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 +185,18 @@ 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. `README.md` の Changelog に新バージョンのエントリを追加
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4. `DEVLOG.md` の先頭に変更詳細エントリを追加
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5. 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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6. `gh release create vX.Y.Z` でリリースを作成
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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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@@ -1,48 +1,65 @@
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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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**📚 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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| Workflow Tab | Models Tab |
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|:---:|:---:|
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|  |  |
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|  |  |
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| Prompt Tab | Settings Tab |
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| Prompt Input Assistance | Gen UI Feeder |
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|:---:|:---:|
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|  |  |
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|  |  |
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| Nodes Tab | Models Tab |
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| GenUI LoRA Stack | GenUI Batch |
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|:---:|:---:|
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|  |  |
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|  |  |
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| Help & Support Tab | |
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| Models Multi-select Menu | Top Bar |
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|:---:|:---:|
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|  | |
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|  |  |
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| Workflow Studio Library (ComfyUI) | ComfyUI Integration |
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| WS Library | Library Information |
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|:---:|:---:|
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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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- **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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### 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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@@ -50,16 +67,47 @@ Browse, organize, and execute workflows directly from a dedicated studio interfa
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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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### GenerateUI Tab (v0.3.5)
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- **5-tab layout** — Input / Model / Settings / Feeder / Batch 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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- **Model tab** — Checkpoint, VAE, LoRA, ControlNet, UNET, TextEncoder, **Hypernetwork** (with Strength field) selectors with filter; Raw JSON on the right
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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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- **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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- **Batch type selector** — check one of the column header checkboxes in the Batch Queue pane (Checkpoint / Lora / Prompt / Workflow) 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** — file-tree checkpoint selection; Filter input to search; All / None buttons; initial state is all unchecked
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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)** — shows items queued for each batch type; column headers (Checkpoint / Lora / Prompt / Workflow) each have a checkbox — check one to activate that batch type (radio behavior: only one at a time); count shown at top
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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 more
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- **Eagle integration** — auto-save generated images to [Eagle](https://eagle.cool/) with metadata
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### Feeder subtab (v0.3.5 / v0.3.42)
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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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**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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**Gallery mode** (v0.3.42) — no external plugin required; uses the built-in **WFS_GalleryFeeder** custom node.
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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`)
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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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- **3-column layout** — AI Assistant (left), Presets/Preset Manager tab-panel (center), Wildcard support (right)
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- **AI chat assistant** — powered by [Ollama](https://ollama.com/), generate and refine prompts interactively
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- **Image attachment** — attach reference images for vision-capable models
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- **Translation** — JA/EN/ZH translation buttons for multilingual prompt creation
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@@ -68,17 +116,57 @@ Browse, organize, and execute workflows directly from a dedicated studio interfa
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- **Group management** — create groups, assign/remove presets, delete groups from the Presets panel
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- **Clipboard copy** — copy positive/negative prompts individually (PP Copy / NP Copy)
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- **GenUI Set** — apply preset prompts directly to the GenerateUI interface
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- **Wildcard input toolbar** — one-click buttons to insert `{|}`, `{n$|}`, `__|__`, `<lora::1:LBW=;>` and other wildcard syntax; wraps selected text when applicable
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- **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
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### Metadata Tab (v0.3.8)
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- **3-column layout** — Drop zone (left) | Model info (center) | LoRA + Prompt (right)
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- **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
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- **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); node types covered: UNETLoader, UnetLoaderGGUF, UNETLoaderGGUF (e.g. HiDream GGUF), CLIPLoader, DualCLIPLoader, TripleCLIPLoader, QuadrupleCLIPLoader (e.g. HiDream 4-CLIP)
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- **LoRA extraction** — lists all LoRA models with `strength_model / strength_clip` values
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- **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
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- **Prompt actions** — Copy to clipboard, **GenUI:P/N** (set GenerateUI positive/negative prompt), **Prompt:P/N** (set Prompt tab preset positive/negative)
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- **Format support** — ComfyUI PNG/WebP/JSON (standard + Flux.2 / Qwen-Image / Z-Image / Ernie Image / WAN2.2 subgraph workflows), SD WebUI, SD Forge, Fooocus
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- **Format note** — supported formats and covered model types are always shown in the left column
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### Settings Tab
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- **2-column layout** — left column for all settings; right column shows the Theme panel fixed in place (sticky)
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- **Collapsible sections** — all settings organized in accordion panels for a clean layout
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- **Theme selection** — 13 built-in themes with visual swatch preview (Dark, Pop, Minimalist, Cyberpunk, Glassmorphism, Neumorphism, Retro Pixel, Pastel, Brutalism, Earthy, Material, Monotone, Corporate)
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- **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)
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- **Workflows directory** — configure which folder to scan for workflows
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- **Gallery output directory** — configure which output folder the Gallery tab scans for images
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- **Eagle connection** — set Eagle API endpoint for auto-save
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- **Ollama connection** — configure Ollama server URL
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- **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
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- **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
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- **Default workflow** — set a workflow to auto-load on startup
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- **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
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- **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
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- **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
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- **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)
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- **Language** — English / Japanese / Chinese
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### Gallery Tab (v0.3.44)
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- **Image browser** — browse ComfyUI output images (Thumbnail / Table views) with server-side scanning optimized for 6,000+ image libraries
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- **Thumbnail / Table views** — switch view modes; Favorites column shown leftmost in Table view
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- **Folder management** — create subfolders ("+ New") or delete the selected folder with all contents ("Del") from the folder tree header
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- **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
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- **Download** — hover over the image preview in the detail panel to reveal a download icon (⬇); click to download the single image
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- **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
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- **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
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- **Prompt search** — text search covers filename, tags, memo, and prompt text (A1111 `parameters` field cached on first detail panel open)
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- **Server-side filtering** — filter by group, favorites, or tags with fast server-side set lookup (no full rescan)
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- **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
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- **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
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- **Favorites** — star images inline without reopening the detail panel
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- **Detail panel** — view filename, path, tags, groups, and metadata in a slide-out panel
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- **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)
|
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- **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
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- **Workflow auto-save** — images generated from the Generate UI tab have their workflow automatically saved to gallery metadata
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- **Output folder configurable** — set the scanned output folder from Settings tab
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- **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
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### Nodes Tab (v0.1.7)
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- **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
|
||||
@@ -87,33 +175,77 @@ Browse, organize, and execute workflows directly from a dedicated studio interfa
|
||||
- **Node Sets** — save multiple nodes + connections as reusable sets from the ComfyUI canvas
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||||
- **Right-click context menu** — "Save as Node Set" option on any node in ComfyUI
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### Models Tab (v0.2.2)
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- **Model Browser** — browse all installed ComfyUI models (Checkpoint, LoRA, VAE, ControlNet, UNET, TextEncoder) with sub-tab switching
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- **Thumbnail / Card / Table views** — switch between view modes with pagination (24 items per page)
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### Models Tab (v0.2.3)
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- **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**
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- **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
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- **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
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- **CivitAI integration** — fetch model metadata by SHA256 hash, view base model, trained words, tags, and model page link
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- **Batch CivitAI fetch** — one-click batch fetch for all models of a type with SSE progress streaming
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- **Detail modal** — preview image, CivitAI info, thumbnail change via file upload
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||||
- **Group management** — create, rename, delete groups and assign/remove models
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||||
- **User-defined badges** — assign free-label badges to models; badge colors shared with the Workflow tab palette
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||||
- **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
|
||||
|
||||
### 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
|
||||
### Tagger Tab (v0.3.38)
|
||||
- **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`
|
||||
|
||||
### AI TOOL Tab (v0.3.14)
|
||||
- **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 or LM Studio; 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 uses `/v1/chat/completions`
|
||||
- **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 (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
|
||||
- **Settings pane** — choose backend (Ollama / LM Studio), set the API URL, test connection, select a model (with refresh button), and configure Free language names for translation source and destination
|
||||
- **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 OpenAI-compatible API (`/v1/chat/completions`, `/v1/models`); VLM images sent as base64 (`images:[]` for Ollama, `image_url` content block for LM Studio)
|
||||
- **URL security** — backend URL validated via `new URL()` to enforce `http://` or `https://` scheme
|
||||
|
||||
### Workflow Studio Library (ComfyUI Side Panel) (v0.3.9)
|
||||
- **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 or LM Studio; 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)
|
||||
- **Sidebar navigation** — 2-column layout: left sidebar (14 topics) + right content pane; click any topic to switch the displayed content
|
||||
- **Support** — fixed at the bottom of the sidebar; shows GitHub and Ko-fi 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
|
||||
|
||||
---
|
||||
|
||||
@@ -134,6 +266,22 @@ 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
|
||||
@@ -168,7 +316,8 @@ 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)
|
||||
- **[Eagle](https://eagle.cool/)** — for auto-saving generated images with metadata
|
||||
|
||||
---
|
||||
@@ -185,6 +334,326 @@ Click the **camera icon** (next to the W button) in ComfyUI's top bar to capture
|
||||
|
||||
## Changelog
|
||||
|
||||
### v0.3.52
|
||||
- **GenerateUI Model tab — fix LoRA selection reset when switching subtabs** — applying a LoRA via "GenUI Model" button and then switching to another subTab (Prompt/Image/Settings) and back to Model caused the LoRA selector, syntax display, and trigger words to revert to the original workflow values; fixed by reading `currentVal` directly from `currentWorkflow` instead of the stale `currentAnalysis`, and by saving/restoring the Single tab's syntax and trigger words across re-renders
|
||||
|
||||
### v0.3.51
|
||||
- **Models tab — Embedding type: GenUI PP / GenUI NP buttons** — Embedding models now show two buttons ("GenUI PP" and "GenUI NP") in the side panel nav, detail side panel, and detail modal (replacing the single "GenUI Model" button); clicking either appends `(embedding:Name:1.0)` to the Positive or Negative prompt of the loaded workflow
|
||||
- **GenerateUI Prompt tab — Embeddings selector** — new section at the bottom of the Prompt tab: Filter input, model select dropdown, Weight input (default 1.0), and Paste button; Paste inserts `(embedding:Stem:weight)` at the cursor position of the last focused textarea (Positive or Negative); focus state is tracked on click/keyup/blur so the button works even after clicking away
|
||||
- **GenerateUI Model tab — Hypernetwork support** — Hypernetwork section added with model selector (filter) and Strength field; applies to `HypernetworkLoader` nodes in the workflow; GenUI Model button in Models tab also triggers Hypernetwork apply
|
||||
- **Models tab — fix Embedding and Hypernetwork models not shown** — `_fetchModelList` now handles the ComfyUI V3 node format (`options` key) in addition to V1/V2 formats; `fetchEmbeddings` switched from the `/embeddings` ComfyUI endpoint to the WFS backend API (`/api/wfm/models/files?type=embedding`) to avoid SPA URL routing conflicts
|
||||
- **Models tab — GenUI Model button in side panel nav** — "GenUI Model" button added to the tab navigation bar (Info / Group / CivitAI) of the side panel; appears for supported model types (Checkpoint, LoRA, VAE, etc.) and hides for unsupported types; Embedding type shows PP/NP nav buttons instead
|
||||
|
||||
### v0.3.50
|
||||
- **Gallery tab — fix prompt search causing ComfyUI hang under large folders** — the v0.3.49 on-demand metadata read ran synchronously on the aiohttp event loop, blocking ComfyUI when folders contained many uncached images; fixed by: (1) removing on-demand reads from `list_images()`; (2) wrapping `list_images()` in `asyncio.to_thread()` to keep the event loop free; (3) adding a background indexer (`start_background_index`) that automatically indexes uncached images after a folder is loaded — processes 10 images per batch with a 50 ms sleep between batches; cancellable on folder switch
|
||||
|
||||
### v0.3.49
|
||||
- **GenerateUI tab — A1111-style SPA-side wildcard expansion** — when prompt text in non-ImpactWildcard nodes (e.g. CLIPTextEncode) contains `__name__` syntax, the SPA expands it at generation time by randomly picking a line from the matching file in the WFS wildcard folder; nested wildcards are re-expanded up to 5 passes; unresolved wildcards are passed through unchanged; ImpactWildcardEncode/Processor nodes are skipped (handled server-side by Impact Pack); works in both single and batch generation
|
||||
- **Gallery tab — fix Export button missing label** — the bulk action bar's Export button had no text label because the `textContent` assignment was missing from `updateUITexts()`; fixed by adding the `galleryBulkExport` i18n key assignment alongside the existing Move/Delete entries
|
||||
- **Gallery tab — fix prompt search not matching images with no cached index** — `prompt_cache` was only built when an image was opened in the detail panel; A1111 images (or any images never opened) were invisible to prompt search; now when search is active and `prompt_cache` is empty, the image file is read on-demand and the cache is populated and persisted for future searches
|
||||
|
||||
### v0.3.48
|
||||
- **Gallery tab — single image download** — hover over a detail-panel preview image to reveal a download overlay (⬇ icon); click to download the original file with its original filename preserved
|
||||
- **Gallery tab — bulk export to ZIP** — multiple selected images can be exported as a ZIP archive via the "Export" button in the bulk action bar; filename: `gallery_export_<timestamp>.zip`; backend: `POST /wfm/gallery/images/export-zip` with path-traversal protection
|
||||
|
||||
### v0.3.47
|
||||
- **Models tab — fix stale group state after file move** — when models were moved to a subdirectory via "Move To...", `state.modelGroups` in the JS client was not updated to reflect the new model paths; the group filter view would lose the moved models immediately after the operation. Now the client-side group membership is updated in sync with the server-side rename (mirrors the existing Python `move_models` logic). Generate tab Batch groups are unaffected as they always fetch fresh from the server.
|
||||
|
||||
### v0.3.46
|
||||
- **Prompt tab AI Assistant — LM Studio support** — switched from Python proxy (`/api/wfm/ollama/*`) to direct browser-to-backend API calls (same pattern as the AI TOOL tab); supports Ollama (`/api/chat`) and LM Studio (`/v1/chat/completions`); image attachments are automatically converted to the correct format per backend
|
||||
- **Settings tab — AI Assistant Settings (Prompt Tab)** — renamed from "Ollama Settings"; added backend selector (Ollama / LM Studio), URL input with per-backend placeholder, model select with Refresh, and Test button; settings saved to localStorage `wfm_prompt_ai_settings` (independent of AI TOOL tab settings)
|
||||
- **Auto-refresh on backend change** — if the backend is switched in Settings tab while Prompt tab is already open, `sendMessage()` now detects the mismatch and automatically re-fetches the model list before sending; model status shows the active backend name (`[ollama]` / `[lmstudio]`)
|
||||
- **Help text updated** (EN/JA/ZH) — Prompt tab, Settings tab, and Troubleshooting sections updated to reflect Ollama / LM Studio dual-backend support
|
||||
|
||||
### v0.3.45
|
||||
- **Group auto-cleanup — Models tab** — stale group entries (models that no longer exist on disk) are automatically removed when the Models tab is loaded or the model type is switched; models moved within the app via "Move To..." also update their group membership immediately
|
||||
- **Group auto-cleanup — Gallery tab (folder view)** — metadata entries for missing files are cleaned up each time a folder is scanned (`list_images`), synchronized with the 60s TTL + folder-mtime cache
|
||||
- **Group auto-cleanup — Gallery tab (group image list)** — `list_images_in_group` (used by the Feeder tab Gallery mode) now filters out non-existent paths and removes them from the group in a single write operation
|
||||
- **Gallery tab — Shift+click range selection** — Shift+click selects all images between the last clicked item (anchor) and the current item; works in both thumbnail and table views; anchor is updated on Ctrl+click and regular click; resets on folder change, image reload, and Deselect All; help text updated (EN/JA/ZH)
|
||||
|
||||
### v0.3.44
|
||||
