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-# ComfyUI-Toggle-Pass
-
-[English](#english) | [中文](#中文)
-
----
-
-<a id="english"></a>
-## English
-
-Toggle-Pass node collection — toggle controls, image load/save/preview, privacy mask, dynamic multi-reference image encoding, unified model loader, and an all-in-one sampler.
-
-### Nodes
-
-#### ToggleUnifiedLoader — Unified Model + LoRA Loader
-
-All-in-one model loading node: UNet + CLIP + VAE + multiple LoRAs in a single node.
-
-**Loader mode** (`加载模式`) — one node, two ways to load:
-
-| Mode | What it does | Parameters shown |
-|---|---|---|
-| `分离组件 (UNet+CLIP+VAE)` (default) | Pick UNet / CLIP / VAE separately | `unet_name`, `clip_name`, `vae_name`, `clip_type` |
-| `Checkpoint 一体包` | Load an all-in-one checkpoint (same as the official **CheckpointLoaderSimple**): UNet + CLIP + VAE are extracted automatically by guessing the config | `ckpt_name` only |
-
-- **The mode is a two-segment switch — `UNet | Checkpoint`**, not a dropdown. Click the left half for
-  separate components, the right half for an all-in-one checkpoint; the current side is highlighted
-- The switch sits **on the node's top row, level with the `模型 / MODEL` output slot**, and its left
-  edge lines up with the dropdown widgets below
-- The `加载模式` value is still stored and serialised normally, so **existing workflows and API calls
-  keep working** — the switch only takes over how it is drawn and clicked
-- Under the Vue node renderer ("modern node design") the switch is drawn inside the combo widget instead
-- In `Checkpoint 一体包` mode the separate model dropdowns are hidden, so the node stays clean
-- **LoRA works in both modes** — the LoRA groups are applied after loading, regardless of mode
-- The VAE comes from the checkpoint itself (same as the official simple loader)
-- **Widget order — the model comes first**: `ckpt_name` → `unet_name` → `clip_name` → `vae_name` →
-  `clip_type` → `加载模式` → LoRA groups → buttons. In `Checkpoint 一体包` mode the checkpoint
-  dropdown is literally the top row
-- Old workflows load correctly: saved widget values are re-mapped **by parameter name** on load, so
-  nothing gets scrambled. Missing values fall back to the default mode (separate components)
-
-**Inputs**:
-- `ckpt_name` — checkpoint file from `models/checkpoints` (only in `Checkpoint 一体包` mode)
-- `unet_name` — UNet model file
-- `clip_name` — CLIP text encoder file
-- `vae_name` — VAE model file
-- `clip_type` — CLIP type (auto-detect from UNet, or manually select: SD, SD3, FLUX, FLUX2, `qwen_image`, etc.)
-  - Auto-detection recognises the Qwen-Image-2.1 UNet class (`QwenImage21`) and maps it to `qwen_image`.
-    ⚠️ This is mandatory for 2.1: only `CLIPType.QWEN_IMAGE` loads the dedicated text encoder (Qwen3-VL 8B)
-    that produces `image_slots`. Without it the generic Qwen3-VL encoder is used and **reference images are silently dropped**.
-  - Manual override available: select `qwen_image` explicitly (recommended if you suspect auto-detection failed)
-- `+ Add LoRA` button — Click to add LoRA slots one by one (up to 10)
-- `- Remove LoRA` button — Click to remove LoRA slots from the last group (up to 10)
-
-**LoRA Controls** (added via button, up to 10 groups):
-- Select a LoRA file → one `Strength` slider appears automatically
-- Select "none" → the slider hides
-- All LoRAs are applied sequentially
-
-> **One strength slider, on purpose.** The CLIP side always follows the MODEL side, so the strength you set is exactly what takes effect.
-
-**LoRA status on the button** — after a run the loader reports back what it actually did, right on the
-`+ Add LoRA` button, so you never have to guess:
-
-| Button text | Meaning |
-|---|---|
-| `· 224p` | the LoRA really was patched into the model — 224 patches (count produced by Python) |
-| `· 192/224p` | 192 of this LoRA's 224 modules landed; the remaining 32 have no target layer in the base checkpoint. Typical for **repacked INT8 all-in-one checkpoints** (e.g. `img_mlp.gate_layer` was fused into `gate_up`), not a node bug |
-| `· ⚠️未写入` | nothing was patched — wrong architecture / incompatible file |
-| `· ⚠️N不匹配` | N LoRAs matched zero weights (wrong base model) |
-| `· ⚠️N跳过` | N LoRAs were skipped (file missing) |
-| `· ⚠️N个强度0` | N selected LoRAs have strength 0 — they do nothing |
-| `· ⚠️CLIP不符` | the text encoder that got built doesn't match the model (split mode: wrong CLIP file / clip type) — conditioning is silently useless |
-
-The label is kept short on purpose (a long one would overflow a narrow node); hover the button to see
-the full per-LoRA breakdown (module coverage, CLIP check result, per-slot patch counts). The label is
-also cleared as soon as you add/remove/switch a LoRA, so a stale count from a previous run never lingers.
-Note this reports whether the patch was **written**, not whether it is visible in the picture —
-a quantized base, a reference-image-dominated edit, or a very subtle LoRA can all make the effect hard to see.
-
-**Outputs**: `MODEL`, `CLIP`, `VAE`
-
-**Global broadcast** (`全局广播` switch, built in — no extra plugin needed):
-
-Turn it **on** and the node's three outputs are automatically wired to every **empty** `MODEL` / `CLIP` /
-`VAE` input in the graph when you press Run. No dragging cables through half the canvas.
-Turn it **off** (default) and nothing changes — you wire everything yourself.
-
-- The real wires are made **only while the prompt is being built** and removed immediately afterwards, so
-  they never get saved into your workflow
-- **While the switch is on, the canvas draws a violet preview curve** for every wire that will be created
-  at run time, with a small dot at both ends landing exactly on the output/input slots. The curves are pure
-  overlay — they create no real links, are not serializable, and disappear as soon as you switch it off
-- **The curves pass behind the nodes**: they are drawn on the background layer, so they show up in the empty
-  space between nodes and are naturally hidden where they cross a node — titles and parameters stay readable
-  (on a very tightly packed graph you will see less of the line, which is the intended trade-off)
-- **It only fills empty inputs.** Anything you connected by hand is left untouched — which is why
-  "auto" and "manual" can coexist in the same graph
-- Covers `MODEL` / `CLIP` / `VAE` only (the node's three outputs)
-- If more than one loader has the switch on, the first one wins for each type and a warning is logged
-- **The switch lives at the top of the node**: directly **below** the `UNet / Checkpoint` pill, on its
-  left half (a small pill labelled `广播` / "broadcast") — click it to toggle. It no longer takes up a
-  full row at the bottom; the bottom now only holds the two LoRA buttons
-- Its size matches the pill: **width = half of the pill**, **height identical to the pill** (14px, edges aligned)
-- The switch state is stored in the node's `properties`, so it is **not** part of `widgets_values` —
-  old workflows keep every parameter in its original position, and the switch simply defaults to off
-- Works alongside `cg-use-everywhere` (it does not touch that plugin's `ue_convert` flag)
-
-> **Limitation**: the broadcast happens on the front-end, so it applies when you press Run or export the
-> API format from the UI. A raw `POST /prompt` with a hand-written API JSON never passes through the
-> front-end, so no auto-wiring happens there — export the API format once and the JSON already has the links.
-
----
-
-#### SaveLoadImage — Save/Load Toggle
-
-Image save node with Save/Load mode toggle.
-- **Save mode**: Save image to output directory, output IMAGE and MASK
-- **Load mode**: Reuse the last saved image, skip saving
-- Click 👁 icon in the title bar to toggle privacy mask (hide/show image preview)
-
-#### ToggleLoadImage — Load Image
-
-Custom image loading node, same functionality as built-in LoadImage, with privacy mask support.
-- Click 👁 icon in the title bar to toggle privacy mask
-
-#### TogglePreviewImage — Preview Image
-
-Custom image preview node, saves to temp (not output), with privacy mask support.
-- Click 👁 icon in the title bar to toggle privacy mask
-
-#### ToggleEmptyLatent — Toggle Empty Latent
-
-Toggleable empty Latent generation node.
-- **ON**: Use width/height parameters
-- **OFF**: Use fallback_width/fallback_height parameters (useful for A/B size comparison)
-
----
-
-#### DynamicRefIndependent — Multi-Reference Independent Encoding (Core Node)
-
-Each reference image is independently VAE-encoded, outputting separate CONDITIONING and LATENT pairs. Ideal for scenarios where multiple images control style/content independently.
-
-**Inputs** (dynamic, controlled by `num_images`):
-- `clip` — CLIP model
-- `vae` — VAE model
-- `num_images` — Number of reference images (1~10)
-- `instruction` — System prompt (leave empty for default)
-- `image1` ~ `imageN` — Reference images
-- `prompt1` ~ `promptN` — Per-image prompts (collapsible widgets inside node)
-
-**Model type**: `Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL`
-- Selecting `Qwen Image 2.1` reveals its own parameter group (see [Qwen Image 2.1](#qwen-image-21-support))
-- Selecting `Qwen Layered` reveals `layers`; latent output is layered (`[1, 16, layers+1, h/8, w/8]`)
-- Selecting `Flux2` / `SD / SDXL` only shows the classic `输出宽度/高度` (old behaviour, unchanged)
-
-**Outputs** (dynamic, N×2):
-- `cond_1`, `latent1`, `cond_2`, `latent2`, ... `cond_N`, `latentN`
-
-**Usage**:
-1. Drag in the node, default is 1 reference image
-2. Adjust `num_images` to desired count (1~10), input/output slots auto-adjust
-3. Connect each reference image and fill in corresponding prompts
-4. Connect `cond_N` to sampler's conditioning, `latentN` to VAE Encode's latent
-
----
-
-#### DynamicRefImageEncode — Dynamic Reference Image Encoding
-
-Single prompt control, dynamically add/remove reference image input slots.
-
-**Inputs**: `clip`, `num_images`, `prompt`, `image1` ~ `imageN`
-**Outputs**: `CONDITIONING`, `LATENT` (all images share the same conditioning, latents output separately)
-
-**Model type**: `Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL`
-- `Qwen Layered`: Adds a `layers` parameter; latent output is a layered latent (`[1, 16, layers+1, h/8, w/8]`), directly connectable to Ksampler's latent input (replaces `EmptyQwenImageLayeredLatentImage`)
-- `Qwen Image 2.1`: Adds its own parameter group for editing + resolution control (see below)
-- Every model type only shows the parameters it actually uses — switching the dropdown hides the rest, so the node stays clean
-- When `匹配原图尺寸` = ON, uses the actual image size; when OFF, uses the 2.1 resolution parameters (or `输出宽度/高度` for other models)
-
-#### DynamicRefLatentInput — Dynamic Latent Input
-
-Dynamically add/remove LATENT input slots, for use with DynamicRef series.
-
-#### ImageBatcher — Image Batcher
-
-Dynamically add/remove IMAGE input slots, batch multiple images together.
-
-#### ToggleImageCompare — 图片对比
-
-Compare multiple "source" images against multiple "result" images by composing them into a single
-side-by-side sheet, previewed right inside the node.
-
-**Inputs**: `num_sources`, `num_results`, `source1`~`source10`, `result1`~`result10`
-- `num_sources` / `num_results` — how many slots to show for each group (1~10), adjusted independently,
-  so **N sources + 1 result** and **N sources + N results** are both supported
-- `source1`~`source10` — the "before" / original images
-- `result1`~`result10` — the "after" / generated images
-
-> In the node UI these are shown in Chinese: slots as `原图1`~`原图10` / `结果1`~`结果10`,
-> counters as `原图数` / `结果数`, and the output as `对比图`. The English keys above are kept
-> unchanged so existing workflows keep loading.
-
-**Parameters**
-- `排版` (layout) — `自动` (auto) / `成对并排` (paired) / `上下两排` (stacked) / `自动网格` (grid)
-  - `自动` picks paired when the two groups have equal counts, otherwise stacked
-- `标签` (labels) — burn `原1 / 结1 …` badges on each tile, or hide them
-
-**Outputs**: `comparison` — one composed IMAGE (feed it into `SaveLoadImage` or a preview node)
-
-Layout is decided by the images **actually connected**, not by how many slots you enabled, so you
-never have to touch the settings when the counts change. Images of different sizes are scaled to a
-common row height (aspect ratio preserved).
-
-#### OneImageMultiPrompt — One Image Multiple Prompts
-
-One reference image with multiple prompts, output separate conditionings (up to 8), sharing a single latent.
-
-**Inputs**: `clip`, `image`, `prompt数量`, `prompt1` ~ `prompt8`
-- `prompt数量` — how many prompts to use (2~8); unused prompt boxes are hidden automatically
-- `模型类型` — `Flux2` / `Qwen Image 2.1` / `SD / SDXL` / `Krea2`. Selecting `Qwen Image 2.1` adds the
-  full resolution parameter group, so one photo can be rendered from multiple viewpoints / edits at once.
-
-**Outputs**: `cond_1` ~ `cond_8`, `latent`
-
----
-
-#### ToggleSampler — All-in-one Sampler (采样器（一体）)
-
-Collapses the official four-node chain into a single node:
-
-```
-RandomNoise + CFGGuider + KSamplerSelect + (BasicScheduler | ManualSigmas)  ->  SamplerCustomAdvanced
-```
-
-**Inputs**: `模型` (MODEL), `正面条件` / `负面条件` (CONDITIONING), `Latent图像` (LATENT)
-**Parameters**: `采样方案`, `采样器`, `调度器`, `步数`, `CFG`, `降噪`, `噪波随机种`, `自定义Sigma表`
-**Outputs**: `Latent`, `降噪Latent`
-
-Model / conditioning / latent stay as **sockets on purpose** — the only thing that really differs
-between models is the sigma curve, so switching models never needs a new node. `采样方案` comes in
-three flavours:
-
-| Scheme | What it does | Which parameters matter |
-|---|---|---|
-| `自动（按步数 + 调度器）` | computes sigmas live — numerically identical to official `BasicScheduler` | `步数` + `调度器` |
-| Preset tables: `Qwen-Image-2.1 turbo 8步`, `LTX-2 视频 8步（蒸馏）`, `LTX-2 视频 3步（低噪）` | fixed distillation trajectory | none — the step count is baked into the table |
-| `自定义表` | hand-typed sigma list | `自定义Sigma表` |
-
-A sigma table must be comma separated, **strictly decreasing**, **end with 0**, and `N` values = `N-1`
-steps (9 values = 8 steps).
-
-Parameters that a preset makes irrelevant are greyed out *and* locked: `步数` flips to the preset's real
-value (still readable, just not editable), and switching back to `自动` restores your own step count.
-`降噪` < 1.0 trims the tail of the table, so low-denoise refinement works with presets too.
-
-For distilled turbo models use `采样器` = `euler` and `CFG` = `1.0`.
-
----
-
-<a id="qwen-image-21-support"></a>
-### Qwen Image 2.1 Support
-
-`DynamicRefImageEncode`, `DynamicRefIndependent` and `OneImageMultiPrompt` all support **Qwen-Image-2.1**
-image editing / multi-view generation. Select `Qwen Image 2.1` in `模型类型` and these parameters appear:
-
-| Parameter | Meaning |
-|---|---|
-| `宽高比` (Aspect ratio) | Preset ratios (1:1 / 3:2 / 16:9 / 21:9 ...), or `自定义（用输出宽度/高度）` |
-| `百万像素` (Megapixels) | Total pixel budget, e.g. `1.0` ≈ 1024×1024, `2.6` ≈ 2016×1344. Range 0.1~16, **do not fill 1024 here** |
-| `倍数` (Multiple) | Alignment step, fixed ≥ **32** (16 will break shapes) |
-| `参考图分辨率` (Ref resolution) | How large the *reference* image is fed to the model (area target). `0` = keep original size |
-| `尺寸适配` (Ref fit mode) | `参考图保比例` (default, no distortion) / `参考图裁剪` / `参考图拉伸` |
-
-Key rules learned from aligning with the official `TextEncodeQwenImage21`:
-
-- **Output size and reference size are independent.** `qwen_image` uses `default_ref_method = "index"`,
-  so the reference and the output live on separate RoPE planes and need not be the same shape.
-  Changing `参考图分辨率` does **not** change the output size.
-- **Instructions must go into the prompt text, not the `instruction` field.**
-  `comfy/text_encoders/qwen_image21.py` drops the system turn, so anything written into `instruction`
-  is silently discarded for 2.1 — the node merges it into the user text automatically.
-  The default Flux-era English instruction is ignored (it would pollute the prompt).
-- **The latent output is all-zero.** Feeding the reference image's real latent turns editing into img2img
-  (nothing changes). The real latent only goes into `reference_latents` in the conditioning.
-- Images must be multiples of **32**; RGBA reference images are composited on white for the vision tower.
-
-Recommended setup for editing: `匹配原图尺寸` = **OFF**, `尺寸适配` = `参考图保比例`,
-`百万像素` = 1.0, `倍数` = 32, `参考图分辨率` = 1024.
-
----
-
-<a id="krea2-support"></a>
-### Krea2 Support
-
-`ToggleUnifiedLoader`, `DynamicRefImageEncode`, `DynamicRefIndependent` and `OneImageMultiPrompt`
-all support **Krea2**. The loader detects `Krea2` from the UNet automatically and picks
-`CLIPType.KREA2` + the `Krea2TEModel` text encoder; the three encoding nodes just need
-`模型类型` = `Krea2`.
-
-⚠️ **VAE must match the model.** This is the single most common failure — mixing them up does not
-fail at load time, it fails deep inside `VAEDecode`:
-
-| Model | VAE file | latent |
-|---|---|---|
-| Qwen-Image **2.1** | `qwen_image_2.1_vae_bf16.safetensors` | 64 ch / 16x / RGBA |
-| **Krea2** | `qwen_image_vae.safetensors` | 16 ch / 8x / RGB |
-
-A wrong VAE is silently padded by `fix_empty_latent_channels` (16 → 64), sampling still finishes, and
-then decode dies with `IndexError: tuple index out of range` from the Wan 2.1 branch.
-The loader now compares `latent_channels` / `latent_dimensions` at load time and prints the exact file
-you should use instead.
-
-Key rules learned from aligning with the official `TextEncodeQwenImageEditPlus`:
-
-- **Reference latent size ≠ output size.** Reference images are VAE-encoded at ~1 MP (multiples of 8),
-  the vision tower gets a ~384 px thumbnail.
-- **The VAE only takes RGB** — alpha is dropped, not composited.
-- **Vision placeholders must be written by hand.** With an explicit `llama_template` the Qwen3-VL
-  tokenizer does *not* auto-insert them, so the node emits
-  `Picture N: <|vision_start|><|image_pad|><|vision_end|>`. Without it the reference image is
-  completely invisible to the model.
-- **`reference_latents_method` is mandatory.** Krea2's `default_ref_method` is `None`, so references
-  are *silently ignored* unless the method is set; the node always sets `index_timestep_zero`
-  (same as the official style-reference template).
-- **The latent output is all-zero** (16 ch / 8x) — same "editing, not img2img" rule as 2.1.
-- **Sigma curve:** Krea2 turbo uses the `自动（按步数 + 调度器）` scheme in `采样器（一体）`.
-  The `Qwen-Image-2.1 turbo 8步` table is *not* interchangeable with it.
-
----
-
-### Canvas Menu Entry
-
-All Toggle-Pass nodes are also reachable from the canvas menu (same place as KJNodes / rgthree entries):
-
-```
-🔄 Toggle-Pass  ▸
-   ├ 采样器（一体）
-   ├ Image Batcher
-   ├ Multi Ref Latent Input
-   ├ One Image Multi Prompt
-   ├ Ref Image Encode
-   ├ Ref Independent
-   └ ...
-```
-
-- The list is **collected at runtime** — new nodes show up automatically, no code change needed
-- Clicking an item spawns the node **at your pointer position**
-
----
-
-### Privacy Mask
-
-SaveLoadImage, ToggleLoadImage, and TogglePreviewImage support privacy mask:
-- Title bar shows 👁 (show image) / 🫣 (hide image) icon
-- Click icon to toggle privacy state; when hidden, image preview area is blank
-- Privacy state is saved/restored with workflow, does not affect image generation
-
----
-
-### Example Workflows
-
-Ready-to-load graphs live in [`example_workflows/`](example_workflows/) — drag a `.json` onto the
-canvas, or open it from the workflow menu.
-
-| File | What it demonstrates | Extra deps |
-| --- | --- | --- |
-| `All-in-One Demo.json` | Every Toggle-Pass node in one graph: `ToggleUnifiedLoader`, `ToggleSampler`, `ToggleLoadImage` ×2, `ToggleImageCompare`, `TogglePreviewImage`, `DynamicRefImageEncode`, `SaveLoadImage` + `VAEDecode`. **Best place to start** — needs no third-party nodes. | — |
-| `Graphic-Text Integration Editing.json` | Text + single reference image editing through `DynamicRefImageEncode`, saved by `SaveLoadImage`. | — |
-| `Batch Editing.json` | Six input images batched into one reference set for batch editing. | `RTXVideoSuperResolution` |
-| `Lazy Person's Multi-angle View.json` | One image + `OneImageMultiPrompt` ("一图多视角") to generate several camera angles in one run. | `CR Upscale Image` |
-
-> The model filenames inside these graphs reflect the author's local setup. If a loader shows red
-> after loading, point it at your own checkpoint / text encoder / VAE.
-
----
-
-### Notes
-
-- When `num_images` changes, input/output slots auto-adjust, **existing connections are not broken**
-- Switching pages and back, nodes auto-restore to correct slot count
-- Requires Qwen2.5-VL or compatible CLIP model; Qwen-Image-2.1 requires Qwen3-VL 8B + `qwen_image` clip type
-- Default instruction is for Qwen-Image-Edit models, modify as needed
-- Only parameters relevant to the selected `模型类型` are shown; the rest are hidden
-- Illegal widget values (e.g. `倍数 = NaN`) fall back to safe defaults on load. **After upgrading,
-  restart ComfyUI and hard-refresh (Ctrl+F5) once**, then save your workflow
-- ⚠️ Modern node design (Vue nodes) breaks `widget.hidden` and custom drawing — with it enabled every
-  parameter stays visible and the privacy icon disappears. Turn it off in ComfyUI settings.
-- ⚠️ On quantized checkpoints (INT8/FP8) a LoRA still takes effect, but it comes out "blurrier": ComfyUI
-  dequantizes → patches → re-quantizes with stochastic rounding, so the delta keeps its magnitude
-  (~115%) while its direction picks up noise (cosine 0.4–0.7). Pose/structure LoRAs stay visible,
-  very subtle style LoRAs may not. This is upstream behavior — official `LoraLoader` is identical
-  (A/B with the same seed gave **pixel-identical** output, meanAbsDiff = 0.00)
-- The loader reports how many patches each LoRA actually wrote **plus module coverage**
-  (`192/224p`) — see the button labels above. A missing fraction usually means the checkpoint was
-  repacked and those layers don't exist in it, not that the LoRA failed to load
-- ⚠️ **CLIP check (split mode)**: after loading, the loader verifies the text-encoder class it actually
-  built (e.g. `QwenImage21TEModel_` vs a wrong `QwenImageTEModel_` / `Flux2TEModel_`). A mismatched CLIP
-  file still runs silently but makes the conditioning useless; the button then shows `⚠️CLIP不符`
-  and the log prints the expected class + what to do
-- ⚠️ **VAE check (split mode)**: the loader also compares latent channels / dimensions against the model.
-  A mismatched VAE never fails at load time — it dies later inside `VAEDecode`
-  (`IndexError: tuple index out of range`), so the loader now warns immediately and names the file to use
-- Hiding/collapsing a LoRA group **reclaims the node height**, so the node never stays as a long empty box
-- **Selection box on canvas follows the mouse again**: ComfyUI 1.57.x clamps the drag-selection rectangle to
-  `.graph-canvas-panel`, which only covers the lower part of the canvas — dragging in the upper half pushed the
-  box's top edge down (and squashed its height to 0), so the box seemed to appear "somewhere else".
-  `js/selection_rect_fix.js` rewrites that box's position from the real mouse points. Disable it with
-  `localStorage.setItem("tp_selrect_fix", "off")` in the browser console + refresh
-- Python changes require a **ComfyUI restart**; pure JS changes only need Ctrl+F5
-
----
-
-<a id="中文"></a>
-## 中文
-
-Toggle-Pass 系列节点——开关控制、图片加载/保存/预览、隐私遮罩、多参考图动态编码、统一模型加载、一体化采样器。
-
-### 节点说明
-
-#### ToggleUnifiedLoader — 统一模型 + LoRA 加载器
-
-一体化模型加载节点：UNet + CLIP + VAE + 多 LoRA 整合到一个节点中。
-
-**加载模式**（`加载模式`）— 一个节点，两种加载方式：
-
-| 模式 | 作用 | 显示的参数 |
-|---|---|---|
-| `分离组件 (UNet+CLIP+VAE)`（默认） | UNet / CLIP / VAE 分别指定 | `unet_name`、`clip_name`、`vae_name`、`clip_type` |
-| `Checkpoint 一体包` | 加载一体化 checkpoint（等同官方 **CheckpointLoaderSimple**）：自动猜配置，UNet + CLIP + VAE 一次出齐 | 只显示 `ckpt_name` |
-
-- **加载模式做成了一个两段式开关 `UNet | Checkpoint`**（不再是下拉框）：点左半是分离组件、点右半是一体包，
-  当前档高亮。开关位于**节点顶部第一行，与输出插槽 `模型 / MODEL` 同一行**，左端与**下方所有下拉框的
-  左边缘对齐**
-- 底层 `加载模式` 的值照常保存与序列化，**旧工作流与 API 调用完全不受影响** —— 开关只是接管了绘制与点击
-- 「现代节点设计」（Vue 节点渲染）下，开关会改为画在下拉框内部
-- 选 `Checkpoint 一体包` 时，分离的三个模型下拉会自动隐藏，界面不眼花
-- **两种模式都能加 LoRA** —— LoRA 组在加载完成后统一应用
-- VAE 用一体包自带的（与官方简易版一致）
-- **参数顺序：模型在最前** —— `ckpt_name` → `unet_name` → `clip_name` → `vae_name` → `clip_type`
-  → `加载模式` → LoRA 组 → 按钮。一体包模式下第一行就是 checkpoint 下拉框
-- 旧工作流能正常加载：加载时会按**参数名**把保存的值对回去，不会串位
-- 缺失的值仍然回退到默认模式（分离组件）
-
-**输入**：
-- `ckpt_name` — `models/checkpoints` 里的 checkpoint 文件（只在「Checkpoint 一体包」模式下显示）
-- `unet_name` — UNet 模型文件
-- `clip_name` — CLIP 文本编码器文件
-- `vae_name` — VAE 模型文件
-- `clip_type` — CLIP 类型（支持 auto 从 UNet 自动检测，也可手动选择：SD、SD3、FLUX、FLUX2、`qwen_image` 等）
-  - 自动检测能识别 Qwen-Image-2.1 的 UNet 类名（`QwenImage21`）并映射到 `qwen_image`
-  - ⚠️ **这是 2.1 的必备条件**：只有 `CLIPType.QWEN_IMAGE` 才能加载 2.1 专用文本编码器（Qwen3-VL 8B），
-    它会生成 `image_slots`；否则会退回通用 Qwen3-VL 编码器，**参考图被静默丢弃**
-  - 也支持手动指定：怀疑自动检测没生效时直接选 `qwen_image`
-- `+ 添加 LoRA` 按钮 — 点击逐个添加 LoRA 槽位（最多 10 组）
-- `- 移除 LoRA` 按钮 — 点击从最后一组开始移除（最多 10 组）
-
-**LoRA 控件**（通过按钮添加，最多 10 组）：
-- 选择 LoRA 文件 → 自动展开**一个**「强度」滑块
-- 选择 "none" → 自动隐藏滑块
-- 多个 LoRA 按顺序依次应用
-
-> **只有一个强度滑块是有意为之**：CLIP 侧**始终跟随 MODEL 侧**，保证「看到的强度 = 实际生效强度」。
-
-**跑完之后按钮上直接告诉你 LoRA 到底写没写进去**（后端回传，不用再猜）：
-
-| 按钮文案 | 含义 |
-|---|---|
-| `· 224p` | LoRA **确实写进了模型**，共 224 个 patch（数字是 Python 侧统计的真实值） |
-| `· 192/224p` | 这份 LoRA 的 224 个模块里只落下 192 个，剩下 32 个在当前底座上**没有对应层**。典型于**被 repack 过的 INT8 一体包**（如 `img_mlp.gate_layer` 被融进 `gate_up`），不是节点的问题 |
-| `· ⚠️未写入` | 一个 patch 都没挂上 —— 架构不匹配 / 文件不对 |
-| `· ⚠️N不匹配` | N 个 LoRA 的键名跟底座完全对不上（拿错底座的 LoRA） |
-| `· ⚠️N跳过` | N 个 LoRA 被跳过（文件找不到） |
-| `· ⚠️N个强度0` | 有 N 个已选 LoRA 强度是 0 —— 等于没加 |
-| `· ⚠️CLIP不符` | 建出的文本编码器与模型不配套（分体模式下换错 CLIP 文件 / 选错 clip 类型）—— 提示词会被错误编码，跑得通但什么都不生效 |
-
-按钮文案故意写得很短（太长会溢出窄节点），鼠标悬停在按钮上能看到**每个 LoRA 的完整明细**
-（模块覆盖率、CLIP 校验结果、每个槽位实际写入的 patch 数）；
-另外只要增删/切换 LoRA，上一次运行的数字会立刻清掉，不会留一个过期读数误导你。
-注意它反映的是「patch 有没有写进去」，不等于「画面上一定看得出来」——量化底座、参考图主导的编辑、过于细微的 LoRA，都会让效果不明显。
-
-**输出**：`MODEL`、`CLIP`、`VAE`
-
-**全局广播**（节点自带开关，不需要装任何额外插件）：
-
-打开后，点「运行」时会把这三个输出**自动接到图里所有空着的** `MODEL` / `CLIP` / `VAE` 输入上，
-不用再横跨半张画布拉线。关掉（默认）就完全保持原样，自己连线。
-
-- 这些线**只在提交的那一刻真实存在**，提交完立刻拆掉 —— 不会被存进工作流
-- **开关打开时，画布上会用紫色曲线画出「运行时会自动接上的每一条线」**，起点/终点各有一个小圆点，
-  精确落在输出口和输入口上。曲线只是视觉预览：不创建真实连线、不参与序列化，关掉开关立刻消失
-- **曲线从节点背后穿过**：画在节点下层，节点之间的空白处正常显示，穿过节点的地方被节点自然遮住，
-  所以永远不会盖住节点标题和参数（节点排得密时可见部分会少一些，这是刻意的取舍）
-- **只补空位**：凡是已经手连的输入一律不动，所以「自动」和「手动」可以在同一张图里混用
-- 只覆盖 `MODEL` / `CLIP` / `VAE`（也就是本节点的三个输出）
-- 如果图里有多个加载器都开了广播，同一类型只取第一个，并在控制台给出提示
-- **开关在节点顶部**：就在 `加载模式` 那个 `UNet / Checkpoint` 胶囊的**正下方、左半边**（一个小胶囊，
-  左边写「广播」）—— 点一下切换。它不再占用节点底部一整行，底部只剩 LoRA 那两个按钮
-- 开关状态存在节点的 `properties` 里，**不占 `widgets_values`** —— 旧工作流的每一项参数位置都不变，
-  开关默认关闭
-- 与 `cg-use-everywhere` 共存，不碰它那套 `ue_convert` 标记
-
-> **限制**：广播发生在前端，所以「界面里点运行」和「从界面导出 API 格式」都算数；
-> 但直接 `POST /prompt` 提交手写的 API JSON 不经过前端，就不会自动接线 ——
-> 需要的话在界面里导出一次 API 格式，导出的 JSON 里线已经带好了。
-
----
-
-#### SaveLoadImage — 保存/加载切换
-
-图片保存节点，支持 Save/Load 模式切换。
-- **Save 模式**：保存图片到 output 目录，同时输出 IMAGE 和 MASK
-- **Load 模式**：直接复用上次保存的图片，跳过保存流程
-- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
-
-#### ToggleLoadImage — 加载图片
-
-自定义加载图像节点，和原生 LoadImage 功能一致，额外支持隐私遮罩。
-- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
-
-#### TogglePreviewImage — 预览图片
-
-自定义预览图像节点，不保存到 output 目录（保存到 temp），支持隐私遮罩。
-- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
-
-#### ToggleEmptyLatent — 开关空 Latent
-
-可切换的空 Latent 生成节点。
-- **ON**：使用 width/height 参数生成
-- **OFF**：使用 fallback_width/fallback_height 参数（可用于 A/B 尺寸对比）
-
----
-
-#### DynamicRefIndependent — 多参考图独立编码（核心节点）
-
-每张参考图独立 VAE 编码，输出各自独立的 CONDITIONING 和 LATENT，适合多图各自控制风格/内容的场景。
-
-**输入**（动态，由 `num_images` 控制）：
-- `clip` — CLIP 模型
-- `vae` — VAE 模型
-- `num_images` — 参考图数量（1~10）
-- `instruction` — 系统提示词（可留空使用默认）
-- `image1` ~ `imageN` — 参考图
-- `prompt1` ~ `promptN` — 每张图对应的提示词（控件在节点内，可折叠）
-
-**模型类型**：`Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL`
-- 选择 `Qwen Image 2.1` 会展开 2.1 专属参数组（见 [Qwen Image 2.1 支持](#qwen-image-21-支持)）
-- 选择 `Qwen Layered` 会展开「图层数」
-- 每种模型只显示自己用得上的参数，其余自动隐藏，界面不会眼花
-
-**输出**（动态，N×2 个）：
-- `cond_1`, `latent1`, `cond_2`, `latent2`, ... `cond_N`, `latentN`
-
-**用法**：
-1. 拖入节点，默认 1 张参考图
-2. 调整 `num_images` 到需要的数量（1~10），输入槽和输出槽自动增减
-3. 每张图连接自己的参考图，填写对应 prompt
-4. `cond_N` 连到 sampler 的 conditioning，`latentN` 连到 VAE Encode 的 latent（供参考图编码使用）
-
----
-
-#### DynamicRefImageEncode — 动态参考图编码
-
-单张 prompt 控制，动态增删参考图输入槽。
-
-**输入**：`clip`, `num_images`, `prompt`, `image1` ~ `imageN`
-**输出**：`CONDITIONING`, `LATENT`（所有图共享同一份 conditioning，分别输出 latent）
-
-**模型类型**：`Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL`
-- `Qwen Layered`：增加「图层数」参数；输出的 latent 为分层格式（`[1, 16, layers+1, h/8, w/8]`），可直接接到 K采样器的 latent 输入（省掉 `EmptyQwenImageLayeredLatentImage` 节点）
-- `Qwen Image 2.1`：增加 2.1 专属的分辨率参数组（详见下方小节）
-- 每个模型类型只显示自己需要的参数，切换下拉框即自动隐藏其余的
-- 「匹配原图尺寸」= ON 时，使用图片实际尺寸；= OFF 时，2.1 走分辨率参数组，其他模型仍用「输出宽度/高度」（旧行为不变）
-
-#### DynamicRefLatentInput — 动态 Latent 输入
-
-动态增删 LATENT 输入槽，配合 DynamicRef 系列使用。
-
-#### ImageBatcher — 图像批处理
-
-动态增删 IMAGE 输入槽，将多张图打包成批次。
-
-#### ToggleImageCompare — 图片对比
-
-把多张「原图」与多张「结果」拼成一张对比图，节点内直接预览。
-
-**输入**：`num_sources`、`num_results`、`source1`~`source10`、`result1`~`result10`
-- `num_sources` / `num_results` — 每组显示几个槽（1~10），两组各自独立，
-  所以 **N 张原图 + 1 张结果** 和 **N 张原图 + N 张结果** 都支持
-- `source1`~`source10` — 原图（before）
-- `result1`~`result10` — 结果（after）
-
-> 节点上显示为 `原图1`~`原图10` / `结果1`~`结果10`、`原图数` / `结果数`、输出 `对比图`；
-> 上面这些英文 key 保持不变，为了兼容已保存的工作流。
-
-**参数**
-- `排版` — `自动` / `成对并排` / `上下两排` / `自动网格`
-  - `自动`：两组数量相等时用「成对并排」，不等时用「上下两排」
-- `标签` — 在每张图上烧 `原1 / 结1 …` 角标，或隐藏
-
-**输出**：`comparison` —— 一张合成图（可直接接 `SaveLoadImage` 或预览节点）
-
-排版按**实际接上的图**算，而不是你开了几个槽，所以数量变了也不用改设置。尺寸不一样的图会
-按行统一高度缩放（保持宽高比）。
-
-#### OneImageMultiPrompt — 单图多提示词（一图多视角）
-
-一张参考图对应多个提示词，分别输出各自的 conditioning（最多 8 组），共享同一个 latent。
-
-**输入**：`clip`、`image`、`prompt数量`、`prompt1` ~ `prompt8`
-- `prompt数量` — 用几个提示词（2~8），多余的 prompt 框会自动隐藏
-- `模型类型` — `Flux2` / `Qwen Image 2.1` / `SD / SDXL` / `Krea2`。选 `Qwen Image 2.1` 会展开完整的分辨率参数组，
-  适合「一张图出多个视角 / 多种编辑」。
-
-**输出**：`cond_1` ~ `cond_8`、`latent`
-
----
-
-#### ToggleSampler — 采样器（一体）
-
-把官方四件套收进一个节点：
-
-```
-随机噪波 + CFG引导器 + K采样器选择 + (基础调度器 | 手动Sigmas)  ->  SamplerCustomAdvanced
-```
-
-**输入**：`模型`（MODEL）、`正面条件` / `负面条件`（CONDITIONING）、`Latent图像`（LATENT）
-**参数**：`采样方案`、`采样器`、`调度器`、`步数`、`CFG`、`降噪`、`噪波随机种`、`自定义Sigma表`
-**输出**：`Latent`、`降噪Latent`
-
-模型 / 条件 / Latent **故意保留成插槽**——真正随模型变的只有 sigma 曲线，所以换模型不用换节点。
-`采样方案` 分三类：
-
-| 方案 | 作用 | 生效参数 |
-|---|---|---|
-| `自动（按步数 + 调度器）` | 现场算 sigma，与官方 `BasicScheduler` 数值完全一致 | `步数` + `调度器` |
-| 预置表：`Qwen-Image-2.1 turbo 8步`、`LTX-2 视频 8步（蒸馏）`、`LTX-2 视频 3步（低噪）` | 蒸馏模型专用的固定轨迹 | 无——步数已写进表里 |
-| `自定义表` | 手填一串 sigma | `自定义Sigma表` |
-
-sigma 表规则：逗号分隔、**严格递减**、**末值必须是 0**、`N` 个点 = `N-1` 步（9 个点 = 8 步）。
-
-选了预置表之后失效的参数会自动变灰**并锁住**：`步数` 会跳到该表真实的步数（看得见但改不动），
-切回 `自动` 会还回你自己设的步数。`降噪` < 1.0 会裁掉表的尾部，所以预置表也能做低降噪细化。
-
-蒸馏 turbo 模型推荐 `采样器` = `euler`、`CFG` = `1.0`。
-
----
-
-<a id="qwen-image-21-支持"></a>
-### Qwen Image 2.1 支持
-
-`DynamicRefImageEncode`（Ref Image Encode）、`DynamicRefIndependent`（Ref Independent）、
-`OneImageMultiPrompt`（一图多视角）均已支持 **Qwen-Image-2.1** 图像编辑 / 多视角生成。
-把「模型类型」选成 `Qwen Image 2.1`，会展开下面这组参数：
-
-| 参数 | 含义 |
-|---|---|
-| `宽高比` | 预设比例（1:1 / 3:2 / 16:9 / 21:9 …），或 `自定义（用输出宽度/高度）` |
-| `百万像素` | 总像素预算：`1.0` ≈ 1024×1024，`2.6` ≈ 2016×1344。范围 0.1~16，**别在这里填 1024** |
-| `倍数` | 对齐步长，硬下限 **32**（填 16 会算出非法形状，例如 5024×3344） |
-| `参考图分辨率` | 参考图喂给模型前被缩到多大（面积基准）；`0` = 保持原尺寸 |
-| `尺寸适配` | `参考图保比例`（默认，不变形）/ `参考图裁剪` / `参考图拉伸` |
-
-对齐官方 `TextEncodeQwenImage21` 时总结出的几条关键规则：
-
-- **出图尺寸和参考图尺寸互不影响。** `qwen_image` 用的是 `default_ref_method = "index"`，
-  参考图和输出各占独立的位置平面，不需要同形。改「参考图分辨率」**不会**让出图变大变小。
-- **指令要写进提示词文本，不能写在 `instruction` 里。**
-  `comfy/text_encoders/qwen_image21.py` 会丢弃 system 段，写进 `instruction` 的内容在 2.1 下会被静默丢弃；
-  节点已自动把它并进 user 段（Flux 时代那句默认英文 instruction 会被忽略，免得污染提示词）。
-- **latent 输出是全零。** 如果把参考图的真实 latent 当采样起点，编辑就会退化成 img2img（内容完全不变）；
-  真实 latent 只进 conditioning 的 `reference_latents`。
-- 图片必须是 **32 的倍数**；带 alpha 的参考图会先合成到白底再送进视觉塔。
-
-推荐设置（2.1 图像编辑）：`匹配原图尺寸` = **OFF**、`尺寸适配` = `参考图保比例`、
-`百万像素` = 1.0、`倍数` = 32、`参考图分辨率` = 1024。
-
----
-
-<a id="krea2-支持"></a>
-### Krea2 支持
-
-`ToggleUnifiedLoader`（统一加载器）、`DynamicRefImageEncode`（Ref Image Encode）、
-`DynamicRefIndependent`（Ref Independent）、`OneImageMultiPrompt`（一图多视角）均已支持 **Krea2**。
-加载器会从 UNet 自动认出 `Krea2`，直接配 `CLIPType.KREA2` + `Krea2TEModel` 文本编码器；
-三个编码节点只要把「模型类型」选成 `Krea2` 即可。
-
-⚠️ **VAE 必须和模型配套。** 这是最常见的翻车点——配错不会在加载时报错，而是在 `VAEDecode` 深处炸：
-
-| 模型 | VAE 文件 | latent |
-|---|---|---|
-| Qwen-Image **2.1** | `qwen_image_2.1_vae_bf16.safetensors` | 64 通道 / 16x / RGBA |
-| **Krea2** | `qwen_image_vae.safetensors` | 16 通道 / 8x / RGB |
-
-配错时 `fix_empty_latent_channels` 会静默把 16 通道补成 64，采样照样跑完，
-然后解码落进 Wan 2.1 分支报 `IndexError: tuple index out of range`。
-现在加载器会在加载时比对 `latent_channels` / `latent_dimensions`，直接把该换哪个文件打到日志里。
-
-对齐官方 `TextEncodeQwenImageEditPlus` 后总结的几条规则：
-
-- **参考图尺寸和出图尺寸无关。** 参考图按约 1 MP（对齐 8 倍数）编码进 VAE，
-  视觉塔只吃约 384 px 的缩略图。
-- **VAE 只吃 RGB** —— alpha 是丢掉，不是合成。
-- **视觉占位符必须手写。** 传了显式 `llama_template` 时 Qwen3-VL 的 tokenizer **不会**自动插占位符，
-  节点会自己拼 `Picture N: <|vision_start|><|image_pad|><|vision_end|>`；
-  漏了就相当于参考图对模型完全不可见。
-- **`reference_latents_method` 必须显式给。** Krea2 的 `default_ref_method` 是 `None`，
-  不设方法时参考图会被**静默忽略**（不报错，只是没效果）；节点固定设为 `index_timestep_zero`
-  （与官方风格参考模板一致）。
-- **latent 输出是全零**（16 通道 / 8x）——和 2.1 一样是「编辑而不是 img2img」。
-- **sigma 曲线：** Krea2 turbo 在 `采样器（一体）` 里用 `自动（按步数 + 调度器）` 方案，
-  `Qwen-Image-2.1 turbo 8步` 那张表不能套过来用。
-
----
-
-### 画布菜单入口
-
-本插件全部节点也能从画布菜单里直接添加（和 KJNodes / rgthree 的入口在同一处）：
-
-```
-🔄 Toggle-Pass  ▸
-   ├ 采样器（一体）
-   ├ Image Batcher
-   ├ Multi Ref Latent Input
-   ├ One Image Multi Prompt
-   ├ Ref Image Encode
-   ├ Ref Independent
-   └ ...
-```
-
-- 列表是**运行时自动收集**的，以后新增节点自动出现，不用改代码
-- 点击任意一项，节点落在**鼠标所在位置**
-
----
-
-### 隐私遮罩功能
-
-SaveLoadImage、ToggleLoadImage、TogglePreviewImage 三个节点均支持隐私遮罩：
-- 标题栏右侧显示 👁（显示图片）/ 🫣（隐藏图片）图标
-- 点击图标切换隐私状态，隐藏时图片预览区域留空
-- 隐私状态随工作流自动保存/恢复，不影响生图流程
-
----
-
-### 示例工作流
-
-现成的图都在 [`example_workflows/`](example_workflows/) 里，把 `.json` 拖到画布上就能打开。
-
-| 文件 | 演示什么 | 额外依赖 |
-| --- | --- | --- |
-| `All-in-One Demo.json` | 一个图里放齐本插件全部节点：`ToggleUnifiedLoader`、`ToggleSampler`、`ToggleLoadImage` ×2、`ToggleImageCompare`、`TogglePreviewImage`、`DynamicRefImageEncode`、`SaveLoadImage` + `VAEDecode`。**想快速上手就从这张开始** —— 不需要任何第三方节点。 | 无 |
-| `Graphic-Text Integration Editing.json` | 单张参考图的「文字 + 参考图」编辑，用 `DynamicRefImageEncode` 编码、`SaveLoadImage` 保存。 | 无 |
-| `Batch Editing.json` | 六张输入图经批处理组成一组参考图，做批量编辑。 | `RTXVideoSuperResolution` |
-| `Lazy Person's Multi-angle View.json` | 一张图 + `OneImageMultiPrompt`（一图多视角），一次生成多个机位。 | `CR Upscale Image` |
-
-> 这些图里的模型文件名是作者本机的配置。加载后如果有加载器变红，在对应节点里改成你自己的
-> checkpoint / 文本编码器 / VAE 即可。
-
----
-
-### 注意事项
-
-- `num_images` 调整时，输入/输出槽会自动增减，**已有连线不会被断开**
-- 切换页面再回来，节点会自动恢复到正确的槽位数量
-- 需要 Qwen2.5-VL 或兼容的 CLIP 模型；用 Qwen-Image-2.1 时需要 Qwen3-VL 8B + `qwen_image` 类型的 clip
-- 默认 instruction 适用于 Qwen-Image-Edit 类模型，可按需修改（2.1 下会自动并进提示词文本）
-- 界面只显示当前「模型类型」用得上的参数，其余自动隐藏
-- 非法的参数值（如 `倍数 = NaN`、`百万像素 = 1024`）在加载时会自动回退到安全默认值。
-  **升级后请重启 ComfyUI + 强刷一次（Ctrl+F5）**，然后保存工作流即可固化
-- ⚠️ 「现代节点设计（Vue 节点）」会导致 `widget.hidden` 与自定义绘制失效 —— 开启后所有参数都会展开、
-  隐私开关图标也会消失。请在 ComfyUI 设置里关掉它
-- ⚠️ 量化底座（INT8/FP8）上 LoRA **依然生效，只是更"糊"**：ComfyUI 走「反量化 → 叠加 patch →
-  再量化回 int8」，但用的是**随机舍入 + 重算 scale**，增量幅度能保留约 113%~120%，代价是方向带噪声
-  （余弦 0.4~0.7）。姿态/结构类 LoRA 看得出来，特别细微的风格类可能看不出来。
-  这是上游行为，官方 `LoraLoader` 一模一样（同 seed A/B 实测：本节点与
-  `CheckpointLoaderSimple + LoraLoader` 输出逐像素完全一致，meanAbsDiff = 0.00）
-- 加载器会把每个 LoRA 实际写入了多少 patch **以及模块覆盖率**（`192/224p`）回传到按钮上
-  （见上面那张表）。覆盖率不满一般是底座被 repack、那几层不存在，而不是 LoRA 没加载
-- ⚠️ **分体模式的 CLIP 校验**：加载完 CLIP 后会比对实际建出的文本编码器类名与模型是否配套
-  （正确类名如 `QwenImage21TEModel_` / `QwenImageTEModel_` / `Flux2TEModel_`，日志会打印）。
-  不配套时按钮显示 `⚠️CLIP不符`，日志会给出预期类名和处理建议 ——
-  否则这种情况会「跑得通但什么都不生效」，极难自查
-- ⚠️ **分体模式的 VAE 校验**：加载器还会比对 latent 通道数 / 维度与模型是否配套。
-  配错的 VAE 在加载时不报错，一路跑到 `VAEDecode` 才炸（`IndexError: tuple index out of range`），
-  所以现在加载时就直接提示，并告诉该换哪个 VAE 文件
-- 隐藏/收起 LoRA 组后会自动**回收节点高度**，节点不会留成一大条空框
-- **画布拖框现在会跟着鼠标走**：ComfyUI 1.57.x 会把拖拽选择框裁剪到 `.graph-canvas-panel`
-  （实测这个容器只覆盖画布中下部），在画布上半部分拖框时框的顶边被强行下压、高度被压成 0，
-  表现就是「鼠标在左上角拉、框却出现在中间/下面」。`js/selection_rect_fix.js` 按真实鼠标点
-  重写框的位置，只改显示、不动画布状态。要关掉：浏览器控制台执行
-  `localStorage.setItem("tp_selrect_fix", "off")` 后刷新
-- Python 改动需要**重启 ComfyUI**，纯 JS 改动只需 Ctrl+F5
-
----
-
-## 更新日志 / Changelog
-
-> 按时间记录**新增 / 变更的功能**，以及怎么用。
-
-### 2026-10-25
-
-- **新增示例工作流 `example_workflows/All-in-One Demo.json`**：一个图里放齐本插件全部交互节点
-  （`ToggleUnifiedLoader` / `ToggleSampler` / `ToggleLoadImage` ×2 / `ToggleImageCompare` /
-  `TogglePreviewImage` / `DynamicRefImageEncode` / `SaveLoadImage` + `VAEDecode`），
-  **不依赖任何第三方节点**，拖进画布即可跑通；原 `Toggle-Pass-Test.json` 已改名为它
-- **README 新增「示例工作流」小节**（中英双语）：列出 `example_workflows/` 里 4 个示例分别演示什么、
-  各自需要哪些额外依赖 —— 此前 README 对示例目录零引用，用户不知道这些文件存在
-
-### 2026-10-24
-
-- **「全局广播」开关搬到节点顶部**：从 widget 列表末尾那整行，移到 `加载模式` 的 `UNet / Checkpoint`
-  胶囊**正下方、左半边**，缩成一个小胶囊（左写「广播」，右侧是开关轨道）
-  - 尺寸与胶囊对齐：**宽度 = 胶囊的一半**，**高度与胶囊完全相同**（都是 14px，上下边沿齐平）
-  - 外观与节点其它控件同语言：淡色托底 + 细轨道 + 白滑块，开启时转高亮蓝（和 `UNet` 选中态呼应），
-    鼠标悬停时托底提亮并浮出一道细描边
-  - 改法是「原生 toggle 保留但隐藏 + 自绘」：widget 对象、名字、`properties` 存储、`serialize:false`
-    全部原样不动 —— `widgets_values` 一项都不动，旧工作流零影响
-  - 顺带节点底部少了一整行，只剩「添加 / 移除 LoRA」两个按钮
-  - 纯前端改动，**不用重启 ComfyUI**，`Ctrl+F5` 即可
-- **修复画布拖框「框不在鼠标那里」**：ComfyUI 1.57.x 会把选择框裁剪到 `.graph-canvas-panel`
-  （实测该容器只覆盖画布中下部 `[64,488,...]`），在画布上半部分拖框时框的顶边被强行压到 488、
-  高度被压成 0，看起来像「鼠标在左上角拉、框跑到中间」。新增 `js/selection_rect_fix.js`
-  按真实鼠标点重写框的位置，纯显示层修正，不改画布状态
-  - 关闭方法：控制台 `localStorage.setItem("tp_selrect_fix", "off")` 后刷新
-- **预览曲线改为从节点背后穿过**：曲线画在背景层（节点下层），节点之间的空白处正常显示，
-  穿过节点的地方被节点自然遮住 —— 不会再盖住节点标题和参数。节点排得密时可见部分会少一些，属正常
-
-### 2026-10-23
-
-- **新增 `ToggleUnifiedLoader` 的「全局广播」开关**（节点自带，不依赖 `cg-use-everywhere`）
-  - 打开后点「运行」自动把 `MODEL` / `CLIP` / `VAE` 接到图里所有**空着的**同类型输入上，
-    提交完立刻拆线，不会被存进工作流
-  - 开关打开期间，画布上用**紫色曲线**画出「运行时会自动接上的线」，两端带小圆点，
-    方便一眼核对接去哪（曲线只是预览，不建真实连线、不参与保存）
-  - 曲线从节点**背后**穿过（画在节点下层）：空白处可见，节点上一根线都不落 —— 不挡标题和参数
-  - 只补空位，手连的一根都不动；关掉就是完全手动
-  - 开关状态存在节点 `properties` 里，**不占 `widgets_values`**，旧工作流参数位置一项不变
-  - 与 `cg-use-everywhere` 共存（不动它的 `ue_convert`）
-  - 纯前端改动，**不用重启 ComfyUI**，`Ctrl+F5` 即可
-- **`采样器（一体）` 初始宽度改为 300**（高度不变），「自动（按步数 + 调度器）」这类长值不再被截断
-
-### 2026-10-21
-
-- **新增 `采样器（一体）`（`ToggleSampler`）** —— 把官方「随机噪波 + CFG引导器 + K采样器选择 +
-  基础调度器/手动Sigmas → SamplerCustomAdvanced」四件套收进一个节点。
-  - 模型 / 条件 / Latent 仍是插槽，换模型不用换节点
-  - `采样方案` 三档：`自动`（= 官方 BasicScheduler，数值等价）、预置表（Qwen-Image-2.1 turbo 8步、
-    LTX-2 视频 8步/3步）、`自定义表`（手填 sigma，接新模型不用改代码）
-  - 选预置表时 `步数` / `调度器` 会变灰并锁住，`步数` 显示该表真实步数；切回 `自动` 自动还原你原来设的步数
-  - sigma 表规则：逗号分隔、严格递减、末值必须是 0、N 个点 = N-1 步
-- **新增 Krea2 支持** —— `ToggleUnifiedLoader`、`DynamicRefImageEncode`、`DynamicRefIndependent`、
-  `OneImageMultiPrompt` 全都能跑 Krea2
-  - 加载器从 UNet 自动识别 `Krea2` → `CLIPType.KREA2` + `Krea2TEModel`，无需手选 clip 类型
-  - 三个编码节点的「模型类型」新增 `Krea2`：参考图按约 1 MP 编码、VAE 只吃 RGB、
-    自动补视觉占位符、自动设 `reference_latents_method = index_timestep_zero`、输出全零 16 通道 latent
-- **加载器改进**
-  - CLIP 类型改为**由模型类推导**，不再二次读取 UNet 去猜（省一次 6.9 GB 读取，也不会再静默失败）
-  - 新增 **VAE 匹配检查**：加载时比对 latent 通道 / 维度，配错直接提示该换哪个 VAE 文件
-    （否则会在 `VAEDecode` 报 `IndexError: tuple index out of range`，很难自查）
-
-### 2026-10-10
-
-- **新增 `ToggleImageCompare` 图片对比节点** —— 把多张原图与多张结果拼成一张对比图，节点内直接预览。
-  两组动态输入（原图 `source1~10`、结果 `result1~10`）各自独立增减，**N 原图 + 1 结果** 与
-  **N 原图 + N 结果** 都支持；排版 `自动` 会按数量自动在「成对并排」和「上下两排」之间选，
-  另有「自动网格」；可给每张图烧 `原1 / 结1` 角标。
-  - **怎么用**：把原图接到 `source*`、结果接到 `result*`，输出直接给 `SaveLoadImage` 或预览节点；
-    排版按**实际接上的图**算，数量变了不用改设置。
-- **加载模式开关（外观与交互定型）** —— `ToggleUnifiedLoader` 的 `加载模式` 在下拉框基础上改成
-  **两段式开关 `UNet | Checkpoint`**：点左半 = 分离组件、点右半 = 一体包，当前档用蓝色滑块高亮，
-  另一半悬停时有淡色反馈。
-  位置在**节点顶部第一行，与输出插槽 `模型 / MODEL` 同一行**；左端与下方所有下拉框的左边缘对齐，
-  宽度固定、两档同宽（切档不跳动），右侧给插槽文字留出安全距离。
-  - **怎么用**：直接点开关左半 / 右半切换，不再弹出下拉菜单；当前档一眼可见。
-  - 底层 `加载模式` 参数照常保存与序列化，**旧工作流与 API 调用零影响** —— 开关只接管绘制与点击，
-    没有新增或删除任何参数。
-- **开关交互修复**：① 点击分界与绘制统一为同一套几何（此前节点较宽时点中段会切错档、点开关右侧空白会误切）；
-  ② 挂在节点上的绘制 / 点击钩子改为**链式调用**，不再覆盖别的插件挂的同名钩子；
-  ③ 鼠标移出开关后悬停高亮不再残留。
-
-### 2026-10-09
-
-- **加载模式开关上线**：`UNet`（分离组件）/ `Checkpoint`（一体包）两段切换，替代原来的下拉框。
-- **模型参数置顶**：`ckpt_name` / `unet_name` / `clip_name` / `vae_name` / `clip_type` 排到参数列表最前，
-  一体包模式下一眼就能看到 checkpoint 行。旧工作流加载时**按参数名自动迁移**，不受顺序变化影响。
-- **LoRA 状态提示**：按钮回传每个 LoRA 实际写入的 patch 数与模块覆盖率（如 `192/224p`）；
-  分体模式下还会比对 CLIP 实例类名与模型是否配套，不匹配时显示 `⚠️CLIP不符`。
-
-### 2026-10-08
-
-- **一体包加载模式**：`ToggleUnifiedLoader` 融合 Checkpoint 加载器，新增 `加载模式` 参数 ——
-  `分离组件 (UNet+CLIP+VAE)`（默认）/ `Checkpoint 一体包`，两种模式都能挂 LoRA。
-- **LoRA 强度合并为一个滑块**：`model` 与 `clip` 两个强度合并显示，CLIP 侧自动跟随 MODEL 侧 ——
-  界面上看到的强度就是实际生效的强度。
-- **LoRA 诊断**：按钮显示已启用的 LoRA 数量与「强度为 0」告警；控制台打印每个 LoRA 最终挂上了多少 patch。
-- 修复：LoRA 组全部移除后节点自动**回收高度**（不再留一大条空框）；「- 移除 LoRA」不再把强度清成 0。
-
-### 2026-10-03
-
-- 修复画布菜单新建节点的落点：改为**鼠标所在位置**（原来是画布可视中心）。
-
-### 2026-09-25
-
-- **画布菜单入口**「🔄 Toggle-Pass」：在画布右键菜单里即可添加本插件的节点，节点列表运行时自动收集。
-  - **怎么用**：画布空白处右键 → 菜单里选 `🔄 Toggle-Pass`。
-- `OneImageMultiPrompt` 支持 **Qwen Image 2.1**；`prompt数量` 自动收起多余的 prompt 输入框。
-
-### 2026-09-24
-
-- `DynamicRefImageEncode` / `DynamicRefIndependent` 支持 **Qwen Image 2.1**。
-- **分辨率控制**：新增 `宽高比` + `百万像素` + `倍数`，以及 `参考图分辨率`、`尺寸适配`。
-- 修复 CLIP 类型检测（此前 Qwen Image 2.1 的专用文本编码器无法加载，参考图会被静默丢弃）。
-- 界面按「模型类型」自动显隐参数；旧工作流的错位脏值自动归位；`倍数` 下限由 16 提到 32。
-
-### 2026-05-12
-
-- `DynamicRefImageEncode` 新增 `Qwen Layered` 模型类型：选中后显示「图层数」，输出分层 latent。
-- `ToggleUnifiedLoader` 新增「- 移除 LoRA」按钮（从最后一组开始移除）。
-
-### 2026-05-10
-
-- 新增 **`ToggleUnifiedLoader`**（统一模型 + LoRA 加载器）：把 UNet / CLIP / VAE / 多 LoRA 整合到单一节点，
-  最多 10 组 LoRA 槽位（选了才显示），支持自动检测 CLIP 类型。
-
-### 2026-05-08
-
-- 新增节点：`SaveLoadImage`（保存/加载切换）、`ToggleLoadImage`、`TogglePreviewImage`、`ToggleEmptyLatent`。
-- 新增**隐私遮罩**（👁 / 🫣 切换，适用于图片类节点）。
-- 新增 **DynamicRef 系列**：`DynamicRefIndependent`、`DynamicRefImageEncode`、`DynamicRefLatentInput`、
-  `ImageBatcher`、`OneImageMultiPrompt`。
-
----
-
-## 安装 / Installation
-
-Place this folder under `ComfyUI/custom_nodes/` and restart ComfyUI.
-
-把本文件夹放到 `ComfyUI/custom_nodes/` 目录下，重启 ComfyUI 即可。
+# ComfyUI-Toggle-Pass
+
+**v1.0.0** · [English](#english) | [中文](#中文)
+
+---
+
+<a id="english"></a>
+## English
+
+Toggle-Pass node collection — toggle controls, image load/save/preview, privacy mask, dynamic multi-reference image encoding, unified model loader, and an all-in-one sampler.
+
+### Nodes
+
+#### ToggleUnifiedLoader — Unified Model + LoRA Loader
+
+All-in-one model loading node: UNet + CLIP + VAE + multiple LoRAs in a single node.
+
+**Loader mode** (`加载模式`) — one node, two ways to load:
+
+| Mode | What it does | Parameters shown |
+|---|---|---|
+| `分离组件 (UNet+CLIP+VAE)` (default) | Pick UNet / CLIP / VAE separately | `unet_name`, `clip_name`, `vae_name`, `clip_type` |
+| `Checkpoint 一体包` | Load an all-in-one checkpoint (same as the official **CheckpointLoaderSimple**): UNet + CLIP + VAE are extracted automatically by guessing the config | `ckpt_name` only |
+
+- **The mode is a two-segment switch — `UNet | Checkpoint`**, not a dropdown. Click the left half for
+  separate components, the right half for an all-in-one checkpoint; the current side is highlighted
+- The switch sits **on the node's top row, level with the `模型 / MODEL` output slot**, and its left
+  edge lines up with the dropdown widgets below
+- The `加载模式` value is still stored and serialised normally, so **existing workflows and API calls
+  keep working** — the switch only takes over how it is drawn and clicked
+- Under the Vue node renderer ("modern node design") the switch is drawn inside the combo widget instead
+- In `Checkpoint 一体包` mode the separate model dropdowns are hidden, so the node stays clean
+- **LoRA works in both modes** — the LoRA groups are applied after loading, regardless of mode
+- The VAE comes from the checkpoint itself (same as the official simple loader)
+- **Widget order — the model comes first**: `ckpt_name` → `unet_name` → `clip_name` → `vae_name` →
+  `clip_type` → `加载模式` → LoRA groups → buttons. In `Checkpoint 一体包` mode the checkpoint
+  dropdown is literally the top row
+- Old workflows load correctly: saved widget values are re-mapped **by parameter name** on load, so
+  nothing gets scrambled. Missing values fall back to the default mode (separate components)
+
+**Inputs**:
+- `ckpt_name` — checkpoint file from `models/checkpoints` (only in `Checkpoint 一体包` mode)
+- `unet_name` — UNet model file
+- `clip_name` — CLIP text encoder file
+- `vae_name` — VAE model file
+- `clip_type` — CLIP type (auto-detect from UNet, or manually select: SD, SD3, FLUX, FLUX2, `qwen_image`, etc.)
+  - Auto-detection recognises the Qwen-Image-2.1 UNet class (`QwenImage21`) and maps it to `qwen_image`.
+    ⚠️ This is mandatory for 2.1: only `CLIPType.QWEN_IMAGE` loads the dedicated text encoder (Qwen3-VL 8B)
+    that produces `image_slots`. Without it the generic Qwen3-VL encoder is used and **reference images are silently dropped**.
+  - Manual override available: select `qwen_image` explicitly (recommended if you suspect auto-detection failed)
+  - `fibo2` and `moss_image` are supported as well (auto-detected from the `Fibo2` / `MOSSImage` UNet class).
+    Both share weight files with `krea2` (FIBO 2.0 uses the same Qwen3-VL-4B; MOSS-Image uses Qwen3.5-**9B**),
+    but they tap completely different layers — a wrong type is not "slightly worse", the conditioning width
+    stops matching. The loader also validates the text encoder class (`Fibo2TEModel_` / `MOSSImageTEModel_`)
+    and the VAE (both use the Flux2 latent, so `flux2-vae*`, never a Qwen VAE).
+- `+ Add LoRA` button — Click to add LoRA slots one by one (up to 10)
+- `- Remove LoRA` button — Click to remove LoRA slots from the last group (up to 10)
+
+**LoRA Controls** (added via button, up to 10 groups):
+- Select a LoRA file → one `Strength` slider appears automatically
+- Select "none" → the slider hides
+- All LoRAs are applied sequentially
+
+> **One strength slider, on purpose.** The CLIP side always follows the MODEL side, so the strength you set is exactly what takes effect.
+
+**LoRA status on the button** — after a run the loader reports back what it actually did, right on the
+`+ Add LoRA` button, so you never have to guess:
+
+| Button text | Meaning |
+|---|---|
+| `· 224p` | the LoRA really was patched into the model — 224 patches (count produced by Python) |
+| `· 192/224p` | 192 of this LoRA's 224 modules landed; the remaining 32 have no target layer in the base checkpoint. Typical for **repacked INT8 all-in-one checkpoints** (e.g. `img_mlp.gate_layer` was fused into `gate_up`), not a node bug |
+| `· ⚠️未写入` | nothing was patched — wrong architecture / incompatible file |
+| `· ⚠️N不匹配` | N LoRAs matched zero weights (wrong base model) |
+| `· ⚠️N跳过` | N LoRAs were skipped (file missing) |
+| `· ⚠️N个强度0` | N selected LoRAs have strength 0 — they do nothing |
+| `· ⚠️CLIP不符` | the text encoder that got built doesn't match the model (split mode: wrong CLIP file / clip type) — conditioning is silently useless |
+
+The label is kept short on purpose (a long one would overflow a narrow node); hover the button to see
+the full per-LoRA breakdown (module coverage, CLIP check result, per-slot patch counts). The label is
+also cleared as soon as you add/remove/switch a LoRA, so a stale count from a previous run never lingers.
+Note this reports whether the patch was **written**, not whether it is visible in the picture —
+a quantized base, a reference-image-dominated edit, or a very subtle LoRA can all make the effect hard to see.
+
+**Outputs**: `MODEL`, `CLIP`, `VAE`
+
+**Global broadcast** (`全局广播` switch, built in — no extra plugin needed):
+
+Turn it **on** and the node's three outputs are automatically wired to every **empty** `MODEL` / `CLIP` /
+`VAE` input in the graph when you press Run. No dragging cables through half the canvas.
+Turn it **off** (default) and nothing changes — you wire everything yourself.
+
+- The real wires are made **only while the prompt is being built** and removed immediately afterwards, so
+  they never get saved into your workflow
+- **While the switch is on, the canvas draws a violet preview curve** for every wire that will be created
+  at run time, with a small dot at both ends landing exactly on the output/input slots. The curves are pure
+  overlay — they create no real links, are not serializable, and disappear as soon as you switch it off
+- **The slots that will be wired get "lit up"**: those inputs are still empty (no real link), so ComfyUI
+  renders them as hollow rings. While the switch is on they get a **solid dot** in the official type colour,
+  matching how a manually connected slot looks — so you can see at a glance which inputs will be wired
+  automatically and which are already connected. Switching it off restores the hollow look
+- **The curves and their end dots both pass behind the nodes**: they are drawn on the background layer, so
+  they show up in the empty space between nodes and are naturally hidden where they cross a node — including
+  the half of each end dot that sits under the node edge. Titles and parameters stay readable, and nothing
+  leaks out when you park a node on top of a wire as a mask
+  (on a very tightly packed graph you will see less of the line, which is the intended trade-off)
+  - Want the old look (dots always on top)? Run `localStorage.tp_dots_layer = "front"` in the console and reload
+- **It only fills empty inputs.** Anything you connected by hand is left untouched — which is why
+  "auto" and "manual" can coexist in the same graph
+- Covers `MODEL` / `CLIP` / `VAE` only (the node's three outputs)
+- If more than one loader has the switch on, the first one wins for each type and a warning is logged
+- **The switch lives at the top of the node**: directly **below** the `UNet / Checkpoint` pill, on its
+  left half (a small pill labelled `广播` / "broadcast") — click it to toggle. It no longer takes up a
+  full row at the bottom; the bottom now only holds the two LoRA buttons
+- Its size matches the pill: **width = half of the pill**, **height identical to the pill** (14px, edges aligned)
+- The switch state is stored in the node's `properties`, so it is **not** part of `widgets_values` —
+  old workflows keep every parameter in its original position, and the switch simply defaults to off
+- Works alongside `cg-use-everywhere` (it does not touch that plugin's `ue_convert` flag)
+
+> **Limitation**: the broadcast happens on the front-end, so it applies when you press Run or export the
+> API format from the UI. A raw `POST /prompt` with a hand-written API JSON never passes through the
+> front-end, so no auto-wiring happens there — export the API format once and the JSON already has the links.
+
+---
+
+#### SaveLoadImage — Save/Load Toggle
+
+Image save node with Save/Load mode toggle.
+- **Save mode**: Save image to output directory, output IMAGE and MASK
+- **Load mode**: Reuse the last saved image, skip saving
+- Click 👁 icon in the title bar to toggle privacy mask (hide/show image preview)
+
+#### ToggleLoadImage — Load Image
+
+Custom image loading node, same functionality as built-in LoadImage, with privacy mask support.
+- Click 👁 icon in the title bar to toggle privacy mask
+
+#### TogglePreviewImage — Preview Image
+
+Custom image preview node, saves to temp (not output), with privacy mask support.
+- Click 👁 icon in the title bar to toggle privacy mask
+
+#### ToggleEmptyLatent — Toggle Empty Latent
+
+Toggleable empty Latent generation node.
+- **ON**: Use width/height parameters
+- **OFF**: Use fallback_width/fallback_height parameters (useful for A/B size comparison)
+- **`latent_type`** — channels and spatial scale, must match the base model's `latent_format`:
+  | Option | Channels | Scale | Models |
+  |---|---|---|---|
+  | `SD / SDXL（4 通道 · 8×）` | 4 | 8× | SD 1.5 / SDXL / Pony |
+  | `SD3 / Flux / Krea 2（16 通道 · 8×）` | 16 | 8× | SD3, Flux.1, **Krea 2** (Wan21 format, `latent_dimensions=3`) |
+  | `Qwen-Image 2.1（64 通道 · 16×）` | 64 | 16× | Qwen-Image 2.1 |
+  | `Flux2（128 通道 · 16×）` | 128 | 16× | FLUX.2 |
+
+  Default is `SD / SDXL` so existing workflows keep their old behaviour. Picking the wrong
+  row does **not** raise an error — KSampler's `fix_empty_latent_channels` silently pads the
+  channels when the latent is all zeros, but any node that consumes the latent directly
+  (without going through a sampler) will get the wrong shape. With the matching row the
+  output is byte-identical to the official `EmptySD3LatentImage` (verified: same shape,
+  dtype, `downscale_ratio_spacial`, tensor diff = 0).
+
+---
+
+#### DynamicRefIndependent — Multi-Reference Independent Encoding (Core Node)
+
+Each reference image is independently VAE-encoded, outputting separate CONDITIONING and LATENT pairs. Ideal for scenarios where multiple images control style/content independently.
+
+**Inputs** (dynamic, controlled by `num_images`):
+- `clip` — CLIP model
+- `vae` — VAE model
+- `num_images` — Number of reference images (1~10)
+- `instruction` — System prompt (leave empty for default)
+- `image1` ~ `imageN` — Reference images
+- `prompt1` ~ `promptN` — Per-image prompts (collapsible widgets inside node)
+
+**Model type**: `Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL / Z-Image`
+- Selecting `Qwen Image 2.1` reveals its own parameter group (see [Qwen Image 2.1](#qwen-image-21-support))
+- Selecting `Qwen Layered` reveals `layers`; latent output is layered (`[1, 16, layers+1, h/8, w/8]`)
+- Selecting `Flux2` / `SD / SDXL / Z-Image` only shows the classic `输出宽度/高度` (old behaviour, unchanged)
+
+**Outputs** (dynamic, N×2):
+- `cond_1`, `latent1`, `cond_2`, `latent2`, ... `cond_N`, `latentN`
+
+**Usage**:
+1. Drag in the node, default is 1 reference image
+2. Adjust `num_images` to desired count (1~10), input/output slots auto-adjust
+3. Connect each reference image and fill in corresponding prompts
+4. Connect `cond_N` to sampler's conditioning, `latentN` to VAE Encode's latent
+
+---
+
+#### DynamicRefImageEncode — Dynamic Reference Image Encoding
+
+Single prompt control, dynamically add/remove reference image input slots.
+
+**Inputs**: `clip`, `num_images`, `prompt`, `image1` ~ `imageN`
+**Outputs**: `CONDITIONING`, `LATENT`, `NEGATIVE` (all images share the same conditioning, latents output separately).
+The second output is a ready-made **empty-text negative** encoded from the same CLIP — wire it into the
+sampler's negative input. Use it instead of `ConditioningZeroOut` on the positive conditioning: at
+CFG = 1.0 both look identical, but as soon as CFG > 1 the zeroed version corrupts the whole frame.
+This output exists only on `Ref Image Encode`; for `Ref Independent` / `OneImageMultiPrompt`
+with CFG > 1, wire a plain empty `CLIPTextEncode` into the negative input.
+
+**Model type**: `Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL / Z-Image` / `Krea2`
+- `Qwen Layered`: Adds a `layers` parameter; latent output is a layered latent (`[1, 16, layers+1, h/8, w/8]`), directly connectable to Ksampler's latent input (replaces `EmptyQwenImageLayeredLatentImage`)
+- `Qwen Image 2.1`: Adds its own parameter group for editing + resolution control (see below)
+- `SD / SDXL / Z-Image`: Generic fallback — emits a 4-channel all-zero latent, which core
+  `fix_empty_latent_channels` pads to whatever channel count the model needs. **This is the correct
+  setting for text-to-image-only models such as Z-Image** (see [Z-Image usage](#z-image-usage)).
+  For those models leave every `imageN` input empty — they have no reference-image path and will
+  output a blocky mosaic if you connect one.
+- `Krea2`: Wan21 latent (16 channels / 8x, `qwen_image_vae`). References go to `reference_latents` only,
+  the sampling start is an all-zero latent, and `reference_latents_method=index` is set automatically
+  (without it the references are silently ignored). **Deliberately not `index_timestep_zero`** — that
+  gives the reference tokens their own t=0 adaLN modulation, which does not match how the krea2_edit
+  LoRAs were trained (one shared tvec for the whole sequence). Measured: with `index_timestep_zero` a
+  line-art reference gets *drawn into* the frame (contour lines on the face); with `index` it is clean.
+  Reference latents are also fitted to the output grid — see the Krea 2 section below.
+- **For `MOSS-Image` / `FIBO 2.0`, pick `Flux2`** — all three share the same latent (128 channels / 16x, `flux2-vae`).
+  Measured: feeding the pose sheet as a second image does re-pose the subject (MOSS 16.7 s / FIBO 10.6 s,
+  no sketch figure left in the frame). What this node does **not** give you is MOSS's `edit_format`
+  (three training layouts that must match the LoRA) / `image_strength`, or FIBO's `negative_prompt`.
+  For those, use the native encoders `TextEncodeMOSSImageEdit` / `TextEncodeFibo2` instead —
+  they take AUTOGROW image inputs, so this node's output still plugs straight in.
+- Every model type only shows the parameters it actually uses — switching the dropdown hides the rest, so the node stays clean
+- When `匹配原图尺寸` = ON, uses the actual image size; when OFF, uses the 2.1 resolution parameters (or `输出宽度/高度` for other models)
+
+#### DynamicRefLatentInput — Dynamic Latent Input
+
+Dynamically add/remove LATENT input slots, for use with DynamicRef series.
+
+#### ImageBatcher — Image Batcher
+
+Dynamically add/remove IMAGE input slots, batch multiple images together.
+
+#### ToggleImageCompare — 图片对比
+
+Compare multiple "source" images against multiple "result" images by composing them into a single
+side-by-side sheet, previewed right inside the node.
+
+**Inputs**: `num_sources`, `num_results`, `source1`~`source10`, `result1`~`result10`
+- `num_sources` / `num_results` — how many slots to show for each group (1~10), adjusted independently,
+  so **N sources + 1 result** and **N sources + N results** are both supported
+- `source1`~`source10` — the "before" / original images
+- `result1`~`result10` — the "after" / generated images
+
+> In the node UI these are shown in Chinese: slots as `原图1`~`原图10` / `结果1`~`结果10`,
+> counters as `原图数` / `结果数`, and the output as `对比图`. The English keys above are kept
+> unchanged so existing workflows keep loading.
+
+**Parameters**
+- `排版` (layout) — `自动` (auto) / `成对并排` (paired) / `上下两排` (stacked) / `自动网格` (grid)
+  - `自动` picks paired when the two groups have equal counts, otherwise stacked
+- `标签` (labels) — burn `原1 / 结1 …` badges on each tile, or hide them
+
+**Outputs**: `comparison` — one composed IMAGE (feed it into `SaveLoadImage` or a preview node)
+
+Layout is decided by the images **actually connected**, not by how many slots you enabled, so you
+never have to touch the settings when the counts change. Images of different sizes are scaled to a
+common row height (aspect ratio preserved).
+
+#### OneImageMultiPrompt — One Image Multiple Prompts
+
+One reference image with multiple prompts, output separate conditionings (up to 8), sharing a single latent.
+
+**Inputs**: `clip`, `image`, `prompt数量`, `prompt1` ~ `prompt8`
+- `prompt数量` — how many prompts to use (2~8); unused prompt boxes are hidden automatically
+- `模型类型` — `Flux2` / `Qwen Image 2.1` / `SD / SDXL / Z-Image` / `Krea2`. Selecting `Qwen Image 2.1` adds the
+  full resolution parameter group, so one photo can be rendered from multiple viewpoints / edits at once.
+
+**Outputs**: `cond_1` ~ `cond_8`, `latent`
+
+---
+
+#### ToggleSampler — All-in-one Sampler (采样器（一体）)
+
+Collapses the official four-node chain into a single node:
+
+```
+RandomNoise + CFGGuider + KSamplerSelect + (BasicScheduler | ManualSigmas)  ->  SamplerCustomAdvanced
+```
+
+**Inputs**: `模型` (MODEL), `正面条件` / `负面条件` (CONDITIONING), `Latent图像` (LATENT)
+**Parameters** (in on-node order): `⚡预设`, `Sigma 曲线`, `采样器`, `调度器`, `步数`, `CFG`, `降噪`, `噪波随机种`, `自定义Sigma表`
+**Outputs**: `Latent`, `降噪Latent`
+
+> Display label and internal name are deliberately separate: `⚡预设` is internally `模型预设`, and
+> `Sigma 曲线` is internally `采样方案`. Only the label is changed — `widgets_values_named` and the
+> `/prompt` `inputs` are keyed by name, so renaming the name would orphan every saved workflow.
+
+Model / conditioning / latent stay as **sockets on purpose** — the only thing that really differs
+between models is the sigma curve, so switching models never needs a new node. `Sigma 曲线` (internal
+name `采样方案`) comes in three flavours:
+
+| Scheme | What it does | Which parameters matter |
+|---|---|---|
+| `自动（按步数 + 调度器）` | computes sigmas live — numerically identical to official `BasicScheduler` | `步数` + `调度器` |
+| Preset tables: `Qwen-Image-2.1 turbo 8步`, `LTX-2 视频 8步（蒸馏）`, `LTX-2 视频 3步（低噪）` | fixed distillation trajectory | none — the step count is baked into the table |
+| `自定义表` | hand-typed sigma list | `自定义Sigma表` |
+
+A sigma table must be comma separated, **strictly decreasing**, **end with 0**, and `N` values = `N-1`
+steps (9 values = 8 steps).
+
+Parameters that a preset makes irrelevant are greyed out *and* locked: `步数` flips to the preset's real
+value (still readable, just not editable), and switching back to `自动` restores your own step count.
+`降噪` < 1.0 trims the tail of the table, so low-denoise refinement works with presets too.
+
+For distilled turbo models use `采样器` = `euler` and `CFG` = `1.0`.
+
+#### ⚡预设 (fill in the usual numbers in one click)
+
+The `⚡预设` dropdown at the **top** of the node is a **one-shot filler**: pick a model and it sets
+`Sigma 曲线 / 采样器 / 调度器 / 步数 / CFG` in one go. `降噪` and `噪波随机种` are left alone
+(they depend on the task, not the model).
+
+It sits at the top because that is the real order of use — pick the model, then fine-tune. The two
+dropdowns also used to be easy to mistake for duplicates, so they are named apart now: `⚡预设` covers
+a whole parameter set, `Sigma 曲线` only the noise trajectory.
+
+| Preset | Sigma curve | Sampler | Scheduler | Steps | CFG |
+|---|---|---|---|---|---|
+| Qwen-Image 2.1 (25 steps) | auto | euler | simple | 25 | 1.0 |
+| Qwen-Image 2.1 turbo (8 steps, fixed table) | preset table | euler | simple | 8 | 1.0 |
+| Krea 2 turbo (8 steps) | auto | euler | simple | 8 | 1.0 |
+| Krea 2 turbo · detail (12 steps) | auto | euler | simple | 12 | 1.0 |
+| FLUX.2 klein (4 steps · beta) | auto | euler | beta | 4 | 1.0 |
+| FLUX.2 klein · upscale refine (4 steps) | auto | euler_ancestral | simple | 4 | 1.0 |
+| Z-Image turbo (8 steps) | auto | euler_ancestral | simple | 8 | 1.0 |
+| Qwen-Image Layered (9 steps · CFG 2.5) | auto | euler | simple | 9 | 2.5 |
+| Qwen-Image Edit 2511 AIO (4 steps) | auto | euler_ancestral | beta | 4 | 1.0 |
+| FIBO 2.0 turbo (4 steps) | auto | euler | simple | 4 | 1.0 |
+| MOSS-Image (30 steps · CFG 4) | auto | euler | normal | 30 | 4.0 |
+| Manual (off) | — | — | — | — | — |
+
+It **fills once, it does not lock**: touch any of those five parameters afterwards and the dropdown
+falls back to `手动（不套用）` by itself. The control therefore always reflects the truth — you will
+never see "Qwen preset" selected while the node is actually running 40 steps.
+
+The table lives only in `MODEL_PRESETS` inside `sampler.py`; the frontend fetches it from
+`GET /toggle_pass/sampler_presets` and keeps **no second copy**, so adding a model touches one file.
+When renaming an entry, add an "old → new" pair to `PRESET_ALIASES`, otherwise workflows saved with
+the old value show a blank dropdown.
+
+Every row was taken from a workflow that actually runs on this machine (the source file is noted in
+the `sampler.py` comments next to each entry) — run it once before changing a number.
+
+---
+
+<a id="pose-reference"></a>
+### Pose Reference
+
+`PoseReference` (参考姿势) picks one pose sheet out of a local library and hands it to any editing
+model's image input as a plain `IMAGE` — no ControlNet, no extra model.
+
+**Library layout** — the node merges up to three roots and de-duplicates by name. Earlier roots win,
+so a sheet of yours with the same name overrides the bundled copy:
+
+| Order | Root | Notes |
+|---|---|---|
+| 1 | `$TP_POSE_ROOT` | only if you set it; point it at your own collection |
+| 2 | `ComfyUI/models/pose_reference/` | recommended home for private sheets — travels with your `models` backup |
+| 3 | `custom_nodes/ComfyUI-Toggle-Pass/pose_assets/` | bundled, ships with the plugin |
+
+Every root uses the same layout:
+
+```
+<root>\
+├─ 01_AI线稿姿势\           lineart set
+│  └─ <category>\<group>\<P###_name>\<P###_view>.webp
+└─ 02_AI深度姿势库\         depth set (same paths, same filenames)
+```
+
+The two sets are path-for-path identical, so the node indexes one and resolves the other by the
+`source` switch. Drop a new folder in and it shows up in the dropdowns within ~15 seconds — no restart,
+no page refresh.
+
+**Inputs**
+
+| Name | What |
+|---|---|
+| `category` | 上半身单人 / 全身单人・标准系 / 全身单人・性感系 / 双人缠绵 |
+| `group` | pose group, e.g. 站姿 / 坐姿 / 蹲姿 (30 in total) |
+| `pose` | `P###_名称`, 288 in total. P-numbers repeat across categories, so the full folder name is the key |
+| `view` | 正面 / 侧面 / 斜侧 / 俯视 / 仰视 |
+| `source` | `线稿` (default) or `深度` |
+| `long_edge` | output long edge, default 1024 — the source sheets are only 300×400. The result is down-aligned to a multiple of 16 (VAE requirement), so the real long edge can be up to 15 px smaller |
+| `flip` | mirror horizontally, so you do not need a second sheet for the mirrored pose |
+
+The first three dropdowns cascade: picking a category narrows `group`, picking a group narrows `pose`.
+**Changing any dropdown repaints the node in place** — the thumbnail of the selected sheet appears right
+below the widgets, no prompt needs to be queued. The node grows itself on first load to make room;
+drag its lower-right corner if you want the picture bigger.
+
+**Wiring**
+
+```
+[Pose Reference] ──image──▶ [TextEncodeQwenImage21] .images ─┐
+                                                             ├─▶ KSampler ─▶ VAEDecode ─▶ SaveImage
+[UNet loader]  [CLIP loader]  [VAE loader] ──────────────────┘
+```
+
+1. Double-click the canvas and search `Pose Reference` (category `Toggle-Pass`).
+2. Pick 类别 → 姿势组 → 姿势 → 视角. The sheet shows up on the node as you click.
+3. Wire `image` into the `images` input of your edit encoder (`TextEncodeQwenImage21`,
+   `TextEncodeFibo2`, …). The rest of the graph is your usual txt2img / edit graph.
+4. `长边` sets the resolution fed to the encoder — 1024 is plenty, the sheets are only 300×400. The output is aligned down to a multiple of 16 so `VAEEncode` never has to pad or crop it (a half-pixel shift is very visible on line art).
+5. Set `翻转` to `开` when you need the mirrored version of the same pose.
+
+**⚠️ Editing models (2511 / FLUX.2 / MOSS-Image / FIBO 2.0) need two images**
+
+Wiring a single sheet into these models **runs fine and produces an image, but the pose never changes**.
+This is not a compatibility problem, it is semantics: the model treats `image1` of the edit encoder as
+"the picture to be modified", so a lineart sheet in `image1` degrades the task into "redraw this lineart
+photorealistically" and nothing in the prompt can bring the original subject back.
+
+Use multi-image editing instead: the **source photo goes to `image1`**, the **pose sheet goes to `image2`**.
+
+```
+[Load Image] ────image───▶ .image1 ┐
+                                   ├─▶ [TextEncodeQwenImageEditPlus] ─▶ KSampler
+[Pose Reference] ─image──▶ .image2 ┘   prompt: keep the subject and outfit of image1,
+                                                change the pose to the one in image2
+```
+
+`image1 / image2 / image3` on `TextEncodeQwenImageEditPlus` are all plain `IMAGE` inputs, so the node
+drops straight in with no converter. Verified on both 2511 and FLUX.2 — the subject is re-posed from
+the `image2` skeleton.
+
+MOSS-Image and FIBO 2.0 work the same way, only the slots are the auto-growing
+`image_0/image_1` (MOSS) and `image_1/image_2` (FIBO):
+
+```
+[Load Image] ────image───▶ .image_0 ┐
+                                    ├─▶ TextEncodeMOSSImageEdit ─▶ KSampler
+[Pose Reference] ─image──▶ .image_1 ┘   edit_format must match the edit LoRA's training format
+```
+
+Measured: both turn a standing photo into the seated pose of the sheet (MOSS 20.2 s, FIBO 16.1 s)
+while keeping face, hair, outfit and background. ⚠️ MOSS only edits when
+`moss_image_edit_lora_bf16` is loaded — without it you get the SFT base and the instruction barely moves.
+
+**Krea 2 — our own nodes can do it; two external packs exist if you need them**
+
+**Start here: no external pack required.** `Ref Image Encode` with `模型类型 = Krea2` reproduces what
+`ComfyUI-krea2Edit` does with two nodes, because ComfyUI's native krea2 `_forward` *already has* the
+in-context reference path (`ref_latents` → clean tokens at frame 1..N, target at frame 0) that
+`Krea2EditModelPatch` rebuilds by hand. Two conditions must hold, and the node now handles both:
+
+1. `reference_latents_method = index` — **not** `index_timestep_zero` (that splits the adaLN
+   modulation into two timesteps and the reference gets traced into the frame).
+2. Every reference latent is fitted to the output grid before VAE encoding — `h/w` identical to the
+   sampling start, so the RoPE positions differ only on the frame axis.
+
+```
+[Pose Reference] ─image─▶ .image1 ┐
+                                  ├─▶ Ref Image Encode (模型类型 = Krea2) ─cond─▶ ToggleSampler ─▶ VAEDecode
+[Load Image] ────image─▶ .image2 ┘                              └latent (all-zero, 16 ch) ┘
+prompt: the woman from image 2, kneeling upright leaning back, chest lifted, back arched,
+        both hands reaching down and back to touch her heels, three-quarter rear-side view;
+        keep face / hair / outfit / background — see the warning below, do NOT shorten this
+```
+
+It also has a dedicated `negative` output (empty-text encode from the same CLIP) — wire that into the
+sampler's negative input, so raising CFG later never corrupts the frame.
+
+⚠️ **Order is a hard convention: the LAST reference is the subject.** Sheet in `image1`, photo in
+`image2`. Swapped, the model renders a line-art mannequin (measured) — the sheet is treated as the
+thing to draw rather than the condition. Measured 8 steps at 720×1440: 70 s, seated pose, clean frame,
+likeness kept, no external nodes.
+
+⚠️⚠️ **The prompt must describe the pose concretely — this is the easiest trap.**
+A vague line like `图二美女参考图一的姿势，重新摆pose` ("the girl in image 2, follow the pose in
+image 1, re-pose") does not make the model *read* the sheet. Measured with identical parameters and
+seed: it treats the mannequin as a **second subject** and outputs a side-by-side collage of
+"real person on the left + line-art mannequin on the right". The pose does not move at all.
+
+Same pose (P068 kneeling, leaning back, hands to heels, three-quarter view), three prompts:
+
+| Prompt | Result |
+| --- | --- |
+| `图二美女参考图一的姿势，重新摆pose` | ❌ person + mannequin collage, pose unchanged |
+| `让图二中的女人摆出图一那个姿势：跪立、身体向后仰、胸部抬起、背部拱起，双手向后下方伸去触摸脚跟，头部微微后仰，采用四分之三侧后方视角。保持她的脸、发型、深蓝色泳装和海滩背景不变。` | ✅ single subject, pose correct |
+| Same in English | ✅ single subject, pose correct (hands slightly closer to the heels) |
+
+Three mandatory parts:
+1. **Name the subject** — "the woman from image 2", not "image 2's beauty"
+2. **Anatomically describe the pose** — torso direction, where each limb goes, where the hands land,
+   head tilt, camera angle. "Follow the pose in image 1" alone conveys nothing
+3. **List what must stay** — face, hair, outfit, background, item by item
+
+Chinese works fine; you do not have to write English. The point is specificity, not language.
+
+⚠️ Use `DynamicRefImageEncode` (`Ref Image Encode`), not `DynamicRefIndependent`: the latter encodes
+each image into its **own** conditioning (that is the design for Qwen 2.1's per-image index planes),
+and merging those does not give Krea2 one coherent reference sequence — measured line-art bleed plus a
+wrong pose.
+
+**If you do reach for an external pack**, these two are mutually incompatible with each other and with
+the two-input semantics above. Mixing them up gives "no error, but the line art gets painted into the
+picture" — a wasted run:
+
+| Node pack | Encoder | Patch node | Where the pose sheet goes | Result |
+| --- | --- | --- | --- | --- |
+| `ComfyUI-Krea2-Ostris-Edit` | `TextEncodeKrea2OstrisEdit` | `Krea2OstrisEditModelPatch` | `image2` (with `image1` = photo) | ✅ skeleton-driven, no line art bleed |
+| `ComfyUI-krea2Edit` | `Krea2EditGroundedEncode` | `Krea2EditModelPatch` | `image_b` + `source_latent_b` | ❌ the sheet is treated as a *second subject* and rendered |
+
+**The Ostris pack is the one that takes a skeleton:**
+
+```
+[Load Image] ─image─▶ [FluxKontextImageScale] ─▶ .image1 ┐
+                                                         ├─▶ TextEncodeKrea2OstrisEdit ─┐
+[Pose Reference] ─image─▶ [FluxKontextImageScale] ─▶ .image2 ┘  (one encoder each, pos/neg) │
+                                                                                            ▼
+[Load Diffusion Model] ─▶ [Krea2OstrisEditModelPatch] ─▶ [Load LoRA] ─▶ KSampler (8 steps, euler/simple, CFG 1)
+```
+
+`image1 / image2 / image3` are plain `IMAGE`, so the pose output drops straight into `image2`.
+Measured with `krea_outfittransfer` + `kv_cache` on, same beach photo and sheet `P017`: 8 steps in
+36.1 s, seated on the sand, **no line art left in frame**, face / hair / swimsuit / background intact.
+
+⚠️ Three things that have to match:
+1. **`kv_cache` must match how the LoRA was trained.** LoRAs trained with ai-toolkit's `kv_cache`
+   kwarg (e.g. `krea_outfittransfer`) need it on; normally trained edit LoRAs drift when it is on.
+2. **The LoRA must match the node pack.** Feeding `krea2_identity_edit_v2` (a krea2Edit-weight) into
+   the Ostris nodes also bleeds the sheet into the image — measured; the two use different geometry.
+3. **Wrap both conditionings in `FluxKontextMultiReferenceLatentMethod`** (`index_timestep_zero`),
+   as the official workflow does.
+
+With only krea2Edit-family LoRAs you can still change the pose, but **by prompt only** — no skeleton image:
+
+```
+[Load Image] ─▶ source_latent / image ─▶ Krea2EditModelPatch ─▶ KSampler (8 steps, euler/simple, CFG 1)
+prompt: have her sit on the sand, chest lifted and leaning back, hands braced beside her hips
+```
+
+Measured: `krea2_identity_edit_v2` took that prompt and turned the standing photo into a seated one,
+but wiring the sheet into `image_b` renders the mannequin too. So: **need a skeleton → Ostris pack;
+only krea2Edit weights → describe the pose in words.**
+
+**Can krea2Edit take a skeleton at all? Four wirings tested, none of them works**
+
+| Wiring | Pose follows the sheet? | Clean frame? |
+|---|---|---|
+| Sheet → `image_b` (+ `source_latent_b`) | yes | ❌ a white mannequin / transparent wireframe gets rendered into the shot, worse as `ref_boost` rises |
+| `source_latent_b` only (no `image_b`, the TE never sees it) | ❌ no effect at all | yes |
+| Sheet as the **main** image, photo as `image_b` (swapped roles) | yes | ❌ outline lines stay on the body |
+| Convert the sheet to a **grey silhouette** (PIL) then feed `image_b` | ❌ pose unchanged | yes |
+
+Rows 2 and 4 were control experiments — the pose sentence was deleted from the prompt entirely,
+and the output stayed in the photo's standing pose, proving all pose change came from the prompt
+and not from the skeleton. Row 4 only fixes the dirty frame, not the driving.
+
+**The likeness difference is measured**: same source photo, same pose target —
+`krea2Edit + krea2_identity_edit_v2` keeps the **same face**, while
+`Ostris + krea_outfittransfer` clearly swaps the person (that LoRA is for outfit transfer).
+So they are not interchangeable:
+
+- **Both at once** — identity kept *and* the pose driven by a sheet → `Ref Image Encode`
+  (`模型类型 = Krea2`) exactly as shown at the top of this section. No external pack, no second pass.
+- **Identity preserved**, text-only edit (restage, outfit change, local edit) → this plugin's Krea2 path
+  or `krea2Edit` + describe the pose in words; both keep the face
+- **Hardest possible skeleton lock**, and you can trade likeness → the `Ostris` pack
+- Only if you already work in the krea2Edit pack and cannot switch → Ostris for the pose, then one
+  `krea2Edit` pass to recover the face
+
+⚠️ `ref_boost` / `grounding_px` are not "higher is better": `ref_boost=1.3` with `grounding_px=1024`
+actually replaced the beach background with seaside rocks. Keep 1.0 / 768 and only nudge up if the face drifts.
+
+**Where the sheets live — three layers, merged at runtime**
+
+| Layer | Location | Ships with the plugin |
+| --- | --- | --- |
+| Built-in | `custom_nodes/ComfyUI-Toggle-Pass/pose_assets/` | yes — 1,530 sheets, only the two non-sensitive categories (`上半身单人`, `全身单人・标准系`) |
+| Private | `ComfyUI/models/pose_reference/` | no — recommended home for your own and private sheets; it travels with your `models` backup |
+| Custom | the `TP_POSE_ROOT` env var | no — only when you keep them somewhere else entirely |
+
+Lookup order is **`TP_POSE_ROOT` → `models/pose_reference` → built-in**, so a sheet of yours wins over
+the built-in one of the same name. Installing the node is enough to browse and generate with the
+built-in set; anything you drop into the private folder is picked up automatically:
+
+- **Drop a new pose group into `models/pose_reference` and the dropdown grows within ~15 s** — no page
+  reload. Deleting it makes it disappear just as fast (the node polls a cheap fingerprint of the library).
+- Need it right now? Right-click the canvas → `🔄 刷新姿势素材库`.
+- A root that does not exist is simply skipped: the node reports where it looked instead of failing
+  silently.
+
+**The workflow carries the sheet with it.** The selected file is inlined into the node as a data URI
+(`properties.tp_pose`), so the graph is self-contained:
+
+- Someone who downloads your workflow **sees the picture and can run it without any local library**;
+- Cost is roughly **+8–14 KB per node** (the sheets are 300×400 webp, 5–10 KB each);
+- When a local library exists it always wins — the inlined copy is only a fallback;
+- If the parameters were changed while the library was missing, the node keeps showing the inlined
+  sheet and says so, rather than going blank.
+
+**Output**: `image` (`IMAGE`) — connect straight to the `images` input of `TextEncodeQwenImage21`,
+`TextEncodeFibo2`, or any other edit encoder.
+
+A ready-made graph ships as `example_workflows/Pose Reference Editing.json` (Qwen-Image-2.1 turbo,
+8 steps). **It carries the selected sheet inside the node** (`properties.tp_pose`, a ~8 KB data URI),
+so it opens and generates even on a machine that has no pose library at all. Together with the built-in
+`pose_assets/`, a fresh install can browse 153 sheets across 12 groups out of the box.
+
+**Why lineart is the default.** Measured on FIBO 2.0 and Qwen-Image-2.1, one pose, two camera angles:
+the depth set fabricated footwear or pads in **3 of 3** runs, the lineart set in **1 of 3**. A depth map
+has no boundary line — the sole and the floor sit at nearly the same brightness, so the model fills the
+bright area in with an object that is not there. Lineart carries an explicit black contour, and
+`lineart → photoreal` is a transform these models saw in training. Depth keeps one advantage: volume,
+which disambiguates occlusion when limbs overlap. Switch to it only for tightly intertwined poses.
+
+---
+
+<a id="qwen-image-21-support"></a>
+### Qwen Image 2.1 Support
+
+`DynamicRefImageEncode`, `DynamicRefIndependent` and `OneImageMultiPrompt` all support **Qwen-Image-2.1**
+image editing / multi-view generation. Select `Qwen Image 2.1` in `模型类型` and these parameters appear:
+
+| Parameter | Meaning |
+|---|---|
+| `宽高比` (Aspect ratio) | Preset ratios (1:1 / 3:2 / 16:9 / 21:9 ...), or `自定义（用输出宽度/高度）` |
+| `百万像素` (Megapixels) | Total pixel budget, e.g. `1.0` ≈ 1024×1024, `2.6` ≈ 2016×1344. Range 0.1~16, **do not fill 1024 here** |
+| `倍数` (Multiple) | Alignment step, fixed ≥ **32** (16 will break shapes) |
+| `参考图分辨率` (Ref resolution) | How large the *reference* image is fed to the model (area target). `0` = keep original size |
+| `尺寸适配` (Ref fit mode) | `参考图保比例` (default, no distortion) / `参考图裁剪` / `参考图拉伸` |
+
+Key rules learned from aligning with the official `TextEncodeQwenImage21`:
+
+- **Output size and reference size are independent.** `qwen_image` uses `default_ref_method = "index"`,
+  so the reference and the output live on separate RoPE planes and need not be the same shape.
+  Changing `参考图分辨率` does **not** change the output size.
+- **Instructions must go into the prompt text, not the `instruction` field.**
+  `comfy/text_encoders/qwen_image21.py` drops the system turn, so anything written into `instruction`
+  is silently discarded for 2.1 — the node merges it into the user text automatically.
+  The default Flux-era English instruction is ignored (it would pollute the prompt).
+- **The latent output is all-zero.** Feeding the reference image's real latent turns editing into img2img
+  (nothing changes). The real latent only goes into `reference_latents` in the conditioning.
+- Images must be multiples of **32**; RGBA reference images are composited on white for the vision tower.
+
+Recommended setup for editing: `匹配原图尺寸` = **OFF**, `尺寸适配` = `参考图保比例`,
+`百万像素` = 1.0, `倍数` = 32, `参考图分辨率` = 1024.
+
+✅ **The negative is already solved — `Ref Image Encode` has a dedicated `negative` output.**
+Wire it straight into the sampler's negative input. It is a real empty-text encode from the same CLIP,
+so it stays correct at **any** CFG (verified pixel-identical to a plain empty `CLIPTextEncode`).
+
+⚠️ Do **not** zero out the positive conditioning with `ConditioningZeroOut` and use that as the
+negative. At CFG = 1.0 the negative is never evaluated, so it looks harmless; raise CFG and the frame
+comes out **fully corrupted** (measured: green/purple blotches at 20 steps / CFG 2.5, both fp8 and
+int8). Older builds of this node only had two outputs and the example workflow used
+`ConditioningZeroOut` — that is exactly what bit people.
+
+**For pose editing, add `VNCCS_QI2_PoseStudioV2`** (`models/loras/Qwen/`, 152 MB, ai-toolkit,
+base = `qwen_image_2`; `LoraLoaderModelOnly` is enough — pure `diffusion_model` keys).
+Same prompt, across the Qwen 2.1 LoRAs on this machine:
+
+| LoRA | Result |
+| --- | --- |
+| bare model | works, 15 s at 8 steps |
+| `VNCCS_QI2_PoseStudioV2` | ✅ **best**: pose tracked most accurately, richest detail, no line-art bleed |
+| `QI2.1_AnyAngle` | ❌ line-art bleed on face/neck (reference gets painted into frame) |
+| `qwen_image_2.1_turbo_lora_avg_rank_178` | usable but skin texture degrades; little pose help |
+
+Two verified setups:
+- **turbo int8 + 8 steps + CFG 1.0** → 16.7 s, fast, good enough
+- **`qwen-image-2.1-UC-fp8` + 20 steps + CFG 2.5** → 43.9 s, full body, best detail (empty-text negative)
+
+---
+
+<a id="krea2-support"></a>
+### Krea2 Support
+
+`ToggleUnifiedLoader`, `DynamicRefImageEncode`, `DynamicRefIndependent` and `OneImageMultiPrompt`
+all support **Krea2**. The loader detects `Krea2` from the UNet automatically and picks
+`CLIPType.KREA2` + the `Krea2TEModel` text encoder; the three encoding nodes just need
+`模型类型` = `Krea2`.
+
+⚠️ **VAE must match the model.** This is the single most common failure — mixing them up does not
+fail at load time, it fails deep inside `VAEDecode`:
+
+| Model | VAE file | latent |
+|---|---|---|
+| Qwen-Image **2.1** | `qwen_image_2.1_vae_bf16.safetensors` | 64 ch / 16x / RGBA |
+| **Krea2** | `qwen_image_vae.safetensors` | 16 ch / 8x / RGB |
+
+A wrong VAE is silently padded by `fix_empty_latent_channels` (16 → 64), sampling still finishes, and
+then decode dies with `IndexError: tuple index out of range` from the Wan 2.1 branch.
+The loader now compares `latent_channels` / `latent_dimensions` at load time and prints the exact file
+you should use instead.
+
+Key rules learned from aligning with the official `TextEncodeQwenImageEditPlus`:
+
+- **Reference latent size = output size.** References are centre-cropped to the output aspect ratio and
+  resized to the exact output pixel grid *before* VAE encoding, so the reference latent matches the
+  sampling start dimension-for-dimension and the RoPE positions differ only on the frame axis — which is
+  the geometry the krea2_edit LoRAs were trained on. The vision tower still gets a ~384 px thumbnail.
+- **The VAE only takes RGB** — alpha is dropped, not composited.
+- **Vision placeholders must be written by hand.** With an explicit `llama_template` the Qwen3-VL
+  tokenizer does *not* auto-insert them, so the node emits
+  `Picture N: <|vision_start|><|image_pad|><|vision_end|>`. Without it the reference image is
+  completely invisible to the model.
+- **`reference_latents_method` is mandatory, and it must be `index`.** Krea2's `default_ref_method` is
+  `None`, so references are *silently ignored* unless the method is set. `index_timestep_zero` runs but
+  is wrong for krea2_edit LoRAs: it splits the adaLN modulation into two timesteps (target = current t,
+  references = t=0) while training used a single shared tvec — a line-art reference then gets traced
+  into the frame. `index` keeps one tvec for the whole sequence and is the one that matches training.
+- **The latent output is all-zero** (16 ch / 8x) — same "editing, not img2img" rule as 2.1.
+- **Sigma curve:** Krea2 turbo uses the `自动（按步数 + 调度器）` scheme in `采样器（一体）`.
+  The `Qwen-Image-2.1 turbo 8步` table is *not* interchangeable with it.
+
+---
+
+<a id="moss-fibo-support"></a>
+### MOSS-Image / FIBO 2.0 Support
+
+`ToggleUnifiedLoader` picks both up automatically — `clip_type` now offers `moss_image` and `fibo2`
+directly (`auto` also detects them from the UNet). Both are **Flux2-latent** models
+(128 channels / 16x, `flux2-vae`), which is why `Ref Image Encode` needs **no extra model type:
+just pick `Flux2`**.
+
+Measured with the pose sheet as the second image (standing beach photo → seated sheet):
+
+| Model | Native encoder | `Ref Image Encode` (Flux2) |
+|---|---|---|
+| MOSS-Image, 20 steps / CFG 4 | 19.6 s — seated, hands on the sand | 16.7 s — seated ✅ |
+| FIBO 2.0, 4 steps / CFG 1 | 15.4 s — kneeling | 10.6 s — kneeling ✅ |
+
+No sketch figure leaked into either frame. What you give up by not using the native encoders:
+
+- **MOSS `edit_format`** — the reference method must match how the edit LoRA was trained
+  (`ai-toolkit` → `index_timestep_zero_isolated`, `ai-toolkit (kv_cache off)` → `index_timestep_zero`,
+  `Moss-Image-Edit` → `index_timestep_zero_text_start`). Picking the wrong one runs fine but edits
+  nothing. `Ref Image Encode` sets no method at all.
+- **MOSS `image_strength`** and **FIBO `negative_prompt`** — neither is exposed there.
+- Both native encoders take **AUTOGROW** image inputs, so a node output still plugs straight in:
+  `TextEncodeMOSSImageEdit` → `images.image_0 / image_1 …`, `TextEncodeFibo2` → `images.image_1 … image_16`.
+
+⚠️ `TextEncodeMOSSImageEdit` has a **single** conditioning output — encode the positive and the
+negative with two separate nodes (the official template does exactly that). Writing `["node", 1]`
+throws `list index out of range`, and the error points at the sampler rather than at the encoder.
+
+Sampler presets: `MOSS-Image（30步·CFG4）` and `FIBO 2.0 turbo（4步）`.
+
+---
+
+<a id="model-type-cheatsheet"></a>
+### Which dropdown do I pick? (`模型类型` vs `clip_type`)
+
+Two dropdowns look like they mean the same thing but are not:
+
+- **`模型类型`** lives on the reference-image encoders. It only decides **which latent shape comes out**
+  (and, for Krea 2, which `reference_latents_method` is used).
+- **`clip_type`** lives on `Toggle Unified Loader`. It decides **how the text encoder is built**.
+
+Getting either one wrong usually fails silently (wrong channel count, wrong conditioning width, or a
+clean-looking image with the prompt ignored).
+
+| Loading | `模型类型` (encoder) | `clip_type` (Unified Loader) | VAE |
+|---|---|---|---|
+| Qwen-Image **Layered** | `Qwen Layered` (+ `图层数`) | `qwen_image` | `qwen_image_layered_vae.safetensors` |
+| Qwen-Image **2.1** | `Qwen Image 2.1` | `qwen_image` | `qwen_image_2.1_vae_bf16.safetensors` |
+| **Krea 2** | `Krea2` | `krea2` | `qwen_image_vae.safetensors` |
+| **FLUX.2 / MOSS-Image / FIBO 2.0** | `Flux2` | `flux2` / `moss_image` / `fibo2` | `flux2-vae.safetensors` |
+| **Z-Image** | `SD / SDXL / Z-Image` | `stable_diffusion` (or `auto`) — **never `flux` / `flux2`** | `ae.safetensors` |
+| **SD 1.5 / SDXL / Pony** | `SD / SDXL / Z-Image` | `stable_diffusion` | the checkpoint's own VAE |
+
+`SD / SDXL / Z-Image` is the *generic* slot on purpose: whenever the encoder takes an image anyway the
+latent comes from `ref_latents[0]`, so this setting behaves the same for SD, SDXL, Pony, Z-Image and
+Qwen-Image 1.0. It only means something ("4-channel all-zero latent") when nothing is connected.
+
+**Qwen-Image Layered specifics** (read off the official `image_qwen_image_layered.json`):
+
+- Text encoder is `qwen_2.5_vl_7b_fp8_scaled` — the **same** encoder as Qwen-Image 1.0 ⇒ `clip_type` = `qwen_image`
+- VAE is **`qwen_image_layered_vae`**, not `qwen_image_vae`. Both are 16-channel, so a mismatch is
+  invisible until `VAEDecode`
+- Latent is 5-D: `[B, 16, layers+1, h/8, w/8]` — exactly what `模型类型 = Qwen Layered` emits,
+  i.e. it replaces the official `EmptyQwenImageLayeredLatentImage`
+- The bundled template actually runs **20 steps / CFG 2.5 / euler / simple / denoise 1** at 640 px with
+  `layers = 2`; its on-canvas note says the *original* settings were 50 steps / CFG 4.0 (even slower).
+- Two variants ship: one starts from `EmptyQwenImageLayeredLatentImage`, the other from `VAE Encode(photo)`.
+  Both use `denoise 1`, so the start latent gets overwritten either way — what actually changes the
+  result is the `layers` count (latent dim 2 = `layers + 1`). **Only** when you want partial-denoise
+  refinement does the start latent matter, and then the encoder's all-zero latent is not what you want.
+
+**Z-Image warning:** do **not** set `clip_type` to `flux` / `flux2`. Z-Image's TE is Qwen3-4B, and
+`comfy/sd.py` routes Qwen3-4B to the Flux encoder when `clip_type` is FLUX/FLUX2 — you get a different
+TE class and the prompt is encoded wrong, with no error.
+
+Measured 2026-11-06 (same Z-Image unet, same `qwen_3_4b` + `ae`, only `clip_type` changed):
+
+| `clip_type` | text encoder actually built | |
+|---|---|---|
+| `stable_diffusion` | `ZImageTEModel_` | ✅ recommended |
+| `auto` | `ZImageTEModel_` | ✅ (detects the class as `Lumina2`) |
+| `qwen_image` | `ZImageTEModel_` | ⚠️ works by luck — Qwen3-4B is dispatched by TE type, not by this value |
+| `flux` | `Flux2TEModel_` | ❌ wrong encoder class, silently bad prompt encoding |
+
+So `qwen_image` will *not* break Z-Image, but pick `stable_diffusion` (or `auto`) so the setting means
+what it says.
+
+<a id="auto-clip-type-en"></a>
+### Is `clip_type = auto` reliable?
+
+**All 5 local models verified correct by real loading; 106 of 108 model classes agree in a static sweep.**
+`auto` does not guess:
+
+1. It **actually loads the UNet** (not a header sniff) and reads the real class name — Qwen-Layered
+   comes out as `QwenImage`, Z-Image as its parent class `Lumina2`;
+2. It resolves `CLIPType` in three layers — **exact table → MRO (base classes) → prefix rules**:
+   - the exact table only lists models where `clip_type` **actually changes the outcome** (table below);
+   - MRO catches subclasses: `FluxSchnell → Flux`, `KOALA_1B → SDXL`, `ZImagePixelSpace → ZImage`;
+   - prefix rules catch variants: `WAN21_I2V` / `WAN22_S2V` / `WAN_Animate2` … 16 WAN classes → `wan` (2 more are in the exact table);
+3. Anything left falls back to `stable_diffusion` — **which is not a wrong guess**, because for that
+   third of the model set ComfyUI never looks at `clip_type` (the TE weights themselves already
+   determine which encoder gets built).
+
+**`clip_type` only matters when one TE weight file can go several ways.** Reading
+`load_text_encoder_state_dicts` in `comfy/sd.py`, only these families are affected:
+
+| TE weights | Needs `clip_type` to distinguish | What you get if it's wrong |
+|---|---|---|
+| T5-XXL | SD3 / LTXV / WAN / CHROMA / PIXART / COGVIDEOX / MOCHI | falls into the `else` branch → **Mochi** TE |
+| Qwen3-VL-8B | QWEN_IMAGE / IDEOGRAM4 / BOOGU / JOYIMAGE | generic `Qwen3VLTEModel_`; Qwen-Image 2.1 loses `image_slots` |
+| Qwen3-VL-4B | KREA2 / FIBO2 / FLUX3 / MAGE / MINIMAX | same, generic Qwen3-VL |
+| Qwen3-4B | FLUX / FLUX2 | Flux-family `Flux2TEModel_` (this is the Z-Image trap) |
+| CLIP-G | SD3 / STABLE_CASCADE / HIDREAM | `SDXLRefinerClipModel` |
+| Qwen2.5-7B | HUNYUAN_IMAGE / LONGCAT_IMAGE | the `qwen_image` TE |
+
+Everything else (Anima / AuraFlow / Cosmos / Lens / OmniGen2 / StableAudio / SAM / the 3D ones)
+behaves identically whatever you pass, so the `auto` fallback is harmless there.
+
+**Measured 2026-11-06 (`clip_type = auto`, real loading):**
+
+| Model | What `auto` decided | TE actually built | |
+|---|---|---|---|
+| Qwen-Image Layered | exact `QwenImage` → `QWEN_IMAGE` | `QwenImageTEModel_` | ✅ |
+| Z-Image | exact `Lumina2` → `STABLE_DIFFUSION` | `ZImageTEModel_` | ✅ |
+| Krea2 | exact `Krea2` → `KREA2` | `Krea2TEModel_` | ✅ |
+| FIBO 2.0 | exact `Fibo2` → `FIBO2` | `Fibo2TEModel_` | ✅ |
+| MOSS-Image | exact `MOSSImage` → `MOSS_IMAGE` | `MOSSImageTEModel_` | ✅ |
+
+**Second safety net: the TE is validated right after loading.** If `auto` ever decides wrong (or
+upstream adds a model class), it will not fail silently — the loader asks the model itself which
+encoder it wants (`model_config.clip_target()`; answerable for 106 of 108 classes) and compares that
+with the class actually built:
+
+```
+[ToggleUnifiedLoader] ⚠️ CLIP 校验失败：模型是 Krea2，但文本编码器建成了 Qwen3VLTEModel_
+（预期 Krea2TEModel，依据 手工表）。...
+```
+
+That check is not decoration: when `auto` gets it wrong, you see it **immediately**, instead of
+discovering later that prompts "just don't work".
+
+**When not to use `auto`:** only if you deliberately want to pair a model with a mismatched encoder
+for testing. In normal use `auto` beats hand-picking, which requires memorising the six rows above.
+
+
+---
+
+<a id="z-image-usage"></a>
+### Z-Image: text-to-image only — pick `SD / SDXL / Z-Image`, never connect a reference image
+
+⚠️ Bottom line: **Z-Image has no reference-image capability. Leave `image1`–`image10` empty.**
+
+Evidence (measured, not guessed):
+
+- Of the five official `z_image` templates, `image_z_image` / `_int8` / `_turbo` / `_turbo_int8`
+  contain **no `LoadImage` at all** — pure text-to-image. The only one taking an image,
+  `image_z_image_turbo_fun_union_controlnet`, drives it through **ControlNet (Canny 0.1 / 0.32)**,
+  a line-art conditioning path completely different from reference-image editing.
+- Z-Image is Lumina2 / NextDiT based and was never trained on reference tokens. If you do connect
+  `image1`, `reference_latents` is still injected into the conditioning, but the model has no matching
+  `reference_latents_text_embeds`, and the output becomes **a full frame of blocky mosaic** —
+  same result across seeds, size buckets and sampling start latents.
+
+**Correct usage:** `模型类型` = `SD / SDXL / Z-Image`, `image1` empty.
+
+This is not a workaround. With no reference images that branch emits a 4-channel all-zero
+latent, which the core `fix_empty_latent_channels` pads to 16 channels — exactly the empty start Z-Image
+needs. At a fixed seed it is pixel-identical to the native
+`UNETLoader + CLIPLoader + VAELoader` graph (all four metrics equal: 51.5 / 214.5 / 46.9).
+
+**If you want Z-Image img2img:** use the native `LoadImage → VAE Encode → sampler (denoise < 1)`
+instead. Prompt and denoise control the amount of change; the photo is the starting latent.
+
+**Reminder built in:** wire it wrong and the log says so (informational only, nothing is changed):
+
+```
+[Toggle-Pass][Ref Image Encode] ⚠️ 文本编码器 = ZImageTEModel_，已接入 1 张参考图，
+但这个底座没有参考图通路。
+```
+
+Z-Image has no separate entry in the `模型类型` dropdown by design — its behaviour is identical to the
+generic `SD / SDXL` one, so the two were merged into the single name `SD / SDXL / Z-Image` instead of
+adding an entry that would imply "pick this one and you can attach images".
+
+> Renamed 2026-11-06: the old value was `SD / SDXL`. Saved workflows are migrated automatically on
+> load (front-end rewrites the widget value, back end also normalises the alias), so old graphs keep
+> working unchanged.
+
+---
+
+### Canvas Menu Entry
+
+All Toggle-Pass nodes are also reachable from the canvas menu (same place as KJNodes / rgthree entries):
+
+```
+🔄 Toggle-Pass  ▸
+   ├ 采样器（一体）
+   ├ Image Batcher
+   ├ Multi Ref Latent Input
+   ├ One Image Multi Prompt
+   ├ Ref Image Encode
+   ├ Ref Independent
+   └ ...
+```
+
+- The list is **collected at runtime** — new nodes show up automatically, no code change needed
+- Clicking an item spawns the node **at your pointer position**
+
+---
+
+### Privacy Mask
+
+SaveLoadImage, ToggleLoadImage, and TogglePreviewImage support privacy mask:
+- Title bar shows 👁 (show image) / 🫣 (hide image) icon
+- Click icon to toggle privacy state; when hidden, image preview area is blank
+- Privacy state is saved/restored with workflow, does not affect image generation
+
+---
+
+### Example Workflows
+
+Ready-to-load graphs live in [`example_workflows/`](example_workflows/) — drag a `.json` onto the
+canvas, or open it from the workflow menu.
+
+| File | What it demonstrates | Extra deps |
+| --- | --- | --- |
+| `All-in-One Demo.json` | Every Toggle-Pass node in one graph: `ToggleUnifiedLoader`, `ToggleSampler`, `ToggleLoadImage` ×2, `ToggleImageCompare`, `TogglePreviewImage`, `DynamicRefImageEncode`, `SaveLoadImage` + `VAEDecode`. **Best place to start** — needs no third-party nodes. | — |
+| `Graphic-Text Integration Editing.json` | Text + single reference image editing through `DynamicRefImageEncode`, saved by `SaveLoadImage`. | — |
+| `Batch Editing.json` | Six input images batched into one reference set for batch editing. | `RTXVideoSuperResolution` |
+| `Lazy Person's Multi-angle View.json` | One image + `OneImageMultiPrompt` ("一图多视角") to generate several camera angles in one run. | `CR Upscale Image` |
+| `Pose Reference Editing.json` | `PoseReference` feeding a Qwen-Image-2.1 turbo edit graph (8 steps). **The selected sheet is embedded in the node**, so it opens and runs with no pose library installed. | — |
+| `Krea2 Pose Editing.json` | 9 nodes, **7 of them from this plugin and zero external packs**: `ToggleUnifiedLoader` (UNet + CLIP + VAE + `krea2_identity_edit_v2` LoRA in one node) + `ToggleLoadImage` + `PoseReference` → `Ref Image Encode` (`模型类型 = Krea2`; pose sheet into `image1`, photo into `image2`, latent and negative both taken from its own outputs) → `ToggleSampler` on the `Krea 2 turbo（8步）` preset → `ToggleImageCompare` / `SaveLoadImage`. Measured 8 steps / 720×1440: ~70 s, standing → seated, no line-art bleed, likeness kept. | none |
+> The model filenames inside these graphs reflect the author's local setup. If a loader shows red
+> after loading, point it at your own checkpoint / text encoder / VAE.
+
+---
+
+### Notes
+
+- When `num_images` changes, input/output slots auto-adjust, **existing connections are not broken**
+- Switching pages and back, nodes auto-restore to correct slot count
+- Requires Qwen2.5-VL or compatible CLIP model; Qwen-Image-2.1 requires Qwen3-VL 8B + `qwen_image` clip type
+- Default instruction is for Qwen-Image-Edit models, modify as needed
+- Only parameters relevant to the selected `模型类型` are shown; the rest are hidden
+- Illegal widget values (e.g. `倍数 = NaN`) fall back to safe defaults on load. **After upgrading,
+  restart ComfyUI and hard-refresh (Ctrl+F5) once**, then save your workflow
+- ⚠️ Modern node design (Vue nodes) breaks `widget.hidden` and custom drawing — with it enabled every
+  parameter stays visible and the privacy icon disappears. Turn it off in ComfyUI settings.
+- ⚠️ On quantized checkpoints (INT8/FP8) a LoRA still takes effect, but it comes out "blurrier": ComfyUI
+  dequantizes → patches → re-quantizes with stochastic rounding, so the delta keeps its magnitude
+  (~115%) while its direction picks up noise (cosine 0.4–0.7). Pose/structure LoRAs stay visible,
+  very subtle style LoRAs may not. This is upstream behavior — official `LoraLoader` is identical
+  (A/B with the same seed gave **pixel-identical** output, meanAbsDiff = 0.00)
+- The loader reports how many patches each LoRA actually wrote **plus module coverage**
+  (`192/224p`) — see the button labels above. A missing fraction usually means the checkpoint was
+  repacked and those layers don't exist in it, not that the LoRA failed to load
+- ⚠️ **CLIP check (split mode)**: after loading, the loader verifies the text-encoder class it actually
+  built (e.g. `QwenImage21TEModel_` vs a wrong `QwenImageTEModel_` / `Flux2TEModel_`). A mismatched CLIP
+  file still runs silently but makes the conditioning useless; the button then shows `⚠️CLIP不符`
+  and the log prints the expected class + what to do
+- ⚠️ **VAE check (split mode)**: the loader also compares latent channels / dimensions against the model.
+  A mismatched VAE never fails at load time — it dies later inside `VAEDecode`
+  (`IndexError: tuple index out of range`), so the loader now warns immediately and names the file to use
+- Hiding/collapsing a LoRA group **reclaims the node height**, so the node never stays as a long empty box
+- **Selection box on canvas follows the mouse again**: ComfyUI 1.57.x clamps the drag-selection rectangle to
+  `.graph-canvas-panel`, which only covers the lower part of the canvas — dragging in the upper half pushed the
+  box's top edge down (and squashed its height to 0), so the box seemed to appear "somewhere else".
+  `js/selection_rect_fix.js` rewrites that box's position from the real mouse points. Disable it with
+  `localStorage.setItem("tp_selrect_fix", "off")` in the browser console + refresh
+- Python changes require a **ComfyUI restart**; pure JS changes only need Ctrl+F5
+
+---
+
+<a id="中文"></a>
+## 中文
+
+Toggle-Pass 系列节点——开关控制、图片加载/保存/预览、隐私遮罩、多参考图动态编码、统一模型加载、一体化采样器。
+
+### 节点说明
+
+#### ToggleUnifiedLoader — 统一模型 + LoRA 加载器
+
+一体化模型加载节点：UNet + CLIP + VAE + 多 LoRA 整合到一个节点中。
+
+**加载模式**（`加载模式`）— 一个节点，两种加载方式：
+
+| 模式 | 作用 | 显示的参数 |
+|---|---|---|
+| `分离组件 (UNet+CLIP+VAE)`（默认） | UNet / CLIP / VAE 分别指定 | `unet_name`、`clip_name`、`vae_name`、`clip_type` |
+| `Checkpoint 一体包` | 加载一体化 checkpoint（等同官方 **CheckpointLoaderSimple**）：自动猜配置，UNet + CLIP + VAE 一次出齐 | 只显示 `ckpt_name` |
+
+- **加载模式做成了一个两段式开关 `UNet | Checkpoint`**（不再是下拉框）：点左半是分离组件、点右半是一体包，
+  当前档高亮。开关位于**节点顶部第一行，与输出插槽 `模型 / MODEL` 同一行**，左端与**下方所有下拉框的
+  左边缘对齐**
+- 底层 `加载模式` 的值照常保存与序列化，**旧工作流与 API 调用完全不受影响** —— 开关只是接管了绘制与点击
+- 「现代节点设计」（Vue 节点渲染）下，开关会改为画在下拉框内部
+- 选 `Checkpoint 一体包` 时，分离的三个模型下拉会自动隐藏，界面不眼花
+- **两种模式都能加 LoRA** —— LoRA 组在加载完成后统一应用
+- VAE 用一体包自带的（与官方简易版一致）
+- **参数顺序：模型在最前** —— `ckpt_name` → `unet_name` → `clip_name` → `vae_name` → `clip_type`
+  → `加载模式` → LoRA 组 → 按钮。一体包模式下第一行就是 checkpoint 下拉框
+- 旧工作流能正常加载：加载时会按**参数名**把保存的值对回去，不会串位
+- 缺失的值仍然回退到默认模式（分离组件）
+
+**输入**：
+- `ckpt_name` — `models/checkpoints` 里的 checkpoint 文件（只在「Checkpoint 一体包」模式下显示）
+- `unet_name` — UNet 模型文件
+- `clip_name` — CLIP 文本编码器文件
+- `vae_name` — VAE 模型文件
+- `clip_type` — CLIP 类型（支持 auto 从 UNet 自动检测，也可手动选择：SD、SD3、FLUX、FLUX2、`qwen_image` 等）
+  - 自动检测能识别 Qwen-Image-2.1 的 UNet 类名（`QwenImage21`）并映射到 `qwen_image`
+  - ⚠️ **这是 2.1 的必备条件**：只有 `CLIPType.QWEN_IMAGE` 才能加载 2.1 专用文本编码器（Qwen3-VL 8B），
+    它会生成 `image_slots`；否则会退回通用 Qwen3-VL 编码器，**参考图被静默丢弃**
+  - 也支持手动指定：怀疑自动检测没生效时直接选 `qwen_image`
+  - 现已支持 `fibo2` / `moss_image`（按 UNet 类名 `Fibo2` / `MOSSImage` 自动识别）。
+    这两个和 `krea2` **共用同一批权重文件**（FIBO 2.0 用 Qwen3-VL-4B，MOSS-Image 用 Qwen3.5-**9B**），
+    但**取层方式完全不同** —— 选错不是「效果差一点」，而是 conditioning 维度对不上。
+    加载器同时校验文本编码器类名（`Fibo2TEModel_` / `MOSSImageTEModel_`）和 VAE
+    （两者都走 Flux2 latent，必须用 `flux2-vae*`，不能用任何 qwen VAE）。
+- `+ 添加 LoRA` 按钮 — 点击逐个添加 LoRA 槽位（最多 10 组）
+- `- 移除 LoRA` 按钮 — 点击从最后一组开始移除（最多 10 组）
+
+**LoRA 控件**（通过按钮添加，最多 10 组）：
+- 选择 LoRA 文件 → 自动展开**一个**「强度」滑块
+- 选择 "none" → 自动隐藏滑块
+- 多个 LoRA 按顺序依次应用
+
+> **只有一个强度滑块是有意为之**：CLIP 侧**始终跟随 MODEL 侧**，保证「看到的强度 = 实际生效强度」。
+
+**跑完之后按钮上直接告诉你 LoRA 到底写没写进去**（后端回传，不用再猜）：
+
+| 按钮文案 | 含义 |
+|---|---|
+| `· 224p` | LoRA **确实写进了模型**，共 224 个 patch（数字是 Python 侧统计的真实值） |
+| `· 192/224p` | 这份 LoRA 的 224 个模块里只落下 192 个，剩下 32 个在当前底座上**没有对应层**。典型于**被 repack 过的 INT8 一体包**（如 `img_mlp.gate_layer` 被融进 `gate_up`），不是节点的问题 |
+| `· ⚠️未写入` | 一个 patch 都没挂上 —— 架构不匹配 / 文件不对 |
+| `· ⚠️N不匹配` | N 个 LoRA 的键名跟底座完全对不上（拿错底座的 LoRA） |
+| `· ⚠️N跳过` | N 个 LoRA 被跳过（文件找不到） |
+| `· ⚠️N个强度0` | 有 N 个已选 LoRA 强度是 0 —— 等于没加 |
+| `· ⚠️CLIP不符` | 建出的文本编码器与模型不配套（分体模式下换错 CLIP 文件 / 选错 clip 类型）—— 提示词会被错误编码，跑得通但什么都不生效 |
+
+按钮文案故意写得很短（太长会溢出窄节点），鼠标悬停在按钮上能看到**每个 LoRA 的完整明细**
+（模块覆盖率、CLIP 校验结果、每个槽位实际写入的 patch 数）；
+另外只要增删/切换 LoRA，上一次运行的数字会立刻清掉，不会留一个过期读数误导你。
+注意它反映的是「patch 有没有写进去」，不等于「画面上一定看得出来」——量化底座、参考图主导的编辑、过于细微的 LoRA，都会让效果不明显。
+
+**输出**：`MODEL`、`CLIP`、`VAE`
+
+**全局广播**（节点自带开关，不需要装任何额外插件）：
+
+打开后，点「运行」时会把这三个输出**自动接到图里所有空着的** `MODEL` / `CLIP` / `VAE` 输入上，
+不用再横跨半张画布拉线。关掉（默认）就完全保持原样，自己连线。
+
+- 这些线**只在提交的那一刻真实存在**，提交完立刻拆掉 —— 不会被存进工作流
+- **开关打开时，画布上会用紫色曲线画出「运行时会自动接上的每一条线」**，起点/终点各有一个小圆点，
+  精确落在输出口和输入口上。曲线只是视觉预览：不创建真实连线、不参与序列化，关掉开关立刻消失
+- **会被接上的槽位会「点亮」**：这些口还是空的（没有真实连线），官方按「未连接」画成空心圈，
+  所以广播开着时会给它们补一个**实心点**，颜色就是该槽位类型的官方配色 —— 和手动连线后的观感一致，
+  一眼就能看出「哪些口会被自动接上」、哪些本来就连好了。关掉开关就恢复空心
+- **曲线和圆点都从节点背后穿过**：画在节点下层，节点之间的空白处正常显示，穿过节点的地方（含端点圆点
+  被节点压住的那半边）被节点自然遮住，所以永远不会盖住节点标题和参数，也不会在你拿节点当遮罩时透出来
+  （节点排得密时可见部分会少一些，这是刻意的取舍）
+  - 想回到「圆点永远画在最上层」的旧观感：控制台执行 `localStorage.tp_dots_layer = "front"` 再刷新
+- **只补空位**：凡是已经手连的输入一律不动，所以「自动」和「手动」可以在同一张图里混用
+- 只覆盖 `MODEL` / `CLIP` / `VAE`（也就是本节点的三个输出）
+- 如果图里有多个加载器都开了广播，同一类型只取第一个，并在控制台给出提示
+- **开关在节点顶部**：就在 `加载模式` 那个 `UNet / Checkpoint` 胶囊的**正下方、左半边**（一个小胶囊，
+  左边写「广播」）—— 点一下切换。它不再占用节点底部一整行，底部只剩 LoRA 那两个按钮
+- 开关状态存在节点的 `properties` 里，**不占 `widgets_values`** —— 旧工作流的每一项参数位置都不变，
+  开关默认关闭
+- 与 `cg-use-everywhere` 共存，不碰它那套 `ue_convert` 标记
+
+> **限制**：广播发生在前端，所以「界面里点运行」和「从界面导出 API 格式」都算数；
+> 但直接 `POST /prompt` 提交手写的 API JSON 不经过前端，就不会自动接线 ——
+> 需要的话在界面里导出一次 API 格式，导出的 JSON 里线已经带好了。
+
+---
+
+#### SaveLoadImage — 保存/加载切换
+
+图片保存节点，支持 Save/Load 模式切换。
+- **Save 模式**：保存图片到 output 目录，同时输出 IMAGE 和 MASK
+- **Load 模式**：直接复用上次保存的图片，跳过保存流程
+- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
+
+#### ToggleLoadImage — 加载图片
+
+自定义加载图像节点，和原生 LoadImage 功能一致，额外支持隐私遮罩。
+- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
+
+#### TogglePreviewImage — 预览图片
+
+自定义预览图像节点，不保存到 output 目录（保存到 temp），支持隐私遮罩。
+- 点击标题栏 👁 图标可切换隐私遮罩（隐藏/显示图片预览）
+
+#### ToggleEmptyLatent — 开关空 Latent
+
+可切换的空 Latent 生成节点。
+- **ON**：使用 width/height 参数生成
+- **OFF**：使用 fallback_width/fallback_height 参数（可用于 A/B 尺寸对比）
+- **`latent类型`** — 通道数与空间压缩比，必须和底模的 `latent_format` 对齐：
+  | 选项 | 通道 | 压缩比 | 适用模型 |
+  |---|---|---|---|
+  | `SD / SDXL（4 通道 · 8×）` | 4 | 8× | SD 1.5 / SDXL / Pony |
+  | `SD3 / Flux / Krea 2（16 通道 · 8×）` | 16 | 8× | SD3、Flux.1、**Krea 2**（走 Wan21 格式，`latent_dimensions=3`）|
+  | `Qwen-Image 2.1（64 通道 · 16×）` | 64 | 16× | Qwen-Image 2.1 |
+  | `Flux2（128 通道 · 16×）` | 128 | 16× | FLUX.2 |
+
+  默认是 `SD / SDXL`，老工作流行为不变。选错**不会报错** —— K采样器发现 latent 全零时
+  会用 `fix_empty_latent_channels` 悄悄补齐通道，但绕过采样器直接用 latent 的节点拿到的形状就是错的。
+  选对时输出与官方 `EmptySD3LatentImage` 完全一致（实测 shape、dtype、`downscale_ratio_spacial` 全等，张量差为 0）。
+
+---
+
+#### DynamicRefIndependent — 多参考图独立编码（核心节点）
+
+每张参考图独立 VAE 编码，输出各自独立的 CONDITIONING 和 LATENT，适合多图各自控制风格/内容的场景。
+
+**输入**（动态，由 `num_images` 控制）：
+- `clip` — CLIP 模型
+- `vae` — VAE 模型
+- `num_images` — 参考图数量（1~10）
+- `instruction` — 系统提示词（可留空使用默认）
+- `image1` ~ `imageN` — 参考图
+- `prompt1` ~ `promptN` — 每张图对应的提示词（控件在节点内，可折叠）
+
+**模型类型**：`Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL / Z-Image`
+- 选择 `Qwen Image 2.1` 会展开 2.1 专属参数组（见 [Qwen Image 2.1 支持](#qwen-image-21-支持)）
+- 选择 `Qwen Layered` 会展开「图层数」
+- `SD / SDXL / Z-Image` 是通用档；**纯文生图模型（如 Z-Image）用它，且参考图槽别接**（见 [Z-Image 用法](#z-image-用法)）
+- 每种模型只显示自己用得上的参数，其余自动隐藏，界面不会眼花
+
+**输出**（动态，N×2 个）：
+- `cond_1`, `latent1`, `cond_2`, `latent2`, ... `cond_N`, `latentN`
+
+**用法**：
+1. 拖入节点，默认 1 张参考图
+2. 调整 `num_images` 到需要的数量（1~10），输入槽和输出槽自动增减
+3. 每张图连接自己的参考图，填写对应 prompt
+4. `cond_N` 连到 sampler 的 conditioning，`latentN` 连到 VAE Encode 的 latent（供参考图编码使用）
+
+---
+
+#### DynamicRefImageEncode — 动态参考图编码
+
+单张 prompt 控制，动态增删参考图输入槽。
+
+**输入**：`clip`, `num_images`, `prompt`, `image1` ~ `imageN`
+**输出**：`CONDITIONING`, `LATENT`, `NEGATIVE`（所有图共享同一份 conditioning，分别输出 latent）。
+第二个输出是**现成的空文本负条件**（用同一个 CLIP 编码），直接接采样器的「负面条件」即可。
+用它替代「把正面条件 `ConditioningZeroOut` 清零当负面」：CFG=1.0 时两者完全一样，
+但 CFG 一超过 1，清零版会把整张图弄花。
+这个输出只在 `Ref Image Encode` 上有；`Ref Independent` / `一图多视角` 要用 CFG>1 时，
+请自己接一个空文本的 `CLIPTextEncode` 到负面。
+
+**模型类型**：`Flux2` / `Qwen Layered` / `Qwen Image 2.1` / `SD / SDXL / Z-Image` / `Krea2`
+- `SD / SDXL / Z-Image`：通用档，输出 4 通道全零 latent，由官方 `fix_empty_latent_channels` 补到模型需要的
+  通道数。**纯文生图模型（如 Z-Image）就用这个档，参考图槽一个都别接** —— Z-Image 没有参考图通路，
+  接上必出方块马赛克，详见 [Z-Image 用法](#z-image-用法)
+- `Qwen Layered`：增加「图层数」参数；输出的 latent 为分层格式（`[1, 16, layers+1, h/8, w/8]`），可直接接到 K采样器的 latent 输入（省掉 `EmptyQwenImageLayeredLatentImage` 节点）
+- `Qwen Image 2.1`：增加 2.1 专属的分辨率参数组（详见下方小节）
+- `Krea2`：latent 走 Wan21（16 通道 / 8x，VAE 是 `qwen_image_vae`）；参考图只进 `reference_latents`，
+  采样起点是全零 latent，并自动补 `reference_latents_method=index`（不给就会被静默忽略）。
+  **故意不用 `index_timestep_zero`** —— 那会给参考图单独一套 t=0 的 adaLN 调制，与 krea2_edit 系 LoRA
+  的训练方式（全序列共用一个 tvec）不符。实测：用 `index_timestep_zero` 时参考线稿会被「描」进画面
+  （脸上出现轮廓线），用 `index` 则干净。参考图的 latent 还会 fit 到出图网格，详见下方 Krea 2 小节
+- **`MOSS-Image` / `FIBO 2.0` 请直接选 `Flux2`** —— 三者 latent 完全同族（128 通道 / 16x，都用 `flux2-vae`）。
+  实测把参考姿势节点接成第二张图能正常改姿势（MOSS 16.7s / FIBO 10.6s，画面内无线稿残留）。
+  但 MOSS 的 `edit_format`（三档训练格式，必须与 LoRA 配对）、`image_strength`，
+  以及 FIBO 的 `negative_prompt` 本节点都不提供 —— 需要精确控制这三项时，
+  请改用官方编码器 `TextEncodeMOSSImageEdit` / `TextEncodeFibo2`（它们是多图 AUTOGROW，本节点接哪个都能直接插）
+- 每个模型类型只显示自己需要的参数，切换下拉框即自动隐藏其余的
+- 「匹配原图尺寸」= ON 时，使用图片实际尺寸；= OFF 时，2.1 走分辨率参数组，其他模型仍用「输出宽度/高度」（旧行为不变）
+
+#### DynamicRefLatentInput — 动态 Latent 输入
+
+动态增删 LATENT 输入槽，配合 DynamicRef 系列使用。
+
+#### ImageBatcher — 图像批处理
+
+动态增删 IMAGE 输入槽，将多张图打包成批次。
+
+#### ToggleImageCompare — 图片对比
+
+把多张「原图」与多张「结果」拼成一张对比图，节点内直接预览。
+
+**输入**：`num_sources`、`num_results`、`source1`~`source10`、`result1`~`result10`
+- `num_sources` / `num_results` — 每组显示几个槽（1~10），两组各自独立，
+  所以 **N 张原图 + 1 张结果** 和 **N 张原图 + N 张结果** 都支持
+- `source1`~`source10` — 原图（before）
+- `result1`~`result10` — 结果（after）
+
+> 节点上显示为 `原图1`~`原图10` / `结果1`~`结果10`、`原图数` / `结果数`、输出 `对比图`；
+> 上面这些英文 key 保持不变，为了兼容已保存的工作流。
+
+**参数**
+- `排版` — `自动` / `成对并排` / `上下两排` / `自动网格`
+  - `自动`：两组数量相等时用「成对并排」，不等时用「上下两排」
+- `标签` — 在每张图上烧 `原1 / 结1 …` 角标，或隐藏
+
+**输出**：`comparison` —— 一张合成图（可直接接 `SaveLoadImage` 或预览节点）
+
+排版按**实际接上的图**算，而不是你开了几个槽，所以数量变了也不用改设置。尺寸不一样的图会
+按行统一高度缩放（保持宽高比）。
+
+#### OneImageMultiPrompt — 单图多提示词（一图多视角）
+
+一张参考图对应多个提示词，分别输出各自的 conditioning（最多 8 组），共享同一个 latent。
+
+**输入**：`clip`、`image`、`prompt数量`、`prompt1` ~ `prompt8`
+- `prompt数量` — 用几个提示词（2~8），多余的 prompt 框会自动隐藏
+- `模型类型` — `Flux2` / `Qwen Image 2.1` / `SD / SDXL / Z-Image` / `Krea2`。选 `Qwen Image 2.1` 会展开完整的分辨率参数组，
+  适合「一张图出多个视角 / 多种编辑」。纯文生图模型（如 Z-Image）选 `SD / SDXL / Z-Image` 且不接参考图，
+  见 [Z-Image 用法](#z-image-用法)。
+
+**输出**：`cond_1` ~ `cond_8`、`latent`
+
+---
+
+#### ToggleSampler — 采样器（一体）
+
+把官方四件套收进一个节点：
+
+```
+随机噪波 + CFG引导器 + K采样器选择 + (基础调度器 | 手动Sigmas)  ->  SamplerCustomAdvanced
+```
+
+**输入**：`模型`（MODEL）、`正面条件` / `负面条件`（CONDITIONING）、`Latent图像`（LATENT）
+**参数**（按节点上的显示顺序）：`⚡预设`、`Sigma 曲线`、`采样器`、`调度器`、`步数`、`CFG`、`降噪`、`噪波随机种`、`自定义Sigma表`
+**输出**：`Latent`、`降噪Latent`
+
+> 控件的**显示名**和内部参数名是分开的：`⚡预设` 的内部名是 `模型预设`，`Sigma 曲线` 的内部名是
+> `采样方案`。之所以只改 label 不改 name —— 存档的 `widgets_values_named` 和 `/prompt` 的 `inputs`
+> 都按 name 走，改 name 等于让所有已存工作流失联。
+
+模型 / 条件 / Latent **故意保留成插槽**——真正随模型变的只有 sigma 曲线，所以换模型不用换节点。
+`Sigma 曲线` 分三类：
+
+| 方案 | 作用 | 生效参数 |
+|---|---|---|
+| `自动（按步数 + 调度器）` | 现场算 sigma，与官方 `BasicScheduler` 数值完全一致 | `步数` + `调度器` |
+| 预置表：`Qwen-Image-2.1 turbo 8步`、`LTX-2 视频 8步（蒸馏）`、`LTX-2 视频 3步（低噪）` | 蒸馏模型专用的固定轨迹 | 无——步数已写进表里 |
+| `自定义表` | 手填一串 sigma | `自定义Sigma表` |
+
+sigma 表规则：逗号分隔、**严格递减**、**末值必须是 0**、`N` 个点 = `N-1` 步（9 个点 = 8 步）。
+
+选了预置表之后失效的参数会自动变灰**并锁住**：`步数` 会跳到该表真实的步数（看得见但改不动），
+切回 `自动` 会还回你自己设的步数。`降噪` < 1.0 会裁掉表的尾部，所以预置表也能做低降噪细化。
+
+蒸馏 turbo 模型推荐 `采样器` = `euler`、`CFG` = `1.0`。
+
+#### ⚡预设（一键填好常用参数）
+
+节点**最上面**的 `⚡预设` 是一个快捷填充：选中某个模型，它会把
+`Sigma 曲线 / 采样器 / 调度器 / 步数 / CFG` 五项一次性改好，不用每次手动调。
+`降噪` 和 `噪波随机种` 不归它管（那两项取决于任务，不是模型）。
+
+放在最上面而不是最下面，是因为真实顺序就是「先定模型、再微调」；两个下拉挨着也容易
+被误认为重复，所以名字也分开了：`⚡预设` 管一整套参数，`Sigma 曲线` 只管噪声轨迹。
+
+| 预设 | Sigma 曲线 | 采样器 | 调度器 | 步数 | CFG |
+|---|---|---|---|---|---|
+| Qwen-Image 2.1（25步） | 自动 | euler | simple | 25 | 1.0 |
+| Qwen-Image 2.1 turbo（8步·固定表） | 预置表 | euler | simple | 8 | 1.0 |
+| Krea 2 turbo（8步） | 自动 | euler | simple | 8 | 1.0 |
+| Krea 2 turbo・细节增强（12步） | 自动 | euler | simple | 12 | 1.0 |
+| FLUX.2 klein（4步·beta） | 自动 | euler | beta | 4 | 1.0 |
+| FLUX.2 klein・高清修复（4步） | 自动 | euler_ancestral | simple | 4 | 1.0 |
+| Z-Image turbo（8步） | 自动 | euler_ancestral | simple | 8 | 1.0 |
+| Qwen-Image Layered（9步·CFG2.5） | 自动 | euler | simple | 9 | 2.5 |
+| Qwen-Image Edit 2511 AIO（4步） | 自动 | euler_ancestral | beta | 4 | 1.0 |
+| FIBO 2.0 turbo（4步） | 自动 | euler | simple | 4 | 1.0 |
+| MOSS-Image（30步·CFG4） | 自动 | euler | normal | 30 | 4.0 |
+| 手动（不套用） | — | — | — | — | — |
+
+**语义是「一键填充」，不是「持续锁定」**：套完预设后你手动改这五项里的任何一个，
+下拉会自己退回 `手动（不套用）`——所以控件显示的永远是真实状态，
+不会出现「写着 Qwen 预设、其实在跑 40 步」。
+
+预设表只写在 `sampler.py` 的 `MODEL_PRESETS` 里，前端通过
+`GET /toggle_pass/sampler_presets` 拉取，**不在 JS 里另抄一份**；加模型只改 Python 一处。
+改名时请在 `PRESET_ALIASES` 里补一条「旧名 → 新名」，否则已经存过旧值的存档会显示空白。
+
+数值全部取自本机 `user/default/workflows/` 里实际跑通的工作流（每个预设的上游文件
+写在 `sampler.py` 的注释里），改之前请先跑一遍再回头调这张表。
+
+---
+
+<a id="pose-reference-zh"></a>
+### 参考姿势（Pose Reference）
+
+`PoseReference` 从本地姿势素材库里挑一张参考图，以普通 `IMAGE` 的形式交给任意编辑模型的图像输入——
+不需要 ControlNet，也不需要额外模型。
+
+**素材目录结构**——节点会合并最多三个根目录，按名字去重，同名以靠前的为准：
+
+| 顺序 | 根目录 | 说明 |
+|---|---|---|
+| 1 | `TP_POSE_ROOT` 环境变量 | 只有你设了才生效，指向完全自定义的位置 |
+| 2 | `ComfyUI/models/pose_reference/` | 推荐放自己的和私密素材——备份 `models` 时会一起带走 |
+| 3 | `custom_nodes/ComfyUI-Toggle-Pass/pose_assets/` | 插件自带，随插件分发 |
+
+每个根目录内部结构一样：
+
+```
+<根目录>\
+├─ 01_AI线稿姿势\           线稿套
+│  └─ <大类>\<姿势组>\<P###_名称>\<P###_视角>.webp
+└─ 02_AI深度姿势库\         深度套（路径、文件名与线稿套一一对应）
+```
+
+两套完全对应，所以节点只索一套，取图时再按 `源` 决定去哪个根目录拿。
+丢一个新目录进去，约 15 秒内下拉里就会自己出现——不用重启，也不用刷新页面。
+
+**输入**
+
+| 参数 | 说明 |
+|---|---|
+| `类别` | 上半身单人 / 全身单人・标准系 / 全身单人・性感系 / 双人缠绵 |
+| `姿势组` | 站姿 / 坐姿 / 蹲姿 等，共 30 组 |
+| `姿势` | `P###_名称`，共 288 个。编号在不同大类里会重复，所以以完整目录名为准 |
+| `视角` | 正面 / 侧面 / 斜侧 / 俯视 / 仰视 |
+| `源` | `线稿`（默认）或 `深度` |
+| `长边` | 输出图长边，默认 1024——素材原始只有 300×400。缩放后会向下对齐到 **16 的倍数**（VAE 下采样要求），所以实际长边可能比设定值小最多 15px |
+| `翻转` | 水平镜像，左右相反的姿势不用另找素材 |
+
+前三个下拉是联动的：选了大类只列该类下的姿势组，选了组只列该组下的姿势。
+**任意一个下拉一改，节点下半部分立刻重画**——选中的那张素材直接显示在参数下面，不用先执行工作流。
+节点首次加载时会自动撑高给图片腾位置；想看得更大就把节点右下角往下拉。
+
+**怎么接**
+
+```
+[参考姿势 Pose Reference] ──image──▶ [文本编码器 Qwen Image 2.1] .images ─┐
+                                                                          ├─▶ K采样器 ─▶ VAE解码 ─▶ 保存图像
+[UNet 加载器]  [CLIP 加载器]  [VAE 加载器] ────────────────────────────────┘
+```
+
+1. 画布空白处双击，搜 `Pose Reference`（分类 `Toggle-Pass`），或用右键菜单 新建节点 → Toggle-Pass → 参考姿势。
+2. 依次选 `类别` → `姿势组` → `姿势` → `视角`，每选一次节点上的图就跟着换，选到满意为止。
+3. 把 `image` 输出接到编辑编码器（`TextEncodeQwenImage21`、`TextEncodeFibo2` 等）的 `images` 输入，
+   其余节点跟你平时的文生图/图编辑工作流完全一样。
+4. `长边` 决定送进编码器的分辨率，1024 足够（素材本身只有 300×400）。输出会自动向下对齐到 16 的倍数，这样 `VAEEncode` 不需要自己 pad/crop——线稿对半像素位移特别敏感，错一点整个人就歪了。
+5. 需要左右镜像的同一姿势，把 `翻转` 设成 `开`，不用另找素材。
+
+**⚠️ 接「编辑模型」（2511 / FLUX.2 / MOSS-Image / FIBO 2.0）必须接两张图**
+
+单张接法在这些模型上**不报错、能出图，但人物姿势一点都不变**——这跟兼容性无关，是语义问题：
+编辑编码器的 `image1` 被模型当成「这张就是要被修改的原图」，你把线稿接到 `image1`，
+任务就退化成「把这张线稿重绘成真人」，提示词里再怎么描述动作都没用。
+
+正确画法是**多图编辑**：待修改的原图接 `image1`，参考姿势接 `image2`。
+
+```
+[加载图像] ────image───▶ .image1 ┐
+                                 ├─▶ [文本编码器 Qwen Image Edit 2511] ─▶ K采样器
+[参考姿势] ────image───▶ .image2 ┘       提示词：保持 image1 的人物和服装，把姿势改成 image2 的姿势
+```
+
+`TextEncodeQwenImageEditPlus` 的 `image1 / image2 / image3` 都是 `IMAGE` 类型，本节点输出可以直接接，
+不用加任何转换节点。实测 2511 与 FLUX.2 在多图接法下都会按 `image2` 的骨架重摆人物。
+
+MOSS-Image 与 FIBO 2.0 同理，只是插槽叫自动增长的 `image_0/image_1`、`image_1/image_2`：
+
+```
+[加载图像] ──▶ .image_0 ┐
+                        ├─▶ TextEncodeMOSSImageEdit ─▶ K采样器
+[参考姿势] ──▶ .image_1 ┘   edit_format 必须与 edit LoRA 的训练格式一致
+```
+
+实测这两个模型都能把一张站姿照片改成素材里的坐姿（MOSS 20.2s / FIBO 16.1s），
+人脸、发型、服装、背景均保持不变。⚠️ MOSS 的编辑能力来自 `moss_image_edit_lora_bf16`，
+不加载它就只有 SFT 底座，指令基本不生效。
+
+**Krea 2：先看本插件的节点；装了两套外部节点包的话再往下看**
+
+**本插件自己的节点就够了，不需要任何外部插件。** `Ref Image Encode` 把「模型类型」选成 `Krea2`，
+就能复现 `ComfyUI-krea2Edit` 两个节点做的事 —— 因为 ComfyUI 原生 krea2 的 `_forward` **本来就有**
+这条 in-context 参考通路（`ref_latents` → 干净 tokens 放 frame 1..N，target 放 frame 0），
+`Krea2EditModelPatch` 只是手工把它重建了一遍。两个前提，节点现在都替你处理了：
+
+1. `reference_latents_method = index`，**不是** `index_timestep_zero`（后者把 adaLN 调制拆成两个
+   时间步，参考线稿会被描进画面）；
+2. 每张参考图的 latent 在 VAE 编码前先 fit 到出图网格 —— `h/w` 与采样起点逐维相同，
+   RoPE 位置只在 frame 轴上不同。
+
+```
+[参考姿势] ─image─▶ .image1 ┐
+                            ├─▶ [Ref Image Encode（模型类型 = Krea2）] ─条件─▶ [采样器（一体）] ─▶ [VAE解码]
+[加载图像] ────image─▶ .image2 ┘                          └latent（全零 16 通道）┘
+```
+
+它还有一个专门的 `negative` 输出（用同一个 CLIP 编出来的空文本条件）—— 接到采样器的「负面条件」，
+这样以后想把 CFG 提上去也不会花屏。
+
+⚠️ **顺序是硬约定：最后一个参考图 = 主体。** 姿势线稿放 `image1`、原照片放 `image2`。
+反过来会直接渲成一个线稿人偶（实测）—— 因为模型会把线稿当成「要画的东西」而不是「条件」。
+实测 8 步 720×1440：70s 出图，坐姿正确、画面干净、人物一致性保持，全程不用外部节点。
+
+⚠️⚠️ **提示词必须把姿势写具体，这是最容易踩的坑。**
+只写「图二美女参考图一的姿势，重新摆pose」这种笼统句子（同参数、同 seed 实测）——
+模型不会去「理解」线稿，而是把线稿人偶当成画面里的**第二个主体**，直接输出
+「左边真人 + 右边线稿人偶」的并排拼贴图。姿势一个字都不会动。
+
+同一个姿势（P068 跪立后仰・手触脚跟 / 斜侧），三种写法实测：
+
+| 提示词 | 结果 |
+| --- | --- |
+| `图二美女参考图一的姿势，重新摆pose` | ❌ 左人＋右线稿人偶的拼贴，姿势未变 |
+| `让图二中的女人摆出图一那个姿势：跪立、身体向后仰、胸部抬起、背部拱起，双手向后下方伸去触摸脚跟，头部微微后仰，采用四分之三侧后方视角。保持她的脸、发型、深蓝色泳装和海滩背景不变。` | ✅ 单人物，姿势到位 |
+| 同上的英文版 | ✅ 单人物，姿势到位（手更贴脚跟一点） |
+
+写提示词的三个必填项：
+1. **主体指认** —— 明确「图二中的女人」（不是「图二美女」）
+2. **姿势的解剖描述** —— 躯干方向、四肢位置、手放哪里、头朝哪、机位角度。
+   光说「参考图一的姿势」等于没说
+3. **保持不变的东西** —— 脸/发型/服装/背景逐项列出
+
+中文可用，不必强行写英文；但**姿势动作词越具体越准**。
+
+⚠️ 请用 `DynamicRefImageEncode`（即 `Ref Image Encode`），不要用 `DynamicRefIndependent`：
+后者把每张图编进**各自独立**的 conditioning（那是为 Qwen 2.1 的每图独立 index 平面设计的），
+合并起来给不了 Krea2 一条连贯的参考序列 —— 实测会线稿残留 + 姿势跑偏。
+
+**如果你要用外部节点包**，本机这两套**互不兼容**，插槽语义完全不同，
+混用会得到「不报错、但线稿被画进画面」的结果。选错节点包 ≈ 白跑：
+
+| 节点包 | 编码器 | patch 节点 | 参考姿势该接哪 | 结果 |
+| --- | --- | --- | --- | --- |
+| `ComfyUI-Krea2-Ostris-Edit` | `TextEncodeKrea2OstrisEdit` | `Krea2OstrisEditModelPatch` | `image2`（配 `image1`=原图） | ✅ 骨架驱动，线稿不进画面 |
+| `ComfyUI-krea2Edit` | `Krea2EditGroundedEncode` | `Krea2EditModelPatch` | `image_b` + `source_latent_b` | ❌ 线稿被当成「第二个主体」画进画面 |
+
+**能用骨架图的是 Ostris 那套**：
+
+```
+[加载图像] ─image─▶ [FluxKontextImageScale] ─▶ .image1 ┐
+                                                      ├─▶ TextEncodeKrea2OstrisEdit ─┐
+[参考姿势] ─image─▶ [FluxKontextImageScale] ─▶ .image2 ┘   （正/负各一个编码器）   │
+                                                                                  ▼
+[加载扩散模型] ─▶ [Krea2OstrisEditModelPatch] ─▶ [加载 LoRA] ─▶ K采样器（8步 euler simple CFG1）
+```
+
+`TextEncodeKrea2OstrisEdit` 的 `image1 / image2 / image3` 都是 `IMAGE`，本节点输出直插 `image2`。
+实测（同一张海边站姿照片 + 素材 `P017_正坐・挺胸后仰`）：`krea_outfittransfer` + `kv_cache=开`，
+8 步 36.1s 变成沙滩坐姿，**画面里没有线稿残留**，脸/发型/比基尼/背景均保持。
+
+⚠️ 三个必须配对的点：
+1. **`kv_cache` 必须跟 LoRA 的训练方式一致**。ai-toolkit 用 `kv_cache` 训练的（如
+   `krea_outfittransfer`）要开；普通训练的编辑 LoRA 开了会跑偏。
+2. **LoRA 必须和节点包配套**。把 `krea2_identity_edit_v2`（krea2Edit 系）接到 Ostris 节点上，
+   线稿同样会被当成主体贴上去——实测过，两者几何不一致。
+3. **条件上要包一层 `FluxKontextMultiReferenceLatentMethod`**（`index_timestep_zero`），
+   正负各一份，官方工作流就是这么接的。
+
+只有 krea2Edit 系 LoRA 时也能改姿势，但只能**用文字描述**，不能给骨架图：
+
+```
+[加载图像] ─▶ source_latent / image ─▶ Krea2EditModelPatch ─▶ K采样器（8步 euler simple CFG1）
+提示词：让她坐在沙滩上，挺胸后仰，双手撑在身侧
+```
+
+实测 `krea2_identity_edit_v2` 靠这句文字把站姿改成了跪坐/坐姿，但把线稿接到 `image_b`
+就会连人偶一起画出来。所以：**只有 krea2Edit 那套节点时，姿势只能写字描述；
+用本插件的 `Ref Image Encode` 就能直接吃骨架图。**
+
+**krea2Edit 到底能不能吃骨架图：四条路都试过了，都不能**
+
+| 接法 | 姿势跟着骨架变？ | 画面干净？ |
+|---|---|---|
+| 线稿 → `image_b`（+ `source_latent_b`） | 是 | ❌ 白色人偶 / 透明线框被实打实画进画面，`ref_boost` 越高越明显 |
+| 只接 `source_latent_b`（不给 `image_b`，TE 不看它） | ❌ 完全不生效 | 是 |
+| 骨架当**主图**、原图当 `image_b`（方向对调） | 是 | ❌ 轮廓线残留在人物身上 |
+| 先用 PIL 把线稿做成**灰度剪影**再喂 `image_b` | ❌ 姿势不动 | 是 |
+
+第 2、4 条都做了控制实验（把提示词里的姿势描述整段删掉）：结果图仍然是原图的站姿，
+说明姿势变化全部来自提示词，骨架本身没起作用。第 4 条只解决"脏画面"，不解决"驱动"。
+
+**一致性的差异是实测的**：同一张原图、同一个姿势目标，`krea2Edit + krea2_identity_edit_v2`
+出来是**同一张脸**；`Ostris + krea_outfittransfer` 出来的脸明显换人（那张 LoRA 是换装用途）。
+所以两者不是"谁替代谁"：
+
+- **两者都要**（人物一致 + 骨架图驱动姿势）→ 本插件 `Ref Image Encode`（模型类型 = Krea2），
+  顶部那条接法就是，一个节点搞定、不用外部插件、也不用二次回收
+- 要**人物一致**、纯文字改图（换姿势/换装/局部改）→ 本插件 Krea2 通路，或 `krea2Edit` + 文字描述
+- 要**极限锁骨架**、可以接受脸飘 → `Ostris` 那套
+- 已经在 krea2Edit 那套里出不了坑 → 先 Ostris 定姿势，再用 `krea2Edit` 做一次身份回收
+
+⚠️ `ref_boost` / `grounding_px` 不是"越高越像"：实测 `ref_boost=1.3` + `grounding_px=1024`
+反而把背景从海边换成了礁石。建议先保持 1.0 / 768，只在掉脸时小步上调。
+
+**素材放哪：分三层，运行时合并**
+
+| 层 | 位置 | 是否随插件分发 |
+| --- | --- | --- |
+| 插件自带 | `custom_nodes/ComfyUI-Toggle-Pass/pose_assets/` | 是 —— 1530 张，只含两个非敏感大类（`上半身单人`、`全身单人・标准系`） |
+| 本地私有 | `ComfyUI/models/pose_reference/` | 否 —— 推荐放自己的和私密素材，跟着 `models` 备份走 |
+| 自定义 | `TP_POSE_ROOT` 环境变量 | 否 —— 只有你把素材放到别处时才用 |
+
+查找顺序是 **`TP_POSE_ROOT` → `models/pose_reference` → 插件自带**，同名素材以靠前的为准。
+所以**只装节点就能用**，往私有目录里丢东西也会自己长出来：
+
+- **把新的姿势组丢进 `models/pose_reference`，约 15 秒内下拉里自动出现**，不用刷新页面；删掉同样会
+  自己消失（节点会轮询一个很轻的素材指纹）。
+- 想立刻生效：画布右键 → `🔄 刷新姿势素材库`。
+- 不存在的那一层直接跳过，不会因此报错；真找不到素材时，报错里会列出都搜过哪些路径。
+
+**工作流自带参考图，可以直接分享。** 选中的素材会以 data URI 写进节点属性（`properties.tp_pose`），
+跟着工作流一起走：
+
+- 别人下载你的工作流，**本机完全没有素材库也能看到这张图，也能直接出图**；
+- 代价约 **+8~14KB/节点**（素材本身就是 300×400 的 webp，单张 5~10KB）；
+- 本机素材库存在时永远优先读素材库，内嵌数据只做兜底；
+- 如果素材库不在了、参数又被改过，节点会继续显示内嵌的那张并给出提示，不会变成空白。
+
+**输出**：`image`（`IMAGE`）——直接接 `TextEncodeQwenImage21`、`TextEncodeFibo2` 或任何编辑编码器的
+`images` 输入。
+
+现成的工作流在 `example_workflows/Pose Reference Editing.json`（Qwen-Image-2.1 turbo，8 步）。
+**素材是跟着节点走的**（`properties.tp_pose`，约 8KB data URI）：别人下载后在**完全没有姿势素材库**
+的机器上也能看到图、也能出图。再加上插件自带的 `pose_assets/`，全新安装就能直接浏览
+两个大类共 153 个姿势 / 12 个组。
+
+**为什么默认线稿。** 用 FIBO 2.0 和 Qwen-Image-2.1 各跑两组对照（同一姿势、同一指令、不同视角）：
+深度套 **3/3** 都自己补出了不存在的鞋或护具，线稿套只有 **1/3** 出现小瑕疵。原因是深度图没有边界线，
+脚底和地面亮度几乎相同，模型就把亮部填成了一个物件；线稿有一圈明确黑线，而且「线稿 → 真人」这个变换
+在这类模型的训练数据里大量存在。深度唯一的优势是有体积感，肢体交叠时能分清前后遮挡关系，所以只在
+复杂缠绕姿势时切过去。
+
+---
+
+<a id="qwen-image-21-支持"></a>
+### Qwen Image 2.1 支持
+
+`DynamicRefImageEncode`（Ref Image Encode）、`DynamicRefIndependent`（Ref Independent）、
+`OneImageMultiPrompt`（一图多视角）均已支持 **Qwen-Image-2.1** 图像编辑 / 多视角生成。
+把「模型类型」选成 `Qwen Image 2.1`，会展开下面这组参数：
+
+| 参数 | 含义 |
+|---|---|
+| `宽高比` | 预设比例（1:1 / 3:2 / 16:9 / 21:9 …），或 `自定义（用输出宽度/高度）` |
+| `百万像素` | 总像素预算：`1.0` ≈ 1024×1024，`2.6` ≈ 2016×1344。范围 0.1~16，**别在这里填 1024** |
+| `倍数` | 对齐步长，硬下限 **32**（填 16 会算出非法形状，例如 5024×3344） |
+| `参考图分辨率` | 参考图喂给模型前被缩到多大（面积基准）；`0` = 保持原尺寸 |
+| `尺寸适配` | `参考图保比例`（默认，不变形）/ `参考图裁剪` / `参考图拉伸` |
+
+对齐官方 `TextEncodeQwenImage21` 时总结出的几条关键规则：
+
+- **出图尺寸和参考图尺寸互不影响。** `qwen_image` 用的是 `default_ref_method = "index"`，
+  参考图和输出各占独立的位置平面，不需要同形。改「参考图分辨率」**不会**让出图变大变小。
+- **指令要写进提示词文本，不能写在 `instruction` 里。**
+  `comfy/text_encoders/qwen_image21.py` 会丢弃 system 段，写进 `instruction` 的内容在 2.1 下会被静默丢弃；
+  节点已自动把它并进 user 段（Flux 时代那句默认英文 instruction 会被忽略，免得污染提示词）。
+- **latent 输出是全零。** 如果把参考图的真实 latent 当采样起点，编辑就会退化成 img2img（内容完全不变）；
+  真实 latent 只进 conditioning 的 `reference_latents`。
+- 图片必须是 **32 的倍数**；带 alpha 的参考图会先合成到白底再送进视觉塔。
+
+推荐设置（2.1 图像编辑）：`匹配原图尺寸` = **OFF**、`尺寸适配` = `参考图保比例`、
+`百万像素` = 1.0、`倍数` = 32、`参考图分辨率` = 1024。
+
+✅ **负面条件已经解决了 —— `Ref Image Encode` 现在有专门的 `negative` 输出**，
+直接连到采样器的「负面条件」即可。它是用同一个 CLIP 编出来的真·空文本条件，
+所以**任何 CFG 下都正确**（实测与手接一个空 `CLIPTextEncode` 逐像素相同）。
+
+⚠️ **不要**再用 `ConditioningZeroOut` 把正面条件清零当负面。CFG = 1.0 时负面根本不参与计算，
+所以看着没事；一提高 CFG 出图就**整张花屏**（实测 fp8 / int8、20 步、CFG 2.5、挂姿势 LoRA，
+绿紫斑块）。以前的版本这个节点只有两个输出、示例工作流接的正是 `ConditioningZeroOut` ——
+坑就是从这里来的。
+
+**姿势编辑推荐挂 `VNCCS_QI2_PoseStudioV2`**（`models/loras/Qwen/`，152 MB，
+ai-toolkit 训练、base = `qwen_image_2`，`LoraLoaderModelOnly` 即可，纯 `diffusion_model` 键）。
+同一句提示词下实测这批 Qwen 2.1 LoRA：
+
+| LoRA | 结果 |
+| --- | --- |
+| 裸模型 | 可用，8 步 15 s |
+| `VNCCS_QI2_PoseStudioV2` | ✅ **最好**：姿势跟得最准、画面最细腻、无线稿残留 |
+| `QI2.1_AnyAngle` | ❌ 脸上/脖子出现线稿残留（参考图内容被「描」进画面） |
+| `qwen_image_2.1_turbo_lora_avg_rank_178` | 可用但皮肤质感变糙，姿势帮助不明显 |
+
+两种跑法都验证过：
+- **turbo int8 + 8 步 + CFG 1.0** → 16.7 s，快，画质够用
+- **`qwen-image-2.1-UC-fp8` + 20 步 + CFG 2.5** → 43.9 s，全身完整、细节最好（负面用空文本）
+
+---
+
+<a id="krea2-支持"></a>
+### Krea2 支持
+
+`ToggleUnifiedLoader`（统一加载器）、`DynamicRefImageEncode`（Ref Image Encode）、
+`DynamicRefIndependent`（Ref Independent）、`OneImageMultiPrompt`（一图多视角）均已支持 **Krea2**。
+加载器会从 UNet 自动认出 `Krea2`，直接配 `CLIPType.KREA2` + `Krea2TEModel` 文本编码器；
+三个编码节点只要把「模型类型」选成 `Krea2` 即可。
+
+⚠️ **VAE 必须和模型配套。** 这是最常见的翻车点——配错不会在加载时报错，而是在 `VAEDecode` 深处炸：
+
+| 模型 | VAE 文件 | latent |
+|---|---|---|
+| Qwen-Image **2.1** | `qwen_image_2.1_vae_bf16.safetensors` | 64 通道 / 16x / RGBA |
+| **Krea2** | `qwen_image_vae.safetensors` | 16 通道 / 8x / RGB |
+
+配错时 `fix_empty_latent_channels` 会静默把 16 通道补成 64，采样照样跑完，
+然后解码落进 Wan 2.1 分支报 `IndexError: tuple index out of range`。
+现在加载器会在加载时比对 `latent_channels` / `latent_dimensions`，直接把该换哪个文件打到日志里。
+
+对齐官方 `TextEncodeQwenImageEditPlus` 后总结的几条规则：
+
+- **参考图 latent 尺寸 = 出图尺寸。** 参考图先中心裁到出图宽高比、再缩到出图像素网格，
+  **然后**才进 VAE。这样参考图 latent 与采样起点逐维相等，RoPE 位置只在 frame 轴上不同 ——
+  正是 krea2_edit 系 LoRA 的训练几何。视觉塔仍只吃约 384 px 的缩略图。
+- **VAE 只吃 RGB** —— alpha 是丢掉，不是合成。
+- **视觉占位符必须手写。** 传了显式 `llama_template` 时 Qwen3-VL 的 tokenizer **不会**自动插占位符，
+  节点会自己拼 `Picture N: <|vision_start|><|image_pad|><|vision_end|>`；
+  漏了就相当于参考图对模型完全不可见。
+- **`reference_latents_method` 必须显式给，而且必须是 `index`。** Krea2 的 `default_ref_method`
+  是 `None`，不设方法时参考图会被**静默忽略**（不报错，只是没效果）。
+  `index_timestep_zero` 能跑但是错的：它把 adaLN 调制拆成两个时间步（target 用当前 t、参考图用 t=0），
+  而训练时全序列共用一个 tvec —— 结果参考线稿会被**描进画面**（脸上出现轮廓线）。
+  `index` 全序列统一 tvec，才对得上训练。
+- **latent 输出是全零**（16 通道 / 8x）——和 2.1 一样是「编辑而不是 img2img」。
+- **sigma 曲线：** Krea2 turbo 在 `采样器（一体）` 里用 `自动（按步数 + 调度器）` 方案，
+  `Qwen-Image-2.1 turbo 8步` 那张表不能套过来用。
+
+---
+
+<a id="moss-fibo-支持"></a>
+### MOSS-Image / FIBO 2.0 支持
+
+`ToggleUnifiedLoader`（统一加载器）会自动识别这两个模型——`CLIP 类型` 里已经能直接选
+`moss_image` / `fibo2`（选 `auto` 也能从 UNet 认出来）。
+两者都是 **Flux2 latent**（128 通道 / 16x，`flux2-vae`），所以 `Ref Image Encode`（参考图编码）
+**不需要新增模型类型，直接选 `Flux2` 就行**。
+
+实测（同一张海边站姿照 + `P017_正坐・挺胸后仰` 线稿作为第二张图）：
+
+| 模型 | 官方编码器 | `Ref Image Encode`（选 Flux2） |
+|---|---|---|
+| MOSS-Image，20 步 / CFG 4 | 19.6s — 坐姿、手撑沙滩 | 16.7s — 坐姿 ✅ |
+| FIBO 2.0，4 步 / CFG 1 | 15.4s — 跪坐 | 10.6s — 跪坐 ✅ |
+
+两组画面里都没有线稿残留。不换成官方编码器的代价是这几点：
+
+- **MOSS 的 `edit_format`** —— 参考方法必须与编辑 LoRA 的训练格式配对
+  （`ai-toolkit` → `index_timestep_zero_isolated`、`ai-toolkit (kv_cache off)` → `index_timestep_zero`、
+  `Moss-Image-Edit` → `index_timestep_zero_text_start`）。选错照样出图，但指令基本不生效。
+  `Ref Image Encode` 完全不设这个字段。
+- **MOSS 的 `image_strength`** 和 **FIBO 的 `negative_prompt`** 它也没有。
+- 两个官方编码器的图片输入都是 **AUTOGROW**，所以本节点的输出照样能直插：
+  `TextEncodeMOSSImageEdit` → `images.image_0 / image_1 …`，`TextEncodeFibo2` → `images.image_1 … image_16`。
+
+⚠️ `TextEncodeMOSSImageEdit` 只有**一个** conditioning 输出，正负条件必须用**两个节点**分别编码
+（官方模板就是两个）。写成 `["节点", 1]` 会报 `list index out of range`，
+而且报错位置会指向采样器、不是编码器。
+
+采样器预设：`MOSS-Image（30步·CFG4）`、`FIBO 2.0 turbo（4步）`。
+
+---
+
+<a id="模型类型对照表"></a>
+### 该选哪个？`模型类型` 和 `clip_type` 不是一回事
+
+两个下拉长得像，作用完全不同：
+
+- **`模型类型`**（在参考图编码器上）只决定**输出什么形状的 latent**（以及 Krea2 走哪种
+  `reference_latents_method`）。
+- **`clip_type`**（在 `Toggle Unified Loader` 上）决定**文本编码器怎么建**。
+
+选错通常**不报错**：要么通道数不对、要么 conditioning 宽度不对、要么图看着挺干净但提示词没生效。
+
+| 要加载的模型 | `模型类型`（编码器） | `clip_type`（统一加载器） | VAE |
+|---|---|---|---|
+| Qwen-Image **Layered** | `Qwen Layered`（+ `图层数`） | `qwen_image` | `qwen_image_layered_vae.safetensors` |
+| Qwen-Image **2.1** | `Qwen Image 2.1` | `qwen_image` | `qwen_image_2.1_vae_bf16.safetensors` |
+| **Krea 2** | `Krea2` | `krea2` | `qwen_image_vae.safetensors` |
+| **FLUX.2 / MOSS-Image / FIBO 2.0** | `Flux2` | `flux2` / `moss_image` / `fibo2` | `flux2-vae.safetensors` |
+| **Z-Image** | `SD / SDXL / Z-Image` | `stable_diffusion`（或 `auto`）—— **千万别选 `flux` / `flux2`** | `ae.safetensors` |
+| **SD 1.5 / SDXL / Pony** | `SD / SDXL / Z-Image` | `stable_diffusion` | 用 checkpoint 自带的 |
+
+`SD / SDXL / Z-Image` 是**故意做成通用档**的：只要真接了参考图，latent 就来自 `ref_latents[0]`，
+所以这个设置在 SD / SDXL / Pony / Z-Image / Qwen-Image 1.0 上表现一致；只有当**一张图都不接**时，
+它才具体意味着「4 通道全零 latent」。
+
+**Qwen-Image Layered 的具体参数**（照官方 `image_qwen_image_layered.json` 抄的）：
+
+- 文本编码器是 `qwen_2.5_vl_7b_fp8_scaled` —— 和 Qwen-Image 1.0 **同一个**编码器 ⇒ `clip_type` 选 `qwen_image`
+- VAE 是 **`qwen_image_layered_vae`**，不是 `qwen_image_vae`。两者都是 16 通道，选错了要等到
+  `VAEDecode` 才炸
+- latent 是 5 维：`[B, 16, layers+1, h/8, w/8]` —— `模型类型 = Qwen Layered` 输出的就是它，
+  等价于官方的 `EmptyQwenImageLayeredLatentImage`
+- 官方模板里实际跑的是 **20 步 / CFG 2.5 / euler / simple / denoise 1**，640 px，`layers = 2`；
+  画布上那段 Note 说的 50 步 / CFG 4.0 是「原始设置」（更慢）
+- 模板给了两条支路：一条从 `EmptyQwenImageLayeredLatentImage` 起步，一条从 `VAE 编码(照片)` 起步。
+  两者都是 `denoise 1`，起点会被完全覆盖 ⇒ 真正决定结果的是 `layers`（latent 第 3 维 = `layers+1`）。
+  **只有**想做「低 denoise 精修」时起点才重要，那时编码器给的全零 latent 就不是你要的
+
+**Z-Image 特别提醒**：`clip_type` **别选 `flux` / `flux2`**。Z-Image 的 TE 是 Qwen3-4B，而
+`comfy/sd.py` 在 `clip_type` 为 FLUX/FLUX2 时会把 Qwen3-4B 建成 Flux 系的编码器 ——
+类都不一样，提示词编码全变，而且不报错。
+
+2026-11-06 实测（同一个 Z-Image unet、同样 `qwen_3_4b` + `ae`，**只改 `clip_type`**）：
+
+| `clip_type` | 实际建出的文本编码器 | |
+|---|---|---|
+| `stable_diffusion` | `ZImageTEModel_` | ✅ 推荐 |
+| `auto` | `ZImageTEModel_` | ✅（模型类识别成 `Lumina2`，走同一档） |
+| `qwen_image` | `ZImageTEModel_` | ⚠️ 撞对的 —— ComfyUI 是按 TE 类型分派，跟这个值无关 |
+| `flux` | `Flux2TEModel_` | ❌ 编码器类不对，提示词静默失效 |
+
+<a id="auto-clip-type"></a>
+### `clip_type = auto` 到底准不准
+
+**本地 5 个模型实测全对，108 个模型类静态核对 106 个一致。** auto 不是瞎猜，它做的是：
+
+1. **真加载 UNet**（不是读 header 猜），拿模型对象的真实类名 —— Qwen-Layered 加载出来是
+   `QwenImage`，Z-Image 是父类 `Lumina2`；
+2. 按「**精确表 → 继承链 → 前缀规则**」三层查 `CLIPType`：
+   - 精确表只登记「`clip_type` 真的改变结果」的模型（见下表）；
+   - 继承链兜子类：`FluxSchnell → Flux`、`KOALA_1B → SDXL`、`ZImagePixelSpace → ZImage`；
+   - 前缀规则兜变体：`WAN21_I2V` / `WAN22_S2V` / `WAN_Animate2` … 16 个 WAN 类 → `wan`（另有 2 个在精确表）；
+3. 剩下的用 `stable_diffusion` 兜底 —— **这不算猜错**，因为那三分之一的模型 ComfyUI 压根
+   不看 `clip_type`（TE 权重本身就唯一确定了是哪种编码器）。
+
+**`clip_type` 只在「同一个 TE 权重能走多条路」时才真正起作用。** 读 `comfy/sd.py` 的
+`load_text_encoder_state_dicts` 可以确认只有这几族：
+
+| TE 权重 | 需要 `clip_type` 区分 | 选错的下场 |
+|---|---|---|
+| T5-XXL | SD3 / LTXV / WAN / CHROMA / PIXART / COGVIDEOX / MOCHI | 掉进 `else` 建成 **Mochi** 的 TE |
+| Qwen3-VL-8B | QWEN_IMAGE / IDEOGRAM4 / BOOGU / JOYIMAGE | 建成通用 `Qwen3VLTEModel_`，Qwen-Image 2.1 的 `image_slots` 丢失 |
+| Qwen3-VL-4B | KREA2 / FIBO2 / FLUX3 / MAGE / MINIMAX | 同上，通用 Qwen3-VL |
+| Qwen3-4B | FLUX / FLUX2 | 建成 Flux 系 `Flux2TEModel_`（Z-Image 就踩这个） |
+| CLIP-G | SD3 / STABLE_CASCADE / HIDREAM | 建成 `SDXLRefinerClipModel` |
+| Qwen2.5-7B | HUNYUAN_IMAGE / LONGCAT_IMAGE | 建成 `qwen_image` 的 TE |
+
+其余模型（Anima / AuraFlow / Cosmos / Lens / OmniGen2 / StableAudio / SAM / 3D 那些）
+`clip_type` 传什么都一样，auto 用兜底值无害。
+
+**实测（2026-11-06，`clip_type = auto`，真加载）**：
+
+| 模型 | auto 判定 | 建出的文本编码器 | |
+|---|---|---|---|
+| Qwen-Image Layered | 精确匹配 `QwenImage` → `QWEN_IMAGE` | `QwenImageTEModel_` | ✅ |
+| Z-Image | 精确匹配 `Lumina2` → `STABLE_DIFFUSION` | `ZImageTEModel_` | ✅ |
+| Krea2 | 精确匹配 `Krea2` → `KREA2` | `Krea2TEModel_` | ✅ |
+| FIBO 2.0 | 精确匹配 `Fibo2` → `FIBO2` | `Fibo2TEModel_` | ✅ |
+| MOSS-Image | 精确匹配 `MOSSImage` → `MOSS_IMAGE` | `MOSSImageTEModel_` | ✅ |
+
+**双保险：加载完立刻校验 TE。** auto 万一判错（或上游加了新模型类），不会静默 ——
+加载器会向模型自己要「我该配哪个编码器」（`model_config.clip_target()`，108 个类里 106 个问得到），
+跟实际建出来的类名比对，不一致就打印：
+
+```
+[ToggleUnifiedLoader] ⚠️ CLIP 校验失败：模型是 Krea2，但文本编码器建成了 Qwen3VLTEModel_
+（预期 Krea2TEModel，依据 手工表）。这个 CLIP 文件与模型不配套 → 提示词会被错误编码
+（跑得通但什么都不生效）。请更换 CLIP 文件，或检查『CLIP 类型』是否选对。
+```
+
+这条校验不是装饰：auto 判错时它会**当场**报出来，而不是等你出图发现「提示词不生效」再回头排查。
+
+**什么时候别用 auto**：只有一种情况 —— 你要故意手选一个与模型不匹配的编码器做实验。
+正常使用 `auto` 比手选更靠谱，因为手选需要记住上表 6 行。
+
+
+所以 `qwen_image` 不会把 Z-Image 弄坏，但还是选 `stable_diffusion`（或 `auto`），让设置名副其实。
+
+---
+
+<a id="z-image-用法"></a>
+### Z-Image：纯文生图，模型类型选 `SD / SDXL / Z-Image`，不要接参考图
+
+⚠️ 先说结论：**Z-Image 没有参考图编辑能力，`image1`~`image10` 一个都别接。**
+
+依据（实测，不是猜的）：
+
+- 官方 5 个 `z_image` 模板里，`image_z_image` / `_int8` / `_turbo` / `_turbo_int8` **全都没有
+  `LoadImage`**，是纯文生图；唯一带图输入的 `image_z_image_turbo_fun_union_controlnet` 走的是
+  **ControlNet（Canny 0.1 / 0.32）**，属于"线条控图"，和参考图编辑是两条完全不同的通路。
+- Z-Image 是 Lumina2 / NextDiT 系，训练时没有参考图 token。真接了 `image1` 之后，
+  `reference_latents` 照样会进 conditioning，但模型缺配套的 `reference_latents_text_embeds`
+  ⇒ 实测出图是**满屏方块马赛克**。换 seed、换尺寸桶、换采样起点都一样。
+
+**正确用法**：`模型类型` 选 `SD / SDXL / Z-Image`，`image1` 空着。
+
+这不是"凑合"。不接图时这个分支输出的就是 4 通道全零 latent，再由官方
+`fix_empty_latent_channels` 补到 16 通道，正是 Z-Image 需要的空起点。同 seed 下与官方
+`UNETLoader + CLIPLoader + VAELoader` 原生链路**逐像素一致**（51.5 / 214.5 / 46.9 四项全等）。
+
+**想要 Z-Image 的图生图**：别用这个节点，走官方的
+`加载图像 → VAE 编码 → 采样器(denoise < 1)`。提示词 + denoise 控制改动力度，照片当采样起点。
+
+**防忘机制**：接反了会在日志里打一条警告（只提示，不改任何行为）：
+
+```
+[Toggle-Pass][Ref Image Encode] ⚠️ 文本编码器 = ZImageTEModel_，已接入 1 张参考图，
+但这个底座没有参考图通路。
+```
+
+`模型类型` 下拉里**没有单独的 Z-Image 项** —— 它的行为和通用档 `SD / SDXL` 完全一样，所以两项
+直接合并成一个名字 `SD / SDXL / Z-Image`，而不是再加一项让人误以为"选了它就能接图"。
+
+> 2026-11-06 改名：旧值是 `SD / SDXL`。老工作流**加载时自动迁移**（前端直接改写 widget 值，
+> 后端收到旧别名也会归一），存过的图不用手动改。
+
+---
+
+### 画布菜单入口
+
+本插件全部节点也能从画布菜单里直接添加（和 KJNodes / rgthree 的入口在同一处）：
+
+```
+🔄 Toggle-Pass  ▸
+   ├ 采样器（一体）
+   ├ Image Batcher
+   ├ Multi Ref Latent Input
+   ├ One Image Multi Prompt
+   ├ Ref Image Encode
+   ├ Ref Independent
+   └ ...
+```
+
+- 列表是**运行时自动收集**的，以后新增节点自动出现，不用改代码
+- 点击任意一项，节点落在**鼠标所在位置**
+
+---
+
+### 隐私遮罩功能
+
+SaveLoadImage、ToggleLoadImage、TogglePreviewImage 三个节点均支持隐私遮罩：
+- 标题栏右侧显示 👁（显示图片）/ 🫣（隐藏图片）图标
+- 点击图标切换隐私状态，隐藏时图片预览区域留空
+- 隐私状态随工作流自动保存/恢复，不影响生图流程
+
+---
+
+### 示例工作流
+
+现成的图都在 [`example_workflows/`](example_workflows/) 里，把 `.json` 拖到画布上就能打开。
+
+| 文件 | 演示什么 | 额外依赖 |
+| --- | --- | --- |
+| `All-in-One Demo.json` | 一个图里放齐本插件全部节点：`ToggleUnifiedLoader`、`ToggleSampler`、`ToggleLoadImage` ×2、`ToggleImageCompare`、`TogglePreviewImage`、`DynamicRefImageEncode`、`SaveLoadImage` + `VAEDecode`。**想快速上手就从这张开始** —— 不需要任何第三方节点。 | 无 |
+| `Graphic-Text Integration Editing.json` | 单张参考图的「文字 + 参考图」编辑，用 `DynamicRefImageEncode` 编码、`SaveLoadImage` 保存。 | 无 |
+| `Batch Editing.json` | 六张输入图经批处理组成一组参考图，做批量编辑。 | `RTXVideoSuperResolution` |
+| `Lazy Person's Multi-angle View.json` | 一张图 + `OneImageMultiPrompt`（一图多视角），一次生成多个机位。 | `CR Upscale Image` |
+| `Pose Reference Editing.json` | `PoseReference` 接 Qwen-Image-2.1 turbo 编辑（8 步）。**选中的素材已内嵌在节点里**，没装姿势素材库也能打开、也能直接跑。 | 无 |
+| `Krea2 Pose Editing.json` | 9 个节点，**7 个是本插件的，外部插件 0 个**：`统一加载器`（UNet + CLIP + VAE + `krea2_identity_edit_v2` LoRA 一个节点搞定）+ `加载图像` + `参考姿势` → `Ref Image Encode`（模型类型 = Krea2；姿势线稿接 `image1`、原照片接 `image2`，latent 和 negative 都直接取它自己的输出）→ `采样器（一体）` 选 `Krea 2 turbo（8步）` → `图像对比` / `保存图像`。实测 720×1440、8 步、70s，站姿改坐姿，线稿不进画面，人物一致性保持。 | 无 |
+> 这些图里的模型文件名是作者本机的配置。加载后如果有加载器变红，在对应节点里改成你自己的
+> checkpoint / 文本编码器 / VAE 即可。
+
+---
+
+### 注意事项
+
+- `num_images` 调整时，输入/输出槽会自动增减，**已有连线不会被断开**
+- 切换页面再回来，节点会自动恢复到正确的槽位数量
+- 需要 Qwen2.5-VL 或兼容的 CLIP 模型；用 Qwen-Image-2.1 时需要 Qwen3-VL 8B + `qwen_image` 类型的 clip
+- 默认 instruction 适用于 Qwen-Image-Edit 类模型，可按需修改（2.1 下会自动并进提示词文本）
+- 界面只显示当前「模型类型」用得上的参数，其余自动隐藏
+- 非法的参数值（如 `倍数 = NaN`、`百万像素 = 1024`）在加载时会自动回退到安全默认值。
+  **升级后请重启 ComfyUI + 强刷一次（Ctrl+F5）**，然后保存工作流即可固化
+- ⚠️ 「现代节点设计（Vue 节点）」会导致 `widget.hidden` 与自定义绘制失效 —— 开启后所有参数都会展开、
+  隐私开关图标也会消失。请在 ComfyUI 设置里关掉它
+- ⚠️ 量化底座（INT8/FP8）上 LoRA **依然生效，只是更"糊"**：ComfyUI 走「反量化 → 叠加 patch →
+  再量化回 int8」，但用的是**随机舍入 + 重算 scale**，增量幅度能保留约 113%~120%，代价是方向带噪声
+  （余弦 0.4~0.7）。姿态/结构类 LoRA 看得出来，特别细微的风格类可能看不出来。
+  这是上游行为，官方 `LoraLoader` 一模一样（同 seed A/B 实测：本节点与
+  `CheckpointLoaderSimple + LoraLoader` 输出逐像素完全一致，meanAbsDiff = 0.00）
+- 加载器会把每个 LoRA 实际写入了多少 patch **以及模块覆盖率**（`192/224p`）回传到按钮上
+  （见上面那张表）。覆盖率不满一般是底座被 repack、那几层不存在，而不是 LoRA 没加载
+- ⚠️ **分体模式的 CLIP 校验**：加载完 CLIP 后会比对实际建出的文本编码器类名与模型是否配套
+  （正确类名如 `QwenImage21TEModel_` / `QwenImageTEModel_` / `Flux2TEModel_`，日志会打印）。
+  不配套时按钮显示 `⚠️CLIP不符`，日志会给出预期类名和处理建议 ——
+  否则这种情况会「跑得通但什么都不生效」，极难自查
+- ⚠️ **分体模式的 VAE 校验**：加载器还会比对 latent 通道数 / 维度与模型是否配套。
+  配错的 VAE 在加载时不报错，一路跑到 `VAEDecode` 才炸（`IndexError: tuple index out of range`），
+  所以现在加载时就直接提示，并告诉该换哪个 VAE 文件
+- 隐藏/收起 LoRA 组后会自动**回收节点高度**，节点不会留成一大条空框
+- **画布拖框现在会跟着鼠标走**：ComfyUI 1.57.x 会把拖拽选择框裁剪到 `.graph-canvas-panel`
+  （实测这个容器只覆盖画布中下部），在画布上半部分拖框时框的顶边被强行下压、高度被压成 0，
+  表现就是「鼠标在左上角拉、框却出现在中间/下面」。`js/selection_rect_fix.js` 按真实鼠标点
+  重写框的位置，只改显示、不动画布状态。要关掉：浏览器控制台执行
+  `localStorage.setItem("tp_selrect_fix", "off")` 后刷新
+- Python 改动需要**重启 ComfyUI**，纯 JS 改动只需 Ctrl+F5
+
+---
+
+## 更新日志 / Changelog
+
+> 按时间记录**新增 / 变更的功能**，以及怎么用。
+
+### 2026-11-07
+
+- **🏷️ 起版本号：`v1.0.0`**。之前一直没有版本标识，用户装了哪一版只能靠 commit 猜。
+  现在三处对齐：`pyproject.toml` 的 `[project].version`（ComfyUI-Manager / Comfy Registry 认这个）、
+  `__init__.py` 的 `__version__`、README 顶部。启动日志会打一行
+  `[Toggle-Pass] v1.0.0 loaded — 13 nodes`，排查「装的是不是新版」一眼就能确认。
+  后续按语义化版本：加功能进 minor，只修 bug 进 patch。
+- **🧹 移除示例 `Krea2 Pose Editing (Ostris).json`**。它硬依赖外部节点包
+  `ComfyUI-Krea2-Ostris-Edit`，而那套接法**会换人**（`krea_outfittransfer` 本质是换装 LoRA）。
+  留着容易让人误以为这是推荐路线。骨架驱动的技术说明仍保留在上面的对照表里，要复现照链路自己接；
+  常态需求用 `Krea2 Pose Editing.json`（零外部依赖，人物一致性保持得住）。
+
+### 2026-11-06
+
+- **🔀 `Ref Image Encode` 输出口顺序调整：`negative` 挪到 `latent` 上面**
+  （顺序变为 `conditioning` / `negative` / `latent`）。
+  - **老工作流自动迁移**：加载时前端按「连线的类型」把线重新归位 ——
+    `LATENT` 的线接到 `latent` 口、`CONDITIONING` 的线接到 `negative` 口，
+    **已存的工作流不用手动改线**。实测两个老样本（2 输出时代的 `All-in-One Demo`、
+    3 输出旧布局的 `Krea2 Pose Editing`）加载后：正面→0、负面→1、latent→2 全部正确，
+    且「保存后重新加载」不会被二次交换。
+  - 迁移**幂等**：靠 link 的类型判断，不依赖存盘标记，加载几次结果都一样。
+  - **连线归位要等 `graph.links` 建好，所以走阶梯重试**：ComfyUI 加载完成后 `graph.links`
+    要几百毫秒才填满（实测 200ms 时还是空表）。改名可以同步做（抢在渲染连线之前），
+    但搬线不能 —— 早期版本只等一帧就搬，结果「槽位名字换了、线没跟着走」，
+    画布上看着就像 `negative` 口插着 `latent` 的线。现在改名同步执行，连线归位按
+    0/50/120/260/550/1000/1800/3000ms 阶梯重试，一旦「0 条需要改」立刻收手，
+    不会干扰你之后的手动接线。
+  - ⚠️ 走 **API / 手写 JSON** 的调用（不经过前端）不会自动迁移 —— 这类调用要把
+    `latent` 从 `["节点", 1]` 改成 `["节点", 2]`、`negative` 从 `2` 改成 `1`。
+- **🎯 `clip_type = auto` 重做，不再靠一张追不上的手工表**。原来 auto 只认 20 个模型类名字面量，
+  ComfyUI 的 `supported_models` 一共有 **108** 个类，没认出来的会静默回落 `stable_diffusion` ——
+  其中 WAN 全系变体（16 个走前缀、2 个在精确表）、Chroma、CogVideoX 正好都在「会建错 TE」那一族里
+  （T5-XXL 的 `else` 分支是 **Mochi** 的编码器）。现在改成三层：
+  - **精确表 → 继承链 → 前缀规则**。精确表只登记「`clip_type` 真的改变结果」的歧义模型；
+    子类走 MRO（`FluxSchnell → Flux`、`KOALA_1B → SDXL`）；WAN 的 17 个变体走前缀规则。
+    静态核对 108 个类：精确 42 / 继承链 17 / 前缀 16 / 通用兜底 33（这 33 个 `clip_type`
+    本来就不参与判断，兜底无害）。
+  - **`auto` 现在会真加载 UNet 拿真实类名**（`load_unet` 返回 `ModelPatcher`，真实模型在
+    `.model` 里；直接 `type(model)` 只会拿到 `ModelPatcher`，实测会让所有模型都推不出来）。
+- **🛡️ CLIP 校验加固：手工表没登记的模型，直接问模型自己要答案**。以前只按 8 条手工前缀表
+  校验，没登记的模型一律「宁可漏报」跳过；现在先查手工表（经过实测，优先），查不到就用
+  `model_config.clip_target()` 反查「这个模型该配哪个编码器」——108 个类里 **106 个问得到**，
+  等于给全部模型类都上了校验。
+  - 实测价值：改代码时一次回归让 auto 全线退化成 `stable_diffusion`，
+    Krea2 / FIBO / MOSS 三个都被这条校验**当场**抓住，没有一个静默出错。
+  - 校验失败的日志会带上「依据 手工表 / 模型自述」，便于区分是表不准还是文件选错。
+- **🔤 `模型类型` 改名：`SD / SDXL` → `SD / SDXL / Z-Image`**。两者 latent 行为完全一样
+  （不接参考图时都输出 4 通道全零 latent，交给官方 `fix_empty_latent_channels` 补通道），
+  合成一个名字省得再开一项，也省得每次去想「Z-Image 到底选哪个」。
+  - 老工作流**自动迁移**：加载时前端直接把控件值从旧名字改成新名字（控制台会打一行
+    `模型类型 已迁移`）；后端也认旧别名，第三方节点转存 / 手改 JSON 一样能走对分支。
+  - 三个编码节点（`Ref Image Encode` / `一图多Prompt` / `Ref Independent`）统一生效。
+  - `ToggleEmptyLatent` 的 `latent类型` **没改**（那个本来就把通道数写在名字里），Z-Image 用
+    `SD / SDXL（4 通道 · 8×）` 那一档即可。
+- **➕ 统一加载器的 `clip_type` 下拉补齐**：原来只登记了 14 项，现在把 ComfyUI 支持的其它类型
+  全追加在末尾（`hunyuan_video` / `lumina2` / `chroma` / `ideogram4` / `flux3` 等 26 项）。
+  用 `getattr` 取枚举，旧版 ComfyUI 少哪个成员就跳过哪个，不会把插件带崩；已有项位置不动，
+  老工作流位置序列化不受影响。
+  - 顺带补了 Z-Image 的自动识别（`ZImage → stable_diffusion` + TE 前缀 `ZImageTEModel`），
+    `clip_type = auto` 时不会再打「未登记映射」的告警。
+- **📋 README 新增「该选哪个？」对照表**（`模型类型` vs `clip_type`），把 Qwen-Image Layered /
+  2.1 / Krea2 / Flux2 系 / Z-Image / SD 各自选什么、配哪个 VAE 一张表列全。
+  见 [模型类型对照表](#模型类型对照表) / [cheat sheet](#model-type-cheatsheet)。
+  - **Qwen-Image Layered → `clip_type` 选 `qwen_image`**：它的 TE 和 Qwen-Image 1.0 是同一个
+    `qwen_2.5_vl_7b_fp8_scaled`，VAE 必须是 `qwen_image_layered_vae`（同为 16 通道，选错要到
+    `VAEDecode` 才炸）；latent 5 维 `[B,16,layers+1,h/8,w/8]` 正好等于 `模型类型 = Qwen Layered`。
+  - **Z-Image → 别选 `flux` / `flux2`**：`comfy/sd.py` 在 FLUX/FLUX2 下会把 Qwen3-4B 建成 Flux
+    Klein 的编码器，类都不一样，提示词全废且不报错。
+- **⚠️ 接错参考图会报警了：Z-Image / Lumina2 系没有参考图通路**。三个编码节点一旦检测到
+  文本编码器是 `ZImageTEModel_` **且**接了 `image1`，日志里会打一条明确警告（含正确做法），
+  **只打印，不改任何参数和接线**。同一个（节点 + 编码器）每次启动只打一次，不会刷日志。
+  - Z-Image 是**纯文生图**模型：官方 5 个 `z_image` 模板里 4 个连 `LoadImage` 都没有，
+    唯一带图的 `fun_union_controlnet` 走的是 **ControlNet**，不是参考图编辑。
+  - 硬接 `image1` 的后果实测是**满屏方块马赛克**（换 seed / 尺寸桶 / 采样起点都一样）。
+  - 正确用法：`模型类型` 选 `SD / SDXL / Z-Image`、`image1` 空着 —— 空 latent 会自动补到 16 通道，
+    同 seed 下与官方原生链路逐像素一致。要图生图请用 `VAE 编码 + 采样器 denoise < 1`。
+  - 下拉里**不单独设 Z-Image 项**（行为和通用档完全一样），已合并进 `SD / SDXL / Z-Image`。
+    详见 [Z-Image 用法](#z-image-用法)。
+- **💡 广播打开时，会被自动接上的槽位小点会「点亮」**（补成实心、用该槽位类型的官方配色，
+  和手动连线后的样子一致）。以前只有紫线画过去、槽位却还是空心圈 —— 因为广播是纯预览，
+  提交前不建真实连线，官方就一直按「未连接」渲染。
+  - 只点亮**空着的**槽位；本来就有真实连线的口不重复画（官方自己就是实心）。
+  - 会被**后绘制的节点压住**的槽位不点亮（按 `graph._nodes` 绘制顺序判断），
+    所以拿节点当遮罩盖内容时不会再有假点透出来。
+  - 节点折叠、槽位位置未确定（`boundingRect` 无效）时跳过。
+  - 实测（无头 Edge，`probe_slot_lit_guard.mjs` 5/5）：点亮后槽位中心色 `255,213,0`
+    **与手动真连线完全相同**；被节点盖住时 `102,85,51`（不点亮）；关掉开关回到 `193,192,0`（空心）。
+- **🐛 修掉「一根紫线往空白处伸出去就断掉」**。原因：广播线的端点优先取槽位的
+  `boundingRect`（官方渲染时算出来的真实盒子），但**节点只要不在可视区内、从没被渲染过**，
+  这个盒子就是 `[0,0,0,0]`，旧代码对零尺寸照样取中心 ⇒ 端点被算成**图坐标原点**，
+  线就画到画布原点那片空白里，看着像一根悬空紫线。
+  - 触发条件：目标节点被拖到视野外（尤其画布上方 `y<0` 那种「半个节点探出去」的情况）。
+    实测 `Toggle-Pass-Test.json` 里的 `VAE解码`（pos.y = -66）就踩中。
+  - 修法：零尺寸一律视为「未布局」，退回 `getConnectionPos` 估算 —— 端点从 `(0,0)`
+    变成真实槽位位置（实测 `(0,0)` → `(1362,-32)`、`(0,0)` → `(955,-52)`，都落回节点内）。
+  - 有 `boundingRect` 的正常槽位行为完全不变（同图另外两条线端点一字未动）。
+  - 纯前端改动，**不用重启**，`Ctrl+F5` 即可。
+- **➕ 新增诊断命令 `__tpAudit()`**（控制台执行）：把当前每一根广播线的**两端坐标 /
+  归属节点 / 是否落在自己节点范围内 / 目标槽 boundingRect** 打成表格，悬空端点会红字警告。
+  以后再遇到「线跑到奇怪的地方」，先跑这个，一眼看出是哪条 pair 算错。
+- **🔧 广播预览的端点圆点改成和线同层（节点能盖住了）**。以前圆点画在 `draw()` 末尾 = 全画布最上层，
+  所以把节点拖到线上当遮罩时，一串紫点会穿出节点浮在上面（一列三颗、看着像脏点）。
+  现在圆点和线一起画进背景层（`bgcanvas`），**节点盖住线的同时把点一起盖住**，观感统一成
+  「线从节点边缘伸出来」，和官方连线一个样子。
+  - 视觉代价：槽位点本身压在节点边线上，所以露在外面的只剩半个圆点（另一半被节点吃掉）。
+  - 想回到旧观感：`localStorage.tp_dots_layer = "front"` 再刷新；删掉这个键就恢复默认。
+  - 纯前端改动，**不用重启**，`Ctrl+F5` 即可。
+  - 实测（无头 Edge 像素级）：同一布局下节点框内新增紫色像素 —— 旧行为 39 px（复现问题）、
+    新默认 **0 px**；bgcanvas 里圆点仍在（两个端点各 20 px）；节点之间空白处的线 200 px 不变。
+
+### 2026-11-05
+
+- **✅ `Ref Image Encode` 新增第三个输出 `negative`（自动生成，不用再接 `ConditioningZeroOut`）**。
+  它是用同一个 CLIP 编码出来的真·空文本负条件，**CFG 调多高都正确**。
+  - 怎么用：`Ref Image Encode` 的 `negative` 口 → 采样器「负面条件」，完事。
+  - 为什么加：之前示例工作流用 `ConditioningZeroOut` 把正面清零当负面，CFG=1.0 时看不出问题，
+    **CFG>1 就整张花屏**（绿紫斑块）。
+  - 兼容：第三个口是**追加在末尾**的，旧工作流的 `conditioning` / `latent` 两个口完全不受影响。
+  - 实测：与手接空 `CLIPTextEncode` **逐像素完全相同**（Qwen 2.1 fp8 / 20 步 / CFG 2.5，max diff 0）；
+    Krea 2 CFG 1.0 下新旧接法差异落在模型自身的重跑随机范围内（同配置重跑 max 142 vs 新旧 139）。
+  - `example_workflows/Krea2 Pose Editing.json` 已改成这个接法（节点数 10 → 9）。
+- **⚠️ CFG > 1 时负面条件不要接 `ConditioningZeroOut`，会整张花屏**。实测（fp8 / int8、20 步、
+  CFG 2.5、挂姿势 LoRA）：把正面条件清零当负面 ⇒ 出图绿紫斑块、完全不可用；改成空文本
+  `CLIPTextEncode` 就正常。CFG 1.0 时负面不参与计算，所以 turbo 8 步的示例一直没暴露这个坑。
+  → 现在直接用 `Ref Image Encode` 的 `negative` 输出即可。
+- **Qwen 2.1 姿势编辑推荐挂 `VNCCS_QI2_PoseStudioV2`**（`models/loras/Qwen/`，152 MB，
+  ai-toolkit 训练、base = `qwen_image_2`，`LoraLoaderModelOnly` 即可）。同 prompt 实测：
+  它姿势跟得最准、画面最细腻、无线稿残留；`QI2.1_AnyAngle` 反而在脸上/脖子画出线稿残留；
+  `qwen_image_2.1_turbo_lora` 皮肤变糙、姿势帮助不大。两种跑法：turbo int8 8 步 16.7s；
+  `UC-fp8` 20 步 CFG 2.5 43.9s（全身最完整）。
+- **⚠️ 提示词必须写具体姿势，否则 Krea 2 会把线稿人偶画进画面**。这是今天实测出来的第二大坑：
+  同参数、同 seed，只写「图二美女参考图一的姿势，重新摆pose」时，模型不读线稿，而是把线稿人偶
+  当成**第二个主体**，直接输出「左边真人 + 右边线稿人偶」的并排拼贴，姿势一点不动。
+  提示词换成「让图二中的女人摆出图一那个姿势：跪立、身体向后仰、胸部抬起、背部拱起，
+  双手向后下方伸去触摸脚跟，头部微微后仰，采用四分之三侧后方视角。保持脸/发型/服装/背景不变」
+  就正常了。**中文完全可用**，关键是三段：主体指认 + 姿势解剖描述 + 保持不变的内容。
+  → README 中英各加一节「提示词怎么写」，并修掉原来那句会误导人的简短示例。
+- **Krea 2 编辑不用再装 `ComfyUI-krea2Edit` 了**：`Ref Image Encode` 把「模型类型」选成 `Krea2`，
+  就能直接做「保身份 + 换姿势」，姿势线稿接 `image1`、原照片接 `image2`。
+  原因是 ComfyUI 原生 krea2 的 `_forward` 本来就有 in-context 参考通路（`ref_latents`），
+  `Krea2EditModelPatch` 只是手工重建了同一件事。实测 8 步 720×1440：70s 出图，坐姿正确、
+  画面干净、人物一致性保持。
+  → 新增示例 `example_workflows/Krea2 Pose Editing.json`（10 节点，**外部插件 0 个**）；
+  原来的 Ostris 版改名为 `Krea2 Pose Editing (Ostris).json`，用途变成「极限锁骨架」。
+- **修 `reference_latents_method`：`index_timestep_zero` → `index`**（Krea2 三处编码节点全改）。
+  `index_timestep_zero` 会给参考图单独一套 **t=0 的 adaLN 调制**，而 krea2_edit 系 LoRA 训练时
+  全序列共用一个 tvec ⇒ 参考线稿会被**「描」进画面**（实测：脸上出现轮廓线和腰线，
+  换 `index` 后完全干净）。这个坑很隐蔽：不报错、能出图、姿势也对，只有画面脏。
+- **Krea2 参考图 latent 改为 fit 到出图网格**：原来是缩到约 1 MP 保持原比例，
+  于是 ref latent 的 `h/w` 与采样起点不同 ⇒ 模型把参考图当「另一块画布」，边缘特征会渗进画面。
+  现在先中心裁到出图宽高比、再 bicubic（**antialias**）缩到出图像素尺寸，
+  ref latent 与零 latent 逐维相等，RoPE 位置只差 frame 轴 —— 与训练几何一致。
+- **★ 顺序是硬约定：最后一个参考图 = 主体。** 姿势线稿放 `image1`、原照片放 `image2`；
+  反过来会渲成一个线稿人偶（实测）。提示词里按 `image 1 / image 2` 指代即可。
+- **`DynamicRefIndependent` 不适合 Krea2**：它把每张图编进各自独立的 conditioning
+  （那是 Qwen 2.1 每图独立 index 平面的设计），合并后给不了 Krea2 一条连贯的参考序列 ——
+  实测线稿残留 + 姿势跑偏。Krea2 请用 `Ref Image Encode`（`DynamicRefImageEncode`）。
+
+### 2026-11-04
+
+- **`空 Latent` 新增 `latent类型`（4 档通道 / 压缩比）**：原来写死 4 通道 · 8×，
+  接 Krea 2（Wan21，16 通道）或 Flux2（128 通道 · 16×）只能靠 K采样器的
+  `fix_empty_latent_channels` 隐式补通道。现在可选 `SD / SDXL`、`SD3 / Flux / Krea 2`、
+  `Qwen-Image 2.1`、`Flux2`，默认仍是 4 通道（老工作流不变）。
+  选对时与官方 `EmptySD3LatentImage` 输出完全一致（实测 shape / dtype /
+  `downscale_ratio_spacial` 全等，张量差 = 0）。
+- **示例 `Krea2 Pose Editing (Ostris).json` 重做为「本插件节点为主」**：16 个节点里 7 个是本插件的
+  （`统一加载器` / `读图` / `参考姿势` / `空 Latent` / `采样器（一体）` / `图像对比` / `存图`），
+  余下 6 个是 ComfyUI 内置（`VAEDecode`、`FluxKontextImageScale`、`FluxKontextMultiReferenceLatentMethod`），
+  只有 3 个来自外部插件 `ComfyUI-Krea2-Ostris-Edit`。画布内新增 `Note` 节点，
+  写明「用到了哪个插件、三个必须配对的条件」，打开就知道缺什么。
+  原版本是从官方模板改的，19 个节点里只有 1 个是本插件的，已作废。
+- **`Ref Image Encode` 接 MOSS-Image / FIBO 2.0：模型类型选 `Flux2`（下拉未新增）**。
+  两者 latent 与 Flux2 同族（128 通道 / 16x，都用 `flux2-vae`），实测参考图能真正生效：
+  MOSS 20 步 16.7s / FIBO 4 步 10.6s 都能把站姿改成素材里的坐姿，画面无线稿残留
+  （对照组用官方编码器分别是 19.6s / 15.4s）。不新增专用类型的理由：MOSS 要 `edit_format`
+  （三档训练格式）+ `image_strength`、FIBO 要 `negative_prompt`，塞进一个类型会变成一半参数无效；
+  需要精确控制这些时直接用 `TextEncodeMOSSImageEdit` / `TextEncodeFibo2`。
+- **修正三处 Flux2 空 latent 的通道 / 尺寸**（`Ref Image Encode`、`OneImageMultiPrompt`、`Ref Independent`）：
+  - `Ref Image Encode` 和 `OneImageMultiPrompt` 的「纯文生图」分支原来写死 4 通道（SD 的 latent），
+    Flux2 实际是 128 通道。走 KSampler 时 `fix_empty_latent_channels` 会自动补齐
+    （4→128 平铺 + 8x→16x 缩放），所以**出图结果完全一致（实测 max abs diff = 0）**；
+    但这是隐式兜底，不经过 KSampler 的场景不会修。
+  - `Ref Independent` 的「空槽兜底」原来是 `torch.zeros(1, 4, 1, 1)`，这是**真的会出问题**：
+    auto-fix 只补通道、不改尺寸，采样照常跑完，最后解码出一张 **16×16 的废图**（不报错，很难查）。
+    已改为按模型类型给正确的通道与尺寸 —— 实测与官方 `EmptyFlux2LatentImage` 逐像素一致
+    （max abs diff = 0），出图从 16×16 恢复成 512×1024。
+- **`参考姿势` 已确认可驱动 Krea 2 编辑（骨架驱动）**，但**只有 Ostris 那套节点能吃骨架图**：
+  `TextEncodeKrea2OstrisEdit` 的 `image1/2/3` 都是 `IMAGE`，原图接 `image1`、参考姿势接 `image2`
+  即可，线稿不会被画进画面。实测 `krea_outfittransfer` + `kv_cache=开`、8 步 euler/simple CFG1，
+  36.1s 把站姿改成素材里的坐姿，脸/发型/服装/背景不变。
+  ⚠️ 另一套 `ComfyUI-krea2Edit`（`Krea2EditGroundedEncode` / `Krea2EditModelPatch`）的
+  `image_b` / `source_latent_b` 语义是「**第二个主体，要放进画面**」，接参考姿势会把人偶一起画出来
+  （实测）；它只能靠文字描述改姿势。**两套节点包不能混用**：把 krea2Edit 系 LoRA
+  （`krea2_identity_edit_v2`）接到 Ostris 节点上同样会贴线稿，几何不一致。
+  另：`kv_cache` 必须与 LoRA 训练方式一致，条件上要包 `FluxKontextMultiReferenceLatentMethod`。
+  当时附过示例 `Krea2 Pose Editing (Ostris).json`（实测 8 步出图，首次含模型加载约 86s），
+  但它对外部节点包有硬依赖、而且会换人，**已在 2026-11-06 移除**。要复现请照上面的链路自己接；
+  常态需求建议直接用 `Krea2 Pose Editing.json`（零外部依赖，人物一致性保持得住）。
+- **`统一加载器` 加载 Krea 2 编辑链已实测**：`clip_type=auto` 正确识别 `Krea2 → CLIPType.KREA2`，
+  TE 建成 `Krea2TEModel_`、VAE 校验 16 通道 / 3 维，LoRA 槽可直接加载 `krea_outfittransfer` 等编辑 LoRA
+  （256 个 patch）。与原生 `UNETLoader + LoraLoaderModelOnly` 对比：**无 LoRA 时逐像素一致（max 0）**；
+  带 LoRA 时 LoRA 影响幅度一致（平均差 60.66 vs 60.69），仅 4.6% 像素有细微差异，画面等效。
+- **`统一加载器` 支持 FIBO 2.0 与 MOSS-Image**：`CLIP 类型` 新增 `fibo2`、`moss_image`，
+  auto 模式按 UNet 类名（`Fibo2` / `MOSSImage`）自动识别。这两个和 `krea2` 共用权重文件，
+  但取层方式完全不同（`fibo2` 取 15 层中段 → 6×7680；`moss_image` 用 Qwen3.5-9B 的 last hidden state），
+  所以选错不是画质问题，是 conditioning 维度直接对不上。加载器同时校验 TE 类名
+  （`Fibo2TEModel_` / `MOSSImageTEModel_`）与 VAE（都走 Flux2 latent，必须 `flux2-vae*`）。
+  实测：同一 seed 下，统一加载器 vs 原生 `UNETLoader + CLIPLoader + VAELoader`
+  **逐像素完全一致**（max abs diff = 0，fibo2 与 moss-image 各一组，moss 的 edit LoRA 走加载器 LoRA 槽同样一致）。
+- **`采样器（一体）` 预设新增两条**：`FIBO 2.0 turbo（4步）`（euler/simple/CFG1）、
+  `MOSS-Image（30步·CFG4）`（euler/normal/CFG4）。FIBO 是 4 步蒸馏、给大反而崩；
+  MOSS 是 SFT 不是蒸馏，30 步 + CFG4 才是训练配置，降到 4 步会糊。数值取自
+  `FIBO2-文生图+图编辑.json` 与 `image_moss_image_edit.json`。
+- **`参考姿势` 已确认可驱动 MOSS-Image / FIBO 2.0 编辑**：这两个编码器的 `images` 都是
+  自动增长输入（MOSS `image_0..image_9`、FIBO `image_1..image_16`），
+  接法和 2511 一样——**原图接第一张、参考姿势接第二张**。实测两个模型都能把站姿照片改成素材里的坐姿，
+  人脸/发型/服装/背景保持不变（MOSS 30 步 20.2s、FIBO 4 步 16.1s）。
+  ⚠️ MOSS 的编辑能力来自 `moss_image_edit_lora_bf16`，且 `edit_format` 必须与 LoRA 训练格式配对
+  （`Moss-Image-Edit` / `ai-toolkit` / `ai-toolkit (kv_cache off)`），选错同样「出图但没改」。
+- **`参考姿势` 接 2511 / FLUX.2 这类编辑模型改为「多图编辑」接法**（README 补说明，代码无改动）：
+  之前单张接 `image1` 时**不报错、能出图，但人物姿势完全不变**——编辑模型把 `image1` 当作
+  「待修改的原图」，线稿进了 `image1` 就等于让它「重绘这张线稿」，提示词失效。
+  现在明确写清：原图接 `image1`、参考姿势接 `image2`，提示词写「保持 image1 的人物，姿势改成 image2」。
+  实测 2511 与 FLUX.2 在多图接法下都能按骨架重摆人物，**不需要 ControlNet，也不需要额外模型**。
+- **`参考姿势` 输出尺寸强制对齐 16 的倍数**：原来只按长边等比缩放，遇到异形比例（如
+  `1234×1000` → `1024×830`）会得到非 16 倍数的宽高，`VAEEncode` 只能自己 pad/crop，
+  边缘产生半像素位移——线稿和深度图对这种位移特别敏感，表现成「人物整体歪了一点」。
+  现在缩放后统一向下对齐到 16 的倍数（宁可少 15px 也不超出设定长边），用缩放而不是裁切，
+  避免切掉脚尖或手指。
+  实测现有 2878 张素材在 512/768/1024/1536 四个档位下**原本就全部对齐**，所以这一条对存量
+  素材没有任何变化，纯粹是给以后自己塞异形比例的图兜底。
+
+### 2026-11-03
+
+- **`采样器（一体）` 新增「⚡预设」下拉**（当天晚些时候由「模型预设」改名并挪到第一行，见下条）：
+  选中模型就把 `Sigma 曲线 / 采样器 / 调度器 / 步数 / CFG` 五项一次填好，不用每次手调。
+  当时 9 个预设：`Qwen-Image 2.1（25步）`、`Qwen-Image 2.1 turbo（8步·固定表）`、`Krea 2 turbo（8步）`、
+  `Krea 2 turbo・细节增强（12步）`、`FLUX.2 klein（4步·beta）`、`FLUX.2 klein・高清修复（4步）`、`Z-Image turbo（8步）`、
+  `Qwen-Image Layered（9步·CFG2.5）`、`Qwen-Image Edit 2511 AIO（4步）`，外加一个 `手动（不套用）`。
+  `降噪` 与 `噪波随机种` 不受预设影响 —— 那两项取决于任务而不是模型。
+- **预设是「一键填充」不是「持续锁定」**：套完预设后手改这五项里的任何一个，下拉会自动退回
+  `手动（不套用）`。这样控件显示永远是真实状态，不会出现「写着 Qwen 预设、其实在跑 40 步」。
+- **预设表只有一份**：写在 `sampler.py` 的 `MODEL_PRESETS`，前端通过
+  `GET /toggle_pass/sampler_presets` 拉取，不在 JS 里另抄一份，以后加模型只改 Python。
+  每一条的数值都来自本机 `user/default/workflows/` 里实际跑通的工作流，来源文件写在
+  `sampler.py` 的注释里。
+- **`⚡预设` 挪到参数控件第一行，并和 `Sigma 曲线` 分了名**：原来叫「模型预设」、排在最后一位，
+  结果两个问题叠在一起 —— 一是名字太长，窄节点上左边的标题被右边的值整个盖住，
+  看着像个无名下拉；二是紧挨着的 `采样方案` 选项也全是「模型名 + 步数」，两个下拉极易被
+  当成重复。现在改名为 `⚡预设` / `Sigma 曲线`（**只改 label，内部 name 一个都没动**），
+  位置换到所有参数控件的最前面。
+- **预设名保持完整模型名，靠节点宽度让标题露出来**：名字一旦缩成 `2511 AIO · 4步` 这种，
+  模型一多就分不清谁是谁，所以预设仍用 `Qwen-Image Edit 2511 AIO（4步）` 这类全名。
+  实测最长一条 `Qwen-Image Layered（9步·CFG2.5）` 占 238px，加上左侧 47px 的 `⚡预设`，
+  节点至少要 350px 宽才不会重叠 ⇒ 新建节点宽度 350，老存档（280/300/340）加载时自动补到 350
+  （只加宽不缩窄）。宽度不够时 litegraph 的值是右对齐画的，会把标题整个盖掉，这正是当初
+  「看着像没有名字」的真正原因。
+- **挪位置不迁移老存档也安全**：1.57 的存档带 `widgets_values_named`（按名字恢复，与顺序无关）；
+  更早的纯位置存档交给 `nodeData.fallbackWidgetsValuesNames` 按老顺序重新对号；万一还是有错位，
+  加载时发现「模型预设」拿到了采样方案的值会自动退回 `手动（不套用）`。
+  已实测四种存档（新建 / 带名字 / 纯位置 10 项 / 纯位置 9 项），七个参数全部各就各位。
+  预设改名同样有 `PRESET_ALIASES` 兜底（现在方向是「中途那版短名 → 当前全名」，
+  权威副本在 `sampler.py`，由接口下发，前端只留一份启动兜底），存过短名的工作流会自动换回全名。
+
+### 2026-11-02
+
+- **新增节点 `PoseReference`（参考姿势）**：从姿势素材库里选一张参考图，输出标准 `IMAGE`，
+  直接接任意编辑模型的 `images` 输入。素材根目录支持三层合并（`TP_POSE_ROOT` 环境变量 →
+  `models/pose_reference` → 插件自带 `pose_assets`），同名以靠前的为准。
+  `类别 / 姿势组 / 姿势` 三级联动下拉（4 大类、30 组、288 个姿势、5 个视角），
+  `源` 可在线稿与深度两套之间切换，`长边` 默认把 300×400 的素材放大到 1024，
+  `翻转` 做水平镜像。
+- **节点内缩略图（不用执行就能看图）**：任意下拉一改，节点下半部分立刻换成新的姿势图——
+  `类别/姿势组/姿势/视角/源/翻转` 六个参数都会触发，120ms 防抖，切得快也不会卡。
+  首次加载自动把节点撑高到够放下整张图，之后随节点大小自适应缩放。
+  图由新增的 `GET /toggle_pass/pose_image` 路由按参数实时渲染（带浏览器缓存），
+  不走 `node.imgs`，因此不依赖前端内部绘制逻辑，换 ComfyUI 版本也不会失效。
+- **工作流自带参考图**：选中的素材以 data URI 内嵌进 `properties.tp_pose`，随工作流保存。
+  别人下载后即使没有 `D:\pose`，也能看到图并直接出图（后端在素材库取图失败时用
+  `extra_pnginfo` 读回内嵌数据）。代价约 +8~14KB/节点；本机素材库存在时优先读素材库。
+  缩略图同时改走 `raw=1` 返回素材原文件，单次 6KB（原先渲染成 512 PNG 要 80KB）。
+- **示例工作流改为随插件分发**：`example_workflows/Pose Reference Editing.json`。因为素材已经内嵌，
+  它不再依赖 `D:\pose`，可直接拖进画布跑通；原先那句「故意不放进 example_workflows」的说法作废。
+  注意：内嵌数据只在**保存工作流**时落盘，改动前保存的旧工作流打开后会自动补上，但需要再存一次。
+- **素材分成两层，插件自带一份可以直接分发**：新增 `pose_assets/`（1530 张 / 9.6MB），只放
+  `上半身单人`、`全身单人・标准系` 两个大类；私密或自建素材留在私有目录。运行时合并成一个索引，
+  查找顺序是 **`TP_POSE_ROOT` → `models/pose_reference` → 插件自带**，同名以靠前的为准。
+  别人装完节点就有 153 个姿势可用，不需要额外下载任何东西。
+- **素材统一收进 `models/pose_reference`**：原先放在 `D:\pose` 的
+  `全身单人・性感系`、`双人缠绵` 两个大类（1348 张 / 8.9MB）迁到
+  `ComfyUI/models/pose_reference/`，与插件自带的两个大类正好互补、互不重叠，
+  合并后仍是 4 类 30 组 288 姿势。这样素材跟着 `models` 备份走，`D:\pose` 不再需要。
+- **素材热更新**：新增 `GET /toggle_pass/pose_stamp` 轻量指纹接口，前端每 15 秒比对一次
+  （画布上没有本节点时直接跳过）。往 `models/pose_reference` 里丢新姿势组，**约 15 秒内下拉自动出现**，
+  删掉同样自动消失，全程不用刷新页面；画布右键另加了 `🔄 刷新姿势素材库` 手动兜底。
+  指纹扫到「大类/姿势组」这一层，只看根目录 mtime 是发现不了往已有大类里加新组的。
+- **修复「素材更新后下拉切不动、必须重启」**：下拉回调原来在挂载时闭包捕获了当时的素材树，
+  素材库热更新后 `node._tpPoseSync` 换了新树，回调却还在用旧的 —— 新增的类别/组切不过去，
+  切走再切回会跳回旧内容。改成回调里每次现取 `node._tpPoseSync`。
+- **修复「换了素材图不变」**：`pose_image` 原来发 `Cache-Control: public, max-age=86400` 且无
+  ETag，浏览器 24 小时内不回源，同名替换素材后永远显示旧图。改成 `no-cache` + `ETag`
+  （mtime+size+参数），没变返回 304，变了立刻拿到新内容；同时刷新素材库时会清掉前端的
+  URL 去重，让图真的重取。
+- **修复「拉目录树失败一次就永久没有联动」**：`fetchTree()` 的 Promise 在失败时会把 `null`
+  缓存住，之后所有新建节点都拿这个 `null`，只能刷新页面才恢复。改成失败不留在缓存里，
+  下一次自动重试；取图失败时也不再锁住 URL，后端恢复后切回同一姿势会重新请求。
+- **修复「生图一开始节点就再也切不动」**：ComfyUI 1.57 前端看到节点有 `node.imgs`，
+  就会自动挂一个 `$$canvas-image-preview` 的官方预览 widget —— 它占掉节点底部一行，
+  并把执行输出的那张 PNG **画在节点预览区上面**。于是从第一次生成开始，节点下部就被
+  一张定死的图盖住：下拉照点、节点自己画的预览在背面也确实换了，但画面上永远不动，
+  看着就是"卡死了，只能重启"。现在 `load()` 不再返回 `ui.images`（从根上不产生 `node.imgs`），
+  前端另外在 `onExecuted` 和 300ms 巡检里把已挂上的官方预览摘掉，
+  已经跑过的工作流不用重建节点也会自己恢复。
+- **修复「切下拉图不换，要重启才变」**：以前预览图只靠下拉的 `callback` 通知刷新，
+  一旦这条通知漏掉（第三方扩展重写回调、程序化改值、前端版本差异），节点就永远停在上一张图上。
+  现在加了两道不依赖回调的自检：绘制时比对参数、以及每 300ms 一次的轻量巡检，
+  参数和已加载的图对不上就自己重取。不管是点下拉、拖参数、加载工作流还是脚本改值，图都会跟上。
+- **修复「取不到图时假装没事」**：以前任何一次取图失败都会静默退回内嵌图，看起来就是"点了没反应"。
+  现在只有内嵌的那张**正好等于当前所选**时才用它兜底，否则节点上直接显示
+  `这张没取到（素材库缺失）`，控制台同步打一条 `[PoseReference] 取图失败`，一眼看出卡在哪。
+- **排查入口**：浏览器控制台敲 `__tpPoseDiag()`，用表格打印每个节点的选中值、上次请求的 URL、
+  已加载图的尺寸、是否来自网络、错误信息。
+- 素材库不存在时不再报一句含糊的错：`build_path` 逐根查找，失败信息会列出每个搜过的路径
+  以及具体原因（没有该套 / 没有该视角），换机器排查快得多。
+- **默认选线稿**：FIBO 2.0 / Qwen-Image-2.1 各两组对照，深度套 3/3 都会自己补出不存在的鞋或护具
+  （深度图没有边界线，脚底与地面亮度太接近），线稿套只有 1/3 出现小瑕疵。
+- 素材索引带 mtime 缓存，首次扫描约 10ms、之后 0.1ms，增删素材会自动重建。
+
+### 2026-10-25
+
+- **新增示例工作流 `example_workflows/All-in-One Demo.json`**：一个图里放齐本插件全部交互节点
+  （`ToggleUnifiedLoader` / `ToggleSampler` / `ToggleLoadImage` ×2 / `ToggleImageCompare` /
+  `TogglePreviewImage` / `DynamicRefImageEncode` / `SaveLoadImage` + `VAEDecode`），
+  **不依赖任何第三方节点**，拖进画布即可跑通；原 `Toggle-Pass-Test.json` 已改名为它
+- **README 新增「示例工作流」小节**（中英双语）：列出 `example_workflows/` 里 4 个示例分别演示什么、
+  各自需要哪些额外依赖 —— 此前 README 对示例目录零引用，用户不知道这些文件存在
+
+### 2026-10-24
+
+- **「全局广播」开关搬到节点顶部**：从 widget 列表末尾那整行，移到 `加载模式` 的 `UNet / Checkpoint`
+  胶囊**正下方、左半边**，缩成一个小胶囊（左写「广播」，右侧是开关轨道）
+  - 尺寸与胶囊对齐：**宽度 = 胶囊的一半**，**高度与胶囊完全相同**（都是 14px，上下边沿齐平）
+  - 外观与节点其它控件同语言：淡色托底 + 细轨道 + 白滑块，开启时转高亮蓝（和 `UNet` 选中态呼应），
+    鼠标悬停时托底提亮并浮出一道细描边
+  - 改法是「原生 toggle 保留但隐藏 + 自绘」：widget 对象、名字、`properties` 存储、`serialize:false`
+    全部原样不动 —— `widgets_values` 一项都不动，旧工作流零影响
+  - 顺带节点底部少了一整行，只剩「添加 / 移除 LoRA」两个按钮
+  - 纯前端改动，**不用重启 ComfyUI**，`Ctrl+F5` 即可
+- **修复画布拖框「框不在鼠标那里」**：ComfyUI 1.57.x 会把选择框裁剪到 `.graph-canvas-panel`
+  （实测该容器只覆盖画布中下部 `[64,488,...]`），在画布上半部分拖框时框的顶边被强行压到 488、
+  高度被压成 0，看起来像「鼠标在左上角拉、框跑到中间」。新增 `js/selection_rect_fix.js`
+  按真实鼠标点重写框的位置，纯显示层修正，不改画布状态
+  - 关闭方法：控制台 `localStorage.setItem("tp_selrect_fix", "off")` 后刷新
+- **预览曲线改为从节点背后穿过**：曲线画在背景层（节点下层），节点之间的空白处正常显示，
+  穿过节点的地方被节点自然遮住 —— 不会再盖住节点标题和参数。节点排得密时可见部分会少一些，属正常
+
+### 2026-10-23
+
+- **新增 `ToggleUnifiedLoader` 的「全局广播」开关**（节点自带，不依赖 `cg-use-everywhere`）
+  - 打开后点「运行」自动把 `MODEL` / `CLIP` / `VAE` 接到图里所有**空着的**同类型输入上，
+    提交完立刻拆线，不会被存进工作流
+  - 开关打开期间，画布上用**紫色曲线**画出「运行时会自动接上的线」，两端带小圆点，
+    方便一眼核对接去哪（曲线只是预览，不建真实连线、不参与保存）
+  - 曲线从节点**背后**穿过（画在节点下层）：空白处可见，节点上一根线都不落 —— 不挡标题和参数
+  - 只补空位，手连的一根都不动；关掉就是完全手动
+  - 开关状态存在节点 `properties` 里，**不占 `widgets_values`**，旧工作流参数位置一项不变
+  - 与 `cg-use-everywhere` 共存（不动它的 `ue_convert`）
+  - 纯前端改动，**不用重启 ComfyUI**，`Ctrl+F5` 即可
+- **`采样器（一体）` 初始宽度改为 300**（高度不变），「自动（按步数 + 调度器）」这类长值不再被截断
+
+### 2026-10-21
+
+- **新增 `采样器（一体）`（`ToggleSampler`）** —— 把官方「随机噪波 + CFG引导器 + K采样器选择 +
+  基础调度器/手动Sigmas → SamplerCustomAdvanced」四件套收进一个节点。
+  - 模型 / 条件 / Latent 仍是插槽，换模型不用换节点
+  - `采样方案` 三档：`自动`（= 官方 BasicScheduler，数值等价）、预置表（Qwen-Image-2.1 turbo 8步、
+    LTX-2 视频 8步/3步）、`自定义表`（手填 sigma，接新模型不用改代码）
+  - 选预置表时 `步数` / `调度器` 会变灰并锁住，`步数` 显示该表真实步数；切回 `自动` 自动还原你原来设的步数
+  - sigma 表规则：逗号分隔、严格递减、末值必须是 0、N 个点 = N-1 步
+- **新增 Krea2 支持** —— `ToggleUnifiedLoader`、`DynamicRefImageEncode`、`DynamicRefIndependent`、
+  `OneImageMultiPrompt` 全都能跑 Krea2
+  - 加载器从 UNet 自动识别 `Krea2` → `CLIPType.KREA2` + `Krea2TEModel`，无需手选 clip 类型
+  - 三个编码节点的「模型类型」新增 `Krea2`：参考图按约 1 MP 编码、VAE 只吃 RGB、
+    自动补视觉占位符、自动设 `reference_latents_method = index_timestep_zero`、输出全零 16 通道 latent
+- **加载器改进**
+  - CLIP 类型改为**由模型类推导**，不再二次读取 UNet 去猜（省一次 6.9 GB 读取，也不会再静默失败）
+  - 新增 **VAE 匹配检查**：加载时比对 latent 通道 / 维度，配错直接提示该换哪个 VAE 文件
+    （否则会在 `VAEDecode` 报 `IndexError: tuple index out of range`，很难自查）
+
+### 2026-10-10
+
+- **新增 `ToggleImageCompare` 图片对比节点** —— 把多张原图与多张结果拼成一张对比图，节点内直接预览。
+  两组动态输入（原图 `source1~10`、结果 `result1~10`）各自独立增减，**N 原图 + 1 结果** 与
+  **N 原图 + N 结果** 都支持；排版 `自动` 会按数量自动在「成对并排」和「上下两排」之间选，
+  另有「自动网格」；可给每张图烧 `原1 / 结1` 角标。
+  - **怎么用**：把原图接到 `source*`、结果接到 `result*`，输出直接给 `SaveLoadImage` 或预览节点；
+    排版按**实际接上的图**算，数量变了不用改设置。
+- **加载模式开关（外观与交互定型）** —— `ToggleUnifiedLoader` 的 `加载模式` 在下拉框基础上改成
+  **两段式开关 `UNet | Checkpoint`**：点左半 = 分离组件、点右半 = 一体包，当前档用蓝色滑块高亮，
+  另一半悬停时有淡色反馈。
+  位置在**节点顶部第一行，与输出插槽 `模型 / MODEL` 同一行**；左端与下方所有下拉框的左边缘对齐，
+  宽度固定、两档同宽（切档不跳动），右侧给插槽文字留出安全距离。
+  - **怎么用**：直接点开关左半 / 右半切换，不再弹出下拉菜单；当前档一眼可见。
+  - 底层 `加载模式` 参数照常保存与序列化，**旧工作流与 API 调用零影响** —— 开关只接管绘制与点击，
+    没有新增或删除任何参数。
+- **开关交互修复**：① 点击分界与绘制统一为同一套几何（此前节点较宽时点中段会切错档、点开关右侧空白会误切）；
+  ② 挂在节点上的绘制 / 点击钩子改为**链式调用**，不再覆盖别的插件挂的同名钩子；
+  ③ 鼠标移出开关后悬停高亮不再残留。
+
+### 2026-10-09
+
+- **加载模式开关上线**：`UNet`（分离组件）/ `Checkpoint`（一体包）两段切换，替代原来的下拉框。
+- **模型参数置顶**：`ckpt_name` / `unet_name` / `clip_name` / `vae_name` / `clip_type` 排到参数列表最前，
+  一体包模式下一眼就能看到 checkpoint 行。旧工作流加载时**按参数名自动迁移**，不受顺序变化影响。
+- **LoRA 状态提示**：按钮回传每个 LoRA 实际写入的 patch 数与模块覆盖率（如 `192/224p`）；
+  分体模式下还会比对 CLIP 实例类名与模型是否配套，不匹配时显示 `⚠️CLIP不符`。
+
+### 2026-10-08
+
+- **一体包加载模式**：`ToggleUnifiedLoader` 融合 Checkpoint 加载器，新增 `加载模式` 参数 ——
+  `分离组件 (UNet+CLIP+VAE)`（默认）/ `Checkpoint 一体包`，两种模式都能挂 LoRA。
+- **LoRA 强度合并为一个滑块**：`model` 与 `clip` 两个强度合并显示，CLIP 侧自动跟随 MODEL 侧 ——
+  界面上看到的强度就是实际生效的强度。
+- **LoRA 诊断**：按钮显示已启用的 LoRA 数量与「强度为 0」告警；控制台打印每个 LoRA 最终挂上了多少 patch。
+- 修复：LoRA 组全部移除后节点自动**回收高度**（不再留一大条空框）；「- 移除 LoRA」不再把强度清成 0。
+
+### 2026-10-03
+
+- 修复画布菜单新建节点的落点：改为**鼠标所在位置**（原来是画布可视中心）。
+
+### 2026-09-25
+
+- **画布菜单入口**「🔄 Toggle-Pass」：在画布右键菜单里即可添加本插件的节点，节点列表运行时自动收集。
+  - **怎么用**：画布空白处右键 → 菜单里选 `🔄 Toggle-Pass`。
+- `OneImageMultiPrompt` 支持 **Qwen Image 2.1**；`prompt数量` 自动收起多余的 prompt 输入框。
+
+### 2026-09-24
+
+- `DynamicRefImageEncode` / `DynamicRefIndependent` 支持 **Qwen Image 2.1**。
+- **分辨率控制**：新增 `宽高比` + `百万像素` + `倍数`，以及 `参考图分辨率`、`尺寸适配`。
+- 修复 CLIP 类型检测（此前 Qwen Image 2.1 的专用文本编码器无法加载，参考图会被静默丢弃）。
+- 界面按「模型类型」自动显隐参数；旧工作流的错位脏值自动归位；`倍数` 下限由 16 提到 32。
+
+### 2026-05-12
+
+- `DynamicRefImageEncode` 新增 `Qwen Layered` 模型类型：选中后显示「图层数」，输出分层 latent。
+- `ToggleUnifiedLoader` 新增「- 移除 LoRA」按钮（从最后一组开始移除）。
+
+### 2026-05-10
+
+- 新增 **`ToggleUnifiedLoader`**（统一模型 + LoRA 加载器）：把 UNet / CLIP / VAE / 多 LoRA 整合到单一节点，
+  最多 10 组 LoRA 槽位（选了才显示），支持自动检测 CLIP 类型。
+
+### 2026-05-08
+
+- 新增节点：`SaveLoadImage`（保存/加载切换）、`ToggleLoadImage`、`TogglePreviewImage`、`ToggleEmptyLatent`。
+- 新增**隐私遮罩**（👁 / 🫣 切换，适用于图片类节点）。
+- 新增 **DynamicRef 系列**：`DynamicRefIndependent`、`DynamicRefImageEncode`、`DynamicRefLatentInput`、
+  `ImageBatcher`、`OneImageMultiPrompt`。
+
+---
+
+## 安装 / Installation
+
+Place this folder under `ComfyUI/custom_nodes/` and restart ComfyUI.
+
+把本文件夹放到 `ComfyUI/custom_nodes/` 目录下，重启 ComfyUI 即可。
diff --git a/__init__.py b/__init__.py
index 5eb0bd2..9d974c4 100644
--- a/__init__.py
+++ b/__init__.py
@@ -2,9 +2,14 @@
 ComfyUI Toggle Pass - Toggle nodes + Image IO + Multi-Ref Encode
 Author: Clever
 """
+# 版本号：与 pyproject.toml 的 [project].version 保持一致。
+# ComfyUI-Manager / Comfy Registry 读 pyproject.toml；这里供运行时打印、方便看日志排查。
+__version__ = "1.0.0"
+
 from .multi_ref_encode import NODE_CLASS_MAPPINGS as MULTI_REF_NODES, NODE_DISPLAY_NAME_MAPPINGS as MULTI_REF_NAMES
 from .unified_loader import NODE_CLASS_MAPPINGS as UNIFIED_NODES, NODE_DISPLAY_NAME_MAPPINGS as UNIFIED_NAMES
 from .sampler import NODE_CLASS_MAPPINGS as SAMPLER_NODES, NODE_DISPLAY_NAME_MAPPINGS as SAMPLER_NAMES
+from .pose_reference import NODE_CLASS_MAPPINGS as POSE_NODES, NODE_DISPLAY_NAME_MAPPINGS as POSE_NAMES
 
 import os
 import json
@@ -298,6 +303,25 @@ class SaveLoadImage:
 
 
 class ToggleEmptyLatent:
+    """开关空 Latent。
+
+    `latent类型` 决定通道数与空间压缩比，必须和底模的 latent_format 对齐：
+      · SD / SDXL           4 通道  8×
+      · SD3 / Flux / Krea 2 16 通道 8×   （Krea2 走 Wan21 格式，latent_dimensions=3）
+      · Qwen-Image 2.1      64 通道 16×
+      · Flux2              128 通道 16×
+    通道给错不会立刻报错 —— K采样器里的 fix_empty_latent_channels 只会在
+    「latent 全零」时按模型补齐通道，属于隐式兜底；直接接别的 latent 处理节点就不会修。
+    """
+
+    LATENT_PRESETS = {
+        "SD / SDXL（4 通道 · 8×）": (4, 8),
+        "SD3 / Flux / Krea 2（16 通道 · 8×）": (16, 8),
+        "Qwen-Image 2.1（64 通道 · 16×）": (64, 16),
+        "Flux2（128 通道 · 16×）": (128, 16),
+    }
+    DEFAULT_LATENT = "SD / SDXL（4 通道 · 8×）"
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -310,6 +334,12 @@ class ToggleEmptyLatent:
             "optional": {
                 "fallback_width": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
                 "fallback_height": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
+                # ⚠️ 必须放在 fallback_height 之后 —— 前端控件顺序是 required 全部 + optional，
+                # 老存档的 widgets_values 是 [width,height,batch_size,enabled,fb_w,fb_h] 六项，
+                # 早先把它插在 enabled 后面（fb_w 之前），老图加载时 fb_w 的 1024 会灌进来，
+                # 点运行直接 value_not_in_list: latent类型 1024 ⇒ 400，看着就是「跑不了」。
+                # 放最后 ⇒ 老图前 6 位对得上，这一项拿 default（4 通道，即老行为）。
+                "latent类型": (list(cls.LATENT_PRESETS.keys()), {"default": cls.DEFAULT_LATENT}),
             },
         }
 
@@ -318,7 +348,8 @@ class ToggleEmptyLatent:
     FUNCTION = "generate"
     CATEGORY = "Toggle-Pass"
 
-    def generate(self, width, height, batch_size=1, enabled="ON", fallback_width=None, fallback_height=None):
+    def generate(self, width, height, batch_size=1, enabled="ON", latent类型=None,
+                 fallback_width=None, fallback_height=None):
         if fallback_width is None:
             fallback_width = 1024
         if fallback_height is None:
@@ -326,10 +357,14 @@ class ToggleEmptyLatent:
         if enabled != "ON":
             width = fallback_width
             height = fallback_height
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8],
+        channels, scale = self.LATENT_PRESETS.get(latent类型, self.LATENT_PRESETS[self.DEFAULT_LATENT])
+        # 宽高先向下对齐到压缩比的倍数，避免出现非整数网格
+        h = max(1, height // scale)
+        w = max(1, width // scale)
+        latent = torch.zeros([batch_size, channels, h, w],
                              device=comfy.model_management.intermediate_device(),
                              dtype=comfy.model_management.intermediate_dtype())
-        return ({"samples": latent, "downscale_ratio_spacial": 8},)
+        return ({"samples": latent, "downscale_ratio_spacial": scale},)
 
 
 # ==================== Image Compare ====================
@@ -579,6 +614,7 @@ NODE_CLASS_MAPPINGS = {
     **MULTI_REF_NODES,
     **UNIFIED_NODES,
     **SAMPLER_NODES,
+    **POSE_NODES,
     "ToggleEmptyLatent": ToggleEmptyLatent,
     "SaveLoadImage": SaveLoadImage,
     "ToggleLoadImage": ToggleLoadImage,
@@ -590,9 +626,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     **MULTI_REF_NAMES,
     **UNIFIED_NAMES,
     **SAMPLER_NAMES,
+    **POSE_NAMES,
     "ToggleEmptyLatent": "Toggle Empty Latent",
     "SaveLoadImage": "Save/Load Image",
     "ToggleLoadImage": "Toggle Load Image",
     "TogglePreviewImage": "Toggle Preview Image",
     "ToggleImageCompare": "图片对比",
 }
+
+print(f"[Toggle-Pass] v{__version__} loaded — {len(NODE_CLASS_MAPPINGS)} nodes")
diff --git a/example_workflows/All-in-One Demo.json b/example_workflows/All-in-One Demo.json
index de521ef..58ad7a8 100644
--- a/example_workflows/All-in-One Demo.json	
+++ b/example_workflows/All-in-One Demo.json	
@@ -1 +1 @@
-{"id":"a6469650-cb96-4ae7-87a1-fd6014dd3877","revision":0,"last_node_id":32,"last_link_id":66,"nodes":[{"id":1,"type":"DynamicRefImageEncode","pos":[548.2320130660914,-65.8117662047309],"size":[270,462],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":null},{"localized_name":"vae","name":"vae","type":"VAE","link":null},{"localized_name":"image1","name":"image1","shape":7,"type":"IMAGE","link":32},{"localized_name":"num_images","name":"num_images","type":"INT","widget":{"name":"num_images"},"link":null},{"localized_name":"prompt","name":"prompt","type":"STRING","widget":{"name":"prompt"},"link":null},{"localized_name":"instruction","name":"instruction","type":"STRING","widget":{"name":"instruction"},"link":null},{"localized_name":"模型类型","name":"模型类型","shape":7,"type":"COMBO","widget":{"name":"模型类型"},"link":null},{"localized_name":"匹配原图尺寸","name":"匹配原图尺寸","shape":7,"type":"COMBO","widget":{"name":"匹配原图尺寸"},"link":null},{"localized_name":"输出宽度","name":"输出宽度","shape":7,"type":"INT","widget":{"name":"输出宽度"},"link":null},{"localized_name":"输出高度","name":"输出高度","shape":7,"type":"INT","widget":{"name":"输出高度"},"link":null},{"localized_name":"尺寸适配","name":"尺寸适配","shape":7,"type":"COMBO","widget":{"name":"尺寸适配"},"link":null},{"localized_name":"图层数","name":"图层数","shape":7,"type":"INT","widget":{"name":"图层数"},"link":null},{"localized_name":"宽高比","name":"宽高比","shape":7,"type":"COMBO","widget":{"name":"宽高比"},"link":null},{"localized_name":"百万像素","name":"百万像素","shape":7,"type":"FLOAT","widget":{"name":"百万像素"},"link":null},{"localized_name":"倍数","name":"倍数","shape":7,"type":"INT","widget":{"name":"倍数"},"link":null},{"localized_name":"参考图分辨率","name":"参考图分辨率","shape":7,"type":"INT","widget":{"name":"参考图分辨率"},"link":null},{"name":"image2","type":"IMAGE","link":33},{"name":"image3","type":"IMAGE","link":null}],"outputs":[{"localized_name":"conditioning","name":"conditioning","type":"CONDITIONING","links":[1,3]},{"localized_name":"latent","name":"latent","type":"LATENT","links":[2]}],"properties":{"Node name for S&R":"DynamicRefImageEncode","ue_properties":{"widget_ue_connectable":{},"input_ue_unconnectable":{},"version":"7.8"}},"widgets_values":[3,"","Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.","Qwen Image 2.1","ON",1920,1080,"参考图保比例",3,"自定义（用输出宽度/高度）",1,32,1024],"widgets_values_named":{"num_images":3,"prompt":"","instruction":"Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.","模型类型":"Qwen Image 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new file mode 100644
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+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name_8"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_model_8",
+          "name": "lora_strength_model_8",
+          "type": "FLOAT",
+          "widget": {
+            "name": "lora_strength_model_8"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_clip_8",
+          "name": "lora_strength_clip_8",
+          "type": "FLOAT",
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+            "name": "lora_strength_clip_8"
+          },
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+        },
+        {
+          "localized_name": "lora_name_9",
+          "name": "lora_name_9",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name_9"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_model_9",
+          "name": "lora_strength_model_9",
+          "type": "FLOAT",
+          "widget": {
+            "name": "lora_strength_model_9"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_clip_9",
+          "name": "lora_strength_clip_9",
+          "type": "FLOAT",
+          "widget": {
+            "name": "lora_strength_clip_9"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_name_10",
+          "name": "lora_name_10",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name_10"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_model_10",
+          "name": "lora_strength_model_10",
+          "type": "FLOAT",
+          "widget": {
+            "name": "lora_strength_model_10"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "lora_strength_clip_10",
+          "name": "lora_strength_clip_10",
+          "type": "FLOAT",
+          "widget": {
+            "name": "lora_strength_clip_10"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "加载模式",
+          "name": "加载模式",
+          "type": "COMBO",
+          "widget": {
+            "name": "加载模式"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "ckpt_name",
+          "name": "ckpt_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "ckpt_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "模型",
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            6
+          ]
+        },
+        {
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            1
+          ]
+        },
+        {
+          "localized_name": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            2,
+            11
+          ]
+        }
+      ],
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+        "ue_properties": {
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+          "input_ue_unconnectable": {}
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+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "ToggleUnifiedLoader"
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+        1,
+        1,
+        "none",
+        1,
+        1,
+        null,
+        null
+      ],
+      "widgets_values_named": {
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+        "unet_name": "krea2\\krea2_turbo_fp8_scaled.safetensors",
+        "clip_name": "krea2\\qwen3vl_4b_fp8_scaled.safetensors",
+        "vae_name": "qwen_image_vae.safetensors",
+        "clip_type": "auto",
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+        "lora_strength_model_1": 1,
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+        "lora_strength_clip_2": 1,
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+        "lora_name_8": "none",
+        "lora_strength_model_8": 1,
+        "lora_strength_clip_8": 1,
+        "lora_name_9": "none",
+        "lora_strength_model_9": 1,
+        "lora_strength_clip_9": 1,
+        "lora_name_10": "none",
+        "lora_strength_model_10": 1,
+        "lora_strength_clip_10": 1,
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+        "_tp_rm_lora_btn": null
+      }
+    },
+    {
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+      "type": "ToggleLoadImage",
+      "pos": [
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+        780
+      ],
+      "size": [
+        270,
+        102
+      ],
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+      "mode": 0,
+      "inputs": [
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+          "name": "image",
+          "type": "COMBO",
+          "widget": {
+            "name": "image"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "upload",
+          "name": "upload",
+          "type": "IMAGEUPLOAD",
+          "widget": {
+            "name": "upload"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "图像",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            4,
+            12
+          ]
+        },
+        {
+          "localized_name": "遮罩",
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "ToggleLoadImage"
+      },
+      "widgets_values": [
+        "20251213_210402.png",
+        "image"
+      ],
+      "widgets_values_named": {
+        "image": "20251213_210402.png",
+        "upload": "image"
+      }
+    },
+    {
+      "id": 3,
+      "type": "PoseReference",
+      "pos": [
+        420,
+        780
+      ],
+      "size": [
+        340,
+        508
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "category",
+          "name": "category",
+          "type": "COMBO",
+          "widget": {
+            "name": "category"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "group",
+          "name": "group",
+          "type": "COMBO",
+          "widget": {
+            "name": "group"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "pose",
+          "name": "pose",
+          "type": "COMBO",
+          "widget": {
+            "name": "pose"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "view",
+          "name": "view",
+          "type": "COMBO",
+          "widget": {
+            "name": "view"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "source",
+          "name": "source",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "source"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "long_edge",
+          "name": "long_edge",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "long_edge"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "flip",
+          "name": "flip",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "flip"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            3
+          ]
+        }
+      ],
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+          "input_ue_unconnectable": {}
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+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "PoseReference",
+        "tp_pose": {
+          "key": "全身单人・性感系|坐姿|P017_正坐・挺胸后仰|正面|线稿",
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"
+        }
+      },
+      "widgets_values": [
+        "全身单人・性感系",
+        "坐姿",
+        "P017_正坐・挺胸后仰",
+        "正面",
+        "线稿",
+        1024,
+        "关"
+      ],
+      "widgets_values_named": {
+        "category": "全身单人・性感系",
+        "group": "坐姿",
+        "pose": "P017_正坐・挺胸后仰",
+        "view": "正面",
+        "source": "线稿",
+        "long_edge": 1024,
+        "flip": "关"
+      }
+    },
+    {
+      "id": 4,
+      "type": "DynamicRefImageEncode",
+      "pos": [
+        780,
+        300
+      ],
+      "size": [
+        270,
+        322
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 1
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 2
+        },
+        {
+          "localized_name": "image1",
+          "name": "image1",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 3
+        },
+        {
+          "localized_name": "num_images",
+          "name": "num_images",
+          "type": "INT",
+          "widget": {
+            "name": "num_images"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "instruction",
+          "name": "instruction",
+          "type": "STRING",
+          "widget": {
+            "name": "instruction"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "模型类型",
+          "name": "模型类型",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "模型类型"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "匹配原图尺寸",
+          "name": "匹配原图尺寸",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "匹配原图尺寸"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "输出宽度",
+          "name": "输出宽度",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "输出宽度"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "输出高度",
+          "name": "输出高度",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "输出高度"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "尺寸适配",
+          "name": "尺寸适配",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "尺寸适配"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "图层数",
+          "name": "图层数",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "图层数"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "宽高比",
+          "name": "宽高比",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "宽高比"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "百万像素",
+          "name": "百万像素",
+          "shape": 7,
+          "type": "FLOAT",
+          "widget": {
+            "name": "百万像素"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "倍数",
+          "name": "倍数",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "倍数"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "参考图分辨率",
+          "name": "参考图分辨率",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "参考图分辨率"
+          },
+          "link": null
+        },
+        {
+          "name": "image2",
+          "type": "IMAGE",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "conditioning",
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "links": [
+            7
+          ]
+        },
+        {
+          "localized_name": "latent",
+          "name": "latent",
+          "type": "LATENT",
+          "links": [
+            9
+          ]
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "DynamicRefImageEncode"
+      },
+      "widgets_values": [
+        2,
+        "Render the woman from image 2 in the exact pose of the mannequin in image 1: seated upright, chest lifted and leaning back, legs together, hands resting beside her hips. Keep her face, hair, white swimsuit and the beach background unchanged.",
+        "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.",
+        "Krea2",
+        "OFF",
+        720,
+        1440,
+        "参考图保比例",
+        3,
+        "1:1 (Square)",
+        1,
+        32,
+        1024
+      ],
+      "widgets_values_named": {
+        "num_images": 2,
+        "prompt": "Render the woman from image 2 in the exact pose of the mannequin in image 1: seated upright, chest lifted and leaning back, legs together, hands resting beside her hips. Keep her face, hair, white swimsuit and the beach background unchanged.",
+        "instruction": "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.",
+        "模型类型": "Krea2",
+        "匹配原图尺寸": "OFF",
+        "输出宽度": 720,
+        "输出高度": 1440,
+        "尺寸适配": "参考图保比例",
+        "图层数": 3,
+        "宽高比": "1:1 (Square)",
+        "百万像素": 1,
+        "倍数": 32,
+        "参考图分辨率": 1024
+      }
+    },
+    {
+      "id": 6,
+      "type": "ToggleSampler",
+      "pos": [
+        1120,
+        80
+      ],
+      "size": [
+        350,
+        364
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "模型",
+          "name": "模型",
+          "type": "MODEL",
+          "link": 6
+        },
+        {
+          "localized_name": "正面条件",
+          "name": "正面条件",
+          "type": "CONDITIONING",
+          "link": 7
+        },
+        {
+          "localized_name": "负面条件",
+          "name": "负面条件",
+          "type": "CONDITIONING",
+          "link": 8
+        },
+        {
+          "localized_name": "Latent图像",
+          "name": "Latent图像",
+          "type": "LATENT",
+          "link": 9
+        },
+        {
+          "localized_name": "模型预设",
+          "name": "模型预设",
+          "type": "COMBO",
+          "widget": {
+            "name": "模型预设"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "采样方案",
+          "name": "采样方案",
+          "type": "COMBO",
+          "widget": {
+            "name": "采样方案"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "采样器",
+          "name": "采样器",
+          "type": "COMBO",
+          "widget": {
+            "name": "采样器"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "调度器",
+          "name": "调度器",
+          "type": "COMBO",
+          "widget": {
+            "name": "调度器"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "步数",
+          "name": "步数",
+          "type": "INT",
+          "widget": {
+            "name": "步数"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "CFG",
+          "name": "CFG",
+          "type": "FLOAT",
+          "widget": {
+            "name": "CFG"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "降噪",
+          "name": "降噪",
+          "type": "FLOAT",
+          "widget": {
+            "name": "降噪"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "噪波随机种",
+          "name": "噪波随机种",
+          "type": "INT",
+          "widget": {
+            "name": "噪波随机种"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "自定义Sigma表",
+          "name": "自定义Sigma表",
+          "type": "STRING",
+          "widget": {
+            "name": "自定义Sigma表"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "Latent",
+          "name": "Latent",
+          "type": "LATENT",
+          "links": [
+            10
+          ]
+        },
+        {
+          "localized_name": "降噪Latent",
+          "name": "降噪Latent",
+          "type": "LATENT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "ToggleSampler"
+      },
+      "widgets_values": [
+        "Krea 2 turbo（8步）",
+        "自动（按步数 + 调度器）",
+        "euler",
+        "simple",
+        8,
+        1,
+        1,
+        0,
+        "randomize",
+        "1.0, 0.978453, 0.95418, 0.926626, 0.89508, 0.845148, 0.704534, 0.414568, 0"
+      ],
+      "widgets_values_named": {
+        "模型预设": "Krea 2 turbo（8步）",
+        "采样方案": "自动（按步数 + 调度器）",
+        "采样器": "euler",
+        "调度器": "simple",
+        "步数": 8,
+        "CFG": 1,
+        "降噪": 1,
+        "噪波随机种": 0,
+        "control_after_generate": "randomize",
+        "自定义Sigma表": "1.0, 0.978453, 0.95418, 0.926626, 0.89508, 0.845148, 0.704534, 0.414568, 0"
+      }
+    },
+    {
+      "id": 7,
+      "type": "VAEDecode",
+      "pos": [
+        1120,
+        560
+      ],
+      "size": [
+        140,
+        46
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "Latent",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 10
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 11
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "图像",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            13,
+            14
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.44.0",
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 8,
+      "type": "ToggleImageCompare",
+      "pos": [
+        1120,
+        760
+      ],
+      "size": [
+        260,
+        150
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "num_sources",
+          "name": "num_sources",
+          "type": "INT",
+          "widget": {
+            "name": "num_sources"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "num_results",
+          "name": "num_results",
+          "type": "INT",
+          "widget": {
+            "name": "num_results"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "排版",
+          "name": "排版",
+          "type": "COMBO",
+          "widget": {
+            "name": "排版"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "标签",
+          "name": "标签",
+          "type": "COMBO",
+          "widget": {
+            "name": "标签"
+          },
+          "link": null
+        },
+        {
+          "label": "原图1",
+          "name": "source1",
+          "type": "IMAGE",
+          "link": 12
+        },
+        {
+          "label": "结果1",
+          "name": "result1",
+          "type": "IMAGE",
+          "link": 13
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "对比图",
+          "name": "对比图",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "ToggleImageCompare"
+      },
+      "widgets_values": [
+        1,
+        1,
+        "成对并排",
+        "显示"
+      ],
+      "widgets_values_named": {
+        "num_sources": 1,
+        "num_results": 1,
+        "排版": "成对并排",
+        "标签": "显示"
+      }
+    },
+    {
+      "id": 9,
+      "type": "SaveLoadImage",
+      "pos": [
+        1480,
+        560
+      ],
+      "size": [
+        270,
+        102
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 14
+        },
+        {
+          "localized_name": "use_saved",
+          "name": "use_saved",
+          "type": "COMBO",
+          "widget": {
+            "name": "use_saved"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "shape": 7,
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "图像",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": null
+        },
+        {
+          "localized_name": "遮罩",
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "SaveLoadImage"
+      },
+      "widgets_values": [
+        "Save",
+        "krea2_pose"
+      ],
+      "widgets_values_named": {
+        "use_saved": "Save",
+        "filename_prefix": "krea2_pose"
+      }
+    },
+    {
+      "id": 10,
+      "type": "Note",
+      "pos": [
+        60,
+        40
+      ],
+      "size": [
+        560,
+        360
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Krea 2 保身份 + 参考姿势（全部用本插件节点，不依赖 krea2Edit 插件）\n\n【接线】\nPoseReference(线稿) → image1，原照片 → image2，两路一起进 Ref Image Encode。\n模型类型选 Krea2；Latent 直接用它的 latent 输出（16 通道零 latent）。\n\n【★ 顺序是硬约定，反了会渲成线稿人偶】\nKrea 2 的 index 方法里「最后一个参考图 = 主体」。所以主体照片必须放 image2，\n条件图（姿势/线稿/场景）放 image1。想换顺序就在提示词里改 image 编号，\n但接线顺序不要动。\n\n【LoRA 必须配对】\n保身份用 krea2_identity_edit_v2（已挂在加载器 LoRA 槽）。\nkrea_outfittransfer 是 Ostris 系的，配这套会换人。\n\n【为什么不用 index_timestep_zero】\n那会让参考图走单独一套 t=0 调制，参考线稿会被「描」进成图（脸上出轮廓线）。\n本节点已固定用 index。\n\n【★★ 提示词必须写具体姿势，否则模型把线稿人偶画进画面】\n实测同参数下：「图二美女参考图一的姿势，重新摆pose」这种笼统句 ⇒ 模型不读线稿，\n而是把线稿人偶当成第二个主体，输出「左边真人 + 右边线稿人偶」的并排拼贴，姿势一点都不动。\n正确写法（中文完全可用，关键是三段）：\n  ① 主体指认：让图二中的女人……\n  ② 姿势解剖：跪立、身体向后仰、胸部抬起、背部拱起、双手向后下方伸去触摸脚跟、\n     头部微微后仰、四分之三侧后方视角\n  ③ 保持不变：脸、发型、服装、背景逐项列出\n本工作流里已填好一版，换姿势时按上面三段改写即可。\n\n【负面条件已经接好了，CFG 随便调】\nRef Image Encode 的第三个输出「negative」就是空文本负条件，直接接到采样器的「负面条件」。\n★ 不要再用 ConditioningZeroOut 把正面清零当负面：CFG=1.0 时它不参与计算所以看不出，\n  一旦 CFG>1 就会崩（实测 Qwen 2.1 20 步 CFG2.5 整张花屏，换空文本即正常）。"
+      ],
+      "widgets_values_named": {
+        "text": "Krea 2 保身份 + 参考姿势（全部用本插件节点，不依赖 krea2Edit 插件）\n\n【接线】\nPoseReference(线稿) → image1，原照片 → image2，两路一起进 Ref Image Encode。\n模型类型选 Krea2；Latent 直接用它的 latent 输出（16 通道零 latent）。\n\n【★ 顺序是硬约定，反了会渲成线稿人偶】\nKrea 2 的 index 方法里「最后一个参考图 = 主体」。所以主体照片必须放 image2，\n条件图（姿势/线稿/场景）放 image1。想换顺序就在提示词里改 image 编号，\n但接线顺序不要动。\n\n【LoRA 必须配对】\n保身份用 krea2_identity_edit_v2（已挂在加载器 LoRA 槽）。\nkrea_outfittransfer 是 Ostris 系的，配这套会换人。\n\n【为什么不用 index_timestep_zero】\n那会让参考图走单独一套 t=0 调制，参考线稿会被「描」进成图（脸上出轮廓线）。\n本节点已固定用 index。"
+      },
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      1,
+      4,
+      0,
+      "CLIP"
+    ],
+    [
+      2,
+      1,
+      2,
+      4,
+      1,
+      "VAE"
+    ],
+    [
+      3,
+      3,
+      0,
+      4,
+      2,
+      "IMAGE"
+    ],
+    [
+      4,
+      2,
+      0,
+      4,
+      16,
+      "IMAGE"
+    ],
+    [
+      6,
+      1,
+      0,
+      6,
+      0,
+      "MODEL"
+    ],
+    [
+      7,
+      4,
+      0,
+      6,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      8,
+      4,
+      2,
+      6,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      9,
+      4,
+      1,
+      6,
+      3,
+      "LATENT"
+    ],
+    [
+      10,
+      6,
+      0,
+      7,
+      0,
+      "LATENT"
+    ],
+    [
+      11,
+      1,
+      2,
+      7,
+      1,
+      "VAE"
+    ],
+    [
+      12,
+      2,
+      0,
+      8,
+      4,
+      "IMAGE"
+    ],
+    [
+      13,
+      7,
+      0,
+      8,
+      5,
+      "IMAGE"
+    ],
+    [
+      14,
+      7,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ue_links": [],
+    "ds": {
+      "scale": 0.9,
+      "offset": [
+        416,
+        110
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/example_workflows/Pose Reference Editing.json b/example_workflows/Pose Reference Editing.json
new file mode 100644
index 0000000..e39cefc
--- /dev/null
+++ b/example_workflows/Pose Reference Editing.json	
@@ -0,0 +1,816 @@
+{
+  "id": "92e0572f-4e3e-4173-9e4f-067267af190c",
+  "revision": 0,
+  "last_node_id": 9,
+  "last_link_id": 10,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "UNETLoader",
+      "pos": [
+        60,
+        110
+      ],
+      "size": [
+        270,
+        82
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "UNet名称",
+          "name": "unet_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "unet_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "数据类型",
+          "name": "weight_dtype",
+          "type": "COMBO",
+          "widget": {
+            "name": "weight_dtype"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "模型",
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "UNETLoader"
+      },
+      "widgets_values": [
+        "Qwen\\qwen_image_2.1_turbo_int8_convrot.safetensors",
+        "default"
+      ],
+      "widgets_values_named": {
+        "unet_name": "Qwen\\qwen_image_2.1_turbo_int8_convrot.safetensors",
+        "weight_dtype": "default"
+      }
+    },
+    {
+      "id": 2,
+      "type": "CLIPLoader",
+      "pos": [
+        60,
+        255
+      ],
+      "size": [
+        270,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "CLIP名称",
+          "name": "clip_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "类型",
+          "name": "type",
+          "type": "COMBO",
+          "widget": {
+            "name": "type"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "设备",
+          "name": "device",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "device"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "CLIPLoader"
+      },
+      "widgets_values": [
+        "qwen\\qwen3vl_8b_int8_convrot.safetensors",
+        "qwen_image",
+        "default"
+      ],
+      "widgets_values_named": {
+        "clip_name": "qwen\\qwen3vl_8b_int8_convrot.safetensors",
+        "type": "qwen_image",
+        "device": "default"
+      }
+    },
+    {
+      "id": 3,
+      "type": "VAELoader",
+      "pos": [
+        60,
+        435
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "vae名称",
+          "name": "vae_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "vae_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            3,
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "qwen_image_2.1_vae_bf16.safetensors"
+      ],
+      "widgets_values_named": {
+        "vae_name": "qwen_image_2.1_vae_bf16.safetensors"
+      }
+    },
+    {
+      "id": 4,
+      "type": "PoseReference",
+      "pos": [
+        440,
+        90
+      ],
+      "size": [
+        340,
+        300
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "category",
+          "name": "category",
+          "type": "COMBO",
+          "widget": {
+            "name": "category"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "group",
+          "name": "group",
+          "type": "COMBO",
+          "widget": {
+            "name": "group"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "pose",
+          "name": "pose",
+          "type": "COMBO",
+          "widget": {
+            "name": "pose"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "view",
+          "name": "view",
+          "type": "COMBO",
+          "widget": {
+            "name": "view"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "source",
+          "name": "source",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "source"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "long_edge",
+          "name": "long_edge",
+          "shape": 7,
+          "type": "INT",
+          "widget": {
+            "name": "long_edge"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "flip",
+          "name": "flip",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "flip"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "aux_id": "electricty00/ComfyUI-Toggle-Pass",
+        "ver": "60665017a9f0f1fb0cb53714f294d7ccee961066",
+        "Node name for S&R": "PoseReference",
+        "tp_pose": {
+          "key": "全身单人・标准系|站姿|P019_站立・双臂上举伸展|斜侧|线稿",
+          "img": 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"
+        }
+      },
+      "widgets_values": [
+        "全身单人・标准系",
+        "站姿",
+        "P019_站立・双臂上举伸展",
+        "斜侧",
+        "线稿",
+        1024,
+        "关"
+      ],
+      "widgets_values_named": {
+        "category": "全身单人・标准系",
+        "group": "站姿",
+        "pose": "P019_站立・双臂上举伸展",
+        "view": "斜侧",
+        "source": "线稿",
+        "long_edge": 1024,
+        "flip": "关"
+      }
+    },
+    {
+      "id": 5,
+      "type": "TextEncodeQwenImage21",
+      "pos": [
+        840,
+        90
+      ],
+      "size": [
+        400,
+        226
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 2
+        },
+        {
+          "localized_name": "image_1",
+          "name": "images.image_1",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 5
+        },
+        {
+          "localized_name": "image_2",
+          "name": "images.image_2",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": 3
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "negative_prompt",
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "negative_prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "resolution",
+          "name": "resolution",
+          "type": "INT",
+          "widget": {
+            "name": "resolution"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ]
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            7
+          ]
+        },
+        {
+          "localized_name": "latent",
+          "name": "latent",
+          "type": "LATENT",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "TextEncodeQwenImage21"
+      },
+      "widgets_values": [
+        "Render this pose reference into a photorealistic full-body photo of a young East Asian woman wearing a simple white dress, standing on a pure white studio background with soft even lighting. Keep her pose, body proportions and camera angle exactly as shown in the reference.",
+        "",
+        1024
+      ],
+      "widgets_values_named": {
+        "prompt": "Render this pose reference into a photorealistic full-body photo of a young East Asian woman wearing a simple white dress, standing on a pure white studio background with soft even lighting. Keep her pose, body proportions and camera angle exactly as shown in the reference.",
+        "negative_prompt": "",
+        "resolution": 1024
+      }
+    },
+    {
+      "id": 6,
+      "type": "KSampler",
+      "pos": [
+        1230,
+        90
+      ],
+      "size": [
+        270,
+        262
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "模型",
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "localized_name": "正面条件",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "localized_name": "负面条件",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 7
+        },
+        {
+          "localized_name": "Latent图像",
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 8
+        },
+        {
+          "localized_name": "种子",
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "步数",
+          "name": "steps",
+          "type": "INT",
+          "widget": {
+            "name": "steps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "cfg",
+          "name": "cfg",
+          "type": "FLOAT",
+          "widget": {
+            "name": "cfg"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "采样器名称",
+          "name": "sampler_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "sampler_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "调度器",
+          "name": "scheduler",
+          "type": "COMBO",
+          "widget": {
+            "name": "scheduler"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "降噪",
+          "name": "denoise",
+          "type": "FLOAT",
+          "widget": {
+            "name": "denoise"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "Latent",
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        20261102,
+        "fixed",
+        8,
+        1,
+        "euler",
+        "simple",
+        1
+      ],
+      "widgets_values_named": {
+        "seed": 20261102,
+        "control_after_generate": "fixed",
+        "steps": 8,
+        "cfg": 1,
+        "sampler_name": "euler",
+        "scheduler": "simple",
+        "denoise": 1
+      }
+    },
+    {
+      "id": 7,
+      "type": "VAEDecode",
+      "pos": [
+        1510,
+        90
+      ],
+      "size": [
+        230,
+        46
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "Latent",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 9
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "图像",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            10
+          ]
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 8,
+      "type": "SaveImage",
+      "pos": [
+        1750,
+        90
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "图片",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 10
+        },
+        {
+          "localized_name": "文件名前缀",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {}
+        },
+        "cnr_id": "comfy-core",
+        "ver": "0.43.0",
+        "Node name for S&R": "SaveImage"
+      },
+      "widgets_values": [
+        "pose_qwen"
+      ],
+      "widgets_values_named": {
+        "filename_prefix": "pose_qwen"
+      }
+    },
+    {
+      "id": 9,
+      "type": "Note",
+      "pos": [
+        440,
+        620
+      ],
+      "size": [
+        980,
+        400
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "参考姿势 · 用姿势素材生成图片\n\n▸ 怎么用\n   1. 在「参考姿势」节点上选：类别 / 姿势组 / 姿势 / 视角（前三个联动）\n   2. 执行，节点上会直接画出选中的素材，先确认姿势对不对\n   3. 改 prompt 描述你要的画面（服装、背景、风格），执行\n\n▸ 源：线稿 / 深度\n   默认「线稿」。实测深度图 3/3 会自己补出不存在的鞋或护具\n   （深度没有边界线，脚底和地面亮度太接近），线稿只有 1/3 有瑕疵。\n   深度唯一的优势是遮挡关系，肢体交叠复杂的姿势可以切过去试。\n\n▸ 长边 / 翻转\n   素材原始只有 300x400，「长边」默认放大到 1024。\n   「翻转」做左右镜像，反方向的姿势不用另找素材。\n\n▸ 换编辑模型\n   这个节点的输出是普通 IMAGE，直接把「参考姿势」接到别的编辑编码器的\n   images 输入即可：\n     · TextEncodeQwenImage21（当前用的，Qwen-Image-2.1）\n     · TextEncodeFibo2（FIBO 2.0，记得 CLIPLoader 类型选 fibo2、VAE 换 flux2-vae）\n     · TextEncodeQwenImageEditPlus / DynamicRefImageEncode 等\n   图片尺寸建议跟 model 的出图尺寸一致，不然模型会自己裁。\n\n▸ 参数\n   Qwen-Image-2.1 turbo：steps 8 / cfg 1.0 / euler / simple / denoise 1.0\n   用文件\n     unet: Qwen/qwen_image_2.1_turbo_int8_convrot.safetensors\n     clip: qwen/qwen3vl_8b_int8_convrot.safetensors（类型 qwen_image）\n     vae : qwen_image_2.1_vae_bf16.safetensors（64 通道，别选 qwen_image_vae）"
+      ],
+      "widgets_values_named": {
+        "text": "参考姿势 · 用姿势素材生成图片\n\n▸ 怎么用\n   1. 在「参考姿势」节点上选：类别 / 姿势组 / 姿势 / 视角（前三个联动）\n   2. 执行，节点上会直接画出选中的素材，先确认姿势对不对\n   3. 改 prompt 描述你要的画面（服装、背景、风格），执行\n\n▸ 源：线稿 / 深度\n   默认「线稿」。实测深度图 3/3 会自己补出不存在的鞋或护具\n   （深度没有边界线，脚底和地面亮度太接近），线稿只有 1/3 有瑕疵。\n   深度唯一的优势是遮挡关系，肢体交叠复杂的姿势可以切过去试。\n\n▸ 长边 / 翻转\n   素材原始只有 300x400，「长边」默认放大到 1024。\n   「翻转」做左右镜像，反方向的姿势不用另找素材。\n\n▸ 换编辑模型\n   这个节点的输出是普通 IMAGE，直接把「参考姿势」接到别的编辑编码器的\n   images 输入即可：\n     · TextEncodeQwenImage21（当前用的，Qwen-Image-2.1）\n     · TextEncodeFibo2（FIBO 2.0，记得 CLIPLoader 类型选 fibo2、VAE 换 flux2-vae）\n     · TextEncodeQwenImageEditPlus / DynamicRefImageEncode 等\n   图片尺寸建议跟 model 的出图尺寸一致，不然模型会自己裁。\n\n▸ 参数\n   Qwen-Image-2.1 turbo：steps 8 / cfg 1.0 / euler / simple / denoise 1.0\n   用文件\n     unet: Qwen/qwen_image_2.1_turbo_int8_convrot.safetensors\n     clip: qwen/qwen3vl_8b_int8_convrot.safetensors（类型 qwen_image）\n     vae : qwen_image_2.1_vae_bf16.safetensors（64 通道，别选 qwen_image_vae）"
+      },
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      6,
+      0,
+      "MODEL"
+    ],
+    [
+      4,
+      3,
+      0,
+      7,
+      1,
+      "VAE"
+    ],
+    [
+      2,
+      2,
+      0,
+      5,
+      0,
+      "CLIP"
+    ],
+    [
+      5,
+      4,
+      0,
+      5,
+      1,
+      "IMAGE"
+    ],
+    [
+      3,
+      3,
+      0,
+      5,
+      3,
+      "VAE"
+    ],
+    [
+      6,
+      5,
+      0,
+      6,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      5,
+      1,
+      6,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      8,
+      5,
+      2,
+      6,
+      3,
+      "LATENT"
+    ],
+    [
+      9,
+      6,
+      0,
+      7,
+      0,
+      "LATENT"
+    ],
+    [
+      10,
+      7,
+      0,
+      8,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "加载模型",
+      "bounding": [
+        20,
+        50,
+        330,
+        490
+      ],
+      "color": "#3f789e",
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "参考姿势 → 出图",
+      "bounding": [
+        410,
+        50,
+        1580,
+        440
+      ],
+      "color": "#8a6d3b",
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.62,
+      "offset": [
+        60,
+        80
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/js/broadcast_toggle.js b/js/broadcast_toggle.js
index a9ae707..07ca6fe 100644
--- a/js/broadcast_toggle.js
+++ b/js/broadcast_toggle.js
@@ -1,865 +1,1037 @@
-/**
- * ComfyUI-Toggle-Pass: ToggleUnifiedLoader —「全局广播」开关（自带，不依赖 cg-use-everywhere）
- *
- * 作用：节点上多一个「开 / 关」开关。
- *   开 -> 运行（以及导出 API）时，把 MODEL / CLIP / VAE 自动接到图里**所有空着的同类型输入**上，
- *         不用再一根根拉线；画布上不会留下这些线（提交时临时连、提交完立刻拆）。
- *         同时画布上会用**紫色曲线**把「将会自动接上的线」画出来，方便核对。
- *   关 -> 完全保持原样，自己拉线（默认）。
- *
- * 为什么不用 cg-use-everywhere 的 `properties.ue_convert`：
- *   那是它的私有机制，装了才有、卸载就没了，而且只在它自己的 queuePrompt 钩子下生效。
- *   这里自己实现一份最小广播：零依赖、导出 API 也照样带线。
- *
- * ⚠️ 序列化安全（这个节点最贵的教训，改动前务必读完）：
- *   - 开关用 `addWidget("toggle", ...)` **追加在 widget 列表最末尾**，并且 `serialize:false`。
- *     `widgets_values` 是按位置存的；它既然不进 widgets_values，旧工作流的每一项位置都不动。
- *     —— 绝不能插在中间，也不能把一个已有参数标成 serialize:false（那样后面的值会整体前移）。
- *   - 开关的状态存在 `node.properties.tp_broadcast`（LiteGraph 原生字段，随工作流保存），
- *     不占用 widgets_values。
- *   - 不动 Python 的 INPUT_TYPES，所以这个特性**不需要重启 ComfyUI**，Ctrl+F5 就行。
- *
- * ⚠️ 只在「前端」提交时生效：用 /prompt 接口直接 POST 原始 API 格式 JSON 时没有前端参与，
- *   广播自然不会发生（想让它生效就先在界面里「导出 API」，导出的 JSON 已经带好线了）。
- */
-
-import { app } from "../../scripts/app.js";
-
-const EXT_NAME = "Toggle-Pass.broadcast";
-const NODE_NAME = "ToggleUnifiedLoader";
-const WIDGET_NAME = "全局广播";
-const PROP = "tp_broadcast";
-const BTN_ON = "开";
-const BTN_OFF = "关";
-
-/* 顶部自绘开关的几何常量 —— 必须与 multi_ref_encode.js 里「加载模式胶囊」那套保持一致，
- * 否则开关会跟上面的 UNet/Checkpoint 胶囊对不齐（两套常量各改各的很容易漂）。 */
-const ROW_SLOT_H = 20;        // 兜底行高（真值优先取 LiteGraph.NODE_SLOT_HEIGHT）
-const ROW_CAP_MAX = 147;      // 胶囊可见宽上限（= multi_ref_encode.js 的 MODE_ROW_MAX）
-const ROW_CAP_MIN = 120;      // 节点极窄时的兜底宽
-const ROW_GAP_RIGHT = 45;     // 胶囊右端到节点右边的最小留白
-const BC_MIN_W = 66;          // 开关自身不窄于此（再窄「广播」+轨道就放不下了）
-const BC_PAD_Y = 3;           // 上下留白 —— 与 multi_ref_encode.js 的 TG_PAD_Y 同值，
-                              // 这样开关高度 = 胶囊高度（都是 行高 - 3*2 = 14）
-
-/* ------------------------------------------------------------------ *
- * 一、节点上的开关
- * ------------------------------------------------------------------ */
-
-function isBroadcastOn(node) {
-    const w = (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
-    if (w) return !!w.value;
-    return !!(node.properties && node.properties[PROP]);
-}
-
-function numAt(v, i) {
-    const x = v?.[i];
-    return typeof x === "number" && isFinite(x) ? x : null;
-}
-
-function addBroadcastWidget(node) {
-    if ((node.widgets || []).some((x) => x && x.name === WIDGET_NAME)) return;
-
-    const on = !!(node.properties && node.properties[PROP]);
-
-    /* 原生 toggle：本机前端 1.53.10 的 widget 工厂支持 "toggle"，
-     * {on, off} 决定两侧文案（ComfyUI 自己的 BOOLEAN 输入就是这么建的）。 */
-    let w = null;
-    try {
-        w = node.addWidget("toggle", WIDGET_NAME, on, (v) => {
-            try {
-                node.properties = node.properties || {};
-                /* 原生 toggle 会把新值当第一个参数传进来；拿不到才回落读 w.value
-                 * （别只读 w.value：某些路径上 callback 先于 value 赋值，会写反） */
-                node.properties[PROP] = typeof v === "boolean" ? v : !!w.value;
-            } catch (e) {
-                console.warn("[Toggle-Pass] 广播开关状态写入 properties 失败", e);
-            }
-            app.graph?.setDirtyCanvas?.(true, true);
-            node.setDirtyCanvas?.(true, true);
-        }, { on: BTN_ON, off: BTN_OFF });
-    } catch (e) {
-        console.warn("[Toggle-Pass] 原生 toggle 控件不可用，跳过广播开关", e);
-        return;
-    }
-    if (!w) return;
-
-    /* 不进 widgets_values / 不进 prompt：旧工作流的位置表一项都不动 */
-    w.options = w.options || {};
-    w.options.serialize = false;
-    w.serialize = false;
-    w.tooltip =
-        "全局广播：开启后运行时把 MODEL / CLIP / VAE 自动接到图里所有空着的同类型输入上，不用拉线。" +
-        "开启时画布上会用紫色曲线画出「将自动接上的线」，方便核对（曲线只是预览，不是真实连线、不会被保存）。" +
-        "关闭则照旧手动连线。";
-    w.label = WIDGET_NAME;
-
-    /* ★ 2026-10-24：开关从「widget 列表末尾的一整行」搬到「顶部胶囊下面」。
-     * 做法：原生 widget 只当**状态容器**（值 / properties / 序列化全部原样不动），
-     *      用 hidden 让它不参与布局与绘制，再自己画一个小开关上去。
-     * 为什么 hidden 是安全的：widgets_values 是按「参与序列化的 widget」顺序存的，
-     *      这个 toggle 本来就是 serialize:false（不进 widgets_values），
-     *      藏起来既不增也不减参与序列化的项 ⇒ 旧工作流的位置表一项都不动。
-     * 顺带的好处：节点底部少了一整行，不再挤在「移除 LoRA」下面。 */
-    w.hidden = true;
-    node._tpBcW = w;
-    attachBroadcastRow(node);
-}
-
-/* ------------------------------------------------------------------ *
- * 一之二、顶部自绘小开关（在 UNet / Checkpoint 胶囊的下一行、左半边）
- *
- * 为什么不用 widget 承载：widget 的 y 由前端布局算死（插槽区之下、按列表顺序往下排），
- *   想放到「第一行插槽左侧的下方」这种位置，只能自己画。
- * 这里复用的是「加载模式」开关同一套打法（见 multi_ref_encode.js 的 attachModeRowToggle）：
- *   · 位置：第 1 行插槽的正下方（= 第 2 行插槽那一行的左半边，右侧 CLIP 标签不受影响）
- *   · 宽度：胶囊的一半（用户要求「一半位置就行」）
- *   · 绘制：node.onDrawForeground；命中：node.onMouseDown（返回真值吃掉事件，避免变成拖动节点）
- * ⚠️ 这两个钩子是**实例属性**，必须存旧引用链式调用 —— 直接赋值会吞掉
- *   multi_ref_encode.js 挂的「加载模式」开关（两边都挂同一对钩子）。
- * ------------------------------------------------------------------ */
-
-/** 与 multi_ref_encode.js 的 tpWidgetMargin() 同源：BaseWidget.margin 默认 15 */
-function bcMargin() {
-    const m = globalThis.BaseWidget?.margin;
-    return (typeof m === "number" && m >= 0 && m <= 40) ? m : 15;
-}
-
-function bcRoundRect(ctx, x, y, w, h, r) {
-    const rr = Math.max(0, Math.min(r, h / 2, w / 2));
-    ctx.beginPath();
-    ctx.moveTo(x + rr, y);
-    ctx.lineTo(x + w - rr, y);
-    ctx.quadraticCurveTo(x + w, y, x + w, y + rr);
-    ctx.lineTo(x + w, y + h - rr);
-    ctx.quadraticCurveTo(x + w, y + h, x + w - rr, y + h);
-    ctx.lineTo(x + rr, y + h);
-    ctx.quadraticCurveTo(x, y + h, x, y + h - rr);
-    ctx.lineTo(x, y + rr);
-    ctx.quadraticCurveTo(x, y, x + rr, y);
-    ctx.closePath();
-}
-
-/** 开关在节点本地坐标里的位置与尺寸（x/w 是我们自己这一块的范围，不含右侧留白） */
-function bcRowGeom(node) {
-    const SH = Number(globalThis.LiteGraph?.NODE_SLOT_HEIGHT) || ROW_SLOT_H;
-    const nw = numAt(node?.size, 0) || 0;
-    const left = bcMargin();
-    /* 先按「加载模式胶囊」的宽度算（保证和上面的胶囊左右端点对得上） */
-    let capW = nw > 0 ? nw - left - ROW_GAP_RIGHT : ROW_CAP_MAX;
-    capW = Math.min(ROW_CAP_MAX, Math.max(0, capW));
-    if (capW < ROW_CAP_MIN) capW = nw > 0 ? Math.min(ROW_CAP_MIN, Math.max(0, nw - left)) : ROW_CAP_MIN;
-    /* 用户要求：占胶囊的一半（红框圈的就是胶囊前一半的宽度） */
-    const w = Math.max(BC_MIN_W, Math.round(capW / 2));
-
-    /* 第一行插槽的纵向位置（与加载模式胶囊同一套反推），再往下挪一行 = 胶囊正下方 */
-    let top = Math.round(SH * 0.2);
-    try {
-        const cy = numAt(node?.getOutputPos?.(0), 1);
-        const py = numAt(node?.pos, 1);
-        if (cy !== null && py !== null) top = Math.max(0, Math.round(cy - py - SH / 2));
-    } catch (e) { /* 用兜底值 */ }
-    return { x: left, y: top + SH, w, h: SH };
-}
-
-/** 自绘：一个完整的小胶囊控件 —— 淡底色托底，左边 label、右边开关轨道。
- *  为什么要有底色：节点里其它控件（下拉框、UNet/Checkpoint 胶囊）都有底色，
- *  只画「裸文字 + 轨道」会显得浮在空白上，跟整体设计语言脱节。
- *  ★ 关键是「托底要淡、轨道要细」：
- *    第一版给外框加了 1px 亮描边，结果和下边框围成**两层框**（外框 + 内轨道），
- *    比上面那条单层胶囊笨重得多。现在外框只有一层很淡的填充、默认无描边，
- *    视觉上退成「一块底色」而不是「一个框」，轨道才是主角。
- *  配色对齐节点现有控件：下拉框底色 / 高亮蓝 #3a96f3。 */
-function drawBroadcastRow(ctx, node, on, hover) {
-    const g = bcRowGeom(node);
-    const cy = g.y + g.h / 2;
-    /* 高度与上面的 UNet/Checkpoint 胶囊完全一致：行高 - BC_PAD_Y*2 = 14
-     * （胶囊那边是 TG_PAD_Y=3、净高 14 —— 两个常量必须同步改，否则一高一矮） */
-    const bh = g.h - BC_PAD_Y * 2;
-    const r = bh / 2;
-    const bx = g.x, bw = g.w;
-
-    try {
-        ctx.save();
-
-        /* ---- 托底：一层淡填充，只在开启/悬停时略提亮 ---- */
-        bcRoundRect(ctx, bx, g.y + BC_PAD_Y, bw, bh, r);
-        if (on) ctx.fillStyle = hover ? "rgba(58,150,243,0.24)" : "rgba(58,150,243,0.13)";
-        else ctx.fillStyle = hover ? "rgba(255,255,255,0.105)" : "rgba(255,255,255,0.06)";
-        ctx.fill();
-        /* 悬停加一道极淡描边：明确「这里能点」，又不会变成第二个框 */
-        if (hover) {
-            ctx.lineWidth = 1;
-            ctx.strokeStyle = on ? "rgba(58,150,243,0.45)" : "rgba(255,255,255,0.12)";
-            ctx.stroke();
-        }
-
-        /* ---- 内容整体居中：先量文字宽，再把「文字 + 间隙 + 轨道」作为一条内容居中摆放 ----
-         * 之前是「文字贴左 8px、轨道贴右 7px」，两边留白各算各的 ⇒ 一宽一窄，看着偏向一边。
-         * 现在统一算 padX，左右留白严格相等。 */
-        const label = "广播";
-        ctx.textAlign = "left";
-        ctx.font = "600 10px sans-serif";     // 用户要求：11px → 10px（小 10%）
-        let tw = 20;                          // 10px「广播」约 20px，量不到时兜底
-        try {
-            const lm = ctx.measureText(label);
-            if (lm && typeof lm.width === "number" && isFinite(lm.width) && lm.width > 0) tw = lm.width;
-        } catch (e) { /* 用兜底 */ }
-
-        const trackW = 24, GAP = 8;
-        const contentW = tw + GAP + trackW;
-        const padX = Math.max(5, (bw - contentW) / 2);   // 左右留白相等 ⇒ 整条内容居中
-        const lx = bx + padX;
-        const tx = lx + tw + GAP;
-
-        /* ---- label：按视觉盒垂直居中 ----
-         * 不用 textBaseline="middle"：Canvas 的 middle 按 (ascent+descent)/2 排，
-         * 对没有降部的中文会整体偏上。改成量出真实上沿、基线放到 cy + 上沿/2，
-         * 这样字形上下留白相等 —— 实测这样仍会低约半个像素，再补 0.5 的视觉修正。 */
-        ctx.fillStyle = on ? "rgba(230,238,250,1)" : "rgba(174,182,196,1)";
-        let asc = 7.3;   // 10px 中文的兜底上沿（量不到时用）
-        try {
-            const lm = ctx.measureText(label);
-            const a = lm && lm.actualBoundingBoxAscent;
-            if (typeof a === "number" && isFinite(a) && a > 0) asc = a;
-        } catch (e) { /* 用兜底 */ }
-        ctx.textBaseline = "alphabetic";
-        ctx.fillText(label, lx, cy + asc / 2 - 0.5);
-
-        /* ---- 右侧开关：细轨道 + 小滑块（位置 tx 已在上面按居中算好） ---- */
-        const th = 10;
-        const ty = cy - th / 2;
-        bcRoundRect(ctx, tx, ty, trackW, th, th / 2);
-        if (on) {
-            const grad = ctx.createLinearGradient(tx, ty, tx, ty + th);
-            grad.addColorStop(0, "#4aa5ff");
-            grad.addColorStop(1, "#2f86e8");
-            ctx.fillStyle = grad;
-        } else {
-            ctx.fillStyle = "rgba(255,255,255,0.13)";
-        }
-        ctx.fill();
-        /* 关闭态补一道内沿，让轨道看起来是「凹槽」而不是一块平色 */
-        if (!on) {
-            ctx.lineWidth = 1;
-            ctx.strokeStyle = "rgba(0,0,0,0.18)";
-            ctx.stroke();
-        }
-
-        /* ---- 滑块：一点点投影，让它「浮」在轨道上 ---- */
-        const kr = 4;
-        const kx = on ? tx + trackW - 1 - kr : tx + 1 + kr;
-        ctx.save();
-        ctx.shadowColor = "rgba(0,0,0,0.30)";
-        ctx.shadowBlur = 2;
-        ctx.shadowOffsetY = 0.5;
-        ctx.beginPath();
-        ctx.arc(kx, cy, kr, 0, Math.PI * 2);
-        ctx.fillStyle = "#ffffff";
-        ctx.fill();
-        ctx.restore();
-    } finally {
-        /* ★ 必须 try/finally：漏 restore 会让整个画布的变换永久跑偏（踩过） */
-        try { ctx.restore(); } catch (e) { /* ignore */ }
-    }
-}
-
-function isBroadcastOnNode(node) {
-    const w = node?._tpBcW || (node?.widgets || []).find((x) => x && x.name === WIDGET_NAME);
-    if (w && typeof w.value === "boolean") return w.value;
-    return !!(node?.properties && node.properties[PROP]);
-}
-
-/** 切开关：真源写 properties，再同步给（隐藏的）原生 widget，前端才知道图被改了 */
-function setBroadcast(node, on) {
-    const v = !!on;
-    try {
-        node.properties = node.properties || {};
-        node.properties[PROP] = v;
-    } catch (e) { /* ignore */ }
-    const w = node._tpBcW || (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
-    if (w) {
-        try {
-            if (typeof w.setValue === "function") w.setValue(v);
-            else w.value = v;
-        } catch (e) {
-            w.value = v;
-        }
-    }
-    node.setDirtyCanvas?.(true, true);
-    app.graph?.setDirtyCanvas?.(true, true);
-    app.graph?.change?.();
-}
-
-/** 把自绘开关挂到节点上（幂等） */
-function attachBroadcastRow(node) {
-    if (node._tpBcRow) return;
-    /* 「现代节点设计」（Vue 节点）不走 canvas 绘制钩子 → 保持原生 widget 可见的回落 */
-    if (globalThis.LiteGraph?.vueNodesMode) {
-        const w = node._tpBcW;
-        if (w) w.hidden = false;
-        return;
-    }
-    node._tpBcRow = true;
-
-    const prevDraw = typeof node.onDrawForeground === "function" ? node.onDrawForeground : null;
-    const prevDown = typeof node.onMouseDown === "function" ? node.onMouseDown : null;
-    const prevMove = typeof node.onMouseMove === "function" ? node.onMouseMove : null;
-
-    node.onDrawForeground = function (ctx, ...rest) {
-        if (prevDraw) {
-            try { prevDraw.call(this, ctx, ...rest); } catch (e) { /* 旧钩子出错不影响开关 */ }
-        }
-        if (!this._tpBcW || this.flags?.collapsed) return;
-        try {
-            drawBroadcastRow(ctx, this, isBroadcastOnNode(this), !!this._tpBcHover);
-        } catch (e) {
-            /* 自绘失败就留空，绝不能抛出去（会中断整块画布绘制） */
-        }
-    };
-
-    /* 悬停高亮：只在「进入/离开」时刷新一次画布，不做逐像素重绘 */
-    node.onMouseMove = function (e, pos, canvas) {
-        if (this._tpBcW && !this.flags?.collapsed) {
-            const x = numAt(pos, 0), y = numAt(pos, 1);
-            let hv = false;
-            if (x !== null && y !== null) {
-                const g = bcRowGeom(this);
-                hv = x >= g.x && x <= g.x + g.w && y >= g.y && y <= g.y + g.h;
-            }
-            if (this._tpBcHover !== hv) {
-                this._tpBcHover = hv;
-                this.setDirtyCanvas?.(true, true);
-            }
-        }
-        return prevMove ? prevMove.call(this, e, pos, canvas) : false;
-    };
-
-    /* ⚠️ 光有 onMouseMove 不够：鼠标一移出节点，前端就不再给它派发 move，
-     *   高亮会**永久卡在亮着**的状态（实测：移开画布后 _tpBcHover 仍是 true）。
-     *   所以补一个离开钩子复位。 */
-    const prevLeave = typeof node.onMouseLeave === "function" ? node.onMouseLeave : null;
-    node.onMouseLeave = function (...args) {
-        if (this._tpBcHover) {
-            this._tpBcHover = false;
-            this.setDirtyCanvas?.(true, true);
-        }
-        return prevLeave ? prevLeave.apply(this, args) : undefined;
-    };
-
-    node.onMouseDown = function (e, pos, canvas) {
-        if (this._tpBcW && !this.flags?.collapsed) {
-            const x = numAt(pos, 0), y = numAt(pos, 1);
-            if (x !== null && y !== null) {
-                const g = bcRowGeom(this);
-                if (x >= g.x && x <= g.x + g.w && y >= g.y && y <= g.y + g.h) {
-                    setBroadcast(this, !isBroadcastOnNode(this));
-                    return true;    // 吃掉点击，避免变成拖动节点
-                }
-            }
-        }
-        return prevDown ? (prevDown.call(this, e, pos, canvas) || false) : false;
-    };
-}
-
-/* 注：原 growToFit()（给追加的那行 widget 补节点高度）已随「开关搬到顶部」一起删除 ——
- * 开关不再占用 widget 布局，节点高度不需要额外补。 */
-
-function syncBroadcastWidget(node) {
-    const w = (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
-    if (!w) return;
-    const v = !!(node.properties && node.properties[PROP]);
-    if (!!w.value !== v) w.value = v;
-    /* 保险：极端情况下（前端没走 onNodeCreated 就直接 configure）自绘钩子可能还没挂 */
-    if (w.hidden) {
-        node._tpBcW = w;
-        attachBroadcastRow(node);
-    }
-}
-
-/* ------------------------------------------------------------------ *
- * 二、提交时临时连线（最小广播实现）
- * ------------------------------------------------------------------ */
-
-/** 输入类型与输出类型是否匹配（兼容 "MODEL" / "A,B" / "*" 这几种写法） */
-function typeMatch(inputType, outputType) {
-    const a = String(inputType ?? "");
-    const b = String(outputType ?? "");
-    if (!a || !b) return false;
-    if (a === b) return true;
-    if (a === "*" || b === "*") return true;
-    return a.split(",").some((s) => s.trim() === b);
-}
-
-/**
- * 计算「广播计划」：哪些空着的输入会被接到哪个输出。
- *
- * ⚠️ 真接线（inflate）和画布上的曲线预览（computeShapes）**必须共用这一份**，
- *    否则会出现「画的线」和「真接的线」不一致 —— 那种 bug 用户根本没法自查。
- */
-function planBroadcast(graph) {
-    const pairs = [];
-    const ambiguous = new Set();
-    if (!graph || !Array.isArray(graph._nodes)) return { pairs, ambiguous };
-
-    /* 每个类型只认一个广播源：多个加载器都开广播时取第一个，并提示一次 */
-    const srcByType = new Map();
-    for (const n of graph._nodes) {
-        if (!n || n.type !== NODE_NAME || !isBroadcastOn(n)) continue;
-        const outs = n.outputs || [];
-        for (let i = 0; i < outs.length; i++) {
-            const t = outs[i] && outs[i].type;
-            if (!t || t === "*") continue;
-            if (srcByType.has(t)) {
-                ambiguous.add(t);
-                continue;
-            }
-            srcByType.set(t, { node: n, slot: i });
-        }
-    }
-    if (!srcByType.size) return { pairs, ambiguous };
-
-    for (const [t, src] of srcByType) {
-        for (const target of graph._nodes) {
-            /* 加载器自己不算，避免自己接自己 */
-            if (!target || target === src.node || target.type === NODE_NAME) continue;
-            const inputs = target.inputs || [];
-            for (let idx = 0; idx < inputs.length; idx++) {
-                const inp = inputs[idx];
-                /* 已经接了线的一律不动 —— 广播只补空位，绝不覆盖用户手连的线 */
-                if (!inp || inp.link != null) continue;
-                if (!typeMatch(inp.type, t)) continue;
-                pairs.push({ srcNode: src.node, srcSlot: src.slot, srcType: t, dstNode: target, dstSlot: idx });
-            }
-        }
-    }
-    return { pairs, ambiguous };
-}
-
-/**
- * 把「开着广播」的加载器的三个输出，临时接到所有空着的同类型输入上。
- * 返回一个还原函数；调用它会把临时连线全部拆掉。
- */
-function inflate(appRef) {
-    const graph = appRef.rootGraph || appRef.graph;
-    const { pairs, ambiguous } = planBroadcast(graph);
-    if (ambiguous.size) {
-        console.warn(
-            "[Toggle-Pass] 有多个加载器开着「全局广播」，同类型输出只取第一个：",
-            Array.from(ambiguous).join(", "),
-        );
-    }
-    const undo = [];
-
-    for (const p of pairs) {
-        try {
-            p.srcNode.connect(p.srcSlot, p.dstNode, p.dstSlot);
-        } catch (e) {
-            continue;
-        }
-        /* connect 失败时不会抛，只表现为 link 仍为空 */
-        if (p.dstNode.inputs?.[p.dstSlot] && p.dstNode.inputs[p.dstSlot].link != null) {
-            const { dstNode, dstSlot } = p;
-            undo.push(() => {
-                try {
-                    dstNode.disconnectInput(dstSlot);
-                } catch (e) {
-                    /* 目标节点已被删掉之类，忽略 */
-                }
-            });
-        }
-    }
-
-    return () => {
-        for (let i = undo.length - 1; i >= 0; i--) undo[i]();
-        try {
-            appRef.graph?.setDirtyCanvas?.(true, true);
-        } catch (e) {
-            /* 无画布环境（导出脚本）忽略 */
-        }
-    };
-}
-
-/* ------------------------------------------------------------------ *
- * 三、画布上的预览曲线（只画，不建真实 link）
- * ------------------------------------------------------------------ */
-
-/* 观感对齐 cg-use-everywhere：紫罗兰实线 + 一层柔光，比青色虚线耐看，
- * 也不会被误当成「报错警告线」。 */
-const PREVIEW_COLOR = "#a78bfa";
-const PREVIEW_GLOW = "#7c5cff";
-
-/** 从槽位的 boundingRect（官方渲染算出来的真实盒子）取中心点，图坐标 */
-function rectCenter(r) {
-    if (!r) return null;
-    if (Array.isArray(r) || typeof r[0] === "number") {
-        const x = numAt(r, 0);
-        const y = numAt(r, 1);
-        if (x == null || y == null) return null;
-        return [x + (numAt(r, 2) || 0) / 2, y + (numAt(r, 3) || 0) / 2];
-    }
-    const x = typeof r.x === "number" ? r.x : r.left;
-    const y = typeof r.y === "number" ? r.y : r.top;
-    if (typeof x !== "number" || typeof y !== "number") return null;
-    const w = r.width ?? r.w ?? 0;
-    const h = r.height ?? r.h ?? 0;
-    return [x + w / 2, y + h / 2];
-}
-
-/**
- * 槽位中心（图坐标）。
- *
- * ⚠️ 优先 `boundingRect`，不要只信 `getConnectionPos`：
- *   本机前端里槽位的 `pos` 字段**经常是 null**（只有走 widget 布局的那几个才被填），
- *   而 `pos` 为 null 时 getConnectionPos 会退化成「按输入顺序估算」的公式
- *   (`node.pos + (index+0.7)*NODE_SLOT_HEIGHT`) —— 一旦节点里有 widget 夹在输入之间，
- *   估出来的位置就和实际渲染对不上，线看起来就是「错位」。
- *   `boundingRect` 是渲染时算出来的真实盒子，节点一移动它就跟着更新。
- */
-function slotPoint(node, isInput, idx) {
-    const list = isInput ? node?.inputs : node?.outputs;
-    const s = list && list[idx];
-    if (!s) return null;
-    const c = rectCenter(s.boundingRect);
-    if (c) return c;
-    try {
-        const p = node.getConnectionPos(isInput, idx);
-        if (p && isFinite(p[0]) && isFinite(p[1])) return [p[0], p[1]];
-    } catch (e) {
-        /* 节点被删/布局未完成 */
-    }
-    return null;
-}
-
-/**
- * 把「运行时会自动接上的线」算成屏幕坐标下的贝塞尔。
- * 不创建任何 link ⇒ 不进工作流、不影响保存，纯视觉提示。
- *
- * 坐标链路：槽位（图坐标）→ `ds.convertOffsetToCanvas` → 屏幕坐标，
- * 然后 setTransform 到 dpr，直接按屏幕坐标画。
- * ⚠️ 不要自己手搓 `(p + offset) * scale`：那是在赌前端内部变换的实现细节。
- */
-function computeShapes(canvas) {
-    /* ⚠️ 必须和 inflate() 用**同一张图**（rootGraph 优先）：
-     *   广播接哪张图，就画哪张图。两边取不同引用（canvas.graph / app.graph）
-     *   在切换工作流或进子图的瞬间会不一致，线就会指向图里已不存在的位置。 */
-    const g = (app && (app.rootGraph || app.graph)) || (canvas && canvas.graph);
-    const ds = canvas && canvas.ds;
-    if (!g || !ds) return null;
-
-    const { pairs } = planBroadcast(g);
-    if (!pairs.length) return null;
-
-    const scale = ds.scale || 1;
-    let dpr = canvas.dpr;
-    if (!dpr) {
-        const r = canvas.canvas?.getBoundingClientRect?.();
-        dpr = r && r.width > 0 ? canvas.canvas.width / r.width : 1;
-    }
-    const off = ds.offset || [0, 0];
-    const toScreen = (p) => {
-        try {
-            const r = ds.convertOffsetToCanvas?.([p[0], p[1]]);
-            if (r && isFinite(r[0]) && isFinite(r[1])) return [r[0], r[1]];
-        } catch (e) {
-            /* 兜底走下面的手算 */
-        }
-        return [(p[0] + (off[0] || 0)) * scale, (p[1] + (off[1] || 0)) * scale];
-    };
-
-    const items = [];
-    for (const p of pairs) {
-        const aG = slotPoint(p.srcNode, false, p.srcSlot);
-        const bG = slotPoint(p.dstNode, true, p.dstSlot);
-        if (!aG || !bG) continue;
-        const a = toScreen(aG);
-        const b = toScreen(bG);
-        if (!isFinite(a[0]) || !isFinite(b[0])) continue;
-        /* 控制点跟官方 Spline 连线同一套：取两点距离的 3 成（不小于 40 图单位），
-         * 起点往右甩、终点从左边进 —— 收口方向和真实连线一致，
-         * 一眼就能看出线是从哪个口出来的。 */
-        const dist = Math.hypot(bG[0] - aG[0], bG[1] - aG[1]);
-        const d = Math.max(40, dist * 0.3) * scale;
-        items.push({
-            aG,
-            bG,
-            a,
-            b,
-            c1: [a[0] + d, a[1]],
-            c2: [b[0] - d, b[1]],
-        });
-    }
-    if (!items.length) return null;
-    return { items, scale, dpr };
-}
-
-/**
- * 取「背景层」的 ctx。
- *
- * ⚠️⚠️ 这里是最容易白忙一场的地方：新版前端把背景画在**离屏** canvas 上
- *   （`this.bgcanvas`），画完之后才 `ctx.drawImage(bgcanvas, ...)` 贴到主画布，
- *   再在主画布上画节点。所以往主 ctx 上画背景层的内容 —— 会立刻被那张贴图盖掉，
- *   代码跑了、坐标也对，就是看不见（实测一个紫色像素都没有）。
- *   必须画进 bgcanvas 自己的 ctx，贴图时才会把它一起带上来，节点再盖在上面。
- *   老架构没有 bgcanvas（背景直接画在主 ctx 上），此时退回主 ctx。
- */
-function bgCtx(canvas) {
-    const bg = canvas && canvas.bgcanvas;
-    if (bg && typeof bg.getContext === "function") {
-        if (!canvas.__tpBgCtx || canvas.__tpBgCtx.canvas !== bg) canvas.__tpBgCtx = bg.getContext("2d");
-        return canvas.__tpBgCtx;
-    }
-    return canvas && canvas.ctx;
-}
-
-/**
- * 线本体 —— 画在**节点下层**（背景层 ctx，交付 `drawBackCanvas` 之后）。
- * 所以线穿过节点时会被节点自然遮住，跟真实连线一个观感：
- * 该露的缝隙里露出来，节点上干干净净，不挡标题也不挡参数。
- */
-function drawBroadcastLines(ctx, canvas) {
-    const plan = computeShapes(canvas);
-    if (!plan || !ctx) return;
-    const { items, scale, dpr } = plan;
-
-    ctx.save();
-    /* ⚠️ save / restore 必须用 try/finally 包住：
-     *   中间任何一次 `it.a` 取值、bezier 计算出问题抛异常，若跳过 restore，
-     *   这个 ctx 就会**永久**留在 `dpr + translate + scale` 变换下 ——
-     *   之后所有绘制（包括官方的选择框、节点）都会整体偏移、放大，
-     *   而且看起来跟本插件毫无关系，极难排查。宁可少画一帧，也不能漏 restore。 */
-    try {
-        ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
-        ctx.lineCap = "round";
-        ctx.lineJoin = "round";
-
-        for (const it of items) {
-            const trace = () => {
-                ctx.beginPath();
-                ctx.moveTo(it.a[0], it.a[1]);
-                ctx.bezierCurveTo(it.c1[0], it.c1[1], it.c2[0], it.c2[1], it.b[0], it.b[1]);
-            };
-
-            /* 一层柔光底：深色画布上紫线才不会是「一根生硬的细线」 */
-            ctx.globalAlpha = 0.16;
-            ctx.strokeStyle = PREVIEW_GLOW;
-            ctx.lineWidth = Math.max(5, 7 * scale);
-            trace();
-            ctx.stroke();
-
-            /* 主线 */
-            ctx.globalAlpha = 0.95;
-            ctx.strokeStyle = PREVIEW_COLOR;
-            ctx.lineWidth = Math.max(1.8, 2.2 * scale);
-            trace();
-            ctx.stroke();
-        }
-    } finally {
-        ctx.restore();
-    }
-
-    if (typeof window !== "undefined") {
-        window.__tpLastDraw = items.map((it) => ({
-            a: [Math.round(it.aG[0]), Math.round(it.aG[1])],
-            b: [Math.round(it.bG[0]), Math.round(it.bG[1])],
-            aS: [Math.round(it.a[0]), Math.round(it.a[1])],
-            bS: [Math.round(it.b[0]), Math.round(it.b[1])],
-            bend: 0,
-        }));
-    }
-}
-
-/**
- * 端点小圆点 —— 画在**节点上层**。
- * 槽位本身就压在节点边线上，放下层会被节点整个盖掉，线头看着像断的；
- * 两颗 3px 的点不挡任何文字，却能把「接在哪」钉死。
- */
-function drawBroadcastDots(ctx, canvas) {
-    const plan = computeShapes(canvas);
-    if (!plan || !ctx) return;
-    const { items, scale, dpr } = plan;
-
-    ctx.save();
-    /* 同 drawBroadcastLines：restore 必须兜底，否则整个画布的变换会被带偏 */
-    try {
-        ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
-        ctx.globalAlpha = 0.95;
-        ctx.fillStyle = PREVIEW_COLOR;
-        const r = Math.max(2.2, 2.6 * scale);
-        for (const it of items) {
-            for (const pt of [it.a, it.b]) {
-                ctx.beginPath();
-                ctx.arc(pt[0], pt[1], r, 0, Math.PI * 2);
-                ctx.fill();
-            }
-        }
-    } finally {
-        ctx.restore();
-    }
-}
-
-/**
- * 装配两处钩子：
- *   ① `drawBackCanvas` **之后** → 线（画进 bgcanvas，节点下层）
- *   ② `draw()` 末尾        → 端点圆点（主 ctx，节点上层）
- *
- * ⚠️ 为什么不用 `onDrawForeground`（踩过的坑）：
- *   ComfyUI 前端用 `watch(() => app.canvas, e => e.onDrawForeground = chain(...))`
- *   维护画布钩子，那个 watch 会在我们之后重新赋值 —— 包装被整个替换掉，
- *   实测 hookLen 变成了别人的 88 字符函数，绘制一次都没执行。
- */
-function installPreviewPainter(appRef) {
-    const canvas = appRef.canvas;
-    if (!canvas || typeof canvas.draw !== "function") return;
-    if (canvas.__tpDrawPatched) return;
-    canvas.__tpDrawPatched = true;
-
-    /* ⚠️ 为什么挂在 `drawBackCanvas` **之后**（踩过的坑）：
-     *   一开始插在 `drawFrontCanvas` **之前**，结果一个像素都看不到 ——
-     *   drawFrontCanvas 内部会把前景层重绘一遍，我们画的东西被它盖掉了。
-     *   画在 drawBackCanvas 之后：背景和真实连线都画完了，节点还没画 ⇒
-     *   线正好落在「节点下层」，节点一盖就自然遮住，节点之间的空白处保留。
-     *
-     * ⚠️ 只用 `this.ctx` / `this`，**不看入参**：实测 drawBackCanvas 的实参
-     *   既不是 ctx 也不是 canvas（另一个入口报过 `ctx.save is not a function`），
-     *   照它取坐标系会一条线都算不出来。参数原样透传，绝不改写。 */
-    const wrapBack = (cv) => {
-        const inner = cv.drawBackCanvas;
-        if (typeof inner !== "function" || inner.__tpPainter) return;
-        const wrapped = function (...args) {
-            const r = inner.apply(this, args);
-            try {
-                drawBroadcastLines(bgCtx(this), this);
-            } catch (e) {
-                if (typeof window !== "undefined") window.__tpDrawErr = "line:" + String((e && e.message) || e);
-            }
-            return r;
-        };
-        wrapped.__tpPainter = true;
-        cv.drawBackCanvas = wrapped;
-    };
-
-    wrapBack(canvas);
-
-    const origDraw = canvas.draw;
-    canvas.draw = function (...args) {
-        /* 前端某些路径会重新赋值 drawBackCanvas（拖拽中断时见过），
-         * 每帧开头兜一次，发现被换掉就重包 —— 成本是一次属性读。 */
-        if (!this.drawBackCanvas || !this.drawBackCanvas.__tpPainter) wrapBack(this);
-        let r;
-        try {
-            r = origDraw.apply(this, args);
-        } finally {
-            try {
-                drawBroadcastDots(this.ctx, this);
-            } catch (e) {
-                if (typeof window !== "undefined") window.__tpDrawErr = "dot:" + String((e && e.message) || e);
-            }
-        }
-        return r;
-    };
-}
-
-function patchGraphToPrompt(appRef) {
-    if (appRef.__tpBroadcastPatched) return;
-
-    /* ⚠️ 必须**两层都包**，只包 graphToPrompt 会漏：
-     *   cg-use-everywhere 在它自己的 setup() 里就把**当时的** app.graphToPrompt 存成变量，
-     *   点「运行」时它走 queuePrompt → 用存下来的原函数（可能绕过我们这一层）→ 广播静默失效。
-     *   包住 queuePrompt 就能兜住这条路径：不管里面谁调谁，临时线在我们这一层就已经接好了。
-     *   用 depth 计数保证「只广播一次」：外层已经接过，内层直接透传、不要重复接、更不要提前拆。 */
-    let depth = 0;
-
-    function around(fn) {
-        if (depth > 0) return fn();
-        depth += 1;
-        const restore = inflate(appRef);
-        const done = () => {
-            depth -= 1;
-            restore();
-        };
-        let ret;
-        try {
-            ret = fn();
-        } catch (e) {
-            done();
-            throw e;
-        }
-        if (ret && typeof ret.then === "function") {
-            return ret.then(
-                (v) => {
-                    done();
-                    return v;
-                },
-                (e) => {
-                    done();
-                    throw e;
-                },
-            );
-        }
-        done();
-        return ret;
-    }
-
-    const origPrompt = appRef.graphToPrompt;
-    if (typeof origPrompt === "function") {
-        appRef.graphToPrompt = function (...args) {
-            return around(() => origPrompt.apply(this, args));
-        };
-    }
-
-    const origQueue = appRef.queuePrompt;
-    if (typeof origQueue === "function") {
-        appRef.queuePrompt = function (...args) {
-            return around(() => origQueue.apply(this, args));
-        };
-    }
-
-    appRef.__tpBroadcastPatched = true;
-    /* 给自检脚本用：直接在「广播生效」上下文里跑一段同步/异步逻辑 */
-    appRef.__tpBroadcastAround = (fn) => around(fn);
-    /* 给自检脚本用：列出当前会广播哪些线（只算不接） */
-    appRef.__tpBroadcastPlan = () =>
-        planBroadcast(appRef.graph).pairs.map(
-            (p) => `${p.dstNode.id}:${p.dstNode.inputs?.[p.dstSlot]?.name}<-${p.srcNode.id}:${p.srcSlot}`,
-        );
-}
-
-/* ------------------------------------------------------------------ *
- * 三、注册
- * ------------------------------------------------------------------ */
-
-app.registerExtension({
-    name: EXT_NAME,
-    async setup() {
-        patchGraphToPrompt(app);
-        installPreviewPainter(app);
-    },
-    beforeRegisterNodeDef(nodeType, nodeData) {
-        if (nodeData.name !== NODE_NAME) return;
-
-        const origCreated = nodeType.prototype.onNodeCreated;
-        nodeType.prototype.onNodeCreated = function () {
-            const r = origCreated ? origCreated.apply(this, arguments) : undefined;
-            const node = this;
-            /* 加载器自己的 onNodeCreated 末尾会 setSize([270,220])，
-             * 所以这里必须排在它之后加控件、再补高度 */
-            addBroadcastWidget(node);
-            /* 前端重建过画布实例的话，钩子会挂在新实例上缺失——这里顺手兜一次 */
-            try {
-                installPreviewPainter(app);
-            } catch (e) {
-                /* 装不上不影响功能 */
-            }
-            return r;
-        };
-
-        /* 读档时 properties 已经恢复，把开关同步成存档里的状态 */
-        const origConfigure = nodeType.prototype.onConfigure;
-        nodeType.prototype.onConfigure = function () {
-            const r = origConfigure ? origConfigure.apply(this, arguments) : undefined;
-            syncBroadcastWidget(this);
-            return r;
-        };
-    },
-});
+/**
+ * ComfyUI-Toggle-Pass: ToggleUnifiedLoader —「全局广播」开关（自带，不依赖 cg-use-everywhere）
+ *
+ * 作用：节点上多一个「开 / 关」开关。
+ *   开 -> 运行（以及导出 API）时，把 MODEL / CLIP / VAE 自动接到图里**所有空着的同类型输入**上，
+ *         不用再一根根拉线；画布上不会留下这些线（提交时临时连、提交完立刻拆）。
+ *         同时画布上会用**紫色曲线**把「将会自动接上的线」画出来，方便核对。
+ *   关 -> 完全保持原样，自己拉线（默认）。
+ *
+ * 为什么不用 cg-use-everywhere 的 `properties.ue_convert`：
+ *   那是它的私有机制，装了才有、卸载就没了，而且只在它自己的 queuePrompt 钩子下生效。
+ *   这里自己实现一份最小广播：零依赖、导出 API 也照样带线。
+ *
+ * ⚠️ 序列化安全（这个节点最贵的教训，改动前务必读完）：
+ *   - 开关用 `addWidget("toggle", ...)` **追加在 widget 列表最末尾**，并且 `serialize:false`。
+ *     `widgets_values` 是按位置存的；它既然不进 widgets_values，旧工作流的每一项位置都不动。
+ *     —— 绝不能插在中间，也不能把一个已有参数标成 serialize:false（那样后面的值会整体前移）。
+ *   - 开关的状态存在 `node.properties.tp_broadcast`（LiteGraph 原生字段，随工作流保存），
+ *     不占用 widgets_values。
+ *   - 不动 Python 的 INPUT_TYPES，所以这个特性**不需要重启 ComfyUI**，Ctrl+F5 就行。
+ *
+ * ⚠️ 只在「前端」提交时生效：用 /prompt 接口直接 POST 原始 API 格式 JSON 时没有前端参与，
+ *   广播自然不会发生（想让它生效就先在界面里「导出 API」，导出的 JSON 已经带好线了）。
+ */
+
+import { app } from "../../scripts/app.js";
+
+const EXT_NAME = "Toggle-Pass.broadcast";
+const NODE_NAME = "ToggleUnifiedLoader";
+const WIDGET_NAME = "全局广播";
+const PROP = "tp_broadcast";
+const BTN_ON = "开";
+const BTN_OFF = "关";
+
+/* 顶部自绘开关的几何常量 —— 必须与 multi_ref_encode.js 里「加载模式胶囊」那套保持一致，
+ * 否则开关会跟上面的 UNet/Checkpoint 胶囊对不齐（两套常量各改各的很容易漂）。 */
+const ROW_SLOT_H = 20;        // 兜底行高（真值优先取 LiteGraph.NODE_SLOT_HEIGHT）
+const ROW_CAP_MAX = 147;      // 胶囊可见宽上限（= multi_ref_encode.js 的 MODE_ROW_MAX）
+const ROW_CAP_MIN = 120;      // 节点极窄时的兜底宽
+const ROW_GAP_RIGHT = 45;     // 胶囊右端到节点右边的最小留白
+const BC_MIN_W = 66;          // 开关自身不窄于此（再窄「广播」+轨道就放不下了）
+const BC_PAD_Y = 3;           // 上下留白 —— 与 multi_ref_encode.js 的 TG_PAD_Y 同值，
+                              // 这样开关高度 = 胶囊高度（都是 行高 - 3*2 = 14）
+
+/* ------------------------------------------------------------------ *
+ * 一、节点上的开关
+ * ------------------------------------------------------------------ */
+
+function isBroadcastOn(node) {
+    const w = (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
+    if (w) return !!w.value;
+    return !!(node.properties && node.properties[PROP]);
+}
+
+function numAt(v, i) {
+    const x = v?.[i];
+    return typeof x === "number" && isFinite(x) ? x : null;
+}
+
+function addBroadcastWidget(node) {
+    if ((node.widgets || []).some((x) => x && x.name === WIDGET_NAME)) return;
+
+    const on = !!(node.properties && node.properties[PROP]);
+
+    /* 原生 toggle：本机前端 1.53.10 的 widget 工厂支持 "toggle"，
+     * {on, off} 决定两侧文案（ComfyUI 自己的 BOOLEAN 输入就是这么建的）。 */
+    let w = null;
+    try {
+        w = node.addWidget("toggle", WIDGET_NAME, on, (v) => {
+            try {
+                node.properties = node.properties || {};
+                /* 原生 toggle 会把新值当第一个参数传进来；拿不到才回落读 w.value
+                 * （别只读 w.value：某些路径上 callback 先于 value 赋值，会写反） */
+                node.properties[PROP] = typeof v === "boolean" ? v : !!w.value;
+            } catch (e) {
+                console.warn("[Toggle-Pass] 广播开关状态写入 properties 失败", e);
+            }
+            app.graph?.setDirtyCanvas?.(true, true);
+            node.setDirtyCanvas?.(true, true);
+        }, { on: BTN_ON, off: BTN_OFF });
+    } catch (e) {
+        console.warn("[Toggle-Pass] 原生 toggle 控件不可用，跳过广播开关", e);
+        return;
+    }
+    if (!w) return;
+
+    /* 不进 widgets_values / 不进 prompt：旧工作流的位置表一项都不动 */
+    w.options = w.options || {};
+    w.options.serialize = false;
+    w.serialize = false;
+    w.tooltip =
+        "全局广播：开启后运行时把 MODEL / CLIP / VAE 自动接到图里所有空着的同类型输入上，不用拉线。" +
+        "开启时画布上会用紫色曲线画出「将自动接上的线」，方便核对（曲线只是预览，不是真实连线、不会被保存）。" +
+        "关闭则照旧手动连线。";
+    w.label = WIDGET_NAME;
+
+    /* ★ 2026-10-24：开关从「widget 列表末尾的一整行」搬到「顶部胶囊下面」。
+     * 做法：原生 widget 只当**状态容器**（值 / properties / 序列化全部原样不动），
+     *      用 hidden 让它不参与布局与绘制，再自己画一个小开关上去。
+     * 为什么 hidden 是安全的：widgets_values 是按「参与序列化的 widget」顺序存的，
+     *      这个 toggle 本来就是 serialize:false（不进 widgets_values），
+     *      藏起来既不增也不减参与序列化的项 ⇒ 旧工作流的位置表一项都不动。
+     * 顺带的好处：节点底部少了一整行，不再挤在「移除 LoRA」下面。 */
+    w.hidden = true;
+    node._tpBcW = w;
+    attachBroadcastRow(node);
+}
+
+/* ------------------------------------------------------------------ *
+ * 一之二、顶部自绘小开关（在 UNet / Checkpoint 胶囊的下一行、左半边）
+ *
+ * 为什么不用 widget 承载：widget 的 y 由前端布局算死（插槽区之下、按列表顺序往下排），
+ *   想放到「第一行插槽左侧的下方」这种位置，只能自己画。
+ * 这里复用的是「加载模式」开关同一套打法（见 multi_ref_encode.js 的 attachModeRowToggle）：
+ *   · 位置：第 1 行插槽的正下方（= 第 2 行插槽那一行的左半边，右侧 CLIP 标签不受影响）
+ *   · 宽度：胶囊的一半（用户要求「一半位置就行」）
+ *   · 绘制：node.onDrawForeground；命中：node.onMouseDown（返回真值吃掉事件，避免变成拖动节点）
+ * ⚠️ 这两个钩子是**实例属性**，必须存旧引用链式调用 —— 直接赋值会吞掉
+ *   multi_ref_encode.js 挂的「加载模式」开关（两边都挂同一对钩子）。
+ * ------------------------------------------------------------------ */
+
+/** 与 multi_ref_encode.js 的 tpWidgetMargin() 同源：BaseWidget.margin 默认 15 */
+function bcMargin() {
+    const m = globalThis.BaseWidget?.margin;
+    return (typeof m === "number" && m >= 0 && m <= 40) ? m : 15;
+}
+
+function bcRoundRect(ctx, x, y, w, h, r) {
+    const rr = Math.max(0, Math.min(r, h / 2, w / 2));
+    ctx.beginPath();
+    ctx.moveTo(x + rr, y);
+    ctx.lineTo(x + w - rr, y);
+    ctx.quadraticCurveTo(x + w, y, x + w, y + rr);
+    ctx.lineTo(x + w, y + h - rr);
+    ctx.quadraticCurveTo(x + w, y + h, x + w - rr, y + h);
+    ctx.lineTo(x + rr, y + h);
+    ctx.quadraticCurveTo(x, y + h, x, y + h - rr);
+    ctx.lineTo(x, y + rr);
+    ctx.quadraticCurveTo(x, y, x + rr, y);
+    ctx.closePath();
+}
+
+/** 开关在节点本地坐标里的位置与尺寸（x/w 是我们自己这一块的范围，不含右侧留白） */
+function bcRowGeom(node) {
+    const SH = Number(globalThis.LiteGraph?.NODE_SLOT_HEIGHT) || ROW_SLOT_H;
+    const nw = numAt(node?.size, 0) || 0;
+    const left = bcMargin();
+    /* 先按「加载模式胶囊」的宽度算（保证和上面的胶囊左右端点对得上） */
+    let capW = nw > 0 ? nw - left - ROW_GAP_RIGHT : ROW_CAP_MAX;
+    capW = Math.min(ROW_CAP_MAX, Math.max(0, capW));
+    if (capW < ROW_CAP_MIN) capW = nw > 0 ? Math.min(ROW_CAP_MIN, Math.max(0, nw - left)) : ROW_CAP_MIN;
+    /* 用户要求：占胶囊的一半（红框圈的就是胶囊前一半的宽度） */
+    const w = Math.max(BC_MIN_W, Math.round(capW / 2));
+
+    /* 第一行插槽的纵向位置（与加载模式胶囊同一套反推），再往下挪一行 = 胶囊正下方 */
+    let top = Math.round(SH * 0.2);
+    try {
+        const cy = numAt(node?.getOutputPos?.(0), 1);
+        const py = numAt(node?.pos, 1);
+        if (cy !== null && py !== null) top = Math.max(0, Math.round(cy - py - SH / 2));
+    } catch (e) { /* 用兜底值 */ }
+    return { x: left, y: top + SH, w, h: SH };
+}
+
+/** 自绘：一个完整的小胶囊控件 —— 淡底色托底，左边 label、右边开关轨道。
+ *  为什么要有底色：节点里其它控件（下拉框、UNet/Checkpoint 胶囊）都有底色，
+ *  只画「裸文字 + 轨道」会显得浮在空白上，跟整体设计语言脱节。
+ *  ★ 关键是「托底要淡、轨道要细」：
+ *    第一版给外框加了 1px 亮描边，结果和下边框围成**两层框**（外框 + 内轨道），
+ *    比上面那条单层胶囊笨重得多。现在外框只有一层很淡的填充、默认无描边，
+ *    视觉上退成「一块底色」而不是「一个框」，轨道才是主角。
+ *  配色对齐节点现有控件：下拉框底色 / 高亮蓝 #3a96f3。 */
+function drawBroadcastRow(ctx, node, on, hover) {
+    const g = bcRowGeom(node);
+    const cy = g.y + g.h / 2;
+    /* 高度与上面的 UNet/Checkpoint 胶囊完全一致：行高 - BC_PAD_Y*2 = 14
+     * （胶囊那边是 TG_PAD_Y=3、净高 14 —— 两个常量必须同步改，否则一高一矮） */
+    const bh = g.h - BC_PAD_Y * 2;
+    const r = bh / 2;
+    const bx = g.x, bw = g.w;
+
+    try {
+        ctx.save();
+
+        /* ---- 托底：一层淡填充，只在开启/悬停时略提亮 ---- */
+        bcRoundRect(ctx, bx, g.y + BC_PAD_Y, bw, bh, r);
+        if (on) ctx.fillStyle = hover ? "rgba(58,150,243,0.24)" : "rgba(58,150,243,0.13)";
+        else ctx.fillStyle = hover ? "rgba(255,255,255,0.105)" : "rgba(255,255,255,0.06)";
+        ctx.fill();
+        /* 悬停加一道极淡描边：明确「这里能点」，又不会变成第二个框 */
+        if (hover) {
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = on ? "rgba(58,150,243,0.45)" : "rgba(255,255,255,0.12)";
+            ctx.stroke();
+        }
+
+        /* ---- 内容整体居中：先量文字宽，再把「文字 + 间隙 + 轨道」作为一条内容居中摆放 ----
+         * 之前是「文字贴左 8px、轨道贴右 7px」，两边留白各算各的 ⇒ 一宽一窄，看着偏向一边。
+         * 现在统一算 padX，左右留白严格相等。 */
+        const label = "广播";
+        ctx.textAlign = "left";
+        ctx.font = "600 10px sans-serif";     // 用户要求：11px → 10px（小 10%）
+        let tw = 20;                          // 10px「广播」约 20px，量不到时兜底
+        try {
+            const lm = ctx.measureText(label);
+            if (lm && typeof lm.width === "number" && isFinite(lm.width) && lm.width > 0) tw = lm.width;
+        } catch (e) { /* 用兜底 */ }
+
+        const trackW = 24, GAP = 8;
+        const contentW = tw + GAP + trackW;
+        const padX = Math.max(5, (bw - contentW) / 2);   // 左右留白相等 ⇒ 整条内容居中
+        const lx = bx + padX;
+        const tx = lx + tw + GAP;
+
+        /* ---- label：按视觉盒垂直居中 ----
+         * 不用 textBaseline="middle"：Canvas 的 middle 按 (ascent+descent)/2 排，
+         * 对没有降部的中文会整体偏上。改成量出真实上沿、基线放到 cy + 上沿/2，
+         * 这样字形上下留白相等 —— 实测这样仍会低约半个像素，再补 0.5 的视觉修正。 */
+        ctx.fillStyle = on ? "rgba(230,238,250,1)" : "rgba(174,182,196,1)";
+        let asc = 7.3;   // 10px 中文的兜底上沿（量不到时用）
+        try {
+            const lm = ctx.measureText(label);
+            const a = lm && lm.actualBoundingBoxAscent;
+            if (typeof a === "number" && isFinite(a) && a > 0) asc = a;
+        } catch (e) { /* 用兜底 */ }
+        ctx.textBaseline = "alphabetic";
+        ctx.fillText(label, lx, cy + asc / 2 - 0.5);
+
+        /* ---- 右侧开关：细轨道 + 小滑块（位置 tx 已在上面按居中算好） ---- */
+        const th = 10;
+        const ty = cy - th / 2;
+        bcRoundRect(ctx, tx, ty, trackW, th, th / 2);
+        if (on) {
+            const grad = ctx.createLinearGradient(tx, ty, tx, ty + th);
+            grad.addColorStop(0, "#4aa5ff");
+            grad.addColorStop(1, "#2f86e8");
+            ctx.fillStyle = grad;
+        } else {
+            ctx.fillStyle = "rgba(255,255,255,0.13)";
+        }
+        ctx.fill();
+        /* 关闭态补一道内沿，让轨道看起来是「凹槽」而不是一块平色 */
+        if (!on) {
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "rgba(0,0,0,0.18)";
+            ctx.stroke();
+        }
+
+        /* ---- 滑块：一点点投影，让它「浮」在轨道上 ---- */
+        const kr = 4;
+        const kx = on ? tx + trackW - 1 - kr : tx + 1 + kr;
+        ctx.save();
+        ctx.shadowColor = "rgba(0,0,0,0.30)";
+        ctx.shadowBlur = 2;
+        ctx.shadowOffsetY = 0.5;
+        ctx.beginPath();
+        ctx.arc(kx, cy, kr, 0, Math.PI * 2);
+        ctx.fillStyle = "#ffffff";
+        ctx.fill();
+        ctx.restore();
+    } finally {
+        /* ★ 必须 try/finally：漏 restore 会让整个画布的变换永久跑偏（踩过） */
+        try { ctx.restore(); } catch (e) { /* ignore */ }
+    }
+}
+
+function isBroadcastOnNode(node) {
+    const w = node?._tpBcW || (node?.widgets || []).find((x) => x && x.name === WIDGET_NAME);
+    if (w && typeof w.value === "boolean") return w.value;
+    return !!(node?.properties && node.properties[PROP]);
+}
+
+/** 切开关：真源写 properties，再同步给（隐藏的）原生 widget，前端才知道图被改了 */
+function setBroadcast(node, on) {
+    const v = !!on;
+    try {
+        node.properties = node.properties || {};
+        node.properties[PROP] = v;
+    } catch (e) { /* ignore */ }
+    const w = node._tpBcW || (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
+    if (w) {
+        try {
+            if (typeof w.setValue === "function") w.setValue(v);
+            else w.value = v;
+        } catch (e) {
+            w.value = v;
+        }
+    }
+    node.setDirtyCanvas?.(true, true);
+    app.graph?.setDirtyCanvas?.(true, true);
+    app.graph?.change?.();
+}
+
+/** 把自绘开关挂到节点上（幂等） */
+function attachBroadcastRow(node) {
+    if (node._tpBcRow) return;
+    /* 「现代节点设计」（Vue 节点）不走 canvas 绘制钩子 → 保持原生 widget 可见的回落 */
+    if (globalThis.LiteGraph?.vueNodesMode) {
+        const w = node._tpBcW;
+        if (w) w.hidden = false;
+        return;
+    }
+    node._tpBcRow = true;
+
+    const prevDraw = typeof node.onDrawForeground === "function" ? node.onDrawForeground : null;
+    const prevDown = typeof node.onMouseDown === "function" ? node.onMouseDown : null;
+    const prevMove = typeof node.onMouseMove === "function" ? node.onMouseMove : null;
+
+    node.onDrawForeground = function (ctx, ...rest) {
+        if (prevDraw) {
+            try { prevDraw.call(this, ctx, ...rest); } catch (e) { /* 旧钩子出错不影响开关 */ }
+        }
+        if (!this._tpBcW || this.flags?.collapsed) return;
+        try {
+            drawBroadcastRow(ctx, this, isBroadcastOnNode(this), !!this._tpBcHover);
+        } catch (e) {
+            /* 自绘失败就留空，绝不能抛出去（会中断整块画布绘制） */
+        }
+    };
+
+    /* 悬停高亮：只在「进入/离开」时刷新一次画布，不做逐像素重绘 */
+    node.onMouseMove = function (e, pos, canvas) {
+        if (this._tpBcW && !this.flags?.collapsed) {
+            const x = numAt(pos, 0), y = numAt(pos, 1);
+            let hv = false;
+            if (x !== null && y !== null) {
+                const g = bcRowGeom(this);
+                hv = x >= g.x && x <= g.x + g.w && y >= g.y && y <= g.y + g.h;
+            }
+            if (this._tpBcHover !== hv) {
+                this._tpBcHover = hv;
+                this.setDirtyCanvas?.(true, true);
+            }
+        }
+        return prevMove ? prevMove.call(this, e, pos, canvas) : false;
+    };
+
+    /* ⚠️ 光有 onMouseMove 不够：鼠标一移出节点，前端就不再给它派发 move，
+     *   高亮会**永久卡在亮着**的状态（实测：移开画布后 _tpBcHover 仍是 true）。
+     *   所以补一个离开钩子复位。 */
+    const prevLeave = typeof node.onMouseLeave === "function" ? node.onMouseLeave : null;
+    node.onMouseLeave = function (...args) {
+        if (this._tpBcHover) {
+            this._tpBcHover = false;
+            this.setDirtyCanvas?.(true, true);
+        }
+        return prevLeave ? prevLeave.apply(this, args) : undefined;
+    };
+
+    node.onMouseDown = function (e, pos, canvas) {
+        if (this._tpBcW && !this.flags?.collapsed) {
+            const x = numAt(pos, 0), y = numAt(pos, 1);
+            if (x !== null && y !== null) {
+                const g = bcRowGeom(this);
+                if (x >= g.x && x <= g.x + g.w && y >= g.y && y <= g.y + g.h) {
+                    setBroadcast(this, !isBroadcastOnNode(this));
+                    return true;    // 吃掉点击，避免变成拖动节点
+                }
+            }
+        }
+        return prevDown ? (prevDown.call(this, e, pos, canvas) || false) : false;
+    };
+}
+
+/* 注：原 growToFit()（给追加的那行 widget 补节点高度）已随「开关搬到顶部」一起删除 ——
+ * 开关不再占用 widget 布局，节点高度不需要额外补。 */
+
+function syncBroadcastWidget(node) {
+    const w = (node.widgets || []).find((x) => x && x.name === WIDGET_NAME);
+    if (!w) return;
+    const v = !!(node.properties && node.properties[PROP]);
+    if (!!w.value !== v) w.value = v;
+    /* 保险：极端情况下（前端没走 onNodeCreated 就直接 configure）自绘钩子可能还没挂 */
+    if (w.hidden) {
+        node._tpBcW = w;
+        attachBroadcastRow(node);
+    }
+}
+
+/* ------------------------------------------------------------------ *
+ * 二、提交时临时连线（最小广播实现）
+ * ------------------------------------------------------------------ */
+
+/** 输入类型与输出类型是否匹配（兼容 "MODEL" / "A,B" / "*" 这几种写法） */
+function typeMatch(inputType, outputType) {
+    const a = String(inputType ?? "");
+    const b = String(outputType ?? "");
+    if (!a || !b) return false;
+    if (a === b) return true;
+    if (a === "*" || b === "*") return true;
+    return a.split(",").some((s) => s.trim() === b);
+}
+
+/**
+ * 计算「广播计划」：哪些空着的输入会被接到哪个输出。
+ *
+ * ⚠️ 真接线（inflate）和画布上的曲线预览（computeShapes）**必须共用这一份**，
+ *    否则会出现「画的线」和「真接的线」不一致 —— 那种 bug 用户根本没法自查。
+ */
+function planBroadcast(graph) {
+    const pairs = [];
+    const ambiguous = new Set();
+    if (!graph || !Array.isArray(graph._nodes)) return { pairs, ambiguous };
+
+    /* 每个类型只认一个广播源：多个加载器都开广播时取第一个，并提示一次 */
+    const srcByType = new Map();
+    for (const n of graph._nodes) {
+        if (!n || n.type !== NODE_NAME || !isBroadcastOn(n)) continue;
+        const outs = n.outputs || [];
+        for (let i = 0; i < outs.length; i++) {
+            const t = outs[i] && outs[i].type;
+            if (!t || t === "*") continue;
+            if (srcByType.has(t)) {
+                ambiguous.add(t);
+                continue;
+            }
+            srcByType.set(t, { node: n, slot: i });
+        }
+    }
+    if (!srcByType.size) return { pairs, ambiguous };
+
+    for (const [t, src] of srcByType) {
+        for (const target of graph._nodes) {
+            /* 加载器自己不算，避免自己接自己 */
+            if (!target || target === src.node || target.type === NODE_NAME) continue;
+            const inputs = target.inputs || [];
+            for (let idx = 0; idx < inputs.length; idx++) {
+                const inp = inputs[idx];
+                /* 已经接了线的一律不动 —— 广播只补空位，绝不覆盖用户手连的线 */
+                if (!inp || inp.link != null) continue;
+                if (!typeMatch(inp.type, t)) continue;
+                pairs.push({ srcNode: src.node, srcSlot: src.slot, srcType: t, dstNode: target, dstSlot: idx });
+            }
+        }
+    }
+    return { pairs, ambiguous };
+}
+
+/**
+ * 把「开着广播」的加载器的三个输出，临时接到所有空着的同类型输入上。
+ * 返回一个还原函数；调用它会把临时连线全部拆掉。
+ */
+function inflate(appRef) {
+    const graph = appRef.rootGraph || appRef.graph;
+    const { pairs, ambiguous } = planBroadcast(graph);
+    if (ambiguous.size) {
+        console.warn(
+            "[Toggle-Pass] 有多个加载器开着「全局广播」，同类型输出只取第一个：",
+            Array.from(ambiguous).join(", "),
+        );
+    }
+    const undo = [];
+
+    for (const p of pairs) {
+        try {
+            p.srcNode.connect(p.srcSlot, p.dstNode, p.dstSlot);
+        } catch (e) {
+            continue;
+        }
+        /* connect 失败时不会抛，只表现为 link 仍为空 */
+        if (p.dstNode.inputs?.[p.dstSlot] && p.dstNode.inputs[p.dstSlot].link != null) {
+            const { dstNode, dstSlot } = p;
+            undo.push(() => {
+                try {
+                    dstNode.disconnectInput(dstSlot);
+                } catch (e) {
+                    /* 目标节点已被删掉之类，忽略 */
+                }
+            });
+        }
+    }
+
+    return () => {
+        for (let i = undo.length - 1; i >= 0; i--) undo[i]();
+        try {
+            appRef.graph?.setDirtyCanvas?.(true, true);
+        } catch (e) {
+            /* 无画布环境（导出脚本）忽略 */
+        }
+    };
+}
+
+/* ------------------------------------------------------------------ *
+ * 三、画布上的预览曲线（只画，不建真实 link）
+ * ------------------------------------------------------------------ */
+
+/* 观感对齐 cg-use-everywhere：紫罗兰实线 + 一层柔光，比青色虚线耐看，
+ * 也不会被误当成「报错警告线」。 */
+const PREVIEW_COLOR = "#a78bfa";
+const PREVIEW_GLOW = "#7c5cff";
+
+/**
+ * ⚠️⚠️ `boundingRect` 只在**画过**这个槽位之后才有效。
+ *   节点在可视区外 / 从未渲染时，槽位的 `boundingRect` 是 `[0,0,0,0]`，
+ *   而 `rectCenter` 对零尺寸会老老实实返回 `[0,0]` —— 于是端点被算成**图坐标原点**，
+ *   线就画到画布左上角那片空白里，看着像一根「悬空紫线」。
+ *   2026-11-06 用户截图里那根从节点往左伸出去就断掉的线就是这个：
+ *   目标是画面外的 `VAE解码`（pos.x=1352，槽位 boundingRect 全 0）。
+ *   ⇒ 零尺寸一律视为「未布局」，退回 `getConnectionPos` 估算。
+ */
+function rectCenter(r) {
+    if (!r) return null;
+    if (Array.isArray(r) || typeof r[0] === "number") {
+        const x = numAt(r, 0);
+        const y = numAt(r, 1);
+        const w = numAt(r, 2);
+        const h = numAt(r, 3);
+        if (x == null || y == null) return null;
+        if (!(w > 0) || !(h > 0)) return null; /* 未布局 */
+        return [x + w / 2, y + h / 2];
+    }
+    const x = typeof r.x === "number" ? r.x : r.left;
+    const y = typeof r.y === "number" ? r.y : r.top;
+    if (typeof x !== "number" || typeof y !== "number") return null;
+    const w = r.width ?? r.w ?? 0;
+    const h = r.height ?? r.h ?? 0;
+    if (!(w > 0) || !(h > 0)) return null; /* 未布局 */
+    return [x + w / 2, y + h / 2];
+}
+
+/**
+ * 槽位中心（图坐标）。
+ *
+ * ⚠️ 优先 `boundingRect`，不要只信 `getConnectionPos`：
+ *   本机前端里槽位的 `pos` 字段**经常是 null**（只有走 widget 布局的那几个才被填），
+ *   而 `pos` 为 null 时 getConnectionPos 会退化成「按输入顺序估算」的公式
+ *   (`node.pos + (index+0.7)*NODE_SLOT_HEIGHT`) —— 一旦节点里有 widget 夹在输入之间，
+ *   估出来的位置就和实际渲染对不上，线看起来就是「错位」。
+ *   `boundingRect` 是渲染时算出来的真实盒子，节点一移动它就跟着更新。
+ */
+function slotPoint(node, isInput, idx) {
+    const list = isInput ? node?.inputs : node?.outputs;
+    const s = list && list[idx];
+    if (!s) return null;
+    const c = rectCenter(s.boundingRect);
+    if (c) return c;
+    try {
+        const p = node.getConnectionPos(isInput, idx);
+        if (p && isFinite(p[0]) && isFinite(p[1])) return [p[0], p[1]];
+    } catch (e) {
+        /* 节点被删/布局未完成 */
+    }
+    return null;
+}
+
+/**
+ * 把「运行时会自动接上的线」算成屏幕坐标下的贝塞尔。
+ * 不创建任何 link ⇒ 不进工作流、不影响保存，纯视觉提示。
+ *
+ * 坐标链路：槽位（图坐标）→ `ds.convertOffsetToCanvas` → 屏幕坐标，
+ * 然后 setTransform 到 dpr，直接按屏幕坐标画。
+ * ⚠️ 不要自己手搓 `(p + offset) * scale`：那是在赌前端内部变换的实现细节。
+ */
+function computeShapes(canvas) {
+    /* ⚠️ 必须和 inflate() 用**同一张图**（rootGraph 优先）：
+     *   广播接哪张图，就画哪张图。两边取不同引用（canvas.graph / app.graph）
+     *   在切换工作流或进子图的瞬间会不一致，线就会指向图里已不存在的位置。 */
+    const g = (app && (app.rootGraph || app.graph)) || (canvas && canvas.graph);
+    const ds = canvas && canvas.ds;
+    if (!g || !ds) return null;
+
+    const { pairs } = planBroadcast(g);
+    if (!pairs.length) return null;
+
+    const scale = ds.scale || 1;
+    let dpr = canvas.dpr;
+    if (!dpr) {
+        const r = canvas.canvas?.getBoundingClientRect?.();
+        dpr = r && r.width > 0 ? canvas.canvas.width / r.width : 1;
+    }
+    const off = ds.offset || [0, 0];
+    const toScreen = (p) => {
+        try {
+            const r = ds.convertOffsetToCanvas?.([p[0], p[1]]);
+            if (r && isFinite(r[0]) && isFinite(r[1])) return [r[0], r[1]];
+        } catch (e) {
+            /* 兜底走下面的手算 */
+        }
+        return [(p[0] + (off[0] || 0)) * scale, (p[1] + (off[1] || 0)) * scale];
+    };
+
+    const items = [];
+    for (const p of pairs) {
+        const aG = slotPoint(p.srcNode, false, p.srcSlot);
+        const bG = slotPoint(p.dstNode, true, p.dstSlot);
+        if (!aG || !bG) continue;
+        const a = toScreen(aG);
+        const b = toScreen(bG);
+        if (!isFinite(a[0]) || !isFinite(b[0])) continue;
+        /* 控制点跟官方 Spline 连线同一套：取两点距离的 3 成（不小于 40 图单位），
+         * 起点往右甩、终点从左边进 —— 收口方向和真实连线一致，
+         * 一眼就能看出线是从哪个口出来的。 */
+        const dist = Math.hypot(bG[0] - aG[0], bG[1] - aG[1]);
+        const d = Math.max(40, dist * 0.3) * scale;
+        items.push({
+            aG,
+            bG,
+            a,
+            b,
+            c1: [a[0] + d, a[1]],
+            c2: [b[0] - d, b[1]],
+            /* 原始引用留着：点亮槽位时要拿槽位的 type（决定颜色）和归属节点（判遮挡） */
+            srcNode: p.srcNode,
+            srcSlot: p.srcSlot,
+            dstNode: p.dstNode,
+            dstSlot: p.dstSlot,
+        });
+    }
+    if (!items.length) return null;
+    return { items, scale, dpr };
+}
+
+/**
+ * 取「背景层」的 ctx。
+ *
+ * ⚠️⚠️ 这里是最容易白忙一场的地方：新版前端把背景画在**离屏** canvas 上
+ *   （`this.bgcanvas`），画完之后才 `ctx.drawImage(bgcanvas, ...)` 贴到主画布，
+ *   再在主画布上画节点。所以往主 ctx 上画背景层的内容 —— 会立刻被那张贴图盖掉，
+ *   代码跑了、坐标也对，就是看不见（实测一个紫色像素都没有）。
+ *   必须画进 bgcanvas 自己的 ctx，贴图时才会把它一起带上来，节点再盖在上面。
+ *   老架构没有 bgcanvas（背景直接画在主 ctx 上），此时退回主 ctx。
+ */
+function bgCtx(canvas) {
+    const bg = canvas && canvas.bgcanvas;
+    if (bg && typeof bg.getContext === "function") {
+        if (!canvas.__tpBgCtx || canvas.__tpBgCtx.canvas !== bg) canvas.__tpBgCtx = bg.getContext("2d");
+        return canvas.__tpBgCtx;
+    }
+    return canvas && canvas.ctx;
+}
+
+/**
+ * 线本体 —— 画在**节点下层**（背景层 ctx，交付 `drawBackCanvas` 之后）。
+ * 所以线穿过节点时会被节点自然遮住，跟真实连线一个观感：
+ * 该露的缝隙里露出来，节点上干干净净，不挡标题也不挡参数。
+ */
+function drawBroadcastLines(ctx, canvas) {
+    const plan = computeShapes(canvas);
+    if (!plan || !ctx) return;
+    const { items, scale, dpr } = plan;
+
+    ctx.save();
+    /* ⚠️ save / restore 必须用 try/finally 包住：
+     *   中间任何一次 `it.a` 取值、bezier 计算出问题抛异常，若跳过 restore，
+     *   这个 ctx 就会**永久**留在 `dpr + translate + scale` 变换下 ——
+     *   之后所有绘制（包括官方的选择框、节点）都会整体偏移、放大，
+     *   而且看起来跟本插件毫无关系，极难排查。宁可少画一帧，也不能漏 restore。 */
+    try {
+        ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+        ctx.lineCap = "round";
+        ctx.lineJoin = "round";
+
+        for (const it of items) {
+            const trace = () => {
+                ctx.beginPath();
+                ctx.moveTo(it.a[0], it.a[1]);
+                ctx.bezierCurveTo(it.c1[0], it.c1[1], it.c2[0], it.c2[1], it.b[0], it.b[1]);
+            };
+
+            /* 一层柔光底：深色画布上紫线才不会是「一根生硬的细线」 */
+            ctx.globalAlpha = 0.16;
+            ctx.strokeStyle = PREVIEW_GLOW;
+            ctx.lineWidth = Math.max(5, 7 * scale);
+            trace();
+            ctx.stroke();
+
+            /* 主线 */
+            ctx.globalAlpha = 0.95;
+            ctx.strokeStyle = PREVIEW_COLOR;
+            ctx.lineWidth = Math.max(1.8, 2.2 * scale);
+            trace();
+            ctx.stroke();
+        }
+    } finally {
+        ctx.restore();
+    }
+
+    if (typeof window !== "undefined") {
+        window.__tpLastDraw = items.map((it) => ({
+            a: [Math.round(it.aG[0]), Math.round(it.aG[1])],
+            b: [Math.round(it.bG[0]), Math.round(it.bG[1])],
+            aS: [Math.round(it.a[0]), Math.round(it.a[1])],
+            bS: [Math.round(it.b[0]), Math.round(it.b[1])],
+            bend: 0,
+        }));
+    }
+}
+
+/**
+ * 端点小圆点 —— 默认和线**同一层**（背景层，被节点盖住）。
+ *
+ * ⚠️ 为什么改了（2026-11-06 用户实测）：
+ *   早先画在 `draw()` 末尾 = 全画布最上层，理由是「槽位压在节点边线上，
+ *   放下层会被盖掉、线头看着像断的」。但这个优先级会让圆点在**任何**节点
+ *   移到线上方时照样穿出来 —— 用户拿节点当遮罩盖内容时，一串紫点浮在
+ *   节点上面，看着像脏点。
+ *   现在和线同层：节点盖住线的同时把点一起盖住，观感统一（线从节点边缘
+ *   伸出来，正好是官方连线的样子）。
+ *   想回到旧行为：`localStorage.tp_dots_layer = "front"` 再刷新。
+ */
+function drawBroadcastDots(ctx, canvas) {
+    const plan = computeShapes(canvas);
+    if (!plan || !ctx) return;
+    const { items, scale, dpr } = plan;
+
+    ctx.save();
+    /* 同 drawBroadcastLines：restore 必须兜底，否则整个画布的变换会被带偏 */
+    try {
+        ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+        ctx.globalAlpha = 0.95;
+        ctx.fillStyle = PREVIEW_COLOR;
+        const r = Math.max(2.2, 2.6 * scale);
+        for (const it of items) {
+            for (const pt of [it.a, it.b]) {
+                ctx.beginPath();
+                ctx.arc(pt[0], pt[1], r, 0, Math.PI * 2);
+                ctx.fill();
+            }
+        }
+    } finally {
+        ctx.restore();
+    }
+}
+
+/**
+ * 取某个类型（MODEL / CLIP / VAE…）的官方配色。
+ * 前端把颜色放在 `LGraphCanvas.link_type_colors`，没命中再看
+ * `canvas.default_connection_color_byType`，都没有就退回中灰。
+ */
+function slotTypeColor(canvas, type) {
+    const t = String(type || "").toUpperCase();
+    const tbl = (typeof window !== "undefined" && window.LGraphCanvas && window.LGraphCanvas.link_type_colors) || {};
+    let c = tbl[t];
+    if (!c && canvas && canvas.default_connection_color_byType) c = canvas.default_connection_color_byType[t];
+    return c || "#9A9";
+}
+
+/**
+ * 这个槽位会不会被**后绘制**的节点盖住？
+ * 画布按 `graph._nodes` 顺序绘制，下标大的在上面。只要有点落在
+ * 某个「更晚绘制」的节点矩形里，就说明它被挡住了 —— 此时不该点亮，
+ * 否则又是一颗浮在别的节点上的假点。
+ */
+function slotHiddenByOtherNode(pt, owner, nodes) {
+    if (!pt || !owner || !nodes) return false;
+    const i = nodes.indexOf(owner);
+    if (i < 0) return false;
+    for (let k = i + 1; k < nodes.length; k++) {
+        const n = nodes[k];
+        if (!n || n === owner || !n.pos || !n.size) continue;
+        const x0 = n.pos[0];
+        const y0 = n.pos[1] - 30;
+        if (pt[0] >= x0 && pt[0] <= x0 + n.size[0] && pt[1] >= y0 && pt[1] <= y0 + n.size[1] + 30) return true;
+    }
+    return false;
+}
+
+/**
+ * 把会被广播接上的槽位**点亮**（补一个和「已连接」一样的实心点）。
+ *
+ * ⚠️ 为什么必须先看 `slot.link`：
+ *   官方槽位是「有 link 填实心、没 link 只描边」。广播只是**预览**，提交前不会
+ *   真的建连线，所以 `slot.link` 一直是 null ⇒ 槽位永远显示成空心，线却已经
+ *   画过去了，看着就是「接上了但没点亮」。
+ *   这里只在「空着、且没被别的节点盖住、位置确定（boundingRect 有效）」时补实心点，
+ *   颜色取该槽位类型的官方配色，所以和手动连线的观感一致。
+ *
+ * 画在**节点之后**（`draw()` 末尾）：槽位点在节点边框上，属于节点自身的一部分，
+ * 画在下层会被节点吃掉。
+ */
+function drawBroadcastSlotLit(ctx, canvas) {
+    const plan = computeShapes(canvas);
+    if (!plan || !ctx) return;
+    const g = (app && (app.rootGraph || app.graph)) || (canvas && canvas.graph);
+    const nodes = (g && g._nodes) || [];
+    const { items, scale, dpr } = plan;
+
+    ctx.save();
+    try {
+        ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+        const r = Math.max(2, 3 * scale);
+        for (const it of items) {
+            const ends = [
+                { node: it.srcNode, idx: it.srcSlot, isInput: false, pt: it.a, ptG: it.aG },
+                { node: it.dstNode, idx: it.dstSlot, isInput: true, pt: it.b, ptG: it.bG },
+            ];
+            for (const q of ends) {
+                const list = q.isInput ? q.node && q.node.inputs : q.node && q.node.outputs;
+                const slot = list && list[q.idx];
+                if (!slot) continue;
+                if (slot.link != null) continue; /* 真连过了，官方自己就画实心 */
+                if (q.node.flags && q.node.flags.collapsed) continue;
+                const br = slot.boundingRect;
+                if (!br || !(numAt(br, 2) > 0)) continue; /* 位置不确定就别瞎点 */
+                if (slotHiddenByOtherNode(q.ptG, q.node, nodes)) continue;
+                ctx.beginPath();
+                ctx.fillStyle = slotTypeColor(canvas, slot.type);
+                ctx.arc(q.pt[0], q.pt[1], r, 0, Math.PI * 2);
+                ctx.fill();
+            }
+        }
+    } finally {
+        ctx.restore();
+    }
+}
+
+/**
+ * 端点圆点放哪一层：
+ *   "back"（默认）→ 跟线一起画进 bgcanvas，节点能盖住
+ *   "front"        → 旧的 draw() 末尾，全画布最上层（能穿出节点）
+ * 用 localStorage 而不是 `app.extensionManager.setting`：后者会 POST 到
+ * 服务端 `comfy.settings.json`，污染用户真实环境。
+ */
+function dotsOnFront() {
+    try {
+        return window.localStorage && window.localStorage.getItem("tp_dots_layer") === "front";
+    } catch (e) {
+        return false;
+    }
+}
+
+/**
+ * 诊断工具：把当前每一根广播线的两端、归属节点、槽位实算位置全部列表打印。
+ * 只读，不改任何行为。控制台执行 `__tpAudit()` 即可。
+ *
+ * 用途：线画到空白处（端点悬空、没有归属节点）时，一眼看出是哪条 pair 算错、
+ * 目标槽位的 `boundingRect` 到底是什么。
+ */
+function installAudit() {
+    if (typeof window === "undefined" || window.__tpAudit) return;
+    window.__tpAudit = function (opts) {
+        const cv = app.canvas;
+        const g = (app && (app.rootGraph || app.graph)) || (cv && cv.graph);
+        if (!g) return "没有图";
+        const near = (p, n) => {
+            if (!p || !n) return "noPoint";
+            const pad = 14;
+            const x0 = n.pos[0] - pad;
+            const y0 = n.pos[1] - 30 - pad;
+            const x1 = n.pos[0] + n.size[0] + pad;
+            const y1 = n.pos[1] + n.size[1] + pad;
+            return p[0] >= x0 && p[0] <= x1 && p[1] >= y0 && p[1] <= y1;
+        };
+        const rows = [];
+        const { pairs } = planBroadcast(g);
+        (pairs || []).forEach((p, i) => {
+            const aG = slotPoint(p.srcNode, false, p.srcSlot);
+            const bG = slotPoint(p.dstNode, true, p.dstSlot);
+            const slot = p.dstNode?.inputs?.[p.dstSlot];
+            rows.push({
+                "#": i,
+                源: (p.srcNode?.title || p.srcNode?.type) + " [" + p.srcSlot + "]",
+                目标: (p.dstNode?.title || p.dstNode?.type) + " [" + p.dstSlot + "] " + (slot?.name || ""),
+                起点: aG ? aG.map(Math.round).join(",") : "-",
+                起点在节点内: near(aG, p.srcNode),
+                终点: bG ? bG.map(Math.round).join(",") : "-",
+                终点在节点内: near(bG, p.dstNode),
+                目标节点矩形: p.dstNode ? [p.dstNode.pos[0], p.dstNode.pos[1], p.dstNode.size[0], p.dstNode.size[1]].map(Math.round).join(",") : "-",
+                终点槽boundingRect: slot && slot.boundingRect ? Array.from(slot.boundingRect).map(Math.round).join(",") : String(slot && slot.boundingRect),
+            });
+        });
+        console.table(rows);
+        const bad = rows.filter((r) => r.起点在节点内 !== true || r.终点在节点内 !== true);
+        if (bad.length) console.warn(`[Toggle-Pass] