ImagePrompt/Style Catalogギャラリー用に実outputフォルダを常に別途保持する
|
||||
# (Settingsで_allowed_rootが別フォルダに変更されていても影響しないようにするため)
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
if output_dir and Path(output_dir).is_dir():
|
||||
_service.set_comfy_output_root(output_dir)
|
||||
except Exception as e:
|
||||
logger.warning("GalleryRoutes: failed to get ComfyUI output dir for image-prompt root: %s", e)
|
||||
|
||||
|
||||
_init_allowed_root()
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/wfm/gallery/folders", list_folders)
|
||||
app.router.add_get("/wfm/gallery/images", list_images)
|
||||
app.router.add_get("/wfm/gallery/image/meta", get_image_meta)
|
||||
app.router.add_get("/wfm/gallery/image/workflow", get_image_workflow)
|
||||
app.router.add_get("/wfm/gallery/image/serve", serve_image)
|
||||
app.router.add_get("/wfm/gallery/image/thumb", serve_thumb)
|
||||
app.router.add_post("/wfm/gallery/image/meta", save_image_meta)
|
||||
app.router.add_get("/wfm/gallery/image-prompt/root", get_image_prompt_root)
|
||||
app.router.add_post("/wfm/gallery/image/image-prompt", save_image_prompt)
|
||||
app.router.add_get("/wfm/gallery/style-catalog/root", get_style_catalog_root)
|
||||
app.router.add_post("/wfm/gallery/image/save", save_image_to_gallery)
|
||||
app.router.add_post("/wfm/gallery/image/save-to-folder", save_image_to_folder_route)
|
||||
app.router.add_post("/wfm/gallery/image/favorite", toggle_favorite)
|
||||
app.router.add_get("/wfm/gallery/groups", list_groups)
|
||||
app.router.add_post("/wfm/gallery/groups", create_group)
|
||||
app.router.add_put("/wfm/gallery/groups/{name}", rename_group)
|
||||
app.router.add_delete("/wfm/gallery/groups/{name}", delete_group)
|
||||
app.router.add_post("/wfm/gallery/groups/ensure", ensure_group)
|
||||
app.router.add_post("/wfm/gallery/groups/{name}/add", add_to_group)
|
||||
app.router.add_post("/wfm/gallery/groups/{name}/remove", remove_from_group)
|
||||
app.router.add_post("/wfm/gallery/groups/{name}/clear", clear_group_images)
|
||||
app.router.add_get("/wfm/gallery/groups/{name}/images", list_group_images)
|
||||
# バルク操作
|
||||
app.router.add_post("/wfm/gallery/bulk/favorite", bulk_favorite)
|
||||
app.router.add_post("/wfm/gallery/bulk/group", bulk_group)
|
||||
# フォルダ・ファイル操作
|
||||
app.router.add_post("/wfm/gallery/folder", create_folder_route)
|
||||
app.router.add_delete("/wfm/gallery/folder", delete_folder_route)
|
||||
app.router.add_post("/wfm/gallery/images/delete", delete_images_route)
|
||||
app.router.add_post("/wfm/gallery/images/move", move_images_route)
|
||||
app.router.add_post("/wfm/gallery/images/export-zip", export_images_zip)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# フォルダツリー
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def list_folders(request: web.Request) -> web.Response:
|
||||
root = request.rel_url.query.get("root", "")
|
||||
if not root:
|
||||
return web.json_response({"error": "root parameter required"}, status=400)
|
||||
try:
|
||||
tree = _service.list_folder_tree(root)
|
||||
return web.json_response(tree)
|
||||
except Exception as e:
|
||||
logger.error("list_folders error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像一覧
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def list_images(request: web.Request) -> web.Response:
|
||||
folder = request.rel_url.query.get("folder", "")
|
||||
search = request.rel_url.query.get("search", "")
|
||||
sort_by = request.rel_url.query.get("sort", "date_desc")
|
||||
favorite_only = request.rel_url.query.get("favorite", "false") == "true"
|
||||
tag_filter = request.rel_url.query.get("tag", "")
|
||||
group_filter = request.rel_url.query.get("group", "")
|
||||
recursive = request.rel_url.query.get("recursive", "false") == "true"
|
||||
|
||||
if not folder:
|
||||
return web.json_response({"error": "folder parameter required"}, status=400)
|
||||
|
||||
try:
|
||||
images = await asyncio.to_thread(
|
||||
_service.list_images,
|
||||
folder,
|
||||
search=search,
|
||||
sort_by=sort_by,
|
||||
favorite_only=favorite_only,
|
||||
tag_filter=tag_filter,
|
||||
group_filter=group_filter,
|
||||
recursive=recursive,
|
||||
)
|
||||
# 検索なしのフォルダロード時にバックグラウンドインデックスを起動
|
||||
if not search:
|
||||
_service.start_background_index(folder)
|
||||
return web.json_response({"images": images, "total": len(images)})
|
||||
except Exception as e:
|
||||
logger.error("list_images error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像メタデータ
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def get_image_meta(request: web.Request) -> web.Response:
|
||||
path = request.rel_url.query.get("path", "")
|
||||
if not path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
try:
|
||||
meta = _service.get_image_metadata(path)
|
||||
return web.json_response(meta)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def get_image_workflow(request: web.Request) -> web.Response:
|
||||
path = request.rel_url.query.get("path", "")
|
||||
if not path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
try:
|
||||
wf = _service.extract_workflow_from_metadata(path)
|
||||
if wf is None:
|
||||
return web.json_response({"workflow": None, "has_workflow": False})
|
||||
return web.json_response({"workflow": wf, "has_workflow": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def serve_image(request: web.Request) -> web.Response:
|
||||
path = request.rel_url.query.get("path", "")
|
||||
if not path:
|
||||
return web.Response(status=400)
|
||||
|
||||
img_path = _service.serve_image(path)
|
||||
if img_path is None:
|
||||
return web.Response(status=404)
|
||||
|
||||
content_type, _ = mimetypes.guess_type(str(img_path))
|
||||
return web.FileResponse(img_path, headers={
|
||||
"Content-Type": content_type or "image/png",
|
||||
"Cache-Control": "max-age=3600",
|
||||
})
|
||||
|
||||
|
||||
async def serve_thumb(request: web.Request) -> web.Response:
|
||||
"""サムネイル配信。Pillowで縮小したJPEGをディスクキャッシュして返す。"""
|
||||
path = request.rel_url.query.get("path", "")
|
||||
if not path:
|
||||
return web.Response(status=400)
|
||||
try:
|
||||
width = int(request.rel_url.query.get("w", "256"))
|
||||
width = max(32, min(512, width))
|
||||
except ValueError:
|
||||
width = 256
|
||||
|
||||
thumb_path = _service.serve_thumbnail(path, width)
|
||||
if thumb_path is None:
|
||||
return web.Response(status=404)
|
||||
|
||||
ext = thumb_path.suffix.lower()
|
||||
if ext in (".jpg", ".jpeg"):
|
||||
content_type = "image/jpeg"
|
||||
elif ext == ".gif":
|
||||
content_type = "image/gif"
|
||||
else:
|
||||
content_type = mimetypes.guess_type(str(thumb_path))[0] or "image/jpeg"
|
||||
|
||||
return web.FileResponse(thumb_path, headers={
|
||||
"Content-Type": content_type,
|
||||
"Cache-Control": "max-age=86400",
|
||||
})
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# バルク操作
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def bulk_favorite(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/bulk/favorite — 複数画像のお気に入りを一括設定"""
|
||||
try:
|
||||
body = await request.json()
|
||||
paths = body.get("paths", [])
|
||||
value = bool(body.get("value", True))
|
||||
if not paths:
|
||||
return web.json_response({"error": "paths required"}, status=400)
|
||||
result = _service.bulk_set_favorite(paths, value)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def bulk_group(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/bulk/group — 複数画像のグループ追加/削除を一括処理"""
|
||||
try:
|
||||
body = await request.json()
|
||||
paths = body.get("paths", [])
|
||||
group = body.get("group", "").strip()
|
||||
action = body.get("action", "add") # "add" or "remove"
|
||||
if not paths or not group:
|
||||
return web.json_response({"error": "paths and group required"}, status=400)
|
||||
if action not in ("add", "remove"):
|
||||
return web.json_response({"error": "action must be 'add' or 'remove'"}, status=400)
|
||||
result = _service.bulk_group_op(paths, group, action)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def save_image_meta(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
if not img_path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
data = {k: v for k, v in body.items() if k != "path"}
|
||||
ok = _service.save_image_meta(img_path, data)
|
||||
return web.json_response({"ok": ok})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def get_image_prompt_root(request: web.Request) -> web.Response:
|
||||
"""GET /wfm/gallery/image-prompt/root — ImagePromptギャラリーのルートフォルダを返す"""
|
||||
root = _service.get_image_prompt_root()
|
||||
if root is None:
|
||||
return web.json_response({"error": "ComfyUI output directory not resolved"}, status=500)
|
||||
return web.json_response({"root": root})
|
||||
|
||||
|
||||
async def save_image_prompt(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/image/image-prompt — 画像の.txtサイドカーにプロンプトを保存"""
|
||||
try:
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
prompt = body.get("prompt", "")
|
||||
if not img_path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
ok = _service.save_image_prompt(img_path, prompt)
|
||||
return web.json_response({"ok": ok})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def get_style_catalog_root(request: web.Request) -> web.Response:
|
||||
"""GET /wfm/gallery/style-catalog/root — Style Catalogギャラリーのルートフォルダを返す"""
|
||||
root = _service.get_style_catalog_root()
|
||||
if root is None:
|
||||
return web.json_response({"error": "ComfyUI output directory not resolved"}, status=500)
|
||||
return web.json_response({"root": root})
|
||||
|
||||
|
||||
async def toggle_favorite(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
if not img_path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
new_val = _service.toggle_favorite(img_path)
|
||||
return web.json_response({"favorite": new_val})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def save_image_to_gallery(request: web.Request) -> web.Response:
|
||||
"""Image Edit Tab / AI Chat からの画像(PNG等)やSVGをギャラリーのルートフォルダに保存する。
|
||||
imageData は data URL (data:<mime>;base64,<data>) を想定。ヘッダがなければPNG扱い（旧互換）。"""
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "").strip()
|
||||
image_data = body.get("imageData", "")
|
||||
|
||||
if not filename or not image_data:
|
||||
return web.json_response({"error": "filename and imageData required"}, status=400)
|
||||
|
||||
if _service._allowed_root is None:
|
||||
return web.json_response({"error": "Gallery root not configured. Open Gallery tab and set output folder first."}, status=500)
|
||||
|
||||
result = _service.save_image_to_folder(str(_service._allowed_root), filename, image_data)
|
||||
if not result["ok"]:
|
||||
return web.json_response({"error": result["error"]}, status=400)
|
||||
|
||||
logger.info("save_image_to_gallery: saved %s", result["path"])
|
||||
return web.json_response({"ok": True, "path": result["path"]})
|
||||
except Exception as e:
|
||||
logger.error("save_image_to_gallery error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def save_image_to_folder_route(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/image/save-to-folder — 任意フォルダへ画像を保存（同名なら上書き）。
|
||||
Style Catalogのカタログ作成で、スタイル名をファイル名として使う際に利用する。"""
|
||||
try:
|
||||
body = await request.json()
|
||||
folder = body.get("folder", "").strip()
|
||||
filename = body.get("filename", "").strip()
|
||||
image_data = body.get("imageData", "")
|
||||
|
||||
if not folder or not filename or not image_data:
|
||||
return web.json_response({"error": "folder, filename and imageData required"}, status=400)
|
||||
|
||||
result = _service.save_image_to_folder(folder, filename, image_data)
|
||||
if not result["ok"]:
|
||||
return web.json_response({"error": result["error"]}, status=400)
|
||||
return web.json_response({"ok": True, "path": result["path"]})
|
||||
except Exception as e:
|
||||
logger.error("save_image_to_folder_route error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# グループ管理
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def list_groups(request: web.Request) -> web.Response:
|
||||
return web.json_response({"groups": _service.list_groups()})
|
||||
|
||||
|
||||
async def create_group(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
name = body.get("name", "").strip()
|
||||
if not name:
|
||||
return web.json_response({"error": "name required"}, status=400)
|
||||
ok = _service.create_group(name)
|
||||
if not ok:
|
||||
return web.json_response({"error": "Group already exists"}, status=409)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
_RESERVED_GROUPS = {"__Feeder__"}
|
||||
|
||||
|
||||
async def rename_group(request: web.Request) -> web.Response:
|
||||
"""PUT /wfm/gallery/groups/{name} - グループ名変更"""
|
||||
try:
|
||||
old_name = request.match_info.get("name", "")
|
||||
if old_name in _RESERVED_GROUPS:
|
||||
return web.json_response({"error": f"Group '{old_name}' is reserved and cannot be renamed"}, status=403)
|
||||
body = await request.json()
|
||||
new_name = body.get("new_name", "").strip()
|
||||
if not old_name or not new_name:
|
||||
return web.json_response({"error": "old_name and new_name required"}, status=400)
|
||||
ok = _service.rename_group(old_name, new_name)
|
||||
if not ok:
|
||||
return web.json_response({"error": "Rename failed (not found or name conflict)"}, status=400)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def delete_group(request: web.Request) -> web.Response:
|
||||
name = request.match_info.get("name", "")
|
||||
if name in _RESERVED_GROUPS:
|
||||
return web.json_response({"error": f"Group '{name}' is reserved and cannot be deleted"}, status=403)
|
||||
ok = _service.delete_group(name)
|
||||
return web.json_response({"ok": ok})
|
||||
|
||||
|
||||
async def add_to_group(request: web.Request) -> web.Response:
|
||||
try:
|
||||
name = request.match_info.get("name", "")
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
ok = _service.add_to_group(img_path, name)
|
||||
return web.json_response({"ok": ok})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def remove_from_group(request: web.Request) -> web.Response:
|
||||
try:
|
||||
name = request.match_info.get("name", "")
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
ok = _service.remove_from_group(img_path, name)
|
||||
return web.json_response({"ok": ok})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def list_group_images(request: web.Request) -> web.Response:
|
||||
name = request.match_info.get("name", "")
|
||||
images = _service.list_images_in_group(name)
|
||||
return web.json_response({"images": images})
|
||||
|
||||
|
||||
async def clear_group_images(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/groups/{name}/clear - グループ内の全画像を除外"""
|
||||
name = request.match_info.get("name", "")
|
||||
if not name:
|
||||
return web.json_response({"error": "name required"}, status=400)
|
||||
ok = _service.clear_group(name)
|
||||
return web.json_response({"ok": ok})
|
||||
|
||||
|
||||
async def ensure_group(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/groups/ensure - グループが存在しない場合のみ作成"""
|
||||
try:
|
||||
body = await request.json()
|
||||
name = body.get("name", "").strip()
|
||||
if not name:
|
||||
return web.json_response({"error": "name required"}, status=400)
|
||||
_service.ensure_group(name)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# フォルダ・ファイル操作
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def create_folder_route(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/folder - 新規フォルダ作成"""
|
||||
try:
|
||||
body = await request.json()
|
||||
parent = body.get("parent", "")
|
||||
name = body.get("name", "").strip()
|
||||
if not parent or not name:
|
||||
return web.json_response({"ok": False, "error": "parent and name required"}, status=400)
|
||||
result = _service.create_folder(parent, name)
|
||||
return web.json_response(result, status=200 if result["ok"] else 400)
|
||||
except Exception as e:
|
||||
return web.json_response({"ok": False, "error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def delete_folder_route(request: web.Request) -> web.Response:
|
||||
"""DELETE /wfm/gallery/folder - フォルダ削除"""
|
||||
try:
|
||||
body = await request.json()
|
||||
folder = body.get("path", "")
|
||||
if not folder:
|
||||
return web.json_response({"ok": False, "error": "path required"}, status=400)
|
||||
result = _service.delete_folder(folder)
|
||||
return web.json_response(result, status=200 if result["ok"] else 400)
|
||||
except Exception as e:
|
||||
return web.json_response({"ok": False, "error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def delete_images_route(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/images/delete - 画像削除（単体・複数）"""
|
||||
try:
|
||||
body = await request.json()
|
||||
paths = body.get("paths", [])
|
||||
if not paths:
|
||||
return web.json_response({"deleted": [], "errors": ["paths required"]}, status=400)
|
||||
result = _service.delete_images(paths)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"deleted": [], "errors": [str(e)]}, status=500)
|
||||
|
||||
|
||||
async def move_images_route(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/images/move - 画像移動（単体・複数）"""
|
||||
try:
|
||||
body = await request.json()
|
||||
paths = body.get("paths", [])
|
||||
dest = body.get("dest", "")
|
||||
if not paths or not dest:
|
||||
return web.json_response({"moved": [], "errors": ["paths and dest required"]}, status=400)
|
||||
result = _service.move_images(paths, dest)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"moved": [], "errors": [str(e)]}, status=500)
|
||||
|
||||
|
||||
async def export_images_zip(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/images/export-zip - 複数画像をZIPファイルにしてダウンロード"""
|
||||
import zipfile
|
||||
import io
|
||||
|
||||
try:
|
||||
body = await request.json()
|
||||
paths = body.get("paths", [])
|
||||
if not paths:
|
||||
return web.json_response({"error": "paths required"}, status=400)
|
||||
|
||||
# ZIPファイルをメモリ上に作成
|
||||
zip_buffer = io.BytesIO()
|
||||
with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zf:
|
||||
for path in paths:
|
||||
file_path = Path(path)
|
||||
# セキュリティ: 許可されたディレクトリ内のみ
|
||||
if not _service._check_path_allowed(file_path):
|
||||
continue
|
||||
if file_path.exists() and file_path.is_file():
|
||||
# ファイル名のみをアーカイブに入れる（パス情報は含めない）
|
||||
zf.write(file_path, arcname=file_path.name)
|
||||
|
||||
zip_buffer.seek(0)
|
||||
return web.Response(
|
||||
body=zip_buffer.getvalue(),
|
||||
content_type="application/zip",
|
||||
headers={"Content-Disposition": "attachment; filename=gallery_export.zip"}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error exporting images to ZIP: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
@@ -0,0 +1,183 @@
|
||||
"""G'MIC-Qt integration API routes."""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
import mimetypes
|
||||
import os
|
||||
import subprocess
|
||||
import threading
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from aiohttp import web
|
||||
|
||||
from ..services.settings_service import SettingsService
|
||||
from ..config import DATA_DIR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_settings = SettingsService()
|
||||
|
||||
# Global job store
|
||||
# key: job_id, value: dict {status, message, result_path, error}
|
||||
_gmic_jobs = {}
|
||||
_gmic_jobs_lock = threading.Lock()
|
||||
|
||||
def _gmic_run_gui(job_id: str, input_path: str, output_path: str, gmic_exe: str):
|
||||
"""Run gmic_qt.exe in a background process/thread and wait for completion.
|
||||
|
||||
G'MIC-Qt Standalone argument order: -o <output> <input>
|
||||
After OK the "filter output" window appears; closing it saves to output_path.
|
||||
"""
|
||||
args = [gmic_exe, "-o", output_path, input_path]
|
||||
logger.info("[gmic] Launching: %s", " ".join(args))
|
||||
try:
|
||||
with _gmic_jobs_lock:
|
||||
_gmic_jobs[job_id]["status"] = "processing"
|
||||
_gmic_jobs[job_id]["message"] = "G'MIC GUIで編集中..."
|
||||
|
||||
import time
|
||||
|
||||
kwargs = {}
|
||||
if os.name == "nt":
|
||||
# Required on Windows to show the GUI window from a background process
|
||||
kwargs["creationflags"] = subprocess.CREATE_NEW_CONSOLE
|
||||
|
||||
proc = subprocess.Popen(args, **kwargs)
|
||||
proc.wait()
|
||||
|
||||
time.sleep(0.5)
|
||||
|
||||
if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
|
||||
with _gmic_jobs_lock:
|
||||
_gmic_jobs[job_id]["status"] = "completed"
|
||||
_gmic_jobs[job_id]["result_path"] = output_path
|
||||
_gmic_jobs[job_id]["message"] = "完了"
|
||||
logger.info("[gmic] Result saved: %s", output_path)
|
||||
else:
|
||||
raise ValueError("G'MIC GUIがキャンセルされました")
|
||||
except Exception as e:
|
||||
with _gmic_jobs_lock:
|
||||
_gmic_jobs[job_id]["status"] = "failed"
|
||||
_gmic_jobs[job_id]["error"] = str(e)
|
||||
_gmic_jobs[job_id]["message"] = f"エラー: {str(e)}"
|
||||
logger.error("[gmic] Error: %s", e)
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
"""Register G'MIC API routes."""
|
||||
app.router.add_post("/api/wfm/gmic/open", handle_open)
|
||||
app.router.add_get("/api/wfm/gmic/status/{job_id}", handle_status)
|
||||
app.router.add_post("/api/wfm/gmic/result", handle_result)
|
||||
|
||||
async def handle_open(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/gmic/open - Open image in G'MIC GUI."""
|
||||
try:
|
||||
body = await request.json()
|
||||
image_b64 = body.get("image_b64", "")
|
||||
if not image_b64:
|
||||
return web.json_response({"error": "image_b64 field is required"}, status=400)
|
||||
|
||||
# Retrieve setting for gmic path
|
||||
data = _settings.load()
|
||||
gmic_exe = data.get("gmic_qt_path", "")
|
||||
if not os.path.exists(gmic_exe):
|
||||
return web.json_response({
|
||||
"error": f"G'MIC executable not found at: {gmic_exe}. Please configure the path in settings."
|
||||
}, status=400)
|
||||
|
||||
ext = ".png"
|
||||
if image_b64.startswith("data:"):
|
||||
header, image_b64 = image_b64.split(",", 1)
|
||||
mime = header.split(";")[0].split(":")[1]
|
||||
guessed = mimetypes.guess_extension(mime)
|
||||
if guessed:
|
||||
ext = ".jpg" if guessed == ".jpe" else guessed
|
||||
|
||||
img_bytes = base64.b64decode(image_b64)
|
||||
|
||||
temp_dir = Path(DATA_DIR) / "gmic_temp"
|
||||
temp_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
now_str = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
input_path = str(temp_dir / f"gmic_input_{now_str}{ext}")
|
||||
output_path = str(temp_dir / f"gmic_output_{now_str}_{str(uuid.uuid4())[:8]}{ext}")
|
||||
|
||||
with open(input_path, "wb") as f:
|
||||
f.write(img_bytes)
|
||||
|
||||
job_id = str(uuid.uuid4())
|
||||
with _gmic_jobs_lock:
|
||||
_gmic_jobs[job_id] = {
|
||||
"status": "pending",
|
||||
"message": "G'MIC GUIを起動中...",
|
||||
"result_path": None,
|
||||
"error": None,
|
||||
}
|
||||
|
||||
# Start G'MIC thread
|
||||
t = threading.Thread(
|
||||
target=_gmic_run_gui,
|
||||
args=(job_id, input_path, output_path, gmic_exe),
|
||||
daemon=True,
|
||||
)
|
||||
t.start()
|
||||
|
||||
return web.json_response({"job_id": job_id, "status": "pending", "message": "起動中"})
|
||||
except Exception as e:
|
||||
logger.error("[gmic] open error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
async def handle_status(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/gmic/status/{job_id} - Check job status."""
|
||||
try:
|
||||
job_id = request.match_info.get("job_id", "")
|
||||
with _gmic_jobs_lock:
|
||||
job = _gmic_jobs.get(job_id)
|
||||
if job is None:
|
||||
return web.json_response({"error": "Job not found"}, status=404)
|
||||
return web.json_response({
|
||||
"job_id": job_id,
|
||||
"status": job["status"],
|
||||
"message": job.get("message", ""),
|
||||
"result_path": job.get("result_path"),
|
||||
"error": job.get("error"),
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("[gmic] status error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
async def handle_result(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/gmic/result - Get result image base64."""
|
||||
try:
|
||||
body = await request.json()
|
||||
result_path = body.get("result_path", "")
|
||||
if not result_path or not os.path.exists(result_path):
|
||||
return web.json_response({"error": f"Result file not found: {result_path}"}, status=404)
|
||||
|
||||
# Path traversal guard: only allow files inside gmic_temp
|
||||
allowed_dir = (Path(DATA_DIR) / "gmic_temp").resolve()
|
||||
try:
|
||||
Path(result_path).resolve().relative_to(allowed_dir)
|
||||
except ValueError:
|
||||
return web.json_response({"error": "Access denied: path outside gmic_temp"}, status=403)
|
||||
|
||||
with open(result_path, "rb") as f:
|
||||
img_bytes = f.read()
|
||||
|
||||
ext = os.path.splitext(result_path)[1].lower()
|
||||
mime_map = {
|
||||
".png": "image/png",
|
||||
".jpg": "image/jpeg",
|
||||
".jpeg": "image/jpeg",
|
||||
".webp": "image/webp",
|
||||
".bmp": "image/bmp",
|
||||
".tiff": "image/tiff"
|
||||
}
|
||||
mime = mime_map.get(ext, "image/png")
|
||||
b64 = base64.b64encode(img_bytes).decode("ascii")
|
||||
return web.json_response({"image_b64": f"data:{mime};base64,{b64}"})
|
||||
except Exception as e:
|
||||
logger.error("[gmic] result error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
@@ -0,0 +1,85 @@
|
||||
"""Lab tab plan file management API routes."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import LAB_PLAN_DIR
|
||||
from ..services.lab_plan_service import LabPlanService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = LabPlanService(LAB_PLAN_DIR)
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/api/wfm/lab/plans", handle_list)
|
||||
app.router.add_get("/api/wfm/lab/plans/content", handle_get_content)
|
||||
app.router.add_post("/api/wfm/lab/plans/save", handle_save)
|
||||
app.router.add_post("/api/wfm/lab/plans/delete", handle_delete)
|
||||
app.router.add_post("/api/wfm/lab/index-image/save-to-output", handle_save_index_to_output)
|
||||
|
||||
|
||||
async def handle_list(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.list_plans)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error listing lab plans: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_get_content(request: web.Request) -> web.Response:
|
||||
filename = request.rel_url.query.get("filename", "")
|
||||
try:
|
||||
data = await asyncio.to_thread(_service.get_plan, filename)
|
||||
return web.json_response({"data": data})
|
||||
except FileNotFoundError:
|
||||
return web.json_response({"error": "not found"}, status=404)
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error getting lab plan content: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
data = body.get("data", {})
|
||||
index_image_base64 = body.get("index_image_base64")
|
||||
result = await asyncio.to_thread(_service.save_plan, filename, data, index_image_base64)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error saving lab plan: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
await asyncio.to_thread(_service.delete_plan, filename)
|
||||
return web.json_response({"status": "ok"})
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error deleting lab plan: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save_index_to_output(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
image_base64 = body.get("image_base64", "")
|
||||
if not image_base64:
|
||||
return web.json_response({"error": "image_base64 required"}, status=400)
|
||||
result = await asyncio.to_thread(_service.save_index_image_to_output, image_base64)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving lab index image to output: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
+258
-7
@@ -27,6 +27,13 @@ def setup_routes(app: web.Application):
|
||||
app.router.add_post("/api/wfm/models/civitai/batch", handle_civitai_batch)
|
||||
app.router.add_post("/api/wfm/models/change-preview", handle_change_preview)
|
||||
app.router.add_get("/api/wfm/models/filepath", handle_get_filepath)
|
||||
app.router.add_get("/api/wfm/models/disabled", handle_get_disabled)
|
||||
app.router.add_post("/api/wfm/models/toggle", handle_toggle_model)
|
||||
app.router.add_post("/api/wfm/models/group-toggle", handle_toggle_group)
|
||||
app.router.add_post("/api/wfm/models/delete", handle_delete_models)
|
||||
app.router.add_get("/api/wfm/models/subdirs", handle_get_subdirs)
|
||||
app.router.add_post("/api/wfm/models/move", handle_move_models)
|
||||
app.router.add_get("/api/wfm/models/files", handle_list_model_files)
|
||||
|
||||
|
||||
# ── Model Metadata ─────────────────────────────────────────
|
||||
@@ -61,9 +68,10 @@ async def handle_save_metadata(request: web.Request) -> web.Response:
|
||||
|
||||
|
||||
async def handle_get_groups(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/models/groups"""
|
||||
"""GET /api/wfm/models/groups?type=checkpoint"""
|
||||
model_type = request.query.get("type", "")
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.get_model_groups)
|
||||
result = await asyncio.to_thread(_service.get_model_groups, model_type or None)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error loading model groups: %s", e)
|
||||
@@ -71,10 +79,17 @@ async def handle_get_groups(request: web.Request) -> web.Response:
|
||||
|
||||
|
||||
async def handle_save_groups(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/models/groups"""
|
||||
"""POST /api/wfm/models/groups
|
||||
|
||||
Body: { "model_type": "checkpoint", "groups": { "groupName": [...] } }
|
||||
"""
|
||||
try:
|
||||
groups = await request.json()
|
||||
result = await asyncio.to_thread(_service.save_model_groups, groups)
|
||||
body = await request.json()
|
||||
model_type = body.get("model_type", "")
|
||||
groups = body.get("groups", {})
|
||||
if not model_type:
|
||||
return web.json_response({"error": "model_type required"}, status=400)
|
||||
result = await asyncio.to_thread(_service.save_model_groups, groups, model_type)
|
||||
return web.json_response({"status": "ok", "groups": result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving model groups: %s", e)
|
||||
@@ -138,10 +153,24 @@ async def handle_civitai_fetch(request: web.Request) -> web.Response:
|
||||
"message": "Model not found on CivitAI"
|
||||
})
|
||||
|
||||
# Auto-set preview if none exists and CivitAI has images
|
||||
preview_saved = False
|
||||
if info.get("images"):
|
||||
has_preview = await asyncio.to_thread(_service.find_preview_image, model_type, model_name)
|
||||
if not has_preview:
|
||||
image_url = info["images"][0]
|
||||
preview_path = file_path.parent / (file_path.stem + ".preview.png")
|
||||
preview_saved = await asyncio.to_thread(
|
||||
CivitaiService.download_image, image_url, preview_path
|
||||
)
|
||||
if preview_saved:
|
||||
logger.info("Auto-saved preview for %s from CivitAI", model_name)
|
||||
|
||||
return web.json_response({
|
||||
"status": "ok",
|
||||
"sha256": sha256,
|
||||
"civitai": info,
|
||||
"preview_saved": preview_saved,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("CivitAI fetch error: %s", e)
|
||||
@@ -257,6 +286,28 @@ async def handle_civitai_batch(request: web.Request) -> web.Response:
|
||||
all_meta[mname]["sha256"] = sha
|
||||
_service._save_metadata(all_meta)
|
||||
|
||||
# Auto-set previews for models without preview image
|
||||
dirs = _get_model_dirs(model_type)
|
||||
preview_saved_count = 0
|
||||
for model_name, result in results.items():
|
||||
civitai = result.get("civitai")
|
||||
if not civitai or not civitai.get("images"):
|
||||
continue
|
||||
if _service.find_preview_image(model_type, model_name):
|
||||
continue
|
||||
model_file = None
|
||||
for d in dirs:
|
||||
p = d / model_name
|
||||
if p.is_file():
|
||||
model_file = p
|
||||
break
|
||||
if not model_file:
|
||||
continue
|
||||
preview_path = model_file.parent / (model_file.stem + ".preview.png")
|
||||
if CivitaiService.download_image(civitai["images"][0], preview_path):
|
||||
preview_saved_count += 1
|
||||
logger.info("Auto-saved preview for %s from CivitAI", model_name)
|
||||
|
||||
# Send final result
|
||||
summary = {
|
||||
"total": len(model_files),
|
||||
@@ -264,6 +315,7 @@ async def handle_civitai_batch(request: web.Request) -> web.Response:
|
||||
"not_found": sum(1 for r in results.values() if r.get("sha256") and not r.get("civitai")),
|
||||
"errors": sum(1 for r in results.values() if r.get("error")),
|
||||
"hashes": {name: r.get("sha256") for name, r in results.items() if r.get("sha256")},
|
||||
"preview_saved": preview_saved_count,
|
||||
}
|
||||
await response.write(send_sse("done", summary))
|
||||
await response.write_eof()
|
||||
@@ -295,7 +347,7 @@ async def handle_get_preview(request: web.Request) -> web.Response:
|
||||
return web.FileResponse(
|
||||
preview_path,
|
||||
headers={"Content-Type": content_type or "image/png",
|
||||
"Cache-Control": "public, max-age=3600"},
|
||||
"Cache-Control": "no-cache"},
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error serving model preview: %s", e)
|
||||
@@ -334,7 +386,7 @@ async def handle_change_preview(request: web.Request) -> web.Response:
|
||||
if image_data is None:
|
||||
return web.json_response({"error": "file required"}, status=400)
|
||||
|
||||
# Resolve model file path
|
||||
# Resolve model file path (also check .disabled variants)
|
||||
from ..services.models_service import _get_model_dirs
|
||||
dirs = _get_model_dirs(model_type)
|
||||
model_path = None
|
||||
@@ -343,6 +395,11 @@ async def handle_change_preview(request: web.Request) -> web.Response:
|
||||
if candidate.is_file():
|
||||
model_path = candidate
|
||||
break
|
||||
# disabled モデルも対象にする
|
||||
disabled_candidate = d / (model_name + ".disabled")
|
||||
if disabled_candidate.is_file():
|
||||
model_path = disabled_candidate
|
||||
break
|
||||
|
||||
if not model_path:
|
||||
return web.json_response({"error": "Model file not found"}, status=404)
|
||||
@@ -386,3 +443,197 @@ async def handle_get_filepath(request: web.Request) -> web.Response:
|
||||
except Exception as e:
|
||||
logger.error("Error resolving model filepath: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ── Model Enable / Disable ────────────────────────────────
|
||||
|
||||
|
||||
async def handle_get_disabled(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/models/disabled?type=checkpoint
|
||||
|
||||
Returns list of disabled model names (normalized, without .disabled suffix).
|
||||
"""
|
||||
model_type = request.query.get("type", "")
|
||||
if not model_type:
|
||||
return web.json_response({"error": "type required"}, status=400)
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.scan_disabled_models, model_type)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error scanning disabled models: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_toggle_model(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/models/toggle
|
||||
|
||||
Body: { "model_type": "checkpoint", "model_name": "v1-5.safetensors", "enabled": false }
|
||||
Renames the model file to add/remove .disabled suffix.
|
||||
"""
|
||||
try:
|
||||
body = await request.json()
|
||||
model_type = body.get("model_type", "")
|
||||
model_name = body.get("model_name", "")
|
||||
enabled = body.get("enabled", True)
|
||||
|
||||
if not model_type or not model_name:
|
||||
return web.json_response({"error": "model_type and model_name required"}, status=400)
|
||||
if ".." in model_name:
|
||||
return web.json_response({"error": "Invalid model name"}, status=400)
|
||||
|
||||
if enabled:
|
||||
await asyncio.to_thread(_service.enable_model, model_type, model_name)
|
||||
else:
|
||||
await asyncio.to_thread(_service.disable_model, model_type, model_name)
|
||||
|
||||
return web.json_response({"status": "ok", "enabled": enabled})
|
||||
except FileNotFoundError as e:
|
||||
return web.json_response({"error": str(e)}, status=404)
|
||||
except OSError as e:
|
||||
logger.error("OS error toggling model: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
except Exception as e:
|
||||
logger.error("Error toggling model: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_toggle_group(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/models/group-toggle
|
||||
|
||||
Body: { "model_type": "checkpoint", "group_name": "MyGroup", "enabled": true }
|
||||
Enables or disables all models belonging to the specified group.
|
||||
"""
|
||||
try:
|
||||
body = await request.json()
|
||||
model_type = body.get("model_type", "")
|
||||
group_name = body.get("group_name", "")
|
||||
enabled = body.get("enabled", True)
|
||||
|
||||
if not model_type or not group_name:
|
||||
return web.json_response({"error": "model_type and group_name required"}, status=400)
|
||||
|
||||
groups = await asyncio.to_thread(_service.get_model_groups, model_type)
|
||||
members = groups.get(group_name, [])
|
||||
|
||||
ok_list = []
|
||||
error_list = []
|
||||
for model_name in members:
|
||||
if ".." in model_name:
|
||||
continue
|
||||
try:
|
||||
if enabled:
|
||||
await asyncio.to_thread(_service.enable_model, model_type, model_name)
|
||||
else:
|
||||
await asyncio.to_thread(_service.disable_model, model_type, model_name)
|
||||
ok_list.append(model_name)
|
||||
except Exception as e:
|
||||
error_list.append({"model": model_name, "error": str(e)})
|
||||
|
||||
return web.json_response({
|
||||
"status": "ok",
|
||||
"enabled": enabled,
|
||||
"ok": ok_list,
|
||||
"errors": error_list,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("Error toggling group: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete_models(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/models/delete
|
||||
|
||||
Body: { "model_type": "checkpoint", "model_names": ["a.safetensors", "b.safetensors"] }
|
||||
Deletes each model file and associated preview/sidecar files, then removes metadata.
|
||||
"""
|
||||
try:
|
||||
body = await request.json()
|
||||
model_type = body.get("model_type", "")
|
||||
model_names = body.get("model_names", [])
|
||||
|
||||
if not model_type or not isinstance(model_names, list) or not model_names:
|
||||
return web.json_response({"error": "model_type and model_names[] required"}, status=400)
|
||||
|
||||
ok_list = []
|
||||
error_list = []
|
||||
for model_name in model_names:
|
||||
if ".." in str(model_name):
|
||||
error_list.append({"model": model_name, "error": "Invalid model name"})
|
||||
continue
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.delete_model, model_type, model_name)
|
||||
ok_list.append({"model": model_name, "deleted": result["deleted"]})
|
||||
logger.info("Deleted model: %s (%s files)", model_name, len(result["deleted"]))
|
||||
except FileNotFoundError as e:
|
||||
error_list.append({"model": model_name, "error": str(e)})
|
||||
except Exception as e:
|
||||
logger.error("Error deleting model %s: %s", model_name, e)
|
||||
error_list.append({"model": model_name, "error": str(e)})
|
||||
|
||||
return web.json_response({"status": "ok", "ok": ok_list, "errors": error_list})
|
||||
except Exception as e:
|
||||
logger.error("Error in delete_models: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
_VALID_MODEL_TYPES = frozenset(
|
||||
["checkpoint", "lora", "vae", "controlnet", "unet", "textencoder", "hypernetwork", "embedding"]
|
||||
)
|
||||
|
||||
|
||||
async def handle_get_subdirs(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/models/subdirs?type=checkpoint
|
||||
Returns list of root-level subdirectory names for the given model type.
|
||||
"""
|
||||
try:
|
||||
model_type = request.rel_url.query.get("type", "")
|
||||
if model_type not in _VALID_MODEL_TYPES:
|
||||
return web.json_response({"error": "Invalid model type"}, status=400)
|
||||
result = await asyncio.to_thread(_service.get_subdirs, model_type)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_move_models(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/models/move
|
||||
|
||||
Body: { "model_type": "checkpoint", "model_names": [...], "dest": "sdxl" }
|
||||
dest: "" = root, or a root-level subfolder name (no path separators, no "..").
|
||||
"""
|
||||
try:
|
||||
body = await request.json()
|
||||
model_type = body.get("model_type", "")
|
||||
model_names = body.get("model_names", [])
|
||||
dest_subdir = body.get("dest", "")
|
||||
if model_type not in _VALID_MODEL_TYPES:
|
||||
return web.json_response({"error": "Invalid model type"}, status=400)
|
||||
if not isinstance(model_names, list) or not model_names:
|
||||
return web.json_response({"error": "model_names must be a non-empty list"}, status=400)
|
||||
if not isinstance(dest_subdir, str):
|
||||
return web.json_response({"error": "dest must be a string"}, status=400)
|
||||
result = await asyncio.to_thread(_service.move_models, model_type, model_names, dest_subdir)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error in move_models: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ── Model File List ───────────────────────────────────────
|
||||
|
||||
|
||||
async def handle_list_model_files(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/models/files?type=<model_type>
|
||||
Returns sorted list of model file names (with extension) for the given type.
|
||||
Used as a fallback when ComfyUI's /embeddings endpoint is not accessible.
|
||||
"""
|
||||
from ..services.models_service import MODEL_TYPE_FOLDER_KEYS
|
||||
model_type = request.query.get("type", "")
|
||||
if not model_type or model_type not in MODEL_TYPE_FOLDER_KEYS:
|
||||
return web.json_response({"error": "invalid type"}, status=400)
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.list_model_files, model_type)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error listing model files for %s: %s", model_type, e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
@@ -82,7 +82,8 @@ async def handle_chat(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
cfg = _get_ollama_config()
|
||||
model = body.get("model", cfg["model"])
|
||||
model = body.get("model") or cfg["model"]
|
||||
base_url = (body.get("url") or cfg["url"]).rstrip("/")
|
||||
messages = body.get("messages", [])
|
||||
|
||||
def _chat():
|
||||
@@ -92,7 +93,7 @@ async def handle_chat(request: web.Request) -> web.Response:
|
||||
"stream": False,
|
||||
}).encode("utf-8")
|
||||
|
||||
url = f"{cfg['url']}/api/chat"
|
||||
url = f"{base_url}/api/chat"
|
||||
req = urllib.request.Request(
|
||||
url,
|
||||
data=payload,
|
||||
|
||||
@@ -1,18 +1,31 @@
|
||||
"""Settings API routes."""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..services.settings_service import SettingsService
|
||||
from ..config import DEFAULT_WORKFLOWS_DIR
|
||||
from ..config import DEFAULT_WORKFLOWS_DIR, DATA_DIR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = SettingsService()
|
||||
|
||||
# Data files included in export/import
|
||||
_DATA_FILES = [
|
||||
"settings.json",
|
||||
"metadata.json",
|
||||
"node_metadata.json",
|
||||
"node_sets.json",
|
||||
"prompts.json",
|
||||
"model_metadata.json",
|
||||
"gallery_metadata.json",
|
||||
"tagger_settings.json",
|
||||
]
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
"""Register settings API routes."""
|
||||
@@ -20,6 +33,11 @@ def setup_routes(app: web.Application):
|
||||
app.router.add_post("/api/wfm/settings", handle_post)
|
||||
app.router.add_get("/api/wfm/settings/workflows-dir", handle_get_workflows_dir)
|
||||
app.router.add_post("/api/wfm/settings/workflows-dir", handle_set_workflows_dir)
|
||||
app.router.add_get("/api/wfm/settings/output-dir", handle_get_output_dir)
|
||||
app.router.add_post("/api/wfm/settings/output-dir", handle_set_output_dir)
|
||||
app.router.add_get("/api/wfm/settings/export", handle_export)
|
||||
app.router.add_post("/api/wfm/settings/import", handle_import)
|
||||
app.router.add_get("/api/wfm/styles", handle_get_styles)
|
||||
|
||||
|
||||
async def handle_get(request: web.Request) -> web.Response:
|
||||
@@ -92,3 +110,163 @@ async def handle_set_workflows_dir(request: web.Request) -> web.Response:
|
||||
except Exception as e:
|
||||
logger.error("Error setting workflows dir: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _get_comfyui_output_dir() -> str:
|
||||
"""ComfyUIのデフォルトoutputフォルダを取得する。"""
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
return str(folder_paths.get_output_directory())
|
||||
except Exception:
|
||||
pass
|
||||
# fallback: config.pyのCOMFYUI_ROOTから推測
|
||||
from ..config import PLUGIN_DIR
|
||||
comfyui_root = PLUGIN_DIR.parent.parent
|
||||
output_dir = comfyui_root / "output"
|
||||
if output_dir.is_dir():
|
||||
return str(output_dir)
|
||||
return ""
|
||||
|
||||
|
||||
async def handle_get_output_dir(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/settings/output-dir - Get gallery output directory."""
|
||||
try:
|
||||
server_settings = await asyncio.to_thread(_service.load)
|
||||
saved = server_settings.get("gallery_output_dir", "").strip()
|
||||
default_dir = await asyncio.to_thread(_get_comfyui_output_dir)
|
||||
return web.json_response({
|
||||
"current": saved or default_dir,
|
||||
"default": default_dir,
|
||||
"saved": saved,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("Error getting output dir: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_set_output_dir(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/settings/output-dir - Save gallery output directory."""
|
||||
try:
|
||||
body = await request.json()
|
||||
new_dir = body.get("gallery_output_dir", "").strip()
|
||||
await asyncio.to_thread(_service.update, {"gallery_output_dir": new_dir})
|
||||
|
||||
default_dir = await asyncio.to_thread(_get_comfyui_output_dir)
|
||||
resolved = new_dir or default_dir
|
||||
|
||||
# GalleryServiceの許可ルートを更新
|
||||
from ..routes.gallery_routes import _service as gallery_service
|
||||
gallery_service.update_output_root(resolved)
|
||||
|
||||
return web.json_response({
|
||||
"status": "ok",
|
||||
"current": resolved,
|
||||
"default": default_dir,
|
||||
"saved": new_dir,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("Error setting output dir: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _load_data_file(filename: str):
|
||||
"""Load a single data file, return empty dict/list if missing."""
|
||||
path = DATA_DIR / filename
|
||||
if not path.exists():
|
||||
return None
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
# エクスポートから除外するsettings.jsonのキー（APIキーなどの機密情報）
|
||||
_SETTINGS_EXPORT_EXCLUDE = {"civitai_api_key"}
|
||||
|
||||
|
||||
def _build_export_bundle() -> dict:
|
||||
"""Collect all data files into a single export bundle."""
|
||||
bundle = {"__version": 1, "__source": "ComfyUI-Workflow-Studio"}
|
||||
for filename in _DATA_FILES:
|
||||
data = _load_data_file(filename)
|
||||
if data is None:
|
||||
continue
|
||||
if filename == "settings.json" and isinstance(data, dict):
|
||||
data = {k: v for k, v in data.items() if k not in _SETTINGS_EXPORT_EXCLUDE}
|
||||
bundle[filename] = data
|
||||
return bundle
|
||||
|
||||
|
||||
async def handle_export(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/settings/export - Download all data as a single JSON bundle."""
|
||||
try:
|
||||
bundle = await asyncio.to_thread(_build_export_bundle)
|
||||
body = json.dumps(bundle, ensure_ascii=False, indent=2)
|
||||
return web.Response(
|
||||
body=body.encode("utf-8"),
|
||||
content_type="application/json",
|
||||
headers={"Content-Disposition": 'attachment; filename="wfm-data-export.json"'},
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error exporting data: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _apply_import_bundle(bundle: dict) -> dict:
|
||||
"""Write imported bundle back to individual data files. Returns summary."""
|
||||
summary = {"imported": [], "skipped": []}
|
||||
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
||||
for filename in _DATA_FILES:
|
||||
if filename not in bundle:
|
||||
summary["skipped"].append(filename)
|
||||
continue
|
||||
try:
|
||||
path = DATA_DIR / filename
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
json.dump(bundle[filename], f, ensure_ascii=False, indent=2)
|
||||
summary["imported"].append(filename)
|
||||
except Exception as e:
|
||||
logger.error("Import failed for %s: %s", filename, e)
|
||||
summary["skipped"].append(filename)
|
||||
return summary
|
||||
|
||||
|
||||
async def handle_import(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/settings/import - Upload and restore a JSON bundle."""
|
||||
try:
|
||||
bundle = await request.json()
|
||||
if not isinstance(bundle, dict):
|
||||
return web.json_response({"error": "Invalid bundle format"}, status=400)
|
||||
summary = await asyncio.to_thread(_apply_import_bundle, bundle)
|
||||
return web.json_response({"status": "ok", **summary})
|
||||
except Exception as e:
|
||||
logger.error("Error importing data: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _load_styles() -> list:
|
||||
"""Load all style JSON files from DATA_DIR/style/ directory."""
|
||||
style_dir = DATA_DIR / "style"
|
||||
styles = []
|
||||
if not style_dir.is_dir():
|
||||
return styles
|
||||
for json_file in sorted(style_dir.glob("*.json")):
|
||||
try:
|
||||
with open(json_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, list):
|
||||
styles.extend(data)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load style file %s: %s", json_file.name, e)
|
||||
return styles
|
||||
|
||||
|
||||
async def handle_get_styles(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/styles - Get all styles from style directory."""
|
||||
try:
|
||||
styles = await asyncio.to_thread(_load_styles)
|
||||
return web.json_response(styles)
|
||||
except Exception as e:
|
||||
logger.error("Error loading styles: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
@@ -0,0 +1,64 @@
|
||||
"""AI skill (.md system prompt) file management API routes."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import AI_SKILLS_DIR
|
||||
from ..services.skill_service import SkillService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = SkillService(AI_SKILLS_DIR)
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/api/wfm/skills", handle_list)
|
||||
app.router.add_get("/api/wfm/skills/content", handle_get_content)
|
||||
app.router.add_post("/api/wfm/skills/save", handle_save)
|
||||
app.router.add_post("/api/wfm/skills/delete", handle_delete)
|
||||
|
||||
|
||||
async def handle_list(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.list_skills)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error listing skills: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_get_content(request: web.Request) -> web.Response:
|
||||
filename = request.rel_url.query.get("filename", "")
|
||||
try:
|
||||
content = await asyncio.to_thread(_service.get_content, filename)
|
||||
return web.json_response({"content": content})
|
||||
except FileNotFoundError:
|
||||
return web.json_response({"error": "not found"}, status=404)
|
||||
except Exception as e:
|
||||
logger.error("Error getting skill content: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
content = body.get("content", "")
|
||||
result = await asyncio.to_thread(_service.save_file, filename, content)
|
||||
return web.json_response({"status": "ok", "file": result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving skill: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
await asyncio.to_thread(_service.delete_file, filename)
|
||||
return web.json_response({"status": "ok"})
|
||||
except Exception as e:
|
||||
logger.error("Error deleting skill: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
@@ -0,0 +1,214 @@
|
||||
"""Tagger API routes."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import TAGGER_DB_FILE, TAGGER_MODELS_DIR, TAGGER_SETTINGS_FILE
|
||||
from ..services.tagger_db_service import TaggerDbService
|
||||
from ..services.tagger_service import TaggerService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_svc = TaggerService(TAGGER_MODELS_DIR, TAGGER_SETTINGS_FILE)
|
||||
_db = TaggerDbService(TAGGER_DB_FILE)
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/wfm/tagger/models", handle_models)
|
||||
app.router.add_post("/wfm/tagger/predict", handle_predict)
|
||||
app.router.add_get("/wfm/tagger/ollama/models", handle_ollama_models)
|
||||
app.router.add_post("/wfm/tagger/ollama/predict", handle_ollama_predict)
|
||||
app.router.add_post("/wfm/tagger/batch/start", handle_batch_start)
|
||||
app.router.add_get("/wfm/tagger/batch/status", handle_batch_status)
|
||||
app.router.add_post("/wfm/tagger/batch/stop", handle_batch_stop)
|
||||
app.router.add_get("/wfm/tagger/db/list", handle_db_list)
|
||||
app.router.add_get("/wfm/tagger/db/search", handle_db_search)
|
||||
app.router.add_put("/wfm/tagger/db/{id}", handle_db_update)
|
||||
app.router.add_delete("/wfm/tagger/db/{id}", handle_db_delete)
|
||||
app.router.add_get("/wfm/tagger/db/export", handle_db_export)
|
||||
app.router.add_post("/wfm/tagger/db/save", handle_db_save)
|
||||
app.router.add_post("/wfm/tagger/write_meta", handle_write_meta)
|
||||
app.router.add_get("/wfm/tagger/settings", handle_settings_get)
|
||||
app.router.add_post("/wfm/tagger/settings", handle_settings_save)
|
||||
|
||||
|
||||
async def handle_models(request: web.Request) -> web.Response:
|
||||
try:
|
||||
models = await asyncio.to_thread(_svc.list_models)
|
||||
return web.json_response({"models": models})
|
||||
except Exception as e:
|
||||
logger.error("handle_models: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_predict(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
b64 = body.get("image_b64", "")
|
||||
model = body.get("model", "")
|
||||
if not b64 or not model:
|
||||
return web.json_response({"error": "image_b64 and model required"}, status=400)
|
||||
result = await asyncio.to_thread(
|
||||
_svc.predict, b64, model,
|
||||
float(body.get("threshold", 0.35)),
|
||||
float(body.get("char_threshold", 0.85)),
|
||||
)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("handle_predict: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_ollama_models(request: web.Request) -> web.Response:
|
||||
api_url = request.rel_url.query.get("api_url", "http://127.0.0.1:11434")
|
||||
try:
|
||||
models = await asyncio.to_thread(_svc.ollama_models, api_url)
|
||||
return web.json_response({"models": models})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_ollama_predict(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
result = await asyncio.to_thread(
|
||||
_svc.ollama_predict,
|
||||
body.get("image_b64", ""),
|
||||
body.get("api_url", "http://127.0.0.1:11434"),
|
||||
body.get("model", ""),
|
||||
body.get("prompt", ""),
|
||||
int(body.get("max_tags", 40)),
|
||||
)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_batch_start(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
result = await asyncio.to_thread(
|
||||
_svc.batch_start,
|
||||
body.get("folder", ""),
|
||||
body.get("model", ""),
|
||||
float(body.get("threshold", 0.35)),
|
||||
float(body.get("char_threshold", 0.85)),
|
||||
bool(body.get("use_ollama", False)),
|
||||
body.get("ollama_api", "http://127.0.0.1:11434"),
|
||||
body.get("ollama_model", ""),
|
||||
body.get("ollama_prompt", ""),
|
||||
int(body.get("ollama_max_tags", 40)),
|
||||
bool(body.get("save_db", True)),
|
||||
bool(body.get("write_file", False)),
|
||||
bool(body.get("write_txt", False)),
|
||||
_db,
|
||||
)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_batch_status(request: web.Request) -> web.Response:
|
||||
return web.json_response(_svc.batch_status())
|
||||
|
||||
|
||||
async def handle_batch_stop(request: web.Request) -> web.Response:
|
||||
_svc.batch_stop()
|
||||
return web.json_response({"ok": True})
|
||||
|
||||
|
||||
async def handle_db_list(request: web.Request) -> web.Response:
|
||||
try:
|
||||
limit = int(request.rel_url.query.get("limit", 100))
|
||||
offset = int(request.rel_url.query.get("offset", 0))
|
||||
rows = await asyncio.to_thread(_db.list, limit, offset)
|
||||
total = await asyncio.to_thread(_db.total)
|
||||
return web.json_response({"rows": rows, "total": total})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_db_search(request: web.Request) -> web.Response:
|
||||
try:
|
||||
q = request.rel_url.query.get("q", "")
|
||||
rows = await asyncio.to_thread(_db.search, q)
|
||||
return web.json_response({"rows": rows})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_db_update(request: web.Request) -> web.Response:
|
||||
try:
|
||||
row_id = int(request.match_info["id"])
|
||||
body = await request.json()
|
||||
await asyncio.to_thread(
|
||||
_db.update, row_id,
|
||||
body.get("interrogator_tags", ""),
|
||||
body.get("vlm_tags", ""),
|
||||
)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_db_delete(request: web.Request) -> web.Response:
|
||||
try:
|
||||
row_id = int(request.match_info["id"])
|
||||
await asyncio.to_thread(_db.delete, row_id)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_db_export(request: web.Request) -> web.Response:
|
||||
try:
|
||||
csv_data = await asyncio.to_thread(_db.export_csv)
|
||||
return web.Response(
|
||||
body=csv_data.encode("utf-8"),
|
||||
content_type="text/csv",
|
||||
headers={"Content-Disposition": "attachment; filename=tagger_tags.csv"},
|
||||
)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_db_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
row_id = await asyncio.to_thread(
|
||||
_db.save,
|
||||
body.get("filename", ""),
|
||||
body.get("interrogator_tags", ""),
|
||||
body.get("vlm_tags", ""),
|
||||
)
|
||||
return web.json_response({"ok": True, "id": row_id})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_write_meta(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
result = await asyncio.to_thread(
|
||||
_svc.write_meta_to_file,
|
||||
body.get("path", ""),
|
||||
body.get("tags", ""),
|
||||
)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_settings_get(request: web.Request) -> web.Response:
|
||||
return web.json_response(_svc.load_settings())
|
||||
|
||||
|
||||
async def handle_settings_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
await asyncio.to_thread(_svc.save_settings, body)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
@@ -0,0 +1,98 @@
|
||||
"""Wildcard file management API routes."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import DATA_DIR, COMFYUI_ROOT
|
||||
from ..services.wildcard_service import WildcardService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = WildcardService(DATA_DIR / "wildcard")
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/api/wfm/wildcards", handle_list)
|
||||
app.router.add_get("/api/wfm/wildcards/content", handle_get_content)
|
||||
app.router.add_post("/api/wfm/wildcards/save", handle_save)
|
||||
app.router.add_post("/api/wfm/wildcards/delete", handle_delete)
|
||||
app.router.add_get("/api/wfm/wildcards/link-status", handle_link_status)
|
||||
app.router.add_post("/api/wfm/wildcards/create-link", handle_create_link)
|
||||
app.router.add_post("/api/wfm/wildcards/remove-link", handle_remove_link)
|
||||
|
||||
|
||||
async def handle_list(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.list_wildcards)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error listing wildcards: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_get_content(request: web.Request) -> web.Response:
|
||||
filename = request.rel_url.query.get("filename", "")
|
||||
try:
|
||||
content = await asyncio.to_thread(_service.get_content, filename)
|
||||
return web.json_response({"content": content})
|
||||
except FileNotFoundError:
|
||||
return web.json_response({"error": "not found"}, status=404)
|
||||
except Exception as e:
|
||||
logger.error("Error getting wildcard content: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
content = body.get("content", "")
|
||||
result = await asyncio.to_thread(_service.save_file, filename, content)
|
||||
return web.json_response({"status": "ok", "file": result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving wildcard: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
await asyncio.to_thread(_service.delete_file, filename)
|
||||
return web.json_response({"status": "ok"})
|
||||
except Exception as e:
|
||||
logger.error("Error deleting wildcard: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_link_status(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.get_link_status, COMFYUI_ROOT)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error getting wildcard link status: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_create_link(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.create_link, COMFYUI_ROOT)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except RuntimeError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error creating wildcard link: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_remove_link(request: web.Request) -> web.Response:
|
||||
try:
|
||||
await asyncio.to_thread(_service.remove_link)
|
||||
return web.json_response({"status": "ok"})
|
||||
except RuntimeError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error removing wildcard link: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
+289
-65
@@ -3,6 +3,8 @@
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import ssl
|
||||
import time
|
||||
from pathlib import Path
|
||||
from urllib.request import Request, urlopen
|
||||
from urllib.error import URLError, HTTPError
|
||||
@@ -11,9 +13,48 @@ from ..config import DATA_DIR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _make_ssl_context():
|
||||
"""Return an SSL context with CA verification.
|
||||
|
||||
1. certifi CA bundle (available in virtually all ComfyUI envs via torch→requests→certifi)
|
||||
2. System default SSL context (OS certificate store)
|
||||
Returns None if both fail — callers fall back to urlopen without context
|
||||
(Python's own default, which also uses certifi when available).
|
||||
SSL verification is never disabled to avoid MitM exposure.
|
||||
"""
|
||||
try:
|
||||
import certifi
|
||||
return ssl.create_default_context(cafile=certifi.where())
|
||||
except Exception:
|
||||
pass
|
||||
try:
|
||||
return ssl.create_default_context()
|
||||
except Exception:
|
||||
pass
|
||||
logger.warning("CivitAI: could not build SSL context; falling back to urllib default")
|
||||
return None
|
||||
|
||||
|
||||
_SSL_CONTEXT = _make_ssl_context # lazy sentinel
|
||||
|
||||
|
||||
def _get_ssl_context():
|
||||
global _SSL_CONTEXT
|
||||
if callable(_SSL_CONTEXT):
|
||||
_SSL_CONTEXT = _make_ssl_context()
|
||||
return _SSL_CONTEXT
|
||||
|
||||
|
||||
CIVITAI_API_BASE = "https://civitai.com/api/v1"
|
||||
CIVITAI_CACHE_FILE = DATA_DIR / "civitai_cache.json"
|
||||
|
||||
# HTTPステータスコード: 指数バックオフでリトライする対象
|
||||
_RETRY_CODES = {429, 500, 502, 503, 504}
|
||||
_MAX_RETRIES = 3
|
||||
# POST /model-versions/by-hash は最大100件まで一括送信可能
|
||||
_BATCH_CHUNK_SIZE = 100
|
||||
|
||||
|
||||
class CivitaiService:
|
||||
"""Fetch and cache CivitAI model metadata."""
|
||||
@@ -21,6 +62,8 @@ class CivitaiService:
|
||||
def __init__(self):
|
||||
self._cache = None
|
||||
|
||||
# ── キャッシュ管理 ────────────────────────────────────────
|
||||
|
||||
def _load_cache(self):
|
||||
if self._cache is not None:
|
||||
return self._cache
|
||||
@@ -42,12 +85,37 @@ class CivitaiService:
|
||||
def get_cached(self, sha256_hash):
|
||||
"""Return cached CivitAI data for a given hash, or None."""
|
||||
cache = self._load_cache()
|
||||
return cache.get(sha256_hash)
|
||||
return cache.get(sha256_hash.lower())
|
||||
|
||||
def get_all_cached(self):
|
||||
"""Return the full cache dict."""
|
||||
return self._load_cache()
|
||||
|
||||
# ── APIキー / ヘッダー ────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _get_api_key():
|
||||
"""Return CivitAI API key. Env var CIVITAI_API_KEY takes priority over settings.json."""
|
||||
import os
|
||||
env_key = os.environ.get("CIVITAI_API_KEY", "").strip()
|
||||
if env_key:
|
||||
return env_key
|
||||
try:
|
||||
from ..services.settings_service import SettingsService
|
||||
return SettingsService().load().get("civitai_api_key", "").strip() or None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _build_headers(self):
|
||||
"""Build request headers, optionally including Bearer token."""
|
||||
headers = {"User-Agent": "ComfyUI-Workflow-Studio/1.0"}
|
||||
api_key = self._get_api_key()
|
||||
if api_key:
|
||||
headers["Authorization"] = f"Bearer {api_key}"
|
||||
return headers
|
||||
|
||||
# ── ハッシュ計算 ──────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def calculate_sha256(file_path, chunk_size=65536):
|
||||
"""Calculate SHA256 hash of a file."""
|
||||
@@ -61,43 +129,130 @@ class CivitaiService:
|
||||
if not chunk:
|
||||
break
|
||||
h.update(chunk)
|
||||
return h.hexdigest()
|
||||
return h.hexdigest() # 小文字16進数
|
||||
|
||||
# ── 単体フェッチ (GET) ────────────────────────────────────
|
||||
|
||||
def fetch_by_hash(self, sha256_hash):
|
||||
"""Fetch model version info from CivitAI by SHA256 hash.
|
||||
"""Fetch model version info from CivitAI by SHA256 hash (GET).
|
||||
|
||||
429/5xx は指数バックオフでリトライ。
|
||||
Returns dict with model info, or None if not found.
|
||||
Caches successful results.
|
||||
"""
|
||||
# Check cache first
|
||||
sha256_lower = sha256_hash.lower()
|
||||
cache = self._load_cache()
|
||||
if sha256_hash in cache:
|
||||
return cache[sha256_hash]
|
||||
if sha256_lower in cache:
|
||||
return cache[sha256_lower]
|
||||
|
||||
url = f"{CIVITAI_API_BASE}/model-versions/by-hash/{sha256_hash}"
|
||||
try:
|
||||
req = Request(url, headers={"User-Agent": "ComfyUI-Workflow-Studio/1.0"})
|
||||
with urlopen(req, timeout=15) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
url = f"{CIVITAI_API_BASE}/model-versions/by-hash/{sha256_hash.upper()}"
|
||||
|
||||
if not data or "id" not in data:
|
||||
for attempt in range(_MAX_RETRIES):
|
||||
try:
|
||||
req = Request(url, headers=self._build_headers())
|
||||
with urlopen(req, timeout=15, context=_get_ssl_context()) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
|
||||
if not data or "id" not in data:
|
||||
return None
|
||||
|
||||
info = self._extract_info(data)
|
||||
cache[sha256_lower] = info
|
||||
self._save_cache()
|
||||
return info
|
||||
|
||||
except HTTPError as e:
|
||||
if e.code == 404:
|
||||
logger.debug("CivitAI: model not found for hash %s", sha256_hash[:16])
|
||||
return None
|
||||
if e.code in _RETRY_CODES and attempt < _MAX_RETRIES - 1:
|
||||
wait = 2 ** attempt
|
||||
logger.warning("CivitAI %s: retrying in %ss (attempt %d/%d)",
|
||||
e.code, wait, attempt + 1, _MAX_RETRIES)
|
||||
time.sleep(wait)
|
||||
continue
|
||||
logger.warning("CivitAI API error: %s %s", e.code, e.reason)
|
||||
return None
|
||||
except (URLError, Exception) as e:
|
||||
if attempt < _MAX_RETRIES - 1:
|
||||
wait = 2 ** attempt
|
||||
logger.warning("CivitAI request failed: %s — retrying in %ss", e, wait)
|
||||
time.sleep(wait)
|
||||
continue
|
||||
logger.warning("CivitAI request failed: %s", e)
|
||||
return None
|
||||
|
||||
# Extract useful fields
|
||||
info = self._extract_info(data)
|
||||
cache[sha256_hash] = info
|
||||
self._save_cache()
|
||||
return info
|
||||
return None
|
||||
|
||||
except HTTPError as e:
|
||||
if e.code == 404:
|
||||
logger.debug("CivitAI: model not found for hash %s", sha256_hash[:16])
|
||||
return None
|
||||
logger.warning("CivitAI API error: %s %s", e.code, e.reason)
|
||||
return None
|
||||
except (URLError, Exception) as e:
|
||||
logger.warning("CivitAI request failed: %s", e)
|
||||
return None
|
||||
# ── 一括フェッチ (POST) ───────────────────────────────────
|
||||
|
||||
def _batch_fetch_post(self, sha256_hashes):
|
||||
"""POST /model-versions/by-hash でハッシュリストを一括取得（最大100件/リクエスト）。
|
||||
|
||||
レスポンスの files[].hashes.SHA256 でリクエストのハッシュと照合する。
|
||||
Returns: { sha256_lower: info_or_none }
|
||||
"""
|
||||
if not sha256_hashes:
|
||||
return {}
|
||||
|
||||
url = f"{CIVITAI_API_BASE}/model-versions/by-hash"
|
||||
results = {h.lower(): None for h in sha256_hashes}
|
||||
cache = self._load_cache()
|
||||
|
||||
for chunk_start in range(0, len(sha256_hashes), _BATCH_CHUNK_SIZE):
|
||||
chunk = sha256_hashes[chunk_start:chunk_start + _BATCH_CHUNK_SIZE]
|
||||
chunk_lower = {h.lower() for h in chunk}
|
||||
|
||||
for attempt in range(_MAX_RETRIES):
|
||||
try:
|
||||
body = json.dumps([h.upper() for h in chunk]).encode("utf-8")
|
||||
req = Request(
|
||||
url,
|
||||
data=body,
|
||||
headers={**self._build_headers(), "Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urlopen(req, timeout=30, context=_get_ssl_context()) as resp:
|
||||
versions = json.loads(resp.read().decode("utf-8"))
|
||||
|
||||
# 各バージョンを files[].hashes.SHA256 でリクエストのハッシュと照合
|
||||
for version_data in versions:
|
||||
info = self._extract_info(version_data)
|
||||
matched = False
|
||||
for f in version_data.get("files", []):
|
||||
file_sha256 = f.get("hashes", {}).get("SHA256", "").lower()
|
||||
if file_sha256 in chunk_lower:
|
||||
cache[file_sha256] = info
|
||||
results[file_sha256] = info
|
||||
matched = True
|
||||
break
|
||||
if not matched:
|
||||
# files にハッシュがない場合は versionId で照合を試みる
|
||||
logger.debug("CivitAI: could not match version %s to a requested hash",
|
||||
version_data.get("id"))
|
||||
break # チャンク成功
|
||||
|
||||
except HTTPError as e:
|
||||
if e.code in _RETRY_CODES and attempt < _MAX_RETRIES - 1:
|
||||
wait = 2 ** attempt
|
||||
logger.warning("CivitAI batch POST %s: retrying in %ss", e.code, wait)
|
||||
time.sleep(wait)
|
||||
continue
|
||||
logger.warning("CivitAI batch POST error: %s %s", e.code, e.reason)
|
||||
break
|
||||
except (URLError, Exception) as e:
|
||||
if attempt < _MAX_RETRIES - 1:
|
||||
wait = 2 ** attempt
|
||||
logger.warning("CivitAI batch POST failed: %s — retrying in %ss", e, wait)
|
||||
time.sleep(wait)
|
||||
continue
|
||||
logger.warning("CivitAI batch POST failed: %s", e)
|
||||
break
|
||||
|
||||
self._save_cache()
|
||||
return results
|
||||
|
||||
# ── 情報抽出 ──────────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _extract_info(data):
|
||||
@@ -106,101 +261,170 @@ class CivitaiService:
|
||||
images = data.get("images", [])
|
||||
files = data.get("files", [])
|
||||
|
||||
# Get primary file info
|
||||
primary_file = None
|
||||
for f in files:
|
||||
if f.get("primary"):
|
||||
primary_file = f
|
||||
break
|
||||
# プライマリファイルを特定
|
||||
primary_file = next((f for f in files if f.get("primary")), None)
|
||||
if not primary_file and files:
|
||||
primary_file = files[0]
|
||||
|
||||
# Get image URLs (first 5)
|
||||
image_urls = []
|
||||
# 画像情報（URL・寸法・NSFWレベル）を最大5件取得
|
||||
image_list = []
|
||||
for img in images[:5]:
|
||||
url = img.get("url", "")
|
||||
if url:
|
||||
# Optimize for thumbnail
|
||||
optimized = url.replace("/original=true", "/width=450,optimized=true")
|
||||
image_urls.append(optimized)
|
||||
img_url = img.get("url", "")
|
||||
if not img_url:
|
||||
continue
|
||||
image_list.append({
|
||||
"url": img_url,
|
||||
"width": img.get("width"),
|
||||
"height": img.get("height"),
|
||||
"nsfwLevel": img.get("nsfwLevel", 0),
|
||||
})
|
||||
|
||||
# プライマリファイルのメタ情報（精度・フォーマット）
|
||||
file_meta = {}
|
||||
file_hashes = {}
|
||||
if primary_file:
|
||||
pm = primary_file.get("metadata", {})
|
||||
file_meta = {
|
||||
"fp": pm.get("fp"),
|
||||
"size": pm.get("size"),
|
||||
"format": pm.get("format"),
|
||||
}
|
||||
# BLAKE3, SHA256, AutoV2 等のハッシュ
|
||||
file_hashes = primary_file.get("hashes", {})
|
||||
|
||||
stats = data.get("stats", {})
|
||||
|
||||
# バッチ POST API は model オブジェクト内に id を含まない場合がある
|
||||
# トップレベルの modelId をフォールバックとして使用する
|
||||
model_id = model.get("id") or data.get("modelId")
|
||||
version_id = data.get("id", "")
|
||||
|
||||
return {
|
||||
"versionId": data.get("id"),
|
||||
"modelId": model.get("id"),
|
||||
"versionId": version_id,
|
||||
"modelId": model_id,
|
||||
"modelName": model.get("name", ""),
|
||||
"versionName": data.get("name", ""),
|
||||
"type": model.get("type", ""),
|
||||
"description": data.get("description") or model.get("description", ""),
|
||||
"tags": model.get("tags", []),
|
||||
"nsfw": model.get("nsfw", False),
|
||||
"nsfwLevel": data.get("nsfwLevel", 0),
|
||||
"air": data.get("air", ""),
|
||||
"creator": data.get("creator", {}).get("username", ""),
|
||||
"images": image_urls,
|
||||
# 後方互換のため URLリストを維持しつつ詳細情報も保存
|
||||
"images": [img["url"] for img in image_list],
|
||||
"imageDetails": image_list,
|
||||
"trainedWords": data.get("trainedWords", []),
|
||||
"baseModel": data.get("baseModel", ""),
|
||||
"fileSize": primary_file.get("sizeKB", 0) if primary_file else 0,
|
||||
"fileMeta": file_meta,
|
||||
"fileHashes": file_hashes,
|
||||
"downloadUrl": data.get("downloadUrl", ""),
|
||||
"modelUrl": f"https://civitai.com/models/{model.get('id', '')}?modelVersionId={data.get('id', '')}",
|
||||
"modelUrl": (
|
||||
f"https://civitai.com/models/{model_id}?modelVersionId={version_id}"
|
||||
if model_id else (
|
||||
f"https://civitai.com/model-versions/{version_id}"
|
||||
if version_id else ""
|
||||
)
|
||||
),
|
||||
"stats": {
|
||||
"downloadCount": stats.get("downloadCount", 0),
|
||||
"thumbsUpCount": stats.get("thumbsUpCount", 0),
|
||||
"thumbsDownCount": stats.get("thumbsDownCount", 0),
|
||||
},
|
||||
"updatedAt": data.get("updatedAt", ""),
|
||||
"publishedAt": data.get("publishedAt", ""),
|
||||
}
|
||||
|
||||
# ── バッチフェッチ ────────────────────────────────────────
|
||||
|
||||
def batch_fetch(self, model_files, progress_callback=None):
|
||||
"""Batch fetch CivitAI info for multiple model files.
|
||||
|
||||
Phase 1: SHA256 計算（"hashing"）
|
||||
Phase 2: POST で一括取得（"fetching"）— キャッシュ済みはスキップ
|
||||
|
||||
Args:
|
||||
model_files: list of (model_name, file_path) tuples
|
||||
progress_callback: fn(current, total, model_name, status) called per model
|
||||
|
||||
Returns: dict of { model_name: { sha256, civitai_info_or_none } }
|
||||
"""
|
||||
import time
|
||||
|
||||
results = {}
|
||||
total = len(model_files)
|
||||
cache = self._load_cache()
|
||||
|
||||
# Phase 1: ハッシュ計算
|
||||
hashes_needed = [] # [(model_name, sha256_lower, original_index)]
|
||||
|
||||
for i, (model_name, file_path) in enumerate(model_files):
|
||||
if progress_callback:
|
||||
progress_callback(i, total, model_name, "hashing")
|
||||
|
||||
# Calculate hash
|
||||
sha256 = self.calculate_sha256(file_path)
|
||||
if not sha256:
|
||||
results[model_name] = {"sha256": None, "civitai": None, "error": "hash_failed"}
|
||||
continue
|
||||
|
||||
results[model_name] = {"sha256": sha256, "civitai": None}
|
||||
|
||||
# Check cache
|
||||
if sha256 in cache:
|
||||
results[model_name]["civitai"] = cache[sha256]
|
||||
if progress_callback:
|
||||
progress_callback(i, total, model_name, "cached")
|
||||
progress_callback(i + 1, total, model_name, "not_found")
|
||||
continue
|
||||
|
||||
# Fetch from API
|
||||
sha256_lower = sha256.lower()
|
||||
results[model_name] = {"sha256": sha256_lower, "civitai": None}
|
||||
|
||||
if sha256_lower in cache:
|
||||
results[model_name]["civitai"] = cache[sha256_lower]
|
||||
if progress_callback:
|
||||
progress_callback(i + 1, total, model_name, "cached")
|
||||
else:
|
||||
hashes_needed.append((model_name, sha256_lower, i))
|
||||
|
||||
# Phase 2: POST で一括取得（未キャッシュ分）
|
||||
if hashes_needed:
|
||||
if progress_callback:
|
||||
progress_callback(i, total, model_name, "fetching")
|
||||
progress_callback(len(results), total, "", "fetching")
|
||||
|
||||
info = self.fetch_by_hash(sha256)
|
||||
if info:
|
||||
results[model_name]["civitai"] = info
|
||||
# 同一ハッシュが複数モデルに対応する場合を考慮
|
||||
sha256_to_entries: dict[str, list] = {}
|
||||
for name, sha256_lower, idx in hashes_needed:
|
||||
sha256_to_entries.setdefault(sha256_lower, []).append((name, idx))
|
||||
|
||||
if progress_callback:
|
||||
status = "found" if info else "not_found"
|
||||
progress_callback(i, total, model_name, status)
|
||||
batch_results = self._batch_fetch_post(list(sha256_to_entries.keys()))
|
||||
|
||||
# Rate limit: small delay between API calls
|
||||
time.sleep(0.5)
|
||||
for sha256_lower, info in batch_results.items():
|
||||
for name, idx in sha256_to_entries.get(sha256_lower, []):
|
||||
results[name]["civitai"] = info
|
||||
if progress_callback:
|
||||
status = "found" if info else "not_found"
|
||||
progress_callback(idx + 1, total, name, status)
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(total, total, "", "done")
|
||||
|
||||
return results
|
||||
|
||||
# ── 画像ダウンロード ──────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def download_image(url, save_path, timeout=15):
|
||||
"""Download an image from URL and save to save_path. Returns True on success."""
|
||||
try:
|
||||
req = Request(url, headers={"User-Agent": "ComfyUI-Workflow-Studio/1.0"})
|
||||
with urlopen(req, timeout=timeout, context=_get_ssl_context()) as resp:
|
||||
data = resp.read()
|
||||
with open(save_path, "wb") as f:
|
||||
f.write(data)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.warning("Failed to download preview from %s: %s", url, e)
|
||||
return False
|
||||
|
||||
# ── キャッシュ操作 ────────────────────────────────────────
|
||||
|
||||
def clear_cache(self, sha256_hash=None):
|
||||
"""Clear cache for a specific hash or all."""
|
||||
cache = self._load_cache()
|
||||
if sha256_hash:
|
||||
cache.pop(sha256_hash, None)
|
||||
cache.pop(sha256_hash.lower(), None)
|
||||
else:
|
||||
cache.clear()
|
||||
self._save_cache()
|
||||
|
||||
@@ -0,0 +1,212 @@
|
||||
"""
|
||||
Gallery Metadata Store - ギャラリー画像のメタデータ永続化
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GalleryMetadataStore:
|
||||
"""
|
||||
gallery_metadata.json の構造:
|
||||
{
|
||||
"images": {
|
||||
"<abs_path>": {
|
||||
"favorite": bool,
|
||||
"tags": [str, ...],
|
||||
"memo": str,
|
||||
"groups": [str, ...]
|
||||
}
|
||||
},
|
||||
"groups": [
|
||||
{"name": str}
|
||||
]
|
||||
}
|
||||
|
||||
self._data（特に "images" 辞書）は、バックグラウンドのフォルダインデックススレッド
|
||||
（gallery_service.pyの_bg_index_folder）と、リクエストハンドラを処理するメインスレッドの
|
||||
双方から並行に読み書きされる。ロックなしで辞書を反復（.items()/.values()/for key in ...や
|
||||
json.dumpによるシリアライズ）している最中に別スレッドがキーを追加/削除すると
|
||||
"dictionary changed size during iteration" が発生するため、_data へアクセスする
|
||||
全メソッドを self._lock で保護する（re-entrant: ensure_group()がcreate_group()を
|
||||
内部呼び出しするため、通常のLockではなくRLockを使う）。
|
||||
"""
|
||||
|
||||
def __init__(self, file_path: Path):
|
||||
self.file_path = file_path
|
||||
self._lock = threading.RLock()
|
||||
self._data: dict = {"images": {}, "groups": []}
|
||||
self._load()
|
||||
|
||||
def _load(self):
|
||||
# コンストラクタからのみ呼ばれ、この時点ではまだ他スレッドと共有されていないためロック不要
|
||||
if self.file_path.exists():
|
||||
try:
|
||||
with open(self.file_path, "r", encoding="utf-8") as f:
|
||||
loaded = json.load(f)
|
||||
self._data = {
|
||||
"images": loaded.get("images", {}),
|
||||
"groups": loaded.get("groups", []),
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("GalleryMetadataStore: load error: %s", e)
|
||||
|
||||
def _save_to_disk(self):
|
||||
# 呼び出し元（save/create_group等）が既にself._lockを保持している前提の内部メソッド
|
||||
try:
|
||||
self.file_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(self.file_path, "w", encoding="utf-8") as f:
|
||||
json.dump(self._data, f, ensure_ascii=False, indent=2)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error("GalleryMetadataStore: save error: %s", e)
|
||||
return False
|
||||
|
||||
def _normalize_path(self, image_path: str) -> str:
|
||||
return str(Path(image_path)).replace("\\", "/")
|
||||
|
||||
def get(self, image_path: str) -> dict:
|
||||
key = self._normalize_path(image_path)
|
||||
with self._lock:
|
||||
return dict(self._data["images"].get(key, {}))
|
||||
|
||||
def save(self, image_path: str, data: dict) -> bool:
|
||||
key = self._normalize_path(image_path)
|
||||
with self._lock:
|
||||
existing = self._data["images"].get(key, {})
|
||||
existing.update(data)
|
||||
allowed = {"favorite", "tags", "memo", "groups", "workflow"}
|
||||
existing = {k: v for k, v in existing.items() if k in allowed}
|
||||
self._data["images"][key] = existing
|
||||
return self._save_to_disk()
|
||||
|
||||
# ── グループ管理 ──────────────────────────────────────────────
|
||||
|
||||
def list_groups(self) -> list[dict]:
|
||||
with self._lock:
|
||||
return list(self._data.get("groups", []))
|
||||
|
||||
def create_group(self, name: str) -> bool:
|
||||
with self._lock:
|
||||
groups = self._data.get("groups", [])
|
||||
if any(g["name"] == name for g in groups):
|
||||
return False
|
||||
groups.append({"name": name})
|
||||
self._data["groups"] = groups
|
||||
return self._save_to_disk()
|
||||
|
||||
def rename_group(self, old_name: str, new_name: str) -> bool:
|
||||
"""グループ名を変更し、全画像のgroupsフィールドも更新する"""
|
||||
with self._lock:
|
||||
groups = self._data.get("groups", [])
|
||||
if not any(g["name"] == old_name for g in groups):
|
||||
return False
|
||||
if any(g["name"] == new_name for g in groups):
|
||||
return False # 新名前が既に存在
|
||||
# グループリスト更新
|
||||
for g in groups:
|
||||
if g["name"] == old_name:
|
||||
g["name"] = new_name
|
||||
break
|
||||
# 全画像のgroupsフィールドを更新
|
||||
for meta in self._data["images"].values():
|
||||
img_groups = meta.get("groups", [])
|
||||
if old_name in img_groups:
|
||||
meta["groups"] = [new_name if g == old_name else g for g in img_groups]
|
||||
return self._save_to_disk()
|
||||
|
||||
def delete_group(self, name: str) -> bool:
|
||||
with self._lock:
|
||||
groups = [g for g in self._data.get("groups", []) if g["name"] != name]
|
||||
self._data["groups"] = groups
|
||||
# 画像のgroupsからも削除
|
||||
for meta in self._data["images"].values():
|
||||
if name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != name]
|
||||
return self._save_to_disk()
|
||||
|
||||
def list_images_in_group(self, group_name: str) -> list[str]:
|
||||
with self._lock:
|
||||
result = []
|
||||
for key, meta in self._data["images"].items():
|
||||
if group_name in meta.get("groups", []):
|
||||
result.append(key)
|
||||
return result
|
||||
|
||||
def remove_stale_paths_from_group(self, group_name: str, stale_paths: list) -> int:
|
||||
"""存在しないパスをグループから一括削除する（1回の保存で完結）"""
|
||||
with self._lock:
|
||||
stale_set = {self._normalize_path(p) for p in stale_paths}
|
||||
count = 0
|
||||
for key, meta in self._data["images"].items():
|
||||
if key in stale_set and group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
count += 1
|
||||
if count:
|
||||
self._save_to_disk()
|
||||
return count
|
||||
|
||||
def get_group_member_set(self, group_name: str) -> set:
|
||||
"""グループメンバーのパスをsetで返す（高速フィルタ用）"""
|
||||
with self._lock:
|
||||
return {
|
||||
key for key, meta in self._data["images"].items()
|
||||
if group_name in meta.get("groups", [])
|
||||
}
|
||||
|
||||
def clear_group(self, group_name: str) -> bool:
|
||||
"""グループ内の全画像を除外する（グループ自体は残す）"""
|
||||
with self._lock:
|
||||
for meta in self._data["images"].values():
|
||||
if group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
return self._save_to_disk()
|
||||
|
||||
def ensure_group(self, name: str) -> bool:
|
||||
"""グループが存在しない場合のみ作成する"""
|
||||
with self._lock:
|
||||
if any(g["name"] == name for g in self._data.get("groups", [])):
|
||||
return True
|
||||
return self.create_group(name)
|
||||
|
||||
def delete(self, image_path: str) -> bool:
|
||||
"""画像のメタデータエントリを削除する"""
|
||||
key = self._normalize_path(image_path)
|
||||
with self._lock:
|
||||
if key in self._data["images"]:
|
||||
del self._data["images"][key]
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
|
||||
def rename_path(self, old_path: str, new_path: str) -> bool:
|
||||
"""画像のパスキーを変更する（ファイル移動後のメタデータ引継ぎ）"""
|
||||
old_key = self._normalize_path(old_path)
|
||||
new_key = self._normalize_path(new_path)
|
||||
with self._lock:
|
||||
if old_key in self._data["images"]:
|
||||
self._data["images"][new_key] = self._data["images"].pop(old_key)
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
|
||||
def cleanup_stale_images(self, folder_path: str, existing_paths: set) -> int:
|
||||
"""folder_path 配下の存在しないファイルのメタデータを削除する。
|
||||
existing_paths: そのフォルダの現在のファイルパス集合（_normalize_path 済み）
|
||||
"""
|
||||
folder_key = self._normalize_path(folder_path)
|
||||
if not folder_key.endswith("/"):
|
||||
folder_key += "/"
|
||||
with self._lock:
|
||||
stale = [
|
||||
key for key in self._data["images"]
|
||||
if key.startswith(folder_key) and key not in existing_paths
|
||||
]
|
||||
if not stale:
|
||||
return 0
|
||||
for key in stale:
|
||||
del self._data["images"][key]
|
||||
self._save_to_disk()
|
||||
logger.info("Cleaned up %d stale metadata entries from %s", len(stale), folder_path)
|
||||
return len(stale)
|
||||
@@ -0,0 +1,967 @@
|
||||
"""
|
||||
Gallery Service - outputフォルダの画像管理、メタデータ閲覧
|
||||
"""
|
||||
import base64
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import mimetypes
|
||||
import os
|
||||
import re
|
||||
import struct
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# サポートする画像拡張子
|
||||
IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".svg"}
|
||||
|
||||
# data URL の MIME タイプ -> 拡張子（save_image_to_gallery / save_image_to_folder共用）
|
||||
_SAVE_EXT_BY_MIME = {
|
||||
"image/png": ".png",
|
||||
"image/jpeg": ".jpg",
|
||||
"image/webp": ".webp",
|
||||
"image/gif": ".gif",
|
||||
"image/svg+xml": ".svg",
|
||||
}
|
||||
|
||||
|
||||
def _decode_image_data_url(image_data: str) -> tuple[bytes, str]:
|
||||
"""data URL (data:<mime>;base64,<data>) をデコードし (バイト列, 拡張子) を返す。
|
||||
ヘッダが無ければPNG扱い（旧互換）。"""
|
||||
raw = image_data
|
||||
mime = "image/png"
|
||||
if raw.startswith("data:"):
|
||||
header, _, raw = raw.partition(",")
|
||||
m = re.match(r"data:([^;]+)", header)
|
||||
if m:
|
||||
mime = m.group(1)
|
||||
ext = _SAVE_EXT_BY_MIME.get(mime, ".png")
|
||||
return base64.b64decode(raw), ext
|
||||
|
||||
|
||||
def _sanitize_save_filename(filename: str, ext: str) -> str:
|
||||
"""OSで使えない文字を除去し、拡張子が無ければ付与する"""
|
||||
safe = re.sub(r'[<>:"/\\|?*\x00-\x1f]', "_", filename)
|
||||
if not safe.lower().endswith(ext):
|
||||
safe += ext
|
||||
return safe
|
||||
|
||||
# メタデータ保存ファイル (gallery専用)
|
||||
from .gallery_metadata import GalleryMetadataStore
|
||||
|
||||
|
||||
# PNGチャンク1個の最大許容サイズ (32MB)
|
||||
_PNG_CHUNK_MAX = 32 * 1024 * 1024
|
||||
# JPEGを読む際の最大ファイルサイズ (64MB)
|
||||
_JPEG_READ_MAX = 64 * 1024 * 1024
|
||||
|
||||
# フォルダキャッシュのTTL秒（フォルダmtimeが同じでも念のため）
|
||||
_CACHE_TTL = 60.0
|
||||
|
||||
# ImagePromptギャラリー: 画像と同名の.txtサイドカーに保存されたプロンプトテキストの最大読み込みサイズ
|
||||
_SIDECAR_PROMPT_MAX = 20_000
|
||||
|
||||
# ImagePromptギャラリーの管理フォルダ名（ComfyUI実outputフォルダ直下）
|
||||
IMAGE_PROMPT_FOLDER_NAME = "ws_image_prompt"
|
||||
|
||||
# Style Catalogギャラリーの管理フォルダ名（ComfyUI実outputフォルダ直下）
|
||||
STYLE_CATALOG_FOLDER_NAME = "ws_style_catalog"
|
||||
|
||||
# ponyxlWildcardsVault形式（.yaml + thumbnails/）を検出するための、画像の祖先フォルダ探索上限
|
||||
_VAULT_ROOT_SEARCH_DEPTH = 8
|
||||
_VAULT_THUMB_DIR_NAMES = ("thumbnails", "thumbnails_option2")
|
||||
|
||||
|
||||
def _clean_vault_tags(text: str) -> str:
|
||||
"""タグ文字列の前後の空白・カンマを整理する（tools/import_style_prompt_seed.py と同じ処理）"""
|
||||
text = text.strip()
|
||||
text = re.sub(r"^[,\s]+|[,\s]+$", "", text)
|
||||
text = re.sub(r"\s*,\s*", ", ", text)
|
||||
return text
|
||||
|
||||
|
||||
def _parse_vault_yaml_leaves(yaml_path: Path) -> dict[str, str]:
|
||||
"""comfyui_prompt_gallery の parseYamlForImages と同じ簡易パーサ（PyYAML不要）。
|
||||
「key:」行の直後が「- タグ文字列」で始まる行なら key をリーフとみなす。
|
||||
戻り値: {leaf_name(小文字): タグ文字列}
|
||||
"""
|
||||
leaves: dict[str, str] = {}
|
||||
try:
|
||||
lines = yaml_path.read_text(encoding="utf-8", errors="replace").split("\n")
|
||||
except OSError:
|
||||
return leaves
|
||||
for i, line in enumerate(lines):
|
||||
trimmed = line.strip()
|
||||
if not trimmed or trimmed.startswith("#") or not trimmed.endswith(":"):
|
||||
continue
|
||||
if i + 1 >= len(lines):
|
||||
continue
|
||||
next_trimmed = lines[i + 1].strip()
|
||||
if not next_trimmed.startswith("-"):
|
||||
continue
|
||||
key = trimmed[:-1].strip()
|
||||
tags = next_trimmed[1:].strip()
|
||||
if not tags or key.lower() == "skip":
|
||||
continue
|
||||
leaves[key.lower()] = _clean_vault_tags(tags)
|
||||
return leaves
|
||||
|
||||
|
||||
class _FolderCache:
|
||||
"""フォルダ単位の画像スキャン結果キャッシュ。
|
||||
フォルダのmtimeが変わった場合、またはTTL超過時に再スキャンする。"""
|
||||
|
||||
def __init__(self):
|
||||
# folder_path -> (mtime, scan_time, [(name, path_str, size, mtime_f), ...])
|
||||
self._cache: dict[str, tuple[float, float, list]] = {}
|
||||
|
||||
def get(self, folder: Path) -> list | None:
|
||||
key = str(folder)
|
||||
if key not in self._cache:
|
||||
return None
|
||||
cached_mtime, scan_time, entries = self._cache[key]
|
||||
# TTL チェック
|
||||
if time.monotonic() - scan_time > _CACHE_TTL:
|
||||
return None
|
||||
# フォルダ自体のmtimeチェック
|
||||
try:
|
||||
current_mtime = folder.stat().st_mtime
|
||||
except OSError:
|
||||
return None
|
||||
if current_mtime != cached_mtime:
|
||||
return None
|
||||
return entries
|
||||
|
||||
def set(self, folder: Path, entries: list):
|
||||
key = str(folder)
|
||||
try:
|
||||
folder_mtime = folder.stat().st_mtime
|
||||
except OSError:
|
||||
folder_mtime = 0.0
|
||||
self._cache[key] = (folder_mtime, time.monotonic(), entries)
|
||||
|
||||
def invalidate(self, folder: Path):
|
||||
self._cache.pop(str(folder), None)
|
||||
|
||||
|
||||
class GalleryService:
|
||||
def __init__(self, data_dir: Path):
|
||||
self.data_dir = data_dir
|
||||
self.metadata_store = GalleryMetadataStore(data_dir / "gallery_metadata.json")
|
||||
self._allowed_root: Path | None = None
|
||||
# ComfyUI実outputフォルダの不変ルート。Settingsでユーザーが_allowed_rootを
|
||||
# 別フォルダに変更しても、ImagePrompt/Style Catalogギャラリー(常に実output配下)への
|
||||
# アクセスが塞がれないようにするため_allowed_rootとは別管理にする
|
||||
self._comfy_output_root: Path | None = None
|
||||
self._folder_cache = _FolderCache()
|
||||
self._bg_cancel = threading.Event()
|
||||
self._bg_cancel.set() # 初期状態: キャンセル済み
|
||||
# ponyxlWildcardsVault形式フォールバック用キャッシュ: category_root文字列 -> (yamlシグネチャ, {leaf: tags})
|
||||
self._vault_leaf_cache: dict[str, tuple[tuple, dict]] = {}
|
||||
|
||||
def update_output_root(self, root_path: str) -> None:
|
||||
"""許可するルートパスを更新する（Settings変更時に呼ぶ）"""
|
||||
p = Path(root_path).resolve() if root_path else None
|
||||
self._allowed_root = p
|
||||
|
||||
def set_comfy_output_root(self, root_path: str) -> None:
|
||||
"""ComfyUI実outputフォルダの不変ルートを設定する（起動時に一度だけ呼ぶ）"""
|
||||
p = Path(root_path).resolve() if root_path else None
|
||||
self._comfy_output_root = p
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# バックグラウンドインデックス
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def start_background_index(self, folder_path: str) -> None:
|
||||
"""フォルダ内の未キャッシュ画像をバックグラウンドでインデックスする。
|
||||
フォルダロード時に呼び出す。前回のインデックス処理は自動キャンセル。"""
|
||||
self._bg_cancel.set() # 前のスレッドをキャンセル
|
||||
cancel = threading.Event()
|
||||
self._bg_cancel = cancel
|
||||
t = threading.Thread(
|
||||
target=self._bg_index_folder,
|
||||
args=(folder_path, cancel),
|
||||
daemon=True,
|
||||
)
|
||||
t.start()
|
||||
|
||||
def _bg_index_folder(self, folder_path: str, cancel: threading.Event) -> None:
|
||||
"""バックグラウンドスレッドで未キャッシュ画像のprompt_cacheを構築する。
|
||||
10枚処理するたびに50msスリープしてメインスレッドへの影響を最小化。"""
|
||||
try:
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir() or not self._check_path_allowed(folder):
|
||||
return
|
||||
entries = self._scan_folder(folder)
|
||||
batch: dict[str, dict] = {}
|
||||
processed = 0
|
||||
for name, abs_path, _, _ in entries:
|
||||
if cancel.is_set():
|
||||
return
|
||||
ext = Path(name).suffix.lower()
|
||||
if ext not in (".png", ".jpg", ".jpeg"):
|
||||
continue
|
||||
meta = self.metadata_store.get(abs_path)
|
||||
if meta.get("prompt_cache"):
|
||||
continue
|
||||
embedded: dict = {}
|
||||
if ext == ".png":
|
||||
embedded = self._read_png_metadata(Path(abs_path))
|
||||
else:
|
||||
embedded = self._read_jpeg_metadata(Path(abs_path))
|
||||
if not embedded:
|
||||
continue
|
||||
prompt_cache = " ".join(
|
||||
str(v) for k, v in embedded.items()
|
||||
if k != "workflow" and isinstance(v, str) and len(v) < 50_000
|
||||
).strip()
|
||||
if not prompt_cache:
|
||||
continue
|
||||
batch[abs_path] = {"prompt_cache": prompt_cache}
|
||||
processed += 1
|
||||
if len(batch) >= 10:
|
||||
for p, d in batch.items():
|
||||
self.metadata_store.save(p, d)
|
||||
batch = {}
|
||||
time.sleep(0.05) # 50ms 他の処理に譲る
|
||||
if batch and not cancel.is_set():
|
||||
for p, d in batch.items():
|
||||
self.metadata_store.save(p, d)
|
||||
if processed:
|
||||
logger.debug("BG index done: %s (%d indexed)", folder_path, processed)
|
||||
except Exception as e:
|
||||
logger.debug("BG index error: %s", e)
|
||||
|
||||
def _check_path_allowed(self, path: Path) -> bool:
|
||||
"""パスが許可ルート配下かチェック（パストラバーサル防止）。
|
||||
Output ギャラリーの _allowed_root、またはComfyUI実outputフォルダの
|
||||
_comfy_output_root のいずれか配下であれば許可する。"""
|
||||
resolved = path.resolve()
|
||||
if self._allowed_root is not None:
|
||||
try:
|
||||
resolved.relative_to(self._allowed_root)
|
||||
return True
|
||||
except ValueError:
|
||||
pass
|
||||
if self._comfy_output_root is not None:
|
||||
try:
|
||||
resolved.relative_to(self._comfy_output_root)
|
||||
return True
|
||||
except ValueError:
|
||||
pass
|
||||
return False
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# フォルダツリー
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def list_folder_tree(self, root_path: str) -> dict:
|
||||
"""outputフォルダのフォルダツリーを返す"""
|
||||
root = Path(root_path).resolve()
|
||||
if not root.is_dir():
|
||||
return {"error": f"Directory not found: {root_path}"}
|
||||
if self._allowed_root is None:
|
||||
self._allowed_root = root
|
||||
|
||||
def build_tree(path: Path, rel_base: Path) -> dict:
|
||||
rel = str(path.relative_to(rel_base)).replace("\\", "/")
|
||||
image_count = 0
|
||||
children = []
|
||||
try:
|
||||
with os.scandir(path) as it:
|
||||
for entry in it:
|
||||
if entry.is_file(follow_symlinks=True):
|
||||
if Path(entry.name).suffix.lower() in IMAGE_EXTENSIONS:
|
||||
image_count += 1
|
||||
elif entry.is_dir(follow_symlinks=True) and not entry.name.startswith("."):
|
||||
children.append(entry.name)
|
||||
except PermissionError:
|
||||
pass
|
||||
children_nodes = [build_tree(path / name, rel_base) for name in sorted(children)]
|
||||
total_count = image_count + sum(c["image_count_total"] for c in children_nodes)
|
||||
return {
|
||||
"name": path.name,
|
||||
"path": rel if rel != "." else "",
|
||||
"abs_path": str(path).replace("\\", "/"),
|
||||
"image_count": image_count,
|
||||
"image_count_total": total_count,
|
||||
"children": children_nodes,
|
||||
}
|
||||
|
||||
return build_tree(root, root)
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像一覧
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def _scan_folder(self, folder: Path) -> list[tuple[str, str, int, float]]:
|
||||
"""os.scandir() で画像ファイルを1回のシステムコールで列挙。
|
||||
キャッシュがあればそれを返す。
|
||||
Returns: [(name, abs_path_str, size, mtime), ...]
|
||||
"""
|
||||
cached = self._folder_cache.get(folder)
|
||||
if cached is not None:
|
||||
return cached
|
||||
|
||||
entries = []
|
||||
try:
|
||||
with os.scandir(folder) as it:
|
||||
for entry in it:
|
||||
if not entry.is_file(follow_symlinks=True):
|
||||
continue
|
||||
if Path(entry.name).suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
continue
|
||||
try:
|
||||
st = entry.stat(follow_symlinks=True)
|
||||
entries.append((
|
||||
entry.name,
|
||||
str(Path(entry.path).resolve()).replace("\\", "/"),
|
||||
st.st_size,
|
||||
st.st_mtime,
|
||||
))
|
||||
except OSError:
|
||||
continue
|
||||
except PermissionError:
|
||||
pass
|
||||
|
||||
self._folder_cache.set(folder, entries)
|
||||
return entries
|
||||
|
||||
def _scan_folder_recursive(self, folder: Path) -> list[tuple[str, str, int, float]]:
|
||||
"""os.walk() でフォルダ配下を再帰的に列挙する（サブフォルダの画像も含む）。
|
||||
ImagePrompt/Style Catalogギャラリーのように深い階層に画像が置かれるケース用。
|
||||
キャッシュはしない（フォルダ数が少なく、都度スキャンしても軽い想定）。
|
||||
"""
|
||||
entries = []
|
||||
try:
|
||||
for root, dirs, files in os.walk(folder):
|
||||
dirs[:] = [d for d in dirs if not d.startswith(".")]
|
||||
for name in files:
|
||||
if Path(name).suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
continue
|
||||
full = Path(root) / name
|
||||
try:
|
||||
st = full.stat()
|
||||
entries.append((
|
||||
name,
|
||||
str(full.resolve()).replace("\\", "/"),
|
||||
st.st_size,
|
||||
st.st_mtime,
|
||||
))
|
||||
except OSError:
|
||||
continue
|
||||
except PermissionError:
|
||||
pass
|
||||
return entries
|
||||
|
||||
def list_images(
|
||||
self,
|
||||
folder_path: str,
|
||||
search: str = "",
|
||||
sort_by: str = "date_desc",
|
||||
favorite_only: bool = False,
|
||||
tag_filter: str = "",
|
||||
group_filter: str = "",
|
||||
recursive: bool = False,
|
||||
) -> list[dict]:
|
||||
"""指定フォルダ内の画像一覧を返す（recursive=Trueならサブフォルダも含める）"""
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir():
|
||||
return []
|
||||
if not self._check_path_allowed(folder):
|
||||
logger.warning("list_images: path not allowed: %s", folder)
|
||||
return []
|
||||
|
||||
# グループフィルタ: サーバーサイドで先にパスSetを取得（転送量削減）
|
||||
group_member_set: set | None = None
|
||||
if group_filter:
|
||||
group_member_set = self.metadata_store.get_group_member_set(group_filter)
|
||||
|
||||
# os.scandir() でファイル情報を一括取得（stat()の個別呼び出しを排除）
|
||||
raw_entries = self._scan_folder_recursive(folder) if recursive else self._scan_folder(folder)
|
||||
|
||||
# 孤立メタデータを自動クリーンアップ（移動・削除されたファイルの残骸を除去）
|
||||
existing_paths = {abs_path for _, abs_path, _, _ in raw_entries}
|
||||
self.metadata_store.cleanup_stale_images(str(folder), existing_paths)
|
||||
|
||||
results = []
|
||||
for name, abs_path, size, mtime in raw_entries:
|
||||
# グループフィルタ（サーバーサイド）
|
||||
if group_member_set is not None and abs_path not in group_member_set:
|
||||
continue
|
||||
|
||||
meta = self.metadata_store.get(abs_path)
|
||||
|
||||
# お気に入りフィルタ（サーバーサイド、早期スキップ）
|
||||
if favorite_only and not meta.get("favorite", False):
|
||||
continue
|
||||
|
||||
# タグフィルタ（サーバーサイド、早期スキップ）
|
||||
tags = meta.get("tags", [])
|
||||
if tag_filter and tag_filter not in tags:
|
||||
continue
|
||||
|
||||
ext = Path(name).suffix.lower()
|
||||
item = {
|
||||
"filename": name,
|
||||
"path": abs_path,
|
||||
"size": size,
|
||||
"mtime": mtime,
|
||||
"ext": ext,
|
||||
"favorite": meta.get("favorite", False),
|
||||
"tags": tags,
|
||||
"memo": meta.get("memo", ""),
|
||||
"groups": meta.get("groups", []),
|
||||
}
|
||||
|
||||
# 検索フィルタ（ファイル名・メモ・タグ・キャッシュ済みプロンプト・.txtサイドカー）
|
||||
if search:
|
||||
s = search.lower()
|
||||
sidecar_prompt = self._read_sidecar_prompt(Path(abs_path)) or ""
|
||||
if not (
|
||||
s in name.lower()
|
||||
or s in item["memo"].lower()
|
||||
or any(s in t.lower() for t in tags)
|
||||
or s in meta.get("prompt_cache", "").lower()
|
||||
or s in sidecar_prompt.lower()
|
||||
):
|
||||
continue
|
||||
|
||||
results.append(item)
|
||||
|
||||
# ソート
|
||||
if sort_by == "name_asc":
|
||||
results.sort(key=lambda x: x["filename"].lower())
|
||||
elif sort_by == "name_desc":
|
||||
results.sort(key=lambda x: x["filename"].lower(), reverse=True)
|
||||
elif sort_by == "date_asc":
|
||||
results.sort(key=lambda x: x["mtime"])
|
||||
else: # date_desc (default)
|
||||
results.sort(key=lambda x: x["mtime"], reverse=True)
|
||||
|
||||
return results
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# ImagePromptギャラリー: .txtサイドカーによるプロンプト保存
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def get_image_prompt_root(self) -> str | None:
|
||||
"""ImagePromptギャラリーのルートフォルダ(ComfyUI実output/ws_image_prompt)を
|
||||
返す。無ければ作成する。"""
|
||||
if self._comfy_output_root is None:
|
||||
return None
|
||||
root = self._comfy_output_root / IMAGE_PROMPT_FOLDER_NAME
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
return str(root).replace("\\", "/")
|
||||
|
||||
def get_style_catalog_root(self) -> str | None:
|
||||
"""Style Catalogギャラリーのルートフォルダ(ComfyUI実output/ws_style_catalog)を
|
||||
返す。無ければ作成する。"""
|
||||
if self._comfy_output_root is None:
|
||||
return None
|
||||
root = self._comfy_output_root / STYLE_CATALOG_FOLDER_NAME
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
return str(root).replace("\\", "/")
|
||||
|
||||
def save_image_to_folder(self, folder_path: str, filename: str, image_data: str) -> dict:
|
||||
"""任意フォルダへ画像を保存する（data URL想定、同名なら上書き）。
|
||||
Style Catalogのカタログ作成（スタイル名でのファイル保存）で使用。"""
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir():
|
||||
return {"ok": False, "error": "Destination folder not found"}
|
||||
if not self._check_path_allowed(folder):
|
||||
return {"ok": False, "error": "Access denied"}
|
||||
try:
|
||||
image_bytes, ext = _decode_image_data_url(image_data)
|
||||
except Exception as e:
|
||||
return {"ok": False, "error": f"Invalid imageData: {e}"}
|
||||
safe = _sanitize_save_filename(filename, ext)
|
||||
if not safe:
|
||||
return {"ok": False, "error": "Invalid filename"}
|
||||
save_path = folder / safe
|
||||
try:
|
||||
save_path.write_bytes(image_bytes)
|
||||
except OSError as e:
|
||||
return {"ok": False, "error": str(e)}
|
||||
return {"ok": True, "path": str(save_path).replace("\\", "/")}
|
||||
|
||||
def _sidecar_path(self, image_path: Path) -> Path:
|
||||
return image_path.with_suffix(".txt")
|
||||
|
||||
def _read_sidecar_prompt(self, image_path: Path) -> str | None:
|
||||
"""画像のプロンプトテキストを返す。
|
||||
1. 画像と同名の .txt サイドカーがあればそれを使う
|
||||
2. 無ければ ponyxlWildcardsVault 形式 (*.yaml + thumbnails/) をフォールバックとして
|
||||
その場で解決する（インポート未実施のフォルダをそのまま置いた場合に対応するため）
|
||||
"""
|
||||
sidecar = self._sidecar_path(image_path)
|
||||
try:
|
||||
if sidecar.is_file() and sidecar.stat().st_size <= _SIDECAR_PROMPT_MAX:
|
||||
text = sidecar.read_text(encoding="utf-8", errors="replace").strip()
|
||||
if text:
|
||||
return text
|
||||
except OSError:
|
||||
pass
|
||||
return self._read_vault_prompt(image_path)
|
||||
|
||||
def _find_vault_category_root(self, image_path: Path) -> Path | None:
|
||||
"""image_path の祖先を辿り、直下に thumbnails(_option2) を持つフォルダ
|
||||
(=ponyxlWildcardsVaultのカテゴリルート) を探す"""
|
||||
cur = image_path.parent
|
||||
for _ in range(_VAULT_ROOT_SEARCH_DEPTH):
|
||||
if any((cur / name).is_dir() for name in _VAULT_THUMB_DIR_NAMES):
|
||||
return cur
|
||||
if cur.parent == cur:
|
||||
break
|
||||
cur = cur.parent
|
||||
return None
|
||||
|
||||
def _load_vault_leaves(self, category_root: Path) -> dict[str, str]:
|
||||
yaml_files = sorted(category_root.glob("*.yaml")) + sorted(category_root.glob("*.yml"))
|
||||
if not yaml_files:
|
||||
return {}
|
||||
try:
|
||||
signature = tuple(sorted((str(f), f.stat().st_mtime) for f in yaml_files))
|
||||
except OSError:
|
||||
signature = ()
|
||||
cache_key = str(category_root)
|
||||
cached = self._vault_leaf_cache.get(cache_key)
|
||||
if cached is not None and cached[0] == signature:
|
||||
return cached[1]
|
||||
leaves: dict[str, str] = {}
|
||||
for yf in yaml_files:
|
||||
leaves.update(_parse_vault_yaml_leaves(yf))
|
||||
self._vault_leaf_cache[cache_key] = (signature, leaves)
|
||||
return leaves
|
||||
|
||||
def _read_vault_prompt(self, image_path: Path) -> str | None:
|
||||
"""ponyxlWildcardsVault形式のフォールバック解決。
|
||||
thumbnails_option2 の "{leaf}.preview3.ext" のような二重拡張子にも対応するため、
|
||||
ファイル名の最初の "." より前をリーフ名候補として使う。"""
|
||||
category_root = self._find_vault_category_root(image_path)
|
||||
if category_root is None:
|
||||
return None
|
||||
leaves = self._load_vault_leaves(category_root)
|
||||
if not leaves:
|
||||
return None
|
||||
leaf_candidate = image_path.name.split(".")[0].lower()
|
||||
return leaves.get(leaf_candidate)
|
||||
|
||||
def save_image_prompt(self, image_path: str, text: str) -> bool:
|
||||
"""画像と同名の.txtサイドカーにプロンプトテキストを保存する"""
|
||||
p = Path(image_path).resolve()
|
||||
if not p.is_file():
|
||||
return False
|
||||
if not self._check_path_allowed(p):
|
||||
return False
|
||||
try:
|
||||
self._sidecar_path(p).write_text((text or "").strip(), encoding="utf-8")
|
||||
return True
|
||||
except OSError as e:
|
||||
logger.warning("save_image_prompt: failed for %s: %s", p, e)
|
||||
return False
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像メタデータ
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def get_image_metadata(self, image_path: str) -> dict:
|
||||
"""PNG/JPEGからメタデータを抽出し、保存済みメタと合わせて返す"""
|
||||
path = Path(image_path).resolve()
|
||||
if not path.is_file():
|
||||
return {"error": "File not found"}
|
||||
if not self._check_path_allowed(path):
|
||||
return {"error": "Access denied"}
|
||||
|
||||
embedded = {}
|
||||
ext = path.suffix.lower()
|
||||
if ext == ".png":
|
||||
embedded = self._read_png_metadata(path)
|
||||
elif ext in {".jpg", ".jpeg"}:
|
||||
embedded = self._read_jpeg_metadata(path)
|
||||
|
||||
saved = self.metadata_store.get(str(path))
|
||||
|
||||
# workflow キーを除く文字列フィールドをプロンプトキャッシュとして保存（検索用）
|
||||
prompt_text = " ".join(
|
||||
str(v) for k, v in embedded.items()
|
||||
if k != "workflow" and isinstance(v, str) and len(v) < 50_000
|
||||
).strip()
|
||||
if prompt_text and prompt_text != saved.get("prompt_cache", ""):
|
||||
self.metadata_store.save(str(path), {"prompt_cache": prompt_text})
|
||||
|
||||
stat = path.stat()
|
||||
return {
|
||||
"filename": path.name,
|
||||
"path": str(path).replace("\\", "/"),
|
||||
"size": stat.st_size,
|
||||
"mtime": stat.st_mtime,
|
||||
"ext": ext,
|
||||
"embedded": embedded,
|
||||
"favorite": saved.get("favorite", False),
|
||||
"tags": saved.get("tags", []),
|
||||
"memo": saved.get("memo", ""),
|
||||
"groups": saved.get("groups", []),
|
||||
"image_prompt": self._read_sidecar_prompt(path),
|
||||
}
|
||||
|
||||
def _read_png_metadata(self, path: Path) -> dict:
|
||||
"""PNGのtEXtチャンクからメタデータを抽出"""
|
||||
result = {}
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
sig = f.read(8)
|
||||
if sig != b"\x89PNG\r\n\x1a\n":
|
||||
return result
|
||||
while True:
|
||||
chunk_len_bytes = f.read(4)
|
||||
if len(chunk_len_bytes) < 4:
|
||||
break
|
||||
chunk_len = struct.unpack(">I", chunk_len_bytes)[0]
|
||||
chunk_type = f.read(4).decode("ascii", errors="ignore")
|
||||
if chunk_len > _PNG_CHUNK_MAX:
|
||||
f.seek(chunk_len + 4, 1)
|
||||
if chunk_type == "IEND":
|
||||
break
|
||||
continue
|
||||
chunk_data = f.read(chunk_len)
|
||||
f.read(4) # CRC
|
||||
|
||||
if chunk_type in ("tEXt", "iTXt"):
|
||||
try:
|
||||
if chunk_type == "tEXt":
|
||||
null_idx = chunk_data.index(b"\x00")
|
||||
key = chunk_data[:null_idx].decode("latin-1")
|
||||
value = chunk_data[null_idx + 1:].decode("latin-1")
|
||||
else:
|
||||
null_idx = chunk_data.index(b"\x00")
|
||||
key = chunk_data[:null_idx].decode("utf-8")
|
||||
rest = chunk_data[null_idx + 1:]
|
||||
rest = rest[2:]
|
||||
null_idx2 = rest.index(b"\x00")
|
||||
rest = rest[null_idx2 + 1:]
|
||||
null_idx3 = rest.index(b"\x00")
|
||||
value = rest[null_idx3 + 1:].decode("utf-8", errors="replace")
|
||||
result[key] = value
|
||||
except (ValueError, UnicodeDecodeError):
|
||||
pass
|
||||
|
||||
if chunk_type == "IEND":
|
||||
break
|
||||
except Exception as e:
|
||||
logger.debug("PNG metadata read error: %s", e)
|
||||
return result
|
||||
|
||||
def _read_jpeg_metadata(self, path: Path) -> dict:
|
||||
"""JPEGのEXIF/commentからメタデータを抽出（簡易）"""
|
||||
result = {}
|
||||
try:
|
||||
file_size = path.stat().st_size
|
||||
read_size = min(file_size, _JPEG_READ_MAX)
|
||||
with open(path, "rb") as f:
|
||||
data = f.read(read_size)
|
||||
idx = 0
|
||||
while idx < len(data) - 3:
|
||||
if data[idx] == 0xFF:
|
||||
marker = data[idx + 1]
|
||||
if marker == 0xFE: # COM
|
||||
length = struct.unpack(">H", data[idx + 2:idx + 4])[0]
|
||||
end = idx + 2 + length
|
||||
comment = data[idx + 4:end].decode("utf-8", errors="replace")
|
||||
result["Comment"] = comment
|
||||
break
|
||||
idx += 2
|
||||
else:
|
||||
idx += 1
|
||||
except Exception as e:
|
||||
logger.debug("JPEG metadata read error: %s", e)
|
||||
return result
|
||||
|
||||
def extract_workflow_from_metadata(self, image_path: str) -> dict | None:
|
||||
"""ワークフローを抽出する。
|
||||
優先順位: PNG[workflow] > PNG[prompt] > gallery_metadata.json[workflow]
|
||||
"""
|
||||
path = Path(image_path).resolve()
|
||||
if not self._check_path_allowed(path):
|
||||
return None
|
||||
|
||||
if path.suffix.lower() == ".png":
|
||||
embedded = self._read_png_metadata(path)
|
||||
# 1. workflow キー (ComfyUI UI形式)
|
||||
for key in ("workflow", "Workflow"):
|
||||
s = embedded.get(key)
|
||||
if s:
|
||||
try:
|
||||
return json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 2. prompt キー (ComfyUI API形式 — 大多数の生成画像)
|
||||
for key in ("prompt", "Prompt"):
|
||||
s = embedded.get(key)
|
||||
if s:
|
||||
try:
|
||||
return json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# 3. gallery_metadata.json に保存されたworkflow
|
||||
saved = self.metadata_store.get(str(path))
|
||||
return saved.get("workflow") or None
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# メタデータ保存
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def save_image_meta(self, image_path: str, data: dict) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
return False
|
||||
return self.metadata_store.save(image_path, data)
|
||||
|
||||
def toggle_favorite(self, image_path: str) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
return False
|
||||
meta = self.metadata_store.get(image_path)
|
||||
new_val = not meta.get("favorite", False)
|
||||
self.metadata_store.save(image_path, {"favorite": new_val})
|
||||
return new_val
|
||||
|
||||
def bulk_set_favorite(self, paths: list[str], value: bool) -> dict:
|
||||
"""複数画像のお気に入りを一括設定する"""
|
||||
ok = fail = 0
|
||||
for p in paths:
|
||||
if not self._check_path_allowed(Path(p).resolve()):
|
||||
fail += 1
|
||||
continue
|
||||
if self.metadata_store.save(p, {"favorite": value}):
|
||||
ok += 1
|
||||
else:
|
||||
fail += 1
|
||||
return {"ok": ok, "fail": fail}
|
||||
|
||||
def bulk_group_op(self, paths: list[str], group_name: str, action: str = "add") -> dict:
|
||||
"""複数画像のグループ追加 / 削除を一括処理する。action は "add" または "remove"。"""
|
||||
ok = fail = 0
|
||||
for p in paths:
|
||||
success = self.add_to_group(p, group_name) if action == "add" else self.remove_from_group(p, group_name)
|
||||
if success:
|
||||
ok += 1
|
||||
else:
|
||||
fail += 1
|
||||
return {"ok": ok, "fail": fail}
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# グループ管理
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def list_groups(self) -> list[dict]:
|
||||
return self.metadata_store.list_groups()
|
||||
|
||||
def create_group(self, name: str) -> bool:
|
||||
return self.metadata_store.create_group(name)
|
||||
|
||||
def rename_group(self, old_name: str, new_name: str) -> bool:
|
||||
return self.metadata_store.rename_group(old_name, new_name)
|
||||
|
||||
def delete_group(self, name: str) -> bool:
|
||||
return self.metadata_store.delete_group(name)
|
||||
|
||||
def add_to_group(self, image_path: str, group_name: str) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
return False
|
||||
meta = self.metadata_store.get(image_path)
|
||||
groups = meta.get("groups", [])
|
||||
if group_name not in groups:
|
||||
groups.append(group_name)
|
||||
return self.metadata_store.save(image_path, {"groups": groups})
|
||||
return True
|
||||
|
||||
def remove_from_group(self, image_path: str, group_name: str) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
return False
|
||||
meta = self.metadata_store.get(image_path)
|
||||
groups = [g for g in meta.get("groups", []) if g != group_name]
|
||||
return self.metadata_store.save(image_path, {"groups": groups})
|
||||
|
||||
def list_images_in_group(self, group_name: str) -> list[str]:
|
||||
all_paths = self.metadata_store.list_images_in_group(group_name)
|
||||
existing = [p for p in all_paths if Path(p).is_file()]
|
||||
stale = [p for p in all_paths if not Path(p).is_file()]
|
||||
if stale:
|
||||
self.metadata_store.remove_stale_paths_from_group(group_name, stale)
|
||||
logger.info("Cleaned up %d stale paths from group '%s'", len(stale), group_name)
|
||||
return existing
|
||||
|
||||
def clear_group(self, group_name: str) -> bool:
|
||||
return self.metadata_store.clear_group(group_name)
|
||||
|
||||
def ensure_group(self, name: str) -> bool:
|
||||
return self.metadata_store.ensure_group(name)
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像配信
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def create_folder(self, parent_path: str, name: str) -> dict:
|
||||
"""選択フォルダ内に新しいサブフォルダを作成する"""
|
||||
parent = Path(parent_path).resolve()
|
||||
if not parent.is_dir():
|
||||
return {"ok": False, "error": "Parent directory not found"}
|
||||
if not self._check_path_allowed(parent):
|
||||
return {"ok": False, "error": "Access denied"}
|
||||
name = name.strip()
|
||||
invalid_chars = set(r'\/:*?"<>|')
|
||||
if not name or any(c in invalid_chars for c in name):
|
||||
return {"ok": False, "error": "Invalid folder name"}
|
||||
new_folder = parent / name
|
||||
if new_folder.exists():
|
||||
return {"ok": False, "error": "Folder already exists"}
|
||||
try:
|
||||
new_folder.mkdir()
|
||||
self._folder_cache.invalidate(parent)
|
||||
return {"ok": True, "path": str(new_folder).replace("\\", "/")}
|
||||
except Exception as e:
|
||||
return {"ok": False, "error": str(e)}
|
||||
|
||||
def delete_folder(self, folder_path: str) -> dict:
|
||||
"""フォルダを再帰的に削除する"""
|
||||
import shutil
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir():
|
||||
return {"ok": False, "error": "Directory not found"}
|
||||
if not self._check_path_allowed(folder):
|
||||
return {"ok": False, "error": "Access denied"}
|
||||
if self._allowed_root and folder == self._allowed_root:
|
||||
return {"ok": False, "error": "Cannot delete root folder"}
|
||||
try:
|
||||
parent = folder.parent
|
||||
shutil.rmtree(folder)
|
||||
self._folder_cache.invalidate(parent)
|
||||
return {"ok": True}
|
||||
except Exception as e:
|
||||
return {"ok": False, "error": str(e)}
|
||||
|
||||
def delete_images(self, paths: list) -> dict:
|
||||
"""画像ファイルを削除する（複数対応）"""
|
||||
deleted = []
|
||||
errors = []
|
||||
for img_path in paths:
|
||||
p = Path(img_path).resolve()
|
||||
if not p.is_file():
|
||||
errors.append(f"{Path(img_path).name}: not found")
|
||||
continue
|
||||
if p.suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
errors.append(f"{p.name}: not an image")
|
||||
continue
|
||||
if not self._check_path_allowed(p):
|
||||
errors.append(f"{p.name}: access denied")
|
||||
continue
|
||||
try:
|
||||
p.unlink()
|
||||
self.metadata_store.delete(str(p))
|
||||
self._folder_cache.invalidate(p.parent)
|
||||
deleted.append(str(p).replace("\\", "/"))
|
||||
except Exception as e:
|
||||
errors.append(f"{p.name}: {e}")
|
||||
return {"deleted": deleted, "errors": errors}
|
||||
|
||||
def move_images(self, paths: list, dest_folder: str) -> dict:
|
||||
"""画像ファイルを別フォルダへ移動する（複数対応）"""
|
||||
dest = Path(dest_folder).resolve()
|
||||
if not dest.is_dir():
|
||||
return {"ok": False, "error": "Destination folder not found", "moved": [], "errors": []}
|
||||
if not self._check_path_allowed(dest):
|
||||
return {"ok": False, "error": "Access denied", "moved": [], "errors": []}
|
||||
moved = []
|
||||
errors = []
|
||||
for img_path in paths:
|
||||
p = Path(img_path).resolve()
|
||||
if not p.is_file():
|
||||
errors.append(f"{Path(img_path).name}: not found")
|
||||
continue
|
||||
if p.suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
errors.append(f"{p.name}: not an image")
|
||||
continue
|
||||
if not self._check_path_allowed(p):
|
||||
errors.append(f"{p.name}: access denied")
|
||||
continue
|
||||
dest_path = dest / p.name
|
||||
counter = 1
|
||||
while dest_path.exists():
|
||||
dest_path = dest / f"{p.stem}_{counter}{p.suffix}"
|
||||
counter += 1
|
||||
try:
|
||||
p.rename(dest_path)
|
||||
old_str = str(p).replace("\\", "/")
|
||||
new_str = str(dest_path).replace("\\", "/")
|
||||
self.metadata_store.rename_path(old_str, new_str)
|
||||
self._folder_cache.invalidate(p.parent)
|
||||
self._folder_cache.invalidate(dest)
|
||||
moved.append({"from": old_str, "to": new_str, "filename": dest_path.name})
|
||||
except Exception as e:
|
||||
errors.append(f"{p.name}: {e}")
|
||||
return {"moved": moved, "errors": errors}
|
||||
|
||||
def serve_image(self, image_path: str):
|
||||
"""画像のPathオブジェクトを返す（ルートで使用）"""
|
||||
p = Path(image_path).resolve()
|
||||
if not p.is_file():
|
||||
return None
|
||||
if p.suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
return None
|
||||
if not self._check_path_allowed(p):
|
||||
logger.warning("serve_image: path not allowed: %s", p)
|
||||
return None
|
||||
return p
|
||||
|
||||
def serve_thumbnail(self, image_path: str, width: int = 256) -> Path | None:
|
||||
"""縮小サムネイルのPathを返す。
|
||||
ディスクキャッシュがあればそれを返し、なければPillowで生成して保存する。
|
||||
GIFはアニメーション保持のため元ファイルをそのまま返す。
|
||||
Pillowが使えない場合は元ファイルにフォールバック。
|
||||
"""
|
||||
p = Path(image_path).resolve()
|
||||
if not p.is_file():
|
||||
return None
|
||||
if p.suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
return None
|
||||
if not self._check_path_allowed(p):
|
||||
logger.warning("serve_thumbnail: path not allowed: %s", p)
|
||||
return None
|
||||
|
||||
# GIF はアニメーション保持のためそのまま返す
|
||||
if p.suffix.lower() == ".gif":
|
||||
return p
|
||||
|
||||
# SVG はベクター画像でPillowが開けないため、元ファイルをそのまま返す
|
||||
# (<img>タグはブラウザ側で任意サイズにスケーリングして表示する)
|
||||
if p.suffix.lower() == ".svg":
|
||||
return p
|
||||
|
||||
try:
|
||||
mtime = int(p.stat().st_mtime * 1000)
|
||||
except OSError:
|
||||
return None
|
||||
|
||||
cache_key = hashlib.md5(f"{p}:{mtime}:{width}".encode()).hexdigest()
|
||||
cache_dir = self.data_dir / "thumb_cache"
|
||||
cache_dir.mkdir(exist_ok=True)
|
||||
thumb_path = cache_dir / f"{cache_key}.jpg"
|
||||
|
||||
if thumb_path.exists():
|
||||
return thumb_path
|
||||
|
||||
try:
|
||||
from PIL import Image
|
||||
with Image.open(p) as img:
|
||||
img = img.convert("RGB")
|
||||
img.thumbnail((width, width), Image.LANCZOS)
|
||||
img.save(thumb_path, "JPEG", quality=85, optimize=True)
|
||||
return thumb_path
|
||||
except Exception as e:
|
||||
logger.warning("serve_thumbnail: failed for %s: %s", p, e)
|
||||
return p # フォールバック: 元ファイル
|
||||
@@ -0,0 +1,138 @@
|
||||
"""Lab tab plan file management service (JSON plan + optional PNG index thumbnail)."""
|
||||
|
||||
import base64
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LabPlanService:
|
||||
def __init__(self, plan_dir: Path):
|
||||
self.plan_dir = plan_dir
|
||||
self.plan_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Path safety
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _safe_path(self, filename: str, ext: str = ".json") -> Path | None:
|
||||
if not filename or not filename.strip():
|
||||
return None
|
||||
name = filename.strip()
|
||||
if not name.endswith(ext):
|
||||
name = name + ext
|
||||
if "/" in name or "\\" in name or name in (".", "..") or "\x00" in name:
|
||||
return None
|
||||
resolved = (self.plan_dir / name).resolve()
|
||||
plan_dir_resolved = self.plan_dir.resolve()
|
||||
if resolved != plan_dir_resolved and not str(resolved).startswith(str(plan_dir_resolved) + "\\") and not str(resolved).startswith(str(plan_dir_resolved) + "/"):
|
||||
return None
|
||||
return resolved
|
||||
|
||||
def _now_iso(self) -> str:
|
||||
return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# CRUD
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def list_plans(self) -> list[dict]:
|
||||
if not self.plan_dir.is_dir():
|
||||
return []
|
||||
result = []
|
||||
for path in sorted(self.plan_dir.glob("*.json")):
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning("Skipping unreadable lab plan %s: %s", path.name, e)
|
||||
continue
|
||||
|
||||
stem = path.stem
|
||||
thumbnail = None
|
||||
thumb_path = self.plan_dir / f"{stem}.png"
|
||||
if thumb_path.is_file():
|
||||
thumbnail = f"/wfm_data/lab_plan/{stem}.png"
|
||||
|
||||
result.append({
|
||||
"filename": path.name,
|
||||
"name": data.get("name", stem),
|
||||
"note": data.get("note", ""),
|
||||
"batch_count": data.get("batch_count", 1),
|
||||
"updated_at": data.get("updated_at", ""),
|
||||
"thumbnail": thumbnail,
|
||||
})
|
||||
result.sort(key=lambda p: p.get("updated_at", ""), reverse=True)
|
||||
return result
|
||||
|
||||
def get_plan(self, filename: str) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError(f"Plan not found: {filename}")
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
def save_plan(self, filename: str, data: dict, index_image_base64: str | None = None) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
|
||||
data = dict(data)
|
||||
data["updated_at"] = self._now_iso()
|
||||
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
if index_image_base64:
|
||||
self.save_index_image(filename, index_image_base64)
|
||||
|
||||
return {"filename": path.name, "updated_at": data["updated_at"]}
|
||||
|
||||
def save_index_image(self, filename: str, image_base64: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
stem = path.stem
|
||||
thumb_path = self.plan_dir / f"{stem}.png"
|
||||
|
||||
# Accept both raw base64 and data: URL forms
|
||||
b64 = image_base64
|
||||
if "," in b64 and b64.strip().lower().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
|
||||
image_bytes = base64.b64decode(b64)
|
||||
with open(thumb_path, "wb") as f:
|
||||
f.write(image_bytes)
|
||||
|
||||
def save_index_image_to_output(self, image_base64: str, filename_prefix: str = "Lab_index") -> dict:
|
||||
"""Saves the contact-sheet index image into ComfyUI's own output folder,
|
||||
alongside the images generated during the run (unlike save_index_image,
|
||||
which saves it next to the plan file in plan_dir)."""
|
||||
b64 = image_base64
|
||||
if "," in b64 and b64.strip().lower().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
image_bytes = base64.b64decode(b64)
|
||||
|
||||
import folder_paths # type: ignore
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(filename_prefix, output_dir)
|
||||
save_name = f"{filename}_{counter:05}_.png"
|
||||
save_path = Path(full_output_folder) / save_name
|
||||
save_path.write_bytes(image_bytes)
|
||||
return {"filename": save_name, "subfolder": subfolder}
|
||||
|
||||
def delete_plan(self, filename: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if path.is_file():
|
||||
path.unlink()
|
||||
thumb_path = self.plan_dir / f"{path.stem}.png"
|
||||
if thumb_path.is_file():
|
||||
thumb_path.unlink()
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
import json
|
||||
import logging
|
||||
import shutil
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
@@ -9,10 +11,23 @@ from ..config import MODEL_METADATA_FILE
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_SCAN_CACHE_TTL = 30 # seconds — reuse scan result within this window
|
||||
|
||||
# Preview image extensions to search for (in priority order)
|
||||
_PREVIEW_EXTENSIONS = [".preview.png", ".preview.jpg", ".preview.jpeg", ".preview.webp",
|
||||
".png", ".jpg", ".jpeg", ".webp"]
|
||||
|
||||
# All sidecar extensions to delete when a model is deleted
|
||||
_SIDECAR_EXTENSIONS = [
|
||||
".preview.png", ".preview.jpg", ".preview.jpeg", ".preview.webp",
|
||||
".png", ".jpg", ".jpeg", ".webp",
|
||||
".metadata.json", ".cm-info.json", ".json", ".civitai.info", ".info",
|
||||
]
|
||||
|
||||
_DISABLED_SUFFIX = ".disabled"
|
||||
|
||||
_MODEL_EXTENSIONS = {".safetensors", ".ckpt", ".pt", ".pth", ".bin", ".gguf", ".pt2"}
|
||||
|
||||
# ComfyUI model type → folder_paths key mapping
|
||||
MODEL_TYPE_FOLDER_KEYS = {
|
||||
"checkpoint": "checkpoints",
|
||||
@@ -21,6 +36,8 @@ MODEL_TYPE_FOLDER_KEYS = {
|
||||
"controlnet": "controlnet",
|
||||
"unet": "diffusion_models",
|
||||
"textencoder": "text_encoders",
|
||||
"hypernetwork": "hypernetworks",
|
||||
"embedding": "embeddings",
|
||||
}
|
||||
|
||||
|
||||
@@ -57,6 +74,7 @@ class ModelsService:
|
||||
|
||||
def __init__(self):
|
||||
self.metadata_file = MODEL_METADATA_FILE
|
||||
self._scan_cache: dict = {} # model_type -> (timestamp, frozenset)
|
||||
|
||||
def _now_iso(self):
|
||||
return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
|
||||
@@ -91,20 +109,187 @@ class ModelsService:
|
||||
entry["memo"] = updates["memo"]
|
||||
if "sha256" in updates:
|
||||
entry["sha256"] = updates["sha256"]
|
||||
if "badges" in updates:
|
||||
entry["badges"] = updates["badges"]
|
||||
entry["updatedAt"] = self._now_iso()
|
||||
data[model_name] = entry
|
||||
self._save_metadata(data)
|
||||
return entry
|
||||
|
||||
def get_model_groups(self):
|
||||
data = self._load_metadata()
|
||||
return data.get("_groups", {})
|
||||
def _scan_model_names(self, model_type: str) -> set:
|
||||
"""モデルタイプの全ファイル名をスキャンして返す（相対パス、/区切り、.disabled除く）。
|
||||
結果は30秒キャッシュされ、起動時の多重スキャンによる高負荷を防ぐ。"""
|
||||
now = time.monotonic()
|
||||
cached = self._scan_cache.get(model_type)
|
||||
if cached is not None:
|
||||
ts, names = cached
|
||||
if now - ts < _SCAN_CACHE_TTL:
|
||||
return names
|
||||
|
||||
def save_model_groups(self, groups):
|
||||
dirs = _get_model_dirs(model_type)
|
||||
names = set()
|
||||
for d in dirs:
|
||||
if not d.is_dir():
|
||||
continue
|
||||
for f in d.rglob("*"):
|
||||
if not f.is_file():
|
||||
continue
|
||||
try:
|
||||
rel = str(f.relative_to(d)).replace("\\", "/")
|
||||
if rel.endswith(_DISABLED_SUFFIX):
|
||||
rel = rel[: -len(_DISABLED_SUFFIX)]
|
||||
names.add(rel)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
self._scan_cache[model_type] = (now, names)
|
||||
return names
|
||||
|
||||
def list_model_files(self, model_type: str) -> list:
|
||||
"""モデルタイプのモデルファイル名をソートして返す（拡張子付き、/区切り）。
|
||||
プレビュー画像やサイドカーファイルを除外し、モデル拡張子のみを返す。"""
|
||||
dirs = _get_model_dirs(model_type)
|
||||
seen = set()
|
||||
for d in dirs:
|
||||
if not d.is_dir():
|
||||
continue
|
||||
for f in d.rglob("*"):
|
||||
if not f.is_file():
|
||||
continue
|
||||
name = f.name
|
||||
if name.endswith(_DISABLED_SUFFIX):
|
||||
name = name[: -len(_DISABLED_SUFFIX)]
|
||||
if Path(name).suffix.lower() not in _MODEL_EXTENSIONS:
|
||||
continue
|
||||
try:
|
||||
rel = str(f.relative_to(d)).replace("\\", "/")
|
||||
if rel.endswith(_DISABLED_SUFFIX):
|
||||
rel = rel[: -len(_DISABLED_SUFFIX)]
|
||||
seen.add(rel)
|
||||
except ValueError:
|
||||
pass
|
||||
return sorted(seen)
|
||||
|
||||
def get_model_groups(self, model_type=None):
|
||||
data = self._load_metadata()
|
||||
data["_groups"] = groups
|
||||
raw = data.get("_groups", {})
|
||||
|
||||
# Migrate old flat format { "groupName": [...] } → per-type { "checkpoint": {...} }
|
||||
if raw and not any(k in MODEL_TYPE_FOLDER_KEYS for k in raw):
|
||||
logger.info("Migrating groups to per-type format (old data moved to _groups_legacy)")
|
||||
data["_groups_legacy"] = raw
|
||||
data["_groups"] = {}
|
||||
self._save_metadata(data)
|
||||
raw = {}
|
||||
|
||||
if model_type:
|
||||
groups = raw.get(model_type, {})
|
||||
|
||||
# Guard: if no model directories are accessible (e.g. network drive disconnected),
|
||||
# skip cleanup entirely to prevent mass-deletion of group members.
|
||||
accessible_dirs = [d for d in _get_model_dirs(model_type) if d.is_dir()]
|
||||
if not accessible_dirs:
|
||||
configured_dirs = _get_model_dirs(model_type)
|
||||
if configured_dirs:
|
||||
logger.warning(
|
||||
"No accessible directories for model_type=%s — skipping group cleanup "
|
||||
"to avoid data loss (drives may be disconnected)", model_type
|
||||
)
|
||||
return {g: [m.replace("\\", "/") for m in members]
|
||||
for g, members in groups.items()}
|
||||
# No directories configured at all — return as-is (nothing to validate against)
|
||||
return {g: [m.replace("\\", "/") for m in members] for g, members in groups.items()}
|
||||
|
||||
valid_names = self._scan_model_names(model_type)
|
||||
cleaned = {}
|
||||
dirty = False
|
||||
for g_name, members in groups.items():
|
||||
# Normalize backslashes (Windows ComfyUI paths) to forward slashes
|
||||
normalized = [m.replace("\\", "/") for m in members]
|
||||
filtered = [m for m in normalized if m in valid_names]
|
||||
cleaned[g_name] = filtered
|
||||
if len(filtered) != len(members) or normalized != list(members):
|
||||
dirty = True
|
||||
if dirty:
|
||||
removed = sum(len(groups[g]) - len(cleaned[g]) for g in groups)
|
||||
if removed > 0:
|
||||
logger.info("Cleaned up %d stale group entries for model_type=%s", removed, model_type)
|
||||
if not isinstance(data.get("_groups"), dict):
|
||||
data["_groups"] = {}
|
||||
data["_groups"][model_type] = cleaned
|
||||
self._save_metadata(data)
|
||||
return cleaned
|
||||
return raw
|
||||
|
||||
def save_model_groups(self, groups, model_type):
|
||||
# Normalize all member names to forward slashes (ComfyUI on Windows uses backslashes)
|
||||
normalized = {g: [m.replace("\\", "/") for m in members] for g, members in groups.items()}
|
||||
data = self._load_metadata()
|
||||
if not isinstance(data.get("_groups"), dict):
|
||||
data["_groups"] = {}
|
||||
data["_groups"][model_type] = normalized
|
||||
self._save_metadata(data)
|
||||
return groups
|
||||
# Also invalidate scan cache so next get_model_groups sees the fresh data
|
||||
self._scan_cache.pop(model_type, None)
|
||||
return normalized
|
||||
|
||||
def find_model_file(self, model_type, model_name):
|
||||
"""Find a model file, checking both enabled and disabled states.
|
||||
|
||||
Returns (Path, is_enabled) or (None, None) if not found.
|
||||
"""
|
||||
dirs = _get_model_dirs(model_type)
|
||||
for d in dirs:
|
||||
enabled = d / model_name
|
||||
if enabled.is_file():
|
||||
return enabled, True
|
||||
disabled = d / (model_name + _DISABLED_SUFFIX)
|
||||
if disabled.is_file():
|
||||
return disabled, False
|
||||
return None, None
|
||||
|
||||
def enable_model(self, model_type, model_name):
|
||||
"""Rename model.safetensors.disabled → model.safetensors."""
|
||||
path, is_enabled = self.find_model_file(model_type, model_name)
|
||||
if path is None:
|
||||
raise FileNotFoundError(f"Model not found: {model_name}")
|
||||
if is_enabled:
|
||||
return
|
||||
target = path.parent / path.name[: -len(_DISABLED_SUFFIX)]
|
||||
path.rename(target)
|
||||
logger.info("Enabled model: %s", model_name)
|
||||
|
||||
def disable_model(self, model_type, model_name):
|
||||
"""Rename model.safetensors → model.safetensors.disabled."""
|
||||
path, is_enabled = self.find_model_file(model_type, model_name)
|
||||
if path is None:
|
||||
raise FileNotFoundError(f"Model not found: {model_name}")
|
||||
if not is_enabled:
|
||||
return
|
||||
target = path.parent / (path.name + _DISABLED_SUFFIX)
|
||||
path.rename(target)
|
||||
logger.info("Disabled model: %s", model_name)
|
||||
|
||||
def scan_disabled_models(self, model_type):
|
||||
"""Scan directories for .disabled files.
|
||||
|
||||
Returns list of normalized model names (without .disabled suffix).
|
||||
"""
|
||||
dirs = _get_model_dirs(model_type)
|
||||
disabled = []
|
||||
for d in dirs:
|
||||
if not d.is_dir():
|
||||
continue
|
||||
for f in d.rglob("*"):
|
||||
if f.is_file() and f.name.endswith(_DISABLED_SUFFIX):
|
||||
try:
|
||||
rel = str(f.relative_to(d))
|
||||
if rel.endswith(_DISABLED_SUFFIX):
|
||||
rel = rel[: -len(_DISABLED_SUFFIX)]
|
||||
disabled.append(rel.replace("\\", "/"))
|
||||
except ValueError:
|
||||
pass
|
||||
return disabled
|
||||
|
||||
def find_preview_image(self, model_type, model_name):
|
||||
"""Find preview image for a model file.
|
||||
@@ -134,7 +319,7 @@ class ModelsService:
|
||||
|
||||
for ext in _PREVIEW_EXTENSIONS:
|
||||
preview = parent / (stem + ext)
|
||||
if preview.is_file():
|
||||
if preview.is_file() and preview.stat().st_size >= 100:
|
||||
logger.debug("Preview found: %s", preview)
|
||||
return preview
|
||||
|
||||
@@ -144,3 +329,189 @@ class ModelsService:
|
||||
|
||||
logger.debug("Preview: model file not found in any dir: %s", model_name)
|
||||
return None
|
||||
|
||||
def delete_model(self, model_type, model_name):
|
||||
"""Delete a model file and all associated preview/sidecar files.
|
||||
|
||||
Also removes the model's metadata entry.
|
||||
Returns dict with deleted file paths.
|
||||
"""
|
||||
if ".." in model_name:
|
||||
raise ValueError(f"Invalid model name: {model_name}")
|
||||
|
||||
path, _ = self.find_model_file(model_type, model_name)
|
||||
if path is None:
|
||||
raise FileNotFoundError(f"Model not found: {model_name}")
|
||||
|
||||
deleted = []
|
||||
parent = path.parent
|
||||
|
||||
# Compute stem from the original (non-disabled) filename.
|
||||
# path.name for a disabled file is e.g. "model.safetensors.disabled"
|
||||
# so we strip the .disabled suffix first, then take Path.stem.
|
||||
orig_name = path.name
|
||||
if orig_name.endswith(_DISABLED_SUFFIX):
|
||||
orig_name = orig_name[: -len(_DISABLED_SUFFIX)]
|
||||
stem = Path(orig_name).stem # "model.safetensors" -> "model"
|
||||
|
||||
# Delete the model file itself
|
||||
path.unlink()
|
||||
deleted.append(str(path))
|
||||
logger.info("Deleted model file: %s", path)
|
||||
|
||||
# Delete all sidecar files (previews, metadata, info)
|
||||
for ext in _SIDECAR_EXTENSIONS:
|
||||
sidecar = parent / (stem + ext)
|
||||
if sidecar.is_file():
|
||||
sidecar.unlink()
|
||||
deleted.append(str(sidecar))
|
||||
logger.info("Deleted sidecar: %s", sidecar)
|
||||
|
||||
# Remove metadata entry
|
||||
data = self._load_metadata()
|
||||
if model_name in data:
|
||||
del data[model_name]
|
||||
self._save_metadata(data)
|
||||
|
||||
return {"deleted": deleted}
|
||||
|
||||
def get_subdirs(self, model_type):
|
||||
"""Return sorted list of root-level subdirectory names for a model type."""
|
||||
dirs = _get_model_dirs(model_type)
|
||||
subdirs = set()
|
||||
for d in dirs:
|
||||
if not d.is_dir():
|
||||
continue
|
||||
for item in d.iterdir():
|
||||
if item.is_dir():
|
||||
subdirs.add(item.name)
|
||||
return sorted(subdirs)
|
||||
|
||||
def move_models(self, model_type, model_names, dest_subdir):
|
||||
"""Move model files + sidecar files to dest_subdir (root-level subfolder name).
|
||||
|
||||
dest_subdir: "" = model root, "sdxl" = <root>/sdxl/
|
||||
Creates the destination directory if it does not exist.
|
||||
Updates metadata keys to reflect new paths.
|
||||
Returns {"moved": [{"from": ..., "to": ...}], "errors": [...]}
|
||||
"""
|
||||
# Validate dest_subdir: must be a plain folder name (no separators, no "..", not absolute)
|
||||
if dest_subdir:
|
||||
if ".." in dest_subdir:
|
||||
return {"moved": [], "errors": [{"model": "*", "error": "Invalid destination: '..' not allowed"}]}
|
||||
if "/" in dest_subdir or "\\" in dest_subdir:
|
||||
return {"moved": [], "errors": [{"model": "*", "error": "Invalid destination: must be a single folder name"}]}
|
||||
if Path(dest_subdir).is_absolute():
|
||||
return {"moved": [], "errors": [{"model": "*", "error": "Invalid destination: absolute paths not allowed"}]}
|
||||
|
||||
moved = []
|
||||
errors = []
|
||||
model_dirs = _get_model_dirs(model_type)
|
||||
|
||||
meta_data = self._load_metadata()
|
||||
meta_changed = False
|
||||
renames = [] # [(old_name, new_name)]
|
||||
|
||||
for model_name in model_names:
|
||||
if ".." in model_name:
|
||||
errors.append({"model": model_name, "error": "Invalid model name"})
|
||||
continue
|
||||
|
||||
path, _is_enabled = self.find_model_file(model_type, model_name)
|
||||
if path is None:
|
||||
errors.append({"model": model_name, "error": f"Model not found: {model_name}"})
|
||||
continue
|
||||
|
||||
try:
|
||||
# Determine which root dir this model lives in
|
||||
root_dir = None
|
||||
for d in model_dirs:
|
||||
try:
|
||||
path.relative_to(d)
|
||||
root_dir = d
|
||||
break
|
||||
except ValueError:
|
||||
pass
|
||||
if root_dir is None:
|
||||
errors.append({"model": model_name, "error": "Cannot determine root directory"})
|
||||
continue
|
||||
|
||||
# Destination directory — verify it stays within root_dir after resolution
|
||||
dest_dir = root_dir / dest_subdir if dest_subdir else root_dir
|
||||
try:
|
||||
dest_dir.resolve().relative_to(root_dir.resolve())
|
||||
except ValueError:
|
||||
errors.append({"model": model_name, "error": "Destination is outside model root"})
|
||||
continue
|
||||
dest_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Original filename without .disabled suffix
|
||||
orig_name = path.name
|
||||
if orig_name.endswith(_DISABLED_SUFFIX):
|
||||
orig_name = orig_name[: -len(_DISABLED_SUFFIX)]
|
||||
stem = Path(orig_name).stem
|
||||
|
||||
new_path = dest_dir / path.name
|
||||
if new_path.resolve() == path.resolve():
|
||||
errors.append({"model": model_name, "error": "Already in destination"})
|
||||
continue
|
||||
|
||||
# Refuse to overwrite an existing file
|
||||
if new_path.exists():
|
||||
errors.append({"model": model_name, "error": f"Destination already exists: {path.name}"})
|
||||
continue
|
||||
|
||||
# Move the model file
|
||||
shutil.move(str(path), str(new_path))
|
||||
logger.info("Moved model: %s → %s", path, new_path)
|
||||
|
||||
# Move all sidecar files (skip if destination already exists)
|
||||
for ext in _SIDECAR_EXTENSIONS:
|
||||
sidecar = path.parent / (stem + ext)
|
||||
if sidecar.is_file():
|
||||
sidecar_dest = dest_dir / (stem + ext)
|
||||
if sidecar_dest.exists():
|
||||
logger.warning("Sidecar destination exists, skipping: %s", sidecar_dest)
|
||||
continue
|
||||
try:
|
||||
shutil.move(str(sidecar), str(sidecar_dest))
|
||||
except Exception as se:
|
||||
logger.warning("Could not move sidecar %s: %s", sidecar, se)
|
||||
|
||||
# Compute new logical model name (relative to root, forward slashes)
|
||||
new_rel = str(new_path.relative_to(root_dir)).replace("\\", "/")
|
||||
if new_rel.endswith(_DISABLED_SUFFIX):
|
||||
new_rel = new_rel[: -len(_DISABLED_SUFFIX)]
|
||||
|
||||
# Update metadata key
|
||||
if model_name in meta_data:
|
||||
meta_data[new_rel] = meta_data.pop(model_name)
|
||||
meta_changed = True
|
||||
|
||||
moved.append({"from": model_name, "to": new_rel})
|
||||
renames.append((model_name, new_rel))
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Error moving model %s: %s", model_name, e)
|
||||
errors.append({"model": model_name, "error": str(e)})
|
||||
|
||||
# Update group entries for renamed/moved models
|
||||
if renames:
|
||||
groups_for_type = meta_data.get("_groups", {}).get(model_type, {})
|
||||
groups_dirty = False
|
||||
for old_name, new_name in renames:
|
||||
for members in groups_for_type.values():
|
||||
for i, m in enumerate(members):
|
||||
if m == old_name:
|
||||
members[i] = new_name
|
||||
groups_dirty = True
|
||||
if groups_dirty:
|
||||
if not isinstance(meta_data.get("_groups"), dict):
|
||||
meta_data["_groups"] = {}
|
||||
meta_data["_groups"][model_type] = groups_for_type
|
||||
meta_changed = True
|
||||
|
||||
if meta_changed:
|
||||
self._save_metadata(meta_data)
|
||||
|
||||
return {"moved": moved, "errors": errors}
|
||||
|
||||
@@ -0,0 +1,228 @@
|
||||
"""AI skill (.md system prompt) file management service."""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
ALLOWED_EXTS = {"md"}
|
||||
|
||||
DEFAULT_SKILL_FILENAME = "svg-icon.md"
|
||||
|
||||
DEFAULT_SKILL_CONTENT = """---
|
||||
name: SVG Icon Generator
|
||||
description: Produces clean, minimal, valid SVG icons and simple illustrations.
|
||||
---
|
||||
|
||||
You are an expert SVG icon and illustration designer. When the user asks for an icon, logo, pictogram, diagram, character, animal, or any vector graphic, respond with valid, clean SVG markup.
|
||||
|
||||
## Composition process (do this before writing markup - skipping this step is the main cause of unrecognizable output)
|
||||
- Break the subject down into 3-8 distinct parts that make it recognizable (e.g. a bird: body, head, beak, eye, wing, tail; a house: wall, roof, door, window). Drop parts that are not essential.
|
||||
- Decide each part's approximate position, size, and draw order within the viewBox (elements listed later in the markup are drawn on top).
|
||||
- Pick the simplest shape that captures each part's silhouette: `<circle>`/`<ellipse>` for round parts, `<rect>` for rectangular parts, `<polygon>` for angular parts (beaks, ears, leaves, roofs). For a rounded organic outline that no primitive can approximate well (a body silhouette, a wing, a leaf edge), use a short `<path>` with 2-4 smooth curve commands (`Q` or `C`) instead of a plain circle - a few deliberate curve points beat a distorted primitive.
|
||||
- Never fall back to drawing an unrelated blob plus a few disconnected decorative lines when a subject is hard to render - always decompose it into the named parts above instead, even if each part is crude.
|
||||
|
||||
## Output rules
|
||||
- Always wrap the SVG markup in a fenced code block labeled `svg`, e.g. ```svg ... ```.
|
||||
- Keep any explanation brief and place it OUTSIDE the code block, never mixed inside the SVG.
|
||||
- Output exactly one `<svg>...</svg>` element per code block.
|
||||
|
||||
## Structure rules
|
||||
- Always include a `viewBox` attribute (e.g. `viewBox="0 0 24 24"`). Do not hardcode large pixel `width`/`height`; if you set them, make them match the viewBox aspect ratio.
|
||||
- Use a small, round coordinate system (0-24 or 0-100).
|
||||
- Round coordinate values to at most 2 decimal places.
|
||||
- Keep every `<path>` short (at most about 6 commands) - long, intricate paths are where quality breaks down most often.
|
||||
- Group related elements with `<g>` when it clarifies structure, but do not over-nest.
|
||||
|
||||
## Color rules
|
||||
- For single-color icons, use `fill="currentColor"` (or `stroke="currentColor"` for line icons) so the icon inherits the surrounding text color.
|
||||
- For multi-color illustrations, use explicit hex colors (e.g. `#4A90D9`) - never named CSS colors like `"blue"`.
|
||||
- Avoid gradients, filters, and blend modes unless the user explicitly asks for a glossy/gradient style - they rarely render correctly from generated coordinates.
|
||||
|
||||
## Accessibility
|
||||
- Include a `<title>` element as the first child of `<svg>` describing what the icon depicts.
|
||||
|
||||
## Security (mandatory - do not violate)
|
||||
- Never include `<script>`, `<foreignObject>`, event handler attributes (`onload`, `onclick`, etc.), or `<a>`/`href` links to external URLs.
|
||||
- Never reference external resources (no `xlink:href` to remote URLs, no `<image>` with external `src`).
|
||||
|
||||
## Example 1 - simple geometric icon (single color, primitives only)
|
||||
|
||||
Request: "Sun icon"
|
||||
|
||||
Parts: disc (circle), 8 rays (lines).
|
||||
|
||||
```svg
|
||||
<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
|
||||
<title>Sun</title>
|
||||
<circle cx="12" cy="12" r="5" fill="currentColor"/>
|
||||
<g stroke="currentColor" stroke-width="2" stroke-linecap="round">
|
||||
<line x1="12" y1="1" x2="12" y2="4"/>
|
||||
<line x1="12" y1="20" x2="12" y2="23"/>
|
||||
<line x1="4.22" y1="4.22" x2="6.34" y2="6.34"/>
|
||||
<line x1="17.66" y1="17.66" x2="19.78" y2="19.78"/>
|
||||
<line x1="1" y1="12" x2="4" y2="12"/>
|
||||
<line x1="20" y1="12" x2="23" y2="12"/>
|
||||
<line x1="4.22" y1="19.78" x2="6.34" y2="17.66"/>
|
||||
<line x1="17.66" y1="6.34" x2="19.78" y2="4.22"/>
|
||||
</g>
|
||||
</svg>
|
||||
```
|
||||
|
||||
## Example 2 - organic subject (multi-color, decomposed into parts)
|
||||
|
||||
Request: "Bird icon"
|
||||
|
||||
Parts: body (rounded outline via a short curved path), head (circle), beak (small triangle), eye (dot), wing (small curved path) - each drawn as its own element, layered head/beak/eye on top of the body.
|
||||
|
||||
```svg
|
||||
<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
|
||||
<title>Bird</title>
|
||||
<path d="M4 16 Q4 10 11 9 Q18 8 20 13 Q20 17 14 18 Q7 19 4 16 Z" fill="#4A90D9"/>
|
||||
<path d="M8 14 Q5 12 3 14 Q6 16 9 16 Z" fill="#3576B8"/>
|
||||
<circle cx="17" cy="10" r="3.2" fill="#4A90D9"/>
|
||||
<polygon points="20,10 23,9.3 20,11.4" fill="#F2B807"/>
|
||||
<circle cx="18" cy="9" r="0.6" fill="#1A1A1A"/>
|
||||
</svg>
|
||||
```
|
||||
|
||||
Follow this pattern for every request: list the parts first, represent each with the simplest adequate shape, then assemble - never skip straight to unstructured shapes.
|
||||
"""
|
||||
|
||||
SKILL_CREATOR_FILENAME = "skill-creator.md"
|
||||
|
||||
SKILL_CREATOR_CONTENT = """---
|
||||
name: Skill Creator
|
||||
description: Interactively interviews you and drafts a new WFS chat skill (.md system prompt) ready to save.
|
||||
---
|
||||
|
||||
You are a skill-design assistant embedded in ComfyUI Workflow Studio's AI TOOL Chat pane. Your job is to interactively help the user design a NEW "skill" - a markdown file with frontmatter (`name`, `description`) followed by system-prompt instructions. Once saved, a skill can be selected from a dropdown and is silently prepended to future chat messages as a system prompt.
|
||||
|
||||
## How to conduct the interview
|
||||
- Ask ONE focused question at a time, never a bulleted list of questions. Wait for the user's answer before asking the next one.
|
||||
- Cover, in this order, only what is not already clear from context:
|
||||
1. What task or domain should the skill specialize in (what should the model do differently once this skill is active)?
|
||||
2. What output format is expected - plain prose, a specific fenced code block language, a fixed structure, a length limit?
|
||||
3. Any hard constraints or things to avoid (tone, forbidden content, security concerns, things past attempts got wrong)?
|
||||
4. Would a short worked example help smaller/local models follow the pattern more reliably? If yes, ask what a good example input/output looks like, or offer to draft one.
|
||||
- Keep it short - 3 to 5 questions is usually enough. If the user says "just generate it", "that's enough", or similar at any point, stop asking and produce the best skill you can from what you already have.
|
||||
|
||||
## Output format for the finished skill
|
||||
- Once you have enough information, output the complete skill file in a single fenced code block labeled `skill` (```skill ... ```). Never mix prose inside that block - explanation goes outside it.
|
||||
- The block must start with frontmatter exactly in this shape:
|
||||
---
|
||||
name: <short display name, shown in the skill dropdown>
|
||||
description: <one sentence, shown as a tooltip in the dropdown>
|
||||
---
|
||||
- After the frontmatter, write clear, imperative system-prompt instructions for the target skill: what role the model should take, concrete output-format rules, hard constraints, and - if useful - one short worked example following a "request -> ideal output" shape.
|
||||
- After the code block, tell the user in one line that they can click the "Save as new skill" button below the message to add it, or copy the block manually into a new skill.
|
||||
"""
|
||||
|
||||
_FRONTMATTER_RE = re.compile(r"^---\r?\n(.*?)\r?\n---\r?\n?", re.DOTALL)
|
||||
|
||||
|
||||
def _parse_frontmatter(content: str) -> dict:
|
||||
"""Extract a shallow `key: value` frontmatter block, if present."""
|
||||
m = _FRONTMATTER_RE.match(content)
|
||||
if not m:
|
||||
return {}
|
||||
fields: dict = {}
|
||||
for line in m.group(1).splitlines():
|
||||
if ":" not in line:
|
||||
continue
|
||||
key, _, value = line.partition(":")
|
||||
key = key.strip()
|
||||
value = value.strip()
|
||||
if key:
|
||||
fields[key] = value
|
||||
return fields
|
||||
|
||||
|
||||
class SkillService:
|
||||
def __init__(self, skills_dir: Path):
|
||||
self.skills_dir = skills_dir
|
||||
self.skills_dir.mkdir(parents=True, exist_ok=True)
|
||||
self._seed_default_skill()
|
||||
|
||||
def _seed_default_skill(self) -> None:
|
||||
"""Seed a starter skill file the first time the directory is empty."""
|
||||
try:
|
||||
has_any = any(
|
||||
p.is_file() and p.suffix.lstrip(".").lower() in ALLOWED_EXTS
|
||||
for p in self.skills_dir.iterdir()
|
||||
)
|
||||
except FileNotFoundError:
|
||||
has_any = False
|
||||
if not has_any:
|
||||
(self.skills_dir / DEFAULT_SKILL_FILENAME).write_text(
|
||||
DEFAULT_SKILL_CONTENT, encoding="utf-8"
|
||||
)
|
||||
(self.skills_dir / SKILL_CREATOR_FILENAME).write_text(
|
||||
SKILL_CREATOR_CONTENT, encoding="utf-8"
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# File CRUD
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def list_skills(self) -> list[dict]:
|
||||
"""Return sorted list of skill files with parsed frontmatter metadata."""
|
||||
if not self.skills_dir.exists():
|
||||
return []
|
||||
files = []
|
||||
for path in sorted(self.skills_dir.glob("*")):
|
||||
if not (path.is_file() and path.suffix.lstrip(".").lower() in ALLOWED_EXTS):
|
||||
continue
|
||||
try:
|
||||
content = path.read_text(encoding="utf-8")
|
||||
except Exception:
|
||||
content = ""
|
||||
meta = _parse_frontmatter(content)
|
||||
files.append({
|
||||
"filename": path.name,
|
||||
"name": meta.get("name") or path.stem,
|
||||
"description": meta.get("description", ""),
|
||||
"size": path.stat().st_size,
|
||||
})
|
||||
return files
|
||||
|
||||
def get_content(self, filename: str) -> str:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError(f"File not found: {filename}")
|
||||
return path.read_text(encoding="utf-8")
|
||||
|
||||
def save_file(self, filename: str, content: str) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
path.write_text(content, encoding="utf-8")
|
||||
meta = _parse_frontmatter(content)
|
||||
return {
|
||||
"filename": path.name,
|
||||
"name": meta.get("name") or path.stem,
|
||||
"description": meta.get("description", ""),
|
||||
"size": path.stat().st_size,
|
||||
}
|
||||
|
||||
def delete_file(self, filename: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if path.is_file():
|
||||
path.unlink()
|
||||
|
||||
def _safe_path(self, filename: str) -> Path | None:
|
||||
if not filename or not filename.strip():
|
||||
return None
|
||||
# Skills are a flat directory - reject any path separator (no subfolders).
|
||||
name = filename.strip()
|
||||
if "/" in name or "\\" in name or name in (".", ".."):
|
||||
return None
|
||||
ext = Path(name).suffix.lstrip(".").lower()
|
||||
if ext not in ALLOWED_EXTS:
|
||||
return None
|
||||
return self.skills_dir / name
|
||||
@@ -0,0 +1,108 @@
|
||||
"""Tagger タグ DB サービス（SQLite）。"""
|
||||
|
||||
import csv
|
||||
import io
|
||||
import logging
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TaggerDbService:
|
||||
def __init__(self, db_file: Path):
|
||||
self.db_file = db_file
|
||||
self._init_db()
|
||||
|
||||
def _conn(self) -> sqlite3.Connection:
|
||||
return sqlite3.connect(str(self.db_file))
|
||||
|
||||
def _init_db(self):
|
||||
self.db_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
with self._conn() as conn:
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS images (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
filename TEXT,
|
||||
interrogator_tags TEXT,
|
||||
vlm_tags TEXT,
|
||||
all_tags TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
for sql in [
|
||||
"CREATE INDEX IF NOT EXISTS idx_all_tags ON images(all_tags)",
|
||||
"CREATE INDEX IF NOT EXISTS idx_filename ON images(filename)",
|
||||
"CREATE INDEX IF NOT EXISTS idx_created_at ON images(created_at DESC)",
|
||||
]:
|
||||
conn.execute(sql)
|
||||
conn.commit()
|
||||
|
||||
def save(self, filename: str, interrogator_tags: str, vlm_tags: str) -> int:
|
||||
all_tags = ", ".join(filter(None, [interrogator_tags, vlm_tags]))
|
||||
with self._conn() as conn:
|
||||
cur = conn.execute(
|
||||
"INSERT INTO images (filename, interrogator_tags, vlm_tags, all_tags) VALUES (?, ?, ?, ?)",
|
||||
(filename, interrogator_tags, vlm_tags, all_tags),
|
||||
)
|
||||
conn.commit()
|
||||
return cur.lastrowid
|
||||
|
||||
def list(self, limit: int = 100, offset: int = 0) -> list:
|
||||
with self._conn() as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT id, filename, interrogator_tags, vlm_tags, all_tags, created_at "
|
||||
"FROM images ORDER BY id DESC LIMIT ? OFFSET ?",
|
||||
(limit, offset),
|
||||
).fetchall()
|
||||
return [self._row(r) for r in rows]
|
||||
|
||||
def total(self) -> int:
|
||||
with self._conn() as conn:
|
||||
return conn.execute("SELECT COUNT(*) FROM images").fetchone()[0]
|
||||
|
||||
def search(self, q: str) -> list:
|
||||
pattern = f"%{q}%"
|
||||
with self._conn() as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT id, filename, interrogator_tags, vlm_tags, all_tags, created_at "
|
||||
"FROM images WHERE all_tags LIKE ? OR filename LIKE ? ORDER BY id DESC LIMIT 200",
|
||||
(pattern, pattern),
|
||||
).fetchall()
|
||||
return [self._row(r) for r in rows]
|
||||
|
||||
def update(self, row_id: int, interrogator_tags: str, vlm_tags: str):
|
||||
all_tags = ", ".join(filter(None, [interrogator_tags, vlm_tags]))
|
||||
with self._conn() as conn:
|
||||
conn.execute(
|
||||
"UPDATE images SET interrogator_tags=?, vlm_tags=?, all_tags=? WHERE id=?",
|
||||
(interrogator_tags, vlm_tags, all_tags, row_id),
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
def delete(self, row_id: int):
|
||||
with self._conn() as conn:
|
||||
conn.execute("DELETE FROM images WHERE id=?", (row_id,))
|
||||
conn.commit()
|
||||
|
||||
def export_csv(self) -> str:
|
||||
with self._conn() as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT id, filename, interrogator_tags, vlm_tags, all_tags, created_at FROM images ORDER BY id"
|
||||
).fetchall()
|
||||
buf = io.StringIO()
|
||||
writer = csv.writer(buf)
|
||||
writer.writerow(["id", "filename", "interrogator_tags", "vlm_tags", "all_tags", "created_at"])
|
||||
writer.writerows(rows)
|
||||
return buf.getvalue()
|
||||
|
||||
@staticmethod
|
||||
def _row(r) -> dict:
|
||||
return {
|
||||
"id": r[0],
|
||||
"filename": r[1] or "",
|
||||
"interrogator_tags": r[2] or "",
|
||||
"vlm_tags": r[3] or "",
|
||||
"all_tags": r[4] or "",
|
||||
"created_at": r[5] or "",
|
||||
}
|
||||
@@ -0,0 +1,402 @@
|
||||
"""WD Tagger / DeepDanbooru / Ollama VLM 推論サービス。"""
|
||||
|
||||
import base64
|
||||
import csv
|
||||
import io
|
||||
import json
|
||||
import logging
|
||||
import tempfile
|
||||
import threading
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps, PngImagePlugin
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_model_lock = threading.Lock()
|
||||
|
||||
|
||||
class TaggerService:
|
||||
def __init__(self, models_dir: Path, settings_file: Path):
|
||||
self.models_dir = models_dir
|
||||
self.settings_file = settings_file
|
||||
self._cache: dict = {}
|
||||
self._batch_state: dict = {
|
||||
"running": False, "total": 0, "done": 0, "log": [], "stop": False,
|
||||
}
|
||||
|
||||
# ── 設定 ──────────────────────────────────────────────────
|
||||
|
||||
def load_settings(self) -> dict:
|
||||
if self.settings_file.exists():
|
||||
try:
|
||||
with open(self.settings_file, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except Exception:
|
||||
pass
|
||||
return {}
|
||||
|
||||
def save_settings(self, data: dict):
|
||||
self.settings_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(self.settings_file, "w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def _effective_models_dir(self) -> Path:
|
||||
custom = self.load_settings().get("models_dir", "").strip()
|
||||
if custom:
|
||||
p = Path(custom)
|
||||
if p.is_dir():
|
||||
return p
|
||||
return self.models_dir
|
||||
|
||||
# ── モデル一覧 ─────────────────────────────────────────────
|
||||
|
||||
def list_models(self) -> list:
|
||||
mdir = self._effective_models_dir()
|
||||
models = []
|
||||
if not mdir.is_dir():
|
||||
return models
|
||||
for item in sorted(mdir.iterdir()):
|
||||
if not item.is_dir():
|
||||
continue
|
||||
exts = {f.suffix.lower() for f in item.iterdir() if f.is_file()}
|
||||
if ".onnx" in exts:
|
||||
models.append({"name": item.name, "type": "onnx"})
|
||||
elif ".h5" in exts:
|
||||
models.append({"name": item.name, "type": "deepdanbooru"})
|
||||
return models
|
||||
|
||||
# ── モデルロード ──────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _validate_model_name(name: str) -> bool:
|
||||
"""パストラバーサル防止: セパレータ・NUL・相対参照を拒否"""
|
||||
return bool(name) and "/" not in name and "\\" not in name and "\x00" not in name and name not in (".", "..")
|
||||
|
||||
def _load_model(self, model_name: str) -> Optional[dict]:
|
||||
if not self._validate_model_name(model_name):
|
||||
logger.error("Invalid model name rejected: %r", model_name)
|
||||
return None
|
||||
if model_name in self._cache:
|
||||
return self._cache[model_name]
|
||||
with _model_lock:
|
||||
if model_name in self._cache:
|
||||
return self._cache[model_name]
|
||||
mdir = self._effective_models_dir()
|
||||
model_dir = mdir / model_name
|
||||
if not model_dir.is_dir():
|
||||
logger.error("Model dir not found: %s", model_dir)
|
||||
return None
|
||||
files = list(model_dir.iterdir())
|
||||
onnx = next((f for f in files if f.suffix == ".onnx"), None)
|
||||
h5 = next((f for f in files if f.suffix == ".h5"), None)
|
||||
if onnx:
|
||||
return self._load_onnx(model_name, onnx, model_dir)
|
||||
if h5:
|
||||
return self._load_deepdanbooru(model_name, h5, model_dir)
|
||||
logger.error("No .onnx or .h5 in %s", model_dir)
|
||||
return None
|
||||
|
||||
def _load_onnx(self, name: str, onnx_file: Path, model_dir: Path) -> Optional[dict]:
|
||||
try:
|
||||
import onnxruntime as ort
|
||||
providers = []
|
||||
try:
|
||||
import torch
|
||||
if torch.cuda.is_available():
|
||||
providers.append("CUDAExecutionProvider")
|
||||
except ImportError:
|
||||
pass
|
||||
providers.append("CPUExecutionProvider")
|
||||
session = ort.InferenceSession(str(onnx_file), providers=providers)
|
||||
labels = self._load_labels(model_dir)
|
||||
data = {
|
||||
"session": session,
|
||||
"labels": labels,
|
||||
"input_name": session.get_inputs()[0].name,
|
||||
"input_shape": session.get_inputs()[0].shape,
|
||||
"type": "onnx",
|
||||
}
|
||||
self._cache[name] = data
|
||||
logger.info("ONNX loaded: %s (%d labels)", name, len(labels))
|
||||
return data
|
||||
except ImportError:
|
||||
logger.error("onnxruntime not installed")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error("ONNX load error %s: %s", name, e)
|
||||
return None
|
||||
|
||||
def _load_deepdanbooru(self, name: str, h5_file: Path, model_dir: Path) -> Optional[dict]:
|
||||
try:
|
||||
import tensorflow as tf
|
||||
except ImportError:
|
||||
logger.warning("TensorFlow not installed — DeepDanbooru unavailable")
|
||||
return None
|
||||
try:
|
||||
model = tf.keras.models.load_model(str(h5_file))
|
||||
labels = self._load_labels(model_dir)
|
||||
data = {"session": model, "labels": labels, "type": "deepdanbooru"}
|
||||
self._cache[name] = data
|
||||
logger.info("DeepDanbooru loaded: %s (%d labels)", name, len(labels))
|
||||
return data
|
||||
except Exception as e:
|
||||
logger.error("DeepDanbooru load error %s: %s", name, e)
|
||||
return None
|
||||
|
||||
def _load_labels(self, model_dir: Path) -> list:
|
||||
for fname in ("selected_tags.csv", "tags.csv", "tags.txt", "tags-general.txt"):
|
||||
p = model_dir / fname
|
||||
if not p.exists():
|
||||
continue
|
||||
try:
|
||||
if p.suffix == ".csv":
|
||||
with open(p, "r", encoding="utf-8-sig", newline="") as f:
|
||||
reader = csv.reader(f)
|
||||
next(reader, None)
|
||||
return [row[1] for row in reader if len(row) > 1]
|
||||
else:
|
||||
with open(p, "r", encoding="utf-8") as f:
|
||||
return [ln.strip() for ln in f if ln.strip()]
|
||||
except Exception as e:
|
||||
logger.warning("Label load failed %s: %s", p, e)
|
||||
return []
|
||||
|
||||
# ── 推論（WD Tagger / DeepDanbooru） ────────────────────
|
||||
|
||||
def predict(self, image_b64: str, model_name: str, threshold: float = 0.35, char_threshold: float = 0.85) -> dict:
|
||||
img = self._decode_b64(image_b64)
|
||||
data = self._load_model(model_name)
|
||||
if data is None:
|
||||
return {"error": f"Model '{model_name}' could not be loaded"}
|
||||
try:
|
||||
if data["type"] == "deepdanbooru":
|
||||
tags = self._predict_dd(img, data, threshold, char_threshold)
|
||||
else:
|
||||
tags = self._predict_onnx(img, data, threshold, char_threshold)
|
||||
return {"tags": ", ".join(tags), "count": len(tags)}
|
||||
except Exception as e:
|
||||
logger.error("predict error: %s", e)
|
||||
return {"error": str(e)}
|
||||
|
||||
def _predict_onnx(self, img: Image.Image, data: dict, thr: float, char_thr: float) -> list:
|
||||
shape = data["input_shape"]
|
||||
fmt = "NHWC"
|
||||
size = (224, 224)
|
||||
if len(shape) == 4:
|
||||
if shape[1] == 3 and isinstance(shape[2], int) and shape[2] > 0:
|
||||
fmt = "NCHW"
|
||||
size = (int(shape[3]), int(shape[2]))
|
||||
elif shape[3] == 3 and isinstance(shape[1], int) and shape[1] > 0:
|
||||
fmt = "NHWC"
|
||||
size = (int(shape[2]), int(shape[1]))
|
||||
|
||||
img = ImageOps.exif_transpose(img)
|
||||
if img.mode != "RGB":
|
||||
img = img.convert("RGB")
|
||||
arr = np.array(img.resize(size, Image.Resampling.LANCZOS), dtype=np.float32) / 255.0
|
||||
if fmt == "NCHW":
|
||||
arr = arr.transpose((2, 0, 1))
|
||||
arr = np.expand_dims(arr, 0)
|
||||
preds = data["session"].run(None, {data["input_name"]: arr})[0][0]
|
||||
return self._filter_tags(preds, data["labels"], thr, char_thr)
|
||||
|
||||
def _predict_dd(self, img: Image.Image, data: dict, thr: float, char_thr: float) -> list:
|
||||
img = ImageOps.exif_transpose(img)
|
||||
if img.mode != "RGB":
|
||||
img = img.convert("RGB")
|
||||
arr = np.array(img.resize((512, 512), Image.Resampling.LANCZOS), dtype=np.float32) / 255.0
|
||||
arr = np.expand_dims(arr, 0)
|
||||
preds = data["session"].predict(arr)[0]
|
||||
return self._filter_tags(preds, data["labels"], thr, char_thr)
|
||||
|
||||
def _filter_tags(self, preds, labels: list, thr: float, char_thr: float) -> list:
|
||||
tags = []
|
||||
for i, score in enumerate(preds):
|
||||
if i >= len(labels):
|
||||
break
|
||||
label = labels[i]
|
||||
if float(score) >= (char_thr if label.startswith("character:") else thr):
|
||||
tags.append(label.replace("character:", ""))
|
||||
return tags
|
||||
|
||||
# ── Ollama VLM ────────────────────────────────────────────
|
||||
|
||||
def ollama_models(self, api_url: str) -> list:
|
||||
try:
|
||||
req = urllib.request.Request(api_url.rstrip("/") + "/api/tags")
|
||||
with urllib.request.urlopen(req, timeout=5) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
return sorted(m["name"] for m in data.get("models", []))
|
||||
except Exception as e:
|
||||
logger.error("ollama_models: %s", e)
|
||||
return []
|
||||
|
||||
def ollama_predict(self, image_b64: str, api_url: str, model: str, prompt: str, max_tags: int = 40) -> dict:
|
||||
payload = json.dumps({
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": prompt, "images": [image_b64]}],
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.7, "num_ctx": 4096},
|
||||
}).encode("utf-8")
|
||||
try:
|
||||
req = urllib.request.Request(
|
||||
api_url.rstrip("/") + "/api/chat",
|
||||
data=payload,
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=180) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
content = data.get("message", {}).get("content", "")
|
||||
tags = [t.strip() for t in content.split(",") if t.strip()][:max_tags]
|
||||
return {"tags": ", ".join(tags), "count": len(tags)}
|
||||
except Exception as e:
|
||||
logger.error("ollama_predict: %s", e)
|
||||
return {"error": str(e)}
|
||||
|
||||
# ── ファイルメタデータ書込 ────────────────────────────────
|
||||
|
||||
_ALLOWED_WRITE_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".gif"}
|
||||
|
||||
def write_meta_to_file(self, image_path: str, tags: str) -> dict:
|
||||
path = Path(image_path).resolve()
|
||||
if path.suffix.lower() not in self._ALLOWED_WRITE_EXTS:
|
||||
return {"error": "Unsupported file type"}
|
||||
if not path.is_file():
|
||||
return {"error": f"File not found: {image_path}"}
|
||||
try:
|
||||
import piexif
|
||||
except ImportError:
|
||||
piexif = None
|
||||
try:
|
||||
with Image.open(path) as img:
|
||||
img.load()
|
||||
ext = path.suffix.lower()
|
||||
if ext in (".jpg", ".jpeg") and piexif:
|
||||
self._write_jpeg(img, path, tags, piexif)
|
||||
elif ext == ".png":
|
||||
self._write_png(img, path, tags)
|
||||
else:
|
||||
self._write_sidecar(path, tags)
|
||||
return {"ok": True}
|
||||
except Exception as e:
|
||||
logger.error("write_meta: %s", e)
|
||||
return {"error": str(e)}
|
||||
|
||||
def _write_jpeg(self, img: Image.Image, path: Path, tags: str, piexif):
|
||||
exif_data = img.info.get("exif")
|
||||
if exif_data:
|
||||
try:
|
||||
exif_dict = piexif.load(exif_data)
|
||||
except Exception:
|
||||
exif_dict = {"0th": {}, "Exif": {}, "1st": {}, "GPS": {}, "Interop": {}}
|
||||
else:
|
||||
exif_dict = {"0th": {}, "Exif": {}, "1st": {}, "GPS": {}, "Interop": {}}
|
||||
exif_dict["0th"][piexif.ImageIFD.ImageDescription] = tags.encode("utf-8")
|
||||
exif_bytes = piexif.dump(exif_dict)
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=path.suffix) as tmp:
|
||||
tmp_path = Path(tmp.name)
|
||||
img.save(str(tmp_path), "JPEG", quality=95, exif=exif_bytes)
|
||||
tmp_path.replace(path)
|
||||
|
||||
def _write_png(self, img: Image.Image, path: Path, tags: str):
|
||||
meta = PngImagePlugin.PngInfo()
|
||||
for k, v in img.info.items():
|
||||
if isinstance(k, str) and isinstance(v, str):
|
||||
meta.add_text(k, v)
|
||||
meta.add_text("Tags", tags)
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
|
||||
tmp_path = Path(tmp.name)
|
||||
img.save(str(tmp_path), "PNG", pnginfo=meta)
|
||||
tmp_path.replace(path)
|
||||
|
||||
def _write_sidecar(self, path: Path, tags: str):
|
||||
sidecar = path.with_suffix(".tags.json")
|
||||
with open(sidecar, "w", encoding="utf-8") as f:
|
||||
json.dump({"tags": tags}, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# ── バッチ処理 ────────────────────────────────────────────
|
||||
|
||||
def batch_status(self) -> dict:
|
||||
s = self._batch_state
|
||||
return {
|
||||
"running": s["running"],
|
||||
"total": s["total"],
|
||||
"done": s["done"],
|
||||
"log": s["log"][-50:],
|
||||
}
|
||||
|
||||
def batch_stop(self):
|
||||
self._batch_state["stop"] = True
|
||||
|
||||
def batch_start(self, folder: str, model_name: str, threshold: float, char_threshold: float,
|
||||
use_ollama: bool, ollama_api: str, ollama_model: str, ollama_prompt: str,
|
||||
ollama_max_tags: int, save_db: bool, write_file: bool, write_txt: bool, db_service) -> dict:
|
||||
if self._batch_state["running"]:
|
||||
return {"error": "Batch already running"}
|
||||
folder_path = Path(folder)
|
||||
if not folder_path.is_dir():
|
||||
return {"error": f"Folder not found: {folder}"}
|
||||
|
||||
exts = {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".gif"}
|
||||
files = sorted(f for f in folder_path.iterdir() if f.is_file() and f.suffix.lower() in exts)
|
||||
if not files:
|
||||
return {"error": "No image files found"}
|
||||
|
||||
self._batch_state = {
|
||||
"running": True, "total": len(files), "done": 0, "log": [], "stop": False,
|
||||
}
|
||||
|
||||
def _run():
|
||||
for fpath in files:
|
||||
if self._batch_state["stop"]:
|
||||
self._batch_state["log"].append("Stopped.")
|
||||
break
|
||||
try:
|
||||
with Image.open(fpath) as img:
|
||||
img.load()
|
||||
buf = io.BytesIO()
|
||||
img.save(buf, format="PNG")
|
||||
b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
|
||||
|
||||
interrogator_tags = ""
|
||||
if model_name:
|
||||
res = self.predict(b64, model_name, threshold, char_threshold)
|
||||
interrogator_tags = res.get("tags", "")
|
||||
|
||||
vlm_tags = ""
|
||||
if use_ollama and ollama_model:
|
||||
res2 = self.ollama_predict(b64, ollama_api, ollama_model, ollama_prompt, ollama_max_tags)
|
||||
vlm_tags = res2.get("tags", "")
|
||||
|
||||
all_tags = ", ".join(filter(None, [interrogator_tags, vlm_tags]))
|
||||
|
||||
if save_db:
|
||||
db_service.save(str(fpath), interrogator_tags, vlm_tags)
|
||||
if write_file and all_tags:
|
||||
self.write_meta_to_file(str(fpath), all_tags)
|
||||
if write_txt and all_tags:
|
||||
txt_path = fpath.with_suffix(".txt")
|
||||
with open(txt_path, "w", encoding="utf-8") as f:
|
||||
f.write(all_tags)
|
||||
|
||||
n = len([t for t in all_tags.split(",") if t.strip()])
|
||||
self._batch_state["log"].append(f"✓ {fpath.name}: {n} tags")
|
||||
except Exception as e:
|
||||
self._batch_state["log"].append(f"✗ {fpath.name}: {e}")
|
||||
self._batch_state["done"] += 1
|
||||
|
||||
self._batch_state["running"] = False
|
||||
|
||||
threading.Thread(target=_run, daemon=True).start()
|
||||
return {"ok": True, "total": len(files)}
|
||||
|
||||
# ── ユーティリティ ────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _decode_b64(b64: str) -> Image.Image:
|
||||
return Image.open(io.BytesIO(base64.b64decode(b64)))
|
||||
@@ -0,0 +1,205 @@
|
||||
"""Wildcard file management service."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import shutil
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
ALLOWED_EXTS = {"txt", "yaml", "yml"}
|
||||
|
||||
|
||||
class WildcardService:
|
||||
def __init__(self, wildcard_dir: Path):
|
||||
self.wildcard_dir = wildcard_dir
|
||||
# Only mkdir if it is a real directory (not a junction/symlink)
|
||||
if not self.wildcard_dir.exists() and not self._is_junction(self.wildcard_dir):
|
||||
self.wildcard_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# File CRUD
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def list_wildcards(self) -> list[dict]:
|
||||
"""Return sorted list of wildcard files recursively (follows junction/symlink)."""
|
||||
if not self.wildcard_dir.exists():
|
||||
return []
|
||||
files = []
|
||||
for path in sorted(self.wildcard_dir.rglob("*")):
|
||||
if path.is_file() and path.suffix.lstrip(".").lower() in ALLOWED_EXTS:
|
||||
rel = path.relative_to(self.wildcard_dir)
|
||||
dir_posix = rel.parent.as_posix()
|
||||
if dir_posix == ".":
|
||||
dir_posix = ""
|
||||
files.append({
|
||||
"name": path.stem,
|
||||
"filename": rel.as_posix(),
|
||||
"ext": path.suffix.lstrip(".").lower(),
|
||||
"size": path.stat().st_size,
|
||||
"dir": dir_posix,
|
||||
"wc_name": rel.with_suffix("").as_posix(),
|
||||
})
|
||||
return files
|
||||
|
||||
def get_content(self, filename: str) -> str:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError(f"File not found: {filename}")
|
||||
return path.read_text(encoding="utf-8")
|
||||
|
||||
def save_file(self, filename: str, content: str) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(content, encoding="utf-8")
|
||||
rel = path.relative_to(self.wildcard_dir)
|
||||
dir_posix = rel.parent.as_posix()
|
||||
if dir_posix == ".":
|
||||
dir_posix = ""
|
||||
return {
|
||||
"name": path.stem,
|
||||
"filename": rel.as_posix(),
|
||||
"ext": path.suffix.lstrip(".").lower(),
|
||||
"size": path.stat().st_size,
|
||||
"dir": dir_posix,
|
||||
"wc_name": rel.with_suffix("").as_posix(),
|
||||
}
|
||||
|
||||
def delete_file(self, filename: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if path.is_file():
|
||||
path.unlink()
|
||||
|
||||
def _safe_path(self, filename: str) -> Path | None:
|
||||
if not filename or not filename.strip():
|
||||
return None
|
||||
# Normalize to forward slashes and strip leading/trailing slashes
|
||||
filename = filename.replace("\\", "/").strip("/")
|
||||
# Validate each path component — block traversal and empty parts
|
||||
parts = filename.split("/")
|
||||
for p in parts:
|
||||
if not p or p == ".." or p == ".":
|
||||
return None
|
||||
ext = Path(parts[-1]).suffix.lstrip(".").lower()
|
||||
if ext not in ALLOWED_EXTS:
|
||||
return None
|
||||
return self.wildcard_dir.joinpath(*parts)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Impact Pack integration
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def find_impact_pack_wildcards(comfyui_root: Path) -> Path | None:
|
||||
"""Return Impact Pack wildcards directory path, or None if not installed."""
|
||||
custom_nodes = comfyui_root / "custom_nodes"
|
||||
if not custom_nodes.is_dir():
|
||||
return None
|
||||
for d in custom_nodes.iterdir():
|
||||
if d.is_dir() and "impact-pack" in d.name.lower():
|
||||
wildcards = d / "wildcards"
|
||||
if wildcards.is_dir():
|
||||
return wildcards
|
||||
return None
|
||||
|
||||
def get_link_status(self, comfyui_root: Path) -> dict:
|
||||
"""Return current Impact Pack integration status."""
|
||||
impact_dir = self.find_impact_pack_wildcards(comfyui_root)
|
||||
is_linked = self._is_junction(self.wildcard_dir) or self.wildcard_dir.is_symlink()
|
||||
|
||||
resolved_target = None
|
||||
if is_linked:
|
||||
try:
|
||||
resolved_target = str(self.wildcard_dir.resolve())
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return {
|
||||
"impact_pack_installed": impact_dir is not None,
|
||||
"impact_pack_wildcards_dir": str(impact_dir) if impact_dir else None,
|
||||
"wfs_wildcard_dir": str(self.wildcard_dir),
|
||||
"is_linked": is_linked,
|
||||
"link_target": resolved_target,
|
||||
}
|
||||
|
||||
def create_link(self, comfyui_root: Path) -> dict:
|
||||
"""Replace WFS wildcard dir with a junction/symlink → Impact Pack wildcards."""
|
||||
impact_dir = self.find_impact_pack_wildcards(comfyui_root)
|
||||
if impact_dir is None:
|
||||
raise RuntimeError("ComfyUI-Impact-Pack is not installed.")
|
||||
|
||||
if self._is_junction(self.wildcard_dir) or self.wildcard_dir.is_symlink():
|
||||
raise RuntimeError("Link already exists.")
|
||||
|
||||
# Migrate existing WFS files to Impact Pack dir
|
||||
migrated: list[str] = []
|
||||
if self.wildcard_dir.is_dir():
|
||||
for f in self.wildcard_dir.iterdir():
|
||||
if f.is_file():
|
||||
dest = impact_dir / f.name
|
||||
if not dest.exists():
|
||||
shutil.copy2(str(f), str(dest))
|
||||
migrated.append(f.name)
|
||||
shutil.rmtree(str(self.wildcard_dir))
|
||||
|
||||
# Create junction (Windows) or symlink (others)
|
||||
self._create_junction_or_symlink(self.wildcard_dir, impact_dir)
|
||||
logger.info("Wildcard link created: %s -> %s", self.wildcard_dir, impact_dir)
|
||||
return {"migrated_files": migrated}
|
||||
|
||||
def remove_link(self) -> None:
|
||||
"""Remove junction/symlink and restore WFS wildcard dir as a plain directory."""
|
||||
if not (self._is_junction(self.wildcard_dir) or self.wildcard_dir.is_symlink()):
|
||||
raise RuntimeError("No link to remove.")
|
||||
self._remove_junction_or_symlink(self.wildcard_dir)
|
||||
self.wildcard_dir.mkdir(parents=True, exist_ok=True)
|
||||
logger.info("Wildcard link removed, restored plain dir: %s", self.wildcard_dir)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Platform helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _is_junction(path: Path) -> bool:
|
||||
"""Return True if path is a Windows directory junction."""
|
||||
if platform.system() != "Windows":
|
||||
return False
|
||||
try:
|
||||
import ctypes
|
||||
FILE_ATTRIBUTE_REPARSE_POINT = 0x0400
|
||||
attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))
|
||||
return attrs != 0xFFFFFFFF and bool(attrs & FILE_ATTRIBUTE_REPARSE_POINT)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _create_junction_or_symlink(link: Path, target: Path) -> None:
|
||||
if platform.system() == "Windows":
|
||||
result = subprocess.run(
|
||||
["cmd", "/c", "mklink", "/J", str(link), str(target)],
|
||||
capture_output=True, text=True,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
raise RuntimeError(f"mklink /J failed: {result.stderr.strip()}")
|
||||
else:
|
||||
os.symlink(target, link, target_is_directory=True)
|
||||
|
||||
@staticmethod
|
||||
def _remove_junction_or_symlink(path: Path) -> None:
|
||||
if platform.system() == "Windows":
|
||||
result = subprocess.run(
|
||||
["cmd", "/c", "rmdir", str(path)],
|
||||
capture_output=True, text=True,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
raise RuntimeError(f"rmdir failed: {result.stderr.strip()}")
|
||||
else:
|
||||
os.unlink(str(path))
|
||||
@@ -3,6 +3,35 @@
|
||||
import os
|
||||
import re
|
||||
|
||||
# プロンプトを直接持つノードタイプ（CLIPTextEncode以外の複合ノード含む）
|
||||
_PROMPT_NODE_TYPES = {
|
||||
"CLIPTextEncode", "CLIPTextEncodeSDXL", "CLIPTextEncodeSDXLRefiner",
|
||||
"CLIPTextEncodeEditPlus", "TextEncodeQwenImageEditPlus", "TextEncodeMageFlowEdit",
|
||||
"SDXLPromptStyler", "SDXLPromptStylerAdvanced", "ImageMetadataPromptLoader",
|
||||
"WFS_PromptText", "ImpactWildcardEncode", "ImpactWildcardProcessor",
|
||||
}
|
||||
|
||||
# 画像を最終出力として保存するノードタイプ
|
||||
_OUTPUT_IMAGE_NODE_TYPES = {"SaveImage", "SaveImageAdvanced"}
|
||||
|
||||
# CLIPLoader / DualCLIPLoader の type フィールド → モデル種別マッピング
|
||||
_CLIP_TYPE_TO_MODEL = {
|
||||
"flux": "Flux",
|
||||
"flux2": "Flux2",
|
||||
"newbie": "NewBie",
|
||||
"ovis": "Ovis",
|
||||
"qwen_image": "Qwen",
|
||||
"sd3": "SD3",
|
||||
"sd3.5": "SD3",
|
||||
"hidream_i": "HiDream",
|
||||
"hidream_e": "HiDream",
|
||||
"hidream": "HiDream",
|
||||
"wan": "Wan",
|
||||
"cosmos": "Cosmos",
|
||||
"lumina": "Lumina",
|
||||
"lumina2": "Z-IMAGE",
|
||||
}
|
||||
|
||||
|
||||
def _model_name_from_ui_node(node):
|
||||
"""Extract model filename from UI format node (lowercase)."""
|
||||
@@ -11,16 +40,65 @@ def _model_name_from_ui_node(node):
|
||||
return val.lower() if isinstance(val, str) else ""
|
||||
|
||||
|
||||
def _clip_type_from_ui_node(node):
|
||||
"""Extract CLIPLoader type string from widgets_values (lowercase)."""
|
||||
ntype = node.get("type", "")
|
||||
vals = node.get("widgets_values", [])
|
||||
if ntype == "CLIPLoader" and len(vals) > 1:
|
||||
return str(vals[1]).lower() if isinstance(vals[1], str) else ""
|
||||
if ntype in ("DualCLIPLoader", "TripleCLIPLoader") and len(vals) > 2:
|
||||
return str(vals[2]).lower() if isinstance(vals[2], str) else ""
|
||||
return ""
|
||||
|
||||
|
||||
def _model_name_from_api_node(node):
|
||||
"""Extract model filename from API format node (lowercase)."""
|
||||
inputs = node.get("inputs", {})
|
||||
for key in ("ckpt_name", "unet_name", "model_name"):
|
||||
for key in ("ckpt_name", "unet_name", "model_name", "clip_name1", "clip_name"):
|
||||
val = inputs.get(key, "")
|
||||
if isinstance(val, str) and val:
|
||||
return val.lower()
|
||||
return ""
|
||||
|
||||
|
||||
def _collect_all_ui_nodes(workflow_data):
|
||||
"""Yield all UI-format nodes including those inside definitions.subgraphs."""
|
||||
for node in workflow_data.get("nodes", []):
|
||||
yield node
|
||||
defs = workflow_data.get("definitions", {})
|
||||
if isinstance(defs, dict):
|
||||
for sg in defs.get("subgraphs", []):
|
||||
if isinstance(sg, dict):
|
||||
for node in sg.get("nodes", []):
|
||||
yield node
|
||||
|
||||
|
||||
def _detect_model_type_from_name(mn_base, model_types):
|
||||
"""Detect model type from a model filename (already lowercased basename)."""
|
||||
if re.search(r"sd1[._-]?5|v1[._-]5", mn_base):
|
||||
model_types.add("SD1.5")
|
||||
elif re.search(r"sdxl|[._-]xl[._-]|[._-]xl$|xl[._-]", mn_base):
|
||||
model_types.add("SDXL")
|
||||
elif re.search(r"flux[-_.]?2", mn_base):
|
||||
model_types.add("Flux2")
|
||||
elif "flux" in mn_base:
|
||||
model_types.add("Flux")
|
||||
elif re.search(r"sd[._-]?3", mn_base):
|
||||
model_types.add("SD3")
|
||||
elif "qwen" in mn_base:
|
||||
model_types.add("Qwen")
|
||||
elif "zimage" in mn_base or "z-image" in mn_base or "zit" in mn_base:
|
||||
model_types.add("Z-IMAGE")
|
||||
elif "newbie" in mn_base:
|
||||
model_types.add("NewBie")
|
||||
elif "ovis" in mn_base:
|
||||
model_types.add("Ovis")
|
||||
elif "hidream" in mn_base:
|
||||
model_types.add("HiDream")
|
||||
elif "wan" in mn_base and re.search(r"wan[-_.]?\d|wan[-_]video", mn_base):
|
||||
model_types.add("Wan")
|
||||
|
||||
|
||||
def analyze_workflow(workflow_data, filename=""):
|
||||
"""Analyze workflow JSON and return model types, input/output node counts."""
|
||||
model_types = set()
|
||||
@@ -36,23 +114,33 @@ def analyze_workflow(workflow_data, filename=""):
|
||||
model_types.add("Qwen")
|
||||
if "zimage" in fn or "z-image" in fn or "zit" in fn:
|
||||
model_types.add("Z-IMAGE")
|
||||
if "flux2" in fn:
|
||||
if re.search(r"flux[-_.]?2", fn):
|
||||
model_types.add("Flux2")
|
||||
elif "flux" in fn:
|
||||
model_types.add("Flux")
|
||||
if "sd3" in fn:
|
||||
model_types.add("SD3")
|
||||
if "newbie" in fn:
|
||||
model_types.add("NewBie")
|
||||
if "ovis" in fn:
|
||||
model_types.add("Ovis")
|
||||
if "hidream" in fn:
|
||||
model_types.add("HiDream")
|
||||
if "wan" in fn and re.search(r"wan[-_.]?\d|wan[-_]video", fn):
|
||||
model_types.add("Wan")
|
||||
|
||||
# Support both UI format (nodes array) and API format (node ID object)
|
||||
raw_nodes = workflow_data.get("nodes", None)
|
||||
if raw_nodes is not None:
|
||||
# UI / app format: collect all nodes including subgraph definitions
|
||||
node_iter = [
|
||||
(
|
||||
node.get("type", ""),
|
||||
node.get("title", "").lower(),
|
||||
_model_name_from_ui_node(node),
|
||||
_clip_type_from_ui_node(node),
|
||||
)
|
||||
for node in raw_nodes
|
||||
for node in _collect_all_ui_nodes(workflow_data)
|
||||
]
|
||||
else:
|
||||
node_iter = []
|
||||
@@ -62,33 +150,32 @@ def analyze_workflow(workflow_data, filename=""):
|
||||
ntype = node.get("class_type", "")
|
||||
title = node.get("_meta", {}).get("title", "").lower()
|
||||
mn = _model_name_from_api_node(node)
|
||||
node_iter.append((ntype, title, mn))
|
||||
node_iter.append((ntype, title, mn, ""))
|
||||
|
||||
for ntype, title, mn in node_iter:
|
||||
for ntype, title, mn, clip_type in node_iter:
|
||||
if (
|
||||
"CheckpointLoader" in ntype
|
||||
or "UNETLoader" in ntype
|
||||
or "UnetLoader" in ntype # UnetLoaderGGUF 等を含む
|
||||
or "unet_name" in ntype
|
||||
):
|
||||
mn_base = os.path.basename(mn.replace("\\", "/"))
|
||||
if re.search(r"sd1[._-]?5|v1[._-]5", mn_base):
|
||||
model_types.add("SD1.5")
|
||||
elif re.search(r"sdxl|[._-]xl[._-]|[._-]xl$|xl[._-]", mn_base):
|
||||
model_types.add("SDXL")
|
||||
elif "flux2" in mn_base:
|
||||
model_types.add("Flux2")
|
||||
elif "flux" in mn_base:
|
||||
model_types.add("Flux")
|
||||
elif re.search(r"sd[._-]?3", mn_base):
|
||||
model_types.add("SD3")
|
||||
elif "qwen" in mn_base:
|
||||
model_types.add("Qwen")
|
||||
elif "zimage" in mn_base or "z-image" in mn_base or "zit" in mn_base:
|
||||
model_types.add("Z-IMAGE")
|
||||
_detect_model_type_from_name(mn_base, model_types)
|
||||
|
||||
# CLIPLoader 系: type フィールドまたはファイル名からモデル種別を検出
|
||||
# QuadrupleCLIPLoader 等、type フィールドを持たない新型ローダーにも対応
|
||||
if "CLIPLoader" in ntype:
|
||||
if clip_type:
|
||||
detected = _CLIP_TYPE_TO_MODEL.get(clip_type)
|
||||
if detected:
|
||||
model_types.add(detected)
|
||||
mn_base = os.path.basename(mn.replace("\\", "/"))
|
||||
if mn_base:
|
||||
_detect_model_type_from_name(mn_base, model_types)
|
||||
|
||||
if "sdxl" in title:
|
||||
model_types.add("SDXL")
|
||||
if "flux2" in title:
|
||||
if re.search(r"flux[-_.]?2", title):
|
||||
model_types.add("Flux2")
|
||||
elif "flux" in title:
|
||||
model_types.add("Flux")
|
||||
@@ -98,26 +185,34 @@ def analyze_workflow(workflow_data, filename=""):
|
||||
model_types.add("Qwen")
|
||||
if "zimage" in title or "z-image" in title or "zit" in title:
|
||||
model_types.add("Z-IMAGE")
|
||||
if "newbie" in title:
|
||||
model_types.add("NewBie")
|
||||
if "ovis" in title:
|
||||
model_types.add("Ovis")
|
||||
if "hidream" in title:
|
||||
model_types.add("HiDream")
|
||||
|
||||
if ntype == "CLIPTextEncode" or "prompt" in title:
|
||||
if ntype in _PROMPT_NODE_TYPES or "prompt" in title:
|
||||
inputs["prompts"] += 1
|
||||
if ntype in ("LoadImage", "LoadImageMask"):
|
||||
inputs["images"] += 1
|
||||
if ntype == "LoadVideo" or "load video" in title:
|
||||
inputs["videos"] += 1
|
||||
if ntype == "SaveImage":
|
||||
if ntype in _OUTPUT_IMAGE_NODE_TYPES:
|
||||
outputs["images"] += 1
|
||||
if ntype in ("SaveVideo", "VH_VideoCombine"):
|
||||
outputs["videos"] += 1
|
||||
|
||||
# Detect workflow format
|
||||
if raw_nodes is not None:
|
||||
# App format: UI-based structure with definitions (subgraphs) or linearMode
|
||||
# App format: simplified web-app-style UI (linearMode) or .app.json filename.
|
||||
# A workflow that merely *contains* subgraphs (definitions.subgraphs) is still a
|
||||
# normal node graph (comfyui-workflow.js's convertUiToApi flattens subgraphs before
|
||||
# conversion), so it's treated as "ui" here too — must match detectFormat() in
|
||||
# static/js/comfyui-workflow.js or the App/UI badge shown to the user disagrees
|
||||
# with what GenerateUI actually does with the file.
|
||||
extra = workflow_data.get("extra", {})
|
||||
if (
|
||||
"definitions" in workflow_data
|
||||
or extra.get("linearMode") is True
|
||||
):
|
||||
if extra.get("linearMode") is True:
|
||||
wf_format = "app"
|
||||
elif filename and filename.lower().endswith(".app.json"):
|
||||
wf_format = "app"
|
||||
|
||||
@@ -112,6 +112,7 @@ class WorkflowService:
|
||||
"summary": meta.get("summary", ""),
|
||||
"modelTypesOverride": override_types,
|
||||
"favorite": bool(meta.get("favorite", False)),
|
||||
"badges": meta.get("badges", []),
|
||||
},
|
||||
"mtime": stat.st_mtime,
|
||||
"thumbnail": thumbnail,
|
||||
@@ -132,7 +133,7 @@ class WorkflowService:
|
||||
"""Save metadata (tags, memo, summary, etc.) for a workflow."""
|
||||
metadata = self._load_metadata()
|
||||
entry = metadata.get(filename, {})
|
||||
for key in ("tags", "memo", "summary", "modelTypesOverride", "favorite"):
|
||||
for key in ("tags", "memo", "summary", "modelTypesOverride", "favorite", "badges"):
|
||||
if key in updates:
|
||||
entry[key] = updates[key]
|
||||
entry["updatedAt"] = self._now_iso()
|
||||
|
||||
@@ -4,7 +4,7 @@ import jinja2
|
||||
from aiohttp import web
|
||||
from pathlib import Path
|
||||
|
||||
from .config import TEMPLATES_DIR, STATIC_DIR, DATA_DIR, WORKFLOWS_DIR
|
||||
from .config import TEMPLATES_DIR, STATIC_DIR, DATA_DIR, WORKFLOWS_DIR, LAB_PLAN_DIR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -41,6 +41,22 @@ async def serve_workflow_file(request: web.Request) -> web.Response:
|
||||
})
|
||||
|
||||
|
||||
async def serve_lab_plan_file(request: web.Request) -> web.Response:
|
||||
"""Serve files (index thumbnails) from the Lab plan directory."""
|
||||
filename = request.match_info.get("filename", "")
|
||||
if not filename or ".." in filename or "/" in filename or "\\" in filename:
|
||||
return web.Response(status=404)
|
||||
|
||||
file_path = LAB_PLAN_DIR / filename
|
||||
if not file_path.is_file():
|
||||
return web.Response(status=404)
|
||||
|
||||
content_type, _ = mimetypes.guess_type(str(file_path))
|
||||
return web.FileResponse(file_path, headers={
|
||||
"Content-Type": content_type or "application/octet-stream",
|
||||
})
|
||||
|
||||
|
||||
class WorkflowStudio:
|
||||
"""Main entry point for Workflow Studio plugin."""
|
||||
|
||||
@@ -57,11 +73,14 @@ class WorkflowStudio:
|
||||
# Dynamic workflow file serving (thumbnails etc.)
|
||||
app.router.add_get("/wfm_data/workflows/{filename}", serve_workflow_file)
|
||||
|
||||
# Dynamic Lab plan file serving (index thumbnails)
|
||||
app.router.add_get("/wfm_data/lab_plan/{filename}", serve_lab_plan_file)
|
||||
|
||||
# Main page
|
||||
app.router.add_get("/wfm", serve_index_page)
|
||||
|
||||
# API routes
|
||||
from .routes import workflow_routes, settings_routes, ollama_routes, eagle_routes, nodes_routes, prompts_routes, models_routes
|
||||
from .routes import workflow_routes, settings_routes, ollama_routes, eagle_routes, nodes_routes, prompts_routes, models_routes, gallery_routes, wildcard_routes, tagger_routes, gmic_routes, skill_routes, lab_routes
|
||||
|
||||
workflow_routes.setup_routes(app)
|
||||
settings_routes.setup_routes(app)
|
||||
@@ -70,5 +89,11 @@ class WorkflowStudio:
|
||||
nodes_routes.setup_routes(app)
|
||||
prompts_routes.setup_routes(app)
|
||||
models_routes.setup_routes(app)
|
||||
gallery_routes.setup_routes(app)
|
||||
wildcard_routes.setup_routes(app)
|
||||
tagger_routes.setup_routes(app)
|
||||
gmic_routes.setup_routes(app)
|
||||
skill_routes.setup_routes(app)
|
||||
lab_routes.setup_routes(app)
|
||||
|
||||
logger.info("Workflow Studio: Routes registered successfully")
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
[project]
|
||||
name = "comfyui-workflow-studio"
|
||||
description = "A comprehensive workflow, asset management, and generation UI plugin for ComfyUI. Manage workflows, models, images, and prompts; batch generation and Image Feeder in the GenerateUI tab; library side panel with canvas drag & drop and AI tools."
|
||||
version = "0.3.95"
|
||||
license = "MIT"
|
||||
requires-python = ">= 3.10"
|
||||
dependencies = ["jinja2"]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/ketle-man/ComfyUI-Workflow-Studio"
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "statsu"
|
||||
DisplayName = "Workflow Studio"
|
||||
Icon = "https://raw.githubusercontent.com/ketle-man/ComfyUI-Workflow-Studio/main/docs/thumb.png"
|
||||
Banner = "https://raw.githubusercontent.com/ketle-man/ComfyUI-Workflow-Studio/main/assets/banner.svg"
|
||||
@@ -0,0 +1,21 @@
|
||||
# ComfyUI-Workflow-Studio - Tagger tab dependencies
|
||||
#
|
||||
# numpy and Pillow are already bundled with ComfyUI and do not need to be installed.
|
||||
# torch is managed by ComfyUI and must NOT be pinned here.
|
||||
#
|
||||
# Install into ComfyUI's embedded Python:
|
||||
# python_embeded\python.exe -m pip install -r requirements.txt
|
||||
#
|
||||
# For GPU-accelerated ONNX inference, install onnxruntime-gpu instead of onnxruntime:
|
||||
# python_embeded\python.exe -m pip install onnxruntime-gpu
|
||||
|
||||
# -- Required for WD Tagger / SwinV2 ONNX inference --
|
||||
onnxruntime>=1.16
|
||||
|
||||
# -- Required for JPEG EXIF tag writing --
|
||||
piexif>=1.1.3
|
||||
|
||||
# -- Optional: DeepDanbooru (.h5 model) support --
|
||||
# Omit if you do not use DeepDanbooru models.
|
||||
# TensorFlow is large (~600MB) and conflicts with some CUDA setups.
|
||||
# tensorflow>=2.10
|
||||
@@ -0,0 +1,994 @@
|
||||
/* ============================================================
|
||||
Gallery Tab Styles
|
||||
============================================================ */
|
||||
|
||||
/* ── サブタブ (Output / Style-Prompt) ──────────────────────── */
|
||||
|
||||
.wfm-gallery-subtab-nav {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-subtab-btn {
|
||||
padding: 6px 16px;
|
||||
font-size: 12px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
margin-bottom: -1px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-subtab-btn:hover { color: var(--wfm-text-primary); }
|
||||
.wfm-gallery-subtab-btn.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-gallery-panel {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-gallery-panel.active { display: flex; }
|
||||
|
||||
/* ── 3カラムレイアウト ──────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-layout {
|
||||
display: flex;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
gap: 0;
|
||||
}
|
||||
|
||||
/* ── 左: フォルダツリーパネル ──────────────────────────────── */
|
||||
|
||||
.wfm-gallery-tree-panel {
|
||||
width: 250px;
|
||||
min-width: 250px;
|
||||
max-width: 250px;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
background: var(--wfm-bg-secondary, #1a1a2e);
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 6px 8px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
gap: 4px;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-title {
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-actions {
|
||||
display: flex;
|
||||
gap: 4px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
|
||||
.wfm-gallery-tree {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
overflow-x: hidden;
|
||||
padding: 4px 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
padding: 3px 8px;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-primary);
|
||||
-webkit-user-select: none;
|
||||
user-select: none;
|
||||
transition: background 0.1s;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-item:hover {
|
||||
background: var(--wfm-bg-hover, rgba(255,255,255,0.06));
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-item.selected {
|
||||
background: var(--wfm-accent, #4f8ef7);
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-item.selected .wfm-gallery-tree-count {
|
||||
background: rgba(255,255,255,0.25);
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-arrow {
|
||||
font-size: 10px;
|
||||
width: 14px;
|
||||
flex-shrink: 0;
|
||||
cursor: pointer;
|
||||
color: var(--wfm-text-secondary);
|
||||
transition: transform 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-label {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-count {
|
||||
font-size: 10px;
|
||||
background: var(--wfm-accent-muted, rgba(79,142,247,0.2));
|
||||
color: var(--wfm-accent, #4f8ef7);
|
||||
border-radius: 8px;
|
||||
padding: 0 5px;
|
||||
line-height: 1.4;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-tree-children {
|
||||
/* 子ノードコンテナ */
|
||||
}
|
||||
|
||||
/* ── 中: 画像一覧パネル ────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-list-panel {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
min-width: 0;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
}
|
||||
|
||||
.wfm-gallery-grid {
|
||||
padding: 8px;
|
||||
}
|
||||
|
||||
/* サムネイルビュー */
|
||||
.wfm-gallery-view-thumb {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fill, minmax(140px, 1fr));
|
||||
gap: 8px;
|
||||
align-items: start;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card {
|
||||
position: relative;
|
||||
background: var(--wfm-bg-secondary, #1a1a2e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 6px;
|
||||
overflow: hidden;
|
||||
cursor: pointer;
|
||||
transition: border-color 0.15s, box-shadow 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card:hover {
|
||||
border-color: var(--wfm-accent, #4f8ef7);
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.3);
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card.selected {
|
||||
border-color: var(--wfm-accent, #4f8ef7);
|
||||
box-shadow: 0 0 0 2px var(--wfm-accent, #4f8ef7);
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card.multi-selected {
|
||||
border-color: #f59e0b;
|
||||
box-shadow: 0 0 0 2px #f59e0b;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-img {
|
||||
width: 100%;
|
||||
aspect-ratio: 1 / 1;
|
||||
object-fit: cover;
|
||||
display: block;
|
||||
background: var(--wfm-bg-hover, #111);
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-placeholder {
|
||||
width: 100%;
|
||||
aspect-ratio: 1 / 1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
background: var(--wfm-bg-hover, #222);
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 24px;
|
||||
}
|
||||
|
||||
/* お気に入りボタン（サムネイルカード右上） */
|
||||
.wfm-gallery-thumb-fav-btn {
|
||||
position: absolute;
|
||||
top: 4px;
|
||||
right: 4px;
|
||||
background: rgba(0,0,0,0.5);
|
||||
border: none;
|
||||
border-radius: 50%;
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
font-size: 13px;
|
||||
line-height: 1;
|
||||
padding: 0;
|
||||
cursor: pointer;
|
||||
color: rgba(255,255,255,0.6);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
opacity: 0;
|
||||
transition: opacity 0.15s, color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card:hover .wfm-gallery-thumb-fav-btn {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-fav-btn.active {
|
||||
color: #fbbf24;
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-fav-btn:hover {
|
||||
background: rgba(0,0,0,0.75);
|
||||
color: #fbbf24;
|
||||
}
|
||||
|
||||
/* Feederグループボタン（サムネイルカード左上） */
|
||||
.wfm-gallery-thumb-feeder-btn {
|
||||
position: absolute;
|
||||
top: 4px;
|
||||
left: 4px;
|
||||
background: rgba(0,0,0,0.5);
|
||||
border: none;
|
||||
border-radius: 50%;
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
font-size: 11px;
|
||||
font-weight: bold;
|
||||
line-height: 1;
|
||||
padding: 0;
|
||||
cursor: pointer;
|
||||
color: rgba(255,255,255,0.6);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
opacity: 0;
|
||||
transition: opacity 0.15s, color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-card:hover .wfm-gallery-thumb-feeder-btn {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-feeder-btn.active {
|
||||
color: #38bdf8;
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-feeder-btn:hover {
|
||||
background: rgba(0,0,0,0.75);
|
||||
color: #38bdf8;
|
||||
}
|
||||
|
||||
/* テーブルビューのお気に入りボタン */
|
||||
.wfm-gallery-table-fav-btn {
|
||||
background: none;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
font-size: 16px;
|
||||
color: rgba(255,255,255,0.3);
|
||||
padding: 2px 4px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-table-fav-btn:hover,
|
||||
.wfm-gallery-table-fav-btn.active {
|
||||
color: #fbbf24;
|
||||
}
|
||||
|
||||
.wfm-gallery-thumb-tags {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 2px;
|
||||
padding: 4px 6px;
|
||||
}
|
||||
|
||||
/* テーブルビュー */
|
||||
.wfm-gallery-view-table {
|
||||
display: block;
|
||||
}
|
||||
|
||||
.wfm-gallery-table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.wfm-gallery-table thead th {
|
||||
position: sticky;
|
||||
top: 0;
|
||||
background: var(--wfm-bg-secondary, #1a1a2e);
|
||||
color: var(--wfm-text-secondary);
|
||||
font-weight: 600;
|
||||
padding: 6px 8px;
|
||||
text-align: left;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-table tbody tr {
|
||||
cursor: pointer;
|
||||
transition: background 0.1s;
|
||||
}
|
||||
|
||||
.wfm-gallery-table tbody tr:hover {
|
||||
background: var(--wfm-bg-hover, rgba(255,255,255,0.05));
|
||||
}
|
||||
|
||||
.wfm-gallery-table tbody tr.selected {
|
||||
background: rgba(79,142,247,0.15);
|
||||
}
|
||||
|
||||
.wfm-gallery-table tbody tr.multi-selected {
|
||||
background: rgba(245,158,11,0.15);
|
||||
}
|
||||
|
||||
.wfm-gallery-table td {
|
||||
padding: 4px 8px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
color: var(--wfm-text-primary);
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
.wfm-gallery-table-thumb {
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
object-fit: cover;
|
||||
border-radius: 3px;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.wfm-gallery-table-name {
|
||||
max-width: 200px;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
/* ── タグバッジ共通 ────────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-tag-badge {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 3px;
|
||||
background: rgba(79,142,247,0.15);
|
||||
color: var(--wfm-accent, #4f8ef7);
|
||||
border: 1px solid rgba(79,142,247,0.3);
|
||||
border-radius: 10px;
|
||||
padding: 1px 7px;
|
||||
font-size: 11px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-gallery-tag-remove {
|
||||
background: none;
|
||||
border: none;
|
||||
color: inherit;
|
||||
cursor: pointer;
|
||||
padding: 0;
|
||||
font-size: 12px;
|
||||
line-height: 1;
|
||||
opacity: 0.7;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
.wfm-gallery-tag-remove:hover {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/* ── 右: 詳細パネル ────────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-detail-panel {
|
||||
width: 280px;
|
||||
min-width: 280px;
|
||||
max-width: 280px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
background: var(--wfm-bg-secondary, #1a1a2e);
|
||||
}
|
||||
|
||||
.wfm-gallery-detail-header {
|
||||
padding: 6px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
}
|
||||
|
||||
.wfm-gallery-detail-filename {
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-primary);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
display: block;
|
||||
}
|
||||
|
||||
/* プレビュー */
|
||||
.wfm-gallery-detail-preview {
|
||||
width: 100%;
|
||||
aspect-ratio: 1 / 1;
|
||||
max-height: 200px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
overflow: hidden;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-preview-wrapper {
|
||||
position: relative;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.wfm-gallery-detail-img {
|
||||
max-width: 100%;
|
||||
max-height: 200px;
|
||||
object-fit: contain;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-gallery-preview-overlay {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
bottom: 0;
|
||||
background: rgba(0,0,0,0.5);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
opacity: 0;
|
||||
transition: opacity 0.2s;
|
||||
}
|
||||
|
||||
.wfm-gallery-preview-wrapper:hover .wfm-gallery-preview-overlay {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-download-btn {
|
||||
background: var(--wfm-accent, #4f8ef7);
|
||||
border: none;
|
||||
border-radius: 50%;
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
font-size: 20px;
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
color: #fff;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.wfm-gallery-download-btn:hover {
|
||||
background: var(--wfm-accent-hover, #3f7fe7);
|
||||
}
|
||||
|
||||
/* タブコンテンツ */
|
||||
.wfm-gallery-detail-tab-content {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 8px;
|
||||
}
|
||||
|
||||
.wfm-gallery-section-title {
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-primary);
|
||||
margin-bottom: 6px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.wfm-gallery-info-section {
|
||||
margin-bottom: 4px;
|
||||
padding: 6px 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-info-row {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-primary);
|
||||
margin-bottom: 3px;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-gallery-image-prompt-text {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-primary);
|
||||
line-height: 1.5;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
padding: 6px 8px;
|
||||
max-height: 160px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.wfm-gallery-tags-display {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 4px;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
|
||||
.wfm-gallery-memo {
|
||||
width: 100%;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
color: var(--wfm-text-primary);
|
||||
font-size: 12px;
|
||||
padding: 6px 8px;
|
||||
resize: vertical;
|
||||
box-sizing: border-box;
|
||||
font-family: inherit;
|
||||
}
|
||||
|
||||
.wfm-gallery-memo:focus {
|
||||
outline: none;
|
||||
border-color: var(--wfm-accent, #4f8ef7);
|
||||
}
|
||||
|
||||
/* ── Metadataタブ（Workflow全幅表示） ─────────────────────── */
|
||||
|
||||
.wfm-gallery-meta-tab {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
padding: 0 !important;
|
||||
overflow: hidden;
|
||||
height: 100%;
|
||||
}
|
||||
|
||||
.wfm-gallery-meta-toolbar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 6px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.wfm-gallery-meta-status {
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-gallery-workflow-json {
|
||||
flex: 1;
|
||||
margin: 0;
|
||||
padding: 10px;
|
||||
font-family: "Consolas", "Monaco", monospace;
|
||||
font-size: 11px;
|
||||
line-height: 1.5;
|
||||
color: var(--wfm-text-primary);
|
||||
background: var(--wfm-bg-secondary, #1a1a2e);
|
||||
overflow: auto;
|
||||
white-space: pre;
|
||||
word-break: normal;
|
||||
border: none;
|
||||
}
|
||||
|
||||
/* グループ */
|
||||
.wfm-gallery-current-groups {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 4px;
|
||||
margin-bottom: 6px;
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
.wfm-gallery-groups-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
}
|
||||
|
||||
.wfm-gallery-group-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 3px 6px;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.wfm-gallery-group-name {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
color: var(--wfm-text-primary);
|
||||
}
|
||||
|
||||
/* ── 一括操作バー ──────────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-bulk-bar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 5px 10px;
|
||||
background: rgba(245,158,11,0.12);
|
||||
border-bottom: 1px solid rgba(245,158,11,0.35);
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.wfm-gallery-bulk-count {
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: #f59e0b;
|
||||
white-space: nowrap;
|
||||
margin-right: 4px;
|
||||
}
|
||||
|
||||
/* ── ライトボックス ─────────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-lightbox {
|
||||
position: fixed;
|
||||
inset: 0;
|
||||
background: rgba(0,0,0,0.9);
|
||||
z-index: 10000;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-gallery-lightbox-inner {
|
||||
position: relative;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
max-width: 90vw;
|
||||
max-height: 90vh;
|
||||
}
|
||||
|
||||
.wfm-gallery-lightbox-img {
|
||||
max-width: 90vw;
|
||||
max-height: 85vh;
|
||||
object-fit: contain;
|
||||
border-radius: 4px;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.wfm-gallery-lightbox-footer {
|
||||
margin-top: 8px;
|
||||
color: rgba(255,255,255,0.7);
|
||||
font-size: 13px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.wfm-gallery-lightbox-close {
|
||||
position: absolute;
|
||||
top: -12px;
|
||||
right: -12px;
|
||||
background: rgba(0,0,0,0.7);
|
||||
border: 1px solid rgba(255,255,255,0.2);
|
||||
color: #fff;
|
||||
border-radius: 50%;
|
||||
width: 28px;
|
||||
height: 28px;
|
||||
font-size: 16px;
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
line-height: 1;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-lightbox-close:hover {
|
||||
background: rgba(255,255,255,0.2);
|
||||
}
|
||||
|
||||
/* ── 画像比較ライトボックス ──────────────────────────────────── */
|
||||
|
||||
.wfm-lightbox-compare .wfm-lightbox-compare-inner {
|
||||
position: relative;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
max-width: 96vw;
|
||||
max-height: 92vh;
|
||||
overflow: auto;
|
||||
}
|
||||
|
||||
.wfm-lightbox-compare-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(var(--compare-cols, 2), 1fr);
|
||||
gap: 12px;
|
||||
padding: 16px;
|
||||
}
|
||||
|
||||
.wfm-lightbox-compare-item {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.wfm-lightbox-compare-img {
|
||||
max-width: calc(92vw / var(--compare-cols, 2) - 12px);
|
||||
max-height: 78vh;
|
||||
object-fit: contain;
|
||||
border-radius: 4px;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.wfm-lightbox-compare-label {
|
||||
color: rgba(255,255,255,0.75);
|
||||
font-size: 12px;
|
||||
text-align: center;
|
||||
max-width: 100%;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-lightbox-compare-close {
|
||||
position: fixed;
|
||||
top: 16px;
|
||||
right: 16px;
|
||||
z-index: 10001;
|
||||
}
|
||||
|
||||
/* ── 詳細パネル アクションボタン行 ───────────────────────────── */
|
||||
|
||||
.wfm-gallery-detail-action-row {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
padding: 4px 8px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
/* ── 詳細パネル アクションボタン ─────────────────────────────── */
|
||||
|
||||
.wfm-gallery-action-btn {
|
||||
padding: 4px 10px;
|
||||
border-radius: 4px;
|
||||
border: 1px solid;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
white-space: nowrap;
|
||||
align-self: center;
|
||||
margin: 2px 3px;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.wfm-gallery-action-btn-green {
|
||||
background: var(--wfm-success, #22c55e);
|
||||
border-color: var(--wfm-success, #22c55e);
|
||||
}
|
||||
.wfm-gallery-action-btn-green:hover {
|
||||
background: #16a34a;
|
||||
border-color: #16a34a;
|
||||
}
|
||||
|
||||
.wfm-gallery-action-btn-primary {
|
||||
background: var(--wfm-primary);
|
||||
border-color: var(--wfm-primary);
|
||||
}
|
||||
.wfm-gallery-action-btn-primary:hover {
|
||||
background: var(--wfm-primary-hover);
|
||||
border-color: var(--wfm-primary-hover);
|
||||
}
|
||||
|
||||
.wfm-gallery-action-btn-tagger {
|
||||
background: var(--wfm-accent, #7c5cfc);
|
||||
border-color: var(--wfm-accent, #7c5cfc);
|
||||
}
|
||||
.wfm-gallery-action-btn-tagger:hover {
|
||||
background: #6a4de0;
|
||||
border-color: #6a4de0;
|
||||
}
|
||||
|
||||
/* ── ファイル操作ボタン (詳細パネル) ─────────────────────────── */
|
||||
|
||||
.wfm-gallery-file-actions {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
padding-top: 4px;
|
||||
}
|
||||
|
||||
.wfm-gallery-file-actions .wfm-btn {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
/* ── 移動先選択モーダル ──────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-move-modal {
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 8px;
|
||||
padding: 20px;
|
||||
min-width: 320px;
|
||||
max-width: 480px;
|
||||
width: 90%;
|
||||
box-shadow: 0 8px 32px rgba(0,0,0,0.5);
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.wfm-gallery-move-modal-title {
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-primary);
|
||||
}
|
||||
|
||||
.wfm-gallery-move-dest-sel {
|
||||
width: 100%;
|
||||
padding: 6px 8px;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.wfm-gallery-move-modal-footer {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
justify-content: flex-end;
|
||||
}
|
||||
|
||||
/* ============================================================
|
||||
ImagePrompt / Style_Catalog ギャラリー (右ペイン)
|
||||
============================================================ */
|
||||
|
||||
.wfm-imageprompt-detail-panel {
|
||||
width: 520px;
|
||||
min-width: 520px;
|
||||
max-width: 520px;
|
||||
flex-direction: row;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col-left {
|
||||
width: 280px;
|
||||
min-width: 280px;
|
||||
max-width: 280px;
|
||||
overflow-y: auto;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding-bottom: 8px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col-right {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
padding: 8px 10px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-section {
|
||||
padding: 8px 10px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-prompt-box {
|
||||
width: 100%;
|
||||
resize: vertical;
|
||||
font-size: 12px;
|
||||
padding: 6px 8px;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
color: var(--wfm-text-primary);
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-btn-row {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
margin-top: 6px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-btn-row .wfm-btn {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chips {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 4px;
|
||||
margin-top: 6px;
|
||||
min-height: 24px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
max-width: 100%;
|
||||
background: rgba(79,142,247,0.15);
|
||||
color: var(--wfm-accent, #4f8ef7);
|
||||
border: 1px solid rgba(79,142,247,0.3);
|
||||
border-radius: 10px;
|
||||
padding: 2px 4px 2px 8px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-text {
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
max-width: 220px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-remove {
|
||||
background: none;
|
||||
border: none;
|
||||
color: inherit;
|
||||
cursor: pointer;
|
||||
padding: 0 2px;
|
||||
font-size: 13px;
|
||||
line-height: 1;
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-remove:hover { opacity: 1; }
|
||||
|
||||
.wfm-imageprompt-final-section {
|
||||
height: 100%;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-final-textarea {
|
||||
margin-top: 4px;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
resize: none;
|
||||
}
|
||||
@@ -0,0 +1,462 @@
|
||||
/* =============================================
|
||||
Image Edit Tab
|
||||
============================================= */
|
||||
|
||||
#wfm-tab-image-edit {
|
||||
padding: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.ie-editor {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
background: var(--wfm-bg);
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Left Toolbar
|
||||
============================================= */
|
||||
|
||||
.ie-toolbar {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
padding: 8px 5px;
|
||||
background: var(--wfm-surface);
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
width: 46px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.ie-tool-btn {
|
||||
width: 34px;
|
||||
height: 34px;
|
||||
border: 1px solid transparent;
|
||||
background: transparent;
|
||||
border-radius: 6px;
|
||||
cursor: pointer;
|
||||
font-size: 15px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
transition: background 0.12s, border-color 0.12s;
|
||||
user-select: none;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.ie-tool-btn:hover {
|
||||
background: var(--wfm-surface-hover);
|
||||
border-color: var(--wfm-border);
|
||||
}
|
||||
|
||||
.ie-tool-btn.active {
|
||||
background: var(--wfm-primary);
|
||||
border-color: var(--wfm-primary);
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.ie-tool-placeholder {
|
||||
opacity: 0.38;
|
||||
}
|
||||
|
||||
.ie-tool-placeholder::after {
|
||||
content: "";
|
||||
position: absolute;
|
||||
bottom: 3px;
|
||||
right: 3px;
|
||||
width: 5px;
|
||||
height: 5px;
|
||||
border-radius: 50%;
|
||||
background: var(--wfm-text-secondary);
|
||||
opacity: 0.5;
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Center column
|
||||
============================================= */
|
||||
|
||||
.ie-center {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
/* Action bar */
|
||||
.ie-action-bar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 5px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-surface);
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
/* Tool options bar */
|
||||
.ie-tool-options {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 14px;
|
||||
padding: 5px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-surface);
|
||||
flex-shrink: 0;
|
||||
flex-wrap: wrap;
|
||||
min-height: 36px;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.ie-opt-group {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 5px;
|
||||
}
|
||||
|
||||
.ie-opt-group label {
|
||||
color: var(--wfm-text-secondary);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.ie-opt-select {
|
||||
font-size: 12px;
|
||||
padding: 2px 5px;
|
||||
background: var(--wfm-surface);
|
||||
color: var(--wfm-text);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.ie-opt-input {
|
||||
font-size: 12px;
|
||||
padding: 2px 5px;
|
||||
background: var(--wfm-surface);
|
||||
color: var(--wfm-text);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.ie-opt-active {
|
||||
background: var(--wfm-primary) !important;
|
||||
color: #fff !important;
|
||||
border-color: var(--wfm-primary) !important;
|
||||
}
|
||||
|
||||
/* Canvas wrap */
|
||||
.ie-canvas-wrap {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
position: relative;
|
||||
background: #1a1a1a;
|
||||
background-image:
|
||||
linear-gradient(45deg, #222 25%, transparent 25%),
|
||||
linear-gradient(-45deg, #222 25%, transparent 25%),
|
||||
linear-gradient(45deg, transparent 75%, #222 75%),
|
||||
linear-gradient(-45deg, transparent 75%, #222 75%);
|
||||
background-size: 20px 20px;
|
||||
background-position: 0 0, 0 10px, 10px -10px, -10px 0;
|
||||
}
|
||||
|
||||
.ie-canvas-container {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
transform-origin: top left;
|
||||
line-height: 0;
|
||||
box-shadow: 0 2px 20px rgba(0,0,0,0.6);
|
||||
}
|
||||
|
||||
.ie-canvas-container canvas {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
display: block;
|
||||
}
|
||||
|
||||
#ie-canvas-bg {
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
#ie-canvas-draw {
|
||||
z-index: 2;
|
||||
cursor: crosshair;
|
||||
}
|
||||
|
||||
#ie-canvas-overlay {
|
||||
z-index: 3;
|
||||
cursor: default;
|
||||
pointer-events: none; /* SelectTool.activate()でautoに変更 */
|
||||
}
|
||||
|
||||
.ie-placeholder {
|
||||
position: absolute;
|
||||
inset: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 14px;
|
||||
pointer-events: none;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.ie-placeholder-icon {
|
||||
font-size: 40px;
|
||||
opacity: 0.3;
|
||||
}
|
||||
|
||||
/* Zoom bar */
|
||||
.ie-zoom-bar {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 4px 10px;
|
||||
border-top: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-surface);
|
||||
font-size: 12px;
|
||||
flex-shrink: 0;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
#ie-zoom-label {
|
||||
min-width: 38px;
|
||||
text-align: right;
|
||||
color: var(--wfm-text);
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Properties Pane (left of Layers panel)
|
||||
============================================= */
|
||||
|
||||
.ie-props-pane {
|
||||
width: 160px;
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
border-left: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-surface);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.ie-props-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
padding: 5px 8px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.4px;
|
||||
color: var(--wfm-text-secondary);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.ie-props-body {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 8px 6px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.ie-props-row {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.ie-props-row label {
|
||||
color: var(--wfm-text-secondary);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.ie-props-row input[type="range"] {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
.ie-props-row span {
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text);
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* Mask layer item highlight */
|
||||
.ie-layer-item[data-type="mask"] .ie-layer-type-icon {
|
||||
color: #e25252;
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Right Panel (Layers)
|
||||
============================================= */
|
||||
|
||||
.ie-right-panel {
|
||||
width: 220px;
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
border-left: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-surface);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.ie-layers-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 2px;
|
||||
padding: 5px 6px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.4px;
|
||||
color: var(--wfm-text-secondary);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.ie-layers-header > span {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.ie-layer-list {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 4px;
|
||||
}
|
||||
|
||||
.ie-layer-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 5px;
|
||||
padding: 4px 5px;
|
||||
border-radius: 5px;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
user-select: none;
|
||||
transition: background 0.1s;
|
||||
}
|
||||
|
||||
.ie-layer-item:hover {
|
||||
background: var(--wfm-surface-hover);
|
||||
}
|
||||
|
||||
.ie-layer-item.active {
|
||||
background: rgba(90, 110, 220, 0.22);
|
||||
outline: 1px solid rgba(90, 110, 220, 0.4);
|
||||
}
|
||||
|
||||
.ie-layer-thumb {
|
||||
width: 30px;
|
||||
height: 30px;
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 3px;
|
||||
flex-shrink: 0;
|
||||
object-fit: cover;
|
||||
background:
|
||||
repeating-conic-gradient(#888 0% 25%, transparent 0% 50%)
|
||||
0 0 / 8px 8px;
|
||||
}
|
||||
|
||||
.ie-layer-name {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.ie-layer-vis-btn {
|
||||
background: none;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
font-size: 13px;
|
||||
padding: 0;
|
||||
line-height: 1;
|
||||
color: var(--wfm-text);
|
||||
opacity: 0.85;
|
||||
flex-shrink: 0;
|
||||
transition: opacity 0.1s;
|
||||
}
|
||||
|
||||
.ie-layer-vis-btn:hover {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.ie-opacity-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 6px 8px;
|
||||
border-top: 1px solid var(--wfm-border);
|
||||
font-size: 12px;
|
||||
flex-shrink: 0;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
.ie-opacity-row input[type="range"] {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
#ie-layer-opacity-label {
|
||||
min-width: 32px;
|
||||
text-align: right;
|
||||
color: var(--wfm-text);
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Text Tool Overlay
|
||||
============================================= */
|
||||
|
||||
.ie-text-overlay {
|
||||
position: absolute;
|
||||
z-index: 100;
|
||||
background: var(--wfm-surface);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 6px;
|
||||
padding: 8px;
|
||||
box-shadow: 0 4px 16px rgba(0,0,0,0.4);
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
min-width: 180px;
|
||||
}
|
||||
|
||||
.ie-text-textarea {
|
||||
width: 200px;
|
||||
resize: vertical;
|
||||
background: var(--wfm-bg);
|
||||
color: var(--wfm-text);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
padding: 6px;
|
||||
font-size: 13px;
|
||||
font-family: inherit;
|
||||
min-height: 60px;
|
||||
}
|
||||
|
||||
.ie-text-btn-row {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
justify-content: flex-end;
|
||||
}
|
||||
|
||||
/* =============================================
|
||||
Scrollbar
|
||||
============================================= */
|
||||
|
||||
.ie-layer-list::-webkit-scrollbar {
|
||||
width: 4px;
|
||||
}
|
||||
|
||||
.ie-layer-list::-webkit-scrollbar-thumb {
|
||||
background: var(--wfm-border);
|
||||
border-radius: 2px;
|
||||
}
|
||||
@@ -0,0 +1,321 @@
|
||||
/* Lab tab — experimental I2I batch generation with per-column keyframes */
|
||||
|
||||
/* .wfm-gen-subtab-content.active defaults to flex-direction:row (main.css);
|
||||
Lab needs its Setting/Results nav stacked above the panel body. */
|
||||
#wfm-gen-subtab-lab.active {
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-nav {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-btn {
|
||||
padding: 6px 16px;
|
||||
font-size: 12px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
margin-bottom: -1px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-btn:hover { color: var(--wfm-text-primary); }
|
||||
.wfm-lab-subtab-btn.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-panel {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
height: calc(100% - 33px);
|
||||
}
|
||||
|
||||
.wfm-lab-panel.active { display: flex; }
|
||||
|
||||
.wfm-lab-planjson-toolbar {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
padding: 8px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-pane-header {
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
padding: 4px 0 6px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* ---------------- Setting layout: left | center (4 columns) | right ---------------- */
|
||||
|
||||
.wfm-lab-setting-layout {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
gap: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-pane {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
min-width: 0;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-pane:last-child { border-right: none; }
|
||||
|
||||
.wfm-lab-left { width: 220px; flex-shrink: 0; }
|
||||
.wfm-lab-center { flex: 1; padding: 0; }
|
||||
.wfm-lab-right { width: 260px; flex-shrink: 0; overflow-y: auto; }
|
||||
|
||||
.wfm-lab-side-btns {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
margin-top: 12px;
|
||||
}
|
||||
|
||||
.wfm-lab-plan-workflow-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 8px;
|
||||
padding: 6px 8px;
|
||||
background: var(--wfm-bg-secondary);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.wfm-lab-plan-workflow-name {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row input {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-note {
|
||||
width: 100%;
|
||||
resize: vertical;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.wfm-lab-checkbox-label {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 8px;
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
/* ---------------- Keyframe columns ---------------- */
|
||||
|
||||
.wfm-lab-columns {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-column {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-column:last-child { border-right: none; }
|
||||
|
||||
.wfm-lab-col-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
gap: 4px;
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-col-btns { display: flex; gap: 2px; }
|
||||
|
||||
.wfm-lab-col-cells {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 6px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.wfm-lab-cell {
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
cursor: pointer;
|
||||
background: var(--wfm-bg-tertiary);
|
||||
transition: border-color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-cell:hover { border-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-cell-index {
|
||||
font-weight: 600;
|
||||
color: var(--wfm-primary);
|
||||
margin-right: 4px;
|
||||
}
|
||||
|
||||
.wfm-lab-cell-value {
|
||||
color: var(--wfm-text-primary);
|
||||
word-break: break-all;
|
||||
}
|
||||
|
||||
.wfm-lab-cell.wfm-lab-cell-revert .wfm-lab-cell-value {
|
||||
color: var(--wfm-text-secondary);
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.wfm-lab-cell-applied {
|
||||
margin-top: 3px;
|
||||
padding-top: 3px;
|
||||
border-top: 1px dashed var(--wfm-border);
|
||||
font-size: 10px;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
.wfm-lab-cell-prompt-line {
|
||||
display: block;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* ---------------- Modal form (checkpoint/vae/prompt/ksampler edit) ---------------- */
|
||||
|
||||
.wfm-lab-modal-row {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-row label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
.wfm-lab-modal-inline {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-btnrow {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 6px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-inline > div {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-checkbox {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 12px;
|
||||
margin-top: 4px;
|
||||
}
|
||||
|
||||
/* ---------------- Results layout ---------------- */
|
||||
|
||||
.wfm-lab-results-layout {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-results-left {
|
||||
width: 260px;
|
||||
flex-shrink: 0;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-results-center {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
padding: 10px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.wfm-lab-results-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fill, minmax(140px, 1fr));
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.wfm-lab-result-thumb {
|
||||
width: 100%;
|
||||
aspect-ratio: 1 / 1;
|
||||
object-fit: cover;
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
border: 1px solid var(--wfm-border);
|
||||
cursor: pointer;
|
||||
transition: border-color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-result-thumb:hover { border-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-result-item { position: relative; }
|
||||
|
||||
.wfm-lab-result-index {
|
||||
position: absolute;
|
||||
top: 2px;
|
||||
left: 2px;
|
||||
background: rgba(0, 0, 0, 0.6);
|
||||
color: #fff;
|
||||
font-size: 10px;
|
||||
padding: 1px 5px;
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
}
|
||||
+2827
-89
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,294 @@
|
||||
/* ============================================================
|
||||
Tagger Tab Styles
|
||||
============================================================ */
|
||||
|
||||
/* ── サブタブナビ ────────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-subtab-nav {
|
||||
display: flex;
|
||||
gap: 0;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
background: var(--wfm-bg-secondary);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-subtab-btn {
|
||||
padding: 8px 20px;
|
||||
font-size: 13px;
|
||||
font-weight: 500;
|
||||
background: transparent;
|
||||
border: none;
|
||||
border-bottom: 2px solid transparent;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
transition: color 0.15s, border-color 0.15s;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-tagger-subtab-btn:hover {
|
||||
color: var(--wfm-text);
|
||||
}
|
||||
|
||||
.wfm-tagger-subtab-btn.active {
|
||||
color: var(--wfm-primary);
|
||||
border-bottom-color: var(--wfm-primary);
|
||||
}
|
||||
|
||||
/* ── サブタブ共通 ────────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-subtab {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 12px;
|
||||
}
|
||||
|
||||
/* ── セクション ─────────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-section {
|
||||
background: var(--wfm-surface);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius);
|
||||
padding: 10px 12px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.wfm-tagger-section-header {
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
/* ── フォームレイアウト ─────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-form-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
|
||||
.wfm-tagger-form-row:last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
min-width: 80px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-tagger-select {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
/* ── スライダー ─────────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-slider {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
accent-color: var(--wfm-primary);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-tagger-slider-val {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
min-width: 36px;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* ── Single サブタブ ─────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-single-layout {
|
||||
display: flex;
|
||||
gap: 12px;
|
||||
height: calc(100vh - var(--wfm-header-height, 96px) - 80px);
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-image-pane {
|
||||
width: 300px;
|
||||
min-width: 240px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-image-sources {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.wfm-tagger-preview-wrap {
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
background: var(--wfm-surface);
|
||||
border: 2px dashed var(--wfm-border);
|
||||
border-radius: var(--wfm-radius);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
overflow: hidden;
|
||||
position: relative;
|
||||
transition: border-color 0.15s, background 0.15s;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-tagger-preview-wrap.drag-over {
|
||||
border-color: var(--wfm-primary);
|
||||
background: rgba(74, 158, 255, 0.08);
|
||||
}
|
||||
|
||||
.wfm-tagger-image-name {
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
text-align: center;
|
||||
word-break: break-all;
|
||||
min-height: 16px;
|
||||
}
|
||||
|
||||
.wfm-tagger-controls-pane {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
overflow-y: auto;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-result-actions {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
margin-top: 6px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
/* ── Batch サブタブ ──────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-batch-layout {
|
||||
max-width: 900px;
|
||||
}
|
||||
|
||||
.wfm-tagger-batch-cols {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.wfm-tagger-progress-bar-wrap {
|
||||
height: 6px;
|
||||
background: var(--wfm-border);
|
||||
border-radius: 3px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-tagger-progress-bar {
|
||||
height: 100%;
|
||||
background: var(--wfm-primary);
|
||||
border-radius: 3px;
|
||||
transition: width 0.3s ease;
|
||||
}
|
||||
|
||||
/* ── DB サブタブ ─────────────────────────────────────────────── */
|
||||
|
||||
.wfm-tagger-db-layout {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
height: calc(100vh - var(--wfm-header-height, 96px) - 80px);
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-toolbar {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
align-items: center;
|
||||
margin-bottom: 10px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table-wrap {
|
||||
flex: 1;
|
||||
overflow: auto;
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius);
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table th,
|
||||
.wfm-tagger-db-table td {
|
||||
padding: 6px 10px;
|
||||
text-align: left;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
max-width: 220px;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table th {
|
||||
background: var(--wfm-bg-secondary);
|
||||
color: var(--wfm-text-secondary);
|
||||
font-weight: 600;
|
||||
text-transform: uppercase;
|
||||
font-size: 11px;
|
||||
letter-spacing: 0.04em;
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table tbody tr {
|
||||
cursor: pointer;
|
||||
transition: background 0.1s;
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table tbody tr:hover {
|
||||
background: var(--wfm-surface-hover);
|
||||
}
|
||||
|
||||
.wfm-tagger-db-table tbody tr.selected {
|
||||
background: rgba(74, 158, 255, 0.15);
|
||||
}
|
||||
|
||||
.wfm-tagger-db-edit-panel {
|
||||
flex-shrink: 0;
|
||||
margin-top: 10px;
|
||||
background: var(--wfm-surface);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius);
|
||||
padding: 10px 12px;
|
||||
}
|
||||
|
||||
/* ── ユーティリティ ─────────────────────────────────────────── */
|
||||
|
||||
#wfm-tab-tagger {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
height: calc(100vh - var(--wfm-header-height, 56px));
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
#wfm-tab-tagger.active {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
.wfm-placeholder {
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 13px;
|
||||
text-align: center;
|
||||
padding: 20px;
|
||||
pointer-events: none;
|
||||
}
|
||||
+1131
File diff suppressed because it is too large
Load Diff
+458
-6
@@ -4,6 +4,7 @@
|
||||
*/
|
||||
|
||||
import { initI18n, t } from "./i18n.js";
|
||||
import { getSettings } from "./util.js";
|
||||
|
||||
// Initialize i18n before anything else
|
||||
initI18n();
|
||||
@@ -90,7 +91,7 @@ function initTabs() {
|
||||
|
||||
function applyI18nToHtml() {
|
||||
// Tab labels
|
||||
const tabMap = { workflow: "tabWorkflow", nodes: "tabNodes", models: "tabModels", generate: "tabGenerate", prompt: "tabPrompt", settings: "tabSettings", help: "tabHelp" };
|
||||
const tabMap = { workflow: "tabWorkflow", nodes: "tabNodes", models: "tabModels", generate: "tabGenerate", prompt: "tabPrompt", metadata: "tabMetadata", gallery: "tabGallery", "image-edit": "tabImageEdit", settings: "tabSettings", help: "tabHelp", ai: "tabAi", tagger: "tabTagger" };
|
||||
document.querySelectorAll(".wfm-tab").forEach((tab) => {
|
||||
const key = tabMap[tab.dataset.tab];
|
||||
if (key) tab.textContent = t(key);
|
||||
@@ -114,7 +115,7 @@ function applyI18nToHtml() {
|
||||
|
||||
const listOpenComfyBtn = document.getElementById("wfm-list-open-comfyui-btn");
|
||||
if (listOpenComfyBtn) {
|
||||
listOpenComfyBtn.textContent = t("openInComfyUI");
|
||||
listOpenComfyBtn.textContent = t("sendToCanvas");
|
||||
if (listOpenComfyBtn.disabled) listOpenComfyBtn.title = t("selectCardFirst");
|
||||
}
|
||||
|
||||
@@ -188,6 +189,97 @@ function applyI18nToHtml() {
|
||||
if (btn.dataset.sideTab === "civitai") btn.textContent = t("modelsSideCivitai");
|
||||
});
|
||||
|
||||
// Gallery tab
|
||||
const gallerySideTabBtns = document.querySelectorAll(".wfm-gallery-detail-tab-btn");
|
||||
gallerySideTabBtns.forEach(btn => {
|
||||
if (btn.dataset.detailTab === "info") btn.textContent = t("modelsSideInfo");
|
||||
if (btn.dataset.detailTab === "meta") btn.textContent = t("gallerySideMetadata");
|
||||
if (btn.dataset.detailTab === "group") btn.textContent = t("modelsSideGroup");
|
||||
});
|
||||
const galleryBulkDeselect = document.getElementById("wfm-gallery-bulk-deselect");
|
||||
if (galleryBulkDeselect) galleryBulkDeselect.textContent = t("galleryBulkDeselectAll");
|
||||
const galleryBulkSelectAll = document.getElementById("wfm-gallery-bulk-select-all");
|
||||
if (galleryBulkSelectAll) galleryBulkSelectAll.textContent = t("galleryBulkSelectAll");
|
||||
const galleryBulkGroupSelect = document.getElementById("wfm-gallery-bulk-group-select");
|
||||
if (galleryBulkGroupSelect) {
|
||||
const opt = galleryBulkGroupSelect.querySelector("option[value='']");
|
||||
if (opt) opt.textContent = t("galleryBulkAddToGroup");
|
||||
}
|
||||
const galleryBulkGroupAdd = document.getElementById("wfm-gallery-bulk-group-add");
|
||||
if (galleryBulkGroupAdd) galleryBulkGroupAdd.textContent = t("galleryBulkAdd");
|
||||
const galleryBulkGroupRemove = document.getElementById("wfm-gallery-bulk-group-remove");
|
||||
if (galleryBulkGroupRemove) galleryBulkGroupRemove.textContent = t("galleryBulkGroupRemove");
|
||||
const galleryBulkFav = document.getElementById("wfm-gallery-bulk-fav");
|
||||
if (galleryBulkFav) galleryBulkFav.textContent = t("galleryBulkFavAll");
|
||||
const galleryBulkUnfav = document.getElementById("wfm-gallery-bulk-unfav");
|
||||
if (galleryBulkUnfav) galleryBulkUnfav.textContent = t("galleryBulkUnfavAll");
|
||||
const galleryBulkMove = document.getElementById("wfm-gallery-bulk-move");
|
||||
if (galleryBulkMove) galleryBulkMove.textContent = t("galleryBulkMoveTo");
|
||||
const galleryBulkExport = document.getElementById("wfm-gallery-bulk-export");
|
||||
if (galleryBulkExport) galleryBulkExport.textContent = t("galleryBulkExport");
|
||||
const galleryBulkDelete = document.getElementById("wfm-gallery-bulk-delete");
|
||||
if (galleryBulkDelete) galleryBulkDelete.textContent = t("galleryBulkDelete");
|
||||
const galleryOpenTaggerBtn = document.getElementById("wfm-gallery-open-tagger-btn");
|
||||
if (galleryOpenTaggerBtn) galleryOpenTaggerBtn.textContent = t("tabTagger");
|
||||
|
||||
// Gallery サブタブ (Output / ImagePrompt / Style_Catalog)
|
||||
const galleryOutputSubtab = document.getElementById("wfm-gallery-subtab-output");
|
||||
if (galleryOutputSubtab) galleryOutputSubtab.textContent = t("galleryTabOutput");
|
||||
const galleryImagePromptSubtab = document.getElementById("wfm-gallery-subtab-imageprompt");
|
||||
if (galleryImagePromptSubtab) galleryImagePromptSubtab.textContent = t("galleryTabImagePrompt");
|
||||
const galleryStyleCatalogSubtab = document.getElementById("wfm-gallery-subtab-styleCatalog");
|
||||
if (galleryStyleCatalogSubtab) galleryStyleCatalogSubtab.textContent = t("galleryTabStyleCatalog");
|
||||
|
||||
// ImagePrompt ギャラリーパネル
|
||||
const imagePromptSearch = document.getElementById("wfm-imageprompt-search");
|
||||
if (imagePromptSearch) imagePromptSearch.placeholder = t("gallerySearchPlaceholder");
|
||||
const imagePromptRefreshBtn = document.getElementById("wfm-imageprompt-refresh-btn");
|
||||
if (imagePromptRefreshBtn) imagePromptRefreshBtn.textContent = t("refresh");
|
||||
const imagePromptTreeTitle = document.getElementById("wfm-imageprompt-tree-title");
|
||||
if (imagePromptTreeTitle) imagePromptTreeTitle.textContent = t("galleryFolders");
|
||||
const imagePromptListPlaceholder = document.getElementById("wfm-imageprompt-list-placeholder");
|
||||
if (imagePromptListPlaceholder) imagePromptListPlaceholder.textContent = t("gallerySelectFolder");
|
||||
const imagePromptNoSelection = document.getElementById("wfm-imageprompt-no-selection");
|
||||
if (imagePromptNoSelection) imagePromptNoSelection.textContent = t("galleryNoSelection");
|
||||
const imagePromptSelectedPromptTitle = document.getElementById("wfm-imageprompt-selected-prompt-title");
|
||||
if (imagePromptSelectedPromptTitle) imagePromptSelectedPromptTitle.textContent = t("imagePromptSelectedPrompt");
|
||||
const imagePromptSelectedPrompt = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
if (imagePromptSelectedPrompt) imagePromptSelectedPrompt.placeholder = t("imagePromptNoPrompt");
|
||||
const imagePromptSavePromptBtn = document.getElementById("wfm-imageprompt-save-prompt-btn");
|
||||
if (imagePromptSavePromptBtn) imagePromptSavePromptBtn.textContent = t("save");
|
||||
const imagePromptAddBtn = document.getElementById("wfm-imageprompt-add-btn");
|
||||
if (imagePromptAddBtn) imagePromptAddBtn.textContent = t("imagePromptAddBtn");
|
||||
const imagePromptChipsTitle = document.getElementById("wfm-imageprompt-chips-title");
|
||||
if (imagePromptChipsTitle) imagePromptChipsTitle.textContent = t("imagePromptChipsTitle");
|
||||
const imagePromptClearBtn = document.getElementById("wfm-imageprompt-clear-btn");
|
||||
if (imagePromptClearBtn) imagePromptClearBtn.textContent = t("imagePromptClearBtn");
|
||||
const imagePromptFinalPromptTitle = document.getElementById("wfm-imageprompt-final-prompt-title");
|
||||
if (imagePromptFinalPromptTitle) imagePromptFinalPromptTitle.textContent = t("imagePromptFinalPromptTitle");
|
||||
const imagePromptCopyBtn = document.getElementById("wfm-imageprompt-copy-btn");
|
||||
if (imagePromptCopyBtn) imagePromptCopyBtn.textContent = t("copy");
|
||||
|
||||
// Style_Catalog ギャラリーパネル
|
||||
const scSearch = document.getElementById("wfm-stylecatalog-search");
|
||||
if (scSearch) scSearch.placeholder = t("stylecatalogSearchPlaceholder");
|
||||
const scRefreshBtn = document.getElementById("wfm-stylecatalog-refresh-btn");
|
||||
if (scRefreshBtn) scRefreshBtn.textContent = t("refresh");
|
||||
const scTreeTitle = document.getElementById("wfm-stylecatalog-tree-title");
|
||||
if (scTreeTitle) scTreeTitle.textContent = t("galleryFolders");
|
||||
const scListPlaceholder = document.getElementById("wfm-stylecatalog-list-placeholder");
|
||||
if (scListPlaceholder) scListPlaceholder.textContent = t("gallerySelectFolder");
|
||||
const scNoSelection = document.getElementById("wfm-stylecatalog-no-selection");
|
||||
if (scNoSelection) scNoSelection.textContent = t("galleryNoSelection");
|
||||
const scPositiveTitle = document.getElementById("wfm-stylecatalog-positive-title");
|
||||
if (scPositiveTitle) scPositiveTitle.textContent = t("stylecatalogPositive");
|
||||
const scCopyPositiveBtn = document.getElementById("wfm-stylecatalog-copy-positive-btn");
|
||||
if (scCopyPositiveBtn) scCopyPositiveBtn.textContent = t("copy");
|
||||
const scNegativeTitle = document.getElementById("wfm-stylecatalog-negative-title");
|
||||
if (scNegativeTitle) scNegativeTitle.textContent = t("stylecatalogNegative");
|
||||
const scCopyNegativeBtn = document.getElementById("wfm-stylecatalog-copy-negative-btn");
|
||||
if (scCopyNegativeBtn) scCopyNegativeBtn.textContent = t("copy");
|
||||
const scSelectStyleBtn = document.getElementById("wfm-stylecatalog-select-style-btn");
|
||||
if (scSelectStyleBtn) scSelectStyleBtn.textContent = t("stylecatalogSelectStyleBtn");
|
||||
|
||||
// Prompt tab
|
||||
const assistantHeader = document.querySelector(".wfm-prompt-split-left .wfm-prompt-split-header");
|
||||
if (assistantHeader) assistantHeader.textContent = t("assistantSubtab");
|
||||
@@ -216,6 +308,90 @@ function applyI18nToHtml() {
|
||||
const presetCopyNegBtn = document.getElementById("wfm-preset-copy-neg-btn");
|
||||
if (presetCopyNegBtn) presetCopyNegBtn.textContent = t("copyNegativePrompt");
|
||||
|
||||
// AI tab
|
||||
document.querySelectorAll(".wfm-ai-subtab-btn").forEach(btn => {
|
||||
if (btn.dataset.aiSubtab === "translate") btn.textContent = t("aiSubTabTranslate");
|
||||
if (btn.dataset.aiSubtab === "vlm") btn.textContent = t("aiSubTabVlm");
|
||||
if (btn.dataset.aiSubtab === "ai-settings") btn.textContent = t("aiSubTabSettings");
|
||||
});
|
||||
const aiLangMap = { ja: "aiLangJa", en: "aiLangEn", zh: "aiLangZh", free: "aiLangFree" };
|
||||
[document.getElementById("wfm-ai-src-lang"), document.getElementById("wfm-ai-dst-lang")].forEach(sel => {
|
||||
if (!sel) return;
|
||||
sel.querySelectorAll("option").forEach(opt => {
|
||||
const key = aiLangMap[opt.value];
|
||||
if (key) opt.textContent = t(key);
|
||||
});
|
||||
});
|
||||
const aiTransInput = document.getElementById("wfm-ai-trans-input");
|
||||
if (aiTransInput) aiTransInput.placeholder = t("aiTransInputPlaceholder");
|
||||
const aiTransOutput = document.getElementById("wfm-ai-trans-output");
|
||||
if (aiTransOutput) aiTransOutput.placeholder = t("aiTransOutputPlaceholder");
|
||||
const aiTransBtn = document.getElementById("wfm-ai-trans-btn");
|
||||
if (aiTransBtn) aiTransBtn.textContent = t("aiTranslateBtn");
|
||||
const aiTransCopyBtn = document.getElementById("wfm-ai-trans-copy-btn");
|
||||
if (aiTransCopyBtn) aiTransCopyBtn.textContent = t("aiCopyBtn");
|
||||
const aiVlmLabel = document.getElementById("wfm-ai-vlm-label");
|
||||
if (aiVlmLabel) aiVlmLabel.textContent = t("aiVlmDropLabel");
|
||||
const aiVlmTask = document.getElementById("wfm-ai-vlm-task");
|
||||
if (aiVlmTask) {
|
||||
aiVlmTask.querySelectorAll("option").forEach(opt => {
|
||||
if (opt.value === "describe") opt.textContent = t("aiVlmDescribe");
|
||||
if (opt.value === "prompt") opt.textContent = t("aiVlmPromptCreate");
|
||||
});
|
||||
}
|
||||
const aiVlmRunBtn = document.getElementById("wfm-ai-vlm-run");
|
||||
if (aiVlmRunBtn) aiVlmRunBtn.textContent = t("aiVlmRunBtn");
|
||||
const aiVlmResult = document.getElementById("wfm-ai-vlm-result");
|
||||
if (aiVlmResult) aiVlmResult.placeholder = t("aiVlmResultPlaceholder");
|
||||
const aiVlmCopyBtn = document.getElementById("wfm-ai-vlm-copy");
|
||||
if (aiVlmCopyBtn) aiVlmCopyBtn.textContent = t("aiCopyBtn");
|
||||
const aiBackendTitle = document.getElementById("wfm-ai-settings-backend-title");
|
||||
if (aiBackendTitle) aiBackendTitle.textContent = t("aiSettingsBackend");
|
||||
const aiConnTitle = document.getElementById("wfm-ai-settings-conn-title");
|
||||
if (aiConnTitle) aiConnTitle.textContent = t("aiSettingsConnection");
|
||||
const aiTestBtn = document.getElementById("wfm-ai-test-btn");
|
||||
if (aiTestBtn) aiTestBtn.textContent = t("aiSettingsTestBtn");
|
||||
const aiModelTitle = document.getElementById("wfm-ai-settings-model-title");
|
||||
if (aiModelTitle) aiModelTitle.textContent = t("aiSettingsModelSection");
|
||||
const aiModelSel = document.getElementById("wfm-ai-model-select");
|
||||
if (aiModelSel) {
|
||||
const firstOpt = aiModelSel.querySelector("option[value='']");
|
||||
if (firstOpt) firstOpt.textContent = t("aiSettingsModelPlaceholder");
|
||||
}
|
||||
const aiModelRefreshBtn = document.getElementById("wfm-ai-model-refresh-btn");
|
||||
if (aiModelRefreshBtn) aiModelRefreshBtn.textContent = t("aiSettingsRefreshBtn");
|
||||
const aiFreeLangTitle = document.getElementById("wfm-ai-settings-freelang-title");
|
||||
if (aiFreeLangTitle) aiFreeLangTitle.textContent = t("aiSettingsFreeLang");
|
||||
const aiFreeSrcLabel = document.getElementById("wfm-ai-free-src-label");
|
||||
if (aiFreeSrcLabel) aiFreeSrcLabel.textContent = t("aiSettingsInputLang");
|
||||
const aiFreeDstLabel = document.getElementById("wfm-ai-free-dst-label");
|
||||
if (aiFreeDstLabel) aiFreeDstLabel.textContent = t("aiSettingsOutputLang");
|
||||
const aiSaveBtn = document.getElementById("wfm-ai-settings-save-btn");
|
||||
if (aiSaveBtn) aiSaveBtn.textContent = t("aiSettingsSaveBtn");
|
||||
const aiChatImgGenLabel = document.getElementById("wfm-ai-chat-imggen-label-text");
|
||||
if (aiChatImgGenLabel) aiChatImgGenLabel.textContent = t("aiChatImgGenToggle");
|
||||
const aiChatGenTitle = document.getElementById("wfm-ai-settings-chatgen-title");
|
||||
if (aiChatGenTitle) aiChatGenTitle.textContent = t("aiSettingsChatGenTitle");
|
||||
const aiChatGenDedicatedLabel = document.getElementById("wfm-ai-chatgen-dedicated-label");
|
||||
if (aiChatGenDedicatedLabel) aiChatGenDedicatedLabel.textContent = t("aiSettingsChatGenDedicated");
|
||||
const aiChatGenI2ITitle = document.getElementById("wfm-ai-settings-chatgen-i2i-title");
|
||||
if (aiChatGenI2ITitle) aiChatGenI2ITitle.textContent = t("aiSettingsChatI2IGenTitle");
|
||||
const aiChatGenI2IDedicatedLabel = document.getElementById("wfm-ai-chatgen-i2i-dedicated-label");
|
||||
if (aiChatGenI2IDedicatedLabel) aiChatGenI2IDedicatedLabel.textContent = t("aiSettingsChatGenDedicated");
|
||||
const aiSkillSel = document.getElementById("wfm-ai-chat-skill-select");
|
||||
if (aiSkillSel) {
|
||||
const firstOpt = aiSkillSel.querySelector("option[value='']");
|
||||
if (firstOpt) firstOpt.textContent = `-- ${t("aiSkillNone")} --`;
|
||||
}
|
||||
const aiSkillManageBtn = document.getElementById("wfm-ai-chat-skill-manage-btn");
|
||||
if (aiSkillManageBtn) aiSkillManageBtn.title = t("aiSkillManageBtnTitle");
|
||||
const aiSkillPanelTitle = document.querySelector(".wfm-ai-skill-panel-title");
|
||||
if (aiSkillPanelTitle) aiSkillPanelTitle.textContent = t("aiSkillPanelTitle");
|
||||
const aiSkillNewBtn = document.getElementById("wfm-ai-skill-new-btn");
|
||||
if (aiSkillNewBtn) aiSkillNewBtn.textContent = t("aiSkillNewBtn");
|
||||
const aiSkillContentTA = document.getElementById("wfm-ai-skill-editor-content");
|
||||
if (aiSkillContentTA) aiSkillContentTA.placeholder = t("aiSkillContentPlaceholder");
|
||||
|
||||
// Group panel labels
|
||||
const propTitle = document.querySelector("#wfm-side-tab-group .wfm-group-section-title");
|
||||
if (propTitle) propTitle.textContent = t("properties");
|
||||
@@ -252,47 +428,286 @@ function applyI18nToHtml() {
|
||||
"wfm-help-about-1": "helpAbout1", "wfm-help-about-2": "helpAbout2",
|
||||
"wfm-help-wf-1": "helpWf1", "wfm-help-wf-2": "helpWf2", "wfm-help-wf-3": "helpWf3",
|
||||
"wfm-help-wf-4": "helpWf4", "wfm-help-wf-5": "helpWf5", "wfm-help-wf-6": "helpWf6",
|
||||
"wfm-help-wf-7": "helpWf7", "wfm-help-wf-8": "helpWf8",
|
||||
"wfm-help-wf-7": "helpWf7",
|
||||
"wfm-help-gen-1": "helpGen1", "wfm-help-gen-2": "helpGen2",
|
||||
"wfm-help-gen-3": "helpGen3", "wfm-help-gen-4": "helpGen4",
|
||||
"wfm-help-gen-5": "helpGen5", "wfm-help-gen-6": "helpGen6", "wfm-help-gen-7": "helpGen7",
|
||||
"wfm-help-gen-8": "helpGen8", "wfm-help-gen-9": "helpGen9", "wfm-help-gen-10": "helpGen10",
|
||||
"wfm-help-gen-11": "helpGen11", "wfm-help-gen-12": "helpGen12",
|
||||
"wfm-help-gen-13": "helpGen13", "wfm-help-gen-14": "helpGen14",
|
||||
"wfm-help-gen-15": "helpGen15", "wfm-help-gen-16": "helpGen16",
|
||||
"wfm-help-gen-17": "helpGen17", "wfm-help-gen-18": "helpGen18",
|
||||
"wfm-help-gen-19": "helpGen19",
|
||||
"wfm-help-gen-20": "helpGen20", "wfm-help-gen-21": "helpGen21",
|
||||
"wfm-help-gen-22": "helpGen22", "wfm-help-gen-23": "helpGen23",
|
||||
"wfm-help-gen-24": "helpGen24", "wfm-help-gen-25": "helpGen25",
|
||||
"wfm-help-feeder-title": "helpFeederTitle",
|
||||
"wfm-help-feeder-desc": "helpFeederDesc",
|
||||
"wfm-help-feeder-imgloop-title": "helpFeederImgloopTitle",
|
||||
"wfm-help-feeder-imgloop-desc": "helpFeederImgloopDesc",
|
||||
"wfm-help-feeder-1": "helpFeeder1", "wfm-help-feeder-2": "helpFeeder2",
|
||||
"wfm-help-feeder-3": "helpFeeder3", "wfm-help-feeder-4": "helpFeeder4",
|
||||
"wfm-help-feeder-5": "helpFeeder5", "wfm-help-feeder-6": "helpFeeder6",
|
||||
"wfm-help-feeder-7": "helpFeeder7", "wfm-help-feeder-8": "helpFeeder8",
|
||||
"wfm-help-feeder-9": "helpFeeder9",
|
||||
"wfm-help-feeder-gal-title": "helpFeederGalTitle",
|
||||
"wfm-help-feeder-gal-desc": "helpFeederGalDesc",
|
||||
"wfm-help-feeder-gal-1": "helpFeederGal1", "wfm-help-feeder-gal-2": "helpFeederGal2",
|
||||
"wfm-help-feeder-gal-3": "helpFeederGal3", "wfm-help-feeder-gal-4": "helpFeederGal4",
|
||||
"wfm-help-feeder-gal-5": "helpFeederGal5", "wfm-help-feeder-gal-6": "helpFeederGal6",
|
||||
"wfm-help-lab-title": "helpLabTitle",
|
||||
"wfm-help-lab-desc": "helpLabDesc",
|
||||
"wfm-help-lab-card-columns-title": "helpLabCardColumnsTitle",
|
||||
"wfm-help-lab-1": "helpLab1", "wfm-help-lab-2": "helpLab2",
|
||||
"wfm-help-lab-3": "helpLab3", "wfm-help-lab-4": "helpLab4",
|
||||
"wfm-help-lab-5": "helpLab5", "wfm-help-lab-6": "helpLab6",
|
||||
"wfm-help-lab-7": "helpLab7", "wfm-help-lab-8": "helpLab8",
|
||||
"wfm-help-lab-9": "helpLab9", "wfm-help-lab-10": "helpLab10",
|
||||
"wfm-help-lab-card-plan-title": "helpLabCardPlanTitle",
|
||||
"wfm-help-lab-plan-1": "helpLabPlan1", "wfm-help-lab-plan-2": "helpLabPlan2",
|
||||
"wfm-help-lab-plan-3": "helpLabPlan3", "wfm-help-lab-plan-4": "helpLabPlan4",
|
||||
"wfm-help-lab-plan-5": "helpLabPlan5", "wfm-help-lab-plan-6": "helpLabPlan6",
|
||||
"wfm-help-lab-plan-7": "helpLabPlan7", "wfm-help-lab-plan-8": "helpLabPlan8",
|
||||
"wfm-help-lab-plan-9": "helpLabPlan9", "wfm-help-lab-plan-10": "helpLabPlan10",
|
||||
"wfm-help-lab-plan-11": "helpLabPlan11",
|
||||
"wfm-help-lab-card-results-title": "helpLabCardResultsTitle",
|
||||
"wfm-help-lab-results-1": "helpLabResults1", "wfm-help-lab-results-2": "helpLabResults2",
|
||||
"wfm-help-lab-results-3": "helpLabResults3",
|
||||
"wfm-help-prompt-1": "helpPrompt1", "wfm-help-prompt-2": "helpPrompt2",
|
||||
"wfm-help-prompt-3": "helpPrompt3", "wfm-help-prompt-4": "helpPrompt4",
|
||||
"wfm-help-prompt-5": "helpPrompt5", "wfm-help-prompt-6": "helpPrompt6",
|
||||
"wfm-help-prompt-7": "helpPrompt7",
|
||||
"wfm-help-prompt-7": "helpPrompt7", "wfm-help-prompt-8": "helpPrompt8",
|
||||
"wfm-help-prompt-9": "helpPrompt9",
|
||||
"wfm-help-settings-1": "helpSettings1", "wfm-help-settings-2": "helpSettings2",
|
||||
"wfm-help-settings-3": "helpSettings3", "wfm-help-settings-4": "helpSettings4",
|
||||
"wfm-help-settings-5": "helpSettings5", "wfm-help-settings-6": "helpSettings6",
|
||||
"wfm-help-settings-7": "helpSettings7", "wfm-help-settings-8": "helpSettings8",
|
||||
"wfm-help-settings-9": "helpSettings9", "wfm-help-settings-10": "helpSettings10",
|
||||
"wfm-help-settings-11": "helpSettings11", "wfm-help-settings-12": "helpSettings12",
|
||||
"wfm-help-settings-13": "helpSettings13",
|
||||
"wfm-help-nodes-1": "helpNodes1", "wfm-help-nodes-2": "helpNodes2",
|
||||
"wfm-help-nodes-3": "helpNodes3", "wfm-help-nodes-4": "helpNodes4",
|
||||
"wfm-help-nodes-5": "helpNodes5", "wfm-help-nodes-6": "helpNodes6",
|
||||
"wfm-help-nodes-7": "helpNodes7",
|
||||
"wfm-help-models-1": "helpModels1", "wfm-help-models-2": "helpModels2",
|
||||
"wfm-help-models-3": "helpModels3", "wfm-help-models-4": "helpModels4",
|
||||
"wfm-help-models-5": "helpModels5", "wfm-help-models-6": "helpModels6",
|
||||
"wfm-help-models-7": "helpModels7",
|
||||
"wfm-help-models-7": "helpModels7", "wfm-help-models-8": "helpModels8",
|
||||
"wfm-help-models-9": "helpModels9", "wfm-help-models-10": "helpModels10",
|
||||
"wfm-help-models-11": "helpModels11", "wfm-help-models-12": "helpModels12",
|
||||
"wfm-help-gallery-title": "helpGalleryTitle",
|
||||
"wfm-help-gallery-1": "helpGallery1", "wfm-help-gallery-2": "helpGallery2",
|
||||
"wfm-help-gallery-3": "helpGallery3", "wfm-help-gallery-4": "helpGallery4",
|
||||
"wfm-help-gallery-5": "helpGallery5", "wfm-help-gallery-6": "helpGallery6",
|
||||
"wfm-help-gallery-7": "helpGallery7", "wfm-help-gallery-8": "helpGallery8",
|
||||
"wfm-help-gallery-9": "helpGallery9", "wfm-help-gallery-10": "helpGallery10",
|
||||
"wfm-help-gallery-11": "helpGallery11", "wfm-help-gallery-12": "helpGallery12",
|
||||
"wfm-help-gallery-13": "helpGallery13", "wfm-help-gallery-14": "helpGallery14",
|
||||
"wfm-help-gallery-15": "helpGallery15", "wfm-help-gallery-16": "helpGallery16",
|
||||
"wfm-help-gallery-17": "helpGallery17",
|
||||
"wfm-help-gallery-18": "helpGallery18", "wfm-help-gallery-19": "helpGallery19",
|
||||
"wfm-help-gallery-20": "helpGallery20", "wfm-help-gallery-21": "helpGallery21",
|
||||
"wfm-help-gallery-22": "helpGallery22", "wfm-help-gallery-23": "helpGallery23",
|
||||
"wfm-help-gallery-24": "helpGallery24",
|
||||
"wfm-help-gallery-card-styleprompt-title": "helpGalleryCardStyleprompt",
|
||||
"wfm-help-gallery-card-stylecatalog-title": "helpGalleryCardStylecatalog",
|
||||
"wfm-help-tagger-title": "helpTaggerTitle",
|
||||
"wfm-help-tagger-1": "helpTagger1", "wfm-help-tagger-2": "helpTagger2",
|
||||
"wfm-help-tagger-3": "helpTagger3", "wfm-help-tagger-4": "helpTagger4",
|
||||
"wfm-help-tagger-5": "helpTagger5", "wfm-help-tagger-6": "helpTagger6",
|
||||
"wfm-help-tagger-7": "helpTagger7", "wfm-help-tagger-8": "helpTagger8",
|
||||
"wfm-help-tagger-9": "helpTagger9",
|
||||
"wfm-help-sidepanel-title": "helpSidepanelTitle",
|
||||
"wfm-help-sidepanel-1": "helpSidepanel1", "wfm-help-sidepanel-2": "helpSidepanel2",
|
||||
"wfm-help-sidepanel-3": "helpSidepanel3", "wfm-help-sidepanel-4": "helpSidepanel4",
|
||||
"wfm-help-sidepanel-5": "helpSidepanel5", "wfm-help-sidepanel-6": "helpSidepanel6",
|
||||
"wfm-help-sidepanel-7": "helpSidepanel7", "wfm-help-sidepanel-8": "helpSidepanel8",
|
||||
"wfm-help-sidepanel-9": "helpSidepanel9", "wfm-help-sidepanel-10": "helpSidepanel10",
|
||||
"wfm-help-sidepanel-11": "helpSidepanel11", "wfm-help-sidepanel-12": "helpSidepanel12",
|
||||
"wfm-help-sidepanel-13": "helpSidepanel13",
|
||||
"wfm-help-sidepanel-14": "helpSidepanel14", "wfm-help-sidepanel-15": "helpSidepanel15",
|
||||
"wfm-help-sidepanel-16": "helpSidepanel16",
|
||||
"wfm-help-sidepanel-17": "helpSidepanel17",
|
||||
"wfm-help-ai-title": "helpAiTitle",
|
||||
"wfm-help-ai-1": "helpAi1", "wfm-help-ai-2": "helpAi2", "wfm-help-ai-3": "helpAi3",
|
||||
"wfm-help-ai-4": "helpAi4", "wfm-help-ai-6": "helpAi6",
|
||||
"wfm-help-ai-7": "helpAi7",
|
||||
"wfm-help-ai-8": "helpAi8",
|
||||
"wfm-help-ai-9": "helpAi9",
|
||||
"wfm-help-ai-card-layout-title": "helpAiCardLayout",
|
||||
"wfm-help-ai-card-translate-title": "helpAiCardTranslate",
|
||||
"wfm-help-ai-card-chat-title": "helpAiCardChat",
|
||||
"wfm-help-ai-card-svg-title": "helpAiCardSvg",
|
||||
"wfm-help-ai-card-skills-title": "helpAiCardSkills",
|
||||
"wfm-help-ai-card-skilltips-title": "helpAiCardSkillTips",
|
||||
"wfm-help-ai-card-tools-title": "helpAiCardTools",
|
||||
"wfm-help-ai-card-settings-title": "helpAiCardSettings",
|
||||
"wfm-help-imageedit-title": "helpImageEditTitle",
|
||||
"wfm-help-imageedit-tools-title": "helpImageEditToolsTitle",
|
||||
"wfm-help-imageedit-layer-title": "helpImageEditLayerTitle",
|
||||
"wfm-help-imageedit-text-title": "helpImageEditTextTitle",
|
||||
"wfm-help-imageedit-export-title": "helpImageEditExportTitle",
|
||||
"wfm-help-imageedit-kbd-title": "helpImageEditKbdTitle",
|
||||
"wfm-help-imageedit-1": "helpImageEdit1", "wfm-help-imageedit-2": "helpImageEdit2", "wfm-help-imageedit-3": "helpImageEdit3",
|
||||
"wfm-help-imageedit-4": "helpImageEdit4", "wfm-help-imageedit-5": "helpImageEdit5",
|
||||
"wfm-help-imageedit-6": "helpImageEdit6", "wfm-help-imageedit-7": "helpImageEdit7",
|
||||
"wfm-help-imageedit-8": "helpImageEdit8", "wfm-help-imageedit-9": "helpImageEdit9",
|
||||
"wfm-help-imageedit-10": "helpImageEdit10", "wfm-help-imageedit-11": "helpImageEdit11",
|
||||
"wfm-help-imageedit-12": "helpImageEdit12", "wfm-help-imageedit-13": "helpImageEdit13",
|
||||
"wfm-help-imageedit-14": "helpImageEdit14", "wfm-help-imageedit-15": "helpImageEdit15",
|
||||
"wfm-help-imageedit-16": "helpImageEdit16", "wfm-help-imageedit-17": "helpImageEdit17",
|
||||
"wfm-help-imageedit-18": "helpImageEdit18", "wfm-help-imageedit-19": "helpImageEdit19",
|
||||
"wfm-help-imageedit-20": "helpImageEdit20", "wfm-help-imageedit-21": "helpImageEdit21",
|
||||
"wfm-help-imageedit-22": "helpImageEdit22", "wfm-help-imageedit-23": "helpImageEdit23",
|
||||
"wfm-help-imageedit-24": "helpImageEdit24", "wfm-help-imageedit-25": "helpImageEdit25",
|
||||
"wfm-help-imageedit-26": "helpImageEdit26", "wfm-help-imageedit-27": "helpImageEdit27",
|
||||
"wfm-help-imageedit-28": "helpImageEdit28", "wfm-help-imageedit-29": "helpImageEdit29",
|
||||
"wfm-help-imageedit-30": "helpImageEdit30",
|
||||
"wfm-help-imageedit-7b": "helpImageEdit7b", "wfm-help-imageedit-7c": "helpImageEdit7c",
|
||||
"wfm-help-imageedit-7d": "helpImageEdit7d", "wfm-help-imageedit-7e": "helpImageEdit7e",
|
||||
"wfm-help-imageedit-8b": "helpImageEdit8b",
|
||||
"wfm-help-imageedit-blur-title": "helpImageEditBlurTitle",
|
||||
"wfm-help-imageedit-blur-1": "helpImageEditBlur1", "wfm-help-imageedit-blur-2": "helpImageEditBlur2",
|
||||
"wfm-help-imageedit-blur-3": "helpImageEditBlur3", "wfm-help-imageedit-blur-4": "helpImageEditBlur4",
|
||||
"wfm-help-imageedit-blur-5": "helpImageEditBlur5", "wfm-help-imageedit-blur-6": "helpImageEditBlur6",
|
||||
"wfm-help-imageedit-bgremove-title": "helpImageEditBgRemoveTitle",
|
||||
"wfm-help-imageedit-bgremove-1": "helpImageEditBgRemove1", "wfm-help-imageedit-bgremove-2": "helpImageEditBgRemove2",
|
||||
"wfm-help-imageedit-bgremove-3": "helpImageEditBgRemove3", "wfm-help-imageedit-bgremove-4": "helpImageEditBgRemove4",
|
||||
"wfm-help-imageedit-bgremove-5": "helpImageEditBgRemove5",
|
||||
"wfm-help-imageedit-mask-title": "helpImageEditMaskTitle",
|
||||
"wfm-help-imageedit-mask-1": "helpImageEditMask1", "wfm-help-imageedit-mask-2": "helpImageEditMask2",
|
||||
"wfm-help-imageedit-mask-3": "helpImageEditMask3", "wfm-help-imageedit-mask-4": "helpImageEditMask4",
|
||||
"wfm-help-imageedit-mask-5": "helpImageEditMask5", "wfm-help-imageedit-mask-6": "helpImageEditMask6",
|
||||
"wfm-help-imageedit-mask-7": "helpImageEditMask7", "wfm-help-imageedit-mask-8": "helpImageEditMask8",
|
||||
"wfm-help-imageedit-mask-9": "helpImageEditMask9", "wfm-help-imageedit-mask-10": "helpImageEditMask10",
|
||||
"wfm-help-imageedit-mask-11": "helpImageEditMask11", "wfm-help-imageedit-mask-12": "helpImageEditMask12",
|
||||
"wfm-help-imageedit-mask-13": "helpImageEditMask13",
|
||||
"wfm-help-imageedit-gmic-title": "helpImageEditGmicTitle",
|
||||
"wfm-help-imageedit-gmic-1": "helpImageEditGmic1", "wfm-help-imageedit-gmic-2": "helpImageEditGmic2",
|
||||
"wfm-help-imageedit-gmic-3": "helpImageEditGmic3", "wfm-help-imageedit-gmic-4": "helpImageEditGmic4",
|
||||
"wfm-help-imageedit-gmic-install-title": "helpImageEditGmicInstallTitle",
|
||||
"wfm-help-imageedit-gmic-install-1": "helpImageEditGmicInstall1",
|
||||
"wfm-help-imageedit-gmic-install-2": "helpImageEditGmicInstall2",
|
||||
"wfm-help-imageedit-gmic-install-3": "helpImageEditGmicInstall3",
|
||||
"wfm-help-imageedit-tabkbd-title": "helpImageEditTabKbdTitle",
|
||||
"wfm-help-imageedit-tabkbd-1": "helpImageEdit22", "wfm-help-imageedit-tabkbd-2": "helpImageEdit23",
|
||||
"wfm-help-imageedit-tabkbd-3": "helpImageEdit24", "wfm-help-imageedit-tabkbd-4": "helpImageEdit25",
|
||||
"wfm-help-imageedit-tabkbd-5": "helpImageEdit26", "wfm-help-imageedit-tabkbd-6": "helpImageEdit27",
|
||||
"wfm-help-imageedit-tabkbd-7": "helpImageEdit28", "wfm-help-imageedit-tabkbd-8": "helpImageEdit29",
|
||||
"wfm-help-shortcuts-title": "helpShortcutsTitle",
|
||||
"wfm-help-shortcuts-1": "helpShortcuts1", "wfm-help-shortcuts-2": "helpShortcuts2",
|
||||
"wfm-help-shortcuts-3": "helpShortcuts3", "wfm-help-shortcuts-4": "helpShortcuts4",
|
||||
"wfm-help-trouble-title": "helpTroubleTitle",
|
||||
"wfm-help-trouble-1": "helpTrouble1", "wfm-help-trouble-2": "helpTrouble2",
|
||||
"wfm-help-trouble-3": "helpTrouble3", "wfm-help-trouble-4": "helpTrouble4",
|
||||
"wfm-help-trouble-5": "helpTrouble5", "wfm-help-trouble-6": "helpTrouble6",
|
||||
"wfm-help-trouble-7": "helpTrouble7", "wfm-help-trouble-8": "helpTrouble8",
|
||||
"wfm-help-trouble-9": "helpTrouble9",
|
||||
"wfm-help-support-title": "helpSupportTitle",
|
||||
"wfm-help-support-desc": "helpSupportDesc",
|
||||
"wfm-help-github-desc": "helpGithubDesc",
|
||||
"wfm-help-kofi-desc": "helpKofiDesc",
|
||||
"wfm-help-thanks": "helpThanks",
|
||||
// -- Help card headings (card-based redesign) --
|
||||
"wfm-help-about-card-getting-started-title": "helpAboutCardGettingStarted",
|
||||
"wfm-help-wf-title": "helpWfTitle",
|
||||
"wfm-help-wf-card-browse-title": "helpWfCardBrowse",
|
||||
"wfm-help-wf-card-groups-title": "helpWfCardGroups",
|
||||
"wfm-help-wf-card-panel-title": "helpWfCardPanel",
|
||||
"wfm-help-gen-title": "helpGenTitle",
|
||||
"wfm-help-gen-card-connect-title": "helpGenCardConnect",
|
||||
"wfm-help-gen-card-tabs-title": "helpGenCardTabs",
|
||||
"wfm-help-gen-card-seed-title": "helpGenCardSeed",
|
||||
"wfm-help-gen-card-batchsetup-title": "helpGenCardBatchSetup",
|
||||
"wfm-help-gen-card-batchtypes-title": "helpGenCardBatchTypes",
|
||||
"wfm-help-gen-card-lora-title": "helpGenCardLora",
|
||||
"wfm-help-prompt-title": "helpPromptTitle",
|
||||
"wfm-help-prompt-card-ai-title": "helpPromptCardAi",
|
||||
"wfm-help-prompt-card-presets-title": "helpPromptCardPresets",
|
||||
"wfm-help-prompt-card-wildcards-title": "helpPromptCardWildcards",
|
||||
"wfm-help-metadata-card-overview-title": "helpMetadataCardOverview",
|
||||
"wfm-help-metadata-card-extracted-title": "helpMetadataCardExtracted",
|
||||
"wfm-help-metadata-card-formats-title": "helpMetadataCardFormats",
|
||||
"wfm-help-settings-title": "helpSettingsTitle",
|
||||
"wfm-help-settings-card-general-title": "helpSettingsCardGeneral",
|
||||
"wfm-help-settings-card-connections-title": "helpSettingsCardConnections",
|
||||
"wfm-help-settings-card-workflow-title": "helpSettingsCardWorkflow",
|
||||
"wfm-help-settings-card-editor-title": "helpSettingsCardEditor",
|
||||
"wfm-help-settings-card-misc-title": "helpSettingsCardMisc",
|
||||
"wfm-help-nodes-title": "helpNodesTitle",
|
||||
"wfm-help-nodes-card-browse-title": "helpNodesCardBrowse",
|
||||
"wfm-help-nodes-card-groups-title": "helpNodesCardGroups",
|
||||
"wfm-help-nodes-card-panel-title": "helpNodesCardPanel",
|
||||
"wfm-help-gallery-card-browse-title": "helpGalleryCardBrowse",
|
||||
"wfm-help-gallery-card-folder-title": "helpGalleryCardFolder",
|
||||
"wfm-help-gallery-card-detail-title": "helpGalleryCardDetail",
|
||||
"wfm-help-gallery-card-actions-title": "helpGalleryCardActions",
|
||||
"wfm-help-gallery-card-formats-title": "helpGalleryCardFormats",
|
||||
"wfm-help-models-title": "helpModelsTitle",
|
||||
"wfm-help-models-card-browse-title": "helpModelsCardBrowse",
|
||||
"wfm-help-models-card-civitai-title": "helpModelsCardCivitai",
|
||||
"wfm-help-models-card-apply-title": "helpModelsCardApply",
|
||||
"wfm-help-models-card-groups-title": "helpModelsCardGroups",
|
||||
"wfm-help-models-card-bulk-title": "helpModelsCardBulk",
|
||||
"wfm-help-sidepanel-card-tabs-title": "helpSidepanelCardTabs",
|
||||
"wfm-help-sidepanel-card-dnd-title": "helpSidepanelCardDnd",
|
||||
"wfm-help-sidepanel-card-info-title": "helpSidepanelCardInfo",
|
||||
"wfm-help-sidepanel-card-ai-title": "helpSidepanelCardAi",
|
||||
"wfm-help-sidepanel-card-nav-title": "helpSidepanelCardNav",
|
||||
"wfm-help-tagger-card-overview-title": "helpTaggerCardOverview",
|
||||
"wfm-help-tagger-card-single-title": "helpTaggerCardSingle",
|
||||
"wfm-help-tagger-card-sending-title": "helpTaggerCardSending",
|
||||
"wfm-help-tagger-card-batch-title": "helpTaggerCardBatch",
|
||||
"wfm-help-imageedit-card-overview-title": "helpImageEditCardOverview",
|
||||
"wfm-help-shortcuts-card-general-title": "helpShortcutsCardGeneral",
|
||||
"wfm-help-trouble-card-connection-title": "helpTroubleCardConnection",
|
||||
"wfm-help-trouble-card-workflow-title": "helpTroubleCardWorkflow",
|
||||
"wfm-help-trouble-card-tagger-title": "helpTroubleCardTagger",
|
||||
};
|
||||
for (const [id, key] of Object.entries(helpIdMap)) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) el.textContent = t(key);
|
||||
}
|
||||
|
||||
// Search box placeholder
|
||||
const helpSearch = document.getElementById("wfm-help-search");
|
||||
if (helpSearch) {
|
||||
helpSearch.placeholder = t("helpSearchPlaceholder");
|
||||
helpSearch.addEventListener("input", _onHelpSearch);
|
||||
}
|
||||
|
||||
// Jump-to-tab buttons
|
||||
document.querySelectorAll(".wfm-help-jump-btn").forEach((btn) => {
|
||||
btn.textContent = t("helpJumpToTab");
|
||||
});
|
||||
}
|
||||
|
||||
function _onHelpSearch(e) {
|
||||
const q = e.target.value.trim().toLowerCase();
|
||||
const navItems = document.querySelectorAll("[data-help-page]");
|
||||
if (!q) {
|
||||
navItems.forEach(btn => btn.classList.remove("search-hidden"));
|
||||
return;
|
||||
}
|
||||
|
||||
const matchedPages = new Set();
|
||||
document.querySelectorAll(".wfm-help-page").forEach(page => {
|
||||
const pageId = page.id.replace("wfm-help-page-", "");
|
||||
if (page.textContent.toLowerCase().includes(q)) matchedPages.add(pageId);
|
||||
});
|
||||
|
||||
let firstBtn = null;
|
||||
navItems.forEach(btn => {
|
||||
const pageId = btn.dataset.helpPage;
|
||||
if (matchedPages.has(pageId)) {
|
||||
btn.classList.remove("search-hidden");
|
||||
if (!firstBtn) firstBtn = btn;
|
||||
} else {
|
||||
btn.classList.add("search-hidden");
|
||||
}
|
||||
});
|
||||
|
||||
if (firstBtn && !firstBtn.classList.contains("active")) firstBtn.click();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
@@ -330,22 +745,59 @@ import { initWorkflowTab } from "./workflow-tab.js";
|
||||
import { initNodesTab } from "./nodes-tab.js";
|
||||
import { initGenerateTab } from "./generate-tab.js";
|
||||
import { initPromptTab } from "./prompt-tab.js";
|
||||
import { initSettingsTab, applyTheme, getSavedTheme } from "./settings-tab.js";
|
||||
import { initSettingsTab, applyTheme, getSavedTheme, applyTextareaFontSize, applyJsonColors } from "./settings-tab.js";
|
||||
import { initModelsTab } from "./models-tab.js";
|
||||
import { initGalleryTab } from "./gallery-tab.js";
|
||||
import { initMetadataTab } from "./metadata-tab.js";
|
||||
import { initAiTab } from "./ai-tab.js";
|
||||
import { initTaggerTab } from "./tagger-tab.js";
|
||||
import { imageEditTab } from "./image-edit-tab.js";
|
||||
|
||||
// Apply saved theme immediately to prevent flash of default theme
|
||||
applyTheme(getSavedTheme());
|
||||
|
||||
// Apply saved textarea font size & JSON highlight colors
|
||||
try {
|
||||
const _s = getSettings();
|
||||
if (_s.textareaFontSize) applyTextareaFontSize(_s.textareaFontSize);
|
||||
applyJsonColors(_s.jsonColors);
|
||||
} catch {}
|
||||
|
||||
function initHelpTab() {
|
||||
document.querySelectorAll(".wfm-help-nav-item").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const page = btn.dataset.helpPage;
|
||||
document.querySelectorAll(".wfm-help-nav-item").forEach((b) => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
document.querySelectorAll(".wfm-help-page").forEach((p) => p.classList.remove("active"));
|
||||
document.getElementById(`wfm-help-page-${page}`)?.classList.add("active");
|
||||
});
|
||||
});
|
||||
|
||||
// Jump-to-tab buttons: switch to the real feature tab this help page documents
|
||||
document.querySelectorAll(".wfm-help-jump-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
document.querySelector(`.wfm-tab[data-tab="${btn.dataset.jumpTab}"]`)?.click();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
document.addEventListener("DOMContentLoaded", () => {
|
||||
applyI18nToHtml();
|
||||
initTabs();
|
||||
initModal();
|
||||
initHelpTab();
|
||||
initSettingsTab(); // Settings first (applies saved URL)
|
||||
initWorkflowTab();
|
||||
initNodesTab();
|
||||
initModelsTab();
|
||||
initGenerateTab();
|
||||
initPromptTab();
|
||||
initMetadataTab();
|
||||
initGalleryTab();
|
||||
initAiTab();
|
||||
initTaggerTab();
|
||||
imageEditTab.init();
|
||||
|
||||
console.log("Workflow Studio: App initialized");
|
||||
});
|
||||
|
||||
+78
-16
@@ -20,6 +20,9 @@ export const comfyUI = {
|
||||
currentPromptId: null,
|
||||
currentWorkflow: null,
|
||||
currentAnalysis: null,
|
||||
// ワークフロー読み込み時点のLora Loader (LoraManager)ノードの loras/text 状態。
|
||||
// Stackグループチェックボックスのトグル時に、OFF側の「読み込み時の状態へ戻す」用に使う。
|
||||
loraManagerSnapshots: {},
|
||||
|
||||
updateUrl(url) {
|
||||
if (!url) {
|
||||
@@ -88,7 +91,18 @@ export const comfyUI = {
|
||||
try {
|
||||
const info = await this.fetchObjectInfo(cls);
|
||||
if (info?.[cls]?.input?.required?.[inputKey]) {
|
||||
return info[cls].input.required[inputKey][0] || [];
|
||||
const inputDef = info[cls].input.required[inputKey];
|
||||
const first = inputDef[0];
|
||||
// V1: [["model1", "model2"], {}]
|
||||
if (Array.isArray(first)) return first;
|
||||
// V2: ["COMBO", { "values": [...] }]
|
||||
if (typeof first === "string" && Array.isArray(inputDef[1]?.values)) {
|
||||
return inputDef[1].values;
|
||||
}
|
||||
// V3 ComfyNode: ["COMBO", { "options": [...] }]
|
||||
if (typeof first === "string" && Array.isArray(inputDef[1]?.options)) {
|
||||
return inputDef[1].options;
|
||||
}
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
@@ -121,19 +135,43 @@ export const comfyUI = {
|
||||
},
|
||||
|
||||
async fetchDiffusionModels() {
|
||||
return this._fetchModelList(
|
||||
["UNETLoader", "UnetLoaderGGUF"],
|
||||
"unet_name"
|
||||
);
|
||||
const [standard, gguf] = await Promise.all([
|
||||
this._fetchModelList(["UNETLoader", "UnetLoaderGGUF"], "unet_name"),
|
||||
this._fetchModelList(["LoaderGGUF", "LoaderGGUFAdvanced"], "gguf_name"),
|
||||
]);
|
||||
return [...new Set([...standard, ...gguf])];
|
||||
},
|
||||
|
||||
async fetchTextEncoders() {
|
||||
const [dual, single] = await Promise.all([
|
||||
this._fetchModelList(["DualCLIPLoader", "DualClipLoaderGGUF"], "clip_name1"),
|
||||
this._fetchModelList(["CLIPLoader", "ClipLoaderGGUF"], "clip_name"),
|
||||
]);
|
||||
return [...new Set([...dual, ...single])];
|
||||
},
|
||||
|
||||
async fetchHypernetworks() {
|
||||
return this._fetchModelList(
|
||||
["DualCLIPLoader", "CLIPLoader"],
|
||||
"clip_name1"
|
||||
["HypernetworkLoader"],
|
||||
"hypernetwork_name"
|
||||
);
|
||||
},
|
||||
|
||||
async fetchEmbeddings() {
|
||||
// Use WFS backend API (reliable, goes through same aiohttp server)
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/files?type=embedding");
|
||||
if (res.ok) {
|
||||
const list = await res.json();
|
||||
return Array.isArray(list) ? list : [];
|
||||
}
|
||||
console.warn("[WFS] /api/wfm/models/files?type=embedding returned", res.status);
|
||||
} catch (e) {
|
||||
console.error("[WFS] fetchEmbeddings error:", e);
|
||||
}
|
||||
return [];
|
||||
},
|
||||
|
||||
async fetchSamplers() {
|
||||
return this._fetchModelList(["KSampler"], "sampler_name");
|
||||
},
|
||||
@@ -143,14 +181,16 @@ export const comfyUI = {
|
||||
},
|
||||
|
||||
// Generation
|
||||
async queuePrompt(workflow) {
|
||||
async queuePrompt(workflow, extraData = null) {
|
||||
const body = {
|
||||
prompt: workflow,
|
||||
client_id: this.clientId,
|
||||
};
|
||||
if (extraData) body.extra_data = extraData;
|
||||
const res = await fetch(`${this.baseUrl}/prompt`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
prompt: workflow,
|
||||
client_id: this.clientId,
|
||||
}),
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
if (!res.ok) {
|
||||
const err = await res.json().catch(() => ({}));
|
||||
@@ -203,7 +243,13 @@ export const comfyUI = {
|
||||
});
|
||||
const res = await fetch(`${this.baseUrl}/view?${params}`);
|
||||
if (!res.ok) throw new Error(`Failed to fetch image: HTTP ${res.status}`);
|
||||
return await res.blob();
|
||||
const blob = await res.blob();
|
||||
// /view はSVGをセキュリティ上application/octet-streamで返すため、
|
||||
// <img>で表示できるよう拡張子から正しいMIMEタイプへ付け替える
|
||||
if (imageData.filename?.toLowerCase().endsWith(".svg") && blob.type !== "image/svg+xml") {
|
||||
return blob.slice(0, blob.size, "image/svg+xml");
|
||||
}
|
||||
return blob;
|
||||
},
|
||||
|
||||
async uploadImage(file, filename, overwrite = true) {
|
||||
@@ -250,8 +296,8 @@ export const comfyUI = {
|
||||
throw new Error("Failed to connect WebSocket");
|
||||
}
|
||||
|
||||
// Queue prompt
|
||||
const result = await this.queuePrompt(workflow);
|
||||
// Queue prompt — pass workflow as extra_pnginfo so SaveImage embeds it in PNG metadata
|
||||
const result = await this.queuePrompt(workflow, { extra_pnginfo: { workflow } });
|
||||
this.currentPromptId = result.prompt_id;
|
||||
|
||||
// Track progress
|
||||
@@ -263,6 +309,9 @@ export const comfyUI = {
|
||||
|
||||
// Extract images from SaveImage outputs only
|
||||
const images = [];
|
||||
// comfyui-tosvg の "Save SVG String" ノードは images ではなく
|
||||
// saved_svg(ファイル名) / path(絶対パス) という独自キーで返す
|
||||
const svgOutputs = [];
|
||||
const outputs = history.outputs || {};
|
||||
for (const nodeOutput of Object.values(outputs)) {
|
||||
if (nodeOutput.images) {
|
||||
@@ -270,10 +319,23 @@ export const comfyUI = {
|
||||
images.push(img);
|
||||
}
|
||||
}
|
||||
if (nodeOutput.saved_svg) {
|
||||
// ComfyUI の実行エンジンは ui 出力の各値をイテラブルとして連結するため、
|
||||
// ノード側が単一文字列で返すと 1 文字ずつ分解された配列になってしまう。
|
||||
// (例: "a.svg" -> ["a",".","s","v","g"]) 文字配列を検出したら結合して復元する。
|
||||
const joinIfCharArray = (v) =>
|
||||
Array.isArray(v) && v.every((c) => typeof c === "string" && c.length === 1)
|
||||
? v.join("")
|
||||
: v;
|
||||
svgOutputs.push({
|
||||
filename: joinIfCharArray(nodeOutput.saved_svg),
|
||||
path: joinIfCharArray(nodeOutput.path) || "",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
onComplete?.(images, seed);
|
||||
return { images, seed };
|
||||
return { images, seed, svgOutputs };
|
||||
} catch (err) {
|
||||
onError?.(err);
|
||||
throw err;
|
||||
|
||||
+1606
-73
File diff suppressed because it is too large
Load Diff
+941
-39
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,859 @@
|
||||
/**
|
||||
* Feeder Tab - ImageFeeder node control (Image Loop mode)
|
||||
* + WFS_GalleryFeeder node control (Gallery mode)
|
||||
*/
|
||||
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { FEEDER_GROUP, ensureFeederGroup } from "./gallery-tab.js";
|
||||
|
||||
// ── Module State ──────────────────────────────────────────────────
|
||||
const _s = {
|
||||
dir: "", // current folder relative path (loop mode)
|
||||
images: [], // filenames in current folder (loop mode)
|
||||
selected: new Set(),// selected filenames (loop mode)
|
||||
presets: {}, // {presetName: {directory, selected_files}}
|
||||
running: false, // feeder run loop active
|
||||
mode: localStorage.getItem("wfm_feeder_mode") || "loop", // "loop" | "gallery"
|
||||
};
|
||||
|
||||
// Gallery mode state
|
||||
const _gs = {
|
||||
groups: [], // [{name}]
|
||||
group: FEEDER_GROUP, // selected group name
|
||||
images: [], // absolute paths in selected group
|
||||
idx: 0, // current index
|
||||
};
|
||||
|
||||
// ── Node helpers ──────────────────────────────────────────────────
|
||||
|
||||
function _feederNodes() {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!wf) return [];
|
||||
return Object.entries(wf)
|
||||
.filter(([, n]) => n?.class_type === "ImageFeeder")
|
||||
.map(([id, n]) => ({ id, title: n._meta?.title || `ImageFeeder` }));
|
||||
}
|
||||
|
||||
function _galFeederNodes() {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!wf) return [];
|
||||
return Object.entries(wf)
|
||||
.filter(([, n]) => n?.class_type === "WFS_GalleryFeeder")
|
||||
.map(([id, n]) => ({ id, title: n._meta?.title || `GalleryFeeder` }));
|
||||
}
|
||||
|
||||
export function refreshFeederNodeList() {
|
||||
if (_s.mode === "gallery") {
|
||||
_refreshGalNodeList();
|
||||
} else {
|
||||
const sel = document.getElementById("wfm-feeder-node-sel");
|
||||
if (!sel) return;
|
||||
const nodes = _feederNodes();
|
||||
if (nodes.length === 0) {
|
||||
sel.innerHTML = `<option value="">-- No ImageFeeder node --</option>`;
|
||||
return;
|
||||
}
|
||||
const prevVal = sel.value;
|
||||
sel.innerHTML = nodes.map(n =>
|
||||
`<option value="${n.id}">${n.title} (ID:${n.id})</option>`
|
||||
).join("");
|
||||
if (prevVal && nodes.find(n => n.id === prevVal)) sel.value = prevVal;
|
||||
_loadFromNode(sel.value || nodes[0].id);
|
||||
}
|
||||
}
|
||||
|
||||
function _refreshGalNodeList() {
|
||||
const sel = document.getElementById("wfm-feeder-gal-node-sel");
|
||||
if (!sel) return;
|
||||
const nodes = _galFeederNodes();
|
||||
if (nodes.length === 0) {
|
||||
sel.innerHTML = `<option value="">-- No WFS_GalleryFeeder node --</option>`;
|
||||
return;
|
||||
}
|
||||
const prevVal = sel.value;
|
||||
sel.innerHTML = nodes.map(n =>
|
||||
`<option value="${n.id}">${n.title} (ID:${n.id})</option>`
|
||||
).join("");
|
||||
if (prevVal && nodes.find(n => n.id === prevVal)) sel.value = prevVal;
|
||||
_loadFromGalNode(sel.value || nodes[0].id);
|
||||
}
|
||||
|
||||
function _loadFromNode(nodeId) {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!wf?.[nodeId]) return;
|
||||
const inp = wf[nodeId].inputs || {};
|
||||
|
||||
_setVal("wfm-feeder-dir-input", inp.directory || "");
|
||||
_setVal("wfm-feeder-sort", inp.sort_mode || "ascending");
|
||||
_setVal("wfm-feeder-index", inp.index ?? 0);
|
||||
_setVal("wfm-feeder-start", inp.start_index ?? 0);
|
||||
_setVal("wfm-feeder-end", inp.end_index ?? 0);
|
||||
_setVal("wfm-feeder-batch", inp.batch_size ?? 1);
|
||||
_setVal("wfm-feeder-seed", inp.seed ?? 0);
|
||||
_setCheck("wfm-feeder-use-sel", inp.use_selection ?? true);
|
||||
|
||||
_s.selected.clear();
|
||||
try {
|
||||
const arr = JSON.parse(inp.selected_files || "[]");
|
||||
if (Array.isArray(arr)) arr.forEach(f => _s.selected.add(f));
|
||||
} catch {}
|
||||
|
||||
const dir = inp.directory || "";
|
||||
if (_s.dir !== dir) {
|
||||
_s.dir = dir;
|
||||
_loadImages(dir);
|
||||
_highlightTreeRow(dir);
|
||||
} else {
|
||||
_refreshGridCbs();
|
||||
}
|
||||
_updateStatus();
|
||||
}
|
||||
|
||||
function _loadFromGalNode(nodeId) {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!wf?.[nodeId]) return;
|
||||
const inp = wf[nodeId].inputs || {};
|
||||
|
||||
const group = inp.group_name || FEEDER_GROUP;
|
||||
_setVal("wfm-feeder-gal-sort", inp.sort_mode || "filename_asc");
|
||||
_setVal("wfm-feeder-gal-index", inp.index ?? 0);
|
||||
_setVal("wfm-feeder-gal-seed", inp.seed ?? 0);
|
||||
_gs.idx = inp.index ?? 0;
|
||||
|
||||
if (_gs.group !== group) {
|
||||
_gs.group = group;
|
||||
_setGalGroupSelect(group);
|
||||
_loadGalImages(group);
|
||||
}
|
||||
}
|
||||
|
||||
function _applyToWorkflow() {
|
||||
const nodeId = document.getElementById("wfm-feeder-node-sel")?.value;
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!nodeId || !wf?.[nodeId]) {
|
||||
showToast(t("feederNoNode"), "error");
|
||||
return;
|
||||
}
|
||||
_applyToNode(nodeId);
|
||||
showToast(t("appliedToWorkflow"), "success");
|
||||
}
|
||||
|
||||
function _applyGalToWorkflow() {
|
||||
const nodeId = document.getElementById("wfm-feeder-gal-node-sel")?.value;
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
if (!nodeId || !wf?.[nodeId]) {
|
||||
showToast(t("feederGalNoNode"), "error");
|
||||
return;
|
||||
}
|
||||
_applyToGalNode(nodeId);
|
||||
showToast(t("appliedToWorkflow"), "success");
|
||||
}
|
||||
|
||||
// ── Run Loop ──────────────────────────────────────────────────────
|
||||
|
||||
function _setRunUI(running) {
|
||||
const runBtn = document.getElementById("wfm-feeder-run-btn");
|
||||
const stopBtn = document.getElementById("wfm-feeder-stop-btn");
|
||||
if (runBtn) runBtn.disabled = running;
|
||||
if (stopBtn) stopBtn.disabled = !running;
|
||||
}
|
||||
|
||||
function _setGalRunUI(running) {
|
||||
const runBtn = document.getElementById("wfm-feeder-gal-run-btn");
|
||||
const stopBtn = document.getElementById("wfm-feeder-gal-stop-btn");
|
||||
if (runBtn) runBtn.disabled = running;
|
||||
if (stopBtn) stopBtn.disabled = !running;
|
||||
}
|
||||
|
||||
function _applyToNode(nodeId) {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
const inp = wf[nodeId].inputs;
|
||||
inp.directory = _s.dir;
|
||||
inp.sort_mode = document.getElementById("wfm-feeder-sort")?.value || "ascending";
|
||||
inp.index = parseInt(document.getElementById("wfm-feeder-index")?.value) || 0;
|
||||
inp.start_index = parseInt(document.getElementById("wfm-feeder-start")?.value) || 0;
|
||||
inp.end_index = parseInt(document.getElementById("wfm-feeder-end")?.value) || 0;
|
||||
inp.batch_size = parseInt(document.getElementById("wfm-feeder-batch")?.value) || 1;
|
||||
inp.seed = parseInt(document.getElementById("wfm-feeder-seed")?.value) || 0;
|
||||
inp.use_selection = document.getElementById("wfm-feeder-use-sel")?.checked ?? true;
|
||||
inp.selected_files = JSON.stringify([..._s.selected]);
|
||||
}
|
||||
|
||||
function _applyToGalNode(nodeId) {
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
const inp = wf[nodeId].inputs;
|
||||
inp.group_name = _gs.group;
|
||||
inp.index = _gs.idx;
|
||||
inp.sort_mode = document.getElementById("wfm-feeder-gal-sort")?.value || "filename_asc";
|
||||
inp.seed = parseInt(document.getElementById("wfm-feeder-gal-seed")?.value) || 0;
|
||||
}
|
||||
|
||||
async function _startRun() {
|
||||
if (_s.running) return;
|
||||
if (!comfyUI.currentWorkflow) { showToast(t("noWorkflowLoaded"), "error"); return; }
|
||||
if (!comfyUI.connected) { showToast(t("notConnectedToComfyUI"), "error"); return; }
|
||||
|
||||
const nodeId = document.getElementById("wfm-feeder-node-sel")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow[nodeId]) {
|
||||
showToast(t("feederNoNode"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const wsOk = await comfyUI.connectWebSocket();
|
||||
if (!wsOk) { showToast(t("wsConnectionFailed"), "error"); return; }
|
||||
|
||||
_s.running = true;
|
||||
_setRunUI(true);
|
||||
|
||||
const progressBar = document.getElementById("wfm-gen-progress-bar");
|
||||
const progressText = document.getElementById("wfm-gen-progress-text");
|
||||
const resultImg = document.getElementById("wfm-gen-result-img");
|
||||
|
||||
let _lastSync = null;
|
||||
const _syncHandler = (ev) => {
|
||||
try {
|
||||
const msg = JSON.parse(ev.data);
|
||||
if (msg.type === "image_loop_node_sync" && String(msg.data?.node_id) === String(nodeId)) {
|
||||
_lastSync = msg.data;
|
||||
if (msg.data.thumbnail_path) {
|
||||
const p = msg.data.thumbnail_path;
|
||||
_showPreview(p, p.split("/").pop());
|
||||
}
|
||||
}
|
||||
} catch {}
|
||||
};
|
||||
comfyUI.socket.addEventListener("message", _syncHandler);
|
||||
|
||||
let count = 0;
|
||||
try {
|
||||
while (_s.running) {
|
||||
_lastSync = null;
|
||||
_applyToNode(nodeId);
|
||||
|
||||
const wf = comfyUI.currentWorkflow;
|
||||
const inp = wf[nodeId].inputs;
|
||||
|
||||
if (progressBar) progressBar.style.width = "0%";
|
||||
if (progressText) progressText.textContent = `#${++count} 0%`;
|
||||
|
||||
const seedMode = document.getElementById("wfm-gen-seed-mode")?.value || "random";
|
||||
const seedValue = parseInt(document.getElementById("wfm-gen-seed-value")?.value) || -1;
|
||||
|
||||
const { images, seed } = await comfyUI.generate(
|
||||
{ ...wf },
|
||||
{
|
||||
seedMode,
|
||||
seedValue,
|
||||
onProgress: (pct) => {
|
||||
if (progressBar) progressBar.style.width = `${(pct * 100).toFixed(1)}%`;
|
||||
if (progressText) progressText.textContent = `#${count} ${(pct * 100).toFixed(0)}%`;
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
const seedEl = document.getElementById("wfm-gen-seed-value");
|
||||
if (seedEl) seedEl.value = seed;
|
||||
|
||||
if (progressBar) progressBar.style.width = "100%";
|
||||
|
||||
if (images.length > 0 && resultImg) {
|
||||
const blob = await comfyUI.getImageBlob(images[0]);
|
||||
resultImg.src = URL.createObjectURL(blob);
|
||||
resultImg.style.display = "block";
|
||||
}
|
||||
|
||||
if (!_s.running) break;
|
||||
|
||||
const control = document.getElementById("wfm-feeder-control-after")?.value || "loop";
|
||||
if (control === "fixed") {
|
||||
// index を変えない
|
||||
} else if (_lastSync) {
|
||||
const indexEl = document.getElementById("wfm-feeder-index");
|
||||
if (indexEl) indexEl.value = _lastSync.next_index;
|
||||
if (control === "increment" && !_lastSync.has_next) {
|
||||
_s.running = false;
|
||||
showToast(t("feederComplete", count), "success");
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (err) {
|
||||
if (_s.running) showToast(t("feederRunError", err.message), "error");
|
||||
} finally {
|
||||
comfyUI.socket?.removeEventListener("message", _syncHandler);
|
||||
_s.running = false;
|
||||
_setRunUI(false);
|
||||
if (progressText) progressText.textContent = `Done (${count} generated)`;
|
||||
}
|
||||
}
|
||||
|
||||
async function _stopRun() {
|
||||
_s.running = false;
|
||||
try { await comfyUI.interrupt(); } catch {}
|
||||
_setRunUI(false);
|
||||
showToast(t("feederStopped"), "info");
|
||||
}
|
||||
|
||||
// ── Gallery Mode Run Loop ─────────────────────────────────────────
|
||||
|
||||
async function _startGalRun() {
|
||||
if (_s.running) return;
|
||||
if (!comfyUI.currentWorkflow) { showToast(t("noWorkflowLoaded"), "error"); return; }
|
||||
if (!comfyUI.connected) { showToast(t("notConnectedToComfyUI"), "error"); return; }
|
||||
|
||||
const nodeId = document.getElementById("wfm-feeder-gal-node-sel")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow[nodeId]) {
|
||||
showToast(t("feederGalNoNode"), "error");
|
||||
return;
|
||||
}
|
||||
if (_gs.images.length === 0) {
|
||||
showToast(t("feederGalEmptyGroup"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const wsOk = await comfyUI.connectWebSocket();
|
||||
if (!wsOk) { showToast(t("wsConnectionFailed"), "error"); return; }
|
||||
|
||||
_s.running = true;
|
||||
_setGalRunUI(true);
|
||||
|
||||
const progressBar = document.getElementById("wfm-gen-progress-bar");
|
||||
const progressText = document.getElementById("wfm-gen-progress-text");
|
||||
const resultImg = document.getElementById("wfm-gen-result-img");
|
||||
const indexEl = document.getElementById("wfm-feeder-gal-index");
|
||||
|
||||
const total = _gs.images.length;
|
||||
let count = 0;
|
||||
try {
|
||||
while (_s.running) {
|
||||
_applyToGalNode(nodeId);
|
||||
|
||||
if (progressBar) progressBar.style.width = "0%";
|
||||
if (progressText) progressText.textContent = `#${++count} [${_gs.idx + 1}/${total}] 0%`;
|
||||
|
||||
const seedMode = document.getElementById("wfm-gen-seed-mode")?.value || "random";
|
||||
const seedValue = parseInt(document.getElementById("wfm-gen-seed-value")?.value) || -1;
|
||||
|
||||
const { images, seed } = await comfyUI.generate(
|
||||
{ ...comfyUI.currentWorkflow },
|
||||
{
|
||||
seedMode,
|
||||
seedValue,
|
||||
onProgress: (pct) => {
|
||||
if (progressBar) progressBar.style.width = `${(pct * 100).toFixed(1)}%`;
|
||||
if (progressText) progressText.textContent = `#${count} [${_gs.idx + 1}/${total}] ${(pct * 100).toFixed(0)}%`;
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
const seedEl = document.getElementById("wfm-gen-seed-value");
|
||||
if (seedEl) seedEl.value = seed;
|
||||
|
||||
if (progressBar) progressBar.style.width = "100%";
|
||||
|
||||
if (images.length > 0 && resultImg) {
|
||||
const blob = await comfyUI.getImageBlob(images[0]);
|
||||
resultImg.src = URL.createObjectURL(blob);
|
||||
resultImg.style.display = "block";
|
||||
}
|
||||
|
||||
if (!_s.running) break;
|
||||
|
||||
const control = document.getElementById("wfm-feeder-gal-control-after")?.value || "loop";
|
||||
const nextIdx = (_gs.idx + 1) % total;
|
||||
const isLast = _gs.idx === total - 1;
|
||||
|
||||
if (control === "fixed") {
|
||||
// idx を変えない
|
||||
} else if (control === "increment" && isLast) {
|
||||
_s.running = false;
|
||||
showToast(t("feederComplete", count), "success");
|
||||
break;
|
||||
} else {
|
||||
_gs.idx = nextIdx;
|
||||
if (indexEl) indexEl.value = _gs.idx;
|
||||
_highlightGalCard(_gs.images[_gs.idx]);
|
||||
}
|
||||
}
|
||||
} catch (err) {
|
||||
if (_s.running) showToast(t("feederRunError", err.message), "error");
|
||||
} finally {
|
||||
_s.running = false;
|
||||
_setGalRunUI(false);
|
||||
if (progressText) progressText.textContent = `Done (${count} generated)`;
|
||||
}
|
||||
}
|
||||
|
||||
async function _stopGalRun() {
|
||||
_s.running = false;
|
||||
try { await comfyUI.interrupt(); } catch {}
|
||||
_setGalRunUI(false);
|
||||
showToast(t("feederStopped"), "info");
|
||||
}
|
||||
|
||||
// ── Folder Tree (loop mode) ───────────────────────────────────────
|
||||
|
||||
async function _loadTree() {
|
||||
const container = document.getElementById("wfm-feeder-tree");
|
||||
if (!container) return;
|
||||
container.innerHTML = `<div class="wfm-feeder-tree-msg">Loading...</div>`;
|
||||
try {
|
||||
const res = await fetch("/image_loop/tree");
|
||||
if (!res.ok) throw new Error(res.status);
|
||||
const data = await res.json();
|
||||
_renderTree(data.tree || [], container);
|
||||
} catch (err) {
|
||||
container.innerHTML = `<div class="wfm-feeder-tree-msg">Cannot reach image-loop API</div>`;
|
||||
console.warn("[Feeder] tree load failed:", err);
|
||||
}
|
||||
}
|
||||
|
||||
function _renderTree(items, container, depth = 0) {
|
||||
if (depth === 0) {
|
||||
container.innerHTML = "";
|
||||
container.appendChild(_makeTreeRow("", "🏠 (root)", 0));
|
||||
}
|
||||
for (const item of items) {
|
||||
const icon = item.children?.length > 0 ? "📂" : "📁";
|
||||
container.appendChild(_makeTreeRow(item.path, `${icon} ${item.name}`, depth + 1));
|
||||
if (item.children?.length > 0) _renderTree(item.children, container, depth + 1);
|
||||
}
|
||||
}
|
||||
|
||||
function _makeTreeRow(path, label, depth) {
|
||||
const row = document.createElement("div");
|
||||
row.className = "wfm-feeder-tree-row" + (_s.dir === path ? " active" : "");
|
||||
row.style.paddingLeft = `${8 + depth * 12}px`;
|
||||
row.dataset.path = path;
|
||||
row.textContent = label;
|
||||
row.title = path || "(root)";
|
||||
row.addEventListener("click", () => _selectDir(path));
|
||||
return row;
|
||||
}
|
||||
|
||||
function _selectDir(path) {
|
||||
if (_s.dir !== path) {
|
||||
_s.selected.clear();
|
||||
_s.dir = path;
|
||||
_setVal("wfm-feeder-dir-input", path);
|
||||
}
|
||||
_highlightTreeRow(path);
|
||||
_loadImages(path);
|
||||
_updateStatus();
|
||||
}
|
||||
|
||||
function _highlightTreeRow(path) {
|
||||
document.querySelectorAll(".wfm-feeder-tree-row").forEach(el => {
|
||||
el.classList.toggle("active", el.dataset.path === path);
|
||||
});
|
||||
}
|
||||
|
||||
// ── Image Grid (loop mode) ────────────────────────────────────────
|
||||
|
||||
async function _loadImages(dir) {
|
||||
const grid = document.getElementById("wfm-feeder-grid");
|
||||
if (!grid) return;
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg">Loading...</div>`;
|
||||
try {
|
||||
const url = "/image_loop/images" + (dir ? `?dir=${encodeURIComponent(dir)}` : "");
|
||||
const res = await fetch(url);
|
||||
if (!res.ok) throw new Error(res.status);
|
||||
const data = await res.json();
|
||||
_s.images = data.images || [];
|
||||
_renderGrid();
|
||||
} catch (err) {
|
||||
_s.images = [];
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg" style="color:var(--wfm-danger)">Load failed: ${err.message}</div>`;
|
||||
}
|
||||
_updateStatus();
|
||||
}
|
||||
|
||||
function _renderGrid() {
|
||||
const grid = document.getElementById("wfm-feeder-grid");
|
||||
if (!grid) return;
|
||||
if (_s.images.length === 0) {
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg">No images found</div>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
for (const fname of _s.images) grid.appendChild(_makeCard(fname));
|
||||
}
|
||||
|
||||
function _makeCard(fname) {
|
||||
const relPath = _s.dir ? `${_s.dir}/${fname}` : fname;
|
||||
const checked = _s.selected.has(fname);
|
||||
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-feeder-card" + (checked ? " selected" : "");
|
||||
card.dataset.name = fname;
|
||||
|
||||
const cb = document.createElement("input");
|
||||
cb.type = "checkbox";
|
||||
cb.className = "wfm-feeder-card-cb";
|
||||
cb.checked = checked;
|
||||
cb.addEventListener("change", e => {
|
||||
e.stopPropagation();
|
||||
if (cb.checked) _s.selected.add(fname);
|
||||
else _s.selected.delete(fname);
|
||||
card.classList.toggle("selected", cb.checked);
|
||||
_updateStatus();
|
||||
});
|
||||
|
||||
const img = document.createElement("img");
|
||||
img.className = "wfm-feeder-card-img";
|
||||
img.loading = "lazy";
|
||||
img.src = `/image_loop/thumbnail?path=${encodeURIComponent(relPath)}`;
|
||||
img.onerror = () => { img.style.display = "none"; card.style.minHeight = "110px"; };
|
||||
|
||||
const name = document.createElement("div");
|
||||
name.className = "wfm-feeder-card-name";
|
||||
name.textContent = fname;
|
||||
name.title = fname;
|
||||
|
||||
card.append(cb, img, name);
|
||||
card.addEventListener("click", e => {
|
||||
if (e.target === cb) return;
|
||||
_showPreview(relPath, fname);
|
||||
});
|
||||
return card;
|
||||
}
|
||||
|
||||
function _refreshGridCbs() {
|
||||
document.querySelectorAll(".wfm-feeder-card").forEach(card => {
|
||||
const sel = _s.selected.has(card.dataset.name);
|
||||
card.classList.toggle("selected", sel);
|
||||
const cb = card.querySelector("input[type=checkbox]");
|
||||
if (cb) cb.checked = sel;
|
||||
});
|
||||
}
|
||||
|
||||
function _selectAll() {
|
||||
_s.images.forEach(f => _s.selected.add(f));
|
||||
_refreshGridCbs();
|
||||
_updateStatus();
|
||||
}
|
||||
|
||||
function _deselectAll() {
|
||||
_s.images.forEach(f => _s.selected.delete(f));
|
||||
_refreshGridCbs();
|
||||
_updateStatus();
|
||||
}
|
||||
|
||||
function _updateStatus() {
|
||||
const el = document.getElementById("wfm-feeder-status");
|
||||
if (!el) return;
|
||||
const inCurrent = _s.images.filter(f => _s.selected.has(f)).length;
|
||||
el.textContent = `📂 ${_s.dir || "(root)"} | ${inCurrent} / ${_s.images.length} selected`;
|
||||
}
|
||||
|
||||
// ── Gallery Mode: Group & Image Grid ─────────────────────────────
|
||||
|
||||
async function _loadGalGroups() {
|
||||
try {
|
||||
const res = await fetch("/wfm/gallery/groups");
|
||||
if (!res.ok) throw new Error(res.status);
|
||||
const data = await res.json();
|
||||
_gs.groups = data.groups || [];
|
||||
_renderGalGroupSelect();
|
||||
} catch (err) {
|
||||
console.warn("[Feeder] loadGalGroups failed:", err);
|
||||
}
|
||||
}
|
||||
|
||||
function _renderGalGroupSelect() {
|
||||
const sel = document.getElementById("wfm-feeder-gal-group");
|
||||
if (!sel) return;
|
||||
sel.innerHTML = _gs.groups
|
||||
.map(g => `<option value="${g.name}"${g.name === _gs.group ? " selected" : ""}>${g.name}</option>`)
|
||||
.join("");
|
||||
if (!_gs.groups.find(g => g.name === _gs.group) && _gs.groups.length > 0) {
|
||||
_gs.group = _gs.groups[0].name;
|
||||
}
|
||||
}
|
||||
|
||||
function _setGalGroupSelect(group) {
|
||||
const sel = document.getElementById("wfm-feeder-gal-group");
|
||||
if (sel) sel.value = group;
|
||||
}
|
||||
|
||||
async function _loadGalImages(group) {
|
||||
const grid = document.getElementById("wfm-feeder-gal-grid");
|
||||
const statusEl = document.getElementById("wfm-feeder-gal-status");
|
||||
if (!grid) return;
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg">Loading...</div>`;
|
||||
try {
|
||||
const res = await fetch(`/wfm/gallery/groups/${encodeURIComponent(group)}/images`);
|
||||
if (!res.ok) throw new Error(res.status);
|
||||
const data = await res.json();
|
||||
_gs.images = data.images || [];
|
||||
_gs.idx = 0;
|
||||
const idxEl = document.getElementById("wfm-feeder-gal-index");
|
||||
if (idxEl) idxEl.value = 0;
|
||||
_renderGalGrid();
|
||||
if (statusEl) statusEl.textContent = `${_gs.images.length} image(s) in "${group}"`;
|
||||
} catch (err) {
|
||||
_gs.images = [];
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg" style="color:var(--wfm-danger)">Load failed: ${err.message}</div>`;
|
||||
if (statusEl) statusEl.textContent = "";
|
||||
}
|
||||
}
|
||||
|
||||
function _renderGalGrid() {
|
||||
const grid = document.getElementById("wfm-feeder-gal-grid");
|
||||
if (!grid) return;
|
||||
if (_gs.images.length === 0) {
|
||||
grid.innerHTML = `<div class="wfm-feeder-grid-msg">No images in this group</div>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
_gs.images.forEach((absPath, i) => grid.appendChild(_makeGalCard(absPath, i)));
|
||||
}
|
||||
|
||||
function _makeGalCard(absPath, idx) {
|
||||
const fname = absPath.replace(/\\/g, "/").split("/").pop();
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-feeder-card" + (idx === _gs.idx ? " selected" : "");
|
||||
card.dataset.path = absPath;
|
||||
card.dataset.idx = idx;
|
||||
|
||||
const img = document.createElement("img");
|
||||
img.className = "wfm-feeder-card-img";
|
||||
img.loading = "lazy";
|
||||
img.src = `/wfm/gallery/image/serve?path=${encodeURIComponent(absPath)}`;
|
||||
img.onerror = () => { img.style.display = "none"; card.style.minHeight = "110px"; };
|
||||
|
||||
const name = document.createElement("div");
|
||||
name.className = "wfm-feeder-card-name";
|
||||
name.textContent = fname;
|
||||
name.title = fname;
|
||||
|
||||
card.append(img, name);
|
||||
card.addEventListener("click", () => {
|
||||
_gs.idx = idx;
|
||||
const idxEl = document.getElementById("wfm-feeder-gal-index");
|
||||
if (idxEl) idxEl.value = idx;
|
||||
_highlightGalCard(absPath);
|
||||
_showGalPreview(absPath, fname);
|
||||
});
|
||||
return card;
|
||||
}
|
||||
|
||||
function _highlightGalCard(absPath) {
|
||||
document.querySelectorAll("#wfm-feeder-gal-grid .wfm-feeder-card").forEach(c => {
|
||||
c.classList.toggle("selected", c.dataset.path === absPath);
|
||||
});
|
||||
}
|
||||
|
||||
// ── Preview Panel ────────────────────────────────────────────────
|
||||
|
||||
async function _showPreview(relPath, fname) {
|
||||
const imgEl = document.getElementById("wfm-feeder-preview-img");
|
||||
const nameEl = document.getElementById("wfm-feeder-preview-name");
|
||||
const infoEl = document.getElementById("wfm-feeder-preview-info");
|
||||
|
||||
if (imgEl) {
|
||||
imgEl.src = `/image_loop/thumbnail?path=${encodeURIComponent(relPath)}`;
|
||||
imgEl.style.display = "block";
|
||||
}
|
||||
if (nameEl) nameEl.textContent = fname;
|
||||
if (infoEl) infoEl.textContent = "Loading...";
|
||||
|
||||
try {
|
||||
const res = await fetch(`/image_loop/image_info?path=${encodeURIComponent(relPath)}`);
|
||||
if (res.ok && infoEl) {
|
||||
const d = await res.json();
|
||||
const kb = d.size_bytes ? (d.size_bytes / 1024).toFixed(1) + " KB" : "";
|
||||
infoEl.textContent = `${d.width ?? "?"}×${d.height ?? "?"} px\n${kb}`;
|
||||
}
|
||||
} catch { if (infoEl) infoEl.textContent = ""; }
|
||||
}
|
||||
|
||||
async function _showGalPreview(absPath, fname) {
|
||||
const imgEl = document.getElementById("wfm-feeder-preview-img");
|
||||
const nameEl = document.getElementById("wfm-feeder-preview-name");
|
||||
const infoEl = document.getElementById("wfm-feeder-preview-info");
|
||||
|
||||
if (imgEl) {
|
||||
imgEl.src = `/wfm/gallery/image/serve?path=${encodeURIComponent(absPath)}`;
|
||||
imgEl.style.display = "block";
|
||||
}
|
||||
if (nameEl) nameEl.textContent = fname;
|
||||
if (infoEl) infoEl.textContent = "";
|
||||
}
|
||||
|
||||
// ── Presets (loop mode) ───────────────────────────────────────────
|
||||
|
||||
async function _loadPresets() {
|
||||
try {
|
||||
const res = await fetch("/image_feeder/presets");
|
||||
if (!res.ok) return;
|
||||
_s.presets = await res.json();
|
||||
} catch { _s.presets = {}; }
|
||||
_renderPresets();
|
||||
}
|
||||
|
||||
function _renderPresets() {
|
||||
const sel = document.getElementById("wfm-feeder-preset-sel");
|
||||
if (!sel) return;
|
||||
const names = Object.keys(_s.presets).sort();
|
||||
sel.innerHTML = `<option value="">-- Select preset --</option>` +
|
||||
names.map(n => `<option value="${n}">${n}</option>`).join("");
|
||||
}
|
||||
|
||||
async function _savePreset() {
|
||||
const name = document.getElementById("wfm-feeder-preset-name")?.value.trim();
|
||||
if (!name) { showToast(t("enterPresetName"), "error"); return; }
|
||||
const sel = _s.images.filter(f => _s.selected.has(f));
|
||||
try {
|
||||
const res = await fetch("/image_feeder/presets", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ name, directory: _s.dir, selected_files: sel }),
|
||||
});
|
||||
if (res.ok) {
|
||||
showToast(t("presetSavedName", name), "success");
|
||||
const nameInput = document.getElementById("wfm-feeder-preset-name");
|
||||
if (nameInput) nameInput.value = "";
|
||||
await _loadPresets();
|
||||
const selEl = document.getElementById("wfm-feeder-preset-sel");
|
||||
if (selEl) selEl.value = name;
|
||||
} else { showToast(t("saveFailed"), "error"); }
|
||||
} catch (err) { showToast(t("saveFailed", err.message), "error"); }
|
||||
}
|
||||
|
||||
async function _applyPreset() {
|
||||
const selEl = document.getElementById("wfm-feeder-preset-sel");
|
||||
const name = selEl?.value;
|
||||
if (!name || !_s.presets[name]) { showToast(t("selectPresetFirst"), "error"); return; }
|
||||
const preset = _s.presets[name];
|
||||
const dir = preset.directory || "";
|
||||
|
||||
_s.selected.clear();
|
||||
(preset.selected_files || []).forEach(f => _s.selected.add(f));
|
||||
_setVal("wfm-feeder-dir-input", dir);
|
||||
|
||||
if (_s.dir !== dir) {
|
||||
_s.dir = dir;
|
||||
_highlightTreeRow(dir);
|
||||
await _loadImages(dir);
|
||||
}
|
||||
_refreshGridCbs();
|
||||
_updateStatus();
|
||||
showToast(t("presetLoaded", name), "success");
|
||||
}
|
||||
|
||||
async function _deletePreset() {
|
||||
const name = document.getElementById("wfm-feeder-preset-sel")?.value;
|
||||
if (!name) return;
|
||||
try {
|
||||
await fetch(`/image_feeder/presets/${encodeURIComponent(name)}`, { method: "DELETE" });
|
||||
showToast(t("presetDeleted", name), "info");
|
||||
await _loadPresets();
|
||||
} catch (err) { showToast(t("deleteFailed"), "error"); }
|
||||
}
|
||||
|
||||
// ── Mode Switch ───────────────────────────────────────────────────
|
||||
|
||||
function _setMode(mode) {
|
||||
_s.mode = mode;
|
||||
localStorage.setItem("wfm_feeder_mode", mode);
|
||||
|
||||
const loopBtn = document.getElementById("wfm-feeder-mode-loop");
|
||||
const galBtn = document.getElementById("wfm-feeder-mode-gallery");
|
||||
const loopSec = document.getElementById("wfm-feeder-loop-section");
|
||||
const galSec = document.getElementById("wfm-feeder-gal-section");
|
||||
const loopLib = document.getElementById("wfm-feeder-loop-library");
|
||||
const galLib = document.getElementById("wfm-feeder-gal-library");
|
||||
|
||||
const isGallery = mode === "gallery";
|
||||
|
||||
if (loopBtn) loopBtn.classList.toggle("wfm-btn-primary", !isGallery);
|
||||
if (galBtn) galBtn.classList.toggle("wfm-btn-primary", isGallery);
|
||||
if (loopSec) loopSec.style.display = isGallery ? "none" : "";
|
||||
if (galSec) galSec.style.display = isGallery ? "" : "none";
|
||||
if (loopLib) loopLib.style.display = isGallery ? "none" : "";
|
||||
if (galLib) galLib.style.display = isGallery ? "" : "none";
|
||||
|
||||
if (isGallery) {
|
||||
_loadGalGroups().then(() => _loadGalImages(_gs.group));
|
||||
_refreshGalNodeList();
|
||||
}
|
||||
}
|
||||
|
||||
// ── Helpers ───────────────────────────────────────────────────────
|
||||
|
||||
function _setVal(id, val) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) el.value = val ?? "";
|
||||
}
|
||||
function _setCheck(id, val) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) el.checked = !!val;
|
||||
}
|
||||
|
||||
// ── Init ─────────────────────────────────────────────────────────
|
||||
|
||||
export async function initFeederTab() {
|
||||
// Mode toggle
|
||||
document.getElementById("wfm-feeder-mode-loop")?.addEventListener("click", () => _setMode("loop"));
|
||||
document.getElementById("wfm-feeder-mode-gallery")?.addEventListener("click", () => _setMode("gallery"));
|
||||
|
||||
// ── Image Loop mode events ──
|
||||
document.getElementById("wfm-feeder-node-sel")?.addEventListener("change", e => {
|
||||
_loadFromNode(e.target.value);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-feeder-apply-btn")?.addEventListener("click", _applyToWorkflow);
|
||||
|
||||
document.getElementById("wfm-feeder-dir-input")?.addEventListener("change", e => {
|
||||
const val = e.target.value.trim();
|
||||
if (_s.dir !== val) {
|
||||
_s.dir = val;
|
||||
_highlightTreeRow(val);
|
||||
_loadImages(val);
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("wfm-feeder-reload-tree")?.addEventListener("click", _loadTree);
|
||||
document.getElementById("wfm-feeder-sel-all")?.addEventListener("click", _selectAll);
|
||||
document.getElementById("wfm-feeder-desel-all")?.addEventListener("click", _deselectAll);
|
||||
|
||||
document.getElementById("wfm-feeder-preset-save")?.addEventListener("click", _savePreset);
|
||||
document.getElementById("wfm-feeder-preset-load")?.addEventListener("click", _applyPreset);
|
||||
document.getElementById("wfm-feeder-preset-del")?.addEventListener("click", _deletePreset);
|
||||
|
||||
document.getElementById("wfm-feeder-run-btn")?.addEventListener("click", _startRun);
|
||||
document.getElementById("wfm-feeder-stop-btn")?.addEventListener("click", _stopRun);
|
||||
|
||||
// ── Gallery mode events ──
|
||||
document.getElementById("wfm-feeder-gal-node-sel")?.addEventListener("change", e => {
|
||||
_loadFromGalNode(e.target.value);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-feeder-gal-apply-btn")?.addEventListener("click", _applyGalToWorkflow);
|
||||
|
||||
document.getElementById("wfm-feeder-gal-group")?.addEventListener("change", e => {
|
||||
_gs.group = e.target.value;
|
||||
_gs.idx = 0;
|
||||
_loadGalImages(_gs.group);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-feeder-gal-index")?.addEventListener("change", e => {
|
||||
_gs.idx = Math.max(0, parseInt(e.target.value) || 0);
|
||||
if (_gs.images[_gs.idx]) _highlightGalCard(_gs.images[_gs.idx]);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-feeder-gal-run-btn")?.addEventListener("click", _startGalRun);
|
||||
document.getElementById("wfm-feeder-gal-stop-btn")?.addEventListener("click", _stopGalRun);
|
||||
|
||||
// 初期化
|
||||
await ensureFeederGroup();
|
||||
_setMode(_s.mode);
|
||||
|
||||
if (_s.mode === "loop") {
|
||||
await Promise.all([_loadTree(), _loadPresets()]);
|
||||
_selectDir("");
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
+2116
-111
File diff suppressed because it is too large
Load Diff
+2377
-83
File diff suppressed because it is too large
Load Diff
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/**
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* Image Edit Tab - Draw Tool
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||||
* Inspired by comfyui-mask-editor-one PaintTool.js
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* Supports color drawing and erasing with a soft round brush.
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||||
* Also supports image brushes loaded from Mask Editor One's ABR brush library.
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*/
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export class DrawTool {
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constructor(canvas) {
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this.canvas = canvas;
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||||
this.ctx = canvas?.getContext("2d") ?? null;
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this.brushSize = 20;
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||||
this.hardness = 0.8; // 0.0 = very soft, 1.0 = hard edge
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this.opacity = 1.0;
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this.color = "#ff0000";
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this.mode = "draw"; // "draw" | "erase"
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this.spacing = 0.25; // stamp interval as fraction of brushSize (image brush only)
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this.angle = 0; // degrees [0, 359]
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this.sizeJitter = false;
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this.sizeJitterAmount = 0.5;
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this.rotationJitter = false;
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||||
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||||
// Image brush from ABR library (null = default circle brush)
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||||
this.brushImage = null;
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||||
this.brushName = "Circle";
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||||
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this._drawing = false;
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||||
this._lastX = 0;
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this._lastY = 0;
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||||
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||||
// Cached circle stamp
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||||
this._stamp = null;
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||||
this._stampSize = 0;
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||||
this._stampHard = 0;
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||||
this._stampColor = null;
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||||
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||||
// Cached image stamp
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||||
this._imgStamp = null;
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||||
this._imgStampSize = 0;
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||||
this._imgStampImg = null;
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||||
this._imgStampColor = null;
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||||
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||||
// Stroke buffer: prevents intra-stroke opacity accumulation (image brush only)
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||||
this._baseCanvas = null;
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||||
this._strokeCanvas = null;
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||||
this._strokeCtx = null;
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||||
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||||
this._onChange = null;
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||||
}
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||||
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||||
setCanvas(canvas) {
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||||
this.canvas = canvas;
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||||
this.ctx = canvas.getContext("2d");
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||||
this._stamp = null;
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||||
this._imgStamp = null;
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||||
}
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||||
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||||
onChange(fn) { this._onChange = fn; }
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||||
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||||
setImageBrush(img, name) {
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this.brushImage = img;
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this.brushName = name;
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this._imgStamp = null;
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}
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||||
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||||
clearImageBrush() {
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||||
this.brushImage = null;
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||||
this.brushName = "Circle";
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||||
this._stamp = null;
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||||
this._imgStamp = null;
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||||
}
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activate() {
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if (this.canvas) this.canvas.style.cursor = "none";
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}
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deactivate() {
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this._drawing = false;
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this._clearStrokeBuffer();
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if (this.canvas) this.canvas.style.cursor = "";
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}
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onMouseDown(x, y) {
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this._drawing = true;
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this._lastX = x;
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this._lastY = y;
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if (this.brushImage) {
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this._initStrokeBuffer();
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this._paintToStroke(x, y);
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this._mergeStroke();
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} else {
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this._paint(x, y);
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||||
}
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||||
}
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||||
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||||
onMouseMove(x, y) {
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if (!this._drawing) return;
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if (this.brushImage) {
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this._paintLineToStroke(this._lastX, this._lastY, x, y);
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this._mergeStroke();
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} else {
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this._paintLine(this._lastX, this._lastY, x, y);
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}
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this._lastX = x;
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||||
this._lastY = y;
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||||
}
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||||
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||||
onMouseUp() {
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||||
if (this._drawing) {
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||||
this._drawing = false;
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||||
this._clearStrokeBuffer();
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if (this._onChange) this._onChange();
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}
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}
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onMouseLeave() {
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if (this._drawing) this.onMouseUp();
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||||
}
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||||
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||||
// ── Stroke buffer (image brush only) ─────────────────────────────
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||||
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||||
_initStrokeBuffer() {
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const w = this.canvas.width;
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||||
const h = this.canvas.height;
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||||
this._baseCanvas = document.createElement("canvas");
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this._baseCanvas.width = w;
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this._baseCanvas.height = h;
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this._baseCanvas.getContext("2d").drawImage(this.canvas, 0, 0);
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this._strokeCanvas = document.createElement("canvas");
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this._strokeCanvas.width = w;
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this._strokeCanvas.height = h;
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||||
this._strokeCtx = this._strokeCanvas.getContext("2d");
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||||
}
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||||
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||||
_clearStrokeBuffer() {
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||||
this._baseCanvas = null;
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||||
this._strokeCanvas = null;
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||||
this._strokeCtx = null;
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||||
}
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||||
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||||
_mergeStroke() {
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||||
const ctx = this.ctx;
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const w = this.canvas.width;
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const h = this.canvas.height;
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ctx.clearRect(0, 0, w, h);
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ctx.drawImage(this._baseCanvas, 0, 0);
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ctx.save();
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if (this.mode === "erase") {
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ctx.globalCompositeOperation = "destination-out";
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} else {
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ctx.globalCompositeOperation = "source-over";
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}
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ctx.globalAlpha = this.opacity;
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ctx.drawImage(this._strokeCanvas, 0, 0);
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||||
ctx.restore();
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||||
}
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||||
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||||
_paintToStroke(x, y) {
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const saved = this.ctx;
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this.ctx = this._strokeCtx;
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if (this.sizeJitter || this.rotationJitter) {
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const savedSize = this.brushSize;
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const savedAngle = this.angle;
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const savedStamp = this._imgStamp;
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const savedStampSize = this._imgStampSize;
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if (this.sizeJitter) {
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this.brushSize = Math.max(1, savedSize * (1 - Math.random() * this.sizeJitterAmount));
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||||
}
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||||
if (this.rotationJitter) {
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this.angle = Math.random() * 360;
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}
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this._paintImageBrush(x, y);
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this.brushSize = savedSize;
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this.angle = savedAngle;
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this._imgStamp = savedStamp;
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this._imgStampSize = savedStampSize;
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} else {
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this._paintImageBrush(x, y);
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}
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this.ctx = saved;
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}
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_paintLineToStroke(x0, y0, x1, y1) {
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const dist = Math.hypot(x1 - x0, y1 - y0);
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const step = Math.max(1, this.brushSize * this.spacing);
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const steps = Math.ceil(dist / step);
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for (let i = 1; i <= steps; i++) {
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const t = i / steps;
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this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
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}
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}
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// ── Circle brush ─────────────────────────────────────────────────
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_getStamp() {
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if (
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this._stamp &&
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this._stampSize === this.brushSize &&
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this._stampHard === this.hardness &&
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this._stampColor === this.color
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) return this._stamp;
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const size = Math.max(1, Math.round(this.brushSize));
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const sc = document.createElement("canvas");
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sc.width = size;
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sc.height = size;
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const sctx = sc.getContext("2d");
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const cx = size / 2, cy = size / 2, r = size / 2;
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const innerR = r * (1 - Math.min(this.hardness, 0.99)) * 0.95;
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const grd = sctx.createRadialGradient(cx, cy, innerR, cx, cy, r);
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grd.addColorStop(0, this.color);
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grd.addColorStop(1, this.color + "00");
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sctx.fillStyle = grd;
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sctx.beginPath();
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sctx.arc(cx, cy, r, 0, Math.PI * 2);
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sctx.fill();
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this._stamp = sc;
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this._stampSize = size;
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this._stampHard = this.hardness;
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this._stampColor = this.color;
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return sc;
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}
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_paint(x, y) {
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const stamp = this._getStamp();
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const s = stamp.width;
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this.ctx.save();
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this.ctx.globalAlpha = this.opacity;
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if (this.mode === "erase") {
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this.ctx.globalCompositeOperation = "destination-out";
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}
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this.ctx.drawImage(stamp, x - s / 2, y - s / 2);
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this.ctx.restore();
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}
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_paintLine(x0, y0, x1, y1) {
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const dist = Math.hypot(x1 - x0, y1 - y0);
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const spacing = Math.max(1, this.brushSize * 0.2);
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const steps = Math.max(1, Math.ceil(dist / spacing));
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for (let i = 1; i <= steps; i++) {
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const t = i / steps;
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this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
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}
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}
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// ── Image brush ──────────────────────────────────────────────────
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_getImageStamp(size) {
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size = Math.max(1, size);
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if (
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this._imgStamp &&
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||||
this._imgStampSize === size &&
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this._imgStampImg === this.brushImage &&
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this._imgStampColor === this.color
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) return this._imgStamp;
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const img = this.brushImage;
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const srcW = img.naturalWidth || img.width;
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const srcH = img.naturalHeight || img.height;
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const aspect = (srcW > 0 && srcH > 0) ? srcW / srcH : 1;
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const stampH = size;
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const stampW = Math.max(1, Math.round(size * aspect));
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const sc = document.createElement("canvas");
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sc.width = stampW;
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sc.height = stampH;
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const stx = sc.getContext("2d");
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stx.drawImage(img, 0, 0, stampW, stampH);
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const imgData = stx.getImageData(0, 0, stampW, stampH);
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const d = imgData.data;
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// Detect alpha channel (ABR-exported PNGs carry alpha = brush density)
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let hasAlpha = false;
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for (let i = 3; i < d.length; i += 4) {
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if (d[i] < 250) { hasAlpha = true; break; }
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}
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||||
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let invertLum = false;
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if (!hasAlpha) {
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||||
const corners = [0, stampW - 1, stampW * (stampH - 1), stampW * stampH - 1];
|
||||
let bgLum = 0;
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for (const ci of corners) {
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||||
const ii = ci * 4;
|
||||
bgLum += (d[ii] * 0.299 + d[ii + 1] * 0.587 + d[ii + 2] * 0.114) / 255;
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}
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invertLum = (bgLum / corners.length) > 0.5;
|
||||
}
|
||||
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||||
// Parse target color
|
||||
const hex = this.color.replace("#", "");
|
||||
const cr = parseInt(hex.slice(0, 2), 16);
|
||||
const cg = parseInt(hex.slice(2, 4), 16);
|
||||
const cb = parseInt(hex.slice(4, 6), 16);
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||||
|
||||
// Convert to target color pixels; alpha = brush density
|
||||
for (let i = 0; i < d.length; i += 4) {
|
||||
let alpha;
|
||||
if (hasAlpha) {
|
||||
alpha = d[i + 3] / 255;
|
||||
} else {
|
||||
const lum = (d[i] * 0.299 + d[i + 1] * 0.587 + d[i + 2] * 0.114) / 255;
|
||||
alpha = invertLum ? 1 - lum : lum;
|
||||
}
|
||||
d[i] = cr; d[i + 1] = cg; d[i + 2] = cb;
|
||||
d[i + 3] = Math.round(alpha * 255);
|
||||
}
|
||||
stx.putImageData(imgData, 0, 0);
|
||||
|
||||
this._imgStamp = sc;
|
||||
this._imgStampSize = size;
|
||||
this._imgStampImg = this.brushImage;
|
||||
this._imgStampColor = this.color;
|
||||
return sc;
|
||||
}
|
||||
|
||||
_paintImageBrush(x, y) {
|
||||
const stamp = this._getImageStamp(Math.round(this.brushSize));
|
||||
this.ctx.globalCompositeOperation = "source-over";
|
||||
if (this.angle) {
|
||||
this.ctx.save();
|
||||
this.ctx.translate(Math.round(x), Math.round(y));
|
||||
this.ctx.rotate((this.angle * Math.PI) / 180);
|
||||
this.ctx.drawImage(stamp, -stamp.width / 2, -stamp.height / 2);
|
||||
this.ctx.restore();
|
||||
} else {
|
||||
this.ctx.drawImage(stamp, Math.round(x - stamp.width / 2), Math.round(y - stamp.height / 2));
|
||||
}
|
||||
this.ctx.globalCompositeOperation = "source-over";
|
||||
}
|
||||
|
||||
static getCanvasPos(canvas, event) {
|
||||
const rect = canvas.getBoundingClientRect();
|
||||
const scaleX = canvas.width / rect.width;
|
||||
const scaleY = canvas.height / rect.height;
|
||||
return {
|
||||
x: (event.clientX - rect.left) * scaleX,
|
||||
y: (event.clientY - rect.top) * scaleY,
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,242 @@
|
||||
/**
|
||||
* Image Edit Tab - Layer & LayerManager
|
||||
* Inspired by comfyui-mask-editor-one LayerManager.js
|
||||
*/
|
||||
|
||||
export class Layer {
|
||||
constructor(name, type, contentW, contentH) {
|
||||
this.id = crypto.randomUUID();
|
||||
this.name = name;
|
||||
this.type = type; // 'draw' | 'image' | 'text'
|
||||
// layer.canvas のサイズ = コンテンツのネイティブ解像度
|
||||
this.canvas = document.createElement("canvas");
|
||||
this.canvas.width = contentW;
|
||||
this.canvas.height = contentH;
|
||||
this.ctx = this.canvas.getContext("2d");
|
||||
// 表示プロパティ
|
||||
this.visible = true;
|
||||
this.opacity = 1.0;
|
||||
this.blendMode = "source-over";
|
||||
// 変換プロパティ（キャンバス座標系での配置）
|
||||
this.x = 0; // 左上X
|
||||
this.y = 0; // 左上Y
|
||||
this.displayW = contentW; // 表示幅
|
||||
this.displayH = contentH; // 表示高さ
|
||||
this.rotation = 0; // 度
|
||||
this.flipX = false;
|
||||
this.flipY = false;
|
||||
this.textProps = null; // テキストレイヤー用プロパティ（再編集・再描画に使用）
|
||||
this.locked = false; // ロック中は SelectTool の変形を禁止
|
||||
this.maskApply = false; // マスクレイヤー: true=クリッピングマスクとして機能
|
||||
this.operation = "add"; // マスクレイヤー合成モード: "add" | "subtract"
|
||||
}
|
||||
|
||||
clear() {
|
||||
this.ctx.clearRect(0, 0, this.canvas.width, this.canvas.height);
|
||||
}
|
||||
|
||||
getThumbnailDataURL(size = 32) {
|
||||
const thumb = document.createElement("canvas");
|
||||
thumb.width = size;
|
||||
thumb.height = size;
|
||||
const ctx = thumb.getContext("2d");
|
||||
|
||||
if (this.type === "mask") {
|
||||
// マスクレイヤー: 黒背景に白マスクのグレースケール表示
|
||||
ctx.fillStyle = "#000";
|
||||
ctx.fillRect(0, 0, size, size);
|
||||
ctx.drawImage(this.canvas, 0, 0, size, size);
|
||||
} else {
|
||||
// 通常レイヤー: チェッカーボード背景（透過表示用）
|
||||
ctx.fillStyle = "#aaa";
|
||||
ctx.fillRect(0, 0, size, size);
|
||||
ctx.fillStyle = "#fff";
|
||||
for (let x = 0; x < size; x += 8)
|
||||
for (let y = 0; y < size; y += 8)
|
||||
if ((x / 8 + y / 8) % 2 === 0) ctx.fillRect(x, y, 8, 8);
|
||||
ctx.drawImage(this.canvas, 0, 0, size, size);
|
||||
}
|
||||
|
||||
return thumb.toDataURL("image/png");
|
||||
}
|
||||
|
||||
toJSON() {
|
||||
return {
|
||||
id: this.id, name: this.name, type: this.type,
|
||||
imageData: this.canvas.toDataURL("image/png"),
|
||||
visible: this.visible, opacity: this.opacity, blendMode: this.blendMode,
|
||||
x: this.x, y: this.y, displayW: this.displayW, displayH: this.displayH,
|
||||
rotation: this.rotation, flipX: this.flipX, flipY: this.flipY,
|
||||
textProps: this.textProps ?? null,
|
||||
locked: this.locked ?? false,
|
||||
maskApply: this.maskApply ?? false,
|
||||
operation: this.operation ?? "add",
|
||||
};
|
||||
}
|
||||
|
||||
static fromJSON(json) {
|
||||
const cw = json.contentW ?? json.displayW ?? 512;
|
||||
const ch = json.contentH ?? json.displayH ?? 512;
|
||||
const layer = new Layer(json.name, json.type, cw, ch);
|
||||
layer.id = json.id;
|
||||
layer.visible = json.visible ?? true;
|
||||
layer.opacity = json.opacity ?? 1.0;
|
||||
layer.blendMode = json.blendMode ?? "source-over";
|
||||
layer.x = json.x ?? 0;
|
||||
layer.y = json.y ?? 0;
|
||||
layer.displayW = json.displayW ?? cw;
|
||||
layer.displayH = json.displayH ?? ch;
|
||||
layer.rotation = json.rotation ?? 0;
|
||||
layer.flipX = json.flipX ?? false;
|
||||
layer.flipY = json.flipY ?? false;
|
||||
layer.textProps = json.textProps ?? null;
|
||||
layer.locked = json.locked ?? false;
|
||||
layer.maskApply = json.maskApply ?? false;
|
||||
layer.operation = json.operation ?? "add";
|
||||
|
||||
if (json.imageData) {
|
||||
return new Promise(resolve => {
|
||||
const img = new Image();
|
||||
img.onload = () => { layer.ctx.drawImage(img, 0, 0, cw, ch); resolve(layer); };
|
||||
img.onerror = () => resolve(layer);
|
||||
img.src = json.imageData;
|
||||
});
|
||||
}
|
||||
return Promise.resolve(layer);
|
||||
}
|
||||
|
||||
/** 変換行列を ctx に適用してから drawImage できる状態にする */
|
||||
static applyTransform(ctx, layer) {
|
||||
const cx = layer.x + layer.displayW / 2;
|
||||
const cy = layer.y + layer.displayH / 2;
|
||||
ctx.translate(cx, cy);
|
||||
if (layer.rotation) ctx.rotate(layer.rotation * Math.PI / 180);
|
||||
ctx.scale(
|
||||
(layer.flipX ? -1 : 1) * (layer.displayW / layer.canvas.width),
|
||||
(layer.flipY ? -1 : 1) * (layer.displayH / layer.canvas.height),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
export class LayerManager {
|
||||
constructor(width, height) {
|
||||
this.width = width;
|
||||
this.height = height;
|
||||
this.layers = []; // 先頭 = 最前面
|
||||
this.activeIndex = 0;
|
||||
this._listeners = [];
|
||||
}
|
||||
|
||||
get activeLayer() { return this.layers[this.activeIndex] ?? null; }
|
||||
|
||||
/**
|
||||
* @param {string} type "draw" | "image" | "text"
|
||||
* @param {string} name
|
||||
* @param {object} opts { contentW, contentH, displayW, displayH, x, y }
|
||||
*/
|
||||
addLayer(type = "draw", name = null, opts = {}) {
|
||||
const n = name ?? `Layer ${this.layers.length + 1}`;
|
||||
const cw = opts.contentW ?? this.width;
|
||||
const ch = opts.contentH ?? this.height;
|
||||
const layer = new Layer(n, type, cw, ch);
|
||||
layer.displayW = opts.displayW ?? cw;
|
||||
layer.displayH = opts.displayH ?? ch;
|
||||
layer.x = opts.x ?? 0;
|
||||
layer.y = opts.y ?? 0;
|
||||
this.layers.splice(this.activeIndex, 0, layer);
|
||||
this._emit("change");
|
||||
return layer;
|
||||
}
|
||||
|
||||
deleteLayer(id) {
|
||||
if (this.layers.length <= 1) return;
|
||||
const idx = this.layers.findIndex(l => l.id === id);
|
||||
if (idx < 0) return;
|
||||
this.layers.splice(idx, 1);
|
||||
this.activeIndex = Math.max(0, Math.min(this.activeIndex, this.layers.length - 1));
|
||||
this._emit("change");
|
||||
}
|
||||
|
||||
setActive(id) {
|
||||
const idx = this.layers.findIndex(l => l.id === id);
|
||||
if (idx >= 0) { this.activeIndex = idx; this._emit("activeChange", this.layers[idx]); }
|
||||
}
|
||||
|
||||
moveUp(id) {
|
||||
const idx = this.layers.findIndex(l => l.id === id);
|
||||
if (idx <= 0) return;
|
||||
[this.layers[idx - 1], this.layers[idx]] = [this.layers[idx], this.layers[idx - 1]];
|
||||
if (this.activeIndex === idx) this.activeIndex = idx - 1;
|
||||
else if (this.activeIndex === idx - 1) this.activeIndex = idx;
|
||||
this._emit("change");
|
||||
}
|
||||
|
||||
moveDown(id) {
|
||||
const idx = this.layers.findIndex(l => l.id === id);
|
||||
if (idx < 0 || idx >= this.layers.length - 1) return;
|
||||
[this.layers[idx], this.layers[idx + 1]] = [this.layers[idx + 1], this.layers[idx]];
|
||||
if (this.activeIndex === idx) this.activeIndex = idx + 1;
|
||||
else if (this.activeIndex === idx + 1) this.activeIndex = idx;
|
||||
this._emit("change");
|
||||
}
|
||||
|
||||
toggleVisible(id) {
|
||||
const layer = this.layers.find(l => l.id === id);
|
||||
if (layer) { layer.visible = !layer.visible; this._emit("change"); }
|
||||
}
|
||||
|
||||
toggleLocked(id) {
|
||||
const layer = this.layers.find(l => l.id === id);
|
||||
if (layer) { layer.locked = !layer.locked; this._emit("change"); }
|
||||
}
|
||||
|
||||
toggleMaskApply(id) {
|
||||
const layer = this.layers.find(l => l.id === id);
|
||||
if (layer && layer.type === "mask") { layer.maskApply = !layer.maskApply; this._emit("change"); }
|
||||
}
|
||||
|
||||
toggleOperation(id) {
|
||||
const layer = this.layers.find(l => l.id === id);
|
||||
if (layer && layer.type === "mask") {
|
||||
layer.operation = layer.operation === "add" ? "subtract" : "add";
|
||||
this._emit("change");
|
||||
}
|
||||
}
|
||||
|
||||
setOpacity(id, opacity) {
|
||||
const layer = this.layers.find(l => l.id === id);
|
||||
if (layer) { layer.opacity = Math.max(0, Math.min(1, opacity)); this._emit("change"); }
|
||||
}
|
||||
|
||||
/** 全レイヤーを下→上の順で target canvas に変換付きで合成 */
|
||||
composite(target) {
|
||||
const ctx = target.getContext("2d");
|
||||
ctx.clearRect(0, 0, target.width, target.height);
|
||||
for (let i = this.layers.length - 1; i >= 0; i--) {
|
||||
const layer = this.layers[i];
|
||||
if (!layer.visible) continue;
|
||||
ctx.save();
|
||||
ctx.globalAlpha = layer.opacity;
|
||||
ctx.globalCompositeOperation = layer.blendMode;
|
||||
Layer.applyTransform(ctx, layer);
|
||||
ctx.drawImage(layer.canvas, -layer.canvas.width / 2, -layer.canvas.height / 2);
|
||||
ctx.restore();
|
||||
}
|
||||
}
|
||||
|
||||
on(event, fn) { this._listeners.push({ event, fn }); }
|
||||
|
||||
_emit(event, data) {
|
||||
for (const l of this._listeners) if (l.event === event) l.fn(data);
|
||||
}
|
||||
|
||||
toJSON() {
|
||||
return { layers: this.layers.map(l => l.toJSON()), width: this.width, height: this.height };
|
||||
}
|
||||
|
||||
async fromJSON(json) {
|
||||
this.layers = await Promise.all((json.layers || []).map(lj => Layer.fromJSON(lj)));
|
||||
this.activeIndex = 0;
|
||||
this._emit("change");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,529 @@
|
||||
/**
|
||||
* Image Edit Tab - Mask Editor One 互換追加ツール
|
||||
* Color, Alpha, Text, Vector, Shape の各マスクツール。
|
||||
* マスクレイヤー canvas に直接白（add）または destination-out（erase）で描画する。
|
||||
*/
|
||||
|
||||
// ── Color Tool ───────────────────────────────────────────────────────────────
|
||||
// bgCanvas（背景合成画像）のクリック色に近いピクセルをマスク選択する。
|
||||
export class MaskColorTool {
|
||||
constructor() {
|
||||
this.tolerance = 40; // 0-255
|
||||
this.feather = 5; // 0-100 (tolerance に対する%)
|
||||
this.mode = "add"; // "add" | "subtract"
|
||||
this._canvas = null;
|
||||
this._ctx = null;
|
||||
this._bgCanvas = null;
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) { this._canvas = canvas; this._ctx = canvas.getContext("2d"); }
|
||||
setBgCanvas(canvas) { this._bgCanvas = canvas; }
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() { if (this._canvas) this._canvas.style.cursor = "crosshair"; }
|
||||
deactivate() { if (this._canvas) this._canvas.style.cursor = ""; }
|
||||
|
||||
onMouseDown(x, y) { this._selectByColor(Math.round(x), Math.round(y)); }
|
||||
onMouseMove() {}
|
||||
onMouseUp() {}
|
||||
onMouseLeave() {}
|
||||
|
||||
_selectByColor(px, py) {
|
||||
if (!this._bgCanvas || !this._ctx || !this._canvas) return;
|
||||
const bgCtx = this._bgCanvas.getContext("2d");
|
||||
const bw = this._bgCanvas.width;
|
||||
const bh = this._bgCanvas.height;
|
||||
if (bw === 0 || bh === 0) return;
|
||||
|
||||
const srcData = bgCtx.getImageData(0, 0, bw, bh);
|
||||
const dw = this._canvas.width;
|
||||
const dh = this._canvas.height;
|
||||
const sx = bw / dw;
|
||||
const sy = bh / dh;
|
||||
|
||||
const bpx = Math.min(bw - 1, Math.round(px * sx));
|
||||
const bpy = Math.min(bh - 1, Math.round(py * sy));
|
||||
const idx = (bpy * bw + bpx) * 4;
|
||||
if (idx < 0 || idx + 3 >= srcData.data.length) return;
|
||||
|
||||
const tr = srcData.data[idx];
|
||||
const tg = srcData.data[idx + 1];
|
||||
const tb = srcData.data[idx + 2];
|
||||
|
||||
const outData = new Uint8ClampedArray(dw * dh * 4);
|
||||
const tol = this.tolerance;
|
||||
|
||||
for (let dy = 0; dy < dh; dy++) {
|
||||
for (let dx = 0; dx < dw; dx++) {
|
||||
const bx = Math.min(bw - 1, Math.round(dx * sx));
|
||||
const by = Math.min(bh - 1, Math.round(dy * sy));
|
||||
const si = (by * bw + bx) * 4;
|
||||
const r = srcData.data[si];
|
||||
const g = srcData.data[si + 1];
|
||||
const b = srcData.data[si + 2];
|
||||
const dist = Math.sqrt((r - tr) ** 2 + (g - tg) ** 2 + (b - tb) ** 2);
|
||||
|
||||
let alpha;
|
||||
if (tol <= 0) {
|
||||
alpha = dist === 0 ? 255 : 0;
|
||||
} else {
|
||||
const fStart = tol * (1 - this.feather / 100);
|
||||
if (dist <= fStart) alpha = 255;
|
||||
else if (dist <= tol) alpha = Math.round(255 * (1 - (dist - fStart) / (tol - fStart)));
|
||||
else alpha = 0;
|
||||
}
|
||||
|
||||
const j = (dy * dw + dx) * 4;
|
||||
outData[j] = outData[j + 1] = outData[j + 2] = 255;
|
||||
outData[j + 3] = alpha;
|
||||
}
|
||||
}
|
||||
|
||||
const tmp = document.createElement("canvas");
|
||||
tmp.width = dw; tmp.height = dh;
|
||||
tmp.getContext("2d").putImageData(new ImageData(outData, dw, dh), 0, 0);
|
||||
|
||||
const ctx = this._ctx;
|
||||
ctx.globalCompositeOperation = this.mode === "subtract" ? "destination-out" : "lighten";
|
||||
ctx.drawImage(tmp, 0, 0);
|
||||
ctx.globalCompositeOperation = "source-over";
|
||||
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
}
|
||||
|
||||
// ── Alpha Tool ───────────────────────────────────────────────────────────────
|
||||
// 元画像のアルファチャンネルをマスクとして抽出する。
|
||||
export class MaskAlphaTool {
|
||||
constructor() {
|
||||
this.threshold = 128; // 0-255
|
||||
this.invert = false;
|
||||
this._canvas = null;
|
||||
this._ctx = null;
|
||||
this._srcImg = null; // HTMLImageElement or HTMLCanvasElement
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) { this._canvas = canvas; this._ctx = canvas.getContext("2d"); }
|
||||
setSourceImage(img) { this._srcImg = img; }
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() { if (this._canvas) this._canvas.style.cursor = "default"; }
|
||||
deactivate() { if (this._canvas) this._canvas.style.cursor = ""; }
|
||||
|
||||
onMouseDown() {}
|
||||
onMouseMove() {}
|
||||
onMouseUp() {}
|
||||
onMouseLeave() {}
|
||||
|
||||
extract() {
|
||||
if (!this._srcImg || !this._ctx || !this._canvas) return;
|
||||
const w = this._canvas.width;
|
||||
const h = this._canvas.height;
|
||||
const tmp = document.createElement("canvas");
|
||||
tmp.width = w; tmp.height = h;
|
||||
tmp.getContext("2d").drawImage(this._srcImg, 0, 0, w, h);
|
||||
const src = tmp.getContext("2d").getImageData(0, 0, w, h);
|
||||
const out = new Uint8ClampedArray(w * h * 4);
|
||||
for (let i = 0; i < src.data.length; i += 4) {
|
||||
let a = src.data[i + 3];
|
||||
if (this.threshold > 0) a = a >= this.threshold ? 255 : 0;
|
||||
if (this.invert) a = 255 - a;
|
||||
out[i] = out[i + 1] = out[i + 2] = 255;
|
||||
out[i + 3] = a;
|
||||
}
|
||||
this._ctx.clearRect(0, 0, w, h);
|
||||
this._ctx.putImageData(new ImageData(out, w, h), 0, 0);
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
}
|
||||
|
||||
// ── Text Tool ────────────────────────────────────────────────────────────────
|
||||
// クリック位置にテキストを白（add）または消去（erase）でスタンプする。
|
||||
export const MASK_TEXT_FONTS = [
|
||||
"Arial", "Arial Black", "Georgia", "Times New Roman",
|
||||
"Courier New", "Verdana", "Trebuchet MS", "Impact",
|
||||
"Comic Sans MS", "Helvetica", "Tahoma",
|
||||
];
|
||||
|
||||
export class MaskTextTool {
|
||||
constructor() {
|
||||
this.text = "Hello";
|
||||
this.fontFamily = "Arial";
|
||||
this.fontSize = 64;
|
||||
this.bold = false;
|
||||
this.italic = false;
|
||||
this.align = "left";
|
||||
this.mode = "add";
|
||||
this._canvas = null;
|
||||
this._ctx = null;
|
||||
this._overlay = null;
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) { this._canvas = canvas; this._ctx = canvas.getContext("2d"); }
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() { if (this._canvas) this._canvas.style.cursor = "crosshair"; }
|
||||
deactivate() { this._closeOverlay(); if (this._canvas) this._canvas.style.cursor = ""; }
|
||||
|
||||
onMouseDown(x, y) {
|
||||
if (this._overlay) { this._closeOverlay(); return; }
|
||||
this._showOverlay(x, y);
|
||||
}
|
||||
onMouseMove() {}
|
||||
onMouseUp() {}
|
||||
onMouseLeave() {}
|
||||
|
||||
_getFont() {
|
||||
const parts = [];
|
||||
if (this.italic) parts.push("italic");
|
||||
if (this.bold) parts.push("bold");
|
||||
parts.push(`${this.fontSize}px`);
|
||||
parts.push(`"${this.fontFamily}", sans-serif`);
|
||||
return parts.join(" ");
|
||||
}
|
||||
|
||||
_showOverlay(canvasX, canvasY) {
|
||||
const cv = this._canvas;
|
||||
const rect = cv.getBoundingClientRect();
|
||||
const cssX = Math.round(canvasX * (rect.width / cv.width)) + rect.left;
|
||||
const cssY = Math.round(canvasY * (rect.height / cv.height)) + rect.top;
|
||||
|
||||
const overlay = document.createElement("div");
|
||||
overlay.style.cssText = [
|
||||
`position:fixed;left:${cssX}px;top:${cssY}px;`,
|
||||
"background:var(--wfm-surface,#2a2a2a);",
|
||||
"border:1px solid var(--wfm-border,#444);",
|
||||
"border-radius:4px;padding:6px;z-index:99999;",
|
||||
"box-shadow:0 4px 16px rgba(0,0,0,0.5);min-width:200px;",
|
||||
].join("");
|
||||
|
||||
const ta = document.createElement("textarea");
|
||||
ta.value = this.text;
|
||||
ta.rows = 3;
|
||||
ta.placeholder = "Enter text…";
|
||||
ta.style.cssText = [
|
||||
"width:100%;resize:vertical;box-sizing:border-box;",
|
||||
"background:var(--wfm-bg,#1a1a1a);color:var(--wfm-text,#ddd);",
|
||||
"border:1px solid var(--wfm-border,#444);border-radius:3px;",
|
||||
"padding:4px;font-size:12px;",
|
||||
].join("");
|
||||
overlay.appendChild(ta);
|
||||
|
||||
const btnRow = document.createElement("div");
|
||||
btnRow.style.cssText = "display:flex;gap:4px;margin-top:4px;";
|
||||
|
||||
const okBtn = document.createElement("button");
|
||||
okBtn.textContent = "Stamp";
|
||||
okBtn.className = "wfm-btn wfm-btn-sm wfm-btn-primary";
|
||||
okBtn.style.flex = "1";
|
||||
okBtn.onclick = () => {
|
||||
if (ta.value.trim()) { this.text = ta.value; this._stamp(canvasX, canvasY); }
|
||||
this._closeOverlay();
|
||||
};
|
||||
|
||||
const cancelBtn = document.createElement("button");
|
||||
cancelBtn.textContent = "Cancel";
|
||||
cancelBtn.className = "wfm-btn wfm-btn-sm";
|
||||
cancelBtn.style.flex = "1";
|
||||
cancelBtn.onclick = () => this._closeOverlay();
|
||||
|
||||
btnRow.appendChild(okBtn);
|
||||
btnRow.appendChild(cancelBtn);
|
||||
overlay.appendChild(btnRow);
|
||||
|
||||
const hint = document.createElement("div");
|
||||
hint.textContent = "Ctrl+Enter: Stamp / Esc: Cancel";
|
||||
hint.style.cssText = "font-size:10px;color:var(--wfm-text-secondary,#888);margin-top:3px;";
|
||||
overlay.appendChild(hint);
|
||||
|
||||
ta.addEventListener("keydown", e => {
|
||||
if (e.key === "Enter" && (e.ctrlKey || e.metaKey)) { e.preventDefault(); okBtn.click(); }
|
||||
else if (e.key === "Escape") cancelBtn.click();
|
||||
e.stopPropagation();
|
||||
});
|
||||
overlay.addEventListener("mousedown", e => e.stopPropagation());
|
||||
|
||||
document.body.appendChild(overlay);
|
||||
this._overlay = overlay;
|
||||
ta.focus(); ta.select();
|
||||
}
|
||||
|
||||
_closeOverlay() {
|
||||
if (this._overlay) { this._overlay.remove(); this._overlay = null; }
|
||||
}
|
||||
|
||||
_stamp(x, y) {
|
||||
const ctx = this._ctx;
|
||||
ctx.save();
|
||||
ctx.font = this._getFont();
|
||||
ctx.textAlign = this.align;
|
||||
ctx.textBaseline = "top";
|
||||
if (this.mode === "erase") {
|
||||
ctx.globalCompositeOperation = "destination-out";
|
||||
ctx.fillStyle = "rgba(0,0,0,1)";
|
||||
} else {
|
||||
ctx.globalCompositeOperation = "source-over";
|
||||
ctx.fillStyle = "white";
|
||||
}
|
||||
const lines = this.text.split("\n");
|
||||
const lineHeight = this.fontSize * 1.2;
|
||||
for (let i = 0; i < lines.length; i++) ctx.fillText(lines[i], x, y + i * lineHeight);
|
||||
ctx.restore();
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
}
|
||||
|
||||
// ── Vector Tool ──────────────────────────────────────────────────────────────
|
||||
// クリックでポイント追加、Catmull-Rom スプラインで閉じたパスを塗りつぶす。
|
||||
const VECTOR_CLOSE_R = 12;
|
||||
|
||||
export class MaskVectorTool {
|
||||
constructor() {
|
||||
this.mode = "add";
|
||||
this._canvas = null;
|
||||
this._ctx = null;
|
||||
this._previewCanvas = null;
|
||||
this._points = [];
|
||||
this._hoverX = null;
|
||||
this._hoverY = null;
|
||||
this.onBeforeCommit = null;
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) { this._canvas = canvas; this._ctx = canvas.getContext("2d"); }
|
||||
setPreviewCanvas(canvas) { this._previewCanvas = canvas; }
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() { if (this._canvas) this._canvas.style.cursor = "crosshair"; this._renderPreview(); }
|
||||
deactivate() { if (this._canvas) this._canvas.style.cursor = ""; this._clearPreview(); }
|
||||
|
||||
reset() { this._points = []; this._hoverX = null; this._hoverY = null; this._clearPreview(); }
|
||||
|
||||
onMouseDown(x, y) {
|
||||
if (this._points.length >= 3) {
|
||||
const f = this._points[0];
|
||||
if (Math.hypot(x - f.x, y - f.y) <= VECTOR_CLOSE_R) { this._commitPath(true); return; }
|
||||
}
|
||||
this._points.push({ x, y });
|
||||
this._renderPreview();
|
||||
}
|
||||
|
||||
onMouseMove(x, y) { this._hoverX = x; this._hoverY = y; this._renderPreview(); }
|
||||
onMouseLeave() { this._hoverX = null; this._hoverY = null; this._renderPreview(); }
|
||||
onMouseUp() {}
|
||||
|
||||
onKeyDown(e) {
|
||||
if (e.key === "Escape") {
|
||||
e.preventDefault(); this.reset();
|
||||
} else if (e.key === "Enter" && this._points.length >= 2) {
|
||||
e.preventDefault(); this._commitPath(false);
|
||||
} else if ((e.key === "Backspace" || e.key === "Delete") && this._points.length > 0) {
|
||||
e.preventDefault(); this._points.pop(); this._renderPreview();
|
||||
}
|
||||
}
|
||||
|
||||
_commitPath(closed) {
|
||||
if (this._points.length < 2) return;
|
||||
if (this.onBeforeCommit) this.onBeforeCommit();
|
||||
const ctx = this._ctx;
|
||||
ctx.save();
|
||||
if (this.mode === "erase") {
|
||||
ctx.globalCompositeOperation = "destination-out";
|
||||
ctx.fillStyle = "rgba(0,0,0,1)";
|
||||
} else {
|
||||
ctx.globalCompositeOperation = "source-over";
|
||||
ctx.fillStyle = "white";
|
||||
}
|
||||
ctx.beginPath();
|
||||
this._buildSpline(ctx, this._points, closed);
|
||||
ctx.fill();
|
||||
ctx.restore();
|
||||
this.reset();
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
|
||||
_buildSpline(ctx, pts, closed) {
|
||||
const n = pts.length;
|
||||
if (n === 0) return;
|
||||
if (n === 1) { ctx.moveTo(pts[0].x, pts[0].y); return; }
|
||||
if (n === 2) {
|
||||
ctx.moveTo(pts[0].x, pts[0].y); ctx.lineTo(pts[1].x, pts[1].y);
|
||||
if (closed) ctx.closePath(); return;
|
||||
}
|
||||
let ext;
|
||||
if (closed) {
|
||||
ext = [pts[n - 1], ...pts, pts[0], pts[1]];
|
||||
} else {
|
||||
ext = [
|
||||
{ x: 2 * pts[0].x - pts[1].x, y: 2 * pts[0].y - pts[1].y },
|
||||
...pts,
|
||||
{ x: 2 * pts[n-1].x - pts[n-2].x, y: 2 * pts[n-1].y - pts[n-2].y },
|
||||
];
|
||||
}
|
||||
ctx.moveTo(ext[1].x, ext[1].y);
|
||||
const count = closed ? n : n - 1;
|
||||
for (let i = 0; i < count; i++) {
|
||||
const [p0, p1, p2, p3] = [ext[i], ext[i+1], ext[i+2], ext[i+3]];
|
||||
ctx.bezierCurveTo(
|
||||
p1.x + (p2.x - p0.x) / 6, p1.y + (p2.y - p0.y) / 6,
|
||||
p2.x - (p3.x - p1.x) / 6, p2.y - (p3.y - p1.y) / 6,
|
||||
p2.x, p2.y,
|
||||
);
|
||||
}
|
||||
if (closed) ctx.closePath();
|
||||
}
|
||||
|
||||
_renderPreview() {
|
||||
if (!this._previewCanvas) return;
|
||||
const pctx = this._previewCanvas.getContext("2d");
|
||||
const w = this._previewCanvas.width, h = this._previewCanvas.height;
|
||||
pctx.clearRect(0, 0, w, h);
|
||||
const pts = this._points, hx = this._hoverX, hy = this._hoverY;
|
||||
if (pts.length === 0 && hx === null) return;
|
||||
|
||||
if (pts.length >= 2) {
|
||||
pctx.save();
|
||||
pctx.strokeStyle = "rgba(255,210,40,0.9)"; pctx.lineWidth = 1.5; pctx.setLineDash([]);
|
||||
pctx.beginPath(); this._buildSpline(pctx, pts, false); pctx.stroke();
|
||||
pctx.restore();
|
||||
}
|
||||
|
||||
if (pts.length >= 1 && hx !== null) {
|
||||
const canClose = pts.length >= 3 && Math.hypot(hx - pts[0].x, hy - pts[0].y) <= VECTOR_CLOSE_R;
|
||||
pctx.save();
|
||||
if (canClose) {
|
||||
pctx.strokeStyle = "rgba(255,100,100,0.55)"; pctx.lineWidth = 1.5; pctx.setLineDash([4, 3]);
|
||||
pctx.beginPath(); this._buildSpline(pctx, pts, true); pctx.stroke();
|
||||
} else {
|
||||
pctx.strokeStyle = "rgba(255,210,40,0.5)"; pctx.lineWidth = 1; pctx.setLineDash([4, 3]);
|
||||
pctx.beginPath();
|
||||
pctx.moveTo(pts[pts.length - 1].x, pts[pts.length - 1].y);
|
||||
pctx.lineTo(hx, hy); pctx.stroke();
|
||||
}
|
||||
pctx.restore();
|
||||
}
|
||||
|
||||
for (let i = 0; i < pts.length; i++) {
|
||||
const p = pts[i], isFirst = i === 0;
|
||||
const closeable = isFirst && pts.length >= 3 && hx !== null && Math.hypot(hx - p.x, hy - p.y) <= VECTOR_CLOSE_R;
|
||||
pctx.beginPath();
|
||||
pctx.arc(p.x, p.y, closeable ? 7 : (isFirst ? 5 : 4), 0, Math.PI * 2);
|
||||
pctx.fillStyle = closeable ? "rgba(255,80,80,0.95)" : (isFirst ? "rgba(255,210,40,0.95)" : "rgba(255,210,40,0.8)");
|
||||
pctx.strokeStyle = "rgba(0,0,0,0.6)"; pctx.lineWidth = 1;
|
||||
pctx.fill(); pctx.stroke();
|
||||
}
|
||||
|
||||
if (hx !== null) {
|
||||
const onFirst = pts.length >= 3 && Math.hypot(hx - pts[0].x, hy - pts[0].y) <= VECTOR_CLOSE_R;
|
||||
if (!onFirst) {
|
||||
pctx.beginPath(); pctx.arc(hx, hy, 3, 0, Math.PI * 2);
|
||||
pctx.fillStyle = "rgba(255,255,255,0.6)"; pctx.strokeStyle = "rgba(200,200,200,0.5)"; pctx.lineWidth = 1;
|
||||
pctx.fill(); pctx.stroke();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_clearPreview() {
|
||||
if (!this._previewCanvas) return;
|
||||
const c = this._previewCanvas;
|
||||
c.getContext("2d").clearRect(0, 0, c.width, c.height);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Shape Tool ───────────────────────────────────────────────────────────────
|
||||
// ドラッグで矩形または楕円をマスクとして描画する（Shift = 正方形/正円）。
|
||||
export class MaskShapeTool {
|
||||
constructor() {
|
||||
this.mode = "add";
|
||||
this.shape = "rect"; // "rect" | "ellipse"
|
||||
this._canvas = null;
|
||||
this._ctx = null;
|
||||
this._previewCanvas = null;
|
||||
this._pCtx = null;
|
||||
this._startX = 0;
|
||||
this._startY = 0;
|
||||
this._curX = 0;
|
||||
this._curY = 0;
|
||||
this._curShift = false;
|
||||
this._drawing = false;
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) { this._canvas = canvas; this._ctx = canvas.getContext("2d"); }
|
||||
setPreviewCanvas(canvas) { this._previewCanvas = canvas; this._pCtx = canvas.getContext("2d"); }
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() { if (this._canvas) this._canvas.style.cursor = "crosshair"; }
|
||||
deactivate() { this._drawing = false; this._clearPreview(); if (this._canvas) this._canvas.style.cursor = ""; }
|
||||
|
||||
onMouseDown(x, y) {
|
||||
this._startX = x; this._startY = y;
|
||||
this._curX = x; this._curY = y; this._curShift = false;
|
||||
this._drawing = true;
|
||||
this._drawPreview(x, y, false);
|
||||
}
|
||||
|
||||
onMouseMove(x, y, e) {
|
||||
if (!this._drawing) return;
|
||||
this._curX = x; this._curY = y; this._curShift = e?.shiftKey ?? false;
|
||||
this._drawPreview(x, y, this._curShift);
|
||||
}
|
||||
|
||||
// x/y を省略した場合は最後に記録した座標を使用（mouseupイベント非経由での呼び出し対応）
|
||||
onMouseUp(x, y, e) {
|
||||
if (!this._drawing) return;
|
||||
const cx = x ?? this._curX;
|
||||
const cy = y ?? this._curY;
|
||||
const shift = e?.shiftKey ?? this._curShift;
|
||||
this._drawing = false;
|
||||
this._clearPreview();
|
||||
this._commit(cx, cy, shift);
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
|
||||
onMouseLeave() {}
|
||||
|
||||
_getRect(x, y, shift) {
|
||||
const sx = this._startX, sy = this._startY;
|
||||
if (shift) {
|
||||
const half = Math.max(Math.abs(x - sx), Math.abs(y - sy));
|
||||
return { x: sx - half, y: sy - half, w: half * 2, h: half * 2 };
|
||||
}
|
||||
return { x: Math.min(sx, x), y: Math.min(sy, y), w: Math.abs(x - sx), h: Math.abs(y - sy) };
|
||||
}
|
||||
|
||||
_drawPreview(x, y, shift) {
|
||||
if (!this._pCtx) return;
|
||||
const pc = this._previewCanvas;
|
||||
this._pCtx.clearRect(0, 0, pc.width, pc.height);
|
||||
const { x: rx, y: ry, w, h } = this._getRect(x, y, shift);
|
||||
if (w < 1 || h < 1) return;
|
||||
this._pCtx.save();
|
||||
this._pCtx.strokeStyle = "rgba(0,180,255,0.9)"; this._pCtx.lineWidth = 1.5; this._pCtx.setLineDash([5, 3]);
|
||||
this._pCtx.beginPath();
|
||||
if (this.shape === "rect") this._pCtx.rect(rx, ry, w, h);
|
||||
else this._pCtx.ellipse(rx + w / 2, ry + h / 2, w / 2, h / 2, 0, 0, Math.PI * 2);
|
||||
this._pCtx.stroke(); this._pCtx.restore();
|
||||
}
|
||||
|
||||
_clearPreview() {
|
||||
if (!this._pCtx) return;
|
||||
this._pCtx.clearRect(0, 0, this._previewCanvas.width, this._previewCanvas.height);
|
||||
}
|
||||
|
||||
_commit(x, y, shift) {
|
||||
const { x: rx, y: ry, w, h } = this._getRect(x, y, shift);
|
||||
if (w < 1 || h < 1) return;
|
||||
const ctx = this._ctx;
|
||||
ctx.save();
|
||||
ctx.globalCompositeOperation = this.mode === "erase" ? "destination-out" : "source-over";
|
||||
ctx.fillStyle = "white";
|
||||
ctx.beginPath();
|
||||
if (this.shape === "rect") ctx.rect(rx, ry, w, h);
|
||||
else ctx.ellipse(rx + w / 2, ry + h / 2, w / 2, h / 2, 0, 0, Math.PI * 2);
|
||||
ctx.fill(); ctx.restore();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,324 @@
|
||||
/**
|
||||
* Image Edit Tab - Mask Tool
|
||||
* White-paint / erase brush for mask layers.
|
||||
* paint mode: draws white (opaque) = mask present
|
||||
* erase mode: destination-out (transparent) = mask absent
|
||||
*
|
||||
* Supports circular soft brush (built-in) and image brushes loaded
|
||||
* from Mask Editor One's ABR brush library (optional).
|
||||
*/
|
||||
|
||||
export class MaskTool {
|
||||
constructor(canvas) {
|
||||
this.canvas = canvas;
|
||||
this.ctx = canvas?.getContext("2d");
|
||||
this.brushSize = 30;
|
||||
this.hardness = 0.85;
|
||||
this.spacing = 0.25; // stamp interval as fraction of brushSize (image brush only)
|
||||
this.mode = "paint"; // "paint" | "erase"
|
||||
|
||||
// Image brush from ABR library (null = default circle brush)
|
||||
this.brushImage = null;
|
||||
this.brushName = "Circle";
|
||||
this.angle = 0; // degrees [0, 359]
|
||||
this.sizeJitter = false;
|
||||
this.sizeJitterAmount = 0.5; // 0–1: fraction of brushSize that can be removed
|
||||
this.rotationJitter = false;
|
||||
|
||||
this._drawing = false;
|
||||
this._lastX = 0;
|
||||
this._lastY = 0;
|
||||
|
||||
// Cached circle stamp
|
||||
this._stamp = null;
|
||||
this._stampSize = 0;
|
||||
this._stampHard = 0;
|
||||
this._stampMode = null;
|
||||
|
||||
// Cached image stamp
|
||||
this._imgStamp = null;
|
||||
this._imgStampSize = 0;
|
||||
this._imgStampImg = null;
|
||||
|
||||
// Stroke buffer: prevents intra-stroke opacity accumulation (image brush only)
|
||||
this._baseCanvas = null;
|
||||
this._strokeCanvas = null;
|
||||
this._strokeCtx = null;
|
||||
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) {
|
||||
this.canvas = canvas;
|
||||
this.ctx = canvas.getContext("2d");
|
||||
this._stamp = null;
|
||||
this._imgStamp = null;
|
||||
}
|
||||
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
setImageBrush(img, name) {
|
||||
this.brushImage = img;
|
||||
this.brushName = name;
|
||||
this._imgStamp = null;
|
||||
}
|
||||
|
||||
clearImageBrush() {
|
||||
this.brushImage = null;
|
||||
this.brushName = "Circle";
|
||||
this._stamp = null;
|
||||
this._imgStamp = null;
|
||||
}
|
||||
|
||||
activate() {
|
||||
if (this.canvas) this.canvas.style.cursor = "none";
|
||||
}
|
||||
|
||||
deactivate() {
|
||||
this._drawing = false;
|
||||
this._clearStrokeBuffer();
|
||||
if (this.canvas) this.canvas.style.cursor = "";
|
||||
}
|
||||
|
||||
onMouseDown(x, y) {
|
||||
this._drawing = true;
|
||||
this._lastX = x;
|
||||
this._lastY = y;
|
||||
if (this.brushImage) {
|
||||
this._initStrokeBuffer();
|
||||
this._paintToStroke(x, y);
|
||||
this._mergeStroke();
|
||||
} else {
|
||||
this._paint(x, y);
|
||||
}
|
||||
}
|
||||
|
||||
onMouseMove(x, y) {
|
||||
if (!this._drawing) return;
|
||||
if (this.brushImage) {
|
||||
this._paintLineToStroke(this._lastX, this._lastY, x, y);
|
||||
this._mergeStroke();
|
||||
} else {
|
||||
this._paintLine(this._lastX, this._lastY, x, y);
|
||||
}
|
||||
this._lastX = x;
|
||||
this._lastY = y;
|
||||
}
|
||||
|
||||
onMouseUp() {
|
||||
if (this._drawing) {
|
||||
this._drawing = false;
|
||||
this._clearStrokeBuffer();
|
||||
if (this._onChange) this._onChange();
|
||||
}
|
||||
}
|
||||
|
||||
onMouseLeave() {
|
||||
if (this._drawing) this.onMouseUp();
|
||||
}
|
||||
|
||||
// ── Stroke buffer (image brush only) ─────────────────────────────
|
||||
|
||||
_initStrokeBuffer() {
|
||||
const w = this.canvas.width;
|
||||
const h = this.canvas.height;
|
||||
this._baseCanvas = document.createElement("canvas");
|
||||
this._baseCanvas.width = w;
|
||||
this._baseCanvas.height = h;
|
||||
this._baseCanvas.getContext("2d").drawImage(this.canvas, 0, 0);
|
||||
this._strokeCanvas = document.createElement("canvas");
|
||||
this._strokeCanvas.width = w;
|
||||
this._strokeCanvas.height = h;
|
||||
this._strokeCtx = this._strokeCanvas.getContext("2d");
|
||||
}
|
||||
|
||||
_clearStrokeBuffer() {
|
||||
this._baseCanvas = null;
|
||||
this._strokeCanvas = null;
|
||||
this._strokeCtx = null;
|
||||
}
|
||||
|
||||
_mergeStroke() {
|
||||
const ctx = this.ctx;
|
||||
const w = this.canvas.width;
|
||||
const h = this.canvas.height;
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
ctx.drawImage(this._baseCanvas, 0, 0);
|
||||
ctx.globalCompositeOperation = this.mode === "erase" ? "destination-out" : "lighten";
|
||||
ctx.drawImage(this._strokeCanvas, 0, 0);
|
||||
ctx.globalCompositeOperation = "source-over";
|
||||
}
|
||||
|
||||
_paintToStroke(x, y) {
|
||||
const saved = this.ctx;
|
||||
this.ctx = this._strokeCtx;
|
||||
|
||||
if (this.sizeJitter || this.rotationJitter) {
|
||||
const savedSize = this.brushSize;
|
||||
const savedAngle = this.angle;
|
||||
const savedStamp = this._imgStamp;
|
||||
const savedStampSize = this._imgStampSize;
|
||||
if (this.sizeJitter) {
|
||||
this.brushSize = Math.max(1, savedSize * (1 - Math.random() * this.sizeJitterAmount));
|
||||
}
|
||||
if (this.rotationJitter) {
|
||||
this.angle = Math.random() * 360;
|
||||
}
|
||||
this._paintImageBrush(x, y);
|
||||
this.brushSize = savedSize;
|
||||
this.angle = savedAngle;
|
||||
this._imgStamp = savedStamp;
|
||||
this._imgStampSize = savedStampSize;
|
||||
} else {
|
||||
this._paintImageBrush(x, y);
|
||||
}
|
||||
|
||||
this.ctx = saved;
|
||||
}
|
||||
|
||||
_paintLineToStroke(x0, y0, x1, y1) {
|
||||
const dist = Math.hypot(x1 - x0, y1 - y0);
|
||||
const step = Math.max(1, this.brushSize * this.spacing);
|
||||
const steps = Math.ceil(dist / step);
|
||||
for (let i = 1; i <= steps; i++) {
|
||||
const t = i / steps;
|
||||
this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Circle brush ─────────────────────────────────────────────────
|
||||
|
||||
_getStamp() {
|
||||
if (
|
||||
this._stamp &&
|
||||
this._stampSize === this.brushSize &&
|
||||
this._stampHard === this.hardness &&
|
||||
this._stampMode === this.mode
|
||||
) return this._stamp;
|
||||
|
||||
const size = Math.max(1, Math.round(this.brushSize));
|
||||
const sc = document.createElement("canvas");
|
||||
sc.width = size;
|
||||
sc.height = size;
|
||||
const sctx = sc.getContext("2d");
|
||||
const cx = size / 2, cy = size / 2, r = size / 2;
|
||||
|
||||
const color = this.mode === "paint" ? "#ffffff" : "#000000";
|
||||
const colorAlpha = this.mode === "paint" ? "rgba(255,255,255,0)" : "rgba(0,0,0,0)";
|
||||
|
||||
const innerR = r * (1 - Math.min(this.hardness, 0.99)) * 0.95;
|
||||
const grd = sctx.createRadialGradient(cx, cy, innerR, cx, cy, r);
|
||||
grd.addColorStop(0, color);
|
||||
grd.addColorStop(1, colorAlpha);
|
||||
sctx.fillStyle = grd;
|
||||
sctx.beginPath();
|
||||
sctx.arc(cx, cy, r, 0, Math.PI * 2);
|
||||
sctx.fill();
|
||||
|
||||
this._stamp = sc;
|
||||
this._stampSize = size;
|
||||
this._stampHard = this.hardness;
|
||||
this._stampMode = this.mode;
|
||||
return sc;
|
||||
}
|
||||
|
||||
_paint(x, y) {
|
||||
const stamp = this._getStamp();
|
||||
const s = stamp.width;
|
||||
this.ctx.save();
|
||||
if (this.mode === "erase") {
|
||||
this.ctx.globalCompositeOperation = "destination-out";
|
||||
}
|
||||
this.ctx.drawImage(stamp, x - s / 2, y - s / 2);
|
||||
this.ctx.restore();
|
||||
}
|
||||
|
||||
_paintLine(x0, y0, x1, y1) {
|
||||
const dist = Math.hypot(x1 - x0, y1 - y0);
|
||||
const spacing = Math.max(1, this.brushSize * 0.2);
|
||||
const steps = Math.max(1, Math.ceil(dist / spacing));
|
||||
for (let i = 1; i <= steps; i++) {
|
||||
const t = i / steps;
|
||||
this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Image brush ──────────────────────────────────────────────────
|
||||
|
||||
_getImageStamp(size) {
|
||||
size = Math.max(1, size);
|
||||
if (
|
||||
this._imgStamp &&
|
||||
this._imgStampSize === size &&
|
||||
this._imgStampImg === this.brushImage
|
||||
) return this._imgStamp;
|
||||
|
||||
const img = this.brushImage;
|
||||
const srcW = img.naturalWidth || img.width;
|
||||
const srcH = img.naturalHeight || img.height;
|
||||
const aspect = (srcW > 0 && srcH > 0) ? srcW / srcH : 1;
|
||||
|
||||
const stampH = size;
|
||||
const stampW = Math.max(1, Math.round(size * aspect));
|
||||
|
||||
const sc = document.createElement("canvas");
|
||||
sc.width = stampW;
|
||||
sc.height = stampH;
|
||||
const stx = sc.getContext("2d");
|
||||
stx.drawImage(img, 0, 0, stampW, stampH);
|
||||
|
||||
const imgData = stx.getImageData(0, 0, stampW, stampH);
|
||||
const d = imgData.data;
|
||||
|
||||
// Detect alpha channel (ABR-exported PNGs carry alpha = brush density)
|
||||
let hasAlpha = false;
|
||||
for (let i = 3; i < d.length; i += 4) {
|
||||
if (d[i] < 250) { hasAlpha = true; break; }
|
||||
}
|
||||
|
||||
let invertLum = false;
|
||||
if (!hasAlpha) {
|
||||
const corners = [0, stampW - 1, stampW * (stampH - 1), stampW * stampH - 1];
|
||||
let bgLum = 0;
|
||||
for (const ci of corners) {
|
||||
const ii = ci * 4;
|
||||
bgLum += (d[ii] * 0.299 + d[ii + 1] * 0.587 + d[ii + 2] * 0.114) / 255;
|
||||
}
|
||||
invertLum = (bgLum / corners.length) > 0.5;
|
||||
}
|
||||
|
||||
// Convert to white pixels; alpha = brush density
|
||||
for (let i = 0; i < d.length; i += 4) {
|
||||
let alpha;
|
||||
if (hasAlpha) {
|
||||
alpha = d[i + 3] / 255;
|
||||
} else {
|
||||
const lum = (d[i] * 0.299 + d[i + 1] * 0.587 + d[i + 2] * 0.114) / 255;
|
||||
alpha = invertLum ? 1 - lum : lum;
|
||||
}
|
||||
d[i] = d[i + 1] = d[i + 2] = 255;
|
||||
d[i + 3] = Math.round(alpha * 255);
|
||||
}
|
||||
stx.putImageData(imgData, 0, 0);
|
||||
|
||||
this._imgStamp = sc;
|
||||
this._imgStampSize = size;
|
||||
this._imgStampImg = this.brushImage;
|
||||
return sc;
|
||||
}
|
||||
|
||||
_paintImageBrush(x, y) {
|
||||
const stamp = this._getImageStamp(Math.round(this.brushSize));
|
||||
this.ctx.globalCompositeOperation = "source-over";
|
||||
if (this.angle) {
|
||||
this.ctx.save();
|
||||
this.ctx.translate(Math.round(x), Math.round(y));
|
||||
this.ctx.rotate((this.angle * Math.PI) / 180);
|
||||
this.ctx.drawImage(stamp, -stamp.width / 2, -stamp.height / 2);
|
||||
this.ctx.restore();
|
||||
} else {
|
||||
this.ctx.drawImage(stamp, Math.round(x - stamp.width / 2), Math.round(y - stamp.height / 2));
|
||||
}
|
||||
this.ctx.globalCompositeOperation = "source-over";
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,336 @@
|
||||
/**
|
||||
* Image Edit Tab - Select Tool
|
||||
* Handles object selection, move, resize, rotate, flip on the overlay canvas.
|
||||
*/
|
||||
|
||||
const HANDLE_R = 5; // ハンドル半径(px)
|
||||
const ROTATE_DIST = 32; // 回転ハンドルまでの距離(px)
|
||||
const MIN_SIZE = 10; // 最小表示サイズ(px)
|
||||
|
||||
export class SelectTool {
|
||||
constructor() {
|
||||
this._overlay = null;
|
||||
this._octx = null;
|
||||
this._layer = null; // 選択中レイヤー
|
||||
this._dragMode = null; // "move"|"rotate"|"resize-tl"|"resize-tr"|"resize-br"|"resize-bl"
|
||||
this._dragStart = null;
|
||||
this._origTransform = null;
|
||||
this._onChange = null;
|
||||
this._cursor = "default";
|
||||
}
|
||||
|
||||
setCanvas(overlayCanvas) {
|
||||
this._overlay = overlayCanvas;
|
||||
this._octx = overlayCanvas.getContext("2d");
|
||||
}
|
||||
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() {
|
||||
if (this._overlay) this._overlay.style.pointerEvents = "auto";
|
||||
this._drawOverlay();
|
||||
}
|
||||
|
||||
deactivate() {
|
||||
this._clearOverlay();
|
||||
if (this._overlay) this._overlay.style.pointerEvents = "none";
|
||||
}
|
||||
|
||||
clearSelection() {
|
||||
this._layer = null;
|
||||
this._dragMode = null;
|
||||
this._clearOverlay();
|
||||
}
|
||||
|
||||
setLayer(layer) {
|
||||
this._layer = layer;
|
||||
this._drawOverlay();
|
||||
}
|
||||
|
||||
getSelectedLayer() { return this._layer; }
|
||||
|
||||
// ── マウスイベント ───────────────────────────
|
||||
|
||||
onMouseDown(x, y, layerMgr) {
|
||||
if (this._layer) {
|
||||
const hit = this._hitHandle(x, y);
|
||||
// ロック中はハンドル操作を禁止（選択済み表示は維持）
|
||||
if (hit && !this._layer.locked) {
|
||||
this._startDrag(hit, x, y);
|
||||
return hit;
|
||||
}
|
||||
}
|
||||
|
||||
// レイヤーヒットテスト（前面から）
|
||||
for (let i = 0; i < layerMgr.layers.length; i++) {
|
||||
const layer = layerMgr.layers[i];
|
||||
if (!layer.visible) continue;
|
||||
if (this._isPointInLayer(x, y, layer)) {
|
||||
this._layer = layer;
|
||||
this._drawOverlay();
|
||||
// ロック中は選択のみ（移動ドラッグは開始しない）
|
||||
if (!layer.locked) this._startDrag("move", x, y);
|
||||
return "select";
|
||||
}
|
||||
}
|
||||
|
||||
// 空白クリック → 選択解除
|
||||
this._layer = null;
|
||||
this._clearOverlay();
|
||||
return null;
|
||||
}
|
||||
|
||||
onMouseMove(x, y) {
|
||||
if (!this._dragMode || !this._layer) {
|
||||
// カーソル変更のみ
|
||||
this._updateCursor(x, y);
|
||||
return;
|
||||
}
|
||||
|
||||
const dx = x - this._dragStart.x;
|
||||
const dy = y - this._dragStart.y;
|
||||
const ot = this._origTransform;
|
||||
|
||||
if (this._dragMode === "move") {
|
||||
this._layer.x = ot.x + dx;
|
||||
this._layer.y = ot.y + dy;
|
||||
} else if (this._dragMode === "rotate") {
|
||||
const cx = ot.x + ot.displayW / 2;
|
||||
const cy = ot.y + ot.displayH / 2;
|
||||
const a0 = Math.atan2(this._dragStart.y - cy, this._dragStart.x - cx);
|
||||
const a1 = Math.atan2(y - cy, x - cx);
|
||||
this._layer.rotation = ot.rotation + (a1 - a0) * 180 / Math.PI;
|
||||
} else if (this._dragMode.startsWith("resize-")) {
|
||||
this._applyResize(dx, dy);
|
||||
}
|
||||
|
||||
this._drawOverlay();
|
||||
if (this._onChange) this._onChange("transforming");
|
||||
}
|
||||
|
||||
onMouseUp() {
|
||||
if (this._dragMode) {
|
||||
this._dragMode = null;
|
||||
this._dragStart = null;
|
||||
this._origTransform = null;
|
||||
if (this._onChange) this._onChange("transformEnd");
|
||||
}
|
||||
}
|
||||
|
||||
onMouseLeave() { this.onMouseUp(); }
|
||||
|
||||
// ── 反転 ─────────────────────────────────────
|
||||
|
||||
flipH() {
|
||||
if (!this._layer) return;
|
||||
this._layer.flipX = !this._layer.flipX;
|
||||
this._drawOverlay();
|
||||
if (this._onChange) this._onChange("transformEnd");
|
||||
}
|
||||
|
||||
flipV() {
|
||||
if (!this._layer) return;
|
||||
this._layer.flipY = !this._layer.flipY;
|
||||
this._drawOverlay();
|
||||
if (this._onChange) this._onChange("transformEnd");
|
||||
}
|
||||
|
||||
// ── 内部ユーティリティ ─────────────────────────
|
||||
|
||||
_startDrag(mode, x, y) {
|
||||
this._dragMode = mode;
|
||||
this._dragStart = { x, y };
|
||||
this._origTransform = {
|
||||
x: this._layer.x, y: this._layer.y,
|
||||
displayW: this._layer.displayW, displayH: this._layer.displayH,
|
||||
rotation: this._layer.rotation,
|
||||
};
|
||||
}
|
||||
|
||||
// 回転を考慮した4頂点を返す（name: "tl"|"tr"|"br"|"bl"）
|
||||
_getCorners(layer) {
|
||||
const hw = layer.displayW / 2;
|
||||
const hh = layer.displayH / 2;
|
||||
const cx = layer.x + hw;
|
||||
const cy = layer.y + hh;
|
||||
const r = (layer.rotation ?? 0) * Math.PI / 180;
|
||||
const cosR = Math.cos(r), sinR = Math.sin(r);
|
||||
return [
|
||||
{ name: "tl", lx: -hw, ly: -hh },
|
||||
{ name: "tr", lx: hw, ly: -hh },
|
||||
{ name: "br", lx: hw, ly: hh },
|
||||
{ name: "bl", lx: -hw, ly: hh },
|
||||
].map(p => ({
|
||||
name: p.name,
|
||||
x: cx + p.lx * cosR - p.ly * sinR,
|
||||
y: cy + p.lx * sinR + p.ly * cosR,
|
||||
}));
|
||||
}
|
||||
|
||||
// 上辺中央から ROTATE_DIST だけ上に出た回転ハンドル
|
||||
_getRotateHandle(layer) {
|
||||
const hw = layer.displayW / 2;
|
||||
const hh = layer.displayH / 2;
|
||||
const cx = layer.x + hw;
|
||||
const cy = layer.y + hh;
|
||||
const r = (layer.rotation ?? 0) * Math.PI / 180;
|
||||
const cosR = Math.cos(r), sinR = Math.sin(r);
|
||||
const dist = hh + ROTATE_DIST;
|
||||
// ローカル座標 (0, -dist) を回転: x' = dist*sin(r), y' = -dist*cos(r)
|
||||
return {
|
||||
x: cx + sinR * dist,
|
||||
y: cy - cosR * dist,
|
||||
};
|
||||
}
|
||||
|
||||
_hitHandle(x, y) {
|
||||
if (!this._layer) return null;
|
||||
const rh = this._getRotateHandle(this._layer);
|
||||
if (Math.hypot(x - rh.x, y - rh.y) <= HANDLE_R * 2.5) return "rotate";
|
||||
|
||||
const corners = this._getCorners(this._layer);
|
||||
for (const c of corners) {
|
||||
if (Math.hypot(x - c.x, y - c.y) <= HANDLE_R * 2.5) return `resize-${c.name}`;
|
||||
}
|
||||
|
||||
if (this._isPointInLayer(x, y, this._layer)) return "move";
|
||||
return null;
|
||||
}
|
||||
|
||||
_isPointInLayer(x, y, layer) {
|
||||
const hw = layer.displayW / 2;
|
||||
const hh = layer.displayH / 2;
|
||||
const cx = layer.x + hw;
|
||||
const cy = layer.y + hh;
|
||||
// 逆回転で点をローカル座標に変換
|
||||
const r = -(layer.rotation ?? 0) * Math.PI / 180;
|
||||
const cosR = Math.cos(r), sinR = Math.sin(r);
|
||||
const lx = (x - cx) * cosR - (y - cy) * sinR;
|
||||
const ly = (x - cx) * sinR + (y - cy) * cosR;
|
||||
return Math.abs(lx) <= hw && Math.abs(ly) <= hh;
|
||||
}
|
||||
|
||||
_applyResize(dx, dy) {
|
||||
const ot = this._origTransform;
|
||||
const mode = this._dragMode;
|
||||
// 回転の逆行列でdx/dyをローカル座標に変換
|
||||
const r = -(ot.rotation ?? 0) * Math.PI / 180;
|
||||
const cosR = Math.cos(r), sinR = Math.sin(r);
|
||||
const ldx = dx * cosR - dy * sinR;
|
||||
const ldy = dx * sinR + dy * cosR;
|
||||
|
||||
let { x, y, displayW, displayH } = ot;
|
||||
|
||||
if (mode === "resize-tl") { x += ldx; y += ldy; displayW -= ldx; displayH -= ldy; }
|
||||
else if (mode === "resize-tr") { y += ldy; displayW += ldx; displayH -= ldy; }
|
||||
else if (mode === "resize-br") { displayW += ldx; displayH += ldy; }
|
||||
else if (mode === "resize-bl") { x += ldx; displayW -= ldx; displayH += ldy; }
|
||||
|
||||
if (displayW < MIN_SIZE) { if (mode.includes("l")) x -= MIN_SIZE - displayW; displayW = MIN_SIZE; }
|
||||
if (displayH < MIN_SIZE) { if (mode.includes("t")) y -= MIN_SIZE - displayH; displayH = MIN_SIZE; }
|
||||
|
||||
// 回転あり: x/yのずれを回転軸中心に合わせて再計算
|
||||
// ローカル左上の変位を逆回転して戻す
|
||||
const r2 = (ot.rotation ?? 0) * Math.PI / 180;
|
||||
const cosR2 = Math.cos(r2), sinR2 = Math.sin(r2);
|
||||
const ox = ot.x + ot.displayW / 2;
|
||||
const oy = ot.y + ot.displayH / 2;
|
||||
// 新しい左上 (ローカル -hw2, -hh2 を回転して絶対座標)
|
||||
const hw2 = displayW / 2;
|
||||
const hh2 = displayH / 2;
|
||||
const dx2 = -hw2 * cosR2 + hh2 * sinR2; // ローカル(-hw2, -hh2)を回転
|
||||
const dy2 = -hw2 * sinR2 - hh2 * cosR2;
|
||||
// ... でも回転付きリサイズは複雑なので、回転がほぼ0のときのみシンプル版を使用
|
||||
if (Math.abs(ot.rotation ?? 0) < 1) {
|
||||
this._layer.x = x; this._layer.y = y;
|
||||
}
|
||||
// 回転あり時は displayW/H のみ更新（位置は維持）
|
||||
this._layer.displayW = displayW;
|
||||
this._layer.displayH = displayH;
|
||||
}
|
||||
|
||||
_updateCursor(x, y) {
|
||||
if (!this._overlay) return;
|
||||
const hit = this._layer ? this._hitHandle(x, y) : null;
|
||||
const cursorMap = {
|
||||
"move": "move", "rotate": "crosshair",
|
||||
"resize-tl": "nwse-resize", "resize-br": "nwse-resize",
|
||||
"resize-tr": "nesw-resize", "resize-bl": "nesw-resize",
|
||||
};
|
||||
this._overlay.style.cursor = hit ? (cursorMap[hit] ?? "default") : "default";
|
||||
}
|
||||
|
||||
// ── オーバーレイ描画 ───────────────────────────
|
||||
|
||||
_drawOverlay() {
|
||||
this._clearOverlay();
|
||||
if (!this._layer || !this._octx) return;
|
||||
|
||||
const ctx = this._octx;
|
||||
const layer = this._layer;
|
||||
const locked = !!layer.locked;
|
||||
const color = locked ? "#e2a04a" : "#4a90e2"; // ロック中はオレンジ
|
||||
const corners = this._getCorners(layer);
|
||||
const rh = this._getRotateHandle(layer);
|
||||
const tl = corners[0], tr = corners[1];
|
||||
const topMid = { x: (tl.x + tr.x) / 2, y: (tl.y + tr.y) / 2 };
|
||||
|
||||
ctx.save();
|
||||
|
||||
// バウンディングボックス
|
||||
ctx.setLineDash([5, 3]);
|
||||
ctx.strokeStyle = color;
|
||||
ctx.lineWidth = 1.5;
|
||||
ctx.beginPath();
|
||||
corners.forEach((c, i) => i === 0 ? ctx.moveTo(c.x, c.y) : ctx.lineTo(c.x, c.y));
|
||||
ctx.closePath();
|
||||
ctx.stroke();
|
||||
ctx.setLineDash([]);
|
||||
|
||||
if (!locked) {
|
||||
// 回転ライン＋ハンドル（ロック中は非表示）
|
||||
ctx.strokeStyle = color;
|
||||
ctx.lineWidth = 1;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(topMid.x, topMid.y);
|
||||
ctx.lineTo(rh.x, rh.y);
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#fff";
|
||||
ctx.strokeStyle = color;
|
||||
ctx.lineWidth = 1.5;
|
||||
ctx.beginPath();
|
||||
ctx.arc(rh.x, rh.y, HANDLE_R, 0, Math.PI * 2);
|
||||
ctx.fill();
|
||||
ctx.stroke();
|
||||
|
||||
// コーナーハンドル
|
||||
for (const c of corners) {
|
||||
ctx.fillStyle = "#fff";
|
||||
ctx.strokeStyle = color;
|
||||
ctx.lineWidth = 1.5;
|
||||
ctx.beginPath();
|
||||
ctx.rect(c.x - HANDLE_R, c.y - HANDLE_R, HANDLE_R * 2, HANDLE_R * 2);
|
||||
ctx.fill();
|
||||
ctx.stroke();
|
||||
}
|
||||
} else {
|
||||
// ロック中: 中央に鍵アイコン
|
||||
const cx = layer.x + layer.displayW / 2;
|
||||
const cy = layer.y + layer.displayH / 2;
|
||||
ctx.font = "18px sans-serif";
|
||||
ctx.textAlign = "center";
|
||||
ctx.textBaseline = "middle";
|
||||
ctx.fillStyle = color;
|
||||
ctx.fillText("🔒", cx, cy);
|
||||
}
|
||||
|
||||
ctx.restore();
|
||||
}
|
||||
|
||||
_clearOverlay() {
|
||||
if (this._octx && this._overlay) {
|
||||
this._octx.clearRect(0, 0, this._overlay.width, this._overlay.height);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,229 @@
|
||||
/**
|
||||
* ShapeTool — rect / ellipse / line / freeline drawing tool for Image Edit tab.
|
||||
* Preview is drawn on the overlay canvas; committed shapes become new draw layers.
|
||||
*/
|
||||
export class ShapeTool {
|
||||
constructor() {
|
||||
// shape settings
|
||||
this.shape = "rect";
|
||||
this.rounded = false;
|
||||
this.fillColor = "#ff0000";
|
||||
this.fillNone = false;
|
||||
this.strokeColor = "#000000";
|
||||
this.strokeNone = true;
|
||||
this.strokeWidth = 5;
|
||||
this.opacity = 1.0;
|
||||
|
||||
// internal state
|
||||
this._active = false;
|
||||
this._canvas = null; // overlay canvas (preview)
|
||||
this._ctx = null;
|
||||
this._dragging = false;
|
||||
this._dragStart = null;
|
||||
this._curPt = null;
|
||||
this._points = []; // freeline points
|
||||
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
// overlay canvas used for live preview
|
||||
setCanvas(canvas) {
|
||||
this._canvas = canvas;
|
||||
this._ctx = canvas ? canvas.getContext("2d") : null;
|
||||
}
|
||||
|
||||
onChange(cb) { this._onChange = cb; }
|
||||
|
||||
activate() {
|
||||
this._active = true;
|
||||
}
|
||||
|
||||
deactivate() {
|
||||
this._active = false;
|
||||
this._dragging = false;
|
||||
this._clearOverlay();
|
||||
if (this._canvas) this._canvas.style.cursor = "";
|
||||
}
|
||||
|
||||
onMouseDown(x, y) {
|
||||
if (!this._active) return;
|
||||
this._dragging = true;
|
||||
this._dragStart = { x, y };
|
||||
this._curPt = { x, y };
|
||||
this._points = this.shape === "freeline" ? [{ x, y }] : [];
|
||||
}
|
||||
|
||||
onMouseMove(x, y) {
|
||||
if (!this._dragging) return;
|
||||
this._curPt = { x, y };
|
||||
if (this.shape === "freeline") this._points.push({ x, y });
|
||||
this._drawPreview(x, y);
|
||||
}
|
||||
|
||||
onMouseUp() {
|
||||
if (!this._dragging) return;
|
||||
this._dragging = false;
|
||||
this._clearOverlay();
|
||||
this._commit();
|
||||
}
|
||||
|
||||
onMouseLeave() {
|
||||
if (!this._dragging) return;
|
||||
this._dragging = false;
|
||||
this._clearOverlay();
|
||||
this._commit();
|
||||
}
|
||||
|
||||
// ── private ───────────────────────────────────────
|
||||
|
||||
_clearOverlay() {
|
||||
if (this._ctx && this._canvas) {
|
||||
this._ctx.clearRect(0, 0, this._canvas.width, this._canvas.height);
|
||||
}
|
||||
}
|
||||
|
||||
_drawPreview(curX, curY) {
|
||||
if (!this._ctx || !this._canvas) return;
|
||||
const ctx = this._ctx;
|
||||
ctx.clearRect(0, 0, this._canvas.width, this._canvas.height);
|
||||
|
||||
const s = this._dragStart;
|
||||
ctx.save();
|
||||
ctx.globalAlpha = this.opacity;
|
||||
|
||||
if (this.shape === "freeline") {
|
||||
// freeline: always draw with stroke color (strokeNone has no meaning here)
|
||||
if (this._points.length >= 2) {
|
||||
ctx.strokeStyle = this.strokeColor;
|
||||
ctx.lineWidth = this.strokeWidth;
|
||||
ctx.lineCap = "round";
|
||||
ctx.lineJoin = "round";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(this._points[0].x, this._points[0].y);
|
||||
for (let i = 1; i < this._points.length; i++) ctx.lineTo(this._points[i].x, this._points[i].y);
|
||||
ctx.stroke();
|
||||
}
|
||||
} else {
|
||||
// dashed blue outline for rect / ellipse / line
|
||||
ctx.strokeStyle = "#0077ff";
|
||||
ctx.lineWidth = Math.max(1, this.strokeWidth || 1);
|
||||
ctx.setLineDash([4, 2]);
|
||||
|
||||
const x1 = Math.min(s.x, curX), y1 = Math.min(s.y, curY);
|
||||
const w = Math.abs(curX - s.x), h = Math.abs(curY - s.y);
|
||||
|
||||
if (this.shape === "ellipse") {
|
||||
ctx.beginPath();
|
||||
ctx.ellipse(x1 + w / 2, y1 + h / 2, w / 2, h / 2, 0, 0, Math.PI * 2);
|
||||
ctx.stroke();
|
||||
} else if (this.shape === "line") {
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(s.x, s.y);
|
||||
ctx.lineTo(curX, curY);
|
||||
ctx.stroke();
|
||||
} else {
|
||||
// rect
|
||||
if (this.rounded) {
|
||||
const r = Math.min(w, h) * 0.15;
|
||||
ctx.beginPath();
|
||||
ctx.roundRect(x1, y1, w, h, r);
|
||||
ctx.stroke();
|
||||
} else {
|
||||
ctx.strokeRect(x1, y1, w, h);
|
||||
}
|
||||
}
|
||||
ctx.setLineDash([]);
|
||||
}
|
||||
ctx.restore();
|
||||
}
|
||||
|
||||
_commit() {
|
||||
const s = this._dragStart, c = this._curPt;
|
||||
if (!s || !c) return;
|
||||
|
||||
const isFreehand = this.shape === "freeline";
|
||||
const dx = Math.abs(c.x - s.x), dy = Math.abs(c.y - s.y);
|
||||
if (!isFreehand && dx < 2 && dy < 2) return;
|
||||
if (isFreehand && this._points.length < 2) return;
|
||||
|
||||
const isStrokeMandatory = ["line", "freeline"].includes(this.shape);
|
||||
const shapeObj = {
|
||||
kind: this.shape,
|
||||
s: { ...s },
|
||||
c: { ...c },
|
||||
points: isFreehand ? this._points.map(p => ({ ...p })) : [],
|
||||
fillColor: this.fillNone ? null : this.fillColor,
|
||||
// line / freeline always use stroke; ignore strokeNone
|
||||
strokeColor: (isStrokeMandatory || !this.strokeNone) ? this.strokeColor : null,
|
||||
strokeWidth: (isStrokeMandatory || !this.strokeNone) ? this.strokeWidth : 0,
|
||||
opacity: this.opacity,
|
||||
rounded: this.rounded,
|
||||
};
|
||||
|
||||
this._onChange?.(shapeObj);
|
||||
}
|
||||
|
||||
// ── static renderer (used by onChange callback to bake shape into layer) ──
|
||||
|
||||
static drawShape(ctx, sh) {
|
||||
const { kind, s, c, points, fillColor, strokeColor, strokeWidth, opacity, rounded } = sh;
|
||||
ctx.save();
|
||||
ctx.globalAlpha = opacity ?? 1;
|
||||
|
||||
if (kind === "freeline") {
|
||||
if (strokeColor && strokeWidth > 0 && points.length >= 2) {
|
||||
ctx.strokeStyle = strokeColor;
|
||||
ctx.lineWidth = strokeWidth;
|
||||
ctx.lineCap = "round";
|
||||
ctx.lineJoin = "round";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(points[0].x, points[0].y);
|
||||
for (let i = 1; i < points.length; i++) ctx.lineTo(points[i].x, points[i].y);
|
||||
ctx.stroke();
|
||||
}
|
||||
} else if (kind === "ellipse") {
|
||||
const x1 = Math.min(s.x, c.x), y1 = Math.min(s.y, c.y);
|
||||
const w = Math.abs(c.x - s.x), h = Math.abs(c.y - s.y);
|
||||
ctx.beginPath();
|
||||
ctx.ellipse(x1 + w / 2, y1 + h / 2, w / 2, h / 2, 0, 0, Math.PI * 2);
|
||||
if (fillColor) { ctx.fillStyle = fillColor; ctx.fill(); }
|
||||
if (strokeColor && strokeWidth > 0) { ctx.strokeStyle = strokeColor; ctx.lineWidth = strokeWidth; ctx.stroke(); }
|
||||
} else if (kind === "line") {
|
||||
if (strokeColor && strokeWidth > 0) {
|
||||
ctx.strokeStyle = strokeColor;
|
||||
ctx.lineWidth = strokeWidth;
|
||||
ctx.lineCap = "round";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(s.x, s.y);
|
||||
ctx.lineTo(c.x, c.y);
|
||||
ctx.stroke();
|
||||
}
|
||||
} else {
|
||||
// rect
|
||||
const x1 = Math.min(s.x, c.x), y1 = Math.min(s.y, c.y);
|
||||
const w = Math.abs(c.x - s.x), h = Math.abs(c.y - s.y);
|
||||
if (rounded) {
|
||||
const r = Math.min(w, h) * 0.15;
|
||||
ctx.beginPath();
|
||||
ctx.roundRect(x1, y1, w, h, r);
|
||||
} else {
|
||||
ctx.beginPath();
|
||||
ctx.rect(x1, y1, w, h);
|
||||
}
|
||||
if (fillColor) { ctx.fillStyle = fillColor; ctx.fill(); }
|
||||
if (strokeColor && strokeWidth > 0) { ctx.strokeStyle = strokeColor; ctx.lineWidth = strokeWidth; ctx.stroke(); }
|
||||
}
|
||||
ctx.restore();
|
||||
}
|
||||
|
||||
// coordinate helper (same signature as DrawTool.getCanvasPos)
|
||||
static getCanvasPos(canvas, event) {
|
||||
const rect = canvas.getBoundingClientRect();
|
||||
const scaleX = canvas.width / rect.width;
|
||||
const scaleY = canvas.height / rect.height;
|
||||
return {
|
||||
x: (event.clientX - rect.left) * scaleX,
|
||||
y: (event.clientY - rect.top) * scaleY,
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,173 @@
|
||||
/**
|
||||
* Image Edit Tab - Text Tool
|
||||
* Measures text bounding box, renders to an off-screen canvas,
|
||||
* and calls onChange(clickX, clickY) so the caller can create
|
||||
* a proper text layer with the exact content size.
|
||||
*/
|
||||
|
||||
export const TEXT_FONTS = [
|
||||
"Arial", "Arial Black", "Georgia", "Times New Roman",
|
||||
"Courier New", "Verdana", "Trebuchet MS", "Impact",
|
||||
"Comic Sans MS", "Tahoma",
|
||||
];
|
||||
|
||||
const TEXT_PADDING = 4; // テキスト描画時の余白(px)
|
||||
|
||||
export class TextTool {
|
||||
constructor(canvas) {
|
||||
this.canvas = canvas;
|
||||
this.text = "Hello";
|
||||
this.fontFamily = "Arial";
|
||||
this.fontSize = 64;
|
||||
this.bold = false;
|
||||
this.italic = false;
|
||||
this.align = "left";
|
||||
this.color = "#ffffff";
|
||||
this._overlay = null;
|
||||
this._onChange = null;
|
||||
}
|
||||
|
||||
setCanvas(canvas) {
|
||||
this._closeOverlay();
|
||||
this.canvas = canvas;
|
||||
}
|
||||
|
||||
onChange(fn) { this._onChange = fn; }
|
||||
|
||||
activate() {
|
||||
if (this.canvas) this.canvas.style.cursor = "text";
|
||||
}
|
||||
|
||||
deactivate() {
|
||||
this._closeOverlay();
|
||||
if (this.canvas) this.canvas.style.cursor = "";
|
||||
}
|
||||
|
||||
onMouseDown(x, y) {
|
||||
if (this._overlay) { this._closeOverlay(); return; }
|
||||
this._showOverlay(x, y);
|
||||
}
|
||||
|
||||
/** 既存テキスト再編集用: プロパティを外部でセット後に呼ぶ */
|
||||
openAt(canvasX, canvasY) {
|
||||
this._closeOverlay();
|
||||
this._showOverlay(canvasX, canvasY);
|
||||
}
|
||||
|
||||
onMouseMove() {}
|
||||
onMouseLeave() {}
|
||||
onMouseUp() {}
|
||||
|
||||
/** テキストのバウンディングボックスサイズのcanvasを返す */
|
||||
createLayerData(clickX, clickY) {
|
||||
const lines = this.text.split("\n");
|
||||
const lineH = this.fontSize * 1.2;
|
||||
const font = this._getCSSFont();
|
||||
|
||||
// measureText 用の仮canvas
|
||||
const tmp = document.createElement("canvas");
|
||||
const tmpCtx = tmp.getContext("2d");
|
||||
tmpCtx.font = font;
|
||||
|
||||
let maxW = 0;
|
||||
for (const line of lines) {
|
||||
const w = line.length > 0 ? tmpCtx.measureText(line).width : this.fontSize * 0.3;
|
||||
if (w > maxW) maxW = w;
|
||||
}
|
||||
|
||||
const tw = Math.max(1, Math.ceil(maxW + TEXT_PADDING * 2));
|
||||
const th = Math.max(1, Math.ceil(lines.length * lineH + TEXT_PADDING * 2));
|
||||
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = tw;
|
||||
canvas.height = th;
|
||||
const ctx = canvas.getContext("2d");
|
||||
ctx.font = font;
|
||||
ctx.fillStyle = this.color;
|
||||
ctx.textBaseline = "top";
|
||||
ctx.textAlign = this.align;
|
||||
|
||||
let drawX = TEXT_PADDING;
|
||||
if (this.align === "center") drawX = tw / 2;
|
||||
else if (this.align === "right") drawX = tw - TEXT_PADDING;
|
||||
|
||||
lines.forEach((line, i) => {
|
||||
ctx.fillText(line, drawX, TEXT_PADDING + i * lineH);
|
||||
});
|
||||
|
||||
return {
|
||||
canvas,
|
||||
width: tw,
|
||||
height: th,
|
||||
x: clickX,
|
||||
y: clickY,
|
||||
};
|
||||
}
|
||||
|
||||
// ── 内部 ──────────────────────────────────────
|
||||
|
||||
_getCSSFont() {
|
||||
const parts = [];
|
||||
if (this.italic) parts.push("italic");
|
||||
if (this.bold) parts.push("bold");
|
||||
parts.push(`${this.fontSize}px`);
|
||||
parts.push(`"${this.fontFamily}", sans-serif`);
|
||||
return parts.join(" ");
|
||||
}
|
||||
|
||||
_showOverlay(canvasX, canvasY) {
|
||||
const cv = this.canvas;
|
||||
const rect = cv.getBoundingClientRect();
|
||||
const scaleX = rect.width / cv.width;
|
||||
const scaleY = rect.height / cv.height;
|
||||
const cssX = Math.round(canvasX * scaleX);
|
||||
const cssY = Math.round(canvasY * scaleY);
|
||||
|
||||
const container = cv.parentElement;
|
||||
const overlay = document.createElement("div");
|
||||
overlay.className = "ie-text-overlay";
|
||||
overlay.style.left = cssX + "px";
|
||||
overlay.style.top = cssY + "px";
|
||||
|
||||
const textarea = document.createElement("textarea");
|
||||
textarea.className = "ie-text-textarea";
|
||||
textarea.value = this.text;
|
||||
textarea.rows = 3;
|
||||
textarea.placeholder = "Enter text…";
|
||||
overlay.appendChild(textarea);
|
||||
|
||||
const btnRow = document.createElement("div");
|
||||
btnRow.className = "ie-text-btn-row";
|
||||
|
||||
const okBtn = document.createElement("button");
|
||||
okBtn.className = "wfm-btn wfm-btn-sm wfm-btn-primary";
|
||||
okBtn.textContent = "OK";
|
||||
okBtn.onclick = () => {
|
||||
this.text = textarea.value;
|
||||
this._closeOverlay();
|
||||
// drawCanvasには描画せず、呼び出し側でレイヤーとして処理する
|
||||
if (this._onChange) this._onChange(canvasX, canvasY);
|
||||
};
|
||||
btnRow.appendChild(okBtn);
|
||||
|
||||
const cancelBtn = document.createElement("button");
|
||||
cancelBtn.className = "wfm-btn wfm-btn-sm";
|
||||
cancelBtn.textContent = "Cancel";
|
||||
cancelBtn.onclick = () => this._closeOverlay();
|
||||
btnRow.appendChild(cancelBtn);
|
||||
|
||||
overlay.appendChild(btnRow);
|
||||
container.appendChild(overlay);
|
||||
this._overlay = overlay;
|
||||
textarea.focus();
|
||||
|
||||
textarea.addEventListener("keydown", e => {
|
||||
if (e.key === "Enter" && !e.shiftKey) { e.preventDefault(); okBtn.click(); }
|
||||
else if (e.key === "Escape") this._closeOverlay();
|
||||
});
|
||||
}
|
||||
|
||||
_closeOverlay() {
|
||||
if (this._overlay) { this._overlay.remove(); this._overlay = null; }
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,363 @@
|
||||
/**
|
||||
* ImagePrompt Gallery — Gallery タブのサブタブ。
|
||||
* ws_image_prompt フォルダ配下の画像+.txtサイドカーを閲覧し、
|
||||
* クリックで選択→「追加」でプロンプトを組み立てて最終的にコピーする。
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
|
||||
const API = {
|
||||
root: () => `/wfm/gallery/image-prompt/root`,
|
||||
folders: (root) => `/wfm/gallery/folders?root=${encodeURIComponent(root)}`,
|
||||
images: (params) => `/wfm/gallery/images?${new URLSearchParams(params)}`,
|
||||
imageMeta: (path) => `/wfm/gallery/image/meta?path=${encodeURIComponent(path)}`,
|
||||
thumb: (path, w = 200) => `/wfm/gallery/image/thumb?path=${encodeURIComponent(path)}&w=${w}`,
|
||||
serveImage: (path) => `/wfm/gallery/image/serve?path=${encodeURIComponent(path)}`,
|
||||
savePrompt: `/wfm/gallery/image/image-prompt`,
|
||||
};
|
||||
|
||||
const state = {
|
||||
root: null,
|
||||
currentFolder: null,
|
||||
search: "",
|
||||
images: [],
|
||||
selectedImage: null,
|
||||
chips: [], // [{ path, filename, prompt }]
|
||||
};
|
||||
|
||||
let _initialized = false;
|
||||
|
||||
async function apiFetch(url, opts = {}) {
|
||||
const res = await fetch(url, opts);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
function debounce(fn, wait) {
|
||||
let timer;
|
||||
return (...args) => {
|
||||
clearTimeout(timer);
|
||||
timer = setTimeout(() => fn(...args), wait);
|
||||
};
|
||||
}
|
||||
|
||||
// ── 初期化 ────────────────────────────────────────────────────
|
||||
|
||||
export function initImagePromptTab() {
|
||||
document.getElementById("wfm-imageprompt-refresh-btn")?.addEventListener("click", () => {
|
||||
loadFolderTree();
|
||||
loadImages();
|
||||
});
|
||||
|
||||
setupSearchClearBtn("wfm-imageprompt-search", "wfm-imageprompt-search-clear-btn", () => {
|
||||
state.search = "";
|
||||
loadImages();
|
||||
});
|
||||
|
||||
const searchInput = document.getElementById("wfm-imageprompt-search");
|
||||
searchInput?.addEventListener("input", debounce(() => {
|
||||
state.search = searchInput.value.trim();
|
||||
loadImages();
|
||||
}, 300));
|
||||
|
||||
document.getElementById("wfm-imageprompt-add-btn")?.addEventListener("click", addSelectedToChips);
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn")?.addEventListener("click", saveSelectedPrompt);
|
||||
document.getElementById("wfm-imageprompt-clear-btn")?.addEventListener("click", clearAllChips);
|
||||
document.getElementById("wfm-imageprompt-copy-btn")?.addEventListener("click", copyFinalPrompt);
|
||||
}
|
||||
|
||||
export async function activateImagePromptTab() {
|
||||
if (_initialized) return;
|
||||
_initialized = true;
|
||||
try {
|
||||
const data = await apiFetch(API.root());
|
||||
state.root = data.root;
|
||||
await loadFolderTree();
|
||||
} catch (e) {
|
||||
const tree = document.getElementById("wfm-imageprompt-tree");
|
||||
if (tree) tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
// ── フォルダツリー ────────────────────────────────────────────
|
||||
|
||||
async function loadFolderTree() {
|
||||
if (!state.root) return;
|
||||
const tree = document.getElementById("wfm-imageprompt-tree");
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
try {
|
||||
const data = await apiFetch(API.folders(state.root));
|
||||
if (data.error) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${escapeHtml(data.error)}</p>`;
|
||||
return;
|
||||
}
|
||||
tree.innerHTML = "";
|
||||
renderTreeNode(data, tree, 0, true);
|
||||
const firstLabel = tree.querySelector(".wfm-gallery-tree-label");
|
||||
if (firstLabel) firstLabel.click();
|
||||
} catch (e) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderTreeNode(node, container, depth, isRoot) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-gallery-tree-item";
|
||||
item.style.paddingLeft = `${depth * 12}px`;
|
||||
item.dataset.path = node.abs_path;
|
||||
|
||||
const hasChildren = node.children && node.children.length > 0;
|
||||
const absPath = node.abs_path;
|
||||
|
||||
const arrow = document.createElement("span");
|
||||
arrow.className = "wfm-gallery-tree-arrow";
|
||||
if (hasChildren) {
|
||||
arrow.textContent = "▶";
|
||||
arrow.dataset.expanded = "false";
|
||||
} else {
|
||||
arrow.textContent = " ";
|
||||
arrow.style.visibility = "hidden";
|
||||
}
|
||||
item.appendChild(arrow);
|
||||
|
||||
const label = document.createElement("span");
|
||||
label.className = "wfm-gallery-tree-label";
|
||||
label.textContent = isRoot ? "[root]" : node.name;
|
||||
// サブフォルダ内も含めた合計件数（クリックで再帰的に全画像を表示するため）
|
||||
const totalCount = node.image_count_total ?? node.image_count;
|
||||
if (totalCount > 0) {
|
||||
const badge = document.createElement("span");
|
||||
badge.className = "wfm-gallery-tree-count";
|
||||
badge.textContent = totalCount;
|
||||
label.appendChild(badge);
|
||||
}
|
||||
item.appendChild(label);
|
||||
|
||||
label.addEventListener("click", () => {
|
||||
document.querySelectorAll("#wfm-imageprompt-tree .wfm-gallery-tree-item.selected").forEach(el => el.classList.remove("selected"));
|
||||
item.classList.add("selected");
|
||||
state.currentFolder = absPath;
|
||||
loadImages();
|
||||
});
|
||||
|
||||
if (hasChildren) {
|
||||
const childContainer = document.createElement("div");
|
||||
childContainer.className = "wfm-gallery-tree-children";
|
||||
childContainer.style.display = "none";
|
||||
|
||||
arrow.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const expanded = arrow.dataset.expanded === "true";
|
||||
if (!expanded) {
|
||||
arrow.dataset.expanded = "true";
|
||||
arrow.textContent = "▼";
|
||||
childContainer.style.display = "";
|
||||
if (childContainer.children.length === 0) {
|
||||
node.children.forEach(child => renderTreeNode(child, childContainer, depth + 1, false));
|
||||
}
|
||||
} else {
|
||||
arrow.dataset.expanded = "false";
|
||||
arrow.textContent = "▶";
|
||||
childContainer.style.display = "none";
|
||||
}
|
||||
});
|
||||
|
||||
container.appendChild(item);
|
||||
container.appendChild(childContainer);
|
||||
} else {
|
||||
container.appendChild(item);
|
||||
}
|
||||
}
|
||||
|
||||
// ── 画像一覧 ─────────────────────────────────────────────────
|
||||
|
||||
async function loadImages() {
|
||||
if (!state.currentFolder) return;
|
||||
const grid = document.getElementById("wfm-imageprompt-grid");
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
|
||||
// 上位フォルダを選択した場合もサブフォルダの画像をまとめて表示する
|
||||
const params = { folder: state.currentFolder, sort: "name_asc", recursive: "true" };
|
||||
if (state.search) params.search = state.search;
|
||||
|
||||
try {
|
||||
const data = await apiFetch(API.images(params));
|
||||
state.images = data.images || [];
|
||||
document.getElementById("wfm-imageprompt-count").textContent = `${state.images.length} images`;
|
||||
renderImages();
|
||||
} catch (e) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderImages() {
|
||||
const grid = document.getElementById("wfm-imageprompt-grid");
|
||||
if (state.images.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("galleryNoImages")}</p>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
const fragment = document.createDocumentFragment();
|
||||
state.images.forEach(img => fragment.appendChild(createThumbCard(img)));
|
||||
grid.appendChild(fragment);
|
||||
}
|
||||
|
||||
function createThumbCard(img) {
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-gallery-thumb-card";
|
||||
card.title = img.filename;
|
||||
card.dataset.path = img.path;
|
||||
if (state.selectedImage && state.selectedImage.path === img.path) {
|
||||
card.classList.add("selected");
|
||||
}
|
||||
|
||||
const imgEl = document.createElement("img");
|
||||
imgEl.className = "wfm-gallery-thumb-img";
|
||||
imgEl.loading = "lazy";
|
||||
imgEl.src = API.thumb(img.path);
|
||||
imgEl.alt = img.filename;
|
||||
imgEl.onerror = () => {
|
||||
imgEl.style.display = "none";
|
||||
const placeholder = document.createElement("div");
|
||||
placeholder.className = "wfm-gallery-thumb-placeholder";
|
||||
placeholder.textContent = "?";
|
||||
card.insertBefore(placeholder, imgEl.nextSibling);
|
||||
};
|
||||
card.appendChild(imgEl);
|
||||
|
||||
card.addEventListener("click", () => selectImage(img));
|
||||
|
||||
return card;
|
||||
}
|
||||
|
||||
// ── 選択画像 / プロンプト表示 ─────────────────────────────────
|
||||
|
||||
async function selectImage(img) {
|
||||
state.selectedImage = img;
|
||||
document.querySelectorAll("#wfm-imageprompt-grid .wfm-gallery-thumb-card").forEach(el => {
|
||||
el.classList.toggle("selected", el.dataset.path === img.path);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-imageprompt-selected-name").textContent = img.filename;
|
||||
const preview = document.getElementById("wfm-imageprompt-selected-preview");
|
||||
preview.innerHTML = "";
|
||||
const previewImg = document.createElement("img");
|
||||
previewImg.src = API.serveImage(img.path);
|
||||
previewImg.alt = img.filename;
|
||||
previewImg.style.cssText = "max-width:100%;max-height:100%;object-fit:contain;";
|
||||
preview.appendChild(previewImg);
|
||||
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
promptBox.value = "";
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn").disabled = true;
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = true;
|
||||
|
||||
try {
|
||||
const meta = await apiFetch(API.imageMeta(img.path));
|
||||
promptBox.value = meta.image_prompt || "";
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn").disabled = false;
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = !meta.image_prompt;
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function saveSelectedPrompt() {
|
||||
if (!state.selectedImage) return;
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
try {
|
||||
const res = await apiFetch(API.savePrompt, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path: state.selectedImage.path, prompt: promptBox.value }),
|
||||
});
|
||||
if (res.ok) {
|
||||
showToast(t("imagePromptSaved"), "success");
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = !promptBox.value.trim();
|
||||
} else {
|
||||
showToast(t("saveFailed"), "error");
|
||||
}
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
|
||||
// ── プロンプトビルダー (チップ / 最終プロンプト) ─────────────────
|
||||
|
||||
function cleanPromptText(text) {
|
||||
return String(text || "")
|
||||
.replace(/^[,\s]+|[,\s]+$/g, "")
|
||||
.replace(/\s*,\s*/g, ", ")
|
||||
.trim();
|
||||
}
|
||||
|
||||
function addSelectedToChips() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
const prompt = cleanPromptText(promptBox.value);
|
||||
if (!prompt) return;
|
||||
if (state.chips.some(c => c.path === img.path)) {
|
||||
showToast(t("imagePromptAlreadyAdded"), "info");
|
||||
return;
|
||||
}
|
||||
state.chips.push({ path: img.path, filename: img.filename, prompt });
|
||||
renderChips();
|
||||
rebuildFinalPrompt();
|
||||
}
|
||||
|
||||
function removeChip(path) {
|
||||
state.chips = state.chips.filter(c => c.path !== path);
|
||||
renderChips();
|
||||
rebuildFinalPrompt();
|
||||
}
|
||||
|
||||
function clearAllChips() {
|
||||
state.chips = [];
|
||||
renderChips();
|
||||
document.getElementById("wfm-imageprompt-final-prompt").value = "";
|
||||
}
|
||||
|
||||
function renderChips() {
|
||||
const container = document.getElementById("wfm-imageprompt-chips");
|
||||
container.innerHTML = "";
|
||||
state.chips.forEach(chip => {
|
||||
const el = document.createElement("span");
|
||||
el.className = "wfm-imageprompt-chip";
|
||||
el.title = chip.prompt;
|
||||
|
||||
const text = document.createElement("span");
|
||||
text.className = "wfm-imageprompt-chip-text";
|
||||
text.textContent = chip.filename.replace(/\.[^.]+$/, "");
|
||||
el.appendChild(text);
|
||||
|
||||
const removeBtn = document.createElement("button");
|
||||
removeBtn.className = "wfm-imageprompt-chip-remove";
|
||||
removeBtn.textContent = "×";
|
||||
removeBtn.addEventListener("click", () => removeChip(chip.path));
|
||||
el.appendChild(removeBtn);
|
||||
|
||||
container.appendChild(el);
|
||||
});
|
||||
}
|
||||
|
||||
function rebuildFinalPrompt() {
|
||||
const combined = state.chips.map(c => c.prompt).join(", ");
|
||||
document.getElementById("wfm-imageprompt-final-prompt").value = cleanPromptText(combined);
|
||||
}
|
||||
|
||||
async function copyFinalPrompt() {
|
||||
const text = document.getElementById("wfm-imageprompt-final-prompt").value;
|
||||
if (!text.trim()) {
|
||||
showToast(t("imagePromptNothingToCopy"), "info");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
await navigator.clipboard.writeText(text);
|
||||
showToast(t("imagePromptCopiedToast"), "success");
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,935 @@
|
||||
/**
|
||||
* Metadata Tab
|
||||
* Drop a PNG/WebP/JSON file to extract and display model/prompt metadata.
|
||||
* Parsing logic adapted from model-and-prompt-from-metadata (workflow_utils.js).
|
||||
*/
|
||||
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml } from "./util.js";
|
||||
import { comfyWorkflow } from "./comfyui-workflow.js";
|
||||
|
||||
// ── File size limit ───────────────────────────────────────────
|
||||
const MAX_FILE_SIZE = 50 * 1024 * 1024;
|
||||
|
||||
// ── Sanitize JSON (NaN/Infinity → null) ──────────────────────
|
||||
function sanitizeJSON(text) {
|
||||
return text
|
||||
.replace(/-Infinity\b/g, "null")
|
||||
.replace(/\bInfinity\b/g, "null")
|
||||
.replace(/\bNaN\b/g, "null");
|
||||
}
|
||||
|
||||
// ── WebP EXIF ────────────────────────────────────────────────
|
||||
async function readWebPEXIFChunk(file) {
|
||||
const buffer = await file.arrayBuffer();
|
||||
const bytes = new Uint8Array(buffer);
|
||||
const view = new DataView(buffer);
|
||||
const ascii = new TextDecoder("latin1");
|
||||
if (bytes.byteLength < 12) return null;
|
||||
if (ascii.decode(bytes.slice(0, 4)) !== "RIFF") return null;
|
||||
if (ascii.decode(bytes.slice(8, 12)) !== "WEBP") return null;
|
||||
let offset = 12;
|
||||
while (offset + 8 <= buffer.byteLength) {
|
||||
const fourcc = ascii.decode(bytes.slice(offset, offset + 4));
|
||||
const chunkSize = view.getUint32(offset + 4, true);
|
||||
if (fourcc === "EXIF") return bytes.slice(offset + 8, offset + 8 + chunkSize);
|
||||
offset += 8 + chunkSize;
|
||||
if (chunkSize % 2 === 1) offset++;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
function extractWorkflowFromEXIF(exifBytes) {
|
||||
const utf8 = new TextDecoder("utf-8", { fatal: false });
|
||||
const text = utf8.decode(exifBytes);
|
||||
for (const key of ["workflow:", "prompt:"]) {
|
||||
const idx = text.indexOf(key + "{");
|
||||
if (idx < 0) continue;
|
||||
let jsonStr = text.slice(idx + key.length);
|
||||
const nullIdx = jsonStr.indexOf("\x00");
|
||||
if (nullIdx >= 0) jsonStr = jsonStr.slice(0, nullIdx);
|
||||
try { return JSON.parse(sanitizeJSON(jsonStr)); } catch {
|
||||
const lb = jsonStr.lastIndexOf("}");
|
||||
if (lb > 0) { try { return JSON.parse(sanitizeJSON(jsonStr.slice(0, lb + 1))); } catch {} }
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
// ── PNG text chunks ───────────────────────────────────────────
|
||||
function findNull(arr, start = 0) {
|
||||
for (let i = start; i < arr.length; i++) if (arr[i] === 0) return i;
|
||||
return -1;
|
||||
}
|
||||
function parseTEXtChunk(data, latin1) {
|
||||
const np = findNull(data);
|
||||
if (np === -1) return null;
|
||||
return { keyword: latin1.decode(data.slice(0, np)), text: latin1.decode(data.slice(np + 1)) };
|
||||
}
|
||||
function parseITXtChunk(data, latin1, utf8) {
|
||||
const np = findNull(data);
|
||||
if (np === -1) return null;
|
||||
const keyword = latin1.decode(data.slice(0, np));
|
||||
let pos = np + 3;
|
||||
pos = findNull(data, pos); if (pos === -1) return null; pos++;
|
||||
pos = findNull(data, pos); if (pos === -1) return null; pos++;
|
||||
return { keyword, text: utf8.decode(data.slice(pos)) };
|
||||
}
|
||||
export async function readAllPNGTextChunks(file) {
|
||||
const buffer = await file.arrayBuffer();
|
||||
const bytes = new Uint8Array(buffer);
|
||||
const PNG_SIG = [0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a];
|
||||
for (let i = 0; i < 8; i++) if (bytes[i] !== PNG_SIG[i]) return null;
|
||||
const view = new DataView(buffer);
|
||||
const latin1 = new TextDecoder("latin1");
|
||||
const utf8 = new TextDecoder("utf-8");
|
||||
let offset = 8;
|
||||
const chunks = {};
|
||||
while (offset + 12 <= buffer.byteLength) {
|
||||
const length = view.getUint32(offset);
|
||||
if (offset + 12 + length > buffer.byteLength) break;
|
||||
const type = latin1.decode(bytes.slice(offset + 4, offset + 8));
|
||||
const data = bytes.slice(offset + 8, offset + 8 + length);
|
||||
if (type === "tEXt") { const c = parseTEXtChunk(data, latin1); if (c) chunks[c.keyword] = c.text; }
|
||||
else if (type === "iTXt") { const c = parseITXtChunk(data, latin1, utf8); if (c) chunks[c.keyword] = c.text; }
|
||||
offset += 12 + length;
|
||||
}
|
||||
return chunks;
|
||||
}
|
||||
|
||||
// ── Workflow extraction helpers ───────────────────────────────
|
||||
function collectUnique(arr) {
|
||||
const seen = new Set(), out = [];
|
||||
for (const v of arr) { if (v && typeof v === "string" && !seen.has(v)) { seen.add(v); out.push(v); } }
|
||||
return out;
|
||||
}
|
||||
function collectAllNodes(workflow) {
|
||||
if (!Array.isArray(workflow.nodes)) return [];
|
||||
const all = [...workflow.nodes];
|
||||
for (const sg of workflow.definitions?.subgraphs ?? []) if (Array.isArray(sg.nodes)) all.push(...sg.nodes);
|
||||
return all;
|
||||
}
|
||||
const META_NODE_TYPES = new Set(["ImageMetadataCheckpointLoader", "ImageMetadataPromptLoader"]);
|
||||
const VAE_NONE = "None";
|
||||
|
||||
function extractCheckpoints(wf) {
|
||||
if (!wf || typeof wf !== "object") return [];
|
||||
if (Array.isArray(wf.nodes)) return collectUnique(collectAllNodes(wf).filter(n => n.type?.toLowerCase().includes("checkpoint") || META_NODE_TYPES.has(n.type)).map(n => n.widgets_values?.[0]));
|
||||
return collectUnique(Object.values(wf).filter(n => n?.class_type?.toLowerCase().includes("checkpoint") || META_NODE_TYPES.has(n?.class_type)).map(n => n.inputs?.ckpt_name));
|
||||
}
|
||||
function extractVAEs(wf) {
|
||||
if (!wf || typeof wf !== "object") return [];
|
||||
if (Array.isArray(wf.nodes)) return collectUnique(collectAllNodes(wf).flatMap(n => { if (n.type === "VAELoader") return [n.widgets_values?.[0]]; if (META_NODE_TYPES.has(n.type ?? "")) { const v = n.widgets_values?.[1]; return v && v !== VAE_NONE ? [v] : []; } return []; }));
|
||||
return collectUnique(Object.values(wf).flatMap(n => { if (!n || typeof n !== "object") return []; if (n.class_type === "VAELoader") return [n.inputs?.vae_name]; if (META_NODE_TYPES.has(n.class_type ?? "")) { const v = n.inputs?.vae_name; return v && v !== VAE_NONE ? [v] : []; } return []; }));
|
||||
}
|
||||
function extractDiffusionModels(wf) {
|
||||
if (!wf || typeof wf !== "object") return [];
|
||||
const UNET_TYPES = new Set(["UNETLoader", "UnetLoaderGGUF", "UNETLoaderGGUF"]);
|
||||
if (Array.isArray(wf.nodes)) return collectUnique(collectAllNodes(wf).filter(n => UNET_TYPES.has(n.type)).map(n => n.widgets_values?.[0]));
|
||||
return collectUnique(Object.values(wf).filter(n => UNET_TYPES.has(n?.class_type)).map(n => n.inputs?.unet_name));
|
||||
}
|
||||
function extractTextEncoders(wf) {
|
||||
if (!wf || typeof wf !== "object") return [];
|
||||
const names = [];
|
||||
if (Array.isArray(wf.nodes)) {
|
||||
for (const n of collectAllNodes(wf)) {
|
||||
if (n.type === "CLIPLoader") { if (n.widgets_values?.[0]) names.push(n.widgets_values[0]); }
|
||||
else if (n.type === "DualCLIPLoader") { [0,1].forEach(i => { if (n.widgets_values?.[i]) names.push(n.widgets_values[i]); }); }
|
||||
else if (n.type === "TripleCLIPLoader") { [0,1,2].forEach(i => { if (n.widgets_values?.[i]) names.push(n.widgets_values[i]); }); }
|
||||
else if (n.type === "QuadrupleCLIPLoader") { [0,1,2,3].forEach(i => { if (n.widgets_values?.[i]) names.push(n.widgets_values[i]); }); }
|
||||
}
|
||||
} else {
|
||||
for (const n of Object.values(wf)) {
|
||||
if (!n || typeof n !== "object") continue;
|
||||
const ct = n.class_type ?? "";
|
||||
if (ct === "CLIPLoader") { if (n.inputs?.clip_name) names.push(n.inputs.clip_name); }
|
||||
else if (ct === "DualCLIPLoader") { if (n.inputs?.clip_name1) names.push(n.inputs.clip_name1); if (n.inputs?.clip_name2) names.push(n.inputs.clip_name2); }
|
||||
else if (ct === "TripleCLIPLoader") { ["clip_name1","clip_name2","clip_name3"].forEach(k => { if (n.inputs?.[k]) names.push(n.inputs[k]); }); }
|
||||
else if (ct === "QuadrupleCLIPLoader") { ["clip_name1","clip_name2","clip_name3","clip_name4"].forEach(k => { if (n.inputs?.[k]) names.push(n.inputs[k]); }); }
|
||||
}
|
||||
}
|
||||
return collectUnique(names);
|
||||
}
|
||||
function extractLoRAs(wf) {
|
||||
if (!wf || typeof wf !== "object") return [];
|
||||
const results = [], seen = new Set();
|
||||
function add(name, sm, sc) {
|
||||
if (!name || typeof name !== "string" || name === "None" || seen.has(name)) return;
|
||||
seen.add(name);
|
||||
const smNum = parseFloat(sm);
|
||||
const scNum = parseFloat(sc);
|
||||
results.push({ name, strength_model: isNaN(smNum) ? 1.0 : smNum, strength_clip: isNaN(scNum) ? 1.0 : scNum });
|
||||
}
|
||||
if (Array.isArray(wf.nodes)) {
|
||||
for (const n of collectAllNodes(wf)) {
|
||||
const type = n.type ?? "";
|
||||
if (type === "LoraLoader") add(n.widgets_values?.[0], n.widgets_values?.[1], n.widgets_values?.[2]);
|
||||
else if (type === "LoraLoaderModelOnly") add(n.widgets_values?.[0], n.widgets_values?.[1], 1.0);
|
||||
else if (type === "ImageMetadataLoRALoader") { for (let i = 0; i < 3; i++) add(n.widgets_values?.[i*3], n.widgets_values?.[i*3+1], n.widgets_values?.[i*3+2]); }
|
||||
else if (type === "Lora Loader (LoraManager)") { const list = n.widgets_values?.find(v => Array.isArray(v)); if (list) for (const l of list) { if (l?.active !== false) add(l?.name, l?.strength ?? 1.0, l?.clipStrength ?? l?.strength ?? 1.0); } }
|
||||
}
|
||||
} else {
|
||||
for (const n of Object.values(wf)) {
|
||||
if (!n || typeof n !== "object") continue;
|
||||
const ct = n.class_type ?? "";
|
||||
if (ct === "LoraLoader") add(n.inputs?.lora_name, n.inputs?.strength_model, n.inputs?.strength_clip);
|
||||
else if (ct === "LoraLoaderModelOnly") add(n.inputs?.lora_name, n.inputs?.strength, 1.0);
|
||||
else if (ct === "Lora Loader (LoraManager)") {
|
||||
const lorasData = n.inputs?.loras;
|
||||
const list = lorasData?.__value__ ?? (Array.isArray(lorasData) ? lorasData : null);
|
||||
if (list) for (const l of list) { if (l?.active !== false) add(l?.name, l?.strength ?? 1.0, l?.clipStrength ?? l?.strength ?? 1.0); }
|
||||
}
|
||||
}
|
||||
}
|
||||
return results;
|
||||
}
|
||||
|
||||
function isTextEncoderNode(ct) { return ct === "CLIPTextEncode" || ct.includes("TextEncode") || ct.includes("TextEncoderSD"); }
|
||||
function isSamplerNode(ct) { return ct === "KSampler" || ct === "KSamplerAdvanced" || ct.includes("KSampler") || ct.includes("Sampler"); }
|
||||
function isPromptStylerNode(ct) { return ct.includes("PromptStyler"); }
|
||||
|
||||
// CLIPTextEncodeEditPlus (model-and-prompt-from-metadata) の encode() と同じ結合ルール。
|
||||
// RAW: text1のみ。EDIT: text_editのみ。front/back: text2(未接続ならtext_edit)をtext1の前/後に結合。
|
||||
function resolveEditPlusText(mode, textEdit, text1, text2) {
|
||||
const t1 = typeof text1 === "string" ? text1 : "";
|
||||
const edit = typeof textEdit === "string" ? textEdit : "";
|
||||
const insert = typeof text2 === "string" && text2 !== "" ? text2 : edit;
|
||||
switch (mode) {
|
||||
case "RAW": return t1;
|
||||
case "EDIT": return edit;
|
||||
case "front": return t1 ? `${insert}, ${t1}` : insert;
|
||||
case "back": return t1 ? `${t1}, ${insert}` : insert;
|
||||
default: return t1 || edit;
|
||||
}
|
||||
}
|
||||
|
||||
function extractPrompts(wf) {
|
||||
if (!wf || typeof wf !== "object") return { positives: [], negatives: [] };
|
||||
return Array.isArray(wf.nodes) ? extractPromptsLiteGraph(wf) : extractPromptsAPI(wf);
|
||||
}
|
||||
|
||||
// LiteGraph形式: リンクを辿ってテキストを解決（ComfySwitchNode等の中継ノードにも対応）
|
||||
function resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const srcNode = nodeMap.get(srcId);
|
||||
if (!srcNode) return null;
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (isPromptStylerNode(srcType)) {
|
||||
const v = srcNode.widgets_values?.[slot];
|
||||
return (v && typeof v === "string") ? v : null;
|
||||
}
|
||||
// ComfySwitchNode ("If/Else Switch") — 片方(TextGenerateなどLLMノード)は静的解決不能なため
|
||||
// on_false/on_true の両方を試し、リテラルへ解決できた方を採用する。
|
||||
if (srcType === "ComfySwitchNode" && Array.isArray(srcNode.inputs)) {
|
||||
for (const name of ["on_false", "on_true"]) {
|
||||
const inp = srcNode.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) continue;
|
||||
const text = resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
if (text) return text;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// WFS_PromptText, PrimitiveStringMultiline 等、任意の STRING 出力ノード
|
||||
const v = srcNode.widgets_values?.[slot] ?? srcNode.widgets_values?.[0];
|
||||
if (v && typeof v === "string") return v;
|
||||
// さらにリンクされている場合（StringReplace等の中継ノード）は追跡する
|
||||
if (Array.isArray(srcNode.inputs)) {
|
||||
const nextInput = srcNode.inputs.find(i => ["text", "value", "prompt", "string"].includes(i.name));
|
||||
if (nextInput?.link != null) {
|
||||
const originId = linkOrigin.get(nextInput.link);
|
||||
const originSlot = linkSlot.get(nextInput.link) ?? 0;
|
||||
if (originId != null) return resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
// CLIPTextEncode + KSampler でプロンプト抽出（トップレベル・サブグラフ共用）
|
||||
// サンプラーが見つかりテキストが取れた場合のみ非null を返す
|
||||
function extractPromptsFromNodeSet(nodes, links) {
|
||||
const nodeMap = new Map();
|
||||
for (const n of nodes) nodeMap.set(n.id, n);
|
||||
const linkOrigin = new Map(), linkSlot = new Map();
|
||||
if (Array.isArray(links)) {
|
||||
for (const lk of links) {
|
||||
if (Array.isArray(lk)) {
|
||||
linkOrigin.set(lk[0], lk[1]);
|
||||
linkSlot.set(lk[0], lk[2] ?? 0);
|
||||
} else if (lk && typeof lk === "object") {
|
||||
const id = lk.id ?? lk[0], origin = lk.origin_id ?? lk[1], slot = lk.origin_slot ?? lk[2] ?? 0;
|
||||
if (id != null && origin != null) { linkOrigin.set(id, origin); linkSlot.set(id, slot); }
|
||||
}
|
||||
}
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const n of nodes) {
|
||||
const type = n.type ?? "";
|
||||
if (type === "CLIPTextEncodeEditPlus") {
|
||||
const resolveNamedInput = (name) => {
|
||||
if (!Array.isArray(n.inputs)) return null;
|
||||
const inp = n.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) return null;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) return null;
|
||||
return resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
};
|
||||
const combined = resolveEditPlusText(n.widgets_values?.[1], n.widgets_values?.[0], resolveNamedInput("text1"), resolveNamedInput("text2"));
|
||||
if (combined) textMap.set(n.id, combined);
|
||||
continue;
|
||||
}
|
||||
if (!isTextEncoderNode(type)) continue;
|
||||
const text = n.widgets_values?.[0];
|
||||
if (text && typeof text === "string") {
|
||||
textMap.set(n.id, text);
|
||||
} else if (Array.isArray(n.inputs)) {
|
||||
const textInput = n.inputs.find(inp => inp.name === "text" || inp.name === "text_g" || inp.name === "prompt");
|
||||
if (textInput?.link != null) {
|
||||
const originId = linkOrigin.get(textInput.link);
|
||||
const originSlot = linkSlot.get(textInput.link) ?? 0;
|
||||
const text2 = resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
if (text2) textMap.set(n.id, text2);
|
||||
}
|
||||
}
|
||||
}
|
||||
const pos = new Set(), neg = new Set();
|
||||
let foundSampler = false;
|
||||
for (const n of nodes) {
|
||||
if (!isSamplerNode(n.type ?? "") || !Array.isArray(n.inputs)) continue;
|
||||
foundSampler = true;
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — it drives a separate
|
||||
// Guider node (CFGGuider/DualCFGGuider/BasicGuider) via its "guider" input. Scan that
|
||||
// node's inputs instead so the loop below can find the positive/negative-role slots.
|
||||
let inputsToScan = n.inputs;
|
||||
const guiderInput = n.inputs.find(inp => inp.name === "guider");
|
||||
if (guiderInput?.link != null) {
|
||||
const guiderId = linkOrigin.get(guiderInput.link);
|
||||
const guiderNode = guiderId != null ? nodeMap.get(guiderId) : null;
|
||||
if (Array.isArray(guiderNode?.inputs)) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const inp of inputsToScan) {
|
||||
if (!inp || inp.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
if (originId == null) continue;
|
||||
const name = inp.name ?? "";
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = name === "positive" || name === "cond1" || name.startsWith("positive");
|
||||
const isNeg = name === "negative" || name.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — one node carries both prompt &
|
||||
// negative_prompt directly, so textMap (single text per node) can't represent both
|
||||
// roles.
|
||||
const srcNode = nodeMap.get(originId);
|
||||
if (srcNode?.type === "TextEncodeMageFlowEdit" || srcNode?.type === "TextEncodeBooguEdit") {
|
||||
const widgetIdx = isPos ? 0 : 1; // widgets_values: [prompt, negative_prompt, ...]
|
||||
const text = srcNode.widgets_values?.[widgetIdx];
|
||||
if (text && typeof text === "string") { if (isPos) pos.add(text); else neg.add(text); }
|
||||
continue;
|
||||
}
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
let resolvedOriginId = originId;
|
||||
if (srcNode?.type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs)) {
|
||||
const innerInput = srcNode.inputs.find(i => i.name === (isPos ? "positive" : "negative"));
|
||||
if (innerInput?.link != null) {
|
||||
const innerId = linkOrigin.get(innerInput.link);
|
||||
if (innerId != null) resolvedOriginId = innerId;
|
||||
}
|
||||
}
|
||||
const text = textMap.get(resolvedOriginId);
|
||||
if (!text) continue;
|
||||
if (isPos) pos.add(text); else neg.add(text);
|
||||
}
|
||||
}
|
||||
if (!foundSampler) return null;
|
||||
// SamplerCustomAdvanced などで positive/negative 直結がない場合、判別不能テキストとして返す
|
||||
if (pos.size === 0 && neg.size === 0) {
|
||||
const allTexts = [...textMap.values()].filter(t => t.trim());
|
||||
if (allTexts.length > 0) return { positives: [], negatives: [], texts: allTexts };
|
||||
return null;
|
||||
}
|
||||
return { positives: [...pos], negatives: [...neg] };
|
||||
}
|
||||
|
||||
// MarkdownNote の **section** → - [name](url) パターンからモデルを抽出
|
||||
// flux/qwen/z-image などのサブグラフ形式ワークフロー向け補完用
|
||||
function extractMarkdownNoteModels(wf) {
|
||||
const allNodes = [];
|
||||
if (Array.isArray(wf.nodes)) allNodes.push(...wf.nodes);
|
||||
for (const sg of wf.definitions?.subgraphs ?? []) if (Array.isArray(sg.nodes)) allNodes.push(...sg.nodes);
|
||||
const result = { checkpoints: [], vaes: [], diffusionModels: [], textEncoders: [], loras: [] };
|
||||
const seen = { checkpoints: new Set(), vaes: new Set(), diffusionModels: new Set(), textEncoders: new Set(), loras: new Set() };
|
||||
function addU(arr, set, name) { if (name && typeof name === "string" && !set.has(name)) { set.add(name); arr.push(name); } }
|
||||
for (const n of allNodes) {
|
||||
if (n.type !== "MarkdownNote") continue;
|
||||
const raw = n.widgets_values;
|
||||
const text = Array.isArray(raw) ? raw[0] : (typeof raw === "string" ? raw : null);
|
||||
if (!text) continue;
|
||||
const sRe = /\*\*([^*\n]+)\*\*/g;
|
||||
let sm;
|
||||
while ((sm = sRe.exec(text)) !== null) {
|
||||
const sec = sm[1].trim().toLowerCase().replace(/\s+/g, "_");
|
||||
if (!["text_encoders", "diffusion_models", "vae", "checkpoints", "loras"].includes(sec)) continue;
|
||||
const rest = text.slice(sm.index + sm[0].length);
|
||||
const end = rest.search(/\n\*\*|\n##/);
|
||||
const content = end >= 0 ? rest.slice(0, end) : rest;
|
||||
const lRe = /^- \[([^\]]+)\]/gm;
|
||||
let lm;
|
||||
while ((lm = lRe.exec(content)) !== null) {
|
||||
const name = lm[1].trim();
|
||||
if (sec === "text_encoders") addU(result.textEncoders, seen.textEncoders, name);
|
||||
else if (sec === "diffusion_models") addU(result.diffusionModels, seen.diffusionModels, name);
|
||||
else if (sec === "vae") addU(result.vaes, seen.vaes, name);
|
||||
else if (sec === "checkpoints") addU(result.checkpoints, seen.checkpoints, name);
|
||||
else if (sec === "loras") addU(result.loras, seen.loras, name);
|
||||
}
|
||||
}
|
||||
}
|
||||
const hasAny = result.checkpoints.length || result.vaes.length || result.diffusionModels.length || result.textEncoders.length || result.loras.length;
|
||||
return hasAny ? result : null;
|
||||
}
|
||||
|
||||
// API形式: リンク参照 [srcNodeId, slot] からテキストを解決
|
||||
function resolveLinkedText(wf, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const src = wf[String(srcId)];
|
||||
if (!src || typeof src !== "object") return null;
|
||||
const ct = src.class_type ?? "";
|
||||
// PromptStyler系: slot 0 = text_positive, slot 1 = text_negative
|
||||
if (isPromptStylerNode(ct)) {
|
||||
const v = slot === 0 ? src.inputs?.text_positive : src.inputs?.text_negative;
|
||||
return (v && typeof v === "string") ? v : null;
|
||||
}
|
||||
// ComfySwitchNode ("If/Else Switch", on_false/on_true/switch) — Ernie Imageのプロンプト強化
|
||||
// トグルなどで使われる。片方(TextGenerateなどLLMノード)は静的解決不能なため両方試し、
|
||||
// リテラルへ解決できた方を採用する。
|
||||
if (ct === "ComfySwitchNode") {
|
||||
for (const key of ["on_false", "on_true"]) {
|
||||
const v = src.inputs?.[key];
|
||||
if (Array.isArray(v)) {
|
||||
const text = resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1);
|
||||
if (text) return text;
|
||||
} else if (typeof v === "string" && v) {
|
||||
return v;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// 汎用テキストキー（"value" は PrimitiveString/PrimitiveStringMultiline）
|
||||
const keys = slot === 0
|
||||
? ["text_positive", "text", "text_g", "prompt", "value"]
|
||||
: ["text_negative", "text_l"];
|
||||
for (const k of keys) {
|
||||
const v = src.inputs?.[k];
|
||||
if (typeof v === "string" && v) return v;
|
||||
if (Array.isArray(v)) {
|
||||
const text = resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1);
|
||||
if (text) return text;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
function extractPromptsAPI(wf) {
|
||||
const metaNodes = Object.values(wf).filter(n => n?.class_type === "ImageMetadataPromptLoader");
|
||||
if (metaNodes.length > 0) {
|
||||
const pos = new Set(), neg = new Set();
|
||||
for (const n of metaNodes) { if (n.inputs?.positive_text) pos.add(n.inputs.positive_text); if (n.inputs?.negative_text) neg.add(n.inputs.negative_text); }
|
||||
if (pos.size > 0 || neg.size > 0) return { positives: [...pos], negatives: [...neg] };
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const [id, n] of Object.entries(wf)) {
|
||||
if (!n) continue;
|
||||
const ct = n.class_type ?? "";
|
||||
if (ct === "CLIPTextEncodeEditPlus") {
|
||||
const resolveField = (key) => {
|
||||
const v = n.inputs?.[key];
|
||||
if (typeof v === "string") return v;
|
||||
if (Array.isArray(v)) return resolveLinkedText(wf, v[0], v[1] ?? 0);
|
||||
return null;
|
||||
};
|
||||
const combined = resolveEditPlusText(n.inputs?.mode, n.inputs?.text_edit, resolveField("text1"), resolveField("text2"));
|
||||
if (combined) textMap.set(id, combined);
|
||||
continue;
|
||||
}
|
||||
if (!isTextEncoderNode(ct)) continue;
|
||||
// "prompt" — TextEncodeQwenImageEdit(Plus)/TextEncodeBooguEdit and similar Image Edit
|
||||
// model text encoders use this key instead of "text"/"text_g".
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? n.inputs?.prompt ?? null;
|
||||
if (raw && typeof raw === "string") {
|
||||
textMap.set(id, raw);
|
||||
} else if (Array.isArray(raw)) {
|
||||
// リンク参照 [srcNodeId, slot] → 解決
|
||||
const text = resolveLinkedText(wf, raw[0], raw[1] ?? 0);
|
||||
if (text) textMap.set(id, text);
|
||||
}
|
||||
}
|
||||
const pos = new Set(), neg = new Set();
|
||||
let foundSampler = false;
|
||||
for (const n of Object.values(wf)) {
|
||||
if (!n || !isSamplerNode(n.class_type ?? "")) continue;
|
||||
foundSampler = true;
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — it drives a separate
|
||||
// Guider node (CFGGuider/DualCFGGuider/BasicGuider) via its "guider" input. Scan that
|
||||
// node's inputs instead so the loop below can find the positive/negative-role keys.
|
||||
let inputsToScan = n.inputs ?? {};
|
||||
if (Array.isArray(n.inputs?.guider)) {
|
||||
const guiderNode = wf[String(n.inputs.guider[0])];
|
||||
if (guiderNode?.inputs) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const [key, val] of Object.entries(inputsToScan)) {
|
||||
if (!Array.isArray(val)) continue;
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = key === "positive" || key === "cond1" || key.startsWith("positive");
|
||||
const isNeg = key === "negative" || key.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — one node carries both prompt &
|
||||
// negative_prompt directly, so textMap (single text per node) can't represent both
|
||||
// roles.
|
||||
const srcNode = wf[String(val[0])];
|
||||
if (srcNode?.class_type === "TextEncodeMageFlowEdit" || srcNode?.class_type === "TextEncodeBooguEdit") {
|
||||
const text = isPos ? srcNode.inputs?.prompt : srcNode.inputs?.negative_prompt;
|
||||
if (text && typeof text === "string") { if (isPos) pos.add(text); else neg.add(text); }
|
||||
continue;
|
||||
}
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
const resolvedId = srcNode?.class_type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs?.[isPos ? "positive" : "negative"])
|
||||
? String(srcNode.inputs[isPos ? "positive" : "negative"][0])
|
||||
: String(val[0]);
|
||||
const text = textMap.get(resolvedId);
|
||||
if (!text) continue;
|
||||
if (isPos) pos.add(text); else neg.add(text);
|
||||
}
|
||||
}
|
||||
if (!foundSampler || (pos.size === 0 && neg.size === 0)) { const all = [...textMap.values()].filter(t => t && t.trim()); return { positives: [], negatives: [], texts: all }; }
|
||||
return { positives: [...pos], negatives: [...neg] };
|
||||
}
|
||||
|
||||
function extractPromptsLiteGraph(wf) {
|
||||
const { nodes, links } = wf;
|
||||
if (!Array.isArray(nodes)) return { positives: [], negatives: [] };
|
||||
|
||||
// 1. ImageMetadataPromptLoader (WFS専用)
|
||||
const metaNodes = nodes.filter(n => n.type === "ImageMetadataPromptLoader");
|
||||
if (metaNodes.length > 0) {
|
||||
const pos = new Set(), neg = new Set();
|
||||
for (const n of metaNodes) { const p = n.widgets_values?.[2], ng = n.widgets_values?.[3]; if (p) pos.add(p); if (ng) neg.add(ng); }
|
||||
if (pos.size > 0 || neg.size > 0) return { positives: [...pos], negatives: [...neg] };
|
||||
}
|
||||
|
||||
// 2. WFS_PromptText (WFS専用)
|
||||
const wfsNodes = nodes.filter(n => n.type === "WFS_PromptText");
|
||||
if (wfsNodes.length > 0) {
|
||||
const pos = new Set(), neg = new Set();
|
||||
for (const n of wfsNodes) { const p = n.widgets_values?.[0], ng = n.widgets_values?.[1]; if (p) pos.add(p); if (ng) neg.add(ng); }
|
||||
if (pos.size > 0 || neg.size > 0) return { positives: [...pos], negatives: [...neg] };
|
||||
}
|
||||
|
||||
// 3. トップレベルの CLIPTextEncode + KSampler
|
||||
const topResult = extractPromptsFromNodeSet(nodes, links ?? []);
|
||||
if (topResult) return topResult;
|
||||
|
||||
// 4. PrimitiveStringMultiline（flux2-klein / ernie など）- サブグラフ内も探す
|
||||
const primTexts = [];
|
||||
for (const n of collectAllNodes(wf)) {
|
||||
if (n.type !== "PrimitiveStringMultiline") continue;
|
||||
const t = Array.isArray(n.widgets_values) ? n.widgets_values[0] : n.widgets_values;
|
||||
if (t && typeof t === "string" && t.trim()) primTexts.push(t.trim());
|
||||
}
|
||||
if (primTexts.length > 0) return { positives: [], negatives: [], texts: primTexts };
|
||||
|
||||
// 5. サブグラフ内の CLIPTextEncode + KSampler（z-image / qwen / flux など）
|
||||
for (const sg of wf.definitions?.subgraphs ?? []) {
|
||||
if (!Array.isArray(sg.nodes)) continue;
|
||||
const sgResult = extractPromptsFromNodeSet(sg.nodes, sg.links ?? []);
|
||||
if (sgResult) return sgResult;
|
||||
}
|
||||
|
||||
// 6. PromptStyler フォールバック
|
||||
const stylerPos = new Set(), stylerNeg = new Set();
|
||||
for (const n of nodes) {
|
||||
if (!isPromptStylerNode(n.type ?? "")) continue;
|
||||
const vals = n.widgets_values ?? [];
|
||||
for (let i = 0; i < vals.length; i++) {
|
||||
if (typeof vals[i] !== "string" || !vals[i].trim()) continue;
|
||||
if (i % 2 === 0) stylerPos.add(vals[i]);
|
||||
else stylerNeg.add(vals[i]);
|
||||
}
|
||||
}
|
||||
if (stylerPos.size > 0 || stylerNeg.size > 0) return { positives: [...stylerPos], negatives: [...stylerNeg] };
|
||||
|
||||
// 7. テキストエンコーダー全テキスト（最終フォールバック）- サブグラフも含む・判別不能
|
||||
const all = [];
|
||||
for (const n of collectAllNodes(wf)) {
|
||||
if (!isTextEncoderNode(n.type ?? "")) continue;
|
||||
const t = n.widgets_values?.[0];
|
||||
if (t && typeof t === "string" && t.trim()) all.push(t);
|
||||
}
|
||||
return { positives: [], negatives: [], texts: all };
|
||||
}
|
||||
|
||||
// ── SD/Fooocus prompt extraction ──────────────────────────────
|
||||
function parseSDAParameters(raw) {
|
||||
const text = raw.replace(/\r\n/g, "\n").replace(/\r/g, "\n");
|
||||
const stepsMatch = text.match(/\nSteps:\s+\d/);
|
||||
if (!stepsMatch) return null;
|
||||
const paramsStart = stepsMatch.index + 1;
|
||||
const promptSection = text.slice(0, paramsStart - 1);
|
||||
const paramsLine = text.slice(paramsStart);
|
||||
const negSep = "\nNegative prompt: ";
|
||||
const negIdx = promptSection.indexOf(negSep);
|
||||
let positive = "", negative = "";
|
||||
if (negIdx !== -1) { positive = promptSection.slice(0, negIdx).trim(); negative = promptSection.slice(negIdx + negSep.length).trim(); }
|
||||
else positive = promptSection.trim();
|
||||
const params = {};
|
||||
const re = /,?\s*([A-Za-z][A-Za-z0-9 ]*):\s*("(?:[^"\\]|\\.)*"|[^,]+)/g;
|
||||
let m;
|
||||
while ((m = re.exec(paramsLine)) !== null) params[m[1].trim()] = m[2].trim().replace(/^"|"$/g, "");
|
||||
return { positive, negative, params };
|
||||
}
|
||||
function parseFooocusMetadata(raw) {
|
||||
let obj; try { obj = JSON.parse(raw); } catch { return null; }
|
||||
if (!obj?.base_model) return null;
|
||||
const toArray = v => !v ? [] : Array.isArray(v) ? v.filter(Boolean) : [String(v)];
|
||||
return { checkpoint: obj.base_model, vae: (obj.vae && obj.vae !== "Default") ? obj.vae : null, positives: toArray(obj.full_prompt ?? obj.prompt), negatives: toArray(obj.full_negative_prompt ?? obj.negative_prompt) };
|
||||
}
|
||||
|
||||
// ── Master extractAllMetadata ─────────────────────────────────
|
||||
export async function extractAllMetadata(file) {
|
||||
const name = file.name.toLowerCase();
|
||||
const isJSON = file.type === "application/json" || name.endsWith(".json");
|
||||
const isWebP = file.type === "image/webp" || name.endsWith(".webp");
|
||||
|
||||
async function fromWorkflow(originalWf, source) {
|
||||
let wf = originalWf;
|
||||
// サブグラフを持つワークフローは、外側のサブグラフノードに実際に設定された値
|
||||
// (ユーザーが見ている値)が内部ノードのwidgets_valuesへ反映されていないことがある
|
||||
// (テンプレートの古いデフォルト値のまま)。convertUiToApi() はこの注入も含めて
|
||||
// 正しく解決するため、そちらを経由してから抽出する。通常のワークフローには影響しない。
|
||||
if (Array.isArray(wf?.nodes) && wf.definitions?.subgraphs?.length > 0) {
|
||||
try {
|
||||
const apiWf = await comfyWorkflow.convertUiToApi(wf);
|
||||
if (apiWf && Object.keys(apiWf).length > 0) wf = apiWf;
|
||||
} catch { /* 変換失敗時は元のUI形式のまま抽出（既存ロジックにフォールバック） */ }
|
||||
}
|
||||
const base = { source, checkpoints: extractCheckpoints(wf), vaes: extractVAEs(wf), diffusionModels: extractDiffusionModels(wf), textEncoders: extractTextEncoders(wf), loras: extractLoRAs(wf), ...extractPrompts(wf) };
|
||||
// MarkdownNote からモデル情報を補完（subgraph形式の flux/qwen/z-image など）。
|
||||
// API変換後は nodes 情報が失われるため、常に元のUI形式ワークフローから抽出する。
|
||||
const mdm = extractMarkdownNoteModels(originalWf);
|
||||
if (mdm) {
|
||||
if (!base.checkpoints.length) base.checkpoints = mdm.checkpoints;
|
||||
if (!base.vaes.length) base.vaes = mdm.vaes;
|
||||
if (!base.diffusionModels.length) base.diffusionModels = mdm.diffusionModels;
|
||||
if (!base.textEncoders.length) base.textEncoders = mdm.textEncoders;
|
||||
if (!base.loras.length) base.loras = mdm.loras;
|
||||
}
|
||||
return base;
|
||||
}
|
||||
|
||||
if (isJSON) {
|
||||
let wf; try { wf = JSON.parse(sanitizeJSON(await file.text())); } catch { return null; }
|
||||
return wf ? await fromWorkflow(wf, "comfyui") : null;
|
||||
}
|
||||
if (isWebP) {
|
||||
const exif = await readWebPEXIFChunk(file);
|
||||
if (!exif) return null;
|
||||
const wf = extractWorkflowFromEXIF(exif);
|
||||
return wf ? await fromWorkflow(wf, "comfyui") : null;
|
||||
}
|
||||
// PNG
|
||||
const chunks = await readAllPNGTextChunks(file);
|
||||
if (!chunks) return null;
|
||||
|
||||
if (chunks.prompt) { let wf; try { wf = JSON.parse(sanitizeJSON(chunks.prompt)); } catch { return null; } return wf ? await fromWorkflow(wf, "comfyui") : null; }
|
||||
if (chunks.workflow) { let wf; try { wf = JSON.parse(sanitizeJSON(chunks.workflow)); } catch { return null; } return wf ? await fromWorkflow(wf, "comfyui") : null; }
|
||||
|
||||
if (chunks.fooocus_scheme === "fooocus" && chunks.parameters) {
|
||||
const f = parseFooocusMetadata(chunks.parameters);
|
||||
if (!f) return null;
|
||||
return { source: "fooocus", checkpoints: [f.checkpoint], vaes: f.vae ? [f.vae] : [], diffusionModels: [], textEncoders: [], loras: [], positives: f.positives, negatives: f.negatives };
|
||||
}
|
||||
if (chunks.parameters) {
|
||||
const p = parseSDAParameters(chunks.parameters);
|
||||
if (!p) return null;
|
||||
const { positive, negative, params } = p;
|
||||
const modelName = params["Model"];
|
||||
if (!modelName) return null;
|
||||
if (params["Module 2"] != null) {
|
||||
const textEncoders = [];
|
||||
for (let i = 2; i <= 9; i++) { const mod = params[`Module ${i}`]; if (!mod) break; textEncoders.push(mod); }
|
||||
return { source: "sd_forge", checkpoints: [], vaes: params["Module 1"] ? [params["Module 1"]] : [], diffusionModels: [modelName], textEncoders, loras: [], positives: positive ? [positive] : [], negatives: negative ? [negative] : [] };
|
||||
}
|
||||
const vaeValue = params["Module 1"] ?? params["VAE"] ?? null;
|
||||
return { source: "sd", checkpoints: [modelName], vaes: vaeValue ? [vaeValue] : [], diffusionModels: [], textEncoders: [], loras: [], positives: positive ? [positive] : [], negatives: negative ? [negative] : [] };
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
// ── UI helpers ────────────────────────────────────────────────
|
||||
|
||||
function buildModelItem(label) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-meta-item";
|
||||
el.title = label;
|
||||
el.innerHTML = `<span class="wfm-meta-item-name">${escapeHtml(label)}</span>`;
|
||||
return el;
|
||||
}
|
||||
|
||||
function buildLoRAItem(lora) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-meta-item";
|
||||
el.title = lora.name;
|
||||
const sm = lora.strength_model.toFixed(2);
|
||||
const sc = lora.strength_clip.toFixed(2);
|
||||
el.innerHTML = `<span class="wfm-meta-item-name">${escapeHtml(lora.name)}</span><span class="wfm-meta-item-badge">${sm}/${sc}</span>`;
|
||||
return el;
|
||||
}
|
||||
|
||||
function buildPromptItem(label, type, full, fullArea, fullLabel, listEl) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-meta-item wfm-meta-item-clickable";
|
||||
const snippet = label.length > 60 ? label.slice(0, 60) + "…" : label;
|
||||
const typeBadge = type === "positive" ? `<span class="wfm-meta-badge-pos">POS</span>`
|
||||
: type === "negative" ? `<span class="wfm-meta-badge-neg">NEG</span>`
|
||||
: ``;
|
||||
el.innerHTML = `${typeBadge}<span class="wfm-meta-item-name">${escapeHtml(snippet)}</span>`;
|
||||
el.addEventListener("click", () => {
|
||||
listEl.querySelectorAll(".wfm-meta-item-clickable").forEach(e => e.classList.remove("selected"));
|
||||
el.classList.add("selected");
|
||||
fullArea.value = full;
|
||||
fullLabel.textContent = type === "positive" ? t("metaPromptPositive")
|
||||
: type === "negative" ? t("metaPromptNegative")
|
||||
: t("metaPromptText") || "Text";
|
||||
});
|
||||
return el;
|
||||
}
|
||||
|
||||
function renderSection(sectionEl, listEl, items, buildFn) {
|
||||
listEl.innerHTML = "";
|
||||
if (items.length === 0) { sectionEl.classList.add("wfm-meta-section-empty"); return; }
|
||||
sectionEl.classList.remove("wfm-meta-section-empty");
|
||||
for (const item of items) listEl.appendChild(buildFn(item));
|
||||
}
|
||||
|
||||
// ── External API ──────────────────────────────────────────────
|
||||
let _externalHandleFile = null;
|
||||
|
||||
export async function loadFileIntoMetadataTab(file) {
|
||||
document.querySelector('.wfm-tab[data-tab="metadata"]')?.click();
|
||||
await new Promise(r => setTimeout(r, 0));
|
||||
if (_externalHandleFile) await _externalHandleFile(file);
|
||||
}
|
||||
|
||||
// ── Tab initialization ────────────────────────────────────────
|
||||
export function initMetadataTab() {
|
||||
const dropZone = document.getElementById("wfm-meta-drop");
|
||||
const dropLabel = document.getElementById("wfm-meta-drop-label");
|
||||
const previewImg = document.getElementById("wfm-meta-preview-img");
|
||||
const fileInfo = document.getElementById("wfm-meta-file-info");
|
||||
const fileInput = document.getElementById("wfm-meta-file-input");
|
||||
|
||||
const ckptSection = document.getElementById("wfm-meta-ckpt-section");
|
||||
const vaeSection = document.getElementById("wfm-meta-vae-section");
|
||||
const diffSection = document.getElementById("wfm-meta-diff-section");
|
||||
const teSection = document.getElementById("wfm-meta-te-section");
|
||||
const loraSection = document.getElementById("wfm-meta-lora-section");
|
||||
const promptSection = document.getElementById("wfm-meta-prompt-section");
|
||||
|
||||
const ckptList = document.getElementById("wfm-meta-ckpt-list");
|
||||
const vaeList = document.getElementById("wfm-meta-vae-list");
|
||||
const diffList = document.getElementById("wfm-meta-diff-list");
|
||||
const teList = document.getElementById("wfm-meta-te-list");
|
||||
const loraList = document.getElementById("wfm-meta-lora-list");
|
||||
const promptList = document.getElementById("wfm-meta-prompt-list");
|
||||
const promptFull = document.getElementById("wfm-meta-prompt-full");
|
||||
const promptFullLabel = document.getElementById("wfm-meta-prompt-full-label");
|
||||
|
||||
if (!dropZone) return;
|
||||
|
||||
// Apply i18n to section titles
|
||||
const titleMap = {
|
||||
"wfm-meta-ckpt-section": "metaSectionCkpt",
|
||||
"wfm-meta-vae-section": "metaSectionVae",
|
||||
"wfm-meta-diff-section": "metaSectionDiff",
|
||||
"wfm-meta-te-section": "metaSectionTe",
|
||||
"wfm-meta-lora-section": "metaSectionLora",
|
||||
"wfm-meta-prompt-section": "metaSectionPrompt",
|
||||
};
|
||||
for (const [id, key] of Object.entries(titleMap)) {
|
||||
const title = document.querySelector(`#${id} .wfm-meta-section-title`);
|
||||
if (title) { const tx = t(key); if (tx && tx !== key) title.textContent = tx; }
|
||||
}
|
||||
|
||||
// Apply i18n to format note
|
||||
const i18nIds = {
|
||||
"wfm-meta-format-note-title": "metaFormatNoteTitle",
|
||||
"wfm-meta-fmt-comfyui": "metaFmtComfyui",
|
||||
"wfm-meta-fmt-sdwebui": "metaFmtSdwebui",
|
||||
"wfm-meta-fmt-fooocus": "metaFmtFooocus",
|
||||
"wfm-meta-format-todo": "metaFormatTodo",
|
||||
};
|
||||
for (const [id, key] of Object.entries(i18nIds)) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) { const tx = t(key); if (tx && tx !== key) el.textContent = tx; }
|
||||
}
|
||||
|
||||
// Apply i18n to help card
|
||||
const helpIds = {
|
||||
"wfm-help-metadata-title": "helpMetadataTitle",
|
||||
"wfm-help-metadata-desc": "helpMetadataDesc",
|
||||
"wfm-help-metadata-1": "helpMetadata1",
|
||||
"wfm-help-metadata-2": "helpMetadata2",
|
||||
"wfm-help-metadata-3": "helpMetadata3",
|
||||
"wfm-help-metadata-4": "helpMetadata4",
|
||||
"wfm-help-metadata-5": "helpMetadata5",
|
||||
};
|
||||
for (const [id, key] of Object.entries(helpIds)) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) { const tx = t(key); if (tx && tx !== key) el.textContent = tx; }
|
||||
}
|
||||
|
||||
function clearAll() {
|
||||
[ckptList, vaeList, diffList, teList, loraList, promptList].forEach(l => { if (l) l.innerHTML = ""; });
|
||||
[ckptSection, vaeSection, diffSection, teSection, loraSection, promptSection].forEach(s => { if (s) s.classList.add("wfm-meta-section-empty"); });
|
||||
if (promptFull) promptFull.value = "";
|
||||
if (promptFullLabel) promptFullLabel.textContent = "";
|
||||
}
|
||||
|
||||
async function handleFile(file) {
|
||||
if (!file) return;
|
||||
if (file.size > MAX_FILE_SIZE) {
|
||||
fileInfo.textContent = t("metaFileTooLarge");
|
||||
fileInfo.style.color = "var(--wfm-warning)";
|
||||
return;
|
||||
}
|
||||
|
||||
fileInfo.textContent = t("metaParsing");
|
||||
fileInfo.style.color = "var(--wfm-text-secondary)";
|
||||
clearAll();
|
||||
|
||||
// Show preview for images
|
||||
const isImage = file.type.startsWith("image/") || file.name.toLowerCase().match(/\.(png|webp|jpg|jpeg)$/);
|
||||
if (isImage) {
|
||||
const url = URL.createObjectURL(file);
|
||||
previewImg.src = url;
|
||||
previewImg.style.display = "block";
|
||||
dropLabel.style.display = "none";
|
||||
previewImg.onload = () => URL.revokeObjectURL(url);
|
||||
} else {
|
||||
previewImg.style.display = "none";
|
||||
dropLabel.style.display = "flex";
|
||||
}
|
||||
|
||||
let meta;
|
||||
try {
|
||||
meta = await extractAllMetadata(file);
|
||||
} catch (err) {
|
||||
console.error("[MetadataTab]", err);
|
||||
fileInfo.textContent = t("metaParseError");
|
||||
fileInfo.style.color = "var(--wfm-danger)";
|
||||
return;
|
||||
}
|
||||
|
||||
if (!meta) {
|
||||
fileInfo.textContent = t("metaNoMetadata");
|
||||
fileInfo.style.color = "var(--wfm-warning)";
|
||||
return;
|
||||
}
|
||||
|
||||
const sizeKB = (file.size / 1024).toFixed(1);
|
||||
const sourceLabel = { comfyui: "ComfyUI", sd: "SD WebUI", sd_forge: "SD Forge", fooocus: "Fooocus" }[meta.source] ?? meta.source;
|
||||
fileInfo.textContent = `${file.name} (${sizeKB} KB · ${sourceLabel})`;
|
||||
fileInfo.style.color = "var(--wfm-text-secondary)";
|
||||
|
||||
renderSection(ckptSection, ckptList, meta.checkpoints, n => buildModelItem(n));
|
||||
renderSection(vaeSection, vaeList, meta.vaes, n => buildModelItem(n));
|
||||
renderSection(diffSection, diffList, meta.diffusionModels, n => buildModelItem(n));
|
||||
renderSection(teSection, teList, meta.textEncoders, n => buildModelItem(n));
|
||||
renderSection(loraSection, loraList, meta.loras, l => buildLoRAItem(l));
|
||||
|
||||
// Prompts
|
||||
promptList.innerHTML = "";
|
||||
const allPrompts = [
|
||||
...meta.positives.map(p => ({ type: "positive", text: p })),
|
||||
...meta.negatives.map(p => ({ type: "negative", text: p })),
|
||||
...(meta.texts ?? []).map(p => ({ type: "text", text: p })),
|
||||
];
|
||||
if (allPrompts.length > 0) {
|
||||
promptSection.classList.remove("wfm-meta-section-empty");
|
||||
for (const { type, text } of allPrompts) {
|
||||
promptList.appendChild(buildPromptItem(text, type, text, promptFull, promptFullLabel, promptList));
|
||||
}
|
||||
// Auto-select first positive
|
||||
const firstPositive = promptList.querySelector(".wfm-meta-item-clickable");
|
||||
if (firstPositive) firstPositive.click();
|
||||
} else {
|
||||
promptSection.classList.add("wfm-meta-section-empty");
|
||||
}
|
||||
}
|
||||
|
||||
// ── Prompt action buttons ─────────────────────────────────
|
||||
function getPromptText() { return promptFull?.value ?? ""; }
|
||||
|
||||
function flashBtn(btn) {
|
||||
btn.classList.add("wfm-meta-btn-flash");
|
||||
setTimeout(() => btn.classList.remove("wfm-meta-btn-flash"), 600);
|
||||
}
|
||||
|
||||
function setTextareaValue(id, text) {
|
||||
const el = document.getElementById(id);
|
||||
if (!el) return false;
|
||||
el.value = text;
|
||||
el.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
return true;
|
||||
}
|
||||
|
||||
document.getElementById("wfm-meta-copy-btn")?.addEventListener("click", function () {
|
||||
const text = getPromptText();
|
||||
if (!text) return;
|
||||
navigator.clipboard.writeText(text).then(() => flashBtn(this));
|
||||
});
|
||||
|
||||
document.getElementById("wfm-meta-genui-pos-btn")?.addEventListener("click", function () {
|
||||
const text = getPromptText();
|
||||
if (!text) return;
|
||||
if (setTextareaValue("wfm-prompt-pos-text", text)) flashBtn(this);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-meta-genui-neg-btn")?.addEventListener("click", function () {
|
||||
const text = getPromptText();
|
||||
if (!text) return;
|
||||
if (setTextareaValue("wfm-prompt-neg-text", text)) flashBtn(this);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-meta-preset-pos-btn")?.addEventListener("click", function () {
|
||||
const text = getPromptText();
|
||||
if (!text) return;
|
||||
if (setTextareaValue("wfm-preset-pos", text)) flashBtn(this);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-meta-preset-neg-btn")?.addEventListener("click", function () {
|
||||
const text = getPromptText();
|
||||
if (!text) return;
|
||||
if (setTextareaValue("wfm-preset-neg", text)) flashBtn(this);
|
||||
});
|
||||
|
||||
_externalHandleFile = handleFile;
|
||||
|
||||
// Drag & Drop
|
||||
dropZone.addEventListener("dragover", e => { e.preventDefault(); e.stopPropagation(); dropZone.classList.add("drag-over"); });
|
||||
dropZone.addEventListener("dragleave", e => { e.stopPropagation(); dropZone.classList.remove("drag-over"); });
|
||||
dropZone.addEventListener("drop", e => { e.preventDefault(); e.stopPropagation(); dropZone.classList.remove("drag-over"); handleFile(e.dataTransfer.files?.[0]); });
|
||||
dropZone.addEventListener("click", e => { if (e.target === previewImg) return; fileInput.click(); });
|
||||
fileInput.addEventListener("change", () => { handleFile(fileInput.files?.[0]); fileInput.value = ""; });
|
||||
|
||||
// Prevent scroll passthrough in lists
|
||||
[ckptList, vaeList, diffList, teList, loraList, promptList].forEach(l => {
|
||||
if (l) l.addEventListener("wheel", e => e.stopPropagation(), { passive: true });
|
||||
});
|
||||
|
||||
clearAll();
|
||||
}
|
||||
+1481
-194
File diff suppressed because it is too large
Load Diff
+363
-121
@@ -5,10 +5,10 @@
|
||||
import { showToast } from "./app.js";
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
|
||||
// ── State ─────────────────────────────────────────────────
|
||||
|
||||
const NODES_PER_PAGE = 50;
|
||||
|
||||
const state = {
|
||||
allNodes: [],
|
||||
@@ -26,6 +26,8 @@ const state = {
|
||||
activeSubView: "browser",
|
||||
loaded: false,
|
||||
currentPage: 0,
|
||||
selectedNodes: new Set(),
|
||||
lastSelectionIndex: -1,
|
||||
};
|
||||
|
||||
// ── Helpers ───────────────────────────────────────────────
|
||||
@@ -51,9 +53,6 @@ function categoryBadgeHtml(cat) {
|
||||
return `<span class="wfm-badge wfm-badge-category">${cat}</span>`;
|
||||
}
|
||||
|
||||
function escapeHtml(s) {
|
||||
return String(s).replace(/&/g, "&").replace(/</g, "<").replace(/>/g, ">").replace(/"/g, """);
|
||||
}
|
||||
|
||||
// ── API ───────────────────────────────────────────────────
|
||||
|
||||
@@ -257,91 +256,78 @@ function renderNodeGrid() {
|
||||
|
||||
if (filtered.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("noNodesFound")}</p>`;
|
||||
clearPagination();
|
||||
return;
|
||||
}
|
||||
|
||||
if (state.viewMode === "table") {
|
||||
renderNodeTableView(grid, filtered);
|
||||
clearPagination();
|
||||
return;
|
||||
}
|
||||
|
||||
// Card view with pagination
|
||||
const totalPages = Math.ceil(filtered.length / NODES_PER_PAGE);
|
||||
if (state.currentPage >= totalPages) state.currentPage = totalPages - 1;
|
||||
if (state.currentPage < 0) state.currentPage = 0;
|
||||
|
||||
const start = state.currentPage * NODES_PER_PAGE;
|
||||
const pageItems = filtered.slice(start, start + NODES_PER_PAGE);
|
||||
|
||||
grid.innerHTML = "";
|
||||
pageItems.forEach(node => {
|
||||
const meta = state.nodeMetadata[node.name] || {};
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-card wfm-node-card";
|
||||
if (state.selectedNode && state.selectedNode.name === node.name) {
|
||||
card.classList.add("wfm-card-selected");
|
||||
}
|
||||
card.dataset.nodeName = node.name;
|
||||
card.style.borderLeft = `3px solid ${packageColor(node.package)}`;
|
||||
filtered.forEach(node => grid.appendChild(createNodeCard(node)));
|
||||
}
|
||||
|
||||
const inputCount = Object.keys(node.input.required || {}).length +
|
||||
Object.keys(node.input.optional || {}).length;
|
||||
const outputCount = (node.output || []).length;
|
||||
const tags = (meta.tags || []).map(tg => `<span class="wfm-tag">#${escapeHtml(tg)}</span>`).join("");
|
||||
const favStar = meta.favorite ? "\u2605" : "\u2606";
|
||||
const favClass = meta.favorite ? "wfm-fav-btn active" : "wfm-fav-btn";
|
||||
function createNodeCard(node) {
|
||||
const meta = state.nodeMetadata[node.name] || {};
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-card wfm-node-card";
|
||||
if (state.selectedNode && state.selectedNode.name === node.name) {
|
||||
card.classList.add("wfm-card-selected");
|
||||
}
|
||||
if (state.selectedNodes.has(node.name)) {
|
||||
card.classList.add("multi-selected");
|
||||
}
|
||||
card.dataset.nodeName = node.name;
|
||||
card.style.borderLeft = `3px solid ${packageColor(node.package)}`;
|
||||
|
||||
card.innerHTML = `
|
||||
<div class="wfm-card-body">
|
||||
<div class="wfm-card-title" title="${escapeHtml(node.name)}">${escapeHtml(node.display_name)}</div>
|
||||
<div class="wfm-card-meta">
|
||||
${categoryBadgeHtml(node.category)}
|
||||
</div>
|
||||
${tags ? `<div class="wfm-card-tags">${tags}</div>` : ""}
|
||||
const tags = (meta.tags || []).map(tg => `<span class="wfm-tag">#${escapeHtml(tg)}</span>`).join("");
|
||||
const favStar = meta.favorite ? "\u2605" : "\u2606";
|
||||
const favClass = meta.favorite ? "wfm-fav-btn active" : "wfm-fav-btn";
|
||||
|
||||
card.innerHTML = `
|
||||
<div class="wfm-card-body">
|
||||
<div class="wfm-card-title" title="${escapeHtml(node.name)}">${escapeHtml(node.display_name)}</div>
|
||||
<div class="wfm-card-meta">
|
||||
${categoryBadgeHtml(node.category)}
|
||||
</div>
|
||||
<button class="${favClass}" title="Favorite">${favStar}</button>`;
|
||||
${tags ? `<div class="wfm-card-tags">${tags}</div>` : ""}
|
||||
</div>
|
||||
<button class="${favClass}" title="Favorite">${favStar}</button>`;
|
||||
|
||||
card.querySelector(".wfm-fav-btn").addEventListener("click", e => {
|
||||
e.stopPropagation();
|
||||
toggleFavorite(node.name, e.currentTarget);
|
||||
});
|
||||
card.addEventListener("click", () => showNodeSidePanel(node));
|
||||
grid.appendChild(card);
|
||||
card.querySelector(".wfm-fav-btn").addEventListener("click", e => {
|
||||
e.stopPropagation();
|
||||
toggleFavorite(node.name, e.currentTarget);
|
||||
});
|
||||
|
||||
renderPagination(filtered.length, totalPages);
|
||||
}
|
||||
|
||||
function renderPagination(totalItems, totalPages) {
|
||||
const container = document.getElementById("wfm-nodes-pagination");
|
||||
if (!container) return;
|
||||
if (totalPages <= 1) { container.innerHTML = ""; return; }
|
||||
|
||||
const start = state.currentPage * NODES_PER_PAGE + 1;
|
||||
const end = Math.min((state.currentPage + 1) * NODES_PER_PAGE, totalItems);
|
||||
container.innerHTML = `
|
||||
<button class="wfm-btn wfm-btn-sm" ${state.currentPage === 0 ? "disabled" : ""} data-page="prev">«</button>
|
||||
<span>${start}-${end} / ${totalItems}</span>
|
||||
<button class="wfm-btn wfm-btn-sm" ${state.currentPage >= totalPages - 1 ? "disabled" : ""} data-page="next">»</button>
|
||||
`;
|
||||
container.querySelector('[data-page="prev"]').addEventListener("click", () => {
|
||||
if (state.currentPage > 0) { state.currentPage--; renderNodeGrid(); scrollGridToTop(); }
|
||||
});
|
||||
container.querySelector('[data-page="next"]').addEventListener("click", () => {
|
||||
if (state.currentPage < totalPages - 1) { state.currentPage++; renderNodeGrid(); scrollGridToTop(); }
|
||||
card.addEventListener("click", (e) => {
|
||||
const filtered = filterNodes();
|
||||
const idx = filtered.findIndex(n => n.name === node.name);
|
||||
if (e.shiftKey && state.lastSelectionIndex !== -1) {
|
||||
const from = Math.min(state.lastSelectionIndex, idx);
|
||||
const to = Math.max(state.lastSelectionIndex, idx);
|
||||
for (let i = from; i <= to; i++) {
|
||||
state.selectedNodes.add(filtered[i].name);
|
||||
}
|
||||
_applyNodeSelectionToDOM();
|
||||
updateNodeBulkBar();
|
||||
} else if (e.ctrlKey || e.metaKey) {
|
||||
if (state.selectedNodes.has(node.name)) {
|
||||
state.selectedNodes.delete(node.name);
|
||||
card.classList.remove("multi-selected");
|
||||
} else {
|
||||
state.selectedNodes.add(node.name);
|
||||
card.classList.add("multi-selected");
|
||||
}
|
||||
state.lastSelectionIndex = idx;
|
||||
updateNodeBulkBar();
|
||||
} else {
|
||||
state.lastSelectionIndex = idx;
|
||||
showNodeSidePanel(node);
|
||||
}
|
||||
});
|
||||
return card;
|
||||
}
|
||||
|
||||
function clearPagination() {
|
||||
const container = document.getElementById("wfm-nodes-pagination");
|
||||
if (container) container.innerHTML = "";
|
||||
}
|
||||
|
||||
function scrollGridToTop() {
|
||||
document.getElementById("wfm-nodes-grid")?.scrollTo(0, 0);
|
||||
}
|
||||
|
||||
function renderNodeTableView(grid, filtered) {
|
||||
grid.innerHTML = "";
|
||||
@@ -363,6 +349,9 @@ function renderNodeTableView(grid, filtered) {
|
||||
if (state.selectedNode && state.selectedNode.name === node.name) {
|
||||
tr.classList.add("wfm-card-selected");
|
||||
}
|
||||
if (state.selectedNodes.has(node.name)) {
|
||||
tr.classList.add("multi-selected");
|
||||
}
|
||||
const inputCount = Object.keys(node.input.required || {}).length +
|
||||
Object.keys(node.input.optional || {}).length;
|
||||
const outputCount = (node.output || []).length;
|
||||
@@ -376,7 +365,31 @@ function renderNodeTableView(grid, filtered) {
|
||||
e.stopPropagation();
|
||||
toggleFavorite(node.name, e.currentTarget);
|
||||
});
|
||||
tr.addEventListener("click", () => showNodeSidePanel(node));
|
||||
tr.addEventListener("click", (e) => {
|
||||
const idx = filtered.findIndex(n => n.name === node.name);
|
||||
if (e.shiftKey && state.lastSelectionIndex !== -1) {
|
||||
const from = Math.min(state.lastSelectionIndex, idx);
|
||||
const to = Math.max(state.lastSelectionIndex, idx);
|
||||
for (let i = from; i <= to; i++) {
|
||||
state.selectedNodes.add(filtered[i].name);
|
||||
}
|
||||
_applyNodeSelectionToDOM();
|
||||
updateNodeBulkBar();
|
||||
} else if (e.ctrlKey || e.metaKey) {
|
||||
if (state.selectedNodes.has(node.name)) {
|
||||
state.selectedNodes.delete(node.name);
|
||||
tr.classList.remove("multi-selected");
|
||||
} else {
|
||||
state.selectedNodes.add(node.name);
|
||||
tr.classList.add("multi-selected");
|
||||
}
|
||||
state.lastSelectionIndex = idx;
|
||||
updateNodeBulkBar();
|
||||
} else {
|
||||
state.lastSelectionIndex = idx;
|
||||
showNodeSidePanel(node);
|
||||
}
|
||||
});
|
||||
tbody.appendChild(tr);
|
||||
});
|
||||
table.appendChild(tbody);
|
||||
@@ -395,13 +408,104 @@ async function toggleFavorite(nodeName, btnEl) {
|
||||
btnEl.classList.toggle("active", newFav);
|
||||
}
|
||||
|
||||
// ── 複数選択ユーティリティ ─────────────────────────────────
|
||||
|
||||
function _applyNodeSelectionToDOM() {
|
||||
const grid = document.getElementById("wfm-nodes-grid");
|
||||
if (!grid) return;
|
||||
grid.querySelectorAll("[data-node-name]").forEach(el => {
|
||||
el.classList.toggle("multi-selected", state.selectedNodes.has(el.dataset.nodeName));
|
||||
});
|
||||
}
|
||||
|
||||
function updateNodeBulkBar() {
|
||||
const bar = document.getElementById("wfm-nodes-bulk-bar");
|
||||
const countEl = document.getElementById("wfm-nodes-bulk-count");
|
||||
if (!bar) return;
|
||||
const count = state.selectedNodes.size;
|
||||
if (count > 0) {
|
||||
bar.style.display = "";
|
||||
if (countEl) countEl.textContent = `${count} ${t("nodesBulkSelected")}`;
|
||||
} else {
|
||||
bar.style.display = "none";
|
||||
}
|
||||
const groupSel = document.getElementById("wfm-nodes-bulk-group-select");
|
||||
if (groupSel) {
|
||||
const current = groupSel.value;
|
||||
const grpNames = Object.keys(state.nodeGroups).sort();
|
||||
groupSel.innerHTML = `<option value="">${t("nodesBulkAddToGroup")}</option>` +
|
||||
grpNames.map(g => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("");
|
||||
if (grpNames.includes(current)) groupSel.value = current;
|
||||
}
|
||||
}
|
||||
|
||||
// ── 一括操作 ──────────────────────────────────────────────
|
||||
|
||||
async function bulkNodeAddToGroup(groupName) {
|
||||
if (!groupName || state.selectedNodes.size === 0) return;
|
||||
const nodeNames = [...state.selectedNodes];
|
||||
if (!state.nodeGroups[groupName]) state.nodeGroups[groupName] = [];
|
||||
let added = 0;
|
||||
nodeNames.forEach(name => {
|
||||
if (!state.nodeGroups[groupName].includes(name)) {
|
||||
state.nodeGroups[groupName].push(name);
|
||||
added++;
|
||||
}
|
||||
});
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
renderFilters();
|
||||
if (state.selectedNode) renderSideGroups(state.selectedNode);
|
||||
showToast(t("addedNImagesToGroup", added, groupName), "success");
|
||||
}
|
||||
|
||||
async function bulkNodeRemoveFromGroup(groupName) {
|
||||
if (!groupName || state.selectedNodes.size === 0) return;
|
||||
if (!state.nodeGroups[groupName]) return;
|
||||
const nodeNames = new Set(state.selectedNodes);
|
||||
const before = state.nodeGroups[groupName].length;
|
||||
state.nodeGroups[groupName] = state.nodeGroups[groupName].filter(n => !nodeNames.has(n));
|
||||
const removed = before - state.nodeGroups[groupName].length;
|
||||
if (state.nodeGroups[groupName].length === 0) delete state.nodeGroups[groupName];
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
renderFilters();
|
||||
if (state.selectedNode) renderSideGroups(state.selectedNode);
|
||||
showToast(t("removedNImagesFromGroup", removed, groupName), "success");
|
||||
}
|
||||
|
||||
async function bulkNodeCreateAndAddToGroup(name) {
|
||||
if (!name || state.selectedNodes.size === 0) return;
|
||||
if (state.nodeGroups[name] !== undefined) {
|
||||
showToast(t("groupExists"), "warning");
|
||||
return;
|
||||
}
|
||||
state.nodeGroups[name] = [...state.selectedNodes];
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
renderFilters();
|
||||
if (state.selectedNode) renderSideGroups(state.selectedNode);
|
||||
updateNodeBulkBar();
|
||||
showToast(t("groupCreated", name), "success");
|
||||
}
|
||||
|
||||
async function bulkNodeSetFavorite(value) {
|
||||
if (state.selectedNodes.size === 0) return;
|
||||
const nodeNames = [...state.selectedNodes].filter(name => {
|
||||
const meta = state.nodeMetadata[name];
|
||||
return (meta?.favorite ?? false) !== value;
|
||||
});
|
||||
if (nodeNames.length === 0) return;
|
||||
await Promise.all(nodeNames.map(name => saveNodeMetadata(name, { favorite: value })));
|
||||
nodeNames.forEach(name => {
|
||||
if (!state.nodeMetadata[name]) state.nodeMetadata[name] = {};
|
||||
state.nodeMetadata[name].favorite = value;
|
||||
});
|
||||
renderNodeGrid();
|
||||
showToast(value ? t("favoritedNImages", nodeNames.length) : t("unfavoritedNImages", nodeNames.length), "success");
|
||||
}
|
||||
|
||||
// ── Side Panel ────────────────────────────────────────────
|
||||
|
||||
function showNodeSidePanel(node) {
|
||||
state.selectedNode = node;
|
||||
const panel = document.getElementById("wfm-nodes-side-panel");
|
||||
if (!panel) return;
|
||||
panel.style.display = "flex";
|
||||
|
||||
// Highlight selected card
|
||||
document.querySelectorAll("#wfm-nodes-grid .wfm-card, #wfm-nodes-grid tr").forEach(el => {
|
||||
@@ -545,42 +649,61 @@ function renderSideGroups(node) {
|
||||
const el = document.getElementById("wfm-nodes-side-group");
|
||||
if (!el) return;
|
||||
|
||||
const groupNames = Object.keys(state.nodeGroups).sort();
|
||||
const memberOf = groupNames.filter(g => (state.nodeGroups[g] || []).includes(node.name));
|
||||
const allGroups = Object.keys(state.nodeGroups).sort();
|
||||
const memberOf = allGroups.filter(g => (state.nodeGroups[g] || []).includes(node.name));
|
||||
const available = allGroups.filter(g => !memberOf.includes(g));
|
||||
|
||||
let html = `<h4 style="margin:0 0 8px;">${t("nodesGroups")}</h4>`;
|
||||
el.innerHTML = `
|
||||
<div style="padding:0 4px;">
|
||||
<div style="margin-bottom:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsCurrentGroups")}</div>
|
||||
${memberOf.length === 0
|
||||
? `<p style="color:var(--wfm-text-secondary);font-size:12px;">${t("modelsNoGroup")}</p>`
|
||||
: memberOf.map(g =>
|
||||
`<div style="display:flex;align-items:center;justify-content:space-between;padding:3px 0;">
|
||||
<span style="font-size:12px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;">${escapeHtml(g)}</span>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger wfm-node-remove-group" data-group="${escapeHtml(g)}" title="${t("modelsRemoveFromGroup")}">×</button>
|
||||
</div>`
|
||||
).join("")}
|
||||
</div>
|
||||
<div style="margin-bottom:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsAssignGroup")}</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<select id="wfm-nodes-assign-group-select" class="wfm-select" style="flex:1;font-size:12px;padding:3px 6px;">
|
||||
${available.length === 0
|
||||
? `<option value="">${t("modelsNoGroupAvailable")}</option>`
|
||||
: available.map(g => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("")}
|
||||
</select>
|
||||
<button id="wfm-nodes-assign-group-btn" class="wfm-btn wfm-btn-sm wfm-btn-primary"
|
||||
${available.length === 0 ? "disabled" : ""}>${t("modelsAdd")}</button>
|
||||
</div>
|
||||
</div>
|
||||
<div style="margin-bottom:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsCreateGroup")}</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<input type="text" id="wfm-nodes-new-group-input" class="wfm-input"
|
||||
style="flex:1;font-size:12px;padding:3px 6px;" placeholder="${t("modelsGroupName")}">
|
||||
<button id="wfm-nodes-create-group-btn" class="wfm-btn wfm-btn-sm wfm-btn-primary">${t("modelsCreate")}</button>
|
||||
</div>
|
||||
</div>
|
||||
<div style="border-top:1px solid var(--wfm-border);padding-top:10px;margin-top:4px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsManageGroups")}</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<select id="wfm-nodes-manage-group-select" class="wfm-select" style="flex:1;font-size:12px;padding:3px 6px;">
|
||||
${allGroups.length === 0
|
||||
? `<option value="">${t("modelsNoGroupAvailable")}</option>`
|
||||
: allGroups.map(g => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("")}
|
||||
</select>
|
||||
<button id="wfm-nodes-rename-group-btn" class="wfm-btn wfm-btn-sm"
|
||||
${allGroups.length === 0 ? "disabled" : ""} title="${t("modelsRename")}">✎</button>
|
||||
<button id="wfm-nodes-delete-group-btn" class="wfm-btn wfm-btn-sm wfm-btn-danger"
|
||||
${allGroups.length === 0 ? "disabled" : ""} title="${t("modelsDelete")}">×</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Current groups
|
||||
if (memberOf.length) {
|
||||
html += memberOf.map(g =>
|
||||
`<div class="wfm-node-group-item">
|
||||
<span>${escapeHtml(g)}</span>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger wfm-node-remove-group" data-group="${escapeHtml(g)}">${t("nodesRemoveGroup")}</button>
|
||||
</div>`
|
||||
).join("");
|
||||
} else {
|
||||
html += `<p style="color:var(--wfm-text-secondary);font-size:12px;">No groups assigned</p>`;
|
||||
}
|
||||
|
||||
// Assign to group
|
||||
const available = groupNames.filter(g => !memberOf.includes(g));
|
||||
html += `<div style="margin-top:12px;display:flex;gap:4px;">
|
||||
<select id="wfm-nodes-assign-group-select" class="wfm-select" style="flex:1;">
|
||||
<option value="">${t("nodesAssignGroup")}</option>
|
||||
${available.map(g => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("")}
|
||||
</select>
|
||||
<button id="wfm-nodes-assign-group-btn" class="wfm-btn wfm-btn-sm wfm-btn-primary">+</button>
|
||||
</div>`;
|
||||
|
||||
// Create new group
|
||||
html += `<div style="margin-top:8px;display:flex;gap:4px;">
|
||||
<input type="text" id="wfm-nodes-new-group-input" class="wfm-input" style="flex:1;" placeholder="${t("nodesGroupName")}">
|
||||
<button id="wfm-nodes-create-group-btn" class="wfm-btn wfm-btn-sm">${t("nodesCreateGroup")}</button>
|
||||
</div>`;
|
||||
|
||||
el.innerHTML = html;
|
||||
|
||||
// Event listeners
|
||||
// グループから除外
|
||||
el.querySelectorAll(".wfm-node-remove-group").forEach(btn => {
|
||||
btn.addEventListener("click", async () => {
|
||||
const group = btn.dataset.group;
|
||||
@@ -592,8 +715,9 @@ function renderSideGroups(node) {
|
||||
});
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-assign-group-btn")?.addEventListener("click", async () => {
|
||||
const sel = document.getElementById("wfm-nodes-assign-group-select");
|
||||
// グループに追加
|
||||
el.querySelector("#wfm-nodes-assign-group-btn")?.addEventListener("click", async () => {
|
||||
const sel = el.querySelector("#wfm-nodes-assign-group-select");
|
||||
const group = sel?.value;
|
||||
if (!group) return;
|
||||
if (!state.nodeGroups[group]) state.nodeGroups[group] = [];
|
||||
@@ -605,25 +729,61 @@ function renderSideGroups(node) {
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-create-group-btn")?.addEventListener("click", async () => {
|
||||
const input = document.getElementById("wfm-nodes-new-group-input");
|
||||
// グループ作成（作成後に現在のノードに追加）
|
||||
el.querySelector("#wfm-nodes-create-group-btn")?.addEventListener("click", async () => {
|
||||
const input = el.querySelector("#wfm-nodes-new-group-input");
|
||||
const name = input?.value?.trim();
|
||||
if (!name) return;
|
||||
if (!state.nodeGroups[name]) state.nodeGroups[name] = [];
|
||||
if (!state.nodeGroups[name].includes(node.name)) {
|
||||
state.nodeGroups[name].push(node.name);
|
||||
if (state.nodeGroups[name]) {
|
||||
showToast(t("groupExists"), "warning");
|
||||
return;
|
||||
}
|
||||
state.nodeGroups[name] = [node.name];
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
input.value = "";
|
||||
renderSideGroups(node);
|
||||
renderFilters();
|
||||
});
|
||||
|
||||
// グループ名変更
|
||||
el.querySelector("#wfm-nodes-rename-group-btn")?.addEventListener("click", async () => {
|
||||
const sel = el.querySelector("#wfm-nodes-manage-group-select");
|
||||
const oldName = sel?.value;
|
||||
if (!oldName) return;
|
||||
const newName = prompt(`Rename group "${oldName}" to:`, oldName);
|
||||
if (!newName || newName === oldName) return;
|
||||
if (state.nodeGroups[newName] !== undefined) {
|
||||
showToast(t("groupNameExists"), "warning");
|
||||
return;
|
||||
}
|
||||
state.nodeGroups[newName] = state.nodeGroups[oldName];
|
||||
delete state.nodeGroups[oldName];
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
if (state.groupFilter === oldName) state.groupFilter = newName;
|
||||
renderSideGroups(node);
|
||||
renderFilters();
|
||||
showToast(t("renamedTo", newName), "success");
|
||||
});
|
||||
|
||||
// グループ削除
|
||||
el.querySelector("#wfm-nodes-delete-group-btn")?.addEventListener("click", async () => {
|
||||
const sel = el.querySelector("#wfm-nodes-manage-group-select");
|
||||
const name = sel?.value;
|
||||
if (!name) return;
|
||||
if (!confirm(`Delete group "${name}"?`)) return;
|
||||
delete state.nodeGroups[name];
|
||||
await saveNodeGroups(state.nodeGroups);
|
||||
if (state.groupFilter === name) state.groupFilter = "";
|
||||
renderSideGroups(node);
|
||||
renderFilters();
|
||||
showToast(t("groupDeleted", name), "success");
|
||||
});
|
||||
}
|
||||
|
||||
function closeNodeSidePanel() {
|
||||
const panel = document.getElementById("wfm-nodes-side-panel");
|
||||
if (panel) panel.style.display = "none";
|
||||
state.selectedNode = null;
|
||||
const titleEl = document.getElementById("wfm-nodes-panel-title");
|
||||
if (titleEl) titleEl.textContent = "";
|
||||
document.querySelectorAll("#wfm-nodes-grid .wfm-card, #wfm-nodes-grid tr").forEach(el => {
|
||||
el.classList.remove("wfm-card-selected");
|
||||
});
|
||||
@@ -1035,6 +1195,11 @@ export function initNodesTab() {
|
||||
renderNodeGrid();
|
||||
});
|
||||
}
|
||||
setupSearchClearBtn("wfm-nodes-search", "wfm-nodes-search-clear-btn", () => {
|
||||
state.searchText = "";
|
||||
state.currentPage = 0;
|
||||
renderNodeGrid();
|
||||
});
|
||||
|
||||
// Filters
|
||||
document.getElementById("wfm-nodes-category-filter")?.addEventListener("change", e => {
|
||||
@@ -1083,8 +1248,6 @@ export function initNodesTab() {
|
||||
loadNodesData();
|
||||
});
|
||||
|
||||
// Side panel close
|
||||
document.getElementById("wfm-nodes-panel-close")?.addEventListener("click", closeNodeSidePanel);
|
||||
|
||||
// Side panel tab switching
|
||||
document.querySelectorAll(".wfm-nodes-side-tab-btn").forEach(btn => {
|
||||
@@ -1102,9 +1265,88 @@ export function initNodesTab() {
|
||||
});
|
||||
});
|
||||
|
||||
// 一括操作バー: テキストをi18nで設定
|
||||
const bulkDeselBtn = document.getElementById("wfm-nodes-bulk-deselect");
|
||||
if (bulkDeselBtn) bulkDeselBtn.textContent = t("nodesBulkDeselect");
|
||||
const bulkGroupAddBtn = document.getElementById("wfm-nodes-bulk-group-add");
|
||||
if (bulkGroupAddBtn) bulkGroupAddBtn.textContent = t("nodesBulkGroupAdd");
|
||||
const bulkGroupRemBtn = document.getElementById("wfm-nodes-bulk-group-remove");
|
||||
if (bulkGroupRemBtn) bulkGroupRemBtn.textContent = t("nodesBulkGroupRemove");
|
||||
const bulkCreateAddBtn = document.getElementById("wfm-nodes-bulk-create-add");
|
||||
if (bulkCreateAddBtn) bulkCreateAddBtn.textContent = t("nodesBulkCreateAdd");
|
||||
const bulkFavBtn = document.getElementById("wfm-nodes-bulk-fav");
|
||||
if (bulkFavBtn) bulkFavBtn.textContent = t("nodesBulkFavAll");
|
||||
const bulkUnfavBtn = document.getElementById("wfm-nodes-bulk-unfav");
|
||||
if (bulkUnfavBtn) bulkUnfavBtn.textContent = t("nodesBulkUnfavAll");
|
||||
const bulkNewGroupInput = document.getElementById("wfm-nodes-bulk-new-group");
|
||||
if (bulkNewGroupInput) bulkNewGroupInput.placeholder = t("nodesBulkNewGroup");
|
||||
|
||||
document.getElementById("wfm-nodes-bulk-deselect")?.addEventListener("click", () => {
|
||||
state.selectedNodes.clear();
|
||||
state.lastSelectionIndex = -1;
|
||||
updateNodeBulkBar();
|
||||
document.querySelectorAll("#wfm-nodes-grid .multi-selected").forEach(el => el.classList.remove("multi-selected"));
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-bulk-group-add")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-nodes-bulk-group-select");
|
||||
if (sel?.value) {
|
||||
bulkNodeAddToGroup(sel.value);
|
||||
} else {
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-bulk-group-remove")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-nodes-bulk-group-select");
|
||||
if (sel?.value) {
|
||||
bulkNodeRemoveFromGroup(sel.value);
|
||||
} else {
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-bulk-create-add")?.addEventListener("click", () => {
|
||||
const input = document.getElementById("wfm-nodes-bulk-new-group");
|
||||
const name = input?.value?.trim();
|
||||
if (!name) return;
|
||||
bulkNodeCreateAndAddToGroup(name).then(() => { if (input) input.value = ""; });
|
||||
});
|
||||
|
||||
document.getElementById("wfm-nodes-bulk-fav")?.addEventListener("click", () => bulkNodeSetFavorite(true));
|
||||
document.getElementById("wfm-nodes-bulk-unfav")?.addEventListener("click", () => bulkNodeSetFavorite(false));
|
||||
|
||||
// Create node set button
|
||||
document.getElementById("wfm-node-set-create-btn")?.addEventListener("click", showCreateSetModal);
|
||||
|
||||
// Clear filters button
|
||||
const nodesClearBtn = document.getElementById("wfm-nodes-clear-filters-btn");
|
||||
if (nodesClearBtn) {
|
||||
nodesClearBtn.textContent = t("clearFilters");
|
||||
nodesClearBtn.addEventListener("click", () => {
|
||||
state.searchText = "";
|
||||
state.categoryFilter = "";
|
||||
state.packageFilter = "";
|
||||
state.tagFilter = "";
|
||||
state.groupFilter = "";
|
||||
state.showFavoritesOnly = false;
|
||||
state.currentPage = 0;
|
||||
const searchInput = document.getElementById("wfm-nodes-search");
|
||||
if (searchInput) searchInput.value = "";
|
||||
const catFilter = document.getElementById("wfm-nodes-category-filter");
|
||||
if (catFilter) catFilter.value = "";
|
||||
const pkgFilter = document.getElementById("wfm-nodes-package-filter");
|
||||
if (pkgFilter) pkgFilter.value = "";
|
||||
const tagFilter = document.getElementById("wfm-nodes-tag-filter");
|
||||
if (tagFilter) tagFilter.value = "";
|
||||
const grpFilter = document.getElementById("wfm-nodes-group-filter");
|
||||
if (grpFilter) grpFilter.value = "";
|
||||
const favBtn = document.getElementById("wfm-nodes-fav-btn");
|
||||
if (favBtn) favBtn.classList.remove("active");
|
||||
renderNodeGrid();
|
||||
});
|
||||
}
|
||||
|
||||
// Lazy load on first tab click
|
||||
document.querySelector('[data-tab="nodes"]')?.addEventListener("click", () => {
|
||||
if (!state.loaded) loadNodesData();
|
||||
|
||||
+540
-74
@@ -4,6 +4,7 @@
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { readJsonStorage, getAiBackendDefaultUrl } from "./util.js";
|
||||
|
||||
// ============================================
|
||||
// State
|
||||
@@ -21,6 +22,7 @@ let pmSearchText = "";
|
||||
let pmSelectedId = null; // currently selected preset id
|
||||
let pmGroups = {}; // { groupName: [presetId, ...] }
|
||||
const PM_GROUPS_KEY = "wfm_prompt_preset_groups";
|
||||
const PROMPT_RESERVED_GROUPS = ["Batch"];
|
||||
|
||||
// ============================================
|
||||
// Preset API helpers
|
||||
@@ -91,15 +93,11 @@ async function migrateLocalStoragePresets() {
|
||||
await apiCreatePreset({
|
||||
name: p.name || "Untitled",
|
||||
text: p.posText || p.text || "",
|
||||
negText: p.negText || "",
|
||||
category: "",
|
||||
tags: [],
|
||||
favorite: false,
|
||||
});
|
||||
// Note: negText is stored separately - we combine it
|
||||
if (p.negText) {
|
||||
// Create a separate entry for negative prompt reference
|
||||
// or store in the text field with marker
|
||||
}
|
||||
}
|
||||
localStorage.removeItem(PRESETS_KEY);
|
||||
} catch { /* ignore migration errors */ }
|
||||
@@ -114,15 +112,17 @@ async function loadAllPresets() {
|
||||
promptPresets = await fetchPresets();
|
||||
|
||||
// Load groups from localStorage
|
||||
try {
|
||||
pmGroups = JSON.parse(localStorage.getItem(PM_GROUPS_KEY) || "{}");
|
||||
} catch { pmGroups = {}; }
|
||||
pmGroups = readJsonStorage(PM_GROUPS_KEY);
|
||||
|
||||
// Clean stale entries from groups
|
||||
// Clean stale entries from groups (preserve reserved groups even if empty)
|
||||
const validIds = new Set(promptPresets.map(p => p.id));
|
||||
for (const g of Object.keys(pmGroups)) {
|
||||
pmGroups[g] = (pmGroups[g] || []).filter(id => validIds.has(id));
|
||||
if (pmGroups[g].length === 0) delete pmGroups[g];
|
||||
if (pmGroups[g].length === 0 && !PROMPT_RESERVED_GROUPS.includes(g)) delete pmGroups[g];
|
||||
}
|
||||
// Ensure reserved groups always exist
|
||||
for (const g of PROMPT_RESERVED_GROUPS) {
|
||||
if (!pmGroups[g]) pmGroups[g] = [];
|
||||
}
|
||||
saveGroups();
|
||||
|
||||
@@ -213,6 +213,25 @@ function matchesSearch(p) {
|
||||
(p.tags || []).some(t => t.toLowerCase().includes(s));
|
||||
}
|
||||
|
||||
function isInBatchPreset(id) {
|
||||
return (pmGroups["Batch"] || []).includes(id);
|
||||
}
|
||||
|
||||
function toggleBatchPreset(id) {
|
||||
const batch = pmGroups["Batch"] || [];
|
||||
const idx = batch.indexOf(id);
|
||||
if (idx >= 0) { batch.splice(idx, 1); } else { batch.push(id); }
|
||||
pmGroups["Batch"] = batch;
|
||||
saveGroups();
|
||||
}
|
||||
|
||||
function clearBatchPresets() {
|
||||
pmGroups["Batch"] = [];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(t("promptBatchClear"), "success");
|
||||
}
|
||||
|
||||
function createPmItem(preset) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-pm-item" + (pmSelectedId === preset.id ? " active" : "");
|
||||
@@ -225,12 +244,15 @@ function createPmItem(preset) {
|
||||
? `<span style="font-size:9px;color:var(--wfm-primary);margin-left:4px;">[${esc(preset.category)}]</span>`
|
||||
: "";
|
||||
|
||||
const inBatch = isInBatchPreset(preset.id);
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="wfm-pm-item-body">
|
||||
<div class="wfm-pm-item-name">${preset.favorite ? '<span style="color:#ffd700;">★</span> ' : ""}${esc(preset.name)}${catBadge}</div>
|
||||
<div class="wfm-pm-item-sub">${esc(previewText)}</div>
|
||||
</div>
|
||||
<div class="wfm-pm-item-actions">
|
||||
<button class="wfm-pm-action-btn pm-batch-btn${inBatch ? " batch-active" : ""}" title="Batch">B</button>
|
||||
<button class="wfm-pm-action-btn pm-fav-btn${preset.favorite ? " fav-active" : ""}" title="Favorite">★</button>
|
||||
<button class="wfm-pm-action-btn pm-del-btn" title="Delete" style="color:var(--wfm-danger);">✕</button>
|
||||
</div>
|
||||
@@ -242,6 +264,13 @@ function createPmItem(preset) {
|
||||
selectPresetInEditor(preset);
|
||||
});
|
||||
|
||||
// Batch toggle
|
||||
el.querySelector(".pm-batch-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleBatchPreset(preset.id);
|
||||
renderPresetManager();
|
||||
});
|
||||
|
||||
// Favorite toggle
|
||||
el.querySelector(".pm-fav-btn").addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
@@ -331,7 +360,8 @@ function renderPmGroups(container) {
|
||||
removeBtn.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
pmGroups[groupName] = pmGroups[groupName].filter(id => id !== p.id);
|
||||
if (pmGroups[groupName].length === 0) delete pmGroups[groupName];
|
||||
// 予約グループ (Batch) は空になってもキーを保持する
|
||||
if (pmGroups[groupName].length === 0 && !PROMPT_RESERVED_GROUPS.includes(groupName)) delete pmGroups[groupName];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
});
|
||||
@@ -345,26 +375,70 @@ function renderPmGroups(container) {
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Ollama API helpers
|
||||
// AI Assistant helpers (Ollama / LM Studio)
|
||||
// ============================================
|
||||
|
||||
async function ollamaModels() {
|
||||
const res = await fetch("/api/wfm/ollama/models");
|
||||
return await res.json();
|
||||
const PROMPT_AI_KEY = "wfm_prompt_ai_settings";
|
||||
|
||||
function loadPromptAiSettings() {
|
||||
try { return JSON.parse(localStorage.getItem(PROMPT_AI_KEY) || "{}"); } catch { return {}; }
|
||||
}
|
||||
|
||||
async function ollamaTest() {
|
||||
const res = await fetch("/api/wfm/ollama/test", { method: "POST" });
|
||||
return await res.json();
|
||||
// バックエンド変更検知用: モデル一覧を取得した時点のバックエンド名を記録
|
||||
let _promptAiBackendForModels = null;
|
||||
|
||||
function getPromptAiConfig() {
|
||||
const s = loadPromptAiSettings();
|
||||
const backend = s.backend || "ollama";
|
||||
const url = (s.backendUrl || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
|
||||
return { backend, url };
|
||||
}
|
||||
|
||||
async function ollamaChat(model, messages) {
|
||||
const res = await fetch("/api/wfm/ollama/chat", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages }),
|
||||
});
|
||||
return await res.json();
|
||||
async function fetchAiModels() {
|
||||
const { backend, url } = getPromptAiConfig();
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/tags`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).models?.map(m => m.name) || [];
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return ((await res.json()).data || []).map(m => m.id);
|
||||
}
|
||||
}
|
||||
|
||||
async function chatWithAi(model, messages) {
|
||||
const { backend, url } = getPromptAiConfig();
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/chat`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).message?.content || "";
|
||||
} else {
|
||||
// LM Studio / Lemonade (OpenAI-compatible): convert Ollama-style messages (with .images) to OpenAI content arrays
|
||||
const openAiMessages = messages.map(msg => {
|
||||
if (msg.images?.length) {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: [
|
||||
{ type: "text", text: msg.content },
|
||||
...msg.images.map(b64 => ({ type: "image_url", image_url: { url: `data:image/jpeg;base64,${b64}` } })),
|
||||
],
|
||||
};
|
||||
}
|
||||
return { role: msg.role, content: msg.content };
|
||||
});
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages: openAiMessages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
@@ -429,7 +503,7 @@ function clearChat() {
|
||||
if (chat) {
|
||||
chat.innerHTML = `
|
||||
<div class="wfm-ollama-welcome">
|
||||
<p>AI Assistant (Ollama)</p>
|
||||
<p>AI Assistant</p>
|
||||
<p>${t("assistantWelcome")}</p>
|
||||
</div>
|
||||
`;
|
||||
@@ -493,6 +567,24 @@ async function sendMessage() {
|
||||
const message = input.value.trim();
|
||||
if (!message) return;
|
||||
|
||||
// バックエンドが変更されていたらモデル一覧を自動更新
|
||||
const { backend } = getPromptAiConfig();
|
||||
if (backend !== _promptAiBackendForModels) {
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
if (status) { status.textContent = `Switching to ${backend}...`; status.className = "wfm-ollama-status"; }
|
||||
try {
|
||||
const models = await fetchAiModels();
|
||||
const savedModel = loadPromptAiSettings().model || "";
|
||||
modelSelect.innerHTML = models.length
|
||||
? models.map(name => `<option value="${name}" ${name === savedModel ? "selected" : ""}>${name}</option>`).join("")
|
||||
: `<option value="">${t("noModelsFound")}</option>`;
|
||||
_promptAiBackendForModels = backend;
|
||||
if (status) { status.textContent = `${models.length} models [${backend}]`; status.className = "wfm-ollama-status connected"; }
|
||||
} catch {
|
||||
if (status) { status.textContent = t("failedConnect"); status.className = "wfm-ollama-status error"; }
|
||||
}
|
||||
}
|
||||
|
||||
const model = modelSelect.value;
|
||||
if (!model) {
|
||||
showToast(t("selectModelFirst"), "error");
|
||||
@@ -512,16 +604,11 @@ async function sendMessage() {
|
||||
if (sendBtn) sendBtn.disabled = true;
|
||||
|
||||
try {
|
||||
const data = await ollamaChat(model, ollamaState.chatHistory);
|
||||
if (data.status === "success" && data.message) {
|
||||
const reply = data.message.content;
|
||||
addChatMessage("assistant", reply);
|
||||
ollamaState.chatHistory.push({ role: "assistant", content: reply });
|
||||
ollamaState.attachedImage = null;
|
||||
updateAttachmentDisplay();
|
||||
} else {
|
||||
showToast(t("error") + ": " + (data.error || data.message || "No response"), "error");
|
||||
}
|
||||
const reply = await chatWithAi(model, ollamaState.chatHistory);
|
||||
addChatMessage("assistant", reply);
|
||||
ollamaState.chatHistory.push({ role: "assistant", content: reply });
|
||||
ollamaState.attachedImage = null;
|
||||
updateAttachmentDisplay();
|
||||
} catch (err) {
|
||||
showToast(t("error") + ": " + err.message, "error");
|
||||
} finally {
|
||||
@@ -564,12 +651,8 @@ async function sendTranslate(direction) {
|
||||
};
|
||||
const prompt = prompts[direction];
|
||||
|
||||
const data = await ollamaChat(model, [{ role: "user", content: prompt }]);
|
||||
if (data.status === "success" && data.message) {
|
||||
addChatMessage("assistant", data.message.content);
|
||||
} else {
|
||||
showToast(t("error") + ": No response", "error");
|
||||
}
|
||||
const reply = await chatWithAi(model, [{ role: "user", content: prompt }]);
|
||||
addChatMessage("assistant", reply);
|
||||
} catch (err) {
|
||||
showToast(t("error") + ": " + err.message, "error");
|
||||
} finally {
|
||||
@@ -593,25 +676,255 @@ function applyToGenerateUI(text) {
|
||||
showToast(t("appliedToGenerateUI"), "success");
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard API helpers
|
||||
// ============================================
|
||||
|
||||
async function wcFetchFiles() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/wildcards");
|
||||
return res.ok ? await res.json() : [];
|
||||
} catch { return []; }
|
||||
}
|
||||
|
||||
async function wcFetchContent(filename) {
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/wildcards/content?filename=${encodeURIComponent(filename)}`);
|
||||
const data = await res.json();
|
||||
return data.content ?? null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
async function wcSaveFile(filename, content) {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/wildcards/save", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename, content }),
|
||||
});
|
||||
const data = await res.json();
|
||||
return data.status === "ok" ? data.file : null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
async function wcDeleteFile(filename) {
|
||||
try {
|
||||
await fetch("/api/wfm/wildcards/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename }),
|
||||
});
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard state & helpers
|
||||
// ============================================
|
||||
|
||||
let wcFiles = [];
|
||||
let wcEditingFilename = null; // null = new file
|
||||
|
||||
// Insert text at cursor; if open+close provided wraps selection
|
||||
function wcInsertAtCursor(textarea, open, close = "") {
|
||||
const start = textarea.selectionStart;
|
||||
const end = textarea.selectionEnd;
|
||||
const selected = textarea.value.substring(start, end);
|
||||
let inserted, cursorStart, cursorEnd;
|
||||
|
||||
if (selected && close) {
|
||||
inserted = open + selected + close;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart + selected.length;
|
||||
} else if (close) {
|
||||
inserted = open + close;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart;
|
||||
} else {
|
||||
inserted = open;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart;
|
||||
}
|
||||
|
||||
textarea.value =
|
||||
textarea.value.substring(0, start) +
|
||||
inserted +
|
||||
textarea.value.substring(end);
|
||||
textarea.setSelectionRange(cursorStart, cursorEnd);
|
||||
textarea.focus();
|
||||
textarea.dispatchEvent(new Event("input"));
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file list rendering
|
||||
// ============================================
|
||||
|
||||
function wcRenderFileList() {
|
||||
const list = document.getElementById("wfm-wc-file-list");
|
||||
if (!list) return;
|
||||
list.innerHTML = "";
|
||||
|
||||
if (wcFiles.length === 0) {
|
||||
list.innerHTML = `<div class="wfm-pm-empty" style="font-size:12px;">No wildcard files yet.<br><small>Click "+ New" to create one.</small></div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
// Group by directory; root ("") first, then alphabetical
|
||||
const grouped = {};
|
||||
for (const f of wcFiles) {
|
||||
const dir = f.dir || "";
|
||||
if (!grouped[dir]) grouped[dir] = [];
|
||||
grouped[dir].push(f);
|
||||
}
|
||||
const dirs = Object.keys(grouped).sort((a, b) => {
|
||||
if (a === "") return -1;
|
||||
if (b === "") return 1;
|
||||
return a.localeCompare(b);
|
||||
});
|
||||
|
||||
function addFileItem(f, indented) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-wc-file-item" + (indented ? " wfm-wc-file-item--sub" : "");
|
||||
item.innerHTML = `
|
||||
<div class="wfm-wc-file-item-info">
|
||||
<span class="wfm-wc-file-name">${esc(f.name)}</span>
|
||||
<span class="wfm-wc-file-ext">.${esc(f.ext)}</span>
|
||||
</div>
|
||||
<div class="wfm-wc-file-item-actions">
|
||||
<button class="wfm-pm-action-btn wfm-wc-use-btn" title="Insert __${esc(f.wc_name)}__ at cursor">Use</button>
|
||||
<button class="wfm-pm-action-btn wfm-wc-edit-btn" title="Edit file">✎</button>
|
||||
</div>
|
||||
`;
|
||||
item.querySelector(".wfm-wc-use-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) wcInsertAtCursor(ta, `__${f.wc_name}__`);
|
||||
});
|
||||
item.querySelector(".wfm-wc-edit-btn").addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
const content = await wcFetchContent(f.filename);
|
||||
wcOpenEditor(f.wc_name, f.ext, content ?? "", f.filename);
|
||||
});
|
||||
item.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) wcInsertAtCursor(ta, `__${f.wc_name}__`);
|
||||
});
|
||||
list.appendChild(item);
|
||||
}
|
||||
|
||||
for (const dir of dirs) {
|
||||
if (dir) {
|
||||
const header = document.createElement("div");
|
||||
header.className = "wfm-wc-dir-header";
|
||||
header.textContent = dir + "/";
|
||||
list.appendChild(header);
|
||||
}
|
||||
for (const f of grouped[dir]) {
|
||||
addFileItem(f, dir !== "");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file editor
|
||||
// ============================================
|
||||
|
||||
function wcOpenEditor(name, ext, content, existingFilename) {
|
||||
wcEditingFilename = existingFilename || null;
|
||||
|
||||
const editor = document.getElementById("wfm-wc-editor");
|
||||
const nameInput = document.getElementById("wfm-wc-editor-name");
|
||||
const extSelect = document.getElementById("wfm-wc-editor-ext");
|
||||
const contentTA = document.getElementById("wfm-wc-editor-content");
|
||||
const deleteBtn = document.getElementById("wfm-wc-editor-delete-btn");
|
||||
|
||||
if (!editor) return;
|
||||
nameInput.value = name || "";
|
||||
extSelect.value = ext || "txt";
|
||||
contentTA.value = content || "";
|
||||
deleteBtn.style.display = existingFilename ? "" : "none";
|
||||
editor.style.display = "";
|
||||
nameInput.focus();
|
||||
}
|
||||
|
||||
function wcCloseEditor() {
|
||||
wcEditingFilename = null;
|
||||
const editor = document.getElementById("wfm-wc-editor");
|
||||
if (editor) editor.style.display = "none";
|
||||
}
|
||||
|
||||
async function wcRefreshFiles() {
|
||||
wcFiles = await wcFetchFiles();
|
||||
wcRenderFileList();
|
||||
wcUpdateFilePicker();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file picker popup
|
||||
// ============================================
|
||||
|
||||
function wcUpdateFilePicker() {
|
||||
const picker = document.getElementById("wfm-wc-file-picker");
|
||||
if (!picker) return;
|
||||
picker.innerHTML = "";
|
||||
if (wcFiles.length === 0) {
|
||||
picker.innerHTML = `<div style="padding:8px;font-size:11px;color:var(--wfm-text-secondary);">No wildcard files</div>`;
|
||||
return;
|
||||
}
|
||||
// Group by directory same as file list
|
||||
const grouped = {};
|
||||
for (const f of wcFiles) {
|
||||
const dir = f.dir || "";
|
||||
if (!grouped[dir]) grouped[dir] = [];
|
||||
grouped[dir].push(f);
|
||||
}
|
||||
const dirs = Object.keys(grouped).sort((a, b) => {
|
||||
if (a === "") return -1;
|
||||
if (b === "") return 1;
|
||||
return a.localeCompare(b);
|
||||
});
|
||||
for (const dir of dirs) {
|
||||
if (dir) {
|
||||
const sep = document.createElement("div");
|
||||
sep.className = "wfm-wc-picker-dir";
|
||||
sep.textContent = dir + "/";
|
||||
picker.appendChild(sep);
|
||||
}
|
||||
for (const f of grouped[dir]) {
|
||||
const btn = document.createElement("button");
|
||||
btn.className = "wfm-wc-picker-item" + (dir ? " wfm-wc-picker-item--sub" : "");
|
||||
btn.textContent = `__${f.wc_name}__`;
|
||||
btn.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) wcInsertAtCursor(ta, `__${f.wc_name}__`);
|
||||
picker.style.display = "none";
|
||||
});
|
||||
picker.appendChild(btn);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Initialize
|
||||
// ============================================
|
||||
|
||||
export function initPromptTab() {
|
||||
// Ollama model refresh
|
||||
// AI model refresh
|
||||
async function refreshModels() {
|
||||
const select = document.getElementById("wfm-ollama-model");
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
const { backend } = getPromptAiConfig();
|
||||
try {
|
||||
const data = await ollamaModels();
|
||||
if (data.status === "success" && data.models) {
|
||||
select.innerHTML = data.models
|
||||
.map((m) => `<option value="${m.name}">${m.name}</option>`)
|
||||
.join("");
|
||||
if (status) {
|
||||
status.textContent = `${data.models.length} models`;
|
||||
status.className = "wfm-ollama-status connected";
|
||||
}
|
||||
const models = await fetchAiModels();
|
||||
const savedModel = loadPromptAiSettings().model || "";
|
||||
if (select) {
|
||||
select.innerHTML = models.length
|
||||
? models.map(name => `<option value="${name}" ${name === savedModel ? "selected" : ""}>${name}</option>`).join("")
|
||||
: `<option value="">${t("noModelsFound")}</option>`;
|
||||
}
|
||||
_promptAiBackendForModels = backend;
|
||||
if (status) {
|
||||
status.textContent = `${models.length} models [${backend}]`;
|
||||
status.className = "wfm-ollama-status connected";
|
||||
}
|
||||
} catch {
|
||||
if (status) {
|
||||
@@ -631,15 +944,11 @@ export function initPromptTab() {
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
if (status) { status.textContent = "Testing..."; status.className = "wfm-ollama-status"; }
|
||||
try {
|
||||
const data = await ollamaTest();
|
||||
if (data.connected) {
|
||||
if (status) { status.textContent = t("connected"); status.className = "wfm-ollama-status connected"; }
|
||||
await refreshModels();
|
||||
} else {
|
||||
if (status) { status.textContent = t("failedConnect"); status.className = "wfm-ollama-status error"; }
|
||||
}
|
||||
await fetchAiModels();
|
||||
if (status) { status.textContent = t("connectedCheck"); status.className = "wfm-ollama-status connected"; }
|
||||
await refreshModels();
|
||||
} catch {
|
||||
if (status) { status.textContent = t("error"); status.className = "wfm-ollama-status error"; }
|
||||
if (status) { status.textContent = t("failedConnect"); status.className = "wfm-ollama-status error"; }
|
||||
}
|
||||
});
|
||||
|
||||
@@ -750,18 +1059,14 @@ export function initPromptTab() {
|
||||
});
|
||||
if (created) {
|
||||
promptPresets.push(created);
|
||||
pmSelectedId = created.id;
|
||||
showToast(t("presetSaved"), "success");
|
||||
}
|
||||
}
|
||||
|
||||
pmSelectedId = null;
|
||||
renderPresetSelect();
|
||||
renderPresetManager();
|
||||
|
||||
// Re-select
|
||||
if (pmSelectedId && presetSelect) {
|
||||
presetSelect.value = pmSelectedId;
|
||||
}
|
||||
if (presetSelect) presetSelect.value = "";
|
||||
});
|
||||
|
||||
// Apply preset to GenerateUI
|
||||
@@ -793,50 +1098,54 @@ export function initPromptTab() {
|
||||
if (!name || !name.trim()) return;
|
||||
const key = name.trim();
|
||||
if (pmGroups[key]) {
|
||||
showToast("Group already exists", "error");
|
||||
showToast(t("groupExists"), "error");
|
||||
return;
|
||||
}
|
||||
pmGroups[key] = [];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(`Group "${key}" created`, "success");
|
||||
showToast(t("groupCreated", key), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-preset-add-to-group-btn")?.addEventListener("click", () => {
|
||||
const groupSelect = document.getElementById("wfm-preset-group-select");
|
||||
const groupName = groupSelect?.value;
|
||||
if (!groupName) {
|
||||
showToast("Select a group first", "error");
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
return;
|
||||
}
|
||||
const id = presetSelect?.value;
|
||||
if (!id) {
|
||||
showToast("Select a preset first", "error");
|
||||
showToast(t("selectPresetFirst"), "error");
|
||||
return;
|
||||
}
|
||||
if (!pmGroups[groupName]) pmGroups[groupName] = [];
|
||||
if (pmGroups[groupName].includes(id)) {
|
||||
showToast("Already in this group", "info");
|
||||
showToast(t("alreadyInGroup"), "info");
|
||||
return;
|
||||
}
|
||||
pmGroups[groupName].push(id);
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast("Added to group", "success");
|
||||
showToast(t("addedToGroup"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-preset-del-group-btn")?.addEventListener("click", () => {
|
||||
const groupSelect = document.getElementById("wfm-preset-group-select");
|
||||
const groupName = groupSelect?.value;
|
||||
if (!groupName) {
|
||||
showToast("Select a group first", "error");
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
return;
|
||||
}
|
||||
if (PROMPT_RESERVED_GROUPS.includes(groupName)) {
|
||||
showToast(t("modelsGroupReserved"), "warning");
|
||||
return;
|
||||
}
|
||||
if (!confirm(`Delete group "${groupName}"?`)) return;
|
||||
delete pmGroups[groupName];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(`Group "${groupName}" deleted`, "success");
|
||||
showToast(t("groupDeleted", groupName), "success");
|
||||
});
|
||||
|
||||
// --- Preset Manager ---
|
||||
@@ -853,4 +1162,161 @@ export function initPromptTab() {
|
||||
pmSearchText = e.target.value.trim();
|
||||
renderPresetManager();
|
||||
});
|
||||
|
||||
document.getElementById("wfm-pm-batch-clear-btn")?.addEventListener("click", () => {
|
||||
clearBatchPresets();
|
||||
});
|
||||
|
||||
// ── Center column tab switching ──────────────────────────
|
||||
document.querySelectorAll(".wfm-prompt-center-tab").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
const target = btn.dataset.ptab;
|
||||
document.querySelectorAll(".wfm-prompt-center-tab").forEach(b => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
document.querySelectorAll(".wfm-prompt-center-pane").forEach(p => {
|
||||
p.style.display = p.id === `wfm-ptab-${target}` ? "" : "none";
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// ── Wildcard syntax buttons ──────────────────────────────
|
||||
document.querySelectorAll(".wfm-wc-btn[data-wc-open]").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
const open = btn.dataset.wcOpen || "";
|
||||
const close = btn.dataset.wcClose || "";
|
||||
wcInsertAtCursor(ta, open, close);
|
||||
});
|
||||
});
|
||||
|
||||
// LoRA LBW template button
|
||||
document.getElementById("wfm-wc-lora-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
wcInsertAtCursor(ta, "<lora::1:LBW=;>");
|
||||
// Move cursor to after "lora:" for the name
|
||||
const pos = ta.selectionStart - "<lora::1:LBW=;>".length + 6;
|
||||
ta.setSelectionRange(pos, pos);
|
||||
ta.focus();
|
||||
});
|
||||
|
||||
// n$${ } button — asks for n then inserts multi-pick template
|
||||
document.getElementById("wfm-wc-nset-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
const n = prompt("Number of items to pick (n):", "2");
|
||||
if (n === null) return;
|
||||
const num = parseInt(n, 10);
|
||||
if (isNaN(num) || num < 1) { showToast(t("enterPositiveInt"), "error"); return; }
|
||||
const template = "{" + num + "$$|}";
|
||||
const start = ta.selectionStart;
|
||||
wcInsertAtCursor(ta, template);
|
||||
// Move cursor between $$ and |
|
||||
const inner = start + ("{" + num + "$$").length;
|
||||
ta.setSelectionRange(inner, inner);
|
||||
ta.focus();
|
||||
});
|
||||
|
||||
// File picker toggle
|
||||
const filePickBtn = document.getElementById("wfm-wc-filepick-btn");
|
||||
const filePicker = document.getElementById("wfm-wc-file-picker");
|
||||
filePickBtn?.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
if (!filePicker) return;
|
||||
const isOpen = filePicker.style.display !== "none";
|
||||
filePicker.style.display = isOpen ? "none" : "";
|
||||
if (!isOpen) wcUpdateFilePicker();
|
||||
});
|
||||
document.addEventListener("click", () => {
|
||||
if (filePicker) filePicker.style.display = "none";
|
||||
});
|
||||
|
||||
// ── Wildcard prompt actions ──────────────────────────────
|
||||
document.getElementById("wfm-wc-copy-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
const text = ta?.value || "";
|
||||
if (!text.trim()) { showToast(t("noTextToCopy"), "error"); return; }
|
||||
navigator.clipboard.writeText(text).then(() => showToast(t("copiedToClipboard"), "success"));
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-to-pos-btn")?.addEventListener("click", () => {
|
||||
const src = document.getElementById("wfm-wc-prompt");
|
||||
const dst = document.getElementById("wfm-preset-pos");
|
||||
if (!src || !dst) return;
|
||||
dst.value = src.value;
|
||||
dst.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
showToast(t("sentToPositive"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-to-neg-btn")?.addEventListener("click", () => {
|
||||
const src = document.getElementById("wfm-wc-prompt");
|
||||
const dst = document.getElementById("wfm-preset-neg");
|
||||
if (!src || !dst) return;
|
||||
dst.value = src.value;
|
||||
dst.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
showToast(t("sentToNegative"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-clear-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) { ta.value = ""; ta.focus(); }
|
||||
});
|
||||
|
||||
// ── Wildcard file manager ────────────────────────────────
|
||||
document.getElementById("wfm-wc-new-file-btn")?.addEventListener("click", () => {
|
||||
wcOpenEditor("", "txt", "", null);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-files-refresh-btn")?.addEventListener("click", () => {
|
||||
wcRefreshFiles();
|
||||
});
|
||||
|
||||
// Editor: save
|
||||
document.getElementById("wfm-wc-editor-save-btn")?.addEventListener("click", async () => {
|
||||
const nameInput = document.getElementById("wfm-wc-editor-name");
|
||||
const extSelect = document.getElementById("wfm-wc-editor-ext");
|
||||
const contentTA = document.getElementById("wfm-wc-editor-content");
|
||||
|
||||
// Normalize separators and strip leading/trailing slashes
|
||||
const rawName = (nameInput?.value || "").trim().replace(/\\/g, "/").replace(/\/+/g, "/").replace(/^\/|\/$/g, "");
|
||||
const ext = extSelect?.value || "txt";
|
||||
const content = contentTA?.value || "";
|
||||
|
||||
if (!rawName) { showToast(t("pleaseEnterFilename"), "error"); return; }
|
||||
// Validate each path component (allow letters, numbers, -, _, ., space)
|
||||
const nameParts = rawName.split("/");
|
||||
if (nameParts.some(p => !p || !/^[\w\-. ]+$/.test(p))) {
|
||||
showToast(t("invalidPathFormat"), "error");
|
||||
return;
|
||||
}
|
||||
const name = rawName;
|
||||
const filename = `${name}.${ext}`;
|
||||
const saved = await wcSaveFile(filename, content);
|
||||
if (saved) {
|
||||
showToast(t("savedAs", filename), "success");
|
||||
wcCloseEditor();
|
||||
wcRefreshFiles();
|
||||
} else {
|
||||
showToast(t("saveFailed"), "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Editor: delete
|
||||
document.getElementById("wfm-wc-editor-delete-btn")?.addEventListener("click", async () => {
|
||||
if (!wcEditingFilename) return;
|
||||
if (!confirm(`Delete "${wcEditingFilename}"?`)) return;
|
||||
await wcDeleteFile(wcEditingFilename);
|
||||
showToast(t("deletedName", wcEditingFilename), "success");
|
||||
wcCloseEditor();
|
||||
wcRefreshFiles();
|
||||
});
|
||||
|
||||
// Editor: cancel
|
||||
document.getElementById("wfm-wc-editor-cancel-btn")?.addEventListener("click", () => {
|
||||
wcCloseEditor();
|
||||
});
|
||||
|
||||
// Initial wildcard file load
|
||||
wcRefreshFiles();
|
||||
}
|
||||
|
||||
+624
-63
@@ -6,6 +6,8 @@ import { showToast } from "./app.js";
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { t, getLang, getSummaryLang, setLang, setSummaryLang, getLanguageOptions, getSummaryLanguageOptions } from "./i18n.js";
|
||||
|
||||
import { getSettings, readJsonStorage, getAiBackendDefaultUrl } from "./util.js";
|
||||
|
||||
const SETTINGS_KEY = "wfm_settings";
|
||||
|
||||
function rgbToHex(rgb) {
|
||||
@@ -15,11 +17,7 @@ function rgbToHex(rgb) {
|
||||
}
|
||||
|
||||
function loadLocalSettings() {
|
||||
try {
|
||||
return JSON.parse(localStorage.getItem(SETTINGS_KEY) || "{}");
|
||||
} catch {
|
||||
return {};
|
||||
}
|
||||
return readJsonStorage(SETTINGS_KEY);
|
||||
}
|
||||
|
||||
function saveLocalSettings(patch) {
|
||||
@@ -314,6 +312,60 @@ function clearCustomOverrides() {
|
||||
if (fontEl) fontEl.remove();
|
||||
}
|
||||
|
||||
const TEXTAREA_FONT_SIZE_DEFAULT = 13;
|
||||
const TEXTAREA_FONT_SIZE_STYLE_ID = "wfm-ta-font-size-style";
|
||||
const TEXTAREA_FONT_SIZE_TARGETS = [
|
||||
"#wfm-prompt-pos-text",
|
||||
"#wfm-prompt-neg-text",
|
||||
"#wfm-ollama-input",
|
||||
"#wfm-preset-pos",
|
||||
"#wfm-preset-neg",
|
||||
"#wfm-wc-prompt",
|
||||
"#wfm-wc-editor-content",
|
||||
"#wfm-meta-prompt-full",
|
||||
].join(",\n ");
|
||||
|
||||
export function applyTextareaFontSize(size) {
|
||||
const px = Math.max(10, Math.min(28, parseInt(size) || TEXTAREA_FONT_SIZE_DEFAULT));
|
||||
let el = document.getElementById(TEXTAREA_FONT_SIZE_STYLE_ID);
|
||||
if (!el) {
|
||||
el = document.createElement("style");
|
||||
el.id = TEXTAREA_FONT_SIZE_STYLE_ID;
|
||||
document.head.appendChild(el);
|
||||
}
|
||||
el.textContent = `${TEXTAREA_FONT_SIZE_TARGETS} { font-size: ${px}px !important; }`;
|
||||
}
|
||||
|
||||
// JSON highlight color settings
|
||||
const JSON_COLOR_STYLE_ID = "wfm-json-color-style";
|
||||
|
||||
const JSON_COLOR_DEFS = [
|
||||
{ id: "base", label: () => t("jsonColorBase") || "通常テキスト", def: "#abb2bf" },
|
||||
{ id: "yellow", label: () => t("jsonColorYellow") || "名前 / スケジューラ", def: "#e5c07b" },
|
||||
{ id: "pink", label: () => t("jsonColorPink") || "タイトル", def: "#c678dd" },
|
||||
{ id: "green", label: () => t("jsonColorGreen") || "Width / Height", def: "#98c379" },
|
||||
{ id: "cyan", label: () => t("jsonColorCyan") || "プロンプト / テキスト", def: "#61afef" },
|
||||
{ id: "red", label: () => t("jsonColorRed") || "画像 / ファイル", def: "#e06c75" },
|
||||
];
|
||||
|
||||
export function applyJsonColors(colors) {
|
||||
const vals = Object.fromEntries(JSON_COLOR_DEFS.map(d => [d.id, colors?.[d.id] || d.def]));
|
||||
let el = document.getElementById(JSON_COLOR_STYLE_ID);
|
||||
if (!el) {
|
||||
el = document.createElement("style");
|
||||
el.id = JSON_COLOR_STYLE_ID;
|
||||
document.head.appendChild(el);
|
||||
}
|
||||
el.textContent = [
|
||||
`.wfm-json-highlight { color: ${vals.base}; }`,
|
||||
`.json-key-yellow, .json-val-yellow { color: ${vals.yellow}; }`,
|
||||
`.json-key-pink, .json-val-pink { color: ${vals.pink}; }`,
|
||||
`.json-key-green, .json-val-green { color: ${vals.green}; }`,
|
||||
`.json-key-cyan, .json-val-cyan { color: ${vals.cyan}; }`,
|
||||
`.json-key-red, .json-val-red { color: ${vals.red}; }`,
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
export function applyTheme(themeId) {
|
||||
if (themeId) {
|
||||
document.documentElement.setAttribute("data-theme", themeId);
|
||||
@@ -339,6 +391,11 @@ export async function initSettingsTab() {
|
||||
const settings = loadLocalSettings();
|
||||
const serverSettings = await loadServerSettings();
|
||||
|
||||
// Sync civitai_host to localStorage so other tabs can read it without an extra fetch
|
||||
if (serverSettings.civitai_host) {
|
||||
localStorage.setItem("wfm_civitai_host", serverSettings.civitai_host);
|
||||
}
|
||||
|
||||
// Load current workflows dir info
|
||||
let workflowsDirInfo = { current: "", default: "" };
|
||||
try {
|
||||
@@ -346,6 +403,12 @@ export async function initSettingsTab() {
|
||||
workflowsDirInfo = await res.json();
|
||||
} catch {}
|
||||
|
||||
// Load gallery output dir info
|
||||
let outputDirInfo = { current: "", default: "", saved: "" };
|
||||
try {
|
||||
const res = await fetch("/api/wfm/settings/output-dir");
|
||||
outputDirInfo = await res.json();
|
||||
} catch {}
|
||||
|
||||
const uiLang = getLang();
|
||||
const summaryLang = getSummaryLang();
|
||||
@@ -353,26 +416,10 @@ export async function initSettingsTab() {
|
||||
container.innerHTML = `
|
||||
<h2 style="font-size:18px;margin-bottom:20px;">${t("settingsTitle")}</h2>
|
||||
|
||||
<!-- Language Settings -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("langLabel")} / ${t("summaryLangLabel")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("langLabel")}</label>
|
||||
<select class="wfm-select" id="wfm-settings-ui-lang">
|
||||
${buildLangOptions(getLanguageOptions(), uiLang)}
|
||||
</select>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("summaryLangLabel")}</label>
|
||||
<select class="wfm-select" id="wfm-settings-summary-lang">
|
||||
${buildLangOptions(getSummaryLanguageOptions(), summaryLang)}
|
||||
</select>
|
||||
<small style="color:var(--wfm-warning);font-size:11px;display:block;margin-top:4px;">
|
||||
⚠ ${t("summaryLangNote")}
|
||||
</small>
|
||||
</div>
|
||||
</details>
|
||||
<div class="wfm-settings-layout">
|
||||
|
||||
<!-- Right column: Theme (floated right) -->
|
||||
<div class="wfm-settings-right-col">
|
||||
<!-- Theme -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("themeLabel")}</summary>
|
||||
@@ -471,6 +518,65 @@ export async function initSettingsTab() {
|
||||
</div>
|
||||
</details>
|
||||
|
||||
</div><!-- /wfm-settings-right-col -->
|
||||
|
||||
<!-- Left column: all settings (Language + rest), flows left of floated Theme -->
|
||||
<div class="wfm-settings-left-col">
|
||||
<!-- Language Settings -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("langLabel")} / ${t("summaryLangLabel")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("langLabel")}</label>
|
||||
<select class="wfm-select" id="wfm-settings-ui-lang">
|
||||
${buildLangOptions(getLanguageOptions(), uiLang)}
|
||||
</select>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("summaryLangLabel")}</label>
|
||||
<select class="wfm-select" id="wfm-settings-summary-lang">
|
||||
${buildLangOptions(getSummaryLanguageOptions(), summaryLang)}
|
||||
</select>
|
||||
<small style="color:var(--wfm-warning);font-size:11px;display:block;margin-top:4px;">
|
||||
⚠ ${t("summaryLangNote")}
|
||||
</small>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Text Size -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("textSizeLabel") || "Text Size"}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label style="display:flex;align-items:center;justify-content:space-between;">
|
||||
<span>${t("textSizeTextarea") || "Prompt / Chat Textarea"}</span>
|
||||
<span id="wfm-ta-font-size-val" style="font-size:12px;color:var(--wfm-text-secondary);">${settings.textareaFontSize || TEXTAREA_FONT_SIZE_DEFAULT}px</span>
|
||||
</label>
|
||||
<input type="range" class="wfm-range" id="wfm-ta-font-size"
|
||||
min="10" max="28" step="1"
|
||||
value="${settings.textareaFontSize || TEXTAREA_FONT_SIZE_DEFAULT}"
|
||||
style="width:100%;margin-top:4px;">
|
||||
<small style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-top:4px;">
|
||||
${t("textSizeHint") || "Applies to: Generate UI prompts, AI Assistant chat, Preset prompts, Wildcard textarea, Metadata prompt preview"}
|
||||
</small>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- RAW JSON Colors -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("jsonColorsSection") || "RAW JSON Colors"}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<div class="wfm-color-grid" id="wfm-json-color-grid">
|
||||
${JSON_COLOR_DEFS.map(d => {
|
||||
const saved = settings.jsonColors?.[d.id] || d.def;
|
||||
return `<div class="wfm-color-item">
|
||||
<label>${d.label()}</label>
|
||||
<input type="color" data-json-color="${d.id}" value="${saved}">
|
||||
</div>`;
|
||||
}).join("")}
|
||||
</div>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-json-color-reset" style="margin-top:8px;">${t("jsonColorReset") || "デフォルトに戻す"}</button>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Workflow Data Folder -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("workflowsDir")}</summary>
|
||||
@@ -492,6 +598,27 @@ export async function initSettingsTab() {
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Gallery Output Folder -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("galleryOutputDir")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("galleryOutputDirLabel")}</label>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<input type="text" class="wfm-input" id="wfm-settings-output-dir"
|
||||
value="${serverSettings.gallery_output_dir || ""}"
|
||||
placeholder="${outputDirInfo.default}">
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-output-dir-apply">${t("workflowsDirApply")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-output-dir-reset">${t("workflowsDirDefault")}</button>
|
||||
</div>
|
||||
<small style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-top:4px;">
|
||||
${t("galleryOutputDirHint")}
|
||||
</small>
|
||||
<div style="font-size:11px;color:var(--wfm-text-secondary);margin-top:6px;">
|
||||
${t("workflowsDirCurrent")}: <code id="wfm-settings-output-dir-current" style="color:var(--wfm-primary);word-break:break-all;">${outputDirInfo.current}</code>
|
||||
</div>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- ComfyUI Settings -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("comfyuiConnection")}</summary>
|
||||
@@ -510,28 +637,95 @@ export async function initSettingsTab() {
|
||||
<div id="wfm-settings-url-status" style="font-size:12px;margin-top:4px;"></div>
|
||||
</details>
|
||||
|
||||
<!-- Ollama Settings -->
|
||||
<!-- Default Checkpoint -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("defaultCheckpointSettings")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label style="display:flex;align-items:center;gap:6px;cursor:pointer;">
|
||||
<input type="checkbox" id="wfm-settings-default-ckpt-enabled" ${serverSettings.default_checkpoint_enabled ? "checked" : ""}>
|
||||
<span id="wfm-settings-default-ckpt-enable-label">${t("defaultCheckpointEnable")}</span>
|
||||
</label>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label id="wfm-settings-default-ckpt-model-label">${t("defaultCheckpointModel")}</label>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<select class="wfm-select" id="wfm-settings-default-ckpt-select" style="flex:1;">
|
||||
<option value="">${t("selectModel")}</option>
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-default-ckpt-refresh">${t("refresh")}</button>
|
||||
</div>
|
||||
<small id="wfm-settings-default-ckpt-hint" style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-top:4px;">
|
||||
${t("defaultCheckpointHint")}
|
||||
</small>
|
||||
</div>
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-default-ckpt-save">${t("save")}</button>
|
||||
</details>
|
||||
|
||||
<!-- AI Assistant Settings (Prompt Tab) -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("ollamaSettings")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("aiSettingsBackend")}</label>
|
||||
<div class="wfm-ai-backend-row">
|
||||
<label class="wfm-ai-radio-label">
|
||||
<input type="radio" name="wfm-prompt-ai-backend" value="ollama"> Ollama
|
||||
</label>
|
||||
<label class="wfm-ai-radio-label">
|
||||
<input type="radio" name="wfm-prompt-ai-backend" value="lmstudio"> LM Studio
|
||||
</label>
|
||||
<label class="wfm-ai-radio-label">
|
||||
<input type="radio" name="wfm-prompt-ai-backend" value="lemonade"> Lemonade
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("ollamaUrl")}</label>
|
||||
<input type="text" class="wfm-input" id="wfm-settings-ollama-url"
|
||||
value="${serverSettings.ollama_url || "http://localhost:11434"}"
|
||||
<input type="text" class="wfm-input" id="wfm-settings-prompt-ai-url"
|
||||
placeholder="http://localhost:11434">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("ollamaDefaultModel")}</label>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<select class="wfm-select" id="wfm-settings-ollama-model" style="flex:1;">
|
||||
<select class="wfm-select" id="wfm-settings-prompt-ai-model" style="flex:1;">
|
||||
<option value="">${t("selectModel")}</option>
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-ollama-refresh">${t("refresh")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-prompt-ai-refresh">${t("refresh")}</button>
|
||||
</div>
|
||||
</div>
|
||||
<div style="display:flex;gap:8px;margin-top:8px;">
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-ollama-save">${t("saveOllama")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-ollama-test">${t("testConnection")}</button>
|
||||
<span id="wfm-settings-ollama-status" style="font-size:12px;line-height:28px;"></span>
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-prompt-ai-save">${t("saveOllama")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-prompt-ai-test">${t("testConnection")}</button>
|
||||
<span id="wfm-settings-prompt-ai-status" style="font-size:12px;line-height:28px;"></span>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- CivitAI Settings -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("civitaiApiKeySetting")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label style="display:flex;align-items:center;gap:4px;">
|
||||
${t("civitaiHostSetting")}
|
||||
<span title="${t("civitaiHostHint")}" style="cursor:help;color:var(--wfm-text-secondary);font-size:13px;">ℹ</span>
|
||||
</label>
|
||||
<select class="wfm-select" id="wfm-settings-civitai-host" style="width:100%;">
|
||||
<option value="civitai.com" ${(serverSettings.civitai_host || "civitai.com") === "civitai.com" ? "selected" : ""}>${t("civitaiHostCom")}</option>
|
||||
<option value="civitai.red" ${serverSettings.civitai_host === "civitai.red" ? "selected" : ""}>${t("civitaiHostRed")}</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<small style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-bottom:8px;">
|
||||
${t("civitaiApiKeyHint")}
|
||||
</small>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<input type="password" class="wfm-input" id="wfm-settings-civitai-api-key"
|
||||
value="${serverSettings.civitai_api_key || ""}"
|
||||
placeholder="${t("civitaiApiKeyPlaceholder")}"
|
||||
style="flex:1;">
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-civitai-api-key-save">
|
||||
${t("civitaiApiKeySave")}
|
||||
</button>
|
||||
</div>
|
||||
<div id="wfm-settings-civitai-api-key-status" style="font-size:12px;margin-top:4px;"></div>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
@@ -571,8 +765,51 @@ export async function initSettingsTab() {
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- G'MIC Integration -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">G'MIC-Qt Integration</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>G'MIC-Qt Executable Path</label>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<input type="text" class="wfm-input" id="wfm-settings-gmic-path"
|
||||
value="${serverSettings.gmic_qt_path || ""}"
|
||||
placeholder="C:\\path\\to\\gmic_qt.exe"
|
||||
style="flex:1;">
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-gmic-path-save">
|
||||
Save
|
||||
</button>
|
||||
</div>
|
||||
<div id="wfm-settings-gmic-path-status" style="font-size:12px;margin-top:4px;"></div>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Data Management -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("dataManagement")}</summary>
|
||||
<p style="font-size:12px;color:var(--wfm-text-secondary);margin-bottom:12px;">${t("dataManagementHint")}</p>
|
||||
<div style="display:flex;gap:8px;flex-wrap:wrap;">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-export">${t("exportData")}</button>
|
||||
<label class="wfm-btn wfm-btn-sm" style="cursor:pointer;margin:0;">
|
||||
${t("importData")}
|
||||
<input type="file" id="wfm-settings-import-file" accept=".json" style="display:none;">
|
||||
</label>
|
||||
</div>
|
||||
<div id="wfm-settings-data-status" style="font-size:12px;margin-top:8px;"></div>
|
||||
</details>
|
||||
|
||||
<!-- Wildcard Integration -->
|
||||
<details class="wfm-settings-section" id="wfm-wildcard-integration-section">
|
||||
<summary class="wfm-settings-summary">Wildcard Integration</summary>
|
||||
<div id="wfm-wildcard-link-status" style="font-size:13px;color:var(--wfm-text-secondary);">Loading...</div>
|
||||
</details>
|
||||
|
||||
<!-- Save Button -->
|
||||
<button class="wfm-btn wfm-btn-primary" id="wfm-settings-save" style="min-width:120px;">${t("saveSettings")}</button>
|
||||
|
||||
</div><!-- /wfm-settings-left-col -->
|
||||
|
||||
<div style="clear:both;"></div>
|
||||
</div><!-- /wfm-settings-layout -->
|
||||
`;
|
||||
|
||||
// --- Theme change handler ---
|
||||
@@ -776,7 +1013,7 @@ export async function initSettingsTab() {
|
||||
setLang(lang);
|
||||
saveLocalSettings({ ...loadLocalSettings(), uiLang: lang });
|
||||
// Re-render the entire page to apply new language
|
||||
showToast("Language changed. Reloading...", "success");
|
||||
showToast(t("languageChanged"), "success");
|
||||
setTimeout(() => location.reload(), 500);
|
||||
});
|
||||
|
||||
@@ -828,56 +1065,166 @@ export async function initSettingsTab() {
|
||||
}
|
||||
});
|
||||
|
||||
// --- Ollama model list loader ---
|
||||
async function loadOllamaModels() {
|
||||
const select = document.getElementById("wfm-settings-ollama-model");
|
||||
// --- Gallery output dir handlers ---
|
||||
document.getElementById("wfm-settings-output-dir-apply")?.addEventListener("click", async () => {
|
||||
const dirInput = document.getElementById("wfm-settings-output-dir");
|
||||
const currentEl = document.getElementById("wfm-settings-output-dir-current");
|
||||
const newDir = dirInput.value.trim();
|
||||
try {
|
||||
const res = await fetch("/api/wfm/ollama/models");
|
||||
const res = await fetch("/api/wfm/settings/output-dir", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ gallery_output_dir: newDir }),
|
||||
});
|
||||
const data = await res.json();
|
||||
const models = data.models || [];
|
||||
const savedModel = serverSettings.ollama_model || "llava";
|
||||
if (data.error) {
|
||||
showToast(`${t("workflowsDirError")}: ${data.error}`, "error");
|
||||
} else {
|
||||
if (currentEl) currentEl.textContent = data.current;
|
||||
showToast(t("workflowsDirChanged"), "success");
|
||||
// gallery-tab に通知して再ロード
|
||||
window.dispatchEvent(new CustomEvent("wfm-output-dir-changed", { detail: { path: data.current } }));
|
||||
}
|
||||
} catch (err) {
|
||||
showToast(`${t("workflowsDirError")}: ${err.message}`, "error");
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("wfm-settings-output-dir-reset")?.addEventListener("click", async () => {
|
||||
const dirInput = document.getElementById("wfm-settings-output-dir");
|
||||
const currentEl = document.getElementById("wfm-settings-output-dir-current");
|
||||
dirInput.value = "";
|
||||
try {
|
||||
const res = await fetch("/api/wfm/settings/output-dir", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ gallery_output_dir: "" }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (currentEl) currentEl.textContent = data.current;
|
||||
showToast(t("workflowsDirChanged"), "success");
|
||||
window.dispatchEvent(new CustomEvent("wfm-output-dir-changed", { detail: { path: data.current } }));
|
||||
} catch (err) {
|
||||
showToast(`${t("workflowsDirError")}: ${err.message}`, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// --- AI Assistant (Prompt Tab) settings ---
|
||||
const PROMPT_AI_KEY = "wfm_prompt_ai_settings";
|
||||
const loadPromptAiCfg = () => {
|
||||
try { return JSON.parse(localStorage.getItem(PROMPT_AI_KEY) || "{}"); } catch { return {}; }
|
||||
};
|
||||
const savePromptAiCfg = (patch) => {
|
||||
const data = { ...loadPromptAiCfg(), ...patch };
|
||||
localStorage.setItem(PROMPT_AI_KEY, JSON.stringify(data));
|
||||
};
|
||||
|
||||
// Restore saved values into UI
|
||||
const promptAiSaved = loadPromptAiCfg();
|
||||
const promptAiBackendSaved = promptAiSaved.backend || "ollama";
|
||||
document.querySelectorAll('input[name="wfm-prompt-ai-backend"]').forEach(r => {
|
||||
r.checked = r.value === promptAiBackendSaved;
|
||||
});
|
||||
const promptAiUrlEl = document.getElementById("wfm-settings-prompt-ai-url");
|
||||
if (promptAiUrlEl) {
|
||||
promptAiUrlEl.value = promptAiSaved.backendUrl || "";
|
||||
promptAiUrlEl.placeholder = getAiBackendDefaultUrl(promptAiBackendSaved);
|
||||
}
|
||||
|
||||
// Backend change → switch URL to the new backend's default
|
||||
document.querySelectorAll('input[name="wfm-prompt-ai-backend"]').forEach(r => {
|
||||
r.addEventListener("change", () => {
|
||||
const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
|
||||
if (promptAiUrlEl) promptAiUrlEl.value = getAiBackendDefaultUrl(backend);
|
||||
});
|
||||
});
|
||||
|
||||
// Load models directly from the selected backend
|
||||
async function loadPromptAiModels() {
|
||||
const select = document.getElementById("wfm-settings-prompt-ai-model");
|
||||
if (!select) return;
|
||||
const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
|
||||
const url = (promptAiUrlEl?.value.trim() || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
|
||||
try {
|
||||
let models = [];
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/tags`);
|
||||
models = (await res.json()).models?.map(m => m.name) || [];
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
models = ((await res.json()).data || []).map(m => m.id);
|
||||
}
|
||||
const saved = loadPromptAiCfg().model || "";
|
||||
select.innerHTML = models.length
|
||||
? models.map((m) => `<option value="${m.name}" ${m.name === savedModel ? "selected" : ""}>${m.name}</option>`).join("")
|
||||
? models.map(name => `<option value="${name}" ${name === saved ? "selected" : ""}>${name}</option>`).join("")
|
||||
: `<option value="">${t("noModelsFound")}</option>`;
|
||||
} catch {
|
||||
select.innerHTML = `<option value="">${t("failedLoadModels")}</option>`;
|
||||
}
|
||||
}
|
||||
await loadOllamaModels();
|
||||
await loadPromptAiModels();
|
||||
|
||||
// Refresh Ollama models
|
||||
document.getElementById("wfm-settings-ollama-refresh")?.addEventListener("click", loadOllamaModels);
|
||||
document.getElementById("wfm-settings-prompt-ai-refresh")?.addEventListener("click", loadPromptAiModels);
|
||||
|
||||
// Test Ollama connection
|
||||
document.getElementById("wfm-settings-ollama-test")?.addEventListener("click", async () => {
|
||||
const statusEl = document.getElementById("wfm-settings-ollama-status");
|
||||
statusEl.textContent = "Testing...";
|
||||
statusEl.style.color = "var(--wfm-text-secondary)";
|
||||
document.getElementById("wfm-settings-prompt-ai-test")?.addEventListener("click", async () => {
|
||||
const statusEl = document.getElementById("wfm-settings-prompt-ai-status");
|
||||
const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
|
||||
const url = (promptAiUrlEl?.value.trim() || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
|
||||
if (statusEl) { statusEl.textContent = t("aiStatusConnecting"); statusEl.style.color = "var(--wfm-text-secondary)"; }
|
||||
try {
|
||||
const res = await fetch("/api/wfm/ollama/test", { method: "POST" });
|
||||
const data = await res.json();
|
||||
statusEl.textContent = data.connected ? t("ollamaConnected") : `${t("ollamaFailed")}: ${data.message}`;
|
||||
statusEl.style.color = data.connected ? "var(--wfm-success)" : "var(--wfm-danger)";
|
||||
let count = 0;
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/tags`);
|
||||
count = ((await res.json()).models || []).length;
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
count = ((await res.json()).data || []).length;
|
||||
}
|
||||
if (statusEl) { statusEl.textContent = `${t("ollamaConnected")} (${count} models)`; statusEl.style.color = "var(--wfm-success)"; }
|
||||
await loadPromptAiModels();
|
||||
} catch (err) {
|
||||
statusEl.textContent = `${t("error")}: ${err.message}`;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
if (statusEl) { statusEl.textContent = `${t("ollamaFailed")}: ${err.message}`; statusEl.style.color = "var(--wfm-danger)"; }
|
||||
}
|
||||
});
|
||||
|
||||
// Save Ollama settings (to server)
|
||||
document.getElementById("wfm-settings-ollama-save")?.addEventListener("click", async () => {
|
||||
const ollamaUrl = document.getElementById("wfm-settings-ollama-url")?.value.trim() || "http://localhost:11434";
|
||||
const ollamaModel = document.getElementById("wfm-settings-ollama-model")?.value || "";
|
||||
document.getElementById("wfm-settings-prompt-ai-save")?.addEventListener("click", () => {
|
||||
const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
|
||||
const url = promptAiUrlEl?.value.trim() || "";
|
||||
const model = document.getElementById("wfm-settings-prompt-ai-model")?.value || "";
|
||||
savePromptAiCfg({ backend, backendUrl: url, model });
|
||||
showToast(t("ollamaSaved"), "success");
|
||||
});
|
||||
|
||||
// CivitAI host select
|
||||
document.getElementById("wfm-settings-civitai-host")?.addEventListener("change", async (e) => {
|
||||
const host = e.target.value;
|
||||
try {
|
||||
await saveServerSettings({ ollama_url: ollamaUrl, ollama_model: ollamaModel });
|
||||
serverSettings.ollama_url = ollamaUrl;
|
||||
serverSettings.ollama_model = ollamaModel;
|
||||
showToast(t("ollamaSaved"), "success");
|
||||
await saveServerSettings({ civitai_host: host });
|
||||
serverSettings.civitai_host = host;
|
||||
localStorage.setItem("wfm_civitai_host", host);
|
||||
showToast(t("civitaiHostSaved"), "success");
|
||||
} catch (err) {
|
||||
showToast(`${t("saveError")}: ${err.message}`, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Save CivitAI API key
|
||||
document.getElementById("wfm-settings-civitai-api-key-save")?.addEventListener("click", async () => {
|
||||
const apiKey = document.getElementById("wfm-settings-civitai-api-key")?.value.trim() || "";
|
||||
const statusEl = document.getElementById("wfm-settings-civitai-api-key-status");
|
||||
try {
|
||||
await saveServerSettings({ civitai_api_key: apiKey });
|
||||
serverSettings.civitai_api_key = apiKey;
|
||||
showToast(t("civitaiApiKeySaved"), "success");
|
||||
if (statusEl) { statusEl.textContent = ""; }
|
||||
} catch (err) {
|
||||
if (statusEl) {
|
||||
statusEl.textContent = `${t("saveError")}: ${err.message}`;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// Test ComfyUI URL
|
||||
document.getElementById("wfm-settings-test-url")?.addEventListener("click", async () => {
|
||||
const urlInput = document.getElementById("wfm-settings-comfyui-url");
|
||||
@@ -890,6 +1237,35 @@ export async function initSettingsTab() {
|
||||
statusEl.style.color = ok ? "var(--wfm-success)" : "var(--wfm-danger)";
|
||||
});
|
||||
|
||||
// Default Checkpoint
|
||||
const defaultCkptSelect = document.getElementById("wfm-settings-default-ckpt-select");
|
||||
|
||||
async function loadDefaultCkptOptions() {
|
||||
if (!defaultCkptSelect) return;
|
||||
try {
|
||||
const list = await comfyUI.fetchCheckpoints();
|
||||
const saved = serverSettings.default_checkpoint_name || "";
|
||||
defaultCkptSelect.innerHTML = `<option value="">${t("selectModel")}</option>` +
|
||||
(list || []).map((m) => `<option value="${m}" ${m === saved ? "selected" : ""}>${m}</option>`).join("");
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
loadDefaultCkptOptions();
|
||||
|
||||
document.getElementById("wfm-settings-default-ckpt-refresh")?.addEventListener("click", loadDefaultCkptOptions);
|
||||
|
||||
document.getElementById("wfm-settings-default-ckpt-save")?.addEventListener("click", async () => {
|
||||
const enabled = !!document.getElementById("wfm-settings-default-ckpt-enabled")?.checked;
|
||||
const ckptName = defaultCkptSelect?.value || "";
|
||||
try {
|
||||
await saveServerSettings({ default_checkpoint_enabled: enabled, default_checkpoint_name: ckptName });
|
||||
serverSettings.default_checkpoint_enabled = enabled;
|
||||
serverSettings.default_checkpoint_name = ckptName;
|
||||
showToast(t("aiToastSettingsSaved"), "success");
|
||||
} catch (err) {
|
||||
showToast(`${t("saveError")}: ${err.message}`, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Test Eagle URL (via server proxy to avoid CORS)
|
||||
document.getElementById("wfm-settings-test-eagle")?.addEventListener("click", async () => {
|
||||
const urlInput = document.getElementById("wfm-settings-eagle-url");
|
||||
@@ -918,7 +1294,7 @@ export async function initSettingsTab() {
|
||||
|
||||
// Clear default workflow
|
||||
document.getElementById("wfm-settings-clear-wf")?.addEventListener("click", () => {
|
||||
const s = JSON.parse(localStorage.getItem("wfm_settings") || "{}");
|
||||
const s = getSettings();
|
||||
delete s.defaultWorkflow;
|
||||
delete s.defaultWorkflowData;
|
||||
localStorage.setItem("wfm_settings", JSON.stringify(s));
|
||||
@@ -928,8 +1304,38 @@ export async function initSettingsTab() {
|
||||
showToast(t("defaultWorkflowCleared"), "success");
|
||||
});
|
||||
|
||||
// --- Textarea font size slider ---
|
||||
document.getElementById("wfm-ta-font-size")?.addEventListener("input", (e) => {
|
||||
const px = parseInt(e.target.value);
|
||||
const valEl = document.getElementById("wfm-ta-font-size-val");
|
||||
if (valEl) valEl.textContent = `${px}px`;
|
||||
applyTextareaFontSize(px);
|
||||
});
|
||||
|
||||
// --- JSON highlight color pickers ---
|
||||
document.getElementById("wfm-json-color-grid")?.addEventListener("input", (e) => {
|
||||
if (e.target.type !== "color") return;
|
||||
const id = e.target.dataset.jsonColor;
|
||||
const jsonColors = { ...(loadLocalSettings().jsonColors || {}), [id]: e.target.value };
|
||||
saveLocalSettings({ jsonColors });
|
||||
applyJsonColors(jsonColors);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-json-color-reset")?.addEventListener("click", () => {
|
||||
const cur = loadLocalSettings();
|
||||
delete cur.jsonColors;
|
||||
localStorage.setItem(SETTINGS_KEY, JSON.stringify(cur));
|
||||
applyJsonColors(undefined);
|
||||
document.querySelectorAll("#wfm-json-color-grid input[type=color]").forEach(inp => {
|
||||
const def = JSON_COLOR_DEFS.find(d => d.id === inp.dataset.jsonColor);
|
||||
if (def) inp.value = def.def;
|
||||
});
|
||||
showToast(t("settingsSaved"), "success");
|
||||
});
|
||||
|
||||
// Save (local settings)
|
||||
document.getElementById("wfm-settings-save")?.addEventListener("click", () => {
|
||||
const taFontSize = parseInt(document.getElementById("wfm-ta-font-size")?.value) || TEXTAREA_FONT_SIZE_DEFAULT;
|
||||
const patch = {
|
||||
comfyuiUrl: document.getElementById("wfm-settings-comfyui-url")?.value.trim() || "",
|
||||
eagleUrl: document.getElementById("wfm-settings-eagle-url")?.value.trim() || "http://localhost:41595",
|
||||
@@ -937,6 +1343,7 @@ export async function initSettingsTab() {
|
||||
uiLang: getLang(),
|
||||
summaryLang: getSummaryLang(),
|
||||
theme: document.documentElement.getAttribute("data-theme") || "",
|
||||
textareaFontSize: taFontSize,
|
||||
};
|
||||
|
||||
saveLocalSettings(patch);
|
||||
@@ -952,4 +1359,158 @@ export async function initSettingsTab() {
|
||||
if (savedUrl) {
|
||||
comfyUI.updateUrl(savedUrl);
|
||||
}
|
||||
|
||||
// --- Wildcard Integration ---
|
||||
async function wcLoadLinkStatus() {
|
||||
const el = document.getElementById("wfm-wildcard-link-status");
|
||||
if (!el) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/wildcards/link-status");
|
||||
const data = await res.json();
|
||||
wcRenderLinkStatus(el, data);
|
||||
} catch (e) {
|
||||
el.innerHTML = `<span style="color:var(--wfm-error,#f44336)">Error: ${e.message}</span>`;
|
||||
}
|
||||
}
|
||||
|
||||
function wcRenderLinkStatus(el, data) {
|
||||
if (!data.impact_pack_installed) {
|
||||
el.innerHTML = `
|
||||
<p style="margin:0 0 8px;">ComfyUI-Impact-Pack がインストールされていません。</p>
|
||||
<p style="margin:0;font-size:12px;color:var(--wfm-text-secondary);">
|
||||
ワイルドカードを Impact Pack と共有するには、先に
|
||||
<a href="https://github.com/ltdrdata/ComfyUI-Impact-Pack" target="_blank" rel="noopener"
|
||||
style="color:var(--wfm-accent);">ComfyUI-Impact-Pack</a>
|
||||
をインストールしてください。
|
||||
</p>`;
|
||||
return;
|
||||
}
|
||||
|
||||
if (data.is_linked) {
|
||||
el.innerHTML = `
|
||||
<p style="margin:0 0 4px;color:var(--wfm-success,#4caf50);">✓ リンク済み</p>
|
||||
<p style="margin:0 0 8px;font-size:12px;color:var(--wfm-text-secondary);">
|
||||
WFS wildcard → ${data.link_target || data.impact_pack_wildcards_dir}
|
||||
</p>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-wc-remove-link">リンクを解除</button>
|
||||
<div id="wfm-wc-link-msg" style="font-size:12px;margin-top:8px;"></div>`;
|
||||
document.getElementById("wfm-wc-remove-link")?.addEventListener("click", async () => {
|
||||
const msg = document.getElementById("wfm-wc-link-msg");
|
||||
try {
|
||||
const r = await fetch("/api/wfm/wildcards/remove-link", { method: "POST" });
|
||||
const d = await r.json();
|
||||
if (!r.ok) throw new Error(d.error || r.statusText);
|
||||
msg.textContent = "リンクを解除しました。";
|
||||
msg.style.color = "var(--wfm-success,#4caf50)";
|
||||
setTimeout(wcLoadLinkStatus, 800);
|
||||
} catch (e) {
|
||||
msg.textContent = "エラー: " + e.message;
|
||||
msg.style.color = "var(--wfm-error,#f44336)";
|
||||
}
|
||||
});
|
||||
} else {
|
||||
el.innerHTML = `
|
||||
<p style="margin:0 0 4px;">Impact Pack wildcards ディレクトリ:</p>
|
||||
<p style="margin:0 0 8px;font-size:12px;color:var(--wfm-text-secondary);">${data.impact_pack_wildcards_dir}</p>
|
||||
<p style="margin:0 0 8px;font-size:12px;color:var(--wfm-text-secondary);">
|
||||
WFS の wildcard ディレクトリを Impact Pack の wildcards ディレクトリへのリンクに置き換えます。
|
||||
既存の WFS ワイルドカードファイルは Impact Pack ディレクトリへ移行されます。
|
||||
</p>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-wc-create-link">リンクを作成</button>
|
||||
<div id="wfm-wc-link-msg" style="font-size:12px;margin-top:8px;"></div>`;
|
||||
document.getElementById("wfm-wc-create-link")?.addEventListener("click", async () => {
|
||||
const msg = document.getElementById("wfm-wc-link-msg");
|
||||
try {
|
||||
const r = await fetch("/api/wfm/wildcards/create-link", { method: "POST" });
|
||||
const d = await r.json();
|
||||
if (!r.ok) throw new Error(d.error || r.statusText);
|
||||
const migrated = d.migrated_files?.length
|
||||
? ` (移行: ${d.migrated_files.join(", ")})`
|
||||
: "";
|
||||
msg.textContent = "リンクを作成しました。" + migrated;
|
||||
msg.style.color = "var(--wfm-success,#4caf50)";
|
||||
setTimeout(wcLoadLinkStatus, 800);
|
||||
} catch (e) {
|
||||
msg.textContent = "エラー: " + e.message;
|
||||
msg.style.color = "var(--wfm-error,#f44336)";
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Load wildcard link status when section is opened
|
||||
document.getElementById("wfm-wildcard-integration-section")?.addEventListener("toggle", (e) => {
|
||||
if (e.target.open) wcLoadLinkStatus();
|
||||
});
|
||||
|
||||
// --- Data Export ---
|
||||
document.getElementById("wfm-settings-export")?.addEventListener("click", async () => {
|
||||
const statusEl = document.getElementById("wfm-settings-data-status");
|
||||
try {
|
||||
const res = await fetch("/api/wfm/settings/export");
|
||||
if (!res.ok) throw new Error(await res.text());
|
||||
const blob = await res.blob();
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement("a");
|
||||
a.href = url;
|
||||
a.download = "wfm-data-export.json";
|
||||
a.click();
|
||||
URL.revokeObjectURL(url);
|
||||
statusEl.textContent = t("exportSuccess");
|
||||
statusEl.style.color = "var(--wfm-success, #4caf50)";
|
||||
} catch (e) {
|
||||
statusEl.textContent = t("exportError") + ": " + e.message;
|
||||
statusEl.style.color = "var(--wfm-error, #f44336)";
|
||||
}
|
||||
});
|
||||
|
||||
// --- Data Import ---
|
||||
document.getElementById("wfm-settings-import-file")?.addEventListener("change", async (e) => {
|
||||
const statusEl = document.getElementById("wfm-settings-data-status");
|
||||
const file = e.target.files?.[0];
|
||||
if (!file) return;
|
||||
try {
|
||||
const text = await file.text();
|
||||
const bundle = JSON.parse(text);
|
||||
const res = await fetch("/api/wfm/settings/import", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(bundle),
|
||||
});
|
||||
const result = await res.json();
|
||||
if (!res.ok) throw new Error(result.error || res.statusText);
|
||||
const imported = result.imported?.join(", ") || "-";
|
||||
statusEl.textContent = t("importSuccess") + ": " + imported;
|
||||
statusEl.style.color = "var(--wfm-success, #4caf50)";
|
||||
} catch (e) {
|
||||
statusEl.textContent = t("importError") + ": " + e.message;
|
||||
statusEl.style.color = "var(--wfm-error, #f44336)";
|
||||
}
|
||||
// Reset file input so same file can be re-selected
|
||||
e.target.value = "";
|
||||
});
|
||||
|
||||
// Save G'MIC path setting
|
||||
document.getElementById("wfm-settings-gmic-path-save")?.addEventListener("click", async () => {
|
||||
const path = document.getElementById("wfm-settings-gmic-path")?.value.trim() || "";
|
||||
const statusEl = document.getElementById("wfm-settings-gmic-path-status");
|
||||
if (statusEl) {
|
||||
statusEl.textContent = "Saving...";
|
||||
statusEl.style.color = "var(--wfm-text-secondary)";
|
||||
}
|
||||
try {
|
||||
await saveServerSettings({ gmic_qt_path: path });
|
||||
serverSettings.gmic_qt_path = path;
|
||||
showToast("G'MIC path saved successfully", "success");
|
||||
if (statusEl) {
|
||||
statusEl.textContent = "Saved successfully!";
|
||||
statusEl.style.color = "var(--wfm-success)";
|
||||
}
|
||||
} catch (err) {
|
||||
if (statusEl) {
|
||||
statusEl.textContent = "Error: " + err.message;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
@@ -0,0 +1,309 @@
|
||||
/**
|
||||
* Style Catalog Gallery — Gallery タブのサブタブ。
|
||||
* ws_style_catalog フォルダ配下の画像（GenerateUIの「カタログ作成」で生成した、
|
||||
* ファイル名=スタイル名のプレビュー画像）を閲覧し、埋め込みPositive/Negative
|
||||
* プロンプトを表示・コピーする。「Select as Style」でGenerateUIの登録済み
|
||||
* Styleドロップダウンをファイル名と同名のStyleに切り替える。
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
import { extractAllMetadata } from "./metadata-tab.js";
|
||||
import { selectStyleByName } from "./generate-tab.js";
|
||||
|
||||
const API = {
|
||||
root: () => `/wfm/gallery/style-catalog/root`,
|
||||
folders: (root) => `/wfm/gallery/folders?root=${encodeURIComponent(root)}`,
|
||||
images: (params) => `/wfm/gallery/images?${new URLSearchParams(params)}`,
|
||||
thumb: (path, w = 200) => `/wfm/gallery/image/thumb?path=${encodeURIComponent(path)}&w=${w}`,
|
||||
serveImage: (path) => `/wfm/gallery/image/serve?path=${encodeURIComponent(path)}`,
|
||||
};
|
||||
|
||||
const state = {
|
||||
root: null,
|
||||
currentFolder: null,
|
||||
search: "",
|
||||
images: [],
|
||||
selectedImage: null,
|
||||
};
|
||||
|
||||
let _initialized = false;
|
||||
|
||||
async function apiFetch(url, opts = {}) {
|
||||
const res = await fetch(url, opts);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
function debounce(fn, wait) {
|
||||
let timer;
|
||||
return (...args) => {
|
||||
clearTimeout(timer);
|
||||
timer = setTimeout(() => fn(...args), wait);
|
||||
};
|
||||
}
|
||||
|
||||
// ── 初期化 ────────────────────────────────────────────────────
|
||||
|
||||
export function initStyleCatalogTab() {
|
||||
document.getElementById("wfm-stylecatalog-refresh-btn")?.addEventListener("click", () => {
|
||||
loadFolderTree();
|
||||
loadImages();
|
||||
});
|
||||
|
||||
setupSearchClearBtn("wfm-stylecatalog-search", "wfm-stylecatalog-search-clear-btn", () => {
|
||||
state.search = "";
|
||||
loadImages();
|
||||
});
|
||||
|
||||
const searchInput = document.getElementById("wfm-stylecatalog-search");
|
||||
searchInput?.addEventListener("input", debounce(() => {
|
||||
state.search = searchInput.value.trim();
|
||||
loadImages();
|
||||
}, 300));
|
||||
|
||||
document.getElementById("wfm-stylecatalog-copy-positive-btn")?.addEventListener("click", () => copyText("wfm-stylecatalog-positive-text"));
|
||||
document.getElementById("wfm-stylecatalog-copy-negative-btn")?.addEventListener("click", () => copyText("wfm-stylecatalog-negative-text"));
|
||||
document.getElementById("wfm-stylecatalog-select-style-btn")?.addEventListener("click", onSelectStyleClick);
|
||||
}
|
||||
|
||||
export async function activateStyleCatalogTab() {
|
||||
if (_initialized) return;
|
||||
_initialized = true;
|
||||
try {
|
||||
const data = await apiFetch(API.root());
|
||||
state.root = data.root;
|
||||
await loadFolderTree();
|
||||
} catch (e) {
|
||||
const tree = document.getElementById("wfm-stylecatalog-tree");
|
||||
if (tree) tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
// ── フォルダツリー ────────────────────────────────────────────
|
||||
|
||||
async function loadFolderTree() {
|
||||
if (!state.root) return;
|
||||
const tree = document.getElementById("wfm-stylecatalog-tree");
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
try {
|
||||
const data = await apiFetch(API.folders(state.root));
|
||||
if (data.error) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${escapeHtml(data.error)}</p>`;
|
||||
return;
|
||||
}
|
||||
tree.innerHTML = "";
|
||||
renderTreeNode(data, tree, 0, true);
|
||||
const firstLabel = tree.querySelector(".wfm-gallery-tree-label");
|
||||
if (firstLabel) firstLabel.click();
|
||||
} catch (e) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderTreeNode(node, container, depth, isRoot) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-gallery-tree-item";
|
||||
item.style.paddingLeft = `${depth * 12}px`;
|
||||
item.dataset.path = node.abs_path;
|
||||
|
||||
const hasChildren = node.children && node.children.length > 0;
|
||||
const absPath = node.abs_path;
|
||||
|
||||
const arrow = document.createElement("span");
|
||||
arrow.className = "wfm-gallery-tree-arrow";
|
||||
if (hasChildren) {
|
||||
arrow.textContent = "▶";
|
||||
arrow.dataset.expanded = "false";
|
||||
} else {
|
||||
arrow.textContent = " ";
|
||||
arrow.style.visibility = "hidden";
|
||||
}
|
||||
item.appendChild(arrow);
|
||||
|
||||
const label = document.createElement("span");
|
||||
label.className = "wfm-gallery-tree-label";
|
||||
label.textContent = isRoot ? "[root]" : node.name;
|
||||
const totalCount = node.image_count_total ?? node.image_count;
|
||||
if (totalCount > 0) {
|
||||
const badge = document.createElement("span");
|
||||
badge.className = "wfm-gallery-tree-count";
|
||||
badge.textContent = totalCount;
|
||||
label.appendChild(badge);
|
||||
}
|
||||
item.appendChild(label);
|
||||
|
||||
label.addEventListener("click", () => {
|
||||
document.querySelectorAll("#wfm-stylecatalog-tree .wfm-gallery-tree-item.selected").forEach(el => el.classList.remove("selected"));
|
||||
item.classList.add("selected");
|
||||
state.currentFolder = absPath;
|
||||
loadImages();
|
||||
});
|
||||
|
||||
if (hasChildren) {
|
||||
const childContainer = document.createElement("div");
|
||||
childContainer.className = "wfm-gallery-tree-children";
|
||||
childContainer.style.display = "none";
|
||||
|
||||
arrow.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const expanded = arrow.dataset.expanded === "true";
|
||||
if (!expanded) {
|
||||
arrow.dataset.expanded = "true";
|
||||
arrow.textContent = "▼";
|
||||
childContainer.style.display = "";
|
||||
if (childContainer.children.length === 0) {
|
||||
node.children.forEach(child => renderTreeNode(child, childContainer, depth + 1, false));
|
||||
}
|
||||
} else {
|
||||
arrow.dataset.expanded = "false";
|
||||
arrow.textContent = "▶";
|
||||
childContainer.style.display = "none";
|
||||
}
|
||||
});
|
||||
|
||||
container.appendChild(item);
|
||||
container.appendChild(childContainer);
|
||||
} else {
|
||||
container.appendChild(item);
|
||||
}
|
||||
}
|
||||
|
||||
// ── 画像一覧 ─────────────────────────────────────────────────
|
||||
|
||||
async function loadImages() {
|
||||
if (!state.currentFolder) return;
|
||||
const grid = document.getElementById("wfm-stylecatalog-grid");
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
|
||||
// 上位フォルダを選択した場合もサブフォルダの画像をまとめて表示する
|
||||
const params = { folder: state.currentFolder, sort: "name_asc", recursive: "true" };
|
||||
if (state.search) params.search = state.search;
|
||||
|
||||
try {
|
||||
const data = await apiFetch(API.images(params));
|
||||
state.images = data.images || [];
|
||||
document.getElementById("wfm-stylecatalog-count").textContent = `${state.images.length} images`;
|
||||
renderImages();
|
||||
} catch (e) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderImages() {
|
||||
const grid = document.getElementById("wfm-stylecatalog-grid");
|
||||
if (state.images.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("galleryNoImages")}</p>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
const fragment = document.createDocumentFragment();
|
||||
state.images.forEach(img => fragment.appendChild(createThumbCard(img)));
|
||||
grid.appendChild(fragment);
|
||||
}
|
||||
|
||||
function createThumbCard(img) {
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-gallery-thumb-card";
|
||||
card.title = img.filename;
|
||||
card.dataset.path = img.path;
|
||||
if (state.selectedImage && state.selectedImage.path === img.path) {
|
||||
card.classList.add("selected");
|
||||
}
|
||||
|
||||
const imgEl = document.createElement("img");
|
||||
imgEl.className = "wfm-gallery-thumb-img";
|
||||
imgEl.loading = "lazy";
|
||||
imgEl.src = API.thumb(img.path);
|
||||
imgEl.alt = img.filename;
|
||||
imgEl.onerror = () => {
|
||||
imgEl.style.display = "none";
|
||||
const placeholder = document.createElement("div");
|
||||
placeholder.className = "wfm-gallery-thumb-placeholder";
|
||||
placeholder.textContent = "?";
|
||||
card.insertBefore(placeholder, imgEl.nextSibling);
|
||||
};
|
||||
card.appendChild(imgEl);
|
||||
|
||||
card.addEventListener("click", () => selectImage(img));
|
||||
|
||||
return card;
|
||||
}
|
||||
|
||||
// ── 選択画像 / Positive・Negative表示 ─────────────────────────
|
||||
|
||||
function styleNameFromFilename(filename) {
|
||||
return filename.replace(/\.[^.]+$/, "");
|
||||
}
|
||||
|
||||
async function selectImage(img) {
|
||||
state.selectedImage = img;
|
||||
document.querySelectorAll("#wfm-stylecatalog-grid .wfm-gallery-thumb-card").forEach(el => {
|
||||
el.classList.toggle("selected", el.dataset.path === img.path);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-stylecatalog-selected-name").textContent = img.filename;
|
||||
const preview = document.getElementById("wfm-stylecatalog-selected-preview");
|
||||
preview.innerHTML = "";
|
||||
const previewImg = document.createElement("img");
|
||||
previewImg.src = API.serveImage(img.path);
|
||||
previewImg.alt = img.filename;
|
||||
previewImg.style.cssText = "max-width:100%;max-height:100%;object-fit:contain;";
|
||||
preview.appendChild(previewImg);
|
||||
|
||||
const positiveText = document.getElementById("wfm-stylecatalog-positive-text");
|
||||
const negativeText = document.getElementById("wfm-stylecatalog-negative-text");
|
||||
const copyPositiveBtn = document.getElementById("wfm-stylecatalog-copy-positive-btn");
|
||||
const copyNegativeBtn = document.getElementById("wfm-stylecatalog-copy-negative-btn");
|
||||
const selectStyleBtn = document.getElementById("wfm-stylecatalog-select-style-btn");
|
||||
|
||||
positiveText.textContent = "";
|
||||
negativeText.textContent = "";
|
||||
copyPositiveBtn.disabled = true;
|
||||
copyNegativeBtn.disabled = true;
|
||||
selectStyleBtn.disabled = false; // ファイル名からのStyle選択はメタデータ抽出に依存しない
|
||||
|
||||
try {
|
||||
const res = await fetch(API.serveImage(img.path));
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const blob = await res.blob();
|
||||
const file = new File([blob], img.filename, { type: blob.type || "image/png" });
|
||||
const meta = await extractAllMetadata(file);
|
||||
|
||||
const positive = meta?.positives?.[0] || "";
|
||||
const negative = meta?.negatives?.[0] || "";
|
||||
|
||||
if (!positive && !negative) {
|
||||
positiveText.textContent = t("stylecatalogNoPromptFound");
|
||||
} else {
|
||||
positiveText.textContent = positive;
|
||||
negativeText.textContent = negative;
|
||||
copyPositiveBtn.disabled = !positive;
|
||||
copyNegativeBtn.disabled = !negative;
|
||||
}
|
||||
} catch (e) {
|
||||
positiveText.textContent = t("stylecatalogNoPromptFound");
|
||||
}
|
||||
}
|
||||
|
||||
function copyText(elementId) {
|
||||
const el = document.getElementById(elementId);
|
||||
const text = el?.textContent || "";
|
||||
if (!text.trim()) return;
|
||||
navigator.clipboard.writeText(text)
|
||||
.then(() => showToast(t("imagePromptCopiedToast"), "success"))
|
||||
.catch((e) => showToast(t("errorWithMsg", e.message), "error"));
|
||||
}
|
||||
|
||||
function onSelectStyleClick() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
const name = styleNameFromFilename(img.filename);
|
||||
const ok = selectStyleByName(name);
|
||||
if (ok) {
|
||||
showToast(t("stylecatalogStyleSelected", name), "success");
|
||||
} else {
|
||||
showToast(t("stylecatalogNoMatch"), "error");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,667 @@
|
||||
/**
|
||||
* Tagger Tab
|
||||
* Handles WD Tagger / DeepDanbooru + Ollama VLM single/batch tagging.
|
||||
*/
|
||||
|
||||
import { t } from "./i18n.js";
|
||||
import { showToast } from "./app.js";
|
||||
|
||||
// ── 状態 ────────────────────────────────────────────────────────
|
||||
|
||||
const state = {
|
||||
/** @type {string|null} 現在の画像 base64 (data URI prefix を除く) */
|
||||
imageB64: null,
|
||||
/** @type {string} 元のファイルパス (Galleryから来た場合) */
|
||||
imagePath: "",
|
||||
/** @type {string} 表示名 */
|
||||
imageName: "",
|
||||
/** @type {NodeJS.Timeout|null} バッチポーリングタイマー */
|
||||
batchPollTimer: null,
|
||||
/** @type {number|null} DB編集中の行ID */
|
||||
dbEditId: null,
|
||||
};
|
||||
|
||||
// ── 初期化 ──────────────────────────────────────────────────────
|
||||
|
||||
export function initTaggerTab() {
|
||||
_applyI18n();
|
||||
_setupSubtabs();
|
||||
_setupSliders();
|
||||
_setupOllamaToggle();
|
||||
_setupSingleActions();
|
||||
_setupBatchActions();
|
||||
_setupDbActions();
|
||||
_loadModels();
|
||||
_loadSettings();
|
||||
}
|
||||
|
||||
/** GalleryタブからTaggerタブへ画像を渡す */
|
||||
export function openImageInTaggerTab(img) {
|
||||
// タブを切り替え
|
||||
document.querySelectorAll(".wfm-tab").forEach(b => b.classList.remove("active"));
|
||||
document.querySelectorAll(".wfm-tab-content").forEach(s => s.classList.remove("active"));
|
||||
const btn = document.querySelector('.wfm-tab[data-tab="tagger"]');
|
||||
const sec = document.getElementById("wfm-tab-tagger");
|
||||
if (btn) btn.classList.add("active");
|
||||
if (sec) sec.classList.add("active");
|
||||
|
||||
// 画像を読み込む (Gallery APIから取得)
|
||||
if (!img?.path) return;
|
||||
const url = `/wfm/gallery/image/serve?path=${encodeURIComponent(img.path)}`;
|
||||
fetch(url)
|
||||
.then(r => {
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return r.blob();
|
||||
})
|
||||
.then(blob => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = e => {
|
||||
_setPreviewFromDataUrl(e.target.result, img.filename || img.path.split(/[\\/]/).pop());
|
||||
state.imagePath = img.path;
|
||||
};
|
||||
reader.readAsDataURL(blob);
|
||||
})
|
||||
.catch(() => showToast(t("taggerNoImage"), "error"));
|
||||
}
|
||||
|
||||
// ── i18n ─────────────────────────────────────────────────────────
|
||||
|
||||
function _applyI18n() {
|
||||
const map = {
|
||||
"wfm-tagger-from-gallery-btn": "taggerFromGallery",
|
||||
"wfm-tagger-upload-label": "taggerUpload",
|
||||
"wfm-tagger-threshold-label": "taggerThreshold",
|
||||
"wfm-tagger-char-threshold-label": "taggerCharThreshold",
|
||||
"wfm-tagger-generate-btn": "taggerGenerateTags",
|
||||
"wfm-tagger-ollama-header-label": "taggerOllamaEnable",
|
||||
"wfm-tagger-ollama-url-label": "taggerOllamaUrl",
|
||||
"wfm-tagger-ollama-model-label": "taggerOllamaModel",
|
||||
"wfm-tagger-ollama-prompt-label": "taggerOllamaPrompt",
|
||||
"wfm-tagger-ollama-max-label": "taggerOllamaMaxTags",
|
||||
"wfm-tagger-result-header": "taggerResult",
|
||||
"wfm-tagger-send-genui-btn": "taggerSendToGenUI",
|
||||
"wfm-tagger-send-prompt-btn": "taggerSendToPrompt",
|
||||
"wfm-tagger-save-gallery-btn": "taggerSaveToGallery",
|
||||
"wfm-tagger-write-file-btn": "taggerWriteToFile",
|
||||
"wfm-tagger-save-db-btn": "taggerSaveToDB",
|
||||
"wfm-tagger-batch-folder-header": "taggerBatchFolder",
|
||||
"wfm-tagger-batch-threshold-label": "taggerThreshold",
|
||||
"wfm-tagger-batch-char-threshold-label": "taggerCharThreshold",
|
||||
"wfm-tagger-batch-url-label": "taggerOllamaUrl",
|
||||
"wfm-tagger-batch-model-label": "taggerOllamaModel",
|
||||
"wfm-tagger-batch-prompt-label": "taggerOllamaPrompt",
|
||||
"wfm-tagger-batch-output-header": "taggerBatchOutput",
|
||||
"wfm-tagger-batch-save-db-label": "taggerSaveToDB",
|
||||
"wfm-tagger-batch-write-file-label": "taggerWriteToFile",
|
||||
"wfm-tagger-batch-write-txt-label": "taggerBatchWriteTxt",
|
||||
"wfm-tagger-batch-start-btn": "taggerBatchStart",
|
||||
"wfm-tagger-batch-stop-btn": "taggerBatchStop",
|
||||
"wfm-tagger-db-search-btn": "taggerDbSearch",
|
||||
"wfm-tagger-db-show-all-btn": "taggerDbShowAll",
|
||||
"wfm-tagger-db-export-btn": "taggerDbExport",
|
||||
"wfm-tagger-db-th-file": "taggerDbFile",
|
||||
"wfm-tagger-db-th-interrogator": "taggerDbInterrogator",
|
||||
"wfm-tagger-db-th-vlm": "taggerDbVlm",
|
||||
"wfm-tagger-db-th-date": "taggerDbDate",
|
||||
"wfm-tagger-db-edit-interr-label": "taggerDbInterrogator",
|
||||
"wfm-tagger-db-edit-vlm-label": "taggerDbVlm",
|
||||
"wfm-tagger-db-save-btn": "taggerDbSave",
|
||||
"wfm-tagger-db-delete-btn": "taggerDbDelete",
|
||||
};
|
||||
for (const [id, key] of Object.entries(map)) {
|
||||
const el = document.getElementById(id);
|
||||
if (el) el.textContent = t(key);
|
||||
}
|
||||
|
||||
// サブタブボタン
|
||||
const subtabKeys = ["taggerSingleTab", "taggerBatchTab", "taggerDbTab"];
|
||||
document.querySelectorAll(".wfm-tagger-subtab-btn").forEach((btn, i) => {
|
||||
btn.textContent = t(subtabKeys[i] || "");
|
||||
});
|
||||
|
||||
// プレースホルダー
|
||||
const ph = document.getElementById("wfm-tagger-preview-placeholder");
|
||||
if (ph) ph.textContent = t("taggerNoImage");
|
||||
}
|
||||
|
||||
// ── サブタブ切り替え ─────────────────────────────────────────────
|
||||
|
||||
function _setupSubtabs() {
|
||||
document.querySelectorAll(".wfm-tagger-subtab-btn").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
document.querySelectorAll(".wfm-tagger-subtab-btn").forEach(b => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
const tab = btn.dataset.taggerTab;
|
||||
document.querySelectorAll(".wfm-tagger-subtab").forEach(s => {
|
||||
s.style.display = s.id === `wfm-tagger-${tab}` ? "" : "none";
|
||||
});
|
||||
if (tab === "db") _dbLoad();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ── スライダー ───────────────────────────────────────────────────
|
||||
|
||||
function _setupSliders() {
|
||||
[
|
||||
["wfm-tagger-threshold", "wfm-tagger-threshold-val"],
|
||||
["wfm-tagger-char-threshold", "wfm-tagger-char-threshold-val"],
|
||||
["wfm-tagger-batch-threshold", "wfm-tagger-batch-threshold-val"],
|
||||
["wfm-tagger-batch-char-threshold", "wfm-tagger-batch-char-threshold-val"],
|
||||
].forEach(([sliderId, valId]) => {
|
||||
const slider = document.getElementById(sliderId);
|
||||
const val = document.getElementById(valId);
|
||||
if (!slider || !val) return;
|
||||
val.textContent = slider.value;
|
||||
slider.addEventListener("input", () => { val.textContent = slider.value; });
|
||||
});
|
||||
}
|
||||
|
||||
// ── Ollama トグル ────────────────────────────────────────────────
|
||||
|
||||
function _setupOllamaToggle() {
|
||||
const chk = document.getElementById("wfm-tagger-ollama-enable");
|
||||
const wrap = document.getElementById("wfm-tagger-ollama-settings");
|
||||
if (chk && wrap) {
|
||||
chk.addEventListener("change", () => {
|
||||
wrap.style.display = chk.checked ? "" : "none";
|
||||
});
|
||||
wrap.style.display = chk.checked ? "" : "none";
|
||||
}
|
||||
}
|
||||
|
||||
// ── モデル一覧ロード ─────────────────────────────────────────────
|
||||
|
||||
async function _loadModels() {
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/models");
|
||||
const data = await res.json();
|
||||
const models = data.models || [];
|
||||
["wfm-tagger-model", "wfm-tagger-batch-model"].forEach(selId => {
|
||||
const sel = document.getElementById(selId);
|
||||
if (!sel) return;
|
||||
sel.innerHTML = `<option value="">${t("taggerNoModel")}</option>` +
|
||||
models.map(m => `<option value="${m.name}">[${m.type}] ${m.name}</option>`).join("");
|
||||
});
|
||||
} catch (e) {
|
||||
console.warn("tagger: _loadModels error", e);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Ollamaモデル一覧ロード ───────────────────────────────────────
|
||||
|
||||
async function _loadOllamaModels(urlId, selId) {
|
||||
const urlEl = document.getElementById(urlId);
|
||||
const selEl = document.getElementById(selId);
|
||||
if (!urlEl || !selEl) return;
|
||||
const api = urlEl.value || "http://127.0.0.1:11434";
|
||||
try {
|
||||
const res = await fetch(`/wfm/tagger/ollama/models?api_url=${encodeURIComponent(api)}`);
|
||||
const data = await res.json();
|
||||
const models = data.models || [];
|
||||
if (models.length === 0) {
|
||||
selEl.innerHTML = `<option value="">(${t("taggerNoModel")})</option>`;
|
||||
} else {
|
||||
selEl.innerHTML = models.map(m => `<option value="${m}">${m}</option>`).join("");
|
||||
}
|
||||
} catch {
|
||||
selEl.innerHTML = `<option value="">(${t("taggerNoModel")})</option>`;
|
||||
}
|
||||
}
|
||||
|
||||
// ── 設定の読み込み/保存 ─────────────────────────────────────────
|
||||
|
||||
async function _loadSettings() {
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/settings");
|
||||
const data = await res.json();
|
||||
if (data.ollama_url) {
|
||||
const el = document.getElementById("wfm-tagger-ollama-url");
|
||||
if (el) el.value = data.ollama_url;
|
||||
}
|
||||
if (data.batch_ollama_url) {
|
||||
const el = document.getElementById("wfm-tagger-batch-ollama-url");
|
||||
if (el) el.value = data.batch_ollama_url;
|
||||
}
|
||||
} catch {
|
||||
// 初回は無視
|
||||
}
|
||||
}
|
||||
|
||||
async function _saveSettings() {
|
||||
try {
|
||||
await fetch("/wfm/tagger/settings", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
ollama_url: document.getElementById("wfm-tagger-ollama-url")?.value || "",
|
||||
batch_ollama_url: document.getElementById("wfm-tagger-batch-ollama-url")?.value || "",
|
||||
}),
|
||||
});
|
||||
} catch {
|
||||
// サイレントに失敗
|
||||
}
|
||||
}
|
||||
|
||||
// ── Single サブタブ ─────────────────────────────────────────────
|
||||
|
||||
function _setupSingleActions() {
|
||||
// ファイルアップロード
|
||||
document.getElementById("wfm-tagger-upload")?.addEventListener("change", e => {
|
||||
const file = e.target.files?.[0];
|
||||
if (!file) return;
|
||||
_loadImageFile(file);
|
||||
e.target.value = "";
|
||||
});
|
||||
|
||||
// ドラッグ＆ドロップ
|
||||
const dropZone = document.getElementById("wfm-tagger-drop-zone");
|
||||
if (dropZone) {
|
||||
dropZone.addEventListener("dragover", e => {
|
||||
e.preventDefault();
|
||||
dropZone.classList.add("drag-over");
|
||||
});
|
||||
dropZone.addEventListener("dragleave", e => {
|
||||
if (!dropZone.contains(e.relatedTarget)) {
|
||||
dropZone.classList.remove("drag-over");
|
||||
}
|
||||
});
|
||||
dropZone.addEventListener("drop", e => {
|
||||
e.preventDefault();
|
||||
dropZone.classList.remove("drag-over");
|
||||
const file = e.dataTransfer.files?.[0];
|
||||
if (file && file.type.startsWith("image/")) {
|
||||
_loadImageFile(file);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// タグ生成
|
||||
document.getElementById("wfm-tagger-generate-btn")?.addEventListener("click", _generateTags);
|
||||
|
||||
// Ollamaリフレッシュ
|
||||
document.getElementById("wfm-tagger-ollama-refresh-btn")?.addEventListener("click", () => {
|
||||
_loadOllamaModels("wfm-tagger-ollama-url", "wfm-tagger-ollama-model");
|
||||
});
|
||||
|
||||
// 結果アクション
|
||||
document.getElementById("wfm-tagger-send-genui-btn")?.addEventListener("click", _sendToGenUI);
|
||||
document.getElementById("wfm-tagger-send-prompt-btn")?.addEventListener("click", _sendToPrompt);
|
||||
document.getElementById("wfm-tagger-save-gallery-btn")?.addEventListener("click", _saveToGallery);
|
||||
document.getElementById("wfm-tagger-write-file-btn")?.addEventListener("click", _writeToFile);
|
||||
document.getElementById("wfm-tagger-save-db-btn")?.addEventListener("click", _saveToDb);
|
||||
}
|
||||
|
||||
function _loadImageFile(file) {
|
||||
const reader = new FileReader();
|
||||
reader.onload = e => {
|
||||
_setPreviewFromDataUrl(e.target.result, file.name);
|
||||
state.imagePath = "";
|
||||
};
|
||||
reader.readAsDataURL(file);
|
||||
}
|
||||
|
||||
function _setPreviewFromDataUrl(dataUrl, name) {
|
||||
state.imageB64 = dataUrl.replace(/^data:[^;]+;base64,/, "");
|
||||
state.imageName = name;
|
||||
const img = document.getElementById("wfm-tagger-preview");
|
||||
const ph = document.getElementById("wfm-tagger-preview-placeholder");
|
||||
const nameEl = document.getElementById("wfm-tagger-image-name");
|
||||
if (img) { img.src = dataUrl; img.style.display = ""; }
|
||||
if (ph) ph.style.display = "none";
|
||||
if (nameEl) nameEl.textContent = name;
|
||||
}
|
||||
|
||||
async function _generateTags() {
|
||||
if (!state.imageB64) {
|
||||
showToast(t("taggerNoImage"), "warning");
|
||||
return;
|
||||
}
|
||||
const model = document.getElementById("wfm-tagger-model")?.value || "";
|
||||
const threshold = parseFloat(document.getElementById("wfm-tagger-threshold")?.value || "0.35");
|
||||
const charThreshold = parseFloat(document.getElementById("wfm-tagger-char-threshold")?.value || "0.85");
|
||||
const useOllama = document.getElementById("wfm-tagger-ollama-enable")?.checked || false;
|
||||
const ollamaUrl = document.getElementById("wfm-tagger-ollama-url")?.value || "http://127.0.0.1:11434";
|
||||
const ollamaModel = document.getElementById("wfm-tagger-ollama-model")?.value || "";
|
||||
const ollamaPrompt = document.getElementById("wfm-tagger-ollama-prompt")?.value || "";
|
||||
const maxTags = parseInt(document.getElementById("wfm-tagger-ollama-max-tags")?.value || "40");
|
||||
|
||||
const generateBtn = document.getElementById("wfm-tagger-generate-btn");
|
||||
if (generateBtn) { generateBtn.disabled = true; generateBtn.textContent = "..."; }
|
||||
|
||||
try {
|
||||
let tags = "";
|
||||
|
||||
if (model) {
|
||||
const res = await fetch("/wfm/tagger/predict", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ image_b64: state.imageB64, model, threshold, char_threshold: charThreshold }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
tags = data.tags || "";
|
||||
}
|
||||
|
||||
if (useOllama && ollamaModel) {
|
||||
const res2 = await fetch("/wfm/tagger/ollama/predict", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
image_b64: state.imageB64, api_url: ollamaUrl,
|
||||
model: ollamaModel, prompt: ollamaPrompt, max_tags: maxTags,
|
||||
}),
|
||||
});
|
||||
const data2 = await res2.json();
|
||||
if (data2.error) throw new Error(data2.error);
|
||||
const vlm = data2.tags || "";
|
||||
tags = [tags, vlm].filter(Boolean).join(", ");
|
||||
}
|
||||
|
||||
if (!tags) { showToast(t("taggerNoTags"), "warning"); return; }
|
||||
document.getElementById("wfm-tagger-result").value = tags;
|
||||
showToast(t("taggerDone"), "success");
|
||||
_saveSettings();
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
} finally {
|
||||
if (generateBtn) { generateBtn.disabled = false; generateBtn.textContent = t("taggerGenerateTags"); }
|
||||
}
|
||||
}
|
||||
|
||||
function _sendToGenUI() {
|
||||
const tags = document.getElementById("wfm-tagger-result")?.value || "";
|
||||
if (!tags) { showToast(t("taggerNoTags"), "warning"); return; }
|
||||
const ta = document.getElementById("wfm-prompt-pos-text");
|
||||
if (!ta) { showToast(t("taggerNoGenUI"), "warning"); return; }
|
||||
const cur = ta.value;
|
||||
ta.value = cur ? `${cur}, ${tags}` : tags;
|
||||
ta.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
document.getElementById("wfm-prompt-pos-apply")?.click();
|
||||
showToast(t("taggerSentToGenUI"), "success");
|
||||
}
|
||||
|
||||
function _sendToPrompt() {
|
||||
const tags = document.getElementById("wfm-tagger-result")?.value || "";
|
||||
if (!tags) { showToast(t("taggerNoTags"), "warning"); return; }
|
||||
const ta = document.getElementById("wfm-preset-pos");
|
||||
if (!ta) { showToast(t("taggerNoGalleryImage"), "warning"); return; }
|
||||
const cur = ta.value;
|
||||
ta.value = cur ? `${cur}, ${tags}` : tags;
|
||||
ta.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
showToast(t("taggerSentToPrompt"), "success");
|
||||
}
|
||||
|
||||
async function _saveToGallery() {
|
||||
const tags = document.getElementById("wfm-tagger-result")?.value || "";
|
||||
if (!tags || !state.imagePath) {
|
||||
showToast(t("taggerNoGalleryImage"), "warning");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const tagsArray = tags.split(",").map(s => s.trim()).filter(Boolean);
|
||||
const res = await fetch("/wfm/gallery/image/meta", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path: state.imagePath, tags: tagsArray }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
showToast(t("taggerSavedToGallery"), "success");
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function _writeToFile() {
|
||||
const tags = document.getElementById("wfm-tagger-result")?.value || "";
|
||||
if (!tags || !state.imagePath) {
|
||||
showToast(t("taggerNoGalleryImage"), "warning");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/write_meta", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path: state.imagePath, tags }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
showToast(t("taggerWrittenToFile"), "success");
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function _saveToDb() {
|
||||
const tags = document.getElementById("wfm-tagger-result")?.value || "";
|
||||
if (!tags) { showToast(t("taggerNoTags"), "warning"); return; }
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/db/save", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename: state.imageName, interrogator_tags: tags, vlm_tags: "" }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
showToast(t("taggerSavedToDB"), "success");
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
// ── Batch サブタブ ───────────────────────────────────────────────
|
||||
|
||||
function _setupBatchActions() {
|
||||
document.getElementById("wfm-tagger-batch-ollama-refresh-btn")?.addEventListener("click", () => {
|
||||
_loadOllamaModels("wfm-tagger-batch-ollama-url", "wfm-tagger-batch-ollama-model");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-tagger-batch-start-btn")?.addEventListener("click", _batchStart);
|
||||
document.getElementById("wfm-tagger-batch-stop-btn")?.addEventListener("click", _batchStop);
|
||||
}
|
||||
|
||||
async function _batchStart() {
|
||||
const folder = document.getElementById("wfm-tagger-batch-folder")?.value || "";
|
||||
if (!folder) { showToast(t("taggerBatchFolder"), "warning"); return; }
|
||||
|
||||
const body = {
|
||||
folder,
|
||||
model: document.getElementById("wfm-tagger-batch-model")?.value || "",
|
||||
threshold: parseFloat(document.getElementById("wfm-tagger-batch-threshold")?.value || "0.35"),
|
||||
char_threshold: parseFloat(document.getElementById("wfm-tagger-batch-char-threshold")?.value || "0.85"),
|
||||
use_ollama: document.getElementById("wfm-tagger-batch-ollama-enable")?.checked || false,
|
||||
ollama_api: document.getElementById("wfm-tagger-batch-ollama-url")?.value || "http://127.0.0.1:11434",
|
||||
ollama_model: document.getElementById("wfm-tagger-batch-ollama-model")?.value || "",
|
||||
ollama_prompt: document.getElementById("wfm-tagger-batch-ollama-prompt")?.value || "",
|
||||
save_db: document.getElementById("wfm-tagger-batch-save-db")?.checked ?? true,
|
||||
write_file: document.getElementById("wfm-tagger-batch-write-file")?.checked || false,
|
||||
write_txt: document.getElementById("wfm-tagger-batch-write-txt")?.checked || false,
|
||||
};
|
||||
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/batch/start", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
|
||||
document.getElementById("wfm-tagger-batch-start-btn").disabled = true;
|
||||
document.getElementById("wfm-tagger-batch-stop-btn").disabled = false;
|
||||
document.getElementById("wfm-tagger-batch-log").value = "";
|
||||
_startBatchPoll();
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function _batchStop() {
|
||||
await fetch("/wfm/tagger/batch/stop", { method: "POST" });
|
||||
}
|
||||
|
||||
function _startBatchPoll() {
|
||||
if (state.batchPollTimer) clearInterval(state.batchPollTimer);
|
||||
state.batchPollTimer = setInterval(_pollBatch, 1000);
|
||||
}
|
||||
|
||||
async function _pollBatch() {
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/batch/status");
|
||||
const data = await res.json();
|
||||
|
||||
const total = data.total || 0;
|
||||
const done = data.done || 0;
|
||||
const pct = total > 0 ? Math.round((done / total) * 100) : 0;
|
||||
|
||||
const bar = document.getElementById("wfm-tagger-batch-progress-bar");
|
||||
const txt = document.getElementById("wfm-tagger-batch-progress-text");
|
||||
if (bar) bar.style.width = pct + "%";
|
||||
if (txt) txt.textContent = `${done} / ${total} (${pct}%)`;
|
||||
|
||||
const logEl = document.getElementById("wfm-tagger-batch-log");
|
||||
if (logEl && data.log?.length) {
|
||||
logEl.value = data.log.join("\n");
|
||||
logEl.scrollTop = logEl.scrollHeight;
|
||||
}
|
||||
|
||||
if (!data.running) {
|
||||
clearInterval(state.batchPollTimer);
|
||||
state.batchPollTimer = null;
|
||||
document.getElementById("wfm-tagger-batch-start-btn").disabled = false;
|
||||
document.getElementById("wfm-tagger-batch-stop-btn").disabled = true;
|
||||
showToast(t("taggerDone"), "success");
|
||||
}
|
||||
} catch {
|
||||
// 無視
|
||||
}
|
||||
}
|
||||
|
||||
// ── DB サブタブ ──────────────────────────────────────────────────
|
||||
|
||||
function _setupDbActions() {
|
||||
document.getElementById("wfm-tagger-db-search-btn")?.addEventListener("click", _dbSearch);
|
||||
document.getElementById("wfm-tagger-db-show-all-btn")?.addEventListener("click", _dbLoad);
|
||||
document.getElementById("wfm-tagger-db-export-btn")?.addEventListener("click", _dbExport);
|
||||
document.getElementById("wfm-tagger-db-search-input")?.addEventListener("keydown", e => {
|
||||
if (e.key === "Enter") _dbSearch();
|
||||
});
|
||||
document.getElementById("wfm-tagger-db-save-btn")?.addEventListener("click", _dbSave);
|
||||
document.getElementById("wfm-tagger-db-delete-btn")?.addEventListener("click", _dbDelete);
|
||||
}
|
||||
|
||||
async function _dbLoad() {
|
||||
try {
|
||||
const res = await fetch("/wfm/tagger/db/list?limit=200&offset=0");
|
||||
const data = await res.json();
|
||||
_renderDbTable(data.rows || []);
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function _dbSearch() {
|
||||
const q = document.getElementById("wfm-tagger-db-search-input")?.value || "";
|
||||
if (!q) { _dbLoad(); return; }
|
||||
try {
|
||||
const res = await fetch(`/wfm/tagger/db/search?q=${encodeURIComponent(q)}`);
|
||||
const data = await res.json();
|
||||
_renderDbTable(data.rows || []);
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
function _renderDbTable(rows) {
|
||||
const tbody = document.getElementById("wfm-tagger-db-tbody");
|
||||
if (!tbody) return;
|
||||
if (!rows.length) {
|
||||
tbody.innerHTML = `<tr><td colspan="5" style="text-align:center;padding:20px;color:var(--wfm-text-secondary)">${t("taggerDbEmpty")}</td></tr>`;
|
||||
return;
|
||||
}
|
||||
tbody.innerHTML = rows.map(row => `
|
||||
<tr data-id="${row.id}" title="${_esc(row.all_tags)}">
|
||||
<td>${row.id}</td>
|
||||
<td>${_esc(row.filename)}</td>
|
||||
<td>${_esc(_truncate(row.interrogator_tags, 60))}</td>
|
||||
<td>${_esc(_truncate(row.vlm_tags, 60))}</td>
|
||||
<td>${_esc(row.created_at?.split("T")[0] || row.created_at || "")}</td>
|
||||
</tr>
|
||||
`).join("");
|
||||
|
||||
tbody.querySelectorAll("tr").forEach(tr => {
|
||||
tr.addEventListener("click", () => {
|
||||
tbody.querySelectorAll("tr").forEach(r => r.classList.remove("selected"));
|
||||
tr.classList.add("selected");
|
||||
const row = rows.find(r => r.id === parseInt(tr.dataset.id));
|
||||
if (row) _openDbEdit(row);
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
function _openDbEdit(row) {
|
||||
state.dbEditId = row.id;
|
||||
const panel = document.getElementById("wfm-tagger-db-edit-panel");
|
||||
const header = document.getElementById("wfm-tagger-db-edit-header");
|
||||
const interrEl = document.getElementById("wfm-tagger-db-edit-interrogator");
|
||||
const vlmEl = document.getElementById("wfm-tagger-db-edit-vlm");
|
||||
if (!panel) return;
|
||||
if (header) header.textContent = `ID: ${row.id} ${row.filename}`;
|
||||
if (interrEl) interrEl.value = row.interrogator_tags;
|
||||
if (vlmEl) vlmEl.value = row.vlm_tags;
|
||||
panel.style.display = "";
|
||||
}
|
||||
|
||||
async function _dbSave() {
|
||||
if (state.dbEditId == null) return;
|
||||
const interrogator_tags = document.getElementById("wfm-tagger-db-edit-interrogator")?.value || "";
|
||||
const vlm_tags = document.getElementById("wfm-tagger-db-edit-vlm")?.value || "";
|
||||
try {
|
||||
const res = await fetch(`/wfm/tagger/db/${state.dbEditId}`, {
|
||||
method: "PUT",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ interrogator_tags, vlm_tags }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
showToast(t("taggerDbSaved"), "success");
|
||||
_dbLoad();
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function _dbDelete() {
|
||||
if (state.dbEditId == null) return;
|
||||
if (!confirm(`Delete DB record #${state.dbEditId}?`)) return;
|
||||
try {
|
||||
const res = await fetch(`/wfm/tagger/db/${state.dbEditId}`, { method: "DELETE" });
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
document.getElementById("wfm-tagger-db-edit-panel").style.display = "none";
|
||||
state.dbEditId = null;
|
||||
showToast(t("taggerDbDeleted"), "success");
|
||||
_dbLoad();
|
||||
} catch (e) {
|
||||
showToast(e.message, "error");
|
||||
}
|
||||
}
|
||||
|
||||
function _dbExport() {
|
||||
const a = document.createElement("a");
|
||||
a.href = "/wfm/tagger/db/export";
|
||||
a.download = "tagger_tags.csv";
|
||||
a.click();
|
||||
}
|
||||
|
||||
// ── ユーティリティ ───────────────────────────────────────────────
|
||||
|
||||
function _esc(str) {
|
||||
return String(str || "").replace(/&/g, "&").replace(/</g, "<").replace(/>/g, ">").replace(/"/g, """);
|
||||
}
|
||||
|
||||
function _truncate(str, len) {
|
||||
if (!str) return "";
|
||||
return str.length > len ? str.slice(0, len) + "…" : str;
|
||||
}
|
||||
@@ -0,0 +1,179 @@
|
||||
/**
|
||||
* Shared utilities
|
||||
*/
|
||||
|
||||
// ============================================
|
||||
// PNG metadata embedding (iTXt chunk)
|
||||
// ============================================
|
||||
// Embeds an arbitrary key/UTF-8-text pair into a canvas-generated PNG data URL as
|
||||
// an iTXt chunk, inserted right after IHDR — same insertion point/CRC approach as
|
||||
// ComfyUI's own workflow-embedding (web/comfyui/top_menu_extension.js's
|
||||
// embedWorkflowInPng), but iTXt instead of tEXt so non-ASCII text (Japanese notes,
|
||||
// prompts, etc.) round-trips correctly through this codebase's PNG chunk reader
|
||||
// (metadata-tab.js's readAllPNGTextChunks already decodes iTXt text as UTF-8;
|
||||
// tEXt text there is decoded as Latin-1, which would mangle non-ASCII payloads).
|
||||
|
||||
function _n2b(n) {
|
||||
return new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]);
|
||||
}
|
||||
|
||||
function _joinBytes(...arrs) {
|
||||
const result = new Uint8Array(arrs.reduce((total, a) => total + a.byteLength, 0));
|
||||
arrs.reduce((offset, a) => { result.set(a, offset); return offset + a.byteLength; }, 0);
|
||||
return result;
|
||||
}
|
||||
|
||||
let _pngCrcTable = null;
|
||||
function _pngCrc32(data) {
|
||||
if (!_pngCrcTable) {
|
||||
_pngCrcTable = [];
|
||||
for (let n = 0; n < 256; n++) {
|
||||
let c = n;
|
||||
for (let k = 0; k < 8; k++) c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1;
|
||||
_pngCrcTable[n] = c;
|
||||
}
|
||||
}
|
||||
let crc = 0 ^ -1;
|
||||
for (let i = 0; i < data.byteLength; i++) crc = (crc >>> 8) ^ _pngCrcTable[(crc ^ data[i]) & 0xff];
|
||||
return (crc ^ -1) >>> 0;
|
||||
}
|
||||
|
||||
function _dataUrlToBytes(dataUrl) {
|
||||
const binary = atob(dataUrl.slice(dataUrl.indexOf(",") + 1));
|
||||
const bytes = new Uint8Array(binary.length);
|
||||
for (let i = 0; i < binary.length; i++) bytes[i] = binary.charCodeAt(i);
|
||||
return bytes;
|
||||
}
|
||||
|
||||
function _bytesToDataUrl(bytes, mime) {
|
||||
let binary = "";
|
||||
const chunkSize = 0x8000;
|
||||
for (let i = 0; i < bytes.length; i += chunkSize) {
|
||||
binary += String.fromCharCode.apply(null, bytes.subarray(i, i + chunkSize));
|
||||
}
|
||||
return `data:${mime};base64,${btoa(binary)}`;
|
||||
}
|
||||
|
||||
export function embedPngTextChunk(dataUrl, keyword, text) {
|
||||
const bytes = _dataUrlToBytes(dataUrl);
|
||||
const view = new DataView(bytes.buffer, bytes.byteOffset, bytes.byteLength);
|
||||
|
||||
const data = _joinBytes(
|
||||
new TextEncoder().encode(`iTXt${keyword}`),
|
||||
new Uint8Array([0, 0, 0, 0, 0]), // keyword-NUL, compression flag(0), compression method(0), empty lang tag+NUL, empty translated keyword+NUL
|
||||
new TextEncoder().encode(text),
|
||||
);
|
||||
const chunk = _joinBytes(_n2b(data.byteLength - 4), data, _n2b(_pngCrc32(data)));
|
||||
|
||||
// Insert after IHDR (8-byte signature + 4-byte length + 4-byte type + IHDR data + 4-byte CRC)
|
||||
const ihdrDataLen = view.getUint32(8);
|
||||
const insertAt = ihdrDataLen + 20;
|
||||
const result = _joinBytes(bytes.subarray(0, insertAt), chunk, bytes.subarray(insertAt));
|
||||
return _bytesToDataUrl(result, "image/png");
|
||||
}
|
||||
|
||||
// HTMLエスケープ（属性値・テキストコンテンツ両方で安全に使える）
|
||||
export function escapeHtml(s) {
|
||||
return String(s)
|
||||
.replace(/&/g, "&")
|
||||
.replace(/</g, "<")
|
||||
.replace(/>/g, ">")
|
||||
.replace(/"/g, """)
|
||||
.replace(/'/g, "'");
|
||||
}
|
||||
|
||||
// localStorage からJSONを安全に読む（不正JSON・未設定時はfallback）
|
||||
export function readJsonStorage(key, fallback = {}) {
|
||||
try {
|
||||
const raw = localStorage.getItem(key);
|
||||
return raw ? JSON.parse(raw) : fallback;
|
||||
} catch {
|
||||
return fallback;
|
||||
}
|
||||
}
|
||||
|
||||
// プラグイン共通設定 (wfm_settings)
|
||||
export function getSettings() {
|
||||
return readJsonStorage("wfm_settings");
|
||||
}
|
||||
|
||||
// AIバックエンド(Ollama/LM Studio/Lemonade)ごとのデフォルトURL
|
||||
export const AI_BACKEND_DEFAULT_URLS = {
|
||||
ollama: "http://localhost:11434",
|
||||
lmstudio: "http://localhost:1234",
|
||||
lemonade: "http://localhost:13305",
|
||||
};
|
||||
|
||||
export function getAiBackendDefaultUrl(backend) {
|
||||
return AI_BACKEND_DEFAULT_URLS[backend] || AI_BACKEND_DEFAULT_URLS.ollama;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Eagle Auto-Save
|
||||
// ============================================
|
||||
|
||||
export function getEagleSettings() {
|
||||
const s = getSettings();
|
||||
return {
|
||||
url: s.eagleUrl || "http://localhost:41595",
|
||||
autoSave: !!s.eagleAutoSave,
|
||||
};
|
||||
}
|
||||
|
||||
export async function saveToEagle(imageUrl, name, tags = [], fileInfo = null) {
|
||||
const eagle = getEagleSettings();
|
||||
if (!eagle.autoSave) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/eagle/add", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
eagleUrl: eagle.url,
|
||||
url: imageUrl,
|
||||
name,
|
||||
tags: ["wfm-comfyui", ...tags],
|
||||
// SVG はサーバー側でローカルパス解決(addFromPath)するために必要
|
||||
filename: fileInfo?.filename || "",
|
||||
subfolder: fileInfo?.subfolder || "",
|
||||
type: fileInfo?.type || "output",
|
||||
// comfyui-tosvg の Save SVG String など、絶対パスが既知の場合に渡す
|
||||
localPath: fileInfo?.localPath || "",
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.status === "success") {
|
||||
console.log("[Eagle] Saved:", name);
|
||||
} else {
|
||||
console.warn("[Eagle] Save failed:", data.message);
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn("[Eagle] Save error:", err.message);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 検索inputにオーバーレイXボタンを設定する。
|
||||
* @param {string} inputId - 検索inputのID
|
||||
* @param {string} clearBtnId - クリアボタンのID
|
||||
* @param {Function} onClear - クリア時に呼び出すコールバック（inputは既に空になった後で呼ばれる）
|
||||
*/
|
||||
export function setupSearchClearBtn(inputId, clearBtnId, onClear) {
|
||||
const input = document.getElementById(inputId);
|
||||
const btn = document.getElementById(clearBtnId);
|
||||
if (!input || !btn) return;
|
||||
|
||||
const sync = () => {
|
||||
btn.style.display = input.value ? "flex" : "none";
|
||||
};
|
||||
|
||||
input.addEventListener("input", sync);
|
||||
btn.addEventListener("click", () => {
|
||||
input.value = "";
|
||||
btn.style.display = "none";
|
||||
input.focus();
|
||||
onClear();
|
||||
});
|
||||
|
||||
// 初期値が入っている場合に備えて同期
|
||||
sync();
|
||||
}
|
||||
+479
-316
File diff suppressed because it is too large
Load Diff
+2431
-259
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,128 @@
|
||||
"""
|
||||
Style/Prompt ギャラリー用シードデータ投入スクリプト。
|
||||
|
||||
comfyui_prompt_gallery / prompt_builder_proto 形式の wildcard データ
|
||||
(pack: set: category: leaf: [tag_string] という4階層YAML + thumbnails/ 配下の対応画像)
|
||||
から、画像とタグ文字列の組が揃っているエントリだけを ws_style_prompt 用フォルダへコピーする。
|
||||
|
||||
- テキストのみのワイルドカード（画像が無いもの）は対象外
|
||||
- サムネイル画像はあってもYAML側にタグが無いもの（プレースホルダー画像等）も対象外
|
||||
- コピー先には画像と同名の .txt サイドカー（タグ文字列そのまま）を書き込む
|
||||
|
||||
Usage:
|
||||
python tools/import_style_prompt_seed.py --source <wildcard_dataディレクトリ> --dest <ws_style_promptディレクトリ>
|
||||
"""
|
||||
import argparse
|
||||
import re
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".webp"}
|
||||
|
||||
|
||||
def clean_tags(text: str) -> str:
|
||||
text = text.strip()
|
||||
text = re.sub(r"^[,\s]+|[,\s]+$", "", text)
|
||||
text = re.sub(r"\s*,\s*", ", ", text)
|
||||
return text
|
||||
|
||||
|
||||
def parse_yaml_leaves(yaml_path: Path) -> dict[str, str]:
|
||||
"""comfyui_prompt_gallery の parseYamlForImages と同じ簡易パーサ（PyYAML不要）。
|
||||
「key:」行の直後が「- タグ文字列」で始まる行なら key をリーフとみなす。
|
||||
戻り値: {leaf_name(小文字): タグ文字列}
|
||||
"""
|
||||
leaves: dict[str, str] = {}
|
||||
lines = yaml_path.read_text(encoding="utf-8", errors="replace").split("\n")
|
||||
for i, line in enumerate(lines):
|
||||
trimmed = line.strip()
|
||||
if not trimmed or trimmed.startswith("#"):
|
||||
continue
|
||||
if not trimmed.endswith(":"):
|
||||
continue
|
||||
if i + 1 >= len(lines):
|
||||
continue
|
||||
next_trimmed = lines[i + 1].strip()
|
||||
if not next_trimmed.startswith("-"):
|
||||
continue
|
||||
key = trimmed[:-1].strip()
|
||||
tags = next_trimmed[1:].strip()
|
||||
if not tags or key.lower() == "skip":
|
||||
continue
|
||||
leaves[key.lower()] = clean_tags(tags)
|
||||
return leaves
|
||||
|
||||
|
||||
def import_category(cat_dir: Path, dest_root: Path) -> tuple[int, int, int]:
|
||||
"""1カテゴリ分を投入する。戻り値: (copied, images_without_prompt, leaves_total)"""
|
||||
category = cat_dir.name
|
||||
yaml_files = sorted(cat_dir.glob("*.yaml")) + sorted(cat_dir.glob("*.yml"))
|
||||
|
||||
leaves: dict[str, str] = {}
|
||||
for yf in yaml_files:
|
||||
leaves.update(parse_yaml_leaves(yf))
|
||||
|
||||
copied = 0
|
||||
images_without_prompt = 0
|
||||
|
||||
# "thumbnails" と、preview違いの "thumbnails_option2"（例: alchemist.preview3.jpeg）の両方を対象にする
|
||||
for thumb_dir_name in ("thumbnails", "thumbnails_option2"):
|
||||
thumbnails_dir = cat_dir / thumb_dir_name
|
||||
if not thumbnails_dir.is_dir():
|
||||
continue
|
||||
for img_path in sorted(thumbnails_dir.rglob("*")):
|
||||
if not img_path.is_file() or img_path.suffix.lower() not in IMAGE_EXTS:
|
||||
continue
|
||||
# "alchemist.preview3.jpeg" のような二重拡張子にも対応するため、最初の "." より前をリーフ名候補にする
|
||||
leaf_candidate = img_path.name.split(".")[0].lower()
|
||||
tags = leaves.get(leaf_candidate)
|
||||
if tags is None:
|
||||
images_without_prompt += 1
|
||||
continue
|
||||
|
||||
rel_parts = img_path.relative_to(thumbnails_dir).parts
|
||||
# 先頭1階層（パック名。例: "ponyxl"）は全カテゴリ共通で冗長なため省く
|
||||
dest_rel = Path(*rel_parts[1:]) if len(rel_parts) > 1 else Path(rel_parts[0])
|
||||
dest_path = dest_root / category / dest_rel
|
||||
dest_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
shutil.copy2(img_path, dest_path)
|
||||
dest_path.with_suffix(".txt").write_text(tags, encoding="utf-8")
|
||||
copied += 1
|
||||
|
||||
return copied, images_without_prompt, len(leaves)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--source", required=True, help="wildcard_data ディレクトリ")
|
||||
parser.add_argument("--dest", required=True, help="投入先 ws_style_prompt ディレクトリ")
|
||||
args = parser.parse_args()
|
||||
|
||||
source = Path(args.source)
|
||||
dest = Path(args.dest)
|
||||
if not source.is_dir():
|
||||
raise SystemExit(f"source not found: {source}")
|
||||
dest.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
categories = [
|
||||
d for d in sorted(source.iterdir())
|
||||
if d.is_dir() and (d / "thumbnails").is_dir()
|
||||
]
|
||||
if not categories:
|
||||
raise SystemExit(f"No category with a thumbnails/ subfolder found under {source}")
|
||||
|
||||
total_copied = 0
|
||||
total_skipped = 0
|
||||
for cat_dir in categories:
|
||||
copied, skipped, leaf_total = import_category(cat_dir, dest)
|
||||
print(f"[{cat_dir.name}] copied={copied} images_without_prompt={skipped} yaml_leaves={leaf_total}")
|
||||
total_copied += copied
|
||||
total_skipped += skipped
|
||||
|
||||
print(f"\nDone. {len(categories)} categories, {total_copied} images copied, {total_skipped} skipped (no matching prompt).")
|
||||
print(f"Destination: {dest}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,178 @@
|
||||
/**
|
||||
* WFS_GalleryFeeder – ComfyUI canvas extension
|
||||
* Adds "After Gen", Run, and Stop controls directly on the node widget area.
|
||||
*/
|
||||
import { app } from "../../scripts/app.js";
|
||||
|
||||
const FEEDER_GROUP = "__Feeder__";
|
||||
|
||||
// Per-node runtime state (keyed by node.id)
|
||||
const _ns = new Map();
|
||||
|
||||
// ── Helpers ───────────────────────────────────────────────────────────
|
||||
|
||||
async function fetchGroupImages(groupName) {
|
||||
try {
|
||||
const res = await fetch(`/wfm/gallery/groups/${encodeURIComponent(groupName)}/images`);
|
||||
if (!res.ok) return [];
|
||||
return (await res.json()).images || [];
|
||||
} catch { return []; }
|
||||
}
|
||||
|
||||
function getOrCreate(nodeId) {
|
||||
if (!_ns.has(nodeId)) {
|
||||
_ns.set(nodeId, { running: false, stopFlag: false, idx: 0, images: [] });
|
||||
}
|
||||
return _ns.get(nodeId);
|
||||
}
|
||||
|
||||
function getInputWidget(node, name) {
|
||||
return node.widgets?.find(w => w.name === name);
|
||||
}
|
||||
|
||||
function setWidgetValue(node, name, value) {
|
||||
const w = getInputWidget(node, name);
|
||||
if (!w) return;
|
||||
w.value = value;
|
||||
if (w.callback) w.callback(value);
|
||||
app.canvas?.setDirty(true, true);
|
||||
}
|
||||
|
||||
/** Resolves when the current prompt finishes (executing=null, interrupted), rejects on error. */
|
||||
function waitForExecution() {
|
||||
return new Promise((resolve, reject) => {
|
||||
const cleanup = () => {
|
||||
app.api.removeEventListener("executing", onExecuting);
|
||||
app.api.removeEventListener("execution_error", onError);
|
||||
app.api.removeEventListener("execution_interrupted", onInterrupted);
|
||||
};
|
||||
const onExecuting = ({ detail }) => { if (detail === null) { cleanup(); resolve(); } };
|
||||
const onError = ({ detail }) => { cleanup(); reject(new Error(detail?.exception_message || "Execution error")); };
|
||||
const onInterrupted = () => { cleanup(); resolve(); }; // treat interrupt as "done"
|
||||
|
||||
app.api.addEventListener("executing", onExecuting);
|
||||
app.api.addEventListener("execution_error", onError);
|
||||
app.api.addEventListener("execution_interrupted", onInterrupted);
|
||||
});
|
||||
}
|
||||
|
||||
function updateRunBtn(node) {
|
||||
const st = getOrCreate(node.id);
|
||||
if (node._wfsRunWidget) node._wfsRunWidget.name = st.running ? "⏳ Running..." : "▶ Run";
|
||||
app.canvas?.setDirty(true, true);
|
||||
}
|
||||
|
||||
function wfsToast(msg, severity = "warn") {
|
||||
try {
|
||||
const tm = app.extensionManager?.toast;
|
||||
if (tm?.add) { tm.add({ severity, summary: "Gallery Feeder", detail: msg, life: 4000 }); return; }
|
||||
} catch {}
|
||||
console[severity === "error" ? "error" : "warn"]("[WFS_GalleryFeeder]", msg);
|
||||
}
|
||||
|
||||
// ── Run / Stop ────────────────────────────────────────────────────────
|
||||
|
||||
async function startLoop(node) {
|
||||
const st = getOrCreate(node.id);
|
||||
if (st.running) return;
|
||||
|
||||
// Re-fetch images for the current group each Run
|
||||
const groupName = getInputWidget(node, "group_name")?.value || FEEDER_GROUP;
|
||||
const images = await fetchGroupImages(groupName);
|
||||
if (images.length === 0) {
|
||||
wfsToast(`No images in group "${groupName}"`, "warn");
|
||||
return;
|
||||
}
|
||||
|
||||
st.running = true;
|
||||
st.stopFlag = false;
|
||||
st.images = images;
|
||||
st.idx = Math.max(0, Math.min(
|
||||
parseInt(getInputWidget(node, "index")?.value) || 0,
|
||||
images.length - 1
|
||||
));
|
||||
|
||||
updateRunBtn(node);
|
||||
|
||||
// Queue first generation with current index
|
||||
setWidgetValue(node, "index", st.idx);
|
||||
app.queuePrompt(0, 1);
|
||||
|
||||
while (st.running && !st.stopFlag) {
|
||||
try {
|
||||
await waitForExecution();
|
||||
} catch (err) {
|
||||
if (st.running) wfsToast(err.message, "error");
|
||||
break;
|
||||
}
|
||||
|
||||
if (!st.running || st.stopFlag) break;
|
||||
|
||||
const afterGen = node._wfsAfterGenWidget?.value || "loop";
|
||||
const total = st.images.length;
|
||||
|
||||
// Increment mode: stop after the last image
|
||||
if (afterGen === "increment" && st.idx >= total - 1) break;
|
||||
|
||||
if (afterGen !== "fixed") {
|
||||
st.idx = (st.idx + 1) % total;
|
||||
setWidgetValue(node, "index", st.idx);
|
||||
}
|
||||
|
||||
app.queuePrompt(0, 1);
|
||||
}
|
||||
|
||||
st.running = false;
|
||||
updateRunBtn(node);
|
||||
}
|
||||
|
||||
function stopLoop(node) {
|
||||
const st = getOrCreate(node.id);
|
||||
if (!st.running) return;
|
||||
st.stopFlag = true;
|
||||
st.running = false;
|
||||
try { app.api.interrupt(); } catch {}
|
||||
updateRunBtn(node);
|
||||
}
|
||||
|
||||
// ── Extension registration ────────────────────────────────────────────
|
||||
|
||||
app.registerExtension({
|
||||
name: "WorkflowStudio.GalleryFeeder",
|
||||
|
||||
async beforeRegisterNodeDef(nodeType, nodeData) {
|
||||
if (nodeData.name !== "WFS_GalleryFeeder") return;
|
||||
|
||||
const origCreated = nodeType.prototype.onNodeCreated;
|
||||
nodeType.prototype.onNodeCreated = function () {
|
||||
origCreated?.call(this);
|
||||
const node = this;
|
||||
|
||||
// ── After Gen selector ────────────────────────────────────
|
||||
// serialize:false → not included in widgets_values / prompt inputs
|
||||
const afterGenW = node.addWidget("combo", "wfs_after_gen", "loop", () => {}, {
|
||||
values: ["loop", "increment", "fixed"],
|
||||
serialize: false,
|
||||
});
|
||||
node._wfsAfterGenWidget = afterGenW;
|
||||
|
||||
// ── Run button ────────────────────────────────────────────
|
||||
const runW = node.addWidget("button", "▶ Run", null, () => startLoop(node));
|
||||
runW.serialize = false;
|
||||
node._wfsRunWidget = runW;
|
||||
|
||||
// ── Stop button ───────────────────────────────────────────
|
||||
const stopW = node.addWidget("button", "■ Stop", null, () => stopLoop(node));
|
||||
stopW.serialize = false;
|
||||
node._wfsStopWidget = stopW;
|
||||
};
|
||||
|
||||
// Clean up when node is removed from graph
|
||||
const origRemoved = nodeType.prototype.onRemoved;
|
||||
nodeType.prototype.onRemoved = function () {
|
||||
stopLoop(this);
|
||||
_ns.delete(this.id);
|
||||
origRemoved?.call(this);
|
||||
};
|
||||
},
|
||||
});
|
||||
+3821
-44
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,818 @@
|
||||
/**
|
||||
* Node Library Sidebar Panel
|
||||
*
|
||||
* Fixed sidebar panel injected into ComfyUI DOM.
|
||||
* Three tabs: Favorites (single nodes), Node Sets (multi-node), Groups.
|
||||
* Items are draggable onto the canvas or click-to-place.
|
||||
*/
|
||||
console.log("[WFM] node_sets_menu.js loading...");
|
||||
import { app } from "../../scripts/app.js";
|
||||
console.log("[WFM] node_sets_menu.js: app imported");
|
||||
|
||||
export const NODE_SETS_TOOLTIP = "Node Library – Browse & drag nodes onto canvas";
|
||||
|
||||
// ============================================
|
||||
// State
|
||||
// ============================================
|
||||
|
||||
const state = {
|
||||
visible: false,
|
||||
activeTab: "favorites", // favorites | sets | groups
|
||||
favorites: [], // [{name, display_name}]
|
||||
nodeSets: [],
|
||||
groups: {}, // {groupName: [nodeName, ...]}
|
||||
metadata: {},
|
||||
searchText: "",
|
||||
loaded: false,
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// API
|
||||
// ============================================
|
||||
|
||||
const fetchNodeSets = async () => {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/node-sets");
|
||||
return res.ok ? await res.json() : [];
|
||||
} catch { return []; }
|
||||
};
|
||||
|
||||
const fetchMetadata = async () => {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/nodes/metadata");
|
||||
return res.ok ? await res.json() : {};
|
||||
} catch { return {}; }
|
||||
};
|
||||
|
||||
const fetchGroups = async () => {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/nodes/groups");
|
||||
return res.ok ? await res.json() : {};
|
||||
} catch { return {}; }
|
||||
};
|
||||
|
||||
const loadData = async () => {
|
||||
const [metadata, nodeSets, groups] = await Promise.all([
|
||||
fetchMetadata(), fetchNodeSets(), fetchGroups(),
|
||||
]);
|
||||
state.metadata = metadata;
|
||||
state.nodeSets = nodeSets;
|
||||
state.groups = groups;
|
||||
|
||||
// Extract favorites from metadata
|
||||
state.favorites = [];
|
||||
for (const [name, meta] of Object.entries(metadata)) {
|
||||
if (name === "_groups") continue;
|
||||
if (meta.favorite) {
|
||||
state.favorites.push({ name, display_name: name });
|
||||
}
|
||||
}
|
||||
state.loaded = true;
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Canvas placement helpers
|
||||
// ============================================
|
||||
|
||||
const getCanvasDropPos = (e) => {
|
||||
const canvas = app.canvas;
|
||||
if (!canvas) return [100, 100];
|
||||
const rect = canvas.canvas.getBoundingClientRect();
|
||||
const x = (e.clientX - rect.left) / canvas.ds.scale - canvas.ds.offset[0];
|
||||
const y = (e.clientY - rect.top) / canvas.ds.scale - canvas.ds.offset[1];
|
||||
return [x, y];
|
||||
};
|
||||
|
||||
const placeSingleNode = (classType, pos) => {
|
||||
const graph = app.graph;
|
||||
if (!graph) return;
|
||||
const node = LiteGraph.createNode(classType);
|
||||
if (!node) {
|
||||
showToast(`Unknown node: ${classType}`, "error");
|
||||
return;
|
||||
}
|
||||
node.pos = pos || [100, 100];
|
||||
graph.add(node);
|
||||
app.canvas.setDirty(true, true);
|
||||
showToast(`Placed: ${node.title || classType}`, "success");
|
||||
};
|
||||
|
||||
const placeNodeSet = (nodeSet, pos) => {
|
||||
const graph = app.graph;
|
||||
const canvas = app.canvas;
|
||||
if (!graph || !canvas) return;
|
||||
|
||||
const nodes = nodeSet.nodes || [];
|
||||
if (nodes.length === 0) return;
|
||||
|
||||
const baseX = pos ? pos[0] : (-canvas.ds.offset[0] + canvas.canvas.width / 2 / canvas.ds.scale);
|
||||
const baseY = pos ? pos[1] : (-canvas.ds.offset[1] + canvas.canvas.height / 2 / canvas.ds.scale);
|
||||
|
||||
const positions = nodes.map(n => n.rel_pos || [0, 0]);
|
||||
const minX = Math.min(...positions.map(p => p[0]));
|
||||
const minY = Math.min(...positions.map(p => p[1]));
|
||||
|
||||
const createdNodes = [];
|
||||
for (const nodeDef of nodes) {
|
||||
if (!nodeDef.class_type) { createdNodes.push(null); continue; }
|
||||
const node = LiteGraph.createNode(nodeDef.class_type);
|
||||
if (!node) { createdNodes.push(null); continue; }
|
||||
const rp = nodeDef.rel_pos || [0, 0];
|
||||
node.pos = [baseX + rp[0] - minX, baseY + rp[1] - minY];
|
||||
if (nodeDef.title) node.title = nodeDef.title;
|
||||
if (nodeDef.widget_values && node.widgets) {
|
||||
for (const [k, v] of Object.entries(nodeDef.widget_values)) {
|
||||
const w = node.widgets.find(ww => ww.name === k);
|
||||
if (w) w.value = v;
|
||||
}
|
||||
}
|
||||
graph.add(node);
|
||||
createdNodes.push(node);
|
||||
}
|
||||
|
||||
for (const link of (nodeSet.links || [])) {
|
||||
const from = createdNodes[link.from_node];
|
||||
const to = createdNodes[link.to_node];
|
||||
if (from && to) {
|
||||
try { from.connect(link.from_slot ?? 0, to, link.to_slot ?? 0); } catch {}
|
||||
}
|
||||
}
|
||||
|
||||
canvas.setDirty(true, true);
|
||||
showToast(`Placed "${nodeSet.name}" (${createdNodes.filter(Boolean).length} nodes)`, "success");
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Drag & Drop onto canvas
|
||||
// ============================================
|
||||
|
||||
let dropHandlerInstalled = false;
|
||||
|
||||
const installCanvasDropHandler = () => {
|
||||
if (dropHandlerInstalled) return;
|
||||
dropHandlerInstalled = true;
|
||||
|
||||
const canvasEl = document.getElementById("graph-canvas");
|
||||
if (!canvasEl) return;
|
||||
|
||||
canvasEl.addEventListener("dragover", (e) => {
|
||||
if (e.dataTransfer.types.includes("application/x-wfm-node")) {
|
||||
e.preventDefault();
|
||||
e.dataTransfer.dropEffect = "copy";
|
||||
}
|
||||
});
|
||||
|
||||
canvasEl.addEventListener("drop", (e) => {
|
||||
const raw = e.dataTransfer.getData("application/x-wfm-node");
|
||||
if (!raw) return;
|
||||
e.preventDefault();
|
||||
|
||||
const data = JSON.parse(raw);
|
||||
const pos = getCanvasDropPos(e);
|
||||
|
||||
if (data.type === "single") {
|
||||
placeSingleNode(data.classType, pos);
|
||||
} else if (data.type === "set") {
|
||||
const set = state.nodeSets.find(s => s.id === data.setId);
|
||||
if (set) placeNodeSet(set, pos);
|
||||
}
|
||||
});
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Sidebar Panel DOM
|
||||
// ============================================
|
||||
|
||||
let panelEl = null;
|
||||
|
||||
const PANEL_ID = "wfm-node-library-panel";
|
||||
|
||||
const createPanel = () => {
|
||||
if (panelEl) return panelEl;
|
||||
|
||||
const panel = document.createElement("div");
|
||||
panel.id = PANEL_ID;
|
||||
panel.innerHTML = `
|
||||
<div class="wfm-nlp-header">
|
||||
<span class="wfm-nlp-title">Node Library</span>
|
||||
<button class="wfm-nlp-refresh" title="Refresh">↻</button>
|
||||
<button class="wfm-nlp-close" title="Close">×</button>
|
||||
</div>
|
||||
<div class="wfm-nlp-tabs">
|
||||
<button class="wfm-nlp-tab active" data-tab="favorites">★ Favorites</button>
|
||||
<button class="wfm-nlp-tab" data-tab="sets">☰ Sets</button>
|
||||
<button class="wfm-nlp-tab" data-tab="groups">📁 Groups</button>
|
||||
</div>
|
||||
<div class="wfm-nlp-search">
|
||||
<input type="text" class="wfm-nlp-search-input" placeholder="Search...">
|
||||
</div>
|
||||
<div class="wfm-nlp-content"></div>
|
||||
`;
|
||||
|
||||
document.body.appendChild(panel);
|
||||
panelEl = panel;
|
||||
|
||||
// Tab switching
|
||||
panel.querySelectorAll(".wfm-nlp-tab").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
state.activeTab = btn.dataset.tab;
|
||||
panel.querySelectorAll(".wfm-nlp-tab").forEach(b => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
renderContent();
|
||||
});
|
||||
});
|
||||
|
||||
// Close
|
||||
panel.querySelector(".wfm-nlp-close").addEventListener("click", () => togglePanel());
|
||||
|
||||
// Search
|
||||
panel.querySelector(".wfm-nlp-search-input").addEventListener("input", (e) => {
|
||||
state.searchText = e.target.value.toLowerCase().trim();
|
||||
renderContent();
|
||||
});
|
||||
|
||||
// Refresh
|
||||
panel.querySelector(".wfm-nlp-refresh").addEventListener("click", async () => {
|
||||
await loadData();
|
||||
renderContent();
|
||||
showToast("Node Library refreshed", "success");
|
||||
});
|
||||
|
||||
injectStyles();
|
||||
return panel;
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Render content
|
||||
// ============================================
|
||||
|
||||
const renderContent = () => {
|
||||
const content = panelEl?.querySelector(".wfm-nlp-content");
|
||||
if (!content) return;
|
||||
|
||||
if (!state.loaded) {
|
||||
content.innerHTML = `<div class="wfm-nlp-empty">Loading...</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
switch (state.activeTab) {
|
||||
case "favorites": renderFavorites(content); break;
|
||||
case "sets": renderSets(content); break;
|
||||
case "groups": renderGroups(content); break;
|
||||
}
|
||||
};
|
||||
|
||||
const renderFavorites = (container) => {
|
||||
let items = state.favorites;
|
||||
if (state.searchText) {
|
||||
items = items.filter(n => n.name.toLowerCase().includes(state.searchText));
|
||||
}
|
||||
|
||||
if (items.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-nlp-empty">
|
||||
${state.favorites.length === 0 ? "No favorites yet.<br><small>Star nodes in Workflow Studio → Nodes tab</small>" : "No matches"}
|
||||
</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
container.innerHTML = "";
|
||||
for (const node of items) {
|
||||
const el = createDraggableItem(node.display_name || node.name, "single", { classType: node.name });
|
||||
el.addEventListener("dblclick", () => {
|
||||
placeSingleNode(node.name);
|
||||
});
|
||||
container.appendChild(el);
|
||||
}
|
||||
};
|
||||
|
||||
const renderSets = (container) => {
|
||||
let items = state.nodeSets;
|
||||
if (state.searchText) {
|
||||
items = items.filter(s =>
|
||||
s.name.toLowerCase().includes(state.searchText) ||
|
||||
(s.tags || []).some(t => t.toLowerCase().includes(state.searchText))
|
||||
);
|
||||
}
|
||||
|
||||
if (items.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-nlp-empty">
|
||||
${state.nodeSets.length === 0 ? "No node sets.<br><small>Create sets in Workflow Studio → Nodes tab</small>" : "No matches"}
|
||||
</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
container.innerHTML = "";
|
||||
for (const set of items) {
|
||||
const count = (set.nodes || []).length;
|
||||
const el = createDraggableItem(
|
||||
`${esc(set.name)} <span class="wfm-nlp-badge">${count} nodes</span>`,
|
||||
"set",
|
||||
{ setId: set.id },
|
||||
set.description
|
||||
);
|
||||
el.addEventListener("dblclick", () => {
|
||||
placeNodeSet(set);
|
||||
});
|
||||
container.appendChild(el);
|
||||
}
|
||||
};
|
||||
|
||||
const renderGroups = (container) => {
|
||||
const groupNames = Object.keys(state.groups).sort();
|
||||
|
||||
if (groupNames.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-nlp-empty">
|
||||
No groups.<br><small>Create groups in Workflow Studio → Nodes tab</small>
|
||||
</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
let filtered = groupNames;
|
||||
if (state.searchText) {
|
||||
filtered = groupNames.filter(g =>
|
||||
g.toLowerCase().includes(state.searchText) ||
|
||||
(state.groups[g] || []).some(n => n.toLowerCase().includes(state.searchText))
|
||||
);
|
||||
}
|
||||
|
||||
container.innerHTML = "";
|
||||
for (const groupName of filtered) {
|
||||
const nodes = state.groups[groupName] || [];
|
||||
|
||||
const section = document.createElement("div");
|
||||
section.className = "wfm-nlp-group-section";
|
||||
|
||||
const header = document.createElement("div");
|
||||
header.className = "wfm-nlp-group-header";
|
||||
header.innerHTML = `<span>${esc(groupName)}</span> <span class="wfm-nlp-badge">${nodes.length}</span>`;
|
||||
header.addEventListener("click", () => {
|
||||
const list = section.querySelector(".wfm-nlp-group-list");
|
||||
list.style.display = list.style.display === "none" ? "block" : "none";
|
||||
header.classList.toggle("collapsed");
|
||||
});
|
||||
section.appendChild(header);
|
||||
|
||||
const list = document.createElement("div");
|
||||
list.className = "wfm-nlp-group-list";
|
||||
for (const nodeName of nodes) {
|
||||
const el = createDraggableItem(nodeName, "single", { classType: nodeName });
|
||||
el.addEventListener("dblclick", () => placeSingleNode(nodeName));
|
||||
list.appendChild(el);
|
||||
}
|
||||
section.appendChild(list);
|
||||
container.appendChild(section);
|
||||
}
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Draggable item factory
|
||||
// ============================================
|
||||
|
||||
const createDraggableItem = (label, type, data, subtitle) => {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-nlp-item";
|
||||
el.draggable = true;
|
||||
el.innerHTML = `
|
||||
<div class="wfm-nlp-item-label">${label}</div>
|
||||
${subtitle ? `<div class="wfm-nlp-item-sub">${esc(subtitle)}</div>` : ""}
|
||||
`;
|
||||
|
||||
el.addEventListener("dragstart", (e) => {
|
||||
e.dataTransfer.effectAllowed = "copy";
|
||||
e.dataTransfer.setData("application/x-wfm-node", JSON.stringify({ type, ...data }));
|
||||
el.classList.add("dragging");
|
||||
});
|
||||
|
||||
el.addEventListener("dragend", () => {
|
||||
el.classList.remove("dragging");
|
||||
});
|
||||
|
||||
return el;
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Toggle & exports
|
||||
// ============================================
|
||||
|
||||
export const togglePanel = async () => {
|
||||
const panel = createPanel();
|
||||
state.visible = !state.visible;
|
||||
panel.style.display = state.visible ? "flex" : "none";
|
||||
|
||||
if (state.visible && !state.loaded) {
|
||||
await loadData();
|
||||
renderContent();
|
||||
}
|
||||
|
||||
installCanvasDropHandler();
|
||||
};
|
||||
|
||||
export const getNodeSetsIcon = () => `
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2">
|
||||
<rect x="3" y="3" width="7" height="7" rx="1"/>
|
||||
<rect x="14" y="3" width="7" height="7" rx="1"/>
|
||||
<rect x="3" y="14" width="7" height="7" rx="1"/>
|
||||
<path d="M17.5 14v7M14 17.5h7"/>
|
||||
</svg>
|
||||
`;
|
||||
|
||||
// ============================================
|
||||
// Toast
|
||||
// ============================================
|
||||
|
||||
const showToast = (message, type = "info") => {
|
||||
const existing = document.getElementById("wfm-ns-toast");
|
||||
if (existing) existing.remove();
|
||||
|
||||
const toast = document.createElement("div");
|
||||
toast.id = "wfm-ns-toast";
|
||||
const colors = {
|
||||
success: { bg: "rgba(46,213,115,0.95)" },
|
||||
error: { bg: "rgba(255,71,87,0.95)" },
|
||||
info: { bg: "rgba(74,158,255,0.95)" },
|
||||
};
|
||||
Object.assign(toast.style, {
|
||||
position: "fixed", bottom: "24px", left: "50%", transform: "translateX(-50%)",
|
||||
padding: "10px 24px", background: (colors[type] || colors.info).bg, color: "#fff",
|
||||
borderRadius: "8px", fontSize: "14px", fontWeight: "500", zIndex: "99999",
|
||||
boxShadow: "0 4px 16px rgba(0,0,0,0.3)", transition: "opacity 0.3s", whiteSpace: "nowrap",
|
||||
});
|
||||
toast.textContent = message;
|
||||
document.body.appendChild(toast);
|
||||
setTimeout(() => { toast.style.opacity = "0"; setTimeout(() => toast.remove(), 300); }, 3000);
|
||||
};
|
||||
|
||||
const esc = (s) => s ? String(s).replace(/&/g, "&").replace(/</g, "<").replace(/>/g, ">").replace(/"/g, """) : "";
|
||||
|
||||
// ============================================
|
||||
// Save selected nodes as Node Set (context menu)
|
||||
// ============================================
|
||||
|
||||
const saveSelectedAsNodeSet = async () => {
|
||||
const selectedNodes = Object.values(app.canvas.selected_nodes || {});
|
||||
if (selectedNodes.length === 0) {
|
||||
showToast("Select at least one node first", "error");
|
||||
return;
|
||||
}
|
||||
|
||||
// Prompt for name
|
||||
const name = prompt("Node Set name:");
|
||||
if (!name || !name.trim()) return;
|
||||
|
||||
// Build node list with relative positions
|
||||
// Use the top-left of the bounding box as origin
|
||||
const minX = Math.min(...selectedNodes.map(n => n.pos[0]));
|
||||
const minY = Math.min(...selectedNodes.map(n => n.pos[1]));
|
||||
|
||||
const nodeIdToIndex = new Map();
|
||||
const nodes = [];
|
||||
selectedNodes.forEach((node, i) => {
|
||||
nodeIdToIndex.set(node.id, i);
|
||||
nodes.push({
|
||||
class_type: node.type || node.comfyClass || "",
|
||||
title: node.title || "",
|
||||
rel_pos: [Math.round(node.pos[0] - minX), Math.round(node.pos[1] - minY)],
|
||||
});
|
||||
});
|
||||
|
||||
// Extract links between selected nodes
|
||||
const links = [];
|
||||
const graph = app.graph;
|
||||
|
||||
for (const node of selectedNodes) {
|
||||
if (!node.inputs) continue;
|
||||
for (let slotIdx = 0; slotIdx < node.inputs.length; slotIdx++) {
|
||||
const input = node.inputs[slotIdx];
|
||||
if (input.link == null) continue;
|
||||
|
||||
// Resolve link data
|
||||
let linkData = null;
|
||||
if (graph.links instanceof Map) {
|
||||
linkData = graph.links.get(input.link);
|
||||
} else if (graph.links) {
|
||||
linkData = graph.links[input.link];
|
||||
}
|
||||
if (!linkData) continue;
|
||||
|
||||
// linkData: [id, origin_id, origin_slot, target_id, target_slot, type]
|
||||
// or object with .origin_id, .origin_slot, etc.
|
||||
const originId = linkData.origin_id ?? linkData[1];
|
||||
const originSlot = linkData.origin_slot ?? linkData[2];
|
||||
const targetSlot = linkData.target_slot ?? linkData[4];
|
||||
|
||||
// Only include if both source and target are in the selection
|
||||
if (nodeIdToIndex.has(originId) && nodeIdToIndex.has(node.id)) {
|
||||
links.push({
|
||||
from_node: nodeIdToIndex.get(originId),
|
||||
from_slot: originSlot,
|
||||
to_node: nodeIdToIndex.get(node.id),
|
||||
to_slot: targetSlot,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Save via API
|
||||
try {
|
||||
const res = await fetch("/api/wfm/node-sets", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ name: name.trim(), description: "", tags: [], nodes, links }),
|
||||
});
|
||||
const result = await res.json();
|
||||
if (result.status === "ok") {
|
||||
showToast(`Node Set "${name.trim()}" saved (${nodes.length} nodes, ${links.length} links)`, "success");
|
||||
// Refresh sidebar if open
|
||||
if (state.loaded) {
|
||||
await loadData();
|
||||
renderContent();
|
||||
}
|
||||
} else {
|
||||
showToast("Failed to save: " + (result.error || "Unknown error"), "error");
|
||||
}
|
||||
} catch (err) {
|
||||
showToast("Failed to save: " + err.message, "error");
|
||||
}
|
||||
};
|
||||
|
||||
// Register context menu item on all nodes
|
||||
app.registerExtension({
|
||||
name: "WorkflowStudio.NodeSetContextMenu",
|
||||
beforeRegisterNodeDef(nodeType, _nodeData, _app) {
|
||||
const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
|
||||
nodeType.prototype.getExtraMenuOptions = function(_, options) {
|
||||
origGetExtraMenuOptions?.apply(this, arguments);
|
||||
|
||||
const selectedCount = Object.keys(app.canvas.selected_nodes || {}).length;
|
||||
|
||||
// Add separator + menu item
|
||||
options.push(null);
|
||||
options.push({
|
||||
content: `Save as Node Set${selectedCount > 1 ? ` (${selectedCount} nodes)` : ""}`,
|
||||
callback: saveSelectedAsNodeSet,
|
||||
});
|
||||
};
|
||||
},
|
||||
});
|
||||
|
||||
// ============================================
|
||||
// Styles
|
||||
// ============================================
|
||||
|
||||
const injectStyles = () => {
|
||||
if (document.getElementById("wfm-nlp-styles")) return;
|
||||
const style = document.createElement("style");
|
||||
style.id = "wfm-nlp-styles";
|
||||
style.textContent = `
|
||||
#${PANEL_ID} {
|
||||
position: fixed;
|
||||
right: 0;
|
||||
top: 0;
|
||||
width: 280px;
|
||||
height: 100vh;
|
||||
background: var(--comfy-menu-bg, #1e1e1e);
|
||||
border-left: 1px solid var(--border-color, #4e4e4e);
|
||||
z-index: 9999;
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
font-family: sans-serif;
|
||||
font-size: 13px;
|
||||
color: var(--input-text, #ddd);
|
||||
box-shadow: -4px 0 16px rgba(0,0,0,0.3);
|
||||
}
|
||||
.wfm-nlp-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 10px 12px;
|
||||
border-bottom: 1px solid var(--border-color, #4e4e4e);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-nlp-title {
|
||||
font-weight: 600;
|
||||
font-size: 14px;
|
||||
flex: 1;
|
||||
}
|
||||
.wfm-nlp-close {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--input-text, #ddd);
|
||||
font-size: 20px;
|
||||
cursor: pointer;
|
||||
padding: 0 4px;
|
||||
opacity: 0.6;
|
||||
}
|
||||
.wfm-nlp-close:hover { opacity: 1; }
|
||||
.wfm-nlp-tabs {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--border-color, #4e4e4e);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-nlp-tab {
|
||||
flex: 1;
|
||||
padding: 8px 4px;
|
||||
background: none;
|
||||
border: none;
|
||||
border-bottom: 2px solid transparent;
|
||||
color: var(--input-text, #ddd);
|
||||
font-size: 11px;
|
||||
cursor: pointer;
|
||||
opacity: 0.6;
|
||||
transition: all 0.15s;
|
||||
white-space: nowrap;
|
||||
}
|
||||
.wfm-nlp-tab:hover { opacity: 0.9; }
|
||||
.wfm-nlp-tab.active {
|
||||
opacity: 1;
|
||||
border-bottom-color: var(--p-button-background, #4a9eff);
|
||||
}
|
||||
.wfm-nlp-search {
|
||||
padding: 8px;
|
||||
border-bottom: 1px solid var(--border-color, #4e4e4e);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-nlp-search-input {
|
||||
width: 100%;
|
||||
padding: 6px 8px;
|
||||
background: var(--comfy-input-bg, #2a2a2a);
|
||||
border: 1px solid var(--border-color, #4e4e4e);
|
||||
border-radius: 4px;
|
||||
color: var(--input-text, #ddd);
|
||||
font-size: 12px;
|
||||
outline: none;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
.wfm-nlp-search-input:focus {
|
||||
border-color: var(--p-button-background, #4a9eff);
|
||||
}
|
||||
.wfm-nlp-content {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 4px 0;
|
||||
}
|
||||
.wfm-nlp-empty {
|
||||
padding: 24px 16px;
|
||||
text-align: center;
|
||||
color: var(--descrip-text, #999);
|
||||
font-size: 12px;
|
||||
line-height: 1.6;
|
||||
}
|
||||
.wfm-nlp-item {
|
||||
padding: 7px 12px;
|
||||
cursor: grab;
|
||||
border-bottom: 1px solid var(--border-color, #3a3a3a);
|
||||
transition: background 0.12s;
|
||||
user-select: none;
|
||||
}
|
||||
.wfm-nlp-item:hover {
|
||||
background: var(--comfy-input-bg, #333);
|
||||
}
|
||||
.wfm-nlp-item.dragging {
|
||||
opacity: 0.5;
|
||||
background: var(--comfy-input-bg, #333);
|
||||
}
|
||||
.wfm-nlp-item-label {
|
||||
font-size: 12px;
|
||||
line-height: 1.3;
|
||||
}
|
||||
.wfm-nlp-item-sub {
|
||||
font-size: 10px;
|
||||
color: var(--descrip-text, #888);
|
||||
margin-top: 2px;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
.wfm-nlp-badge {
|
||||
display: inline-block;
|
||||
font-size: 10px;
|
||||
padding: 1px 5px;
|
||||
background: var(--comfy-input-bg, #444);
|
||||
border-radius: 3px;
|
||||
color: var(--descrip-text, #aaa);
|
||||
margin-left: 4px;
|
||||
vertical-align: middle;
|
||||
}
|
||||
.wfm-nlp-group-section {
|
||||
border-bottom: 1px solid var(--border-color, #3a3a3a);
|
||||
}
|
||||
.wfm-nlp-group-header {
|
||||
padding: 8px 12px;
|
||||
cursor: pointer;
|
||||
font-weight: 500;
|
||||
font-size: 12px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
transition: background 0.12s;
|
||||
}
|
||||
.wfm-nlp-group-header:hover {
|
||||
background: var(--comfy-input-bg, #333);
|
||||
}
|
||||
.wfm-nlp-group-header::before {
|
||||
content: "\\25BC";
|
||||
font-size: 8px;
|
||||
margin-right: 6px;
|
||||
transition: transform 0.15s;
|
||||
}
|
||||
.wfm-nlp-group-header.collapsed::before {
|
||||
transform: rotate(-90deg);
|
||||
}
|
||||
.wfm-nlp-group-list {
|
||||
padding-left: 8px;
|
||||
}
|
||||
.wfm-nlp-group-list .wfm-nlp-item {
|
||||
padding: 5px 12px;
|
||||
font-size: 11px;
|
||||
}
|
||||
.wfm-nlp-refresh {
|
||||
background: none;
|
||||
border: none;
|
||||
color: var(--input-text, #ddd);
|
||||
font-size: 16px;
|
||||
cursor: pointer;
|
||||
opacity: 0.7;
|
||||
}
|
||||
.wfm-nlp-refresh:hover { opacity: 1; }
|
||||
`;
|
||||
document.head.appendChild(style);
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Context Menu — Save selected nodes as Node Set
|
||||
// ============================================
|
||||
|
||||
const saveSelectedAsNodeSet = async () => {
|
||||
const selectedNodes = Object.values(app.canvas.selected_nodes || {});
|
||||
if (selectedNodes.length === 0) {
|
||||
showToast("Select at least one node first", "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const name = prompt("Node Set name:");
|
||||
if (!name || !name.trim()) return;
|
||||
|
||||
const minX = Math.min(...selectedNodes.map(n => n.pos[0]));
|
||||
const minY = Math.min(...selectedNodes.map(n => n.pos[1]));
|
||||
|
||||
const nodeIdToIndex = new Map();
|
||||
const nodes = [];
|
||||
selectedNodes.forEach((node, i) => {
|
||||
nodeIdToIndex.set(node.id, i);
|
||||
nodes.push({
|
||||
class_type: node.type || node.comfyClass || "",
|
||||
title: node.title || "",
|
||||
rel_pos: [Math.round(node.pos[0] - minX), Math.round(node.pos[1] - minY)],
|
||||
});
|
||||
});
|
||||
|
||||
const links = [];
|
||||
const graph = app.graph;
|
||||
for (const node of selectedNodes) {
|
||||
if (!node.inputs) continue;
|
||||
for (let slotIdx = 0; slotIdx < node.inputs.length; slotIdx++) {
|
||||
const input = node.inputs[slotIdx];
|
||||
if (input.link == null) continue;
|
||||
|
||||
let linkData = null;
|
||||
if (graph.links instanceof Map) {
|
||||
linkData = graph.links.get(input.link);
|
||||
} else if (graph.links) {
|
||||
linkData = graph.links[input.link];
|
||||
}
|
||||
if (!linkData) continue;
|
||||
|
||||
const originId = linkData.origin_id ?? linkData[1];
|
||||
const originSlot = linkData.origin_slot ?? linkData[2];
|
||||
const targetSlot = linkData.target_slot ?? linkData[4];
|
||||
|
||||
if (nodeIdToIndex.has(originId) && nodeIdToIndex.has(node.id)) {
|
||||
links.push({
|
||||
from_node: nodeIdToIndex.get(originId),
|
||||
from_slot: originSlot,
|
||||
to_node: nodeIdToIndex.get(node.id),
|
||||
to_slot: targetSlot,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/wfm/node-sets", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ name: name.trim(), description: "", tags: [], nodes, links }),
|
||||
});
|
||||
const result = await res.json();
|
||||
if (result.status === "ok") {
|
||||
showToast(`Node Set "${name.trim()}" saved (${nodes.length} nodes, ${links.length} links)`, "success");
|
||||
if (state.loaded) { await loadData(); renderContent(); }
|
||||
} else {
|
||||
showToast("Failed to save: " + (result.error || "Unknown error"), "error");
|
||||
}
|
||||
} catch (err) {
|
||||
showToast("Failed to save: " + err.message, "error");
|
||||
}
|
||||
};
|
||||
|
||||
export { saveSelectedAsNodeSet };
|
||||
@@ -605,50 +605,61 @@ app.registerExtension({
|
||||
};
|
||||
injectStyles();
|
||||
|
||||
const applyButtonIcon = (button, className, getIconFn, extraStyles) => {
|
||||
if (button.querySelector("svg")) return;
|
||||
button.classList.add(className);
|
||||
button.innerHTML = getIconFn();
|
||||
button.style.borderRadius = "4px";
|
||||
button.style.padding = "6px";
|
||||
if (extraStyles) Object.assign(button.style, extraStyles);
|
||||
const svg = button.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
};
|
||||
|
||||
const buttonConfigs = [
|
||||
{ tooltip: BUTTON_TOOLTIP, className: "wfm-top-menu-button", getIcon: getWfmIcon, styles: { backgroundColor: "var(--primary-bg)" } },
|
||||
{ tooltip: SNAPSHOT_TOOLTIP, className: "wfm-snapshot-button", getIcon: getSnapshotIcon, styles: null },
|
||||
{ tooltip: NODE_SETS_TOOLTIP, className: "wfm-node-sets-btn", getIcon: getNodeSetsIcon, styles: null },
|
||||
];
|
||||
|
||||
const replaceAllIcons = () => {
|
||||
const applyIconToButton = (btn, cfg) => {
|
||||
// Skip if the SVG icon is already in place
|
||||
if (btn.querySelector("svg")) return;
|
||||
btn.classList.add(cfg.className);
|
||||
btn.innerHTML = cfg.getIcon();
|
||||
btn.style.borderRadius = "4px";
|
||||
btn.style.padding = "6px";
|
||||
if (cfg.styles) Object.assign(btn.style, cfg.styles);
|
||||
const svg = btn.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
};
|
||||
|
||||
// Initial application — retry until buttons are rendered by Vue
|
||||
const pollUntilFound = () => {
|
||||
let foundAny = false;
|
||||
for (const cfg of buttonConfigs) {
|
||||
document.querySelectorAll(`button[aria-label="${cfg.tooltip}"]`).forEach((btn) => {
|
||||
applyButtonIcon(btn, cfg.className, cfg.getIcon, cfg.styles);
|
||||
foundAny = true;
|
||||
applyIconToButton(btn, cfg);
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
const waitAndObserve = () => {
|
||||
const firstBtn = document.querySelector(`button[aria-label="${BUTTON_TOOLTIP}"]`);
|
||||
if (!firstBtn) {
|
||||
requestAnimationFrame(waitAndObserve);
|
||||
return;
|
||||
}
|
||||
replaceAllIcons();
|
||||
|
||||
const container = firstBtn.closest(".actionbar-container") || firstBtn.parentElement;
|
||||
if (container) {
|
||||
new MutationObserver(() => replaceAllIcons()).observe(container, {
|
||||
childList: true,
|
||||
subtree: true,
|
||||
});
|
||||
if (!foundAny) {
|
||||
requestAnimationFrame(pollUntilFound);
|
||||
}
|
||||
};
|
||||
requestAnimationFrame(waitAndObserve);
|
||||
requestAnimationFrame(pollUntilFound);
|
||||
|
||||
// MutationObserver: re-apply icons after Vue re-renders the action bar
|
||||
// (e.g. when the Properties Panel is toggled inside a subgraph)
|
||||
if (typeof MutationObserver !== "undefined") {
|
||||
const observer = new MutationObserver(() => {
|
||||
for (const cfg of buttonConfigs) {
|
||||
document.querySelectorAll(`button[aria-label="${cfg.tooltip}"]`).forEach((btn) => {
|
||||
// Only re-apply when Vue has reset innerHTML back to <i>
|
||||
if (btn.querySelector("i")) {
|
||||
applyIconToButton(btn, cfg);
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
const watchNode =
|
||||
document.querySelector("[data-testid='action-bar-buttons']") ||
|
||||
document.querySelector(".actionbar-container") ||
|
||||
document.body;
|
||||
observer.observe(watchNode, { childList: true, subtree: true });
|
||||
window.__wfmIconObserver = observer;
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
@@ -0,0 +1,565 @@
|
||||
console.log("[WFM] top_menu_extension.js loading...");
|
||||
import { app } from "../../scripts/app.js";
|
||||
console.log("[WFM] app imported:", !!app);
|
||||
import { togglePanel, getNodeSetsIcon, NODE_SETS_TOOLTIP, saveSelectedAsNodeSet } from "./node_sets_menu.js";
|
||||
console.log("[WFM] node_sets_menu imports loaded");
|
||||
|
||||
const BUTTON_TOOLTIP = "Launch Workflow Studio (Shift+Click opens in new window)";
|
||||
const SNAPSHOT_TOOLTIP = "Save workflow canvas image as thumbnail";
|
||||
const WFM_PATH = "/wfm";
|
||||
const NEW_WINDOW_FEATURES = "width=1400,height=900,resizable=yes,scrollbars=yes,status=yes";
|
||||
const MAX_ATTACH_ATTEMPTS = 120;
|
||||
const BUTTON_GROUP_CLASS = "wfm-top-menu-group";
|
||||
|
||||
const MIN_VERSION_FOR_ACTION_BAR = [1, 33, 9];
|
||||
|
||||
const openWorkflowStudio = (event) => {
|
||||
const url = `${window.location.origin}${WFM_PATH}`;
|
||||
|
||||
if (event.shiftKey) {
|
||||
window.open(url, "_blank", NEW_WINDOW_FEATURES);
|
||||
return;
|
||||
}
|
||||
|
||||
window.open(url, "_blank");
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Canvas Snapshot - Export workflow as PNG thumbnail
|
||||
// ============================================
|
||||
|
||||
const getWorkflowFilename = () => {
|
||||
const now = new Date();
|
||||
const pad = (n) => String(n).padStart(2, "0");
|
||||
const stamp = `${now.getFullYear()}${pad(now.getMonth() + 1)}${pad(now.getDate())}${pad(now.getHours())}${pad(now.getMinutes())}${pad(now.getSeconds())}`;
|
||||
return `wf_${stamp}.json`;
|
||||
};
|
||||
|
||||
// ---- PNG tEXt chunk embedding helpers ----
|
||||
|
||||
const n2b = (n) => new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]);
|
||||
|
||||
const joinArrayBuffer = (...bufs) => {
|
||||
const result = new Uint8Array(bufs.reduce((total, buf) => total + buf.byteLength, 0));
|
||||
bufs.reduce((offset, buf) => {
|
||||
result.set(buf, offset);
|
||||
return offset + buf.byteLength;
|
||||
}, 0);
|
||||
return result;
|
||||
};
|
||||
|
||||
let _crcTable = null;
|
||||
const crc32 = (data) => {
|
||||
if (!_crcTable) {
|
||||
_crcTable = [];
|
||||
for (let n = 0; n < 256; n++) {
|
||||
let c = n;
|
||||
for (let k = 0; k < 8; k++) {
|
||||
c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1;
|
||||
}
|
||||
_crcTable[n] = c;
|
||||
}
|
||||
}
|
||||
let crc = 0 ^ -1;
|
||||
for (let i = 0; i < data.byteLength; i++) {
|
||||
crc = (crc >>> 8) ^ _crcTable[(crc ^ data[i]) & 0xff];
|
||||
}
|
||||
return (crc ^ -1) >>> 0;
|
||||
};
|
||||
|
||||
const embedWorkflowInPng = async (blob, workflowJson) => {
|
||||
const buffer = await blob.arrayBuffer();
|
||||
const typedArr = new Uint8Array(buffer);
|
||||
const view = new DataView(buffer);
|
||||
|
||||
// Create tEXt chunk: "tEXtworkflow\0{json}"
|
||||
const data = new TextEncoder().encode(`tEXtworkflow\0${workflowJson}`);
|
||||
const chunk = joinArrayBuffer(n2b(data.byteLength - 4), data, n2b(crc32(data)));
|
||||
|
||||
// Insert after IHDR chunk (8-byte signature + IHDR length field(4) + IHDR type(4) + IHDR data + CRC(4))
|
||||
const ihdrDataLen = view.getUint32(8);
|
||||
const sz = ihdrDataLen + 20; // 8(sig) + 4(len) + 4(type) + ihdrDataLen + 4(crc)
|
||||
const result = joinArrayBuffer(typedArr.subarray(0, sz), chunk, typedArr.subarray(sz));
|
||||
|
||||
return new Blob([result], { type: "image/png" });
|
||||
};
|
||||
|
||||
// ---- Canvas capture ----
|
||||
|
||||
const captureCanvasSnapshot = () => {
|
||||
return new Promise((resolve, reject) => {
|
||||
try {
|
||||
const graph = app.graph;
|
||||
const canvas = app.canvas;
|
||||
if (!graph || !canvas || !graph._nodes?.length) {
|
||||
reject(new Error("No workflow loaded"));
|
||||
return;
|
||||
}
|
||||
|
||||
// Serialize workflow data before changing canvas state
|
||||
const workflowJson = JSON.stringify(graph.serialize());
|
||||
|
||||
// Calculate bounds of all nodes
|
||||
const bounds = graph._nodes.reduce(
|
||||
(p, n) => {
|
||||
if (n.pos[0] < p[0]) p[0] = n.pos[0];
|
||||
if (n.pos[1] < p[1]) p[1] = n.pos[1];
|
||||
const b = n.getBounding();
|
||||
const r = n.pos[0] + b[2];
|
||||
const bot = n.pos[1] + b[3];
|
||||
if (r > p[2]) p[2] = r;
|
||||
if (bot > p[3]) p[3] = bot;
|
||||
return p;
|
||||
},
|
||||
[99999, 99999, -99999, -99999]
|
||||
);
|
||||
|
||||
// Add padding
|
||||
const padding = 80;
|
||||
bounds[0] -= padding;
|
||||
bounds[1] -= padding;
|
||||
bounds[2] += padding;
|
||||
bounds[3] += padding;
|
||||
|
||||
// Save current canvas state
|
||||
const savedState = {
|
||||
scale: canvas.ds.scale,
|
||||
width: canvas.canvas.width,
|
||||
height: canvas.canvas.height,
|
||||
offset: [...canvas.ds.offset],
|
||||
transform: canvas.canvas.getContext("2d").getTransform(),
|
||||
};
|
||||
|
||||
// Set canvas to render full workflow
|
||||
const dpr = window.devicePixelRatio || 1;
|
||||
canvas.ds.scale = 1;
|
||||
canvas.canvas.width = (bounds[2] - bounds[0]) * dpr;
|
||||
canvas.canvas.height = (bounds[3] - bounds[1]) * dpr;
|
||||
canvas.ds.offset = [-bounds[0], -bounds[1]];
|
||||
canvas.canvas.getContext("2d").setTransform(dpr, 0, 0, dpr, 0, 0);
|
||||
|
||||
// Render
|
||||
canvas.draw(true, true);
|
||||
|
||||
// Capture to blob
|
||||
canvas.canvas.toBlob(async (rawBlob) => {
|
||||
// Restore canvas state
|
||||
canvas.ds.scale = savedState.scale;
|
||||
canvas.canvas.width = savedState.width;
|
||||
canvas.canvas.height = savedState.height;
|
||||
canvas.ds.offset = savedState.offset;
|
||||
canvas.canvas.getContext("2d").setTransform(savedState.transform);
|
||||
canvas.draw(true, true);
|
||||
|
||||
if (!rawBlob) {
|
||||
reject(new Error("Failed to generate canvas image"));
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
// Embed workflow JSON into PNG tEXt chunk
|
||||
const blob = await embedWorkflowInPng(rawBlob, workflowJson);
|
||||
resolve(blob);
|
||||
} catch (embedErr) {
|
||||
console.warn("Workflow Studio: failed to embed workflow metadata, saving without it:", embedErr);
|
||||
resolve(rawBlob);
|
||||
}
|
||||
}, "image/png");
|
||||
} catch (err) {
|
||||
reject(err);
|
||||
}
|
||||
});
|
||||
};
|
||||
|
||||
const saveCanvasToWorkflowStudio = async () => {
|
||||
const filename = getWorkflowFilename();
|
||||
|
||||
try {
|
||||
showNotification("Capturing workflow image...", "info");
|
||||
const blob = await captureCanvasSnapshot();
|
||||
|
||||
const fd = new FormData();
|
||||
fd.append("filename", filename);
|
||||
fd.append("image", blob, "canvas.png");
|
||||
|
||||
const res = await fetch("/api/wfm/workflows/save-canvas-image", {
|
||||
method: "POST",
|
||||
body: fd,
|
||||
});
|
||||
|
||||
const data = await res.json();
|
||||
if (data.error) {
|
||||
throw new Error(data.error);
|
||||
}
|
||||
|
||||
showNotification(`Workflow saved: ${filename.replace(/\.json$/, "")}`, "success");
|
||||
} catch (err) {
|
||||
showNotification("Error: " + err.message, "error");
|
||||
}
|
||||
};
|
||||
|
||||
const showNotification = (message, type = "info") => {
|
||||
// Try using ComfyUI's built-in toast if available
|
||||
try {
|
||||
if (app.ui?.dialog) {
|
||||
// Fallback: use a simple temporary notification
|
||||
}
|
||||
} catch (e) { /* ignore */ }
|
||||
|
||||
// Simple toast notification
|
||||
const existing = document.getElementById("wfm-snapshot-toast");
|
||||
if (existing) existing.remove();
|
||||
|
||||
const toast = document.createElement("div");
|
||||
toast.id = "wfm-snapshot-toast";
|
||||
const colors = {
|
||||
success: { bg: "rgba(46,213,115,0.95)", color: "#fff" },
|
||||
error: { bg: "rgba(255,71,87,0.95)", color: "#fff" },
|
||||
info: { bg: "rgba(74,158,255,0.95)", color: "#fff" },
|
||||
};
|
||||
const c = colors[type] || colors.info;
|
||||
Object.assign(toast.style, {
|
||||
position: "fixed",
|
||||
bottom: "24px",
|
||||
left: "50%",
|
||||
transform: "translateX(-50%)",
|
||||
padding: "10px 24px",
|
||||
background: c.bg,
|
||||
color: c.color,
|
||||
borderRadius: "8px",
|
||||
fontSize: "14px",
|
||||
fontWeight: "500",
|
||||
zIndex: "99999",
|
||||
boxShadow: "0 4px 16px rgba(0,0,0,0.3)",
|
||||
transition: "opacity 0.3s ease",
|
||||
whiteSpace: "nowrap",
|
||||
});
|
||||
toast.textContent = message;
|
||||
document.body.appendChild(toast);
|
||||
|
||||
if (type !== "info") {
|
||||
setTimeout(() => {
|
||||
toast.style.opacity = "0";
|
||||
setTimeout(() => toast.remove(), 300);
|
||||
}, 3000);
|
||||
} else {
|
||||
// Info messages will be replaced by success/error
|
||||
setTimeout(() => {
|
||||
if (toast.parentNode) {
|
||||
toast.style.opacity = "0";
|
||||
setTimeout(() => toast.remove(), 300);
|
||||
}
|
||||
}, 10000);
|
||||
}
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Version Detection & UI
|
||||
// ============================================
|
||||
|
||||
const getComfyUIFrontendVersion = async () => {
|
||||
try {
|
||||
if (window["__COMFYUI_FRONTEND_VERSION__"]) {
|
||||
return window["__COMFYUI_FRONTEND_VERSION__"];
|
||||
}
|
||||
} catch (error) {
|
||||
console.warn("Workflow Studio: unable to read __COMFYUI_FRONTEND_VERSION__:", error);
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch("/system_stats");
|
||||
const data = await response.json();
|
||||
|
||||
if (data?.system?.comfyui_frontend_version) {
|
||||
return data.system.comfyui_frontend_version;
|
||||
}
|
||||
|
||||
if (data?.system?.required_frontend_version) {
|
||||
return data.system.required_frontend_version;
|
||||
}
|
||||
} catch (error) {
|
||||
console.warn("Workflow Studio: unable to fetch system_stats:", error);
|
||||
}
|
||||
|
||||
return "0.0.0";
|
||||
};
|
||||
|
||||
const parseVersion = (versionStr) => {
|
||||
if (!versionStr || typeof versionStr !== "string") {
|
||||
return [0, 0, 0];
|
||||
}
|
||||
|
||||
const cleanVersion = versionStr.replace(/^[vV]/, "").split("-")[0];
|
||||
const parts = cleanVersion.split(".").map((part) => parseInt(part, 10) || 0);
|
||||
|
||||
while (parts.length < 3) {
|
||||
parts.push(0);
|
||||
}
|
||||
|
||||
return parts;
|
||||
};
|
||||
|
||||
const compareVersions = (version1, version2) => {
|
||||
const v1 = typeof version1 === "string" ? parseVersion(version1) : version1;
|
||||
const v2 = typeof version2 === "string" ? parseVersion(version2) : version2;
|
||||
|
||||
for (let i = 0; i < 3; i++) {
|
||||
if (v1[i] > v2[i]) return 1;
|
||||
if (v1[i] < v2[i]) return -1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
};
|
||||
|
||||
const supportsActionBarButtons = async () => {
|
||||
const version = await getComfyUIFrontendVersion();
|
||||
return compareVersions(version, MIN_VERSION_FOR_ACTION_BAR) >= 0;
|
||||
};
|
||||
|
||||
const getWfmIcon = () => {
|
||||
return `
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path d="M3 5 L7 17 L12 7 L17 17 L21 5" stroke-width="2.3"/>
|
||||
<path d="M5 20 Q9 17 12 20 Q15 23 19 20" stroke-width="1.8"/>
|
||||
</svg>
|
||||
`;
|
||||
};
|
||||
|
||||
const getSnapshotIcon = () => {
|
||||
return `
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2">
|
||||
<rect x="3" y="5" width="18" height="14" rx="2"/>
|
||||
<circle cx="12" cy="12" r="3"/>
|
||||
<path d="M8 5 L9 3 L15 3 L16 5"/>
|
||||
</svg>
|
||||
`;
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Button Creation
|
||||
// ============================================
|
||||
|
||||
const createTopMenuButton = async () => {
|
||||
const { ComfyButton } = await import("../../scripts/ui/components/button.js");
|
||||
|
||||
const button = new ComfyButton({
|
||||
icon: "wfm",
|
||||
tooltip: BUTTON_TOOLTIP,
|
||||
app,
|
||||
enabled: true,
|
||||
classList: "comfyui-button comfyui-menu-mobile-collapse primary",
|
||||
});
|
||||
|
||||
button.element.setAttribute("aria-label", BUTTON_TOOLTIP);
|
||||
button.element.title = BUTTON_TOOLTIP;
|
||||
|
||||
if (button.iconElement) {
|
||||
button.iconElement.innerHTML = getWfmIcon();
|
||||
button.iconElement.style.width = "1.2rem";
|
||||
button.iconElement.style.height = "1.2rem";
|
||||
}
|
||||
|
||||
button.element.addEventListener("click", openWorkflowStudio);
|
||||
return button;
|
||||
};
|
||||
|
||||
const createSnapshotButton = async () => {
|
||||
const { ComfyButton } = await import("../../scripts/ui/components/button.js");
|
||||
|
||||
const button = new ComfyButton({
|
||||
icon: "snapshot",
|
||||
tooltip: SNAPSHOT_TOOLTIP,
|
||||
app,
|
||||
enabled: true,
|
||||
classList: "comfyui-button comfyui-menu-mobile-collapse",
|
||||
});
|
||||
|
||||
button.element.setAttribute("aria-label", SNAPSHOT_TOOLTIP);
|
||||
button.element.title = SNAPSHOT_TOOLTIP;
|
||||
|
||||
if (button.iconElement) {
|
||||
button.iconElement.innerHTML = getSnapshotIcon();
|
||||
button.iconElement.style.width = "1.2rem";
|
||||
button.iconElement.style.height = "1.2rem";
|
||||
}
|
||||
|
||||
button.element.addEventListener("click", saveCanvasToWorkflowStudio);
|
||||
return button;
|
||||
};
|
||||
|
||||
const createNodeSetsButton = async () => {
|
||||
const { ComfyButton } = await import("../../scripts/ui/components/button.js");
|
||||
|
||||
const button = new ComfyButton({
|
||||
icon: "node-sets",
|
||||
tooltip: NODE_SETS_TOOLTIP,
|
||||
app,
|
||||
enabled: true,
|
||||
classList: "comfyui-button comfyui-menu-mobile-collapse wfm-node-sets-btn",
|
||||
});
|
||||
|
||||
button.element.setAttribute("aria-label", NODE_SETS_TOOLTIP);
|
||||
button.element.title = NODE_SETS_TOOLTIP;
|
||||
|
||||
if (button.iconElement) {
|
||||
button.iconElement.innerHTML = getNodeSetsIcon();
|
||||
button.iconElement.style.width = "1.2rem";
|
||||
button.iconElement.style.height = "1.2rem";
|
||||
}
|
||||
|
||||
button.element.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
togglePanel();
|
||||
});
|
||||
|
||||
return button;
|
||||
};
|
||||
|
||||
const attachTopMenuButton = async (attempt = 0) => {
|
||||
if (document.querySelector(`.${BUTTON_GROUP_CLASS}`)) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Try settingsGroup first (legacy ComfyUI)
|
||||
const settingsGroup = app.menu?.settingsGroup;
|
||||
// Fallback: find the legacy topbar container used by newer ComfyUI
|
||||
const legacyContainer = document.querySelector('[data-testid="legacy-topbar-container"] .flex');
|
||||
|
||||
if (!settingsGroup?.element?.parentElement && !legacyContainer) {
|
||||
if (attempt >= MAX_ATTACH_ATTEMPTS) {
|
||||
console.warn("Workflow Studio: unable to locate the ComfyUI menu container.");
|
||||
return;
|
||||
}
|
||||
|
||||
requestAnimationFrame(() => attachTopMenuButton(attempt + 1));
|
||||
return;
|
||||
}
|
||||
|
||||
const wfmButton = await createTopMenuButton();
|
||||
const snapshotButton = await createSnapshotButton();
|
||||
const nodeSetsButton = await createNodeSetsButton();
|
||||
const { ComfyButtonGroup } = await import("../../scripts/ui/components/buttonGroup.js");
|
||||
|
||||
const buttonGroup = new ComfyButtonGroup(wfmButton, snapshotButton, nodeSetsButton);
|
||||
buttonGroup.element.classList.add(BUTTON_GROUP_CLASS);
|
||||
|
||||
if (settingsGroup?.element?.parentElement) {
|
||||
settingsGroup.element.before(buttonGroup.element);
|
||||
} else if (legacyContainer) {
|
||||
legacyContainer.prepend(buttonGroup.element);
|
||||
}
|
||||
};
|
||||
|
||||
app.registerExtension({
|
||||
name: "WorkflowStudio.TopMenu",
|
||||
actionBarButtons: [
|
||||
{
|
||||
icon: "icon-[mdi--file-document-multiple] size-4",
|
||||
tooltip: BUTTON_TOOLTIP,
|
||||
onClick: openWorkflowStudio,
|
||||
},
|
||||
{
|
||||
icon: "icon-[mdi--camera] size-4",
|
||||
tooltip: SNAPSHOT_TOOLTIP,
|
||||
onClick: saveCanvasToWorkflowStudio,
|
||||
},
|
||||
{
|
||||
icon: "icon-[mdi--view-grid-plus] size-4",
|
||||
tooltip: NODE_SETS_TOOLTIP,
|
||||
onClick: () => togglePanel(),
|
||||
},
|
||||
],
|
||||
beforeRegisterNodeDef(nodeType, _nodeData, _app) {
|
||||
const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
|
||||
nodeType.prototype.getExtraMenuOptions = function (_, options) {
|
||||
origGetExtraMenuOptions?.apply(this, arguments);
|
||||
const selectedCount = Object.keys(app.canvas.selected_nodes || {}).length;
|
||||
options.push(null);
|
||||
options.push({
|
||||
content: `Save as Node Set${selectedCount > 1 ? ` (${selectedCount} nodes)` : ""}`,
|
||||
callback: saveSelectedAsNodeSet,
|
||||
});
|
||||
};
|
||||
},
|
||||
async setup() {
|
||||
const injectStyles = () => {
|
||||
const styleId = "wfm-top-menu-button-styles";
|
||||
if (document.getElementById(styleId)) return;
|
||||
|
||||
const style = document.createElement("style");
|
||||
style.id = styleId;
|
||||
style.textContent = `
|
||||
button[aria-label="${BUTTON_TOOLTIP}"].wfm-top-menu-button {
|
||||
transition: all 0.2s ease;
|
||||
border: 1px solid transparent;
|
||||
}
|
||||
button[aria-label="${BUTTON_TOOLTIP}"].wfm-top-menu-button:hover {
|
||||
background-color: var(--primary-hover-bg) !important;
|
||||
}
|
||||
button[aria-label="${SNAPSHOT_TOOLTIP}"].wfm-snapshot-button {
|
||||
transition: all 0.2s ease;
|
||||
border: 1px solid transparent;
|
||||
}
|
||||
button[aria-label="${SNAPSHOT_TOOLTIP}"].wfm-snapshot-button:hover {
|
||||
background-color: var(--primary-hover-bg) !important;
|
||||
}
|
||||
button[aria-label="${NODE_SETS_TOOLTIP}"].wfm-node-sets-btn {
|
||||
transition: all 0.2s ease;
|
||||
border: 1px solid transparent;
|
||||
}
|
||||
button[aria-label="${NODE_SETS_TOOLTIP}"].wfm-node-sets-btn:hover {
|
||||
background-color: var(--primary-hover-bg) !important;
|
||||
}
|
||||
`;
|
||||
document.head.appendChild(style);
|
||||
};
|
||||
injectStyles();
|
||||
|
||||
const replaceButtonIcon = () => {
|
||||
const wfmButtons = document.querySelectorAll(`button[aria-label="${BUTTON_TOOLTIP}"]`);
|
||||
wfmButtons.forEach((button) => {
|
||||
button.classList.add("wfm-top-menu-button");
|
||||
button.innerHTML = getWfmIcon();
|
||||
button.style.borderRadius = "4px";
|
||||
button.style.padding = "6px";
|
||||
button.style.backgroundColor = "var(--primary-bg)";
|
||||
const svg = button.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
});
|
||||
|
||||
const snapButtons = document.querySelectorAll(`button[aria-label="${SNAPSHOT_TOOLTIP}"]`);
|
||||
snapButtons.forEach((button) => {
|
||||
button.classList.add("wfm-snapshot-button");
|
||||
button.innerHTML = getSnapshotIcon();
|
||||
button.style.borderRadius = "4px";
|
||||
button.style.padding = "6px";
|
||||
const svg = button.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
});
|
||||
|
||||
const nsButtons = document.querySelectorAll(`button[aria-label="${NODE_SETS_TOOLTIP}"]`);
|
||||
nsButtons.forEach((button) => {
|
||||
button.classList.add("wfm-node-sets-btn");
|
||||
button.innerHTML = getNodeSetsIcon();
|
||||
button.style.borderRadius = "4px";
|
||||
button.style.padding = "6px";
|
||||
const svg = button.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
});
|
||||
|
||||
if (wfmButtons.length === 0) {
|
||||
requestAnimationFrame(replaceButtonIcon);
|
||||
}
|
||||
};
|
||||
requestAnimationFrame(replaceButtonIcon);
|
||||
},
|
||||
});
|
||||
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|
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|
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Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user