- **Gallery tab — server-side thumbnail generation** — `GET /wfm/gallery/image/thumb?path=...&w=256` endpoint generates 256px JPEG thumbnails via Pillow and caches them to `data/thumb_cache/` keyed by `md5(path:mtime:width)`; GIF served as-is to preserve animation; falls back to the original file if Pillow is unavailable; thumbnail view and table view both switched from `/image/serve` to `/image/thumb`
|
||||
- **Gallery tab — infinite-scroll paging** — thumbnail view renders 50 images initially; an IntersectionObserver sentinel (rootMargin 300px) appends the next page on scroll; sentinel is disconnected and removed when all images are rendered; paging resets on every `renderImages()` call (folder change, filter, sort)
|
||||
- **Gallery tab — bulk API endpoints** — `POST /wfm/gallery/bulk/favorite` and `POST /wfm/gallery/bulk/group` handle favorite set and group add/remove for any number of images in a single request; replaced N-parallel individual fetches in `bulkSetFavorite()` and `bulkAddToGroup()`
|
||||
- **Gallery tab — image compare mode** — Ctrl+click 2–4 images, then click "Compare" in the Bulk Bar to open a side-by-side lightbox; CSS grid adapts to the image count via `--compare-cols` custom property; close button is `position:fixed` at the top-right corner to avoid `overflow:auto` clipping
|
||||
- **Gallery tab — "Remove from Group" bulk button** — new button in the Bulk Bar (right of "Add to Group"); uses the same group selector dropdown; calls `POST /wfm/gallery/bulk/group` with `action:"remove"`
|
||||
- **Gallery tab — bulk bar UI labels** — dropdown placeholder "Add to Group..." → "Select Group"; "Add" button → "Add to Group"; "Delete" button → "Delete File" (distinguishes file deletion from group removal)
|
||||
- **Gallery tab — prompt search** — text search now covers A1111 `parameters` text embedded in PNG/JPEG metadata; prompt text is extracted on first detail-panel open and stored as `prompt_cache` in `metadata.json`; subsequent `list_images` searches include `prompt_cache` without re-reading image files
|
||||
- **Help tab — Gallery mode i18n** — Feeder subtab Gallery mode section (h4, description, 6 bullet points) now translated to Japanese and Chinese via `i18n.js`
|
||||
- **Help tab — font size ×1.5** — nav items, headings, body text, link cards, and support card text all scaled up in `main.css`
|
||||
- **Help tab — search box** — live search input above the nav sidebar filters matching pages and auto-clicks the first result; placeholder text localized (EN/JA/ZH)
|
||||
|
||||
### v0.3.43
|
||||
- **Send to Canvas — direct canvas load via `window.opener`** — clicking "Send to Canvas" (Workflow tab) or "Copy & Send Canvas" (Gallery tab) now sends the workflow directly to the ComfyUI canvas without requiring a title drag; uses `window.opener.wfmReceiveWorkflow()` registered by `node_sets_menu.js` via `app.handleFile()`; both UI and API formats are supported; fallback to localStorage + title drag when `window.opener` is unavailable (e.g. opened from a bookmark), UI-format only
|
||||
- **Workflow Studio Library — API-format DnD enabled** — API-format workflows in the W (Workflows) sub-tab are no longer grayed out; drag & drop and double-click now work for all formats via `app.handleFile()`
|
||||
|
||||
### v0.3.42
|
||||
- **Feeder tab — Gallery mode** — new [Image Loop] / [Gallery] toggle in the Feeder tab; Gallery mode drives a WFS_GalleryFeeder node without any external plugin: node selector, group select, sort mode / index / seed, After Gen (Loop / Increment / Fixed), Run / Stop; center pane shows the selected group's images — click any image to jump to that index; `__Feeder__` group is auto-created on first open
|
||||
- **WFS_GalleryFeeder custom node** — new ComfyUI node (Workflow Studio category) that reads images from a Gallery group and outputs one `IMAGE` at a time; inputs: `group_name`, `index`, `sort_mode` (filename_asc / filename_desc / random), `seed`; skips missing files; `IS_CHANGED` ensures re-execution on every queue
|
||||
- **WFS_GalleryFeeder — ComfyUI canvas controls** — `gallery_feeder_extension.js` adds After Gen combo (loop / increment / fixed), ▶ Run button, and ■ Stop button as non-serialized widgets directly on the node in the ComfyUI canvas; manages per-node loop state with `waitForExecution()` Promise pattern; `onRemoved` hook auto-stops the loop on node deletion
|
||||
- **Gallery tab — thumbnail F button** — cyan overlay button (top-left of each image card) toggles `__Feeder__` group membership; active state is always visible in cyan, inactive shows only on hover; initial state derived from `img.groups` returned by `list_images()`
|
||||
- **Gallery tab — `__Feeder__` reserved group** — shown with 🔒 prefix in group dropdowns; rename and delete blocked server-side (403) and client-side (buttons disabled); **FC** toolbar button clears all group members without deleting the group
|
||||
- **Security: XSS fixes in `gallery-tab.js`** — tag names, filenames, and server error messages now escaped via `escapeHtml()` before insertion into `innerHTML` (tag badge list, detail panel `alt` attributes, lightbox footer, folder tree errors, grid errors); `util.js` `escapeHtml` updated to also escape single quotes (`'` → `'`)
|
||||
- **Security: path validation in `gallery_service.py`** — `save_image_meta`, `toggle_favorite`, `add_to_group`, `remove_from_group` now call `_check_path_allowed()` before any file operation; previously these four methods were missing the guard that other write methods already had
|
||||
- **Security: `_init_allowed_root()` in `gallery_routes.py`** — `_allowed_root` is now initialized at module load from saved `gallery_output_dir` (falls back to ComfyUI default output dir); prevents `list_folder_tree` from overriding `_allowed_root` via a crafted `?root=` request, and also fixes serve_image returning 404 in Feeder Gallery mode before the Gallery tab is opened
|
||||
- **Bug fix: WFS_GalleryFeeder seed validation error** — seed `max` changed from `0x7FFFFFFF` to `0xffffffffffffffff` (matching KSampler); `applySeedToWorkflow()` was writing values exceeding the old max, causing `value_bigger_than_max` validation failures
|
||||
|
||||
### v0.3.41
|
||||
- **Generate UI — ImpactWildcardEncode / ImpactWildcardProcessor prompt detection** — fixed: wildcard text inputs from comfy-impact-pack nodes were not shown in the Prompt tab of Generate UI
|
||||
- `CLIPTextEncodeEditPlus` node (used as the text encoder in these workflows) now correctly propagates positive/negative roles upstream to ImpactWildcard nodes via BFS
|
||||
- `CLIPTextEncodeEditPlus` with an empty `text_edit` field is no longer added to `prompt_nodes` ahead of the upstream ImpactWildcard node, preventing a blank text area
|
||||
- `widgets[0]` is now always written to `wildcard_text` in the API conversion step, regardless of `object_info` widget-order differences between impact-pack versions
|
||||
- Static fallback mapping added for `ImpactWildcardProcessor` and `ImpactWildcardEncode` so detection works even when the ComfyUI server is offline
|
||||
|
||||
### v0.3.40
|
||||
- **AI TOOL tab — Chat pane** — new pane (between Translation and TOOLS) for multi-turn conversation with the LLM; full conversation history sent each turn for context; Enter to send, Shift+Enter for newline; Clear button resets history; Ollama uses `/api/chat`, LM Studio uses `/v1/chat/completions`
|
||||
- **AI TOOL tab — Wildcard generation** — new "Create wildcards" task in the TOOLS pane dropdown; enter a category name and count, click Run to generate plain-text entries one per line (no markdown, no numbering); output can be pasted directly into wildcard `.txt` files
|
||||
- **Library A tab — Chat and Wildcards** — both features added to the side panel's A tab: Chat as a new sub-tab (Translation → Chat → TOOLS → Settings), wildcard generation as a new option in the TOOLS sub-tab dropdown
|
||||
- **Help updated** — AI TOOL tab help card updated to 6 items (Chat and Wildcards added); Library panel sidepanel-16 updated; EN/JA/ZH
|
||||
|
||||
### v0.3.39
|
||||
- **Workflow tab — Send to Canvas button** — replaced "Open in ComfyUI" (toolbar and detail modal) with "Send to Canvas"; stores the selected workflow in `localStorage` for title-drag to the canvas without opening a new browser tab
|
||||
- **Gallery tab — Copy & Send Canvas button** — renamed "Copy Workflow" to "Copy & Send Canvas"; copies the embedded workflow JSON to the clipboard and simultaneously stores it for title-drag
|
||||
- **Workflow Studio Library — title drag** — after "Send to Canvas" or "Copy & Send Canvas" is clicked, the "Workflow Studio Library" panel title turns blue with a green ● indicator; drag the title onto the ComfyUI canvas to load the workflow; title resets automatically after loading
|
||||
|
||||
### v0.3.38
|
||||
- **Tagger tab** — new tab for automatic image tagging; supports WD Tagger (ONNX), SwinV2, DeepDanbooru (.h5), and Ollama VLM; 3 sub-tabs: Single (drag & drop / upload, 5 output targets), Batch (folder processing with real-time progress), DB (searchable SQLite database with CSV export)
|
||||
- **Tagger single — GenUI:P button** — new button (left of "Send to Prompt") that appends generated tags to the GenerateUI positive prompt and immediately applies them to the loaded workflow; shows a warning if no workflow is loaded in GenerateUI
|
||||
- **Tagger batch — Write .txt output** — new output option that creates `<filename>.txt` alongside each processed image containing all generated tags (comma-separated)
|
||||
- **Gallery detail panel — Tagger button** — new action button in the Gallery detail panel opens the selected image directly in the Tagger tab for instant single-image tagging
|
||||
- **Gallery detail panel layout** — tabs (Info / JSON / Groups) and action buttons (Metadata / Load GenUI / Tagger) are now on separate rows, preventing button overflow on narrow panels
|
||||
- **Gallery — Save to Gallery tags fix** — tags saved from the Tagger tab were stored as a string instead of an array, causing `forEach is not a function` errors on the next Gallery refresh; now correctly stored as a string array
|
||||
- **GenerateUI — ZIT/Lumina2 workflow prompt fix** — `ConditioningZeroOut` removed from the CONDITIONING passthrough set; previously it propagated the "negative" role upstream through the shared CLIPTextEncode node, marking it as "unknown" and hiding the positive prompt in the GenerateUI Input tab; ZIT (Z-Image Turbo) workflows now correctly show the positive prompt
|
||||
- **Workflow analyzer — lumina2 model type** — CLIPLoader `type: "lumina2"` is now correctly classified as Z-IMAGE in the workflow type detector
|
||||
|
||||
### v0.3.37
|
||||
- **Models tab — Select All button** — new button added to the right of "Deselect All" in the multi-select action bar; selects all models currently visible after applying the active search / filter / group filters
|
||||
- **Gallery tab — Select All button** — new button added to the right of "Deselect All" in the bulk action bar; selects all images in the current folder view
|
||||
- **Gallery bulk bar — i18n** — all bulk action bar buttons and the selection count were hard-coded English strings; they now use the `t()` translation system (EN / JA / ZH) so the bar respects the selected UI language
|
||||
|
||||
### v0.3.36
|
||||
- **GenerateUI Save — overwrite protection** — saving now checks for an existing file and asks for confirmation before overwriting; if the target file is in UI format, a dedicated warning explains that saving in API format will lose the node layout
|
||||
- **GenerateUI Save — Raw JSON sync** — unapplied Raw JSON edits are now included in the save (parsed and synced back to the editor); invalid JSON aborts the save with an error instead of silently saving stale content
|
||||
- **GenerateUI Save — hardening** — filename is set via DOM (no HTML-attribute injection), image extensions (`.png` etc.) are stripped from the default name, and a double-submit guard prevents duplicate POSTs
|
||||
- **Help i18n fixed** — v0.3.35 help text updates (Save button, table sort, JSON tab rename) were being overwritten at startup by stale i18n strings and never displayed; all help keys synced in EN / JA / ZH and new entries registered in the help ID map
|
||||
- **Toast i18n** — ~115 hard-coded English toasts replaced with translated messages (~60 new shared keys in EN / JA / ZH); Settings labels for Text Size / RAW JSON Colors that displayed raw key names are now properly defined
|
||||
- **Refactoring** — new shared `util.js` (`escapeHtml` unified across 5 files — the Metadata tab copy was missing `"` escaping; `readJsonStorage` / `getSettings` replace 15 direct `localStorage` parses); Models table sort now precomputes sort keys (faster on large libraries); Gallery action buttons moved off the tab-button class, removing all `!important` CSS overrides
|
||||
- **README refreshed** — intro rewritten around the three pillars (Management / GenerateUI / Library side panel); new screenshots: GenUI LoRA Stack, GenUI Batch, Models Multi-select Menu; WS Library image updated
|
||||
|
||||
### v0.3.35
|
||||
- **Models tab — Table column sort** — click any column header to sort ascending (▲), click again for descending (▼), click a third time to clear; active column name highlighted in accent color; Enable/Disable column header now labeled **E/D**
|
||||
- **Gallery tab — Load GenUI button** — new button to the right of the Metadata button in the detail panel; loads the embedded ComfyUI workflow from the selected image directly into the GenerateUI tab; warning toast shown if no workflow is embedded or the format is unsupported; Metadata button restyled green, Load GenUI uses primary accent color
|
||||
- **GenerateUI tab — Save button** — new button 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; workflow name label updates to the saved filename on success
|
||||
|
||||
### v0.3.33
|
||||
- **LoRA pane — workflow load no longer overwrites LoraManager** — removed the auto-apply block that replaced `inputs.loras` / `inputs.text` with Stack group contents on every workflow load; Stack is now applied only when the Apply button is pressed
|
||||
- **LoRA pane — 🔄 button no longer resets to Single tab** — active tab state is saved before `innerHTML` rebuild and restored afterwards; staying on Stack tab after refresh
|
||||
- **Models tab — Stack group hidden for non-LoRA types** — when switching to Checkpoint or other non-LoRA types, the Stack group is now excluded from the group filter dropdown and side-panel group management (LoRA-only concept)
|
||||
- **CLIPTextEncodeEditPlus widget mapping fix** — `convertUiToApi` no longer advances `wIdx` for UI slot inputs that have an actual link, preventing index misalignment when `object_info` lists optional STRING inputs (e.g. `text1`) before widget inputs (e.g. `text_edit`); fixes `+af` appearing as the positive prompt instead of the edit text
|
||||
- **Stack Apply — trigger words now applied correctly on first press** — trigger words (`currentAllTriggers` / `currentActiveTriggers`) are now recomputed at click time from the latest `_stackActive` + `metadata` + `civitaiCache`; previously used a stale snapshot captured at render time, causing trigger words to be missing on the first Apply and to linger after disabling a model
|
||||
|
||||
### v0.3.32
|
||||
- **Lora Loader (LoraManager) LoRA detection** — I tab and Metadata tab now correctly detect LoRAs from `Lora Loader (LoraManager)` nodes in API format (`inputs.loras.__value__`); `strength`/`clipStrength` stored as strings (e.g. `"0.20"`) are now parsed correctly
|
||||
- **LoraManager node placement — LoRA syntax** — placing a `Lora Loader (LoraManager)` node via drag now also updates the `text` widget with `<lora:name:strength>` syntax (previously only the `loras` array widget was set)
|
||||
- **M tab Groups — LoRA → LoraManager drop** — LoRA groups in the Library M tab show an **All N LoRAs** item; drag to canvas places a `Lora Loader (LoraManager)` node with all group LoRAs; double-click also places at canvas center
|
||||
- **P tab — Groups sub-tab** — new Groups sub-tab (row 2) in the Library P tab; groups are read from `localStorage["wfm_prompt_preset_groups"]` (shared with Batch); expanding a group shows draggable preset items
|
||||
|
||||
### v0.3.31
|
||||
- **LoRA pane — Single/Stack tabs** — LoRA pane split into two tabs: Single (filter, model dropdown, M/C strength, target node, Apply, P, LORA SYNTAX, TRIGGER WORDS) and Stack (target node, Apply, P, Toggle-all, global strength adjuster, LORA SYNTAX, TRIGGER WORDS, model list); each tab has independent strength settings
|
||||
- **GenUI Model — LoRA improvements** — now correctly handles `Lora Loader (LoraManager)` nodes (previously failed to find the node); on apply: switches to Single tab, disables all Stack models, and updates LORA SYNTAX / TRIGGER WORDS in the Single tab panel
|
||||
|
||||
### v0.3.30
|
||||
- **LoRA Stack trigger words fix** — inactive stack models no longer show their trigger words in the Trigger words display; both initial render and per-model toggle/toggle-all now filter to active models only
|
||||
- **Help troubleshooting** — added entry for "No such file or directory" error when running LoRA Stack (comfyui-lora-manager compatibility issue)
|
||||
|
||||
### v0.3.29
|
||||
- **LoRA section unified** — single LoRA and Stack sections merged into one pane; Apply button handles both cases (stack models present → apply all active + prompt sync; no stack → apply selected single LoRA + prompt sync); Ctrl+Apply removed, now plain Apply by default
|
||||
- **LoRA prompt sync** — Apply inserts `<lora:stem:M:C>` syntax and CivitAI trigger words into the Positive prompt; re-applying updates the prompt by diff (removes stale tokens, re-adds active ones); works for both single LoRA and Stack
|
||||
- **P button** — placed to the right of Apply; immediately writes the current Positive prompt textarea to the target workflow node without switching tabs
|
||||
- **LoRA UI layout fixes** — "Stack" label moved from ID row to Strength row left of the toggle-all checkbox; ID dropdown expanded to `flex:1` (no max-width cap); M/C input width 54 px → 60 px to prevent value truncation
|
||||
- **Models tab — CivitAI default** — side panel opens to the CivitAI tab by default when a model is selected
|
||||
- **Help tab — sidebar navigation** — redesigned as 2-column layout: left sidebar (14 topics + Support fixed at bottom) + right content pane; content switches on click
|
||||
- **Input tab — Prompt/Image tabs + taller textboxes** — Prompt and Image split into inner tabs; Positive prompt rows 4 → 8, Negative prompt rows 3 → 6
|
||||
- **Reset Workflow button** — added to the GenerateUI toolbar; re-reads the loaded workflow file from disk and reloads the editor
|
||||
- **LoRA Stack trigger words** — CivitAI trigger words for active stack models appended to Positive prompt on Apply; diff-updated on re-apply
|
||||
- **Feeder subtab UI** — (root) folder selected automatically on tab open; PREVIEW pane moved to the far right of the layout; preview auto-updates to the current processing image during a RUN
|
||||
- **Batch tab — Sampler / Scheduler** — left pane gains Sampler and Scheduler tabs (lists fetched from ComfyUI); Batch Queue gains two new columns; batch runs each value through KSampler sequentially
|
||||
- **Lora Loader (LoraManager) support** — workflow analysis, single Apply, Stack Apply, and Batch all write `inputs.loras.__value__` array + `inputs.text` syntax for LoraManager nodes
|
||||
- **Stack enable/disable** — per-model checkbox and Toggle All checkbox; disabled models excluded from syntax/prompt; LoraManager apply preserves them as `active: false`
|
||||
- **Global strength adjuster** — `[−][step][+]` controls for Model and CLIP strength applied to all stack models at once
|
||||
- **model-and-prompt-from-metadata node support** — `CLIPTextEncodeEditPlus`, `ImageMetadataCheckpointLoader`, `ImageMetadataPromptLoader`, `ImageMetadataLoRALoader` added to workflow analysis
|
||||
- **Models thumbnail bug fixes** — preview endpoint now sends `Cache-Control: no-cache`; files under 100 bytes skipped; `.disabled` model files included in preview change lookup
|
||||
|
||||
### v0.3.23
|
||||
- **Bug fix: workflow batch 404** — `_runBatchGenerate()` workflow case was fetching `/api/wfm/workflows/{filename}` (non-existent route); corrected to `/api/wfm/workflows/raw?filename={filename}`
|
||||
- **Bug fix: rename double-submit** — `addEventListener("blur", commitRename)` was accumulating on the persistent `#wfm-modal-title-input` element each time the modal opened; subsequent renames fired the handler twice, sending two rename requests back-to-back (first succeeds, second returns 409 or 404); fixed by storing the listener reference on `titleInput._commitRename` and calling `removeEventListener` before re-registering
|
||||
|
||||
### v0.3.22
|
||||
- **Card view removed** — Workflow tab and Models tab no longer offer Card view; Thumbnail and Table remain; if `"card"` was previously stored in localStorage it falls back to Thumbnail automatically
|
||||
- **Batch type selector** — replaced the Checkpoint Batch toggle with per-type checkboxes on the Batch Queue column headers (Checkpoint / Lora / Prompt / Workflow); radio behavior ensures only one type is active at a time; the Batch panel below Generate repurposed as a status display showing the active type, amber progress bar, and Pause/Resume / Stop controls
|
||||
- **4-type batch generation** — `_runBatchGenerate()` dispatches to Checkpoint (file-tree + group selection), Lora (group selection), Prompt (preset group items applied to positive/negative nodes), and Workflow (group selection, restores original workflow after batch); all types use the same generic `_runBatchLoop()` with pause/abort support
|
||||
- **Help updated** — EN / JA / ZH strings updated for `helpWf1`, `helpModels2`, `helpGen11`, `helpGen13`, `helpGen14`
|
||||
|
||||
### v0.3.21
|
||||
- **CivitAI Host setting** — new dropdown in Settings tab (CivitAI section): choose `civitai.com` (SFW only) or `civitai.red` (unrestricted); selection is saved to `settings.json` and synced to `localStorage` so the Models tab uses it immediately without extra server calls
|
||||
- **CivitAI panel — Info / Sample sub-tabs** — the CivitAI side panel is split into two sub-tabs; Info tab contains model details and actions; Sample tab shows all sample images (count displayed in label); images open full-size in a new browser tab on click
|
||||
- **Bug fix: model page URL for existing cache** — `modelId: null` entries (the majority of the cache) previously generated broken links (`/models?modelVersionId=…`); both the Python service and the JS renderer now fall back to `/{host}/model-versions/{versionId}` which redirects correctly to the model page; all 324 existing cache entries are fixed immediately without re-fetching
|
||||
- **Help updated** — Batch tab entries (gen-12 / gen-13 / gen-14) and Settings entries (settings-11 Text Size / settings-12 RAW JSON Colors / settings-13 CivitAI Host) are now fully localized in EN / JA / ZH; `helpGen3` updated to reflect 5-tab layout; `helpGen11` rewritten to describe the Batch toggle; JA/ZH translations no longer contain untranslated English terms (Filter, Pause/Resume, Stop)
|
||||
|
||||
### v0.3.20
|
||||
- **CivitAI panel — Type / Base Model / Hash display** — side panel CivitAI tab now shows a Type badge (e.g. CHECKPOINT, LORA), Base Model row, and Hash row (BLAKE3 preferred, SHA256 fallback from CivitAI API or local metadata); click the hash to copy the full value to clipboard
|
||||
- **CivitAI panel — images open in new tab** — sample images in the CivitAI tab are now wrapped in links; clicking an image opens the full-size version in a new browser tab
|
||||
- **CivitAI panel — 3-state UI** — the panel now distinguishes between "not yet checked" (blue fetch button), "checked but not found on CivitAI" (re-check button with a notice), and "found" (full info); no more ambiguity after running batch fetch
|
||||
- **CivitAI tab click refresh** — clicking the CivitAI tab in the side panel now re-renders its content, ensuring the latest fetch state is always shown without needing to reselect the model
|
||||
- **Bug fix: model page URL broken after batch fetch** — the batch `POST /model-versions/by-hash` endpoint can omit `model.id` inside the version object; `_extract_info` now falls back to the top-level `modelId` field so the model page link is always correct; existing cached entries with a broken URL are also fixed client-side from `modelId` + `versionId`
|
||||
- **CivitAI cache — `fileHashes` field added** — `_extract_info` now stores the primary file's full hash map (BLAKE3, SHA256, AutoV2, etc.) so the Hash row is populated for newly fetched or refreshed models
|
||||
|
||||
### v0.3.19
|
||||
- **CivitAI batch fetch — major speedup** — switched from one-by-one `GET` requests (0.5 s delay each) to `POST /model-versions/by-hash` batch endpoint; up to 100 hashes per request, reducing 100-model fetch from ~50 s to a few seconds
|
||||
- **Retry with exponential backoff** — `fetch_by_hash` and the new batch POST both retry on HTTP 429 / 500 / 502 / 503 / 504 with 1 s → 2 s → 4 s delays (max 3 attempts), as recommended in the CivitAI API docs
|
||||
- **CivitAI API key support** — optional Bearer token can be set in Settings tab (CivitAI API Key section); environment variable `CIVITAI_API_KEY` takes priority over the stored key; API keys are excluded from data exports
|
||||
- **Richer metadata** — `_extract_info` now captures `nsfwLevel` (numeric), `air` (AIR URN identifier), `stats` (download count, thumbs-up/down), `updatedAt`, `publishedAt`, image dimensions / per-image NSFW level (`imageDetails`), and primary file precision / format (`fileMeta`)
|
||||
- **Hash consistency** — cache keys use lowercase hex; API requests send uppercase hex as required by the CivitAI spec
|
||||
- **Bug fix: CivitAI tab blank after batch fetch** — the `done` SSE event now applies `data.hashes` directly to `state.modelMetadata` so the side-panel CivitAI tab renders correctly even when the subsequent `fetchModelMetadata()` call races ahead of the server flush
|
||||
|
||||
### v0.3.18
|
||||
- **Batch tab** — new 5th subtab in GenerateUI with a dedicated 3-pane layout for assembling checkpoint batch queues; left pane: file-tree checkpoint selection (filter, All/None, initially unchecked); center pane: group-based selection with inner tabs (Checkpoint / Lora / Prompt / Workflow — Lora/Prompt/Workflow coming soon); right pane: live Batch Queue preview combining both pane selections with duplicates removed
|
||||
- **Checkpoint selector moved** — the checkpoint dropdown is removed from the right panel; selection is now done exclusively in the Batch tab left and center panes; the right panel retains only the enable/disable toggle and progress display
|
||||
- **Group selection fix** — batch group selection now stores selected group names (not raw model names), so changes to group membership in the Models tab are always reflected in the queue without stale entries
|
||||
- **Models tab bulk operations expanded** — bulk action bar now includes: Deselect All (clear all selections without exiting select mode), ★ Favorite / ☆ Unfavorite, +Badge / −Badge (apply or remove a badge from all selected models); existing group and delete operations unchanged
|
||||
|
||||
### v0.3.17
|
||||
- **CivitAI SSL fix** — resolved `CERTIFICATE_VERIFY_FAILED` errors when fetching model info on Windows Portable Python environments; uses `certifi` CA bundle when available, falls back to system store, and disables verification only as a last resort with a warning log
|
||||
- **Lora Manager support** — `Lora Loader (LoraManager)` nodes (ComfyUI-Lora-Manager) are now correctly converted from UI format to API format in the GenerateUI tab; the `loras` widget array is wrapped to `{"__value__": [...]}` as required by the node, enabling generation with LoRA Manager workflows
|
||||
|
||||
### v0.3.16
|
||||
- **CivitAI preview fallback** — model cards and the detail panel's Info tab now display the first CivitAI image directly when no local preview file exists; if the backend download fails (e.g. network restriction server-side), the browser fetches the image URL from the CivitAI cache as a fallback so the preview is always shown after fetching
|
||||
|
||||
### v0.3.15
|
||||
- **Sample workflows** — added `workflows/` directory containing 13 sample workflows (SD1.5 / SDXL / DWPose / Face Detailer / Image Editing) with PNG thumbnails, bundled in the package
|
||||
- **CivitAI auto-preview** — when fetching CivitAI info (individual or batch), the first CivitAI image is automatically downloaded and saved as `{model_stem}.preview.png` if no preview exists; batch summary reports the count of auto-saved previews
|
||||
- **TOOLS pane — Create Tags** — new task option in the VLM dropdown (both SPA AI TOOL tab and Library A tab): generates a comma-separated list of descriptive English tags from the image
|
||||
|
||||
### v0.3.14
|
||||
- **AI TOOL tab redesign** — tab renamed from "A" to "AI TOOL"; sub-tab navigation removed in favor of a permanent 3-pane layout (Translation 40% | TOOLS 40% | Settings 20%) — all panes always visible simultaneously; "VLM" pane renamed to "TOOLS"
|
||||
- **Settings — RAW JSON Colors** — new accordion section with 6 color pickers to customize the syntax highlight colors for the Raw JSON editor in Generate UI: Default Text, Name/Scheduler, Title, Width/Height, Prompt/Text, Image/File; changes apply immediately on color pick; Reset Defaults restores the original scheme; saved to `localStorage` under `wfm_settings.jsonColors` and applied on startup
|
||||
- **Help tab updated** — AI TOOL tab card rewritten; Settings card updated with RAW JSON Colors entry; Library panel sidepanel-16 entry updated
|
||||
|
||||
### v0.3.13
|
||||
- **AI Tab (A)** — new tab added to both the SPA and the Workflow Studio Library side panel (rightmost tab)
|
||||
- **Translation sub-tab** — translate text via Ollama or LM Studio between Japanese, English, Chinese, or a custom Free language; ⇄ swap button exchanges both language selectors and text content simultaneously; language selections persisted automatically
|
||||
- **VLM sub-tab** — drop zone (110px, matching the I tab) accepts images via drag & drop or click; task selector (Describe Image / Create Prompt); Run button streams result into output area with Copy button; Ollama uses `images:[base64]` payload, LM Studio uses `image_url` content block
|
||||
- **Settings sub-tab** — backend radio (Ollama / LM Studio), API URL input, connection test with live status, model selector with refresh, Free language name fields (source and destination); saved to `localStorage` key `wfm_ai_settings` and shared between SPA and Library panel
|
||||
- **URL security** — backend URL validated via `new URL()` to enforce `http://` or `https://` scheme before any network call
|
||||
- **Settings Tab — Ollama section renamed** — "Ollama 設定" → "Ollama 設定（プロンプトタブ）" to distinguish it from the new AI tab's settings
|
||||
- **Help Tab updated** — added AI Tab (A) feature card and Library panel A tab entry; i18n support in EN/JA/ZH
|
||||
|
||||
### v0.3.12
|
||||
- **GenerateUI — Raw JSON search** — always-visible search bar above the Raw JSON editor; finds all matches as you type with match count (`3/12`); navigate forward/back with ↑/↓ buttons, Enter / Shift+Enter, or Escape / ✕ to clear; current match highlighted in orange, other matches in yellow; search overlay syncs with editor scroll and stays updated while editing
|
||||
- **Workflow analyzer — extended model type detection** — added CLIPLoader `type` field mapping to model family (`flux`, `hidream_i`, `wan`, `cosmos`, `lumina`, `ovis`, etc.) via `_CLIP_TYPE_TO_MODEL`; subgraph nodes (`definitions.subgraphs`) now scanned for model detection; `UnetLoaderGGUF` and similar loaders matched via `"UnetLoader" in ntype`; new families added: NewBie, Ovis, HiDream, Wan, Cosmos, Lumina; model-name detection refactored into reusable `_detect_model_type_from_name()`
|
||||
- **Screenshots refreshed** — replaced tab-by-tab screenshots with 8 feature-focused images
|
||||
|
||||
### v0.3.11
|
||||
- **Metadata Tab — extended loader support** — `metadata-tab.js` now recognizes `UnetLoaderGGUF` and `UNETLoaderGGUF` as Diffusion Model (e.g. HiDream GGUF workflows), and `QuadrupleCLIPLoader` as Text Encoder (e.g. HiDream 4-CLIP); previously these were detected only in the side panel I tab, not in the Metadata tab itself
|
||||
- **Settings Tab — Text Size** — new slider (10–28 px) in the Settings tab adjusts font size for all prompt and chat text areas at once: Generate UI positive/negative prompts, AI Assistant chat input, Preset prompts, Wildcard text area and file editor, and Metadata prompt full preview; applied immediately on drag and persisted with Save Settings
|
||||
|
||||
### v0.3.10
|
||||
- **Information tab — drag to canvas** — model, lora, and Prompts sub-tabs in the side panel I tab now support drag & drop onto the ComfyUI canvas; double-click also places nodes at canvas center
|
||||
- model sub-tab: each item places the corresponding loader node (Checkpoint → `CheckpointLoaderSimple`, VAE → `VAELoader`, Diffusion Model → `UNETLoader`, Text Encoder → `CLIPLoader`)
|
||||
- lora sub-tab: drag individual LoRA to place `LoraLoader`; **Multiple LORA** section (shown for 1+ LoRAs) places all LoRAs in a single `Lora Loader (LoraManager)` node
|
||||
- Prompts sub-tab: drag a prompt to place `CLIPTextEncode` with the full text pre-filled
|
||||
- **Extended node type support** — `UnetLoaderGGUF` / `UNETLoaderGGUF` now detected as Diffusion Model; `QuadrupleCLIPLoader` (HiDream and similar 4-CLIP workflows) now detected as Text Encoder
|
||||
- **Bug fix: CLIPLoader widget name** — `textencoder` type in `MODEL_NODE_MAP` was incorrectly using `clip_name1` (DualCLIPLoader's widget) instead of `clip_name`; Text Encoder models now drop with the correct filename
|
||||
- **Preview area fixed height** — Information tab drop zone fixed at 110px so model/prompt lists are no longer compressed when a large image is loaded
|
||||
|
||||
### v0.3.9
|
||||
- **Side panel I tab** — new Information tab in the Workflow Studio Library side panel; drop a ComfyUI PNG/WebP/JSON directly in the panel to inspect models, LoRAs, and prompts without opening the Metadata tab; sub-tabs: model / lora / Prompts with full-text preview and Copy button
|
||||
- **Side panel tab redesign** — tabs shortened to single letters (W / N / P / M / I) with full name shown on hover
|
||||
- **Bug fix: top bar icons** — icons (Workflow Studio / camera / Node Sets) were invisible in newer ComfyUI versions because Iconify converted the MDI CSS class to an inline SVG before the custom SVG could be injected; fixed by removing the early-return guard and adopting the same simple `requestAnimationFrame` retry pattern used by lora-manager
|
||||
|
||||
### v0.3.8
|
||||
- **Metadata Tab: extended subgraph support** — added extraction for Flux.2 Dev fp8 (`SamplerCustomAdvanced`), Flux.2 Klein 4B Distilled (image edit), Ernie Image, Qwen-Image-Edit 2511 (`TextEncodeQwenImageEditPlus`), and WAN2.2 14B Animate (top-level CLIPTextEncode + subgraph KSampler)
|
||||
- **Prompt type: Text (no badge)** — when positive/negative polarity cannot be determined (indirect sampler connections, cross-level links, `PrimitiveStringMultiline` etc.), prompts are listed without a POS/NEG badge; action buttons still work normally
|
||||
- **Prompt extraction improvements** — `extractPromptsFromNodeSet` now resolves link sources from any STRING-output node (e.g. `ComfySwitchNode`, `PrimitiveStringMultiline`) without type restriction; `PrimitiveStringMultiline` search and final text-encoder fallback both extended to include subgraph nodes via `collectAllNodes`
|
||||
- **i18n** — added `metaPromptText` key (EN: "Text" / JA: "テキスト" / ZH: "文本")
|
||||
|
||||
### v0.3.7
|
||||
- **Metadata Tab: subgraph workflow support** — Flux.2 (Dev fp8 / Klein), Qwen-Image-Edit (/ 2511 / Layered), and Z-Image (Base / Turbo) official templates now correctly extract models and prompts; these use ComfyUI's `definitions.subgraphs` format with `UNETLoader`, `CLIPLoader`, and `VAELoader` inside the subgraph
|
||||
- **Prompt extraction improvements** — 7-stage fallback chain: ImageMetadataPromptLoader → WFS_PromptText → top-level CLIPTextEncode+KSampler → `PrimitiveStringMultiline` (flux2-klein) → subgraph CLIPTextEncode+KSampler → PromptStyler → all CLIPTextEncode text
|
||||
- **MarkdownNote model fallback** — `extractMarkdownNoteModels()` parses `**section** → - [model](url)` patterns from MarkdownNote nodes as a supplemental model source
|
||||
- **Format note updated** — "Flux.2 / Qwen-Image / Z-Image subgraph workflows supported" (EN/JA/ZH)
|
||||
|
||||
### v0.3.6
|
||||
- **Metadata Tab** — new tab (between Prompt and Gallery) that extracts model and prompt information from ComfyUI-generated PNG/WebP images and workflow JSON files; 3-column layout: drop zone with image preview (left), Checkpoint/VAE/Diffusion Model/Text Encoder lists (center), LoRA list with strength values + Prompt list with full-text viewer (right)
|
||||
- **Prompt action buttons** — Copy, GenUI:P/N (set GenerateUI prompts), Prompt:P/N (set Prompt tab preset prompts) directly from the extracted prompt text
|
||||
- **SDXL Prompt Styler support** — improved prompt extraction resolves `PromptStyler → CLIPTextEncode` link chains in both LiteGraph and API workflow formats
|
||||
- **Format support** — ComfyUI (PNG/WebP/JSON), SD WebUI, SD Forge, Fooocus
|
||||
|
||||
### v0.3.5
|
||||
- **Feeder subtab** — new subtab in GenerateUI for controlling [comfyui-image-feeder](https://github.com/ketle-man/comfyui-image-feeder) nodes; left pane: node selector + all ImageFeeder parameters + presets + Run/Stop controls; center pane: folder tree, image grid with checkbox selection, and preview panel
|
||||
- **Run loop with After gen modes** — Loop (wrap and continue), Increment (advance and auto-stop), Fixed (constant index); index updates automatically via WebSocket `image_loop_node_sync` after each generation; seed comes from the right-pane seed setting
|
||||
- **Workflow analysis improvements** — `analyzeWorkflow` now handles multi-hop CONDITIONING chains via BFS (up to 5 iterations), CLIPTextEncodeSDXL / CLIPTextEncodeSDXLRefiner, SDXLPromptStyler / SDXLPromptStylerAdvanced, KSamplerAdvanced (`noise_seed`), TextEncodeQwenImageEditPlus, PrimitiveStringMultiline, Power Lora Loader (rgthree), and `Checkpoint Loader` (WAS) with space in the class name
|
||||
|
||||
### v0.3.4
|
||||
- **Wildcard support panel** — new right column in the Prompt tab with a one-click toolbar for inserting wildcard syntax (`{|}`, `{n$|}`, `__|__`, `:`, `;`, `$`, `<lora::1:LBW=;>`, `[]`, single/multi pick) and wrapping selected text; dedicated prompt textarea and wildcard file picker (click to insert `__filename__`)
|
||||
- **Wildcard file manager** — create, view, edit, and delete `.txt` / `.yaml` wildcard files stored in `user/default/Workflow-Studio/wildcard/`; editor opens inline with filename input and save/cancel controls
|
||||
- **Preset/Preset Manager tab switch** — the center pane of the Prompt tab is now a two-tab panel (Presets / Preset Manager) to reclaim horizontal space for the new wildcard panel
|
||||
- **Wildcard Integration setting** — new accordion section in Settings tab; auto-detects ComfyUI-Impact-Pack; when installed, provides a button to create a directory junction (Windows) or symlink (other OS) from the WFS wildcard directory to Impact Pack's `wildcards/` directory; existing WFS files are migrated automatically; a Remove Link button reverses the operation
|
||||
|
||||
### v0.3.2
|
||||
- **Checkpoint Batch** — new panel in the GenerateUI right column (below Seed, above results); enable via checkbox to auto-generate once per checkpoint model; filter included/excluded subfolders by comma-separated names (empty = all checkpoints); real-time preview of matched model count; amber progress bar shows current model name and index; Stop button aborts the loop after the current generation completes; failed models are counted and reported in the summary toast
|
||||
- **Seed layout fix** — seed value input and mode selector (Random / Fixed / Increment / Decrement) now stacked vertically so neither is clipped in narrow panels
|
||||
|
||||
### v0.3.1
|
||||
- **Folder operations** — create subfolders and delete folders (with all contents) directly from the folder tree header buttons
|
||||
- **File operations** — move or delete individual images from the detail panel; bulk move and bulk delete from the multi-select bar
|
||||
- **Tree state preserved** — folder expansion state is maintained after create/delete/move/refresh operations
|
||||
- **Workflow viewer improved** — Metadata tab now reads both `prompt` (API format, the default for most ComfyUI-generated images) and `workflow` (UI format) keys from PNG metadata, covering virtually all ComfyUI output
|
||||
- **Workflow auto-save** — after each generation in the Generate UI tab, the current workflow is automatically saved to gallery metadata for the generated images; shown in the Metadata tab even if PNG embedding is absent
|
||||
|
||||
### v0.3.0
|
||||
- **Cross-type group filter** — the group filter dropdown in the Models tab now shows groups from all model types simultaneously, each prefixed with a type label (`[Checkpoint]`, `[LoRA]`, etc.); selecting a group automatically switches to the corresponding model type
|
||||
- **Sidebar group display fixed** — the ComfyUI sidebar "Model Groups" view now correctly renders per-type groups (introduced in v0.2.9) with a type label on each group header; fixes a crash where `.filter is not a function` was thrown on the new nested group structure
|
||||
|
||||
### v0.2.9
|
||||
- **Model Enable/Disable** — hide models from ComfyUI by renaming the file extension to `.disabled`; toggle per model (⏸ button on each card/row), per group (Enable All / Disable All buttons in the Group panel), or filter the grid by status (All / Enabled only / Disabled only). Changes take effect after ComfyUI refresh.
|
||||
- **Per-type model groups** — groups are now scoped to each model type; checkpoint groups only appear in the Checkpoint tab, LoRA groups only in the LoRA tab, etc. Existing flat groups are automatically migrated.
|
||||
- **Multi-select mode** — click the Select button in the toolbar to enter selection mode; check individual models across Thumbnail / Card / Table views
|
||||
- **Bulk group operations** — with models selected, add or remove them from any group in one click, or create a new group and add them simultaneously
|
||||
- **Bulk file delete** — permanently delete selected model files along with all associated preview images and sidecar files (`.json`, `.civitai.info`, `.info`); requires confirmation before deleting
|
||||
- **Bug fix** — models in subdirectories could not be disabled due to a path-doubling error; fixed by computing the rename target from `path.name` instead of `model_name`
|
||||
- **Bug fix** — Hypernetwork tab showed `C`, `O`, `M`, `B` as fake model items; caused by the new ComfyUI API format returning `"COMBO"` as the first element; fixed in `comfyui-client.js`
|
||||
|
||||
### v0.2.8
|
||||
- **Data storage moved** — plugin data (settings, metadata, prompts, etc.) is now stored in `ComfyUI/user/default/Workflow-Studio/` instead of `custom_nodes/ComfyUI-Workflow-Studio/data/`; falls back to the old `data/` directory if `user/default/` does not exist
|
||||
- **Data Management section** — new Settings tab section to export all plugin data to a single JSON file and import it back; useful for migration, backup, and reinstallation
|
||||
|
||||
### v0.2.7
|
||||
- **Gallery tab** — new image browser for ComfyUI output folder; Thumbnail/Table views, multi-select with Bulk Bar, server-side group/tag/favorite filtering, group management (rename support), performance-optimized with + mtime folder cache
|
||||
- **Group UI unified** — Workflow, Nodes, and Gallery group panels rebuilt to match the Models tab 4-section pattern (Current Groups / Add to Group / Create New Group / Manage Groups); Rename support added to Workflow and Nodes tabs
|
||||
- **Settings 2-column layout** — Theme panel fixed on the right (50% width, sticky); all other settings flow continuously on the left with no visual gap
|
||||
- **Gallery output directory setting** — configure the output folder scanned by Gallery tab from Settings
|
||||
- **Help tab** — Gallery tab description card added
|
||||
|
||||
### v0.2.5
|
||||
- **Sidebar Category/Package sub-tabs** — added 📂 Category and 🧩 Package sub-tabs to the Nodes tab in Workflow Studio Library; each shows a dropdown to filter nodes by category or custom node package (reads `/object_info` for accurate package detection)
|
||||
- **Sidebar theme settings** — added ⚙ button in the panel header to customize background, sub-header background, text, border, and secondary text colors with live preview; settings saved to localStorage
|
||||
- **Sidebar TextEncoder (By Type)** — fixed TextEncoder not appearing in Models → By Type; now uses `DualCLIPLoader` → `CLIPLoader` fallback to retrieve `clip_name1` list
|
||||
- **Hide `.index.json`** — excluded `.index.json` from both the Workflow tab and the Workflow Studio Library side panel
|
||||
|
||||
### v0.2.4
|
||||
- **GenerateUI tab redesign** — reorganized from 5 tabs (Prompt/Image/Model/Settings/Raw JSON) to 3 tabs (Input/Model/Settings); each tab shows a Raw JSON column (540px) for instant JSON preview and direct editing from any tab
|
||||
- **Input tab** — Prompt and Image stacked vertically in the left column
|
||||
- **Settings tab** — KSampler and LatentImage stacked vertically in the left column
|
||||
- **Raw JSON tab removed** — Raw JSON is now always visible as a right-column panel within each tab
|
||||
|
||||
### v0.2.3
|
||||
- **Badge system unified** — Workflow and Models tabs now share a single badge palette (`⚙ Badge` button); workflow auto-analysis model type system removed in favor of user-defined free-label badges
|
||||
- **GenUI Model button** — apply any model from the Models tab directly to the corresponding node in GenerateUI's current workflow (available in detail modal and side panel; Checkpoint / LoRA / VAE / ControlNet / UNET / TextEncoder)
|
||||
- **Sidebar Models tab** — added All / ★ Favorites / Groups / By Type sub-tabs to the Models section of Workflow Studio Library
|
||||
- **Sidebar All views** — ★ star displayed for favorite items in Workflows and Nodes All sub-tabs
|
||||
- **Sidebar panel width** — widened from 280px to 310px
|
||||
|
||||
### 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
|
||||
@@ -263,7 +732,8 @@ 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
|
||||
@@ -282,7 +752,7 @@ ComfyUI-Workflow-Studio/
|
||||
│ ├── settings_service.py # Settings persistence
|
||||
│ └── png_extractor.py # PNG metadata extraction
|
||||
├── 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 +760,26 @@ 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 (image browser, groups, metadata)
|
||||
│ ├── 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)
|
||||
└── data/ # Fallback data dir (used when ComfyUI user/default/ is not found)
|
||||
```
|
||||
|
||||
---
|
||||
@@ -317,6 +794,9 @@ 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
|
||||
|
||||
+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>
|
||||
|
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|
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|
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|
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|
After Width: | Height: | Size: 1.7 MiB |
+19
-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 +68,9 @@ 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"
|
||||
|
||||
# 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"
|
||||
@@ -0,0 +1,450 @@
|
||||
"""
|
||||
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)
|
||||
|
||||
|
||||
_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_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", "")
|
||||
|
||||
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,
|
||||
)
|
||||
# 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 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)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 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)
|
||||
+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)
|
||||
|
||||
@@ -1,18 +1,30 @@
|
||||
"""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",
|
||||
]
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
"""Register settings API routes."""
|
||||
@@ -20,6 +32,10 @@ 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)
|
||||
|
||||
|
||||
async def handle_get(request: web.Request) -> web.Response:
|
||||
@@ -92,3 +108,136 @@ 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)
|
||||
|
||||
@@ -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 +1,186 @@
|
||||
"""
|
||||
Gallery Metadata Store - ギャラリー画像のメタデータ永続化
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
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}
|
||||
]
|
||||
}
|
||||
"""
|
||||
|
||||
def __init__(self, file_path: Path):
|
||||
self.file_path = file_path
|
||||
self._data: dict = {"images": {}, "groups": []}
|
||||
self._load()
|
||||
|
||||
def _load(self):
|
||||
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):
|
||||
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)
|
||||
return dict(self._data["images"].get(key, {}))
|
||||
|
||||
def save(self, image_path: str, data: dict) -> bool:
|
||||
key = self._normalize_path(image_path)
|
||||
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]:
|
||||
return list(self._data.get("groups", []))
|
||||
|
||||
def create_group(self, name: str) -> bool:
|
||||
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"""
|
||||
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:
|
||||
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]:
|
||||
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）"""
|
||||
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）"""
|
||||
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）"""
|
||||
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"""
|
||||
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)
|
||||
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)
|
||||
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 += "/"
|
||||
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 +1,709 @@
|
||||
"""
|
||||
Gallery Service - outputフォルダの画像管理、メタデータ閲覧
|
||||
"""
|
||||
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"}
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
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
|
||||
self._folder_cache = _FolderCache()
|
||||
self._bg_cancel = threading.Event()
|
||||
self._bg_cancel.set() # 初期状態: キャンセル済み
|
||||
|
||||
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
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 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）"""
|
||||
if self._allowed_root is None:
|
||||
return False
|
||||
try:
|
||||
path.resolve().relative_to(self._allowed_root)
|
||||
return True
|
||||
except ValueError:
|
||||
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)]
|
||||
return {
|
||||
"name": path.name,
|
||||
"path": rel if rel != "." else "",
|
||||
"abs_path": str(path).replace("\\", "/"),
|
||||
"image_count": image_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 list_images(
|
||||
self,
|
||||
folder_path: str,
|
||||
search: str = "",
|
||||
sort_by: str = "date_desc",
|
||||
favorite_only: bool = False,
|
||||
tag_filter: str = "",
|
||||
group_filter: str = "",
|
||||
) -> list[dict]:
|
||||
"""指定フォルダ内の画像一覧を返す（サブフォルダなし）"""
|
||||
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(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）
|
||||
if search:
|
||||
s = search.lower()
|
||||
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()
|
||||
):
|
||||
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
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 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", []),
|
||||
}
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
@@ -2,6 +2,7 @@
|
||||
|
||||
import json
|
||||
import logging
|
||||
import shutil
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
@@ -13,6 +14,17 @@ logger = logging.getLogger(__name__)
|
||||
_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 +33,8 @@ MODEL_TYPE_FOLDER_KEYS = {
|
||||
"controlnet": "controlnet",
|
||||
"unet": "diffusion_models",
|
||||
"textencoder": "text_encoders",
|
||||
"hypernetwork": "hypernetworks",
|
||||
"embedding": "embeddings",
|
||||
}
|
||||
|
||||
|
||||
@@ -91,21 +105,155 @@ 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）"""
|
||||
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
|
||||
return names
|
||||
|
||||
def save_model_groups(self, groups):
|
||||
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, {})
|
||||
valid_names = self._scan_model_names(model_type)
|
||||
cleaned = {}
|
||||
dirty = False
|
||||
for g_name, members in groups.items():
|
||||
filtered = [m for m in members if m in valid_names]
|
||||
cleaned[g_name] = filtered
|
||||
if len(filtered) != len(members):
|
||||
dirty = True
|
||||
if dirty:
|
||||
removed = sum(len(groups[g]) - len(cleaned[g]) for g in groups)
|
||||
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):
|
||||
data = self._load_metadata()
|
||||
if not isinstance(data.get("_groups"), dict):
|
||||
data["_groups"] = {}
|
||||
data["_groups"][model_type] = groups
|
||||
self._save_metadata(data)
|
||||
return groups
|
||||
|
||||
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 +282,7 @@ 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 +292,189 @@ 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 +1,108 @@
|
||||
"""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 +3,24 @@
|
||||
import os
|
||||
import re
|
||||
|
||||
# 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 +29,65 @@ 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 +103,33 @@ 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 +139,32 @@ 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")
|
||||
@@ -98,6 +174,12 @@ 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:
|
||||
inputs["prompts"] += 1
|
||||
|
||||
@@ -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()
|
||||
|
||||
@@ -61,7 +61,7 @@ class WorkflowStudio:
|
||||
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
|
||||
|
||||
workflow_routes.setup_routes(app)
|
||||
settings_routes.setup_routes(app)
|
||||
@@ -70,5 +70,8 @@ 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)
|
||||
|
||||
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 = "0.3.52"
|
||||
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 +1,835 @@
|
||||
/* ============================================================
|
||||
Gallery Tab Styles
|
||||
============================================================ */
|
||||
|
||||
/* ── 3カラムレイアウト ──────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-layout {
|
||||
display: flex;
|
||||
height: calc(100vh - var(--wfm-header-height, 96px));
|
||||
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-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;
|
||||
}
|
||||
+2555
-91
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;
|
||||
}
|
||||
@@ -0,0 +1,597 @@
|
||||
/**
|
||||
* AI Tab - Translation, VLM, Settings
|
||||
* Supports Ollama and LM Studio as backends
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { readJsonStorage } from "./util.js";
|
||||
|
||||
const SETTINGS_KEY = "wfm_ai_settings";
|
||||
|
||||
const LANG_NAMES = {
|
||||
ja: "Japanese",
|
||||
en: "English",
|
||||
zh: "Chinese",
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Settings persistence
|
||||
// ============================================
|
||||
|
||||
function loadAiSettings() {
|
||||
return readJsonStorage(SETTINGS_KEY);
|
||||
}
|
||||
|
||||
function saveAiSettings(patch) {
|
||||
const data = { ...loadAiSettings(), ...patch };
|
||||
localStorage.setItem(SETTINGS_KEY, JSON.stringify(data));
|
||||
return data;
|
||||
}
|
||||
|
||||
const VLM_PROMPTS = {
|
||||
describe: "Describe this image in detail.",
|
||||
prompt: "Create a detailed Stable Diffusion image generation prompt based on this image. Output only the prompt text, nothing else.",
|
||||
tags: "Generate a list of descriptive tags for this image. Output only comma-separated tags in English, nothing else.",
|
||||
};
|
||||
|
||||
function fileToBase64(file) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = e => {
|
||||
const b64 = e.target.result.split(",")[1];
|
||||
resolve({ base64: b64, mimeType: file.type });
|
||||
};
|
||||
reader.onerror = reject;
|
||||
reader.readAsDataURL(file);
|
||||
});
|
||||
}
|
||||
|
||||
async function callVLM(url, backend, model, prompt, base64Image, mimeType) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/generate`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, images: [base64Image], stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).response || "";
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
messages: [{ role: "user", content: [
|
||||
{ type: "text", text: prompt },
|
||||
{ type: "image_url", image_url: { url: `data:${mimeType};base64,${base64Image}` } },
|
||||
]}],
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// URL validation
|
||||
// ============================================
|
||||
|
||||
function isValidBackendUrl(url) {
|
||||
try {
|
||||
const u = new URL(url);
|
||||
return u.protocol === "http:" || u.protocol === "https:";
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// API helpers
|
||||
// ============================================
|
||||
|
||||
async function fetchModels(url, backend) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/tags`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return (data.models || []).map((m) => m.name);
|
||||
} else {
|
||||
// LM Studio (OpenAI-compatible)
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return (data.data || []).map((m) => m.id);
|
||||
}
|
||||
}
|
||||
|
||||
async function testConnection(url, backend) {
|
||||
const models = await fetchModels(url, backend);
|
||||
return models.length;
|
||||
}
|
||||
|
||||
async function callLLM(url, backend, model, prompt) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/generate`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return data.response || "";
|
||||
} else {
|
||||
// LM Studio (OpenAI-compatible)
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
messages: [{ role: "user", content: prompt }],
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return data.choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Translation prompt builder
|
||||
// ============================================
|
||||
|
||||
function buildTranslationPrompt(text, srcLang, dstLang, settings) {
|
||||
const srcName = srcLang === "free" ? (settings.freeSrcLang || "Auto") : LANG_NAMES[srcLang];
|
||||
const dstName = dstLang === "free" ? (settings.freeDstLang || "English") : LANG_NAMES[dstLang];
|
||||
const fromPart = srcLang === "free" ? "" : `from ${srcName} `;
|
||||
return `Translate the following text ${fromPart}to ${dstName}. Output only the translated text, nothing else.\n\n${text}`;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Translation tab
|
||||
// ============================================
|
||||
|
||||
function initTranslateTab() {
|
||||
const srcLangSel = document.getElementById("wfm-ai-src-lang");
|
||||
const dstLangSel = document.getElementById("wfm-ai-dst-lang");
|
||||
const swapBtn = document.getElementById("wfm-ai-trans-swap-btn");
|
||||
const inputEl = document.getElementById("wfm-ai-trans-input");
|
||||
const outputEl = document.getElementById("wfm-ai-trans-output");
|
||||
const transBtn = document.getElementById("wfm-ai-trans-btn");
|
||||
const copyBtn = document.getElementById("wfm-ai-trans-copy-btn");
|
||||
const statusEl = document.getElementById("wfm-ai-trans-status");
|
||||
|
||||
if (!transBtn) return;
|
||||
|
||||
// Restore saved language selections
|
||||
const saved = loadAiSettings();
|
||||
if (saved.srcLang && srcLangSel) srcLangSel.value = saved.srcLang;
|
||||
if (saved.dstLang && dstLangSel) dstLangSel.value = saved.dstLang;
|
||||
|
||||
// Persist language selections
|
||||
srcLangSel?.addEventListener("change", () => saveAiSettings({ srcLang: srcLangSel.value }));
|
||||
dstLangSel?.addEventListener("change", () => saveAiSettings({ dstLang: dstLangSel.value }));
|
||||
|
||||
// Swap languages
|
||||
swapBtn?.addEventListener("click", () => {
|
||||
const tmp = srcLangSel.value;
|
||||
srcLangSel.value = dstLangSel.value;
|
||||
dstLangSel.value = tmp;
|
||||
saveAiSettings({ srcLang: srcLangSel.value, dstLang: dstLangSel.value });
|
||||
// Swap text content too
|
||||
const tmpText = inputEl.value;
|
||||
inputEl.value = outputEl.value;
|
||||
outputEl.value = tmpText;
|
||||
});
|
||||
|
||||
// Translate
|
||||
transBtn.addEventListener("click", async () => {
|
||||
const text = inputEl.value.trim();
|
||||
if (!text) {
|
||||
showToast(t("aiToastNoText"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const settings = loadAiSettings();
|
||||
const { backend = "ollama", backendUrl, model } = settings;
|
||||
const url = backendUrl || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234");
|
||||
|
||||
if (!isValidBackendUrl(url)) {
|
||||
showToast(t("aiToastInvalidUrl"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
if (!model) {
|
||||
showToast(t("aiToastNoModel"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
transBtn.disabled = true;
|
||||
statusEl.textContent = t("aiStatusTranslating");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-working";
|
||||
outputEl.value = "";
|
||||
|
||||
try {
|
||||
const prompt = buildTranslationPrompt(text, srcLangSel.value, dstLangSel.value, settings);
|
||||
const result = await callLLM(url, backend, model, prompt);
|
||||
outputEl.value = result.trim();
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
} catch (err) {
|
||||
statusEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
showToast(t("aiToastTransFailed") + err.message, "error");
|
||||
} finally {
|
||||
transBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
// Copy translated text
|
||||
copyBtn?.addEventListener("click", () => {
|
||||
const text = outputEl.value;
|
||||
if (!text) {
|
||||
showToast(t("aiToastNoCopyText"), "error");
|
||||
return;
|
||||
}
|
||||
navigator.clipboard.writeText(text).then(() => {
|
||||
showToast(t("aiToastCopied"), "success");
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Settings tab
|
||||
// ============================================
|
||||
|
||||
async function refreshModels() {
|
||||
const settings = loadAiSettings();
|
||||
const backend = document.querySelector("input[name='wfm-ai-backend']:checked")?.value || "ollama";
|
||||
const url = document.getElementById("wfm-ai-backend-url")?.value?.trim() || "";
|
||||
const modelSel = document.getElementById("wfm-ai-model-select");
|
||||
const refreshBtn = document.getElementById("wfm-ai-model-refresh-btn");
|
||||
|
||||
if (!modelSel) return;
|
||||
|
||||
if (refreshBtn) refreshBtn.disabled = true;
|
||||
try {
|
||||
const models = await fetchModels(url, backend);
|
||||
modelSel.innerHTML = `<option value="">-- ${t("aiSettingsModelPlaceholder")} --</option>`;
|
||||
models.forEach((name) => {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = name;
|
||||
opt.textContent = name;
|
||||
if (name === settings.model) opt.selected = true;
|
||||
modelSel.appendChild(opt);
|
||||
});
|
||||
if (models.length === 0) showToast(t("aiToastNoModels"), "error");
|
||||
} catch (err) {
|
||||
showToast(t("aiToastModelsFailed") + err.message, "error");
|
||||
} finally {
|
||||
if (refreshBtn) refreshBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
function initSettingsTab() {
|
||||
const saved = loadAiSettings();
|
||||
|
||||
// Restore saved values
|
||||
const backendRadios = document.querySelectorAll("input[name='wfm-ai-backend']");
|
||||
backendRadios.forEach((r) => {
|
||||
if (r.value === (saved.backend || "ollama")) r.checked = true;
|
||||
});
|
||||
|
||||
const urlInput = document.getElementById("wfm-ai-backend-url");
|
||||
if (urlInput && saved.backendUrl) urlInput.value = saved.backendUrl;
|
||||
|
||||
// Update URL placeholder when backend changes
|
||||
backendRadios.forEach((r) => {
|
||||
r.addEventListener("change", () => {
|
||||
if (!urlInput) return;
|
||||
const defaultUrl = r.value === "ollama" ? "http://localhost:11434" : "http://localhost:1234";
|
||||
if (!saved.backendUrl) urlInput.value = defaultUrl;
|
||||
});
|
||||
});
|
||||
|
||||
// Connection test
|
||||
document.getElementById("wfm-ai-test-btn")?.addEventListener("click", async () => {
|
||||
const testBtn = document.getElementById("wfm-ai-test-btn");
|
||||
const resultEl = document.getElementById("wfm-ai-test-result");
|
||||
const backend = document.querySelector("input[name='wfm-ai-backend']:checked")?.value || "ollama";
|
||||
const url = urlInput?.value?.trim() || "";
|
||||
|
||||
if (!isValidBackendUrl(url)) {
|
||||
if (resultEl) {
|
||||
resultEl.textContent = t("aiToastInvalidUrlInput");
|
||||
resultEl.className = "wfm-ai-test-result wfm-ai-status-error";
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (testBtn) testBtn.disabled = true;
|
||||
if (resultEl) {
|
||||
resultEl.textContent = t("aiStatusConnecting");
|
||||
resultEl.className = "wfm-ai-test-result wfm-ai-status-working";
|
||||
}
|
||||
|
||||
try {
|
||||
const count = await testConnection(url, backend);
|
||||
if (resultEl) {
|
||||
resultEl.textContent = `${t("aiStatusConnectOk")} (${count} ${t("aiModels")})`;
|
||||
resultEl.className = "wfm-ai-test-result wfm-ai-status-ok";
|
||||
}
|
||||
await refreshModels();
|
||||
} catch (err) {
|
||||
if (resultEl) {
|
||||
resultEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
resultEl.className = "wfm-ai-test-result wfm-ai-status-error";
|
||||
}
|
||||
} finally {
|
||||
if (testBtn) testBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
// Model refresh
|
||||
document.getElementById("wfm-ai-model-refresh-btn")?.addEventListener("click", () => refreshModels());
|
||||
|
||||
// Free language inputs
|
||||
const freeSrcInput = document.getElementById("wfm-ai-free-src-lang");
|
||||
const freeDstInput = document.getElementById("wfm-ai-free-dst-lang");
|
||||
if (freeSrcInput && saved.freeSrcLang) freeSrcInput.value = saved.freeSrcLang;
|
||||
if (freeDstInput && saved.freeDstLang) freeDstInput.value = saved.freeDstLang;
|
||||
|
||||
// Save settings
|
||||
document.getElementById("wfm-ai-settings-save-btn")?.addEventListener("click", () => {
|
||||
const backend = document.querySelector("input[name='wfm-ai-backend']:checked")?.value || "ollama";
|
||||
const url = urlInput?.value?.trim() || "";
|
||||
const model = document.getElementById("wfm-ai-model-select")?.value || "";
|
||||
const freeSrcLang = freeSrcInput?.value?.trim() || "";
|
||||
const freeDstLang = freeDstInput?.value?.trim() || "";
|
||||
|
||||
if (url && !isValidBackendUrl(url)) {
|
||||
showToast(t("aiToastInvalidUrl"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
saveAiSettings({ backend, backendUrl: url, model, freeSrcLang, freeDstLang });
|
||||
showToast(t("aiToastSettingsSaved"), "success");
|
||||
});
|
||||
|
||||
// Load models on init if settings exist
|
||||
if (saved.backendUrl && saved.backend) {
|
||||
refreshModels().catch(() => {});
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Chat tab
|
||||
// ============================================
|
||||
|
||||
async function callChat(url, backend, model, messages) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/chat`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).message?.content || "";
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
function appendChatBubble(messagesEl, role, content) {
|
||||
const div = document.createElement("div");
|
||||
div.className = `wfm-ai-chat-msg wfm-ai-chat-msg-${role}`;
|
||||
div.textContent = content;
|
||||
messagesEl.appendChild(div);
|
||||
messagesEl.scrollTop = messagesEl.scrollHeight;
|
||||
return div;
|
||||
}
|
||||
|
||||
function initChatTab() {
|
||||
const messagesEl = document.getElementById("wfm-ai-chat-messages");
|
||||
const inputEl = document.getElementById("wfm-ai-chat-input");
|
||||
const sendBtn = document.getElementById("wfm-ai-chat-send-btn");
|
||||
const clearBtn = document.getElementById("wfm-ai-chat-clear-btn");
|
||||
const statusEl = document.getElementById("wfm-ai-chat-status");
|
||||
|
||||
if (!messagesEl || !inputEl || !sendBtn) return;
|
||||
|
||||
let chatHistory = [];
|
||||
|
||||
async function sendMessage() {
|
||||
const text = inputEl.value.trim();
|
||||
if (!text) return;
|
||||
|
||||
const settings = loadAiSettings();
|
||||
const { backend = "ollama", backendUrl, model } = settings;
|
||||
const url = backendUrl || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234");
|
||||
|
||||
if (!isValidBackendUrl(url)) {
|
||||
showToast(t("aiToastInvalidUrl"), "error");
|
||||
return;
|
||||
}
|
||||
if (!model) {
|
||||
showToast(t("aiToastNoModel"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
inputEl.value = "";
|
||||
inputEl.style.height = "";
|
||||
chatHistory.push({ role: "user", content: text });
|
||||
appendChatBubble(messagesEl, "user", text);
|
||||
|
||||
sendBtn.disabled = true;
|
||||
statusEl.textContent = t("aiStatusChatting");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-working";
|
||||
|
||||
try {
|
||||
const reply = await callChat(url, backend, model, chatHistory);
|
||||
chatHistory.push({ role: "assistant", content: reply });
|
||||
appendChatBubble(messagesEl, "assistant", reply);
|
||||
statusEl.textContent = "";
|
||||
statusEl.className = "wfm-ai-trans-status";
|
||||
} catch (err) {
|
||||
chatHistory.pop();
|
||||
if (messagesEl.lastChild) messagesEl.removeChild(messagesEl.lastChild);
|
||||
inputEl.value = text;
|
||||
statusEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
showToast(t("aiToastChatFailed") + err.message, "error");
|
||||
} finally {
|
||||
sendBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
sendBtn.addEventListener("click", sendMessage);
|
||||
|
||||
inputEl.addEventListener("keydown", (e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault();
|
||||
sendMessage();
|
||||
}
|
||||
});
|
||||
|
||||
clearBtn.addEventListener("click", () => {
|
||||
chatHistory = [];
|
||||
messagesEl.innerHTML = "";
|
||||
statusEl.textContent = "";
|
||||
statusEl.className = "wfm-ai-trans-status";
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// VLM tab
|
||||
// ============================================
|
||||
|
||||
function initVlmTab() {
|
||||
const dropEl = document.getElementById("wfm-ai-vlm-drop");
|
||||
const previewEl = document.getElementById("wfm-ai-vlm-preview");
|
||||
const labelEl = document.getElementById("wfm-ai-vlm-label");
|
||||
const fileInput = document.getElementById("wfm-ai-vlm-file");
|
||||
const taskSel = document.getElementById("wfm-ai-vlm-task");
|
||||
const runBtn = document.getElementById("wfm-ai-vlm-run");
|
||||
const statusEl = document.getElementById("wfm-ai-vlm-status");
|
||||
const resultEl = document.getElementById("wfm-ai-vlm-result");
|
||||
const copyBtn = document.getElementById("wfm-ai-vlm-copy");
|
||||
const wcInputs = document.getElementById("wfm-ai-wc-inputs");
|
||||
const wcNameEl = document.getElementById("wfm-ai-wc-name");
|
||||
const wcCountEl = document.getElementById("wfm-ai-wc-count");
|
||||
|
||||
if (!dropEl) return;
|
||||
|
||||
let vlmImage = null; // { base64, mimeType }
|
||||
|
||||
function updateTaskUI() {
|
||||
const isWildcard = taskSel?.value === "wildcard";
|
||||
dropEl.style.display = isWildcard ? "none" : "";
|
||||
if (wcInputs) wcInputs.style.display = isWildcard ? "" : "none";
|
||||
}
|
||||
taskSel?.addEventListener("change", updateTaskUI);
|
||||
updateTaskUI();
|
||||
|
||||
const loadImage = async (file) => {
|
||||
if (!file || !file.type.startsWith("image/")) return;
|
||||
vlmImage = await fileToBase64(file);
|
||||
previewEl.src = `data:${vlmImage.mimeType};base64,${vlmImage.base64}`;
|
||||
previewEl.style.display = "block";
|
||||
labelEl.style.display = "none";
|
||||
};
|
||||
|
||||
dropEl.addEventListener("click", () => fileInput.click());
|
||||
fileInput.addEventListener("change", e => { if (e.target.files[0]) loadImage(e.target.files[0]); });
|
||||
dropEl.addEventListener("dragover", e => { e.preventDefault(); dropEl.classList.add("drag-over"); });
|
||||
dropEl.addEventListener("dragleave", () => dropEl.classList.remove("drag-over"));
|
||||
dropEl.addEventListener("drop", e => {
|
||||
e.preventDefault();
|
||||
dropEl.classList.remove("drag-over");
|
||||
if (e.dataTransfer.files[0]) loadImage(e.dataTransfer.files[0]);
|
||||
});
|
||||
|
||||
runBtn.addEventListener("click", async () => {
|
||||
const task = taskSel?.value || "describe";
|
||||
const settings = loadAiSettings();
|
||||
const { backend = "ollama", backendUrl, model } = settings;
|
||||
const url = backendUrl || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234");
|
||||
|
||||
if (!isValidBackendUrl(url)) { showToast(t("aiToastInvalidUrl"), "error"); return; }
|
||||
if (!model) { showToast(t("aiToastNoModel"), "error"); return; }
|
||||
|
||||
if (task === "wildcard") {
|
||||
const name = wcNameEl?.value.trim() || "";
|
||||
const count = Math.max(1, parseInt(wcCountEl?.value) || 20);
|
||||
if (!name) { showToast(t("aiToastWcNoName"), "error"); return; }
|
||||
|
||||
runBtn.disabled = true;
|
||||
statusEl.textContent = t("aiStatusRunning");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-working";
|
||||
resultEl.value = "";
|
||||
|
||||
const prompt = `Generate ${count} wildcard entries for the category "${name}". Output only plain text in English, one entry per line, no numbers, no markdown, no asterisks, no bold, nothing else.`;
|
||||
|
||||
try {
|
||||
const result = await callLLM(url, backend, model, prompt);
|
||||
resultEl.value = result.trim()
|
||||
.split("\n")
|
||||
.map(l => l.replace(/\*\*/g, "").replace(/^\*\s*/, "").replace(/^\d+\.\s*/, "").trim())
|
||||
.filter(l => l.length > 0)
|
||||
.join("\n");
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
} catch (err) {
|
||||
statusEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
showToast(t("aiToastVlmFailed") + err.message, "error");
|
||||
} finally {
|
||||
runBtn.disabled = false;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (!vlmImage) { showToast(t("aiToastNoImage"), "error"); return; }
|
||||
|
||||
runBtn.disabled = true;
|
||||
statusEl.textContent = t("aiStatusRunning");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-working";
|
||||
resultEl.value = "";
|
||||
|
||||
try {
|
||||
const result = await callVLM(url, backend, model, VLM_PROMPTS[task], vlmImage.base64, vlmImage.mimeType);
|
||||
resultEl.value = result.trim();
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
} catch (err) {
|
||||
statusEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
showToast(t("aiToastVlmFailed") + err.message, "error");
|
||||
} finally {
|
||||
runBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
copyBtn.addEventListener("click", () => {
|
||||
const text = resultEl.value;
|
||||
if (!text) { showToast(t("aiToastNoCopyText"), "error"); return; }
|
||||
navigator.clipboard.writeText(text).then(() => showToast(t("aiToastCopied"), "success"));
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Export
|
||||
// ============================================
|
||||
|
||||
export function initAiTab() {
|
||||
initTranslateTab();
|
||||
initChatTab();
|
||||
initVlmTab();
|
||||
initSettingsTab();
|
||||
}
|
||||
+212
-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", 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 +189,39 @@ 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");
|
||||
|
||||
// Prompt tab
|
||||
const assistantHeader = document.querySelector(".wfm-prompt-split-left .wfm-prompt-split-header");
|
||||
if (assistantHeader) assistantHeader.textContent = t("assistantSubtab");
|
||||
@@ -216,6 +250,67 @@ 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");
|
||||
|
||||
// Group panel labels
|
||||
const propTitle = document.querySelector("#wfm-side-tab-group .wfm-group-section-title");
|
||||
if (propTitle) propTitle.textContent = t("properties");
|
||||
@@ -252,37 +347,85 @@ 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-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-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-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-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-5": "helpAi5", "wfm-help-ai-6": "helpAi6",
|
||||
"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-support-title": "helpSupportTitle",
|
||||
"wfm-help-support-desc": "helpSupportDesc",
|
||||
"wfm-help-github-desc": "helpGithubDesc",
|
||||
@@ -293,6 +436,41 @@ function applyI18nToHtml() {
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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 +508,50 @@ 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";
|
||||
|
||||
// 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");
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
document.addEventListener("DOMContentLoaded", () => {
|
||||
applyI18nToHtml();
|
||||
initTabs();
|
||||
initModal();
|
||||
initHelpTab();
|
||||
initSettingsTab(); // Settings first (applies saved URL)
|
||||
initWorkflowTab();
|
||||
initNodesTab();
|
||||
initModelsTab();
|
||||
initGenerateTab();
|
||||
initPromptTab();
|
||||
initMetadataTab();
|
||||
initGalleryTab();
|
||||
initAiTab();
|
||||
initTaggerTab();
|
||||
|
||||
console.log("Workflow Studio: App initialized");
|
||||
});
|
||||
|
||||
@@ -88,7 +88,18 @@ 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 {}
|
||||
}
|
||||
@@ -134,6 +145,28 @@ export const comfyUI = {
|
||||
);
|
||||
},
|
||||
|
||||
async fetchHypernetworks() {
|
||||
return this._fetchModelList(
|
||||
["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 +176,16 @@ 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(() => ({}));
|
||||
@@ -250,8 +285,8 @@ 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
|
||||
|
||||
+703
-34
@@ -5,6 +5,107 @@
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { syncJsonHighlight } from "./json-highlight.js";
|
||||
|
||||
// ── LoRA pane stack state ─────────────────────────────────
|
||||
// { modelFullPath: { m: number, c: number } }
|
||||
let _stackStrengths = {};
|
||||
// { modelFullPath: boolean } — true = active (default)
|
||||
let _stackActive = {};
|
||||
// { id: string|null, selStart: number, selEnd: number }
|
||||
let _lastPromptFocus = { id: null, selStart: 0, selEnd: 0 };
|
||||
|
||||
function _loraBasename(fullPath) {
|
||||
const name = (fullPath || "").replace(/\\/g, "/").split("/").pop() || fullPath;
|
||||
return name.replace(/\.[^.]+$/, "");
|
||||
}
|
||||
|
||||
function _buildLoraSyntax(stackModels) {
|
||||
return stackModels
|
||||
.filter((m) => _stackActive[m] !== false)
|
||||
.map((m) => {
|
||||
const stem = _loraBasename(m);
|
||||
const str = (_stackStrengths[m]?.m ?? 1.0).toFixed(2).replace(/\.?0+$/, "") || "1";
|
||||
return `<lora:${stem}:${str}>`;
|
||||
})
|
||||
.join(", ");
|
||||
}
|
||||
|
||||
function _buildLoraManagerSyntax(stackModels) {
|
||||
return stackModels
|
||||
.filter((m) => _stackActive[m] !== false)
|
||||
.map((m) => {
|
||||
const stem = _loraBasename(m);
|
||||
const strM = (_stackStrengths[m]?.m ?? 1.0).toFixed(2).replace(/\.?0+$/, "") || "1";
|
||||
const strC = (_stackStrengths[m]?.c ?? 1.0).toFixed(2).replace(/\.?0+$/, "") || "1";
|
||||
return `<lora:${stem}:${strM}:${strC}>`;
|
||||
})
|
||||
.join(" ");
|
||||
}
|
||||
|
||||
function _applyLoraToNode(nodeId, loraPath, strModel, strClip, isLoraManager) {
|
||||
if (isLoraManager) {
|
||||
const stem = _loraBasename(loraPath);
|
||||
comfyUI.currentWorkflow[nodeId].inputs.loras = {
|
||||
__value__: [{ name: stem, strength: strModel, active: true, expanded: false, clipStrength: strClip, locked: false }],
|
||||
};
|
||||
comfyUI.currentWorkflow[nodeId].inputs.text = `<lora:${stem}:${strModel}:${strClip}>`;
|
||||
} else {
|
||||
comfyUI.currentWorkflow[nodeId].inputs.lora_name = loraPath;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_model = strModel;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_clip = strClip;
|
||||
}
|
||||
}
|
||||
|
||||
function _refreshLoraPaneDynamic(stackModels, metadata, civitaiCache) {
|
||||
const syntaxEl = document.getElementById("wfm-lora-stack-syntax");
|
||||
if (syntaxEl) syntaxEl.textContent = _buildLoraSyntax(stackModels) || "—";
|
||||
|
||||
const triggersEl = document.getElementById("wfm-lora-stack-triggers");
|
||||
if (triggersEl && metadata && civitaiCache) {
|
||||
const activeWords = [];
|
||||
stackModels.forEach((m) => {
|
||||
if (_stackActive[m] === false) return;
|
||||
const sha = (metadata[m] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
if (civInfo?.trainedWords?.length) activeWords.push(...civInfo.trainedWords);
|
||||
});
|
||||
triggersEl.innerHTML = activeWords.length
|
||||
? activeWords.map((w) => `<span class="wfm-lora-trigger-word">${w}</span>`).join(" ")
|
||||
: `<span style="color:var(--wfm-text-secondary);font-size:12px;">—</span>`;
|
||||
}
|
||||
}
|
||||
|
||||
function _refreshLoraSingleDynamic(metadata, civitaiCache) {
|
||||
const modelName = document.getElementById("wfm-lora-select")?.value;
|
||||
const syntaxEl = document.getElementById("wfm-lora-single-syntax");
|
||||
const triggersEl = document.getElementById("wfm-lora-single-triggers");
|
||||
if (!modelName) {
|
||||
if (syntaxEl) syntaxEl.textContent = "—";
|
||||
if (triggersEl) triggersEl.innerHTML = `<span style="color:var(--wfm-text-secondary);font-size:12px;">—</span>`;
|
||||
return;
|
||||
}
|
||||
const stem = _loraBasename(modelName);
|
||||
const strM = parseFloat(document.getElementById("wfm-lora-str-model")?.value) || 1.0;
|
||||
const strC = parseFloat(document.getElementById("wfm-lora-str-clip")?.value) || 1.0;
|
||||
if (syntaxEl) syntaxEl.textContent = `<lora:${stem}:${strM}:${strC}>`;
|
||||
if (triggersEl) {
|
||||
const sha = (metadata[modelName] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
const words = civInfo?.trainedWords || [];
|
||||
triggersEl.innerHTML = words.length
|
||||
? words.map(w => `<span class="wfm-lora-trigger-word">${w}</span>`).join(" ")
|
||||
: `<span style="color:var(--wfm-text-secondary);font-size:12px;">—</span>`;
|
||||
}
|
||||
}
|
||||
|
||||
function _syncStackToggleAll(stackModels) {
|
||||
const cb = document.getElementById("wfm-lora-stack-toggle-all");
|
||||
if (!cb) return;
|
||||
const allOn = stackModels.every((m) => _stackActive[m] !== false);
|
||||
const allOff = stackModels.every((m) => _stackActive[m] === false);
|
||||
cb.checked = allOn;
|
||||
cb.indeterminate = !allOn && !allOff;
|
||||
}
|
||||
|
||||
/** Sync comfyUI.currentWorkflow to Raw JSON textarea + highlight */
|
||||
function _syncRawJson() {
|
||||
const rawTextarea = document.getElementById("wfm-gen-raw-json");
|
||||
@@ -24,6 +125,8 @@ export const comfyEditor = {
|
||||
diffusionModels: [],
|
||||
textEncoders: [],
|
||||
controlNets: [],
|
||||
hypernetworks: [],
|
||||
embeddings: [],
|
||||
samplers: [],
|
||||
schedulers: [],
|
||||
lastError: null,
|
||||
@@ -31,13 +134,15 @@ export const comfyEditor = {
|
||||
|
||||
async loadModelLists() {
|
||||
try {
|
||||
const [ckpt, vae, lora, diff, enc, cn, samp, sched] = await Promise.all([
|
||||
const [ckpt, vae, lora, diff, enc, cn, hn, emb, samp, sched] = await Promise.all([
|
||||
comfyUI.fetchCheckpoints(),
|
||||
comfyUI.fetchVaes(),
|
||||
comfyUI.fetchLoras(),
|
||||
comfyUI.fetchDiffusionModels(),
|
||||
comfyUI.fetchTextEncoders(),
|
||||
comfyUI.fetchControlNets(),
|
||||
comfyUI.fetchHypernetworks(),
|
||||
comfyUI.fetchEmbeddings(),
|
||||
comfyUI.fetchSamplers(),
|
||||
comfyUI.fetchSchedulers(),
|
||||
]);
|
||||
@@ -47,6 +152,8 @@ export const comfyEditor = {
|
||||
this.models.diffusionModels = diff;
|
||||
this.models.textEncoders = enc;
|
||||
this.models.controlNets = cn;
|
||||
this.models.hypernetworks = hn;
|
||||
this.models.embeddings = emb;
|
||||
this.models.samplers = samp;
|
||||
this.models.schedulers = sched;
|
||||
this.models.lastError = null;
|
||||
@@ -60,7 +167,9 @@ export const comfyEditor = {
|
||||
this.renderPromptTab(analysis, "wfm-gen-prompt-fields");
|
||||
this.renderImageTab(analysis, "wfm-gen-image-fields");
|
||||
this.renderModelTab(analysis, "wfm-gen-model-fields");
|
||||
this.renderLoraPane(analysis, "wfm-gen-lora-fields"); // async, fires independently
|
||||
this.renderSettingsTab(analysis, "wfm-gen-settings-fields");
|
||||
_syncRawJson();
|
||||
},
|
||||
|
||||
renderPromptTab(analysis, containerId) {
|
||||
@@ -70,29 +179,42 @@ export const comfyEditor = {
|
||||
const positiveNodes = (analysis.prompt_nodes || []).filter((n) => n.role === "positive");
|
||||
const negativeNodes = (analysis.prompt_nodes || []).filter((n) => n.role === "negative");
|
||||
const nodeOpts = _nodeOptions(analysis.all_nodes);
|
||||
const embeddings = this.models.embeddings || [];
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="wfm-form-group">
|
||||
<label>Positive Prompt</label>
|
||||
<div style="display:flex;gap:8px;margin-bottom:6px;">
|
||||
<select id="wfm-prompt-pos-target" class="wfm-select" style="width:auto;flex:1">
|
||||
${positiveNodes.map((n) => `<option value="${n.id}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${positiveNodes.map((n) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${nodeOpts}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-prompt-pos-apply">Apply</button>
|
||||
</div>
|
||||
<textarea class="wfm-textarea" id="wfm-prompt-pos-text" rows="4">${positiveNodes[0]?.text || ""}</textarea>
|
||||
<textarea class="wfm-textarea" id="wfm-prompt-pos-text" rows="6">${positiveNodes[0]?.text || ""}</textarea>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Negative Prompt</label>
|
||||
<div style="display:flex;gap:8px;margin-bottom:6px;">
|
||||
<select id="wfm-prompt-neg-target" class="wfm-select" style="width:auto;flex:1">
|
||||
${negativeNodes.map((n) => `<option value="${n.id}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${negativeNodes.map((n) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${nodeOpts}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-prompt-neg-apply">Apply</button>
|
||||
</div>
|
||||
<textarea class="wfm-textarea" id="wfm-prompt-neg-text" rows="3">${negativeNodes[0]?.text || ""}</textarea>
|
||||
<textarea class="wfm-textarea" id="wfm-prompt-neg-text" rows="6">${negativeNodes[0]?.text || ""}</textarea>
|
||||
</div>
|
||||
<div class="wfm-form-group" style="border-top:1px solid var(--wfm-border);margin-top:12px;padding-top:12px;">
|
||||
<label>Embeddings</label>
|
||||
<input type="text" class="wfm-input" id="wfm-embedding-filter" placeholder="Filter..." style="margin-bottom:4px;">
|
||||
<select class="wfm-select" id="wfm-embedding-select" style="margin-bottom:4px;">
|
||||
${embeddings.map((m) => `<option value="${m}">${m}</option>`).join("")}
|
||||
</select>
|
||||
<div style="display:flex;gap:6px;align-items:center;margin-top:4px;">
|
||||
<label style="font-size:12px;white-space:nowrap;color:var(--wfm-text-secondary);">Weight</label>
|
||||
<input type="number" class="wfm-input" id="wfm-embedding-weight" value="1.0" step="0.1" min="-10" max="10" style="width:70px;">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-embedding-paste">Paste</button>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
@@ -100,7 +222,9 @@ export const comfyEditor = {
|
||||
const nodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
const text = document.getElementById("wfm-prompt-pos-text")?.value;
|
||||
if (nodeId && comfyUI.currentWorkflow?.[nodeId]) {
|
||||
comfyUI.currentWorkflow[nodeId].inputs.text = text;
|
||||
const promptNode = (analysis.prompt_nodes || []).find(n => n.id === nodeId);
|
||||
const textKey = promptNode?.textKey || "text";
|
||||
comfyUI.currentWorkflow[nodeId].inputs[textKey] = text;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
@@ -109,7 +233,66 @@ export const comfyEditor = {
|
||||
const nodeId = document.getElementById("wfm-prompt-neg-target")?.value;
|
||||
const text = document.getElementById("wfm-prompt-neg-text")?.value;
|
||||
if (nodeId && comfyUI.currentWorkflow?.[nodeId]) {
|
||||
comfyUI.currentWorkflow[nodeId].inputs.text = text;
|
||||
const promptNode = (analysis.prompt_nodes || []).find(n => n.id === nodeId);
|
||||
const textKey = promptNode?.textKey || "text";
|
||||
comfyUI.currentWorkflow[nodeId].inputs[textKey] = text;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
|
||||
// Track last focused prompt textarea for Paste button
|
||||
["wfm-prompt-pos-text", "wfm-prompt-neg-text"].forEach((taId) => {
|
||||
const ta = document.getElementById(taId);
|
||||
if (!ta) return;
|
||||
["click", "keyup", "blur"].forEach((evt) => {
|
||||
ta.addEventListener(evt, () => {
|
||||
_lastPromptFocus = { id: taId, selStart: ta.selectionStart, selEnd: ta.selectionEnd };
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// Embedding filter
|
||||
document.getElementById("wfm-embedding-filter")?.addEventListener("input", (e) => {
|
||||
const filter = e.target.value.toLowerCase();
|
||||
const select = document.getElementById("wfm-embedding-select");
|
||||
if (!select) return;
|
||||
select.innerHTML = (this.models.embeddings || [])
|
||||
.filter((m) => m.toLowerCase().includes(filter))
|
||||
.map((m) => `<option value="${m}">${m}</option>`)
|
||||
.join("");
|
||||
});
|
||||
|
||||
// Embedding Paste button — inserts at cursor position of last focused prompt textarea
|
||||
document.getElementById("wfm-embedding-paste")?.addEventListener("click", () => {
|
||||
const select = document.getElementById("wfm-embedding-select");
|
||||
if (!select?.value) return;
|
||||
const rawWeight = parseFloat(document.getElementById("wfm-embedding-weight")?.value);
|
||||
const weight = isNaN(rawWeight) ? 1.0 : rawWeight;
|
||||
const stem = select.value.replace(/\\/g, "/").split("/").pop().replace(/\.[^.]+$/, "");
|
||||
const weightStr = Number.isInteger(weight) ? `${weight}.0` : String(weight);
|
||||
const syntax = `(embedding:${stem}:${weightStr})`;
|
||||
|
||||
const targetId = _lastPromptFocus.id === "wfm-prompt-neg-text" ? "wfm-prompt-neg-text" : "wfm-prompt-pos-text";
|
||||
const promptType = targetId === "wfm-prompt-neg-text" ? "negative" : "positive";
|
||||
const ta = document.getElementById(targetId);
|
||||
if (!ta) return;
|
||||
|
||||
const selStart = _lastPromptFocus.id === targetId ? _lastPromptFocus.selStart : ta.value.length;
|
||||
const selEnd = _lastPromptFocus.id === targetId ? _lastPromptFocus.selEnd : ta.value.length;
|
||||
const before = ta.value.substring(0, selStart);
|
||||
const after = ta.value.substring(selEnd);
|
||||
const sep = before && !/[,\s]$/.test(before) ? ", " : "";
|
||||
const newText = before + sep + syntax + after;
|
||||
ta.value = newText;
|
||||
|
||||
const newCursor = selStart + sep.length + syntax.length;
|
||||
ta.focus();
|
||||
ta.setSelectionRange(newCursor, newCursor);
|
||||
_lastPromptFocus = { id: targetId, selStart: newCursor, selEnd: newCursor };
|
||||
|
||||
const wfNode = (comfyUI.currentAnalysis?.prompt_nodes || []).find((n) => n.role === promptType);
|
||||
if (wfNode && comfyUI.currentWorkflow?.[wfNode.id]) {
|
||||
comfyUI.currentWorkflow[wfNode.id].inputs[wfNode.textKey || "text"] = newText;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
@@ -122,10 +305,13 @@ export const comfyEditor = {
|
||||
const sections = [
|
||||
{ label: "Checkpoint", key: "checkpoints", nodes: analysis.checkpoint_nodes, inputKey: "ckpt_name" },
|
||||
{ label: "VAE", key: "vaes", nodes: analysis.vae_nodes, inputKey: "vae_name" },
|
||||
{ label: "LoRA", key: "loras", nodes: analysis.lora_nodes, inputKey: "lora_name" },
|
||||
{ label: "Diffusion Model", key: "diffusionModels", nodes: analysis.diffusion_model_nodes, inputKey: "unet_name" },
|
||||
{ label: "Text Encoder", key: "textEncoders", nodes: analysis.text_encoder_nodes, inputKey: "clip_name1" },
|
||||
{ label: "ControlNet", key: "controlNets", nodes: analysis.controlnet_nodes, inputKey: "control_net_name" },
|
||||
{
|
||||
label: "Hypernetwork", key: "hypernetworks", nodes: analysis.hypernetwork_nodes, inputKey: "hypernetwork_name",
|
||||
extras: [{ label: "Strength", inputKey: "strength", type: "number", defaultVal: 1.0, step: 0.01, min: -10, max: 10 }],
|
||||
},
|
||||
];
|
||||
|
||||
el.innerHTML = sections
|
||||
@@ -135,6 +321,16 @@ export const comfyEditor = {
|
||||
const targetOpts = s.nodes
|
||||
.map((n) => `<option value="${n.id}">ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
const extrasHtml = (s.extras || []).map((ex) => {
|
||||
const curVal = s.nodes?.[0]?.[ex.inputKey] ?? ex.defaultVal;
|
||||
return `<div style="display:flex;gap:6px;align-items:center;margin-top:4px;">
|
||||
<label style="font-size:12px;white-space:nowrap;color:var(--wfm-text-secondary);">${ex.label}</label>
|
||||
<input type="${ex.type}" class="wfm-input wfm-model-extra" id="wfm-model-${s.key}-${ex.inputKey}"
|
||||
data-key="${s.key}" data-input-key="${ex.inputKey}"
|
||||
value="${curVal}" step="${ex.step}" min="${ex.min}" max="${ex.max}"
|
||||
style="width:80px;">
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
return `
|
||||
<div class="wfm-form-group" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;">
|
||||
@@ -143,17 +339,11 @@ export const comfyEditor = {
|
||||
<select class="wfm-select" id="wfm-model-${s.key}" style="margin-bottom:4px;">
|
||||
${models.map((m) => `<option value="${m}" ${m === currentVal ? "selected" : ""}>${m}</option>`).join("")}
|
||||
</select>
|
||||
<div style="display:flex;gap:8px;align-items:center;">
|
||||
${extrasHtml}
|
||||
<div style="display:flex;gap:8px;align-items:center;margin-top:4px;">
|
||||
<select class="wfm-select" id="wfm-model-${s.key}-target" style="flex:1;">${targetOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-model-apply" data-key="${s.key}" data-input="${s.inputKey}">Apply</button>
|
||||
</div>
|
||||
${s.key === "loras" ? `
|
||||
<div style="display:flex;gap:8px;margin-top:6px;align-items:center;">
|
||||
<label style="font-size:12px;">Strength M:</label>
|
||||
<input type="number" class="wfm-input" id="wfm-lora-str-model" value="1.0" step="0.1" min="0" max="2" style="width:70px;">
|
||||
<label style="font-size:12px;">C:</label>
|
||||
<input type="number" class="wfm-input" id="wfm-lora-str-clip" value="1.0" step="0.1" min="0" max="2" style="width:70px;">
|
||||
</div>` : ""}
|
||||
</div>
|
||||
`;
|
||||
})
|
||||
@@ -187,19 +377,435 @@ export const comfyEditor = {
|
||||
const nodeId = targetSelect.value;
|
||||
if (nodeId && comfyUI.currentWorkflow?.[nodeId]) {
|
||||
comfyUI.currentWorkflow[nodeId].inputs[inputKey] = value;
|
||||
// Apply LoRA strengths
|
||||
if (key === "loras") {
|
||||
const strModel = parseFloat(document.getElementById("wfm-lora-str-model")?.value) || 1.0;
|
||||
const strClip = parseFloat(document.getElementById("wfm-lora-str-clip")?.value) || 1.0;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_model = strModel;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_clip = strClip;
|
||||
}
|
||||
// Apply extras (e.g. strength for Hypernetwork)
|
||||
el.querySelectorAll(`.wfm-model-extra[data-key="${key}"]`).forEach((ex) => {
|
||||
const exInputKey = ex.dataset.inputKey;
|
||||
const exVal = parseFloat(ex.value);
|
||||
if (!isNaN(exVal)) comfyUI.currentWorkflow[nodeId].inputs[exInputKey] = exVal;
|
||||
});
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
});
|
||||
},
|
||||
|
||||
async renderLoraPane(analysis, containerId) {
|
||||
const el = document.getElementById(containerId);
|
||||
if (!el) return;
|
||||
|
||||
const loraNodes = analysis.lora_nodes || [];
|
||||
const loras = this.models.loras || [];
|
||||
const defaultStackTarget = (loraNodes.find((n) => n.is_lora_manager) || loraNodes[0])?.id;
|
||||
const nodeOpts = loraNodes
|
||||
.map((n) => `<option value="${n.id}">ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
const stackTargetOpts = loraNodes
|
||||
.map((n) => `<option value="${n.id}" ${String(n.id) === String(defaultStackTarget) ? "selected" : ""}>ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
// currentWorkflowから直接取得（applyToGenUIによる変更を正確に反映）
|
||||
let currentVal = loraNodes[0]?.lora_name || "";
|
||||
const _firstLoraNode = loraNodes[0];
|
||||
if (_firstLoraNode && comfyUI.currentWorkflow?.[_firstLoraNode.id]) {
|
||||
const _wfNode = comfyUI.currentWorkflow[_firstLoraNode.id];
|
||||
if (!_firstLoraNode.is_lora_manager) {
|
||||
currentVal = _wfNode.inputs?.lora_name || currentVal;
|
||||
} else {
|
||||
// LoraManager: loras.__value__[0].nameからフルパスを復元
|
||||
const _wfLoras = _wfNode.inputs?.loras?.__value__;
|
||||
if (Array.isArray(_wfLoras) && _wfLoras.length > 0) {
|
||||
const _stem = _wfLoras[0].name;
|
||||
const _found = (this.models.loras || []).find(m => _loraBasename(m) === _stem);
|
||||
currentVal = _found || _stem;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fetch Stack group, metadata, CivitAI cache in parallel
|
||||
let stackModels = [];
|
||||
let metadata = {};
|
||||
let civitaiCache = {};
|
||||
try {
|
||||
const [grpRes, metaRes, civRes] = await Promise.all([
|
||||
fetch("/api/wfm/models/groups?type=lora"),
|
||||
fetch("/api/wfm/models/metadata"),
|
||||
fetch("/api/wfm/models/civitai/cache"),
|
||||
]);
|
||||
const groups = grpRes.ok ? await grpRes.json() : {};
|
||||
stackModels = groups["Stack"] || [];
|
||||
metadata = metaRes.ok ? await metaRes.json() : {};
|
||||
civitaiCache = civRes.ok ? await civRes.json() : {};
|
||||
} catch { /* ignore */ }
|
||||
|
||||
stackModels.forEach((m) => {
|
||||
if (!_stackStrengths[m]) _stackStrengths[m] = { m: 1.0, c: 1.0 };
|
||||
if (_stackActive[m] === undefined) _stackActive[m] = true;
|
||||
});
|
||||
|
||||
// Build Stack trigger words
|
||||
const activeTriggerWords = [];
|
||||
const allStackTriggers = [];
|
||||
stackModels.forEach((m) => {
|
||||
const sha = (metadata[m] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
if (civInfo?.trainedWords?.length) {
|
||||
allStackTriggers.push(...civInfo.trainedWords);
|
||||
if (_stackActive[m] !== false) activeTriggerWords.push(...civInfo.trainedWords);
|
||||
}
|
||||
});
|
||||
const stackTriggerHtml = activeTriggerWords.length
|
||||
? activeTriggerWords.map((w) => `<span class="wfm-lora-trigger-word">${w}</span>`).join(" ")
|
||||
: `<span style="color:var(--wfm-text-secondary);font-size:12px;">—</span>`;
|
||||
const stackLoraSyntax = _buildLoraSyntax(stackModels);
|
||||
const allActive = stackModels.every((m) => _stackActive[m] !== false);
|
||||
const anyActive = stackModels.some((m) => _stackActive[m] !== false);
|
||||
|
||||
const stackModelRows = stackModels.map((m) => {
|
||||
const stem = _loraBasename(m);
|
||||
const str = _stackStrengths[m] || { m: 1.0, c: 1.0 };
|
||||
const active = _stackActive[m] !== false;
|
||||
return `
|
||||
<div class="wfm-lora-stack-model-row${active ? "" : " wfm-lora-stack-model-row--off"}" data-model="${m.replace(/"/g, """)}">
|
||||
<input type="checkbox" class="wfm-lora-stack-active-cb" ${active ? "checked" : ""} title="Enable/Disable">
|
||||
<span class="wfm-lora-stack-model-name" title="${m}">${stem}</span>
|
||||
<div class="wfm-lora-stack-strengths">
|
||||
<input type="number" class="wfm-input wfm-lora-stack-str-m" value="${str.m}" step="0.05" min="0" max="2" style="width:64px;" ${active ? "" : "disabled"}>
|
||||
<input type="number" class="wfm-input wfm-lora-stack-str-c" value="${str.c}" step="0.05" min="0" max="2" style="width:64px;" ${active ? "" : "disabled"}>
|
||||
</div>
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
const _prevActiveTab = el.querySelector(".wfm-lora-tab-btn.active")?.dataset?.tab || "single";
|
||||
// applyToGenUIで設定したSingle表示の状態を保存（再描画後に復元）
|
||||
const _prevSingleSyntax = document.getElementById("wfm-lora-single-syntax")?.textContent || "";
|
||||
const _prevSingleTriggers = document.getElementById("wfm-lora-single-triggers")?.innerHTML || "";
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="wfm-lora-tab-header">
|
||||
<button class="wfm-lora-tab-btn active" data-tab="single">Single</button>
|
||||
<button class="wfm-lora-tab-btn" data-tab="stack">Stack</button>
|
||||
</div>
|
||||
|
||||
<!-- Single tab -->
|
||||
<div class="wfm-lora-tab-content" id="wfm-lora-panel-single">
|
||||
<input type="text" class="wfm-input" id="wfm-lora-filter" placeholder="Filter...">
|
||||
<select class="wfm-select" id="wfm-lora-select">
|
||||
${loras.map((m) => `<option value="${m}" ${m === currentVal ? "selected" : ""}>${m}</option>`).join("")}
|
||||
</select>
|
||||
<div class="wfm-lora-strength-single">
|
||||
<span>M</span>
|
||||
<input type="number" class="wfm-input" id="wfm-lora-str-model" value="1.0" step="0.05" min="0" max="2">
|
||||
<span>C</span>
|
||||
<input type="number" class="wfm-input" id="wfm-lora-str-clip" value="1.0" step="0.05" min="0" max="2">
|
||||
</div>
|
||||
<div class="wfm-lora-stack-header">
|
||||
<select class="wfm-select" id="wfm-lora-single-target" style="flex:1;min-width:0;">${nodeOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-lora-single-apply" title="Apply LoRA to node and sync to Positive prompt">Apply</button>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-lora-p-btn" id="wfm-lora-single-pos-apply" title="Apply Positive prompt to workflow">P</button>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-info-block">
|
||||
<div class="wfm-lora-stack-info-label">Lora syntax</div>
|
||||
<div id="wfm-lora-single-syntax" class="wfm-lora-stack-syntax">—</div>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-info-block">
|
||||
<div class="wfm-lora-stack-info-label">Trigger words</div>
|
||||
<div id="wfm-lora-single-triggers" class="wfm-lora-stack-triggers"><span style="color:var(--wfm-text-secondary);font-size:12px;">—</span></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Stack tab -->
|
||||
<div class="wfm-lora-tab-content" id="wfm-lora-panel-stack" style="display:none;">
|
||||
<div class="wfm-lora-stack-header">
|
||||
<select class="wfm-select" id="wfm-lora-stack-target" style="flex:1;min-width:0;">${stackTargetOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-lora-stack-apply" title="Apply Stack to node and sync to Positive prompt">Apply</button>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-lora-p-btn" id="wfm-lora-pos-apply" title="Apply Positive prompt to workflow">P</button>
|
||||
</div>
|
||||
<div class="wfm-lora-strength-combined">
|
||||
<span class="wfm-lora-stack-label" style="margin-right:4px;flex-shrink:0;">Stack</span>
|
||||
<input type="checkbox" id="wfm-lora-stack-toggle-all" ${allActive ? "checked" : ""} title="Toggle all stack models" style="flex-shrink:0;margin-right:8px;">
|
||||
${stackModels.length > 0 ? `
|
||||
<div class="wfm-lora-stack-global-adj-groups">
|
||||
<div class="wfm-lora-stack-global-adj-group">
|
||||
<span>Str M</span>
|
||||
<button class="wfm-btn wfm-btn-xs" id="wfm-stack-adj-m-dec">−</button>
|
||||
<input type="number" id="wfm-stack-adj-step-m" class="wfm-input wfm-lora-stack-adj-step" value="0.05" step="0.05" min="0.01" max="2.0">
|
||||
<button class="wfm-btn wfm-btn-xs" id="wfm-stack-adj-m-inc">+</button>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-global-adj-group">
|
||||
<span>C</span>
|
||||
<button class="wfm-btn wfm-btn-xs" id="wfm-stack-adj-c-dec">−</button>
|
||||
<input type="number" id="wfm-stack-adj-step-c" class="wfm-input wfm-lora-stack-adj-step" value="0.05" step="0.05" min="0.01" max="2.0">
|
||||
<button class="wfm-btn wfm-btn-xs" id="wfm-stack-adj-c-inc">+</button>
|
||||
</div>
|
||||
</div>
|
||||
` : ""}
|
||||
</div>
|
||||
<div class="wfm-lora-stack-info-block">
|
||||
<div class="wfm-lora-stack-info-label">Lora syntax</div>
|
||||
<div id="wfm-lora-stack-syntax" class="wfm-lora-stack-syntax">—</div>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-info-block">
|
||||
<div class="wfm-lora-stack-info-label">Trigger words</div>
|
||||
<div id="wfm-lora-stack-triggers" class="wfm-lora-stack-triggers">${stackTriggerHtml}</div>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-models">
|
||||
${stackModelRows || `<p class="wfm-placeholder">No models in Stack group</p>`}
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Fix: overwrite textContent to avoid HTML parsing of <lora:...> syntax
|
||||
const _synEl = document.getElementById("wfm-lora-stack-syntax");
|
||||
if (_synEl) _synEl.textContent = stackLoraSyntax || "—";
|
||||
|
||||
// Single: applyToGenUIで設定したsyntax/triggersを復元
|
||||
if (_prevSingleSyntax && _prevSingleSyntax !== "—") {
|
||||
const _sEl = document.getElementById("wfm-lora-single-syntax");
|
||||
if (_sEl) _sEl.textContent = _prevSingleSyntax;
|
||||
}
|
||||
if (_prevSingleTriggers) {
|
||||
const _tEl = document.getElementById("wfm-lora-single-triggers");
|
||||
if (_tEl && !_prevSingleTriggers.includes(">—<")) _tEl.innerHTML = _prevSingleTriggers;
|
||||
}
|
||||
|
||||
// ── Tab switching ────────────────────────────────────
|
||||
el.querySelectorAll(".wfm-lora-tab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
el.querySelectorAll(".wfm-lora-tab-btn").forEach(b => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
const tab = btn.dataset.tab;
|
||||
document.getElementById("wfm-lora-panel-single").style.display = tab === "single" ? "" : "none";
|
||||
document.getElementById("wfm-lora-panel-stack").style.display = tab === "stack" ? "" : "none";
|
||||
});
|
||||
});
|
||||
|
||||
// Restore previously active tab after re-render
|
||||
if (_prevActiveTab === "stack") {
|
||||
el.querySelector('[data-tab="single"]')?.classList.remove("active");
|
||||
el.querySelector('[data-tab="stack"]')?.classList.add("active");
|
||||
document.getElementById("wfm-lora-panel-single").style.display = "none";
|
||||
document.getElementById("wfm-lora-panel-stack").style.display = "";
|
||||
}
|
||||
|
||||
// ── Single: filter ───────────────────────────────────
|
||||
document.getElementById("wfm-lora-filter")?.addEventListener("input", (e) => {
|
||||
const filter = e.target.value.toLowerCase();
|
||||
const select = document.getElementById("wfm-lora-select");
|
||||
if (!select) return;
|
||||
select.innerHTML = loras
|
||||
.filter((m) => m.toLowerCase().includes(filter))
|
||||
.map((m) => `<option value="${m}">${m}</option>`)
|
||||
.join("");
|
||||
_refreshLoraSingleDynamic(metadata, civitaiCache);
|
||||
});
|
||||
|
||||
// ── Single: update SYNTAX/TRIGGERS on model or strength change ──
|
||||
document.getElementById("wfm-lora-select")?.addEventListener("change", () => _refreshLoraSingleDynamic(metadata, civitaiCache));
|
||||
document.getElementById("wfm-lora-str-model")?.addEventListener("input", () => _refreshLoraSingleDynamic(metadata, civitaiCache));
|
||||
document.getElementById("wfm-lora-str-clip")?.addEventListener("input", () => _refreshLoraSingleDynamic(metadata, civitaiCache));
|
||||
|
||||
// ── Single: Apply button ─────────────────────────────
|
||||
document.getElementById("wfm-lora-single-apply")?.addEventListener("click", () => {
|
||||
const nodeId = document.getElementById("wfm-lora-single-target")?.value;
|
||||
const select = document.getElementById("wfm-lora-select");
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId] || !select?.value) return;
|
||||
|
||||
const node = loraNodes.find((n) => String(n.id) === String(nodeId));
|
||||
const strModel = parseFloat(document.getElementById("wfm-lora-str-model")?.value) || 1.0;
|
||||
const strClip = parseFloat(document.getElementById("wfm-lora-str-clip")?.value) || 1.0;
|
||||
_applyLoraToNode(nodeId, select.value, strModel, strClip, node?.is_lora_manager);
|
||||
_syncRawJson();
|
||||
|
||||
// Sync to Positive prompt
|
||||
const posTextarea = document.getElementById("wfm-prompt-pos-text");
|
||||
if (!posTextarea) return;
|
||||
const stem = _loraBasename(select.value);
|
||||
const loraSyntax = `<lora:${stem}:${strModel}:${strClip}>`;
|
||||
const sha = (metadata[select.value] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
const triggerWords = civInfo?.trainedWords || [];
|
||||
|
||||
let cleaned = posTextarea.value;
|
||||
const escapedStem = stem.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
|
||||
cleaned = cleaned.replace(new RegExp(`,?\\s*<lora:${escapedStem}:[^>]*>`, "gi"), "");
|
||||
if (triggerWords.length > 0) {
|
||||
const wordSet = new Set(triggerWords.map(w => w.trim().toLowerCase()));
|
||||
cleaned = cleaned.split(",").map(p => p.trim()).filter(p => p && !wordSet.has(p.toLowerCase())).join(", ");
|
||||
}
|
||||
cleaned = cleaned.replace(/,\s*$/, "").trim();
|
||||
const append = triggerWords.length > 0 ? `${loraSyntax}, ${triggerWords.join(", ")}` : loraSyntax;
|
||||
posTextarea.value = cleaned ? `${cleaned}, ${append}` : append;
|
||||
|
||||
const posNodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
if (posNodeId && comfyUI.currentWorkflow?.[posNodeId]) {
|
||||
const promptNode = comfyUI.currentAnalysis?.prompt_nodes?.find(n => String(n.id) === String(posNodeId));
|
||||
comfyUI.currentWorkflow[posNodeId].inputs[promptNode?.textKey || "text"] = posTextarea.value;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
|
||||
// ── Single: P button ─────────────────────────────────
|
||||
document.getElementById("wfm-lora-single-pos-apply")?.addEventListener("click", () => {
|
||||
const nodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
const text = document.getElementById("wfm-prompt-pos-text")?.value;
|
||||
if (nodeId && comfyUI.currentWorkflow?.[nodeId]) {
|
||||
const promptNode = comfyUI.currentAnalysis?.prompt_nodes?.find(n => String(n.id) === String(nodeId));
|
||||
comfyUI.currentWorkflow[nodeId].inputs[promptNode?.textKey || "text"] = text;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
|
||||
// ── Stack: per-model strength + toggle ───────────────
|
||||
el.querySelectorAll(".wfm-lora-stack-model-row").forEach((row) => {
|
||||
const modelName = row.dataset.model;
|
||||
const inputM = row.querySelector(".wfm-lora-stack-str-m");
|
||||
const inputC = row.querySelector(".wfm-lora-stack-str-c");
|
||||
const cbActive = row.querySelector(".wfm-lora-stack-active-cb");
|
||||
|
||||
const onStrChange = () => {
|
||||
_stackStrengths[modelName] = { m: parseFloat(inputM.value) || 1.0, c: parseFloat(inputC.value) || 1.0 };
|
||||
_refreshLoraPaneDynamic(stackModels, metadata, civitaiCache);
|
||||
};
|
||||
inputM?.addEventListener("input", onStrChange);
|
||||
inputC?.addEventListener("input", onStrChange);
|
||||
|
||||
cbActive?.addEventListener("change", () => {
|
||||
const on = cbActive.checked;
|
||||
_stackActive[modelName] = on;
|
||||
row.classList.toggle("wfm-lora-stack-model-row--off", !on);
|
||||
if (inputM) inputM.disabled = !on;
|
||||
if (inputC) inputC.disabled = !on;
|
||||
_syncStackToggleAll(stackModels);
|
||||
_refreshLoraPaneDynamic(stackModels, metadata, civitaiCache);
|
||||
});
|
||||
});
|
||||
|
||||
// ── Stack: toggle-all checkbox ───────────────────────
|
||||
const toggleAllCb = document.getElementById("wfm-lora-stack-toggle-all");
|
||||
if (toggleAllCb) {
|
||||
toggleAllCb.indeterminate = !allActive && anyActive;
|
||||
toggleAllCb.addEventListener("change", () => {
|
||||
const on = toggleAllCb.checked;
|
||||
stackModels.forEach((m) => { _stackActive[m] = on; });
|
||||
el.querySelectorAll(".wfm-lora-stack-model-row").forEach((row) => {
|
||||
const cb = row.querySelector(".wfm-lora-stack-active-cb");
|
||||
const inM = row.querySelector(".wfm-lora-stack-str-m");
|
||||
const inC = row.querySelector(".wfm-lora-stack-str-c");
|
||||
if (cb) cb.checked = on;
|
||||
if (inM) inM.disabled = !on;
|
||||
if (inC) inC.disabled = !on;
|
||||
row.classList.toggle("wfm-lora-stack-model-row--off", !on);
|
||||
});
|
||||
_refreshLoraPaneDynamic(stackModels, metadata, civitaiCache);
|
||||
});
|
||||
}
|
||||
|
||||
// ── Stack: global strength adjustment ────────────────
|
||||
if (stackModels.length > 0) {
|
||||
const adjApply = (key, sign) => {
|
||||
const delta = sign * (parseFloat(document.getElementById(`wfm-stack-adj-step-${key}`)?.value) || 0.05);
|
||||
stackModels.forEach((m) => {
|
||||
const str = _stackStrengths[m] || { m: 1.0, c: 1.0 };
|
||||
str[key] = Math.max(0, Math.round((str[key] + delta) * 1000) / 1000);
|
||||
_stackStrengths[m] = str;
|
||||
});
|
||||
el.querySelectorAll(".wfm-lora-stack-model-row").forEach((row) => {
|
||||
const str = _stackStrengths[row.dataset.model];
|
||||
if (!str) return;
|
||||
const inp = row.querySelector(`.wfm-lora-stack-str-${key}`);
|
||||
if (inp) inp.value = str[key];
|
||||
});
|
||||
_refreshLoraPaneDynamic(stackModels, metadata, civitaiCache);
|
||||
};
|
||||
document.getElementById("wfm-stack-adj-m-inc")?.addEventListener("click", () => adjApply("m", 1));
|
||||
document.getElementById("wfm-stack-adj-m-dec")?.addEventListener("click", () => adjApply("m", -1));
|
||||
document.getElementById("wfm-stack-adj-c-inc")?.addEventListener("click", () => adjApply("c", 1));
|
||||
document.getElementById("wfm-stack-adj-c-dec")?.addEventListener("click", () => adjApply("c", -1));
|
||||
}
|
||||
|
||||
// ── Stack: Apply button ──────────────────────────────
|
||||
document.getElementById("wfm-lora-stack-apply")?.addEventListener("click", () => {
|
||||
const nodeId = document.getElementById("wfm-lora-stack-target")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
|
||||
const node = loraNodes.find((n) => String(n.id) === String(nodeId));
|
||||
if (node?.is_lora_manager) {
|
||||
const loraValue = stackModels.map((m) => {
|
||||
const stem = _loraBasename(m);
|
||||
const str = _stackStrengths[m] || { m: 1.0, c: 1.0 };
|
||||
const active = _stackActive[m] !== false;
|
||||
return { name: stem, strength: str.m, active, expanded: false, clipStrength: str.c, locked: false };
|
||||
});
|
||||
comfyUI.currentWorkflow[nodeId].inputs.loras = { __value__: loraValue };
|
||||
comfyUI.currentWorkflow[nodeId].inputs.text = _buildLoraManagerSyntax(stackModels);
|
||||
} else {
|
||||
const first = stackModels[0];
|
||||
if (first) {
|
||||
comfyUI.currentWorkflow[nodeId].inputs.lora_name = first;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_model = _stackStrengths[first]?.m ?? 1.0;
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_clip = _stackStrengths[first]?.c ?? 1.0;
|
||||
}
|
||||
}
|
||||
_syncRawJson();
|
||||
|
||||
// Sync Stack LORA SYNTAX + TRIGGER WORDS to Positive prompt
|
||||
const posTextarea = document.getElementById("wfm-prompt-pos-text");
|
||||
if (!posTextarea) return;
|
||||
const effectiveSyntax = _buildLoraSyntax(stackModels);
|
||||
|
||||
// Recompute trigger words at apply-time using the current _stackActive state
|
||||
const currentAllTriggers = [];
|
||||
const currentActiveTriggers = [];
|
||||
stackModels.forEach((m) => {
|
||||
const sha = (metadata[m] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
if (civInfo?.trainedWords?.length) {
|
||||
currentAllTriggers.push(...civInfo.trainedWords);
|
||||
if (_stackActive[m] !== false) currentActiveTriggers.push(...civInfo.trainedWords);
|
||||
}
|
||||
});
|
||||
|
||||
let cleaned = posTextarea.value;
|
||||
for (const m of stackModels) {
|
||||
const stem = _loraBasename(m).replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
|
||||
cleaned = cleaned.replace(new RegExp(`,?\\s*<lora:${stem}:[^>]*>`, "gi"), "");
|
||||
}
|
||||
if (currentAllTriggers.length > 0) {
|
||||
const wordSet = new Set(currentAllTriggers.map(w => w.trim().toLowerCase()));
|
||||
cleaned = cleaned.split(",").map(p => p.trim()).filter(p => p && !wordSet.has(p.toLowerCase())).join(", ");
|
||||
}
|
||||
cleaned = cleaned.replace(/,\s*$/, "").trim();
|
||||
if (effectiveSyntax) {
|
||||
const append = currentActiveTriggers.length > 0
|
||||
? `${effectiveSyntax}, ${currentActiveTriggers.join(", ")}`
|
||||
: effectiveSyntax;
|
||||
posTextarea.value = cleaned ? `${cleaned}, ${append}` : append;
|
||||
} else {
|
||||
posTextarea.value = cleaned;
|
||||
}
|
||||
|
||||
const posNodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
if (posNodeId && comfyUI.currentWorkflow?.[posNodeId]) {
|
||||
const promptNode = comfyUI.currentAnalysis?.prompt_nodes?.find(n => String(n.id) === String(posNodeId));
|
||||
comfyUI.currentWorkflow[posNodeId].inputs[promptNode?.textKey || "text"] = posTextarea.value;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
|
||||
// ── Stack: P button ──────────────────────────────────
|
||||
document.getElementById("wfm-lora-pos-apply")?.addEventListener("click", () => {
|
||||
const nodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
const text = document.getElementById("wfm-prompt-pos-text")?.value;
|
||||
if (nodeId && comfyUI.currentWorkflow?.[nodeId]) {
|
||||
const promptNode = comfyUI.currentAnalysis?.prompt_nodes?.find(n => String(n.id) === String(nodeId));
|
||||
comfyUI.currentWorkflow[nodeId].inputs[promptNode?.textKey || "text"] = text;
|
||||
_syncRawJson();
|
||||
}
|
||||
});
|
||||
|
||||
// Initial single tab display
|
||||
_refreshLoraSingleDynamic(metadata, civitaiCache);
|
||||
},
|
||||
|
||||
renderSettingsTab(analysis, containerId) {
|
||||
const el = document.getElementById(containerId);
|
||||
if (!el) return;
|
||||
@@ -208,11 +814,11 @@ export const comfyEditor = {
|
||||
const latent = analysis.latent_nodes?.[0];
|
||||
|
||||
el.innerHTML = `
|
||||
<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;">
|
||||
<div>
|
||||
<h3 style="font-size:14px;margin-bottom:12px;">KSampler</h3>
|
||||
<div style="display:flex;flex-direction:row;gap:0;align-items:flex-start;">
|
||||
<div style="flex:1;min-width:0;padding-right:14px;border-right:1px solid var(--wfm-border);">
|
||||
<h3 style="font-size:13px;font-weight:600;text-transform:uppercase;letter-spacing:0.5px;color:var(--wfm-text-secondary);margin:0 0 12px;">KSampler</h3>
|
||||
${sampler ? `
|
||||
<input type="hidden" id="wfm-settings-sampler-id" value="${sampler.id}">
|
||||
<input type="hidden" id="wfm-settings-sampler-id" value="${sampler.id}" data-seed-key="${sampler.seedKey || "seed"}">
|
||||
<div class="wfm-form-group">
|
||||
<label>Seed</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-seed" value="${sampler.seed ?? -1}">
|
||||
@@ -244,8 +850,8 @@ export const comfyEditor = {
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-sampler-apply">Apply</button>
|
||||
` : "<p class='wfm-placeholder'>No KSampler node found</p>"}
|
||||
</div>
|
||||
<div>
|
||||
<h3 style="font-size:14px;margin-bottom:12px;">Latent Image</h3>
|
||||
<div style="flex:1;min-width:0;padding-left:14px;">
|
||||
<h3 style="font-size:13px;font-weight:600;text-transform:uppercase;letter-spacing:0.5px;color:var(--wfm-text-secondary);margin:0 0 12px;">Latent Image</h3>
|
||||
${latent ? `
|
||||
<input type="hidden" id="wfm-settings-latent-id" value="${latent.id}">
|
||||
<div class="wfm-form-group">
|
||||
@@ -270,7 +876,9 @@ export const comfyEditor = {
|
||||
const nodeId = document.getElementById("wfm-settings-sampler-id")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
const inputs = comfyUI.currentWorkflow[nodeId].inputs;
|
||||
inputs.seed = parseInt(document.getElementById("wfm-settings-seed")?.value) || -1;
|
||||
// KSamplerAdvanced uses noise_seed; fall back to seed
|
||||
const seedKey = document.getElementById("wfm-settings-sampler-id")?.dataset?.seedKey || "seed";
|
||||
inputs[seedKey] = parseInt(document.getElementById("wfm-settings-seed")?.value) || -1;
|
||||
inputs.steps = parseInt(document.getElementById("wfm-settings-steps")?.value) || 20;
|
||||
inputs.cfg = parseFloat(document.getElementById("wfm-settings-cfg")?.value) || 7;
|
||||
inputs.sampler_name = document.getElementById("wfm-settings-sampler-name")?.value;
|
||||
@@ -401,18 +1009,79 @@ export const comfyEditor = {
|
||||
|
||||
syncToWorkflow() {
|
||||
// Sync prompt texts before generation
|
||||
const posTarget = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
const posSelect = document.getElementById("wfm-prompt-pos-target");
|
||||
const posTarget = posSelect?.value;
|
||||
const posText = document.getElementById("wfm-prompt-pos-text")?.value;
|
||||
if (posTarget && comfyUI.currentWorkflow?.[posTarget]) {
|
||||
comfyUI.currentWorkflow[posTarget].inputs.text = posText;
|
||||
const posTextKey = posSelect.selectedOptions[0]?.dataset?.textKey || "text";
|
||||
comfyUI.currentWorkflow[posTarget].inputs[posTextKey] = posText;
|
||||
}
|
||||
|
||||
const negTarget = document.getElementById("wfm-prompt-neg-target")?.value;
|
||||
const negSelect = document.getElementById("wfm-prompt-neg-target");
|
||||
const negTarget = negSelect?.value;
|
||||
const negText = document.getElementById("wfm-prompt-neg-text")?.value;
|
||||
if (negTarget && comfyUI.currentWorkflow?.[negTarget]) {
|
||||
comfyUI.currentWorkflow[negTarget].inputs.text = negText;
|
||||
const negTextKey = negSelect.selectedOptions[0]?.dataset?.textKey || "text";
|
||||
comfyUI.currentWorkflow[negTarget].inputs[negTextKey] = negText;
|
||||
}
|
||||
},
|
||||
|
||||
disableAllStack(containerId = "wfm-gen-lora-fields") {
|
||||
for (const k of Object.keys(_stackActive)) {
|
||||
_stackActive[k] = false;
|
||||
}
|
||||
const toggleAll = document.getElementById("wfm-lora-stack-toggle-all");
|
||||
if (toggleAll) toggleAll.checked = false;
|
||||
const container = document.getElementById(containerId);
|
||||
if (!container) return;
|
||||
container.querySelectorAll(".wfm-lora-stack-model-row").forEach((row) => {
|
||||
row.classList.add("wfm-lora-stack-model-row--off");
|
||||
const cb = row.querySelector(".wfm-lora-stack-active-cb");
|
||||
const inM = row.querySelector(".wfm-lora-stack-str-m");
|
||||
const inC = row.querySelector(".wfm-lora-stack-str-c");
|
||||
if (cb) cb.checked = false;
|
||||
if (inM) inM.disabled = true;
|
||||
if (inC) inC.disabled = true;
|
||||
});
|
||||
},
|
||||
|
||||
appendEmbeddingToPrompt(syntax, promptType) {
|
||||
const promptNodes = (comfyUI.currentAnalysis?.prompt_nodes || []).filter((n) => n.role === promptType);
|
||||
const textareaId = promptType === "positive" ? "wfm-prompt-pos-text" : "wfm-prompt-neg-text";
|
||||
const textarea = document.getElementById(textareaId);
|
||||
|
||||
if (promptNodes.length > 0 && comfyUI.currentWorkflow) {
|
||||
const node = promptNodes[0];
|
||||
const wfNode = comfyUI.currentWorkflow[node.id];
|
||||
if (wfNode) {
|
||||
const textKey = node.textKey || "text";
|
||||
const current = wfNode.inputs[textKey] || "";
|
||||
const newText = current ? `${current}, ${syntax}` : syntax;
|
||||
wfNode.inputs[textKey] = newText;
|
||||
if (textarea) textarea.value = newText;
|
||||
_syncRawJson();
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback: update textarea only
|
||||
if (textarea) {
|
||||
const current = textarea.value;
|
||||
textarea.value = current ? `${current}, ${syntax}` : syntax;
|
||||
}
|
||||
},
|
||||
|
||||
switchLoraSingleTab() {
|
||||
const container = document.getElementById("wfm-gen-lora-fields");
|
||||
if (!container) return;
|
||||
container.querySelectorAll(".wfm-lora-tab-btn").forEach(b => b.classList.remove("active"));
|
||||
const singleBtn = container.querySelector(".wfm-lora-tab-btn[data-tab='single']");
|
||||
if (singleBtn) singleBtn.classList.add("active");
|
||||
const singlePanel = document.getElementById("wfm-lora-panel-single");
|
||||
const stackPanel = document.getElementById("wfm-lora-panel-stack");
|
||||
if (singlePanel) singlePanel.style.display = "";
|
||||
if (stackPanel) stackPanel.style.display = "none";
|
||||
},
|
||||
};
|
||||
|
||||
function _nodeOptions(nodes) {
|
||||
|
||||
+300
-15
@@ -295,16 +295,36 @@ export const comfyWorkflow = {
|
||||
const nodeLinks = linkMap[node.id] || {};
|
||||
const inputDefs = node.inputs || [];
|
||||
const linkedInputNames = new Set();
|
||||
// UI slot inputs with an actual link don't have a widget value entry in
|
||||
// widgets_values (modern ComfyUI format). Track these separately so the
|
||||
// widget-mapping loop won't advance wIdx for them.
|
||||
const linkedSlotNames = new Set();
|
||||
inputDefs.forEach((inp, idx) => {
|
||||
if (nodeLinks[idx]) {
|
||||
inputs[inp.name] = nodeLinks[idx];
|
||||
linkedInputNames.add(inp.name);
|
||||
linkedSlotNames.add(inp.name);
|
||||
}
|
||||
});
|
||||
|
||||
// 2. Map widgets_values to widget input names using object_info
|
||||
const widgets = node.widgets_values || [];
|
||||
if (widgets.length > 0) {
|
||||
// Lora Loader (LoraManager): use __lm_widget_ids from node properties
|
||||
// widgets_values: [autocomplete_meta_obj, text_str, loras_array]
|
||||
// API format: {__lm_autocomplete_meta_text: obj, text: str, loras: {__value__: arr}}
|
||||
const lmWidgetIds = node.properties?.__lm_widget_ids;
|
||||
if (lmWidgetIds && Array.isArray(lmWidgetIds)) {
|
||||
lmWidgetIds.forEach((name, idx) => {
|
||||
if (idx >= widgets.length || linkedInputNames.has(name)) return;
|
||||
let val = widgets[idx];
|
||||
if (name === "loras" && Array.isArray(val)) {
|
||||
val = { "__value__": val };
|
||||
}
|
||||
inputs[name] = val;
|
||||
});
|
||||
} else {
|
||||
|
||||
const widgetNames = _getWidgetInputNames(objectInfo, node.type);
|
||||
|
||||
if (widgetNames.length > 0) {
|
||||
@@ -321,13 +341,15 @@ export const comfyWorkflow = {
|
||||
const expectedType = widgetTypes[nIdx];
|
||||
// Skip if this input is already linked
|
||||
if (linkedInputNames.has(name)) {
|
||||
// Still consume widget value(s) since widgets_values
|
||||
// includes entries for linked inputs too
|
||||
wIdx++;
|
||||
// Also skip control_after_generate if present
|
||||
if (wIdx < widgets.length && _isExtraWidgetValue(widgets[wIdx], expectedType)) {
|
||||
if (!linkedSlotNames.has(name)) {
|
||||
// Not a UI slot input — old-style linked input that still
|
||||
// occupies a slot in widgets_values; consume it.
|
||||
wIdx++;
|
||||
if (wIdx < widgets.length && _isExtraWidgetValue(widgets[wIdx], expectedType)) {
|
||||
wIdx++;
|
||||
}
|
||||
}
|
||||
// UI slot inputs with a real link have no widget value entry — don't advance wIdx.
|
||||
continue;
|
||||
}
|
||||
// Check if current widget value matches expected type
|
||||
@@ -343,6 +365,24 @@ export const comfyWorkflow = {
|
||||
wIdx++;
|
||||
}
|
||||
}
|
||||
// For COMBO inputs, validate the value against the available choices.
|
||||
// Dynamic COMBO options (e.g. Impact Pack's "Select to add Wildcard") may
|
||||
// differ between the saved workflow and the current ComfyUI instance
|
||||
// (e.g. "Select Wildcard 🟢 Full Cache" vs "Select Wildcard"), causing
|
||||
// "Prompt outputs failed validation". Fall back to first choice when mismatch.
|
||||
if (expectedType === "COMBO") {
|
||||
const allInputDefs = {
|
||||
...(objectInfo[node.type]?.input?.required || {}),
|
||||
...(objectInfo[node.type]?.input?.optional || {}),
|
||||
};
|
||||
const spec = allInputDefs[name];
|
||||
if (spec) {
|
||||
const choices = Array.isArray(spec[0]) ? spec[0] : null;
|
||||
if (choices && choices.length > 0 && !choices.includes(val)) {
|
||||
val = choices[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
inputs[name] = val;
|
||||
}
|
||||
} else {
|
||||
@@ -356,6 +396,15 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
}
|
||||
} // end else (non-LoraManager path)
|
||||
}
|
||||
|
||||
// ImpactWildcardEncode / ImpactWildcardProcessor: widgets[0] is always wildcard_text.
|
||||
// Always overwrite: object_info ordering may misalign when widget order differs between
|
||||
// impact-pack versions; widgets[0] is stable across all known versions.
|
||||
if ((node.type === "ImpactWildcardEncode" || node.type === "ImpactWildcardProcessor")
|
||||
&& widgets.length > 0) {
|
||||
inputs["wildcard_text"] = widgets[0];
|
||||
}
|
||||
|
||||
api[nodeId] = {
|
||||
@@ -441,12 +490,13 @@ export const comfyWorkflow = {
|
||||
text_encoder_nodes: [],
|
||||
diffusion_model_nodes: [],
|
||||
controlnet_nodes: [],
|
||||
hypernetwork_nodes: [],
|
||||
all_nodes: [],
|
||||
};
|
||||
|
||||
if (!workflow) return result;
|
||||
|
||||
// Build sampler connections for prompt classification
|
||||
// === Pass 1: Collect sampler refs + all_nodes ===
|
||||
const samplerPositiveRef = {};
|
||||
const samplerNegativeRef = {};
|
||||
|
||||
@@ -462,40 +512,217 @@ export const comfyWorkflow = {
|
||||
if (Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
|
||||
if (Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
|
||||
|
||||
// KSamplerAdvanced uses noise_seed instead of seed
|
||||
const seedKey = "seed" in inputs ? "seed" : "noise_seed";
|
||||
result.sampler_nodes.push({
|
||||
id, type: ct, title,
|
||||
seed: inputs.seed, steps: inputs.steps, cfg: inputs.cfg,
|
||||
seed: inputs.seed ?? inputs.noise_seed,
|
||||
seedKey,
|
||||
steps: inputs.steps, cfg: inputs.cfg,
|
||||
sampler_name: inputs.sampler_name, scheduler: inputs.scheduler,
|
||||
denoise: inputs.denoise,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// === Pass 1b: Expand refs through CONDITIONING graph (BFS, 5 iterations) ===
|
||||
// Nodes that pass CONDITIONING through without changing positive/negative role
|
||||
const COND_PASSTHROUGH = new Set([
|
||||
"ConditioningCombine", "ConditioningConcat", "ConditioningAverage",
|
||||
"ConditioningSetTimestepRange",
|
||||
// ConditioningZeroOut is intentionally excluded: it discards upstream text entirely
|
||||
// (used as a no-text negative in ZIT/Lumina2 workflows). Propagating the negative
|
||||
// role upstream would incorrectly mark the shared positive CLIPTextEncode as "unknown".
|
||||
"ControlNetApply", "ControlNetApplyAdvanced",
|
||||
"IPAdapterApply", "IPAdapterApplyFaceID",
|
||||
"StyleModelApply",
|
||||
]);
|
||||
|
||||
for (let iter = 0; iter < 5; iter++) {
|
||||
for (const [id, node] of Object.entries(workflow)) {
|
||||
if (!node?.class_type) continue;
|
||||
const ct = node.class_type;
|
||||
const inputs = node.inputs || {};
|
||||
const isPos = !!samplerPositiveRef[id];
|
||||
const isNeg = !!samplerNegativeRef[id];
|
||||
if (!isPos && !isNeg) continue;
|
||||
|
||||
if (COND_PASSTHROUGH.has(ct)) {
|
||||
for (const v of Object.values(inputs)) {
|
||||
if (Array.isArray(v)) {
|
||||
if (isPos) samplerPositiveRef[v[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[v[0]] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Propagate role through text encoder linked inputs (CLIPTextEncode → upstream text source)
|
||||
if (ct === "CLIPTextEncode") {
|
||||
const tv = inputs.text;
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
if (ct === "CLIPTextEncodeSDXL" || ct === "CLIPTextEncodeSDXLRefiner") {
|
||||
for (const key of ["text_g", "text_l"]) {
|
||||
const tv = inputs[key];
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (ct === "ImpactWildcardEncode" || ct === "ImpactWildcardProcessor") {
|
||||
const tv = inputs.wildcard_text;
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
// CLIPTextEncodeEditPlus — propagate role to its STRING inputs (text1, text2)
|
||||
if (ct === "CLIPTextEncodeEditPlus") {
|
||||
for (const key of ["text1", "text2"]) {
|
||||
const tv = inputs[key];
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// === Pass 2: Main analysis ===
|
||||
for (const [id, node] of Object.entries(workflow)) {
|
||||
if (!node?.class_type) continue;
|
||||
const ct = node.class_type;
|
||||
const inputs = node.inputs || {};
|
||||
const title = node._meta?.title || ct;
|
||||
|
||||
if (ct.includes("CheckpointLoader")) {
|
||||
const isPos = !!samplerPositiveRef[id];
|
||||
const isNeg = !!samplerNegativeRef[id];
|
||||
|
||||
const getRole = () => {
|
||||
if (isPos && !isNeg) return "positive";
|
||||
if (isNeg && !isPos) return "negative";
|
||||
if (/pos|正/i.test(title)) return "positive";
|
||||
if (/neg|負/i.test(title)) return "negative";
|
||||
return "unknown";
|
||||
};
|
||||
|
||||
// --- Checkpoint nodes ---
|
||||
// Matches: CheckpointLoaderSimple, CheckpointLoader, "Checkpoint Loader" (WAS), etc.
|
||||
if (ct.includes("CheckpointLoader") || ct === "Checkpoint Loader") {
|
||||
result.checkpoint_nodes.push({
|
||||
id, type: ct, title,
|
||||
ckpt_name: inputs.ckpt_name,
|
||||
});
|
||||
}
|
||||
|
||||
// ImageMetadataPromptLoader — checkpoint + positive/negative prompts in one node
|
||||
if (ct === "ImageMetadataPromptLoader") {
|
||||
result.checkpoint_nodes.push({ id, type: ct, title, ckpt_name: inputs.ckpt_name });
|
||||
if (typeof inputs.positive_text === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [positive]`, role: "positive",
|
||||
text: inputs.positive_text, textKey: "positive_text",
|
||||
});
|
||||
}
|
||||
if (typeof inputs.negative_text === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [negative]`, role: "negative",
|
||||
text: inputs.negative_text, textKey: "negative_text",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// --- Prompt nodes ---
|
||||
|
||||
if (ct === "CLIPTextEncode") {
|
||||
let role = "unknown";
|
||||
if (samplerPositiveRef[id]) role = "positive";
|
||||
else if (samplerNegativeRef[id]) role = "negative";
|
||||
else if (/pos|正/i.test(title)) role = "positive";
|
||||
else if (/neg|負/i.test(title)) role = "negative";
|
||||
// Only add when text is a direct string (if linked, upstream node will be detected)
|
||||
const textVal = inputs.text;
|
||||
if (typeof textVal === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: textVal, textKey: "text",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// SDXL dual-text encoder
|
||||
if (ct === "CLIPTextEncodeSDXL" || ct === "CLIPTextEncodeSDXLRefiner") {
|
||||
const textG = inputs.text_g;
|
||||
const textL = inputs.text_l;
|
||||
// Add only if at least one text field is a direct string
|
||||
if (typeof textG === "string" || typeof textL === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: typeof textG === "string" ? textG : (typeof textL === "string" ? textL : ""),
|
||||
textKey: typeof textG === "string" ? "text_g" : "text_l",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// CLIPTextEncodeEditPlus — text_edit is the locally editable override; text1 is always a link.
|
||||
// Skip when text_edit is empty: it means upstream nodes (ImpactWildcardEncode etc.) supply
|
||||
// the actual prompt text and the edit field is just an optional local override.
|
||||
if (ct === "CLIPTextEncodeEditPlus") {
|
||||
const textVal = inputs.text_edit;
|
||||
if (typeof textVal === "string" && textVal !== "") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: textVal, textKey: "text_edit",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Qwen-based image+text encoder
|
||||
if (ct === "TextEncodeQwenImageEditPlus") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role,
|
||||
text: inputs.text,
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: inputs.prompt || "",
|
||||
textKey: "prompt",
|
||||
});
|
||||
}
|
||||
|
||||
// SDXLPromptStyler / SDXLPromptStylerAdvanced — contains both pos & neg in one node
|
||||
if (ct === "SDXLPromptStyler" || ct === "SDXLPromptStylerAdvanced") {
|
||||
if (inputs.text_positive !== undefined) {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [positive]`, role: "positive",
|
||||
text: inputs.text_positive || "", textKey: "text_positive",
|
||||
});
|
||||
}
|
||||
if (inputs.text_negative !== undefined) {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [negative]`, role: "negative",
|
||||
text: inputs.text_negative || "", textKey: "text_negative",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// ImpactWildcardEncode / ImpactWildcardProcessor — treat wildcard_text as prompt
|
||||
if (ct === "ImpactWildcardEncode" || ct === "ImpactWildcardProcessor") {
|
||||
const textVal = inputs.wildcard_text;
|
||||
if (typeof textVal === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: textVal, textKey: "wildcard_text",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// PrimitiveStringMultiline / PrimitiveString — when feeding a prompt node
|
||||
if ((ct === "PrimitiveStringMultiline" || ct === "PrimitiveString") && (isPos || isNeg)) {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: inputs.value || "",
|
||||
textKey: "value",
|
||||
});
|
||||
}
|
||||
|
||||
// --- Latent nodes ---
|
||||
if (ct === "EmptyLatentImage" || ct === "EmptySD3LatentImage") {
|
||||
result.latent_nodes.push({
|
||||
id, type: ct, title,
|
||||
@@ -504,6 +731,7 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
|
||||
// --- LoRA nodes ---
|
||||
if (ct === "LoraLoader" || ct === "LoraLoaderModelOnly") {
|
||||
result.lora_nodes.push({
|
||||
id, type: ct, title,
|
||||
@@ -513,14 +741,51 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
|
||||
// Power Lora Loader (rgthree) — dynamic lora_N inputs in API format
|
||||
if (ct === "Power Lora Loader (rgthree)") {
|
||||
for (const [k, v] of Object.entries(inputs)) {
|
||||
if (/^lora_\d+$/.test(k) && typeof v === "string") {
|
||||
result.lora_nodes.push({
|
||||
id, type: ct, title,
|
||||
lora_name: v,
|
||||
strength_model: inputs[k.replace("lora_", "strength_")] ?? 1,
|
||||
strength_clip: inputs[k.replace("lora_", "strength_clip_")] ?? 1,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Lora Loader (LoraManager) — loras stored in inputs.loras.__value__
|
||||
if (ct === "Lora Loader (LoraManager)") {
|
||||
result.lora_nodes.push({ id, type: ct, title, is_lora_manager: true });
|
||||
}
|
||||
|
||||
// ImageMetadataLoRALoader — up to 3 LoRA slots; skip "None" entries
|
||||
if (ct === "ImageMetadataLoRALoader") {
|
||||
for (let i = 1; i <= 3; i++) {
|
||||
const loraName = inputs[`lora_${i}`];
|
||||
if (typeof loraName === "string" && loraName !== "None") {
|
||||
result.lora_nodes.push({
|
||||
id, type: ct, title,
|
||||
lora_name: loraName,
|
||||
strength_model: inputs[`strength_model_${i}`] ?? 1.0,
|
||||
strength_clip: inputs[`strength_clip_${i}`] ?? 1.0,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// --- VAE nodes ---
|
||||
if (ct === "VAELoader") {
|
||||
result.vae_nodes.push({ id, type: ct, title, vae_name: inputs.vae_name });
|
||||
}
|
||||
|
||||
// --- Save/preview nodes ---
|
||||
if (ct === "SaveImage" || ct === "PreviewImage") {
|
||||
result.save_nodes.push({ id, type: ct, title });
|
||||
}
|
||||
|
||||
// --- Load image nodes ---
|
||||
if (ct === "LoadImage" || ct === "LoadImageMask"
|
||||
|| (ct.toLowerCase().includes("load") && ct.toLowerCase().includes("image") && inputs.image !== undefined)) {
|
||||
result.load_image_nodes.push({
|
||||
@@ -528,6 +793,7 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
|
||||
// --- Text encoder (CLIP) nodes ---
|
||||
if (ct === "DualCLIPLoader" || ct === "CLIPLoader") {
|
||||
result.text_encoder_nodes.push({
|
||||
id, type: ct, title,
|
||||
@@ -536,18 +802,29 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
|
||||
// --- Diffusion model nodes ---
|
||||
if (ct === "UNETLoader" || ct === "UnetLoaderGGUF") {
|
||||
result.diffusion_model_nodes.push({
|
||||
id, type: ct, title, unet_name: inputs.unet_name,
|
||||
});
|
||||
}
|
||||
|
||||
// --- ControlNet loader nodes ---
|
||||
if (ct.includes("ControlNetLoader")) {
|
||||
result.controlnet_nodes.push({
|
||||
id, type: ct, title,
|
||||
control_net_name: inputs.control_net_name,
|
||||
});
|
||||
}
|
||||
|
||||
// --- Hypernetwork loader nodes ---
|
||||
if (ct === "HypernetworkLoader") {
|
||||
result.hypernetwork_nodes.push({
|
||||
id, type: ct, title,
|
||||
hypernetwork_name: inputs.hypernetwork_name,
|
||||
strength: inputs.strength ?? 1.0,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
result.all_nodes.sort((a, b) => Number(a.id) - Number(b.id));
|
||||
@@ -592,6 +869,10 @@ function _getWidgetMapping(nodeType) {
|
||||
CheckpointLoaderSimple: ["ckpt_name"],
|
||||
KSampler: ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"],
|
||||
CLIPTextEncode: ["text"],
|
||||
CLIPTextEncodeEditPlus: ["text_edit", "mode"],
|
||||
ImageMetadataCheckpointLoader: ["ckpt_name", "vae_name", "_metadata_json"],
|
||||
ImageMetadataPromptLoader: ["ckpt_name", "vae_name", "positive_text", "negative_text", "_metadata_json"],
|
||||
ImageMetadataLoRALoader: ["lora_1", "strength_model_1", "strength_clip_1", "lora_2", "strength_model_2", "strength_clip_2", "lora_3", "strength_model_3", "strength_clip_3"],
|
||||
EmptyLatentImage: ["width", "height", "batch_size"],
|
||||
LoraLoader: ["lora_name", "strength_model", "strength_clip"],
|
||||
VAELoader: ["vae_name"],
|
||||
@@ -603,6 +884,10 @@ function _getWidgetMapping(nodeType) {
|
||||
CLIPLoader: ["clip_name", "type", "device"],
|
||||
FluxGuidance: ["guidance"],
|
||||
CFGNorm: ["strength"],
|
||||
// Impact Pack wildcard nodes — only map wildcard_text (index 0); remaining widgets
|
||||
// vary between versions and include frontend-only extras, so we leave them unmapped.
|
||||
ImpactWildcardProcessor: ["wildcard_text"],
|
||||
ImpactWildcardEncode: ["wildcard_text"],
|
||||
};
|
||||
return mappings[nodeType] || null;
|
||||
}
|
||||
|
||||
@@ -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
+1632
-85
File diff suppressed because it is too large
Load Diff
+1395
-83
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,770 @@
|
||||
/**
|
||||
* 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";
|
||||
|
||||
// ── 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)) };
|
||||
}
|
||||
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"); }
|
||||
|
||||
function extractPrompts(wf) {
|
||||
if (!wf || typeof wf !== "object") return { positives: [], negatives: [] };
|
||||
return Array.isArray(wf.nodes) ? extractPromptsLiteGraph(wf) : extractPromptsAPI(wf);
|
||||
}
|
||||
|
||||
// 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) {
|
||||
if (!isTextEncoderNode(n.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 srcNode = originId != null ? nodeMap.get(originId) : null;
|
||||
if (srcNode) {
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (isPromptStylerNode(srcType)) {
|
||||
const v = srcNode.widgets_values?.[originSlot];
|
||||
if (v && typeof v === "string") textMap.set(n.id, v);
|
||||
} else {
|
||||
// WFS_PromptText, PrimitiveStringMultiline, ComfySwitchNode 等、任意の STRING 出力ノード
|
||||
const v = srcNode.widgets_values?.[originSlot] ?? srcNode.widgets_values?.[0];
|
||||
if (v && typeof v === "string") textMap.set(n.id, v);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
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;
|
||||
for (const inp of n.inputs) {
|
||||
if (!inp || inp.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
if (originId == null) continue;
|
||||
const text = textMap.get(originId);
|
||||
if (!text) continue;
|
||||
const name = inp.name ?? "";
|
||||
if (name === "positive" || name.startsWith("positive")) pos.add(text);
|
||||
else if (name === "negative" || name.startsWith("negative")) 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) {
|
||||
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;
|
||||
}
|
||||
// 汎用テキストキー
|
||||
const keys = slot === 0
|
||||
? ["text_positive", "text", "text_g", "prompt"]
|
||||
: ["text_negative", "text_l"];
|
||||
for (const k of keys) {
|
||||
const v = src.inputs?.[k];
|
||||
if (v && typeof v === "string") return v;
|
||||
}
|
||||
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 || !isTextEncoderNode(n.class_type ?? "")) continue;
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? 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;
|
||||
for (const [key, val] of Object.entries(n.inputs ?? {})) {
|
||||
if (!Array.isArray(val)) continue;
|
||||
const text = textMap.get(String(val[0]));
|
||||
if (!text) continue;
|
||||
if (key === "positive" || key.startsWith("positive")) pos.add(text);
|
||||
else if (key === "negative" || key.startsWith("negative")) 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 ─────────────────────────────────
|
||||
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");
|
||||
|
||||
function fromWorkflow(wf, source) {
|
||||
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）
|
||||
const mdm = extractMarkdownNoteModels(wf);
|
||||
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 ? fromWorkflow(wf, "comfyui") : null;
|
||||
}
|
||||
if (isWebP) {
|
||||
const exif = await readWebPEXIFChunk(file);
|
||||
if (!exif) return null;
|
||||
const wf = extractWorkflowFromEXIF(exif);
|
||||
return wf ? 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 ? fromWorkflow(wf, "comfyui") : null; }
|
||||
if (chunks.workflow) { let wf; try { wf = JSON.parse(sanitizeJSON(chunks.workflow)); } catch { return null; } return wf ? 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();
|
||||
}
|
||||
+1421
-194
File diff suppressed because it is too large
Load Diff
+128
-121
@@ -5,10 +5,10 @@
|
||||
import { showToast } from "./app.js";
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml } from "./util.js";
|
||||
|
||||
// ── State ─────────────────────────────────────────────────
|
||||
|
||||
const NODES_PER_PAGE = 50;
|
||||
|
||||
const state = {
|
||||
allNodes: [],
|
||||
@@ -51,9 +51,6 @@ 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 +254,50 @@ 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");
|
||||
}
|
||||
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);
|
||||
card.addEventListener("click", () => showNodeSidePanel(node));
|
||||
return card;
|
||||
}
|
||||
|
||||
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" ${state.currentPage >= totalPages - 1 ? "disabled" : ""} data-page="next">»</button>
|
||||
`;
|
||||
container.querySelector('[data-page="prev"]').addEventListener("click", () => {
|
||||
if (state.currentPage > 0) { state.currentPage--; renderNodeGrid(); scrollGridToTop(); }
|
||||
});
|
||||
container.querySelector('[data-page="next"]').addEventListener("click", () => {
|
||||
if (state.currentPage < totalPages - 1) { state.currentPage++; renderNodeGrid(); scrollGridToTop(); }
|
||||
});
|
||||
}
|
||||
|
||||
function clearPagination() {
|
||||
const container = document.getElementById("wfm-nodes-pagination");
|
||||
if (container) container.innerHTML = "";
|
||||
}
|
||||
|
||||
function scrollGridToTop() {
|
||||
document.getElementById("wfm-nodes-grid")?.scrollTo(0, 0);
|
||||
}
|
||||
|
||||
function renderNodeTableView(grid, filtered) {
|
||||
grid.innerHTML = "";
|
||||
@@ -399,9 +355,6 @@ async function toggleFavorite(nodeName, btnEl) {
|
||||
|
||||
function showNodeSidePanel(node) {
|
||||
state.selectedNode = node;
|
||||
const panel = document.getElementById("wfm-nodes-side-panel");
|
||||
if (!panel) return;
|
||||
panel.style.display = "flex";
|
||||
|
||||
// Highlight selected card
|
||||
document.querySelectorAll("#wfm-nodes-grid .wfm-card, #wfm-nodes-grid tr").forEach(el => {
|
||||
@@ -545,42 +498,61 @@ function renderSideGroups(node) {
|
||||
const el = document.getElementById("wfm-nodes-side-group");
|
||||
if (!el) return;
|
||||
|
||||
const groupNames = Object.keys(state.nodeGroups).sort();
|
||||
const memberOf = groupNames.filter(g => (state.nodeGroups[g] || []).includes(node.name));
|
||||
const allGroups = Object.keys(state.nodeGroups).sort();
|
||||
const memberOf = allGroups.filter(g => (state.nodeGroups[g] || []).includes(node.name));
|
||||
const available = allGroups.filter(g => !memberOf.includes(g));
|
||||
|
||||
let html = `<h4 style="margin:0 0 8px;">${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 +564,9 @@ 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 +578,61 @@ 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");
|
||||
});
|
||||
@@ -1083,8 +1092,6 @@ 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 => {
|
||||
|
||||
+537
-68
@@ -4,6 +4,7 @@
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { readJsonStorage } 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
|
||||
@@ -114,15 +116,17 @@ 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 +217,25 @@ 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 +248,15 @@ 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 +268,13 @@ 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();
|
||||
@@ -345,26 +378,70 @@ 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 || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234")).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 ${res.status}`);
|
||||
return (await res.json()).message?.content || "";
|
||||
} else {
|
||||
// LM Studio: convert Ollama-style messages (with .images) to OpenAI content arrays
|
||||
const openAiMessages = messages.map(msg => {
|
||||
if (msg.images?.length) {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: [
|
||||
{ type: "text", text: msg.content },
|
||||
...msg.images.map(b64 => ({ type: "image_url", image_url: { url: `data:image/jpeg;base64,${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 ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
@@ -429,7 +506,7 @@ function clearChat() {
|
||||
if (chat) {
|
||||
chat.innerHTML = `
|
||||
<div class="wfm-ollama-welcome">
|
||||
<p>AI Assistant (Ollama)</p>
|
||||
<p>AI Assistant</p>
|
||||
<p>${t("assistantWelcome")}</p>
|
||||
</div>
|
||||
`;
|
||||
@@ -493,6 +570,24 @@ 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 +607,11 @@ 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 +654,8 @@ 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 +679,255 @@ 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 +947,11 @@ 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 +1062,14 @@ 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 +1101,54 @@ 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 +1165,161 @@ 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();
|
||||
}
|
||||
|
||||
+526
-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 } 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">${t("comfyuiConnection")}</summary>
|
||||
@@ -510,28 +637,68 @@ export async function initSettingsTab() {
|
||||
<div id="wfm-settings-url-status" style="font-size:12px;margin-top:4px;"></div>
|
||||
</details>
|
||||
|
||||
<!-- Ollama Settings -->
|
||||
<!-- AI Assistant Settings (Prompt Tab) -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("ollamaSettings")}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("aiSettingsBackend")}</label>
|
||||
<div class="wfm-ai-backend-row">
|
||||
<label class="wfm-ai-radio-label">
|
||||
<input type="radio" name="wfm-prompt-ai-backend" value="ollama"> Ollama
|
||||
</label>
|
||||
<label class="wfm-ai-radio-label">
|
||||
<input type="radio" name="wfm-prompt-ai-backend" value="lmstudio"> LM Studio
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>${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">
|
||||
${t("civitaiApiKeySave")}
|
||||
</button>
|
||||
</div>
|
||||
<div id="wfm-settings-civitai-api-key-status" style="font-size:12px;margin-top:4px;"></div>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
@@ -571,8 +738,33 @@ export async function initSettingsTab() {
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Data Management -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${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 +968,7 @@ 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 +1020,166 @@ 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 = promptAiBackendSaved === "ollama" ? "http://localhost:11434" : "http://localhost:1234";
|
||||
}
|
||||
|
||||
// Backend change → update URL placeholder
|
||||
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.placeholder = backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234";
|
||||
});
|
||||
});
|
||||
|
||||
// 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() || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234")).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() || (backend === "ollama" ? "http://localhost:11434" : "http://localhost:1234")).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")}: ${err.message}`;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// Test ComfyUI URL
|
||||
document.getElementById("wfm-settings-test-url")?.addEventListener("click", async () => {
|
||||
const urlInput = document.getElementById("wfm-settings-comfyui-url");
|
||||
@@ -918,7 +1220,7 @@ export async function initSettingsTab() {
|
||||
|
||||
// Clear default workflow
|
||||
document.getElementById("wfm-settings-clear-wf")?.addEventListener("click", () => {
|
||||
const s = JSON.parse(localStorage.getItem("wfm_settings") || "{}");
|
||||
const s = getSettings();
|
||||
delete s.defaultWorkflow;
|
||||
delete s.defaultWorkflowData;
|
||||
localStorage.setItem("wfm_settings", JSON.stringify(s));
|
||||
@@ -928,8 +1230,38 @@ export async function initSettingsTab() {
|
||||
showToast(t("defaultWorkflowCleared"), "success");
|
||||
});
|
||||
|
||||
// --- Textarea font size slider ---
|
||||
document.getElementById("wfm-ta-font-size")?.addEventListener("input", (e) => {
|
||||
const px = parseInt(e.target.value);
|
||||
const valEl = document.getElementById("wfm-ta-font-size-val");
|
||||
if (valEl) valEl.textContent = `${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 +1269,7 @@ export async function initSettingsTab() {
|
||||
uiLang: getLang(),
|
||||
summaryLang: getSummaryLang(),
|
||||
theme: document.documentElement.getAttribute("data-theme") || "",
|
||||
textareaFontSize: taFontSize,
|
||||
};
|
||||
|
||||
saveLocalSettings(patch);
|
||||
@@ -952,4 +1285,134 @@ 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 = "";
|
||||
});
|
||||
}
|
||||
|
||||
@@ -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 +1,28 @@
|
||||
/**
|
||||
* Shared utilities
|
||||
*/
|
||||
|
||||
// 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");
|
||||
}
|
||||
+365
-314
File diff suppressed because it is too large
Load Diff
+1417
-259
File diff suppressed because it is too large
Load Diff
@@ -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);
|
||||
};
|
||||
},
|
||||
});
|
||||
+2937
-43
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, ${links.length} links)`, "success");
|
||||
if (state.loaded) { await loadData(); renderContent(); }
|
||||
} else {
|
||||
showToast("Failed to save: " + (result.error || "Unknown error"), "error");
|
||||
}
|
||||
} catch (err) {
|
||||
showToast("Failed to save: " + err.message, "error");
|
||||
}
|
||||
};
|
||||
|
||||
export { saveSelectedAsNodeSet };
|
||||
@@ -605,50 +605,33 @@ app.registerExtension({
|
||||
};
|
||||
injectStyles();
|
||||
|
||||
const applyButtonIcon = (button, className, getIconFn, extraStyles) => {
|
||||
if (button.querySelector("svg")) return;
|
||||
button.classList.add(className);
|
||||
button.innerHTML = getIconFn();
|
||||
button.style.borderRadius = "4px";
|
||||
button.style.padding = "6px";
|
||||
if (extraStyles) Object.assign(button.style, extraStyles);
|
||||
const svg = button.querySelector("svg");
|
||||
if (svg) {
|
||||
svg.style.width = "20px";
|
||||
svg.style.height = "20px";
|
||||
}
|
||||
};
|
||||
|
||||
const buttonConfigs = [
|
||||
{ tooltip: BUTTON_TOOLTIP, className: "wfm-top-menu-button", getIcon: getWfmIcon, styles: { backgroundColor: "var(--primary-bg)" } },
|
||||
{ tooltip: SNAPSHOT_TOOLTIP, className: "wfm-snapshot-button", getIcon: getSnapshotIcon, styles: null },
|
||||
{ tooltip: NODE_SETS_TOOLTIP, className: "wfm-node-sets-btn", getIcon: getNodeSetsIcon, styles: null },
|
||||
];
|
||||
|
||||
const replaceAllIcons = () => {
|
||||
const replaceButtonIcons = () => {
|
||||
let foundAny = false;
|
||||
for (const cfg of buttonConfigs) {
|
||||
document.querySelectorAll(`button[aria-label="${cfg.tooltip}"]`).forEach((btn) => {
|
||||
applyButtonIcon(btn, cfg.className, cfg.getIcon, cfg.styles);
|
||||
foundAny = true;
|
||||
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";
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
const waitAndObserve = () => {
|
||||
const firstBtn = document.querySelector(`button[aria-label="${BUTTON_TOOLTIP}"]`);
|
||||
if (!firstBtn) {
|
||||
requestAnimationFrame(waitAndObserve);
|
||||
return;
|
||||
}
|
||||
replaceAllIcons();
|
||||
|
||||
const container = firstBtn.closest(".actionbar-container") || firstBtn.parentElement;
|
||||
if (container) {
|
||||
new MutationObserver(() => replaceAllIcons()).observe(container, {
|
||||
childList: true,
|
||||
subtree: true,
|
||||
});
|
||||
if (!foundAny) {
|
||||
requestAnimationFrame(replaceButtonIcons);
|
||||
}
|
||||
};
|
||||
requestAnimationFrame(waitAndObserve);
|
||||
requestAnimationFrame(replaceButtonIcons);
|
||||
},
|
||||
});
|
||||
|
||||
@@ -0,0 +1,565 @@
|
||||
console.log("[WFM] top_menu_extension.js loading...");
|
||||
import { app } from "../../scripts/app.js";
|
||||
console.log("[WFM] app imported:", !!app);
|
||||
import { togglePanel, getNodeSetsIcon, NODE_SETS_TOOLTIP, saveSelectedAsNodeSet } from "./node_sets_menu.js";
|
||||
console.log("[WFM] node_sets_menu imports loaded");
|
||||
|
||||
const BUTTON_TOOLTIP = "Launch Workflow Studio (Shift+Click opens in new window)";
|
||||
const SNAPSHOT_TOOLTIP = "Save workflow canvas image as thumbnail";
|
||||
const WFM_PATH = "/wfm";
|
||||
const NEW_WINDOW_FEATURES = "width=1400,height=900,resizable=yes,scrollbars=yes,status=yes";
|
||||
const MAX_ATTACH_ATTEMPTS = 120;
|
||||
const BUTTON_GROUP_CLASS = "wfm-top-menu-group";
|
||||
|
||||
const MIN_VERSION_FOR_ACTION_BAR = [1, 33, 9];
|
||||
|
||||
const openWorkflowStudio = (event) => {
|
||||
const url = `${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);
|
||||
},
|
||||
});
|
||||
File diff suppressed because one or more lines are too long
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|
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Reference in New Issue
Block a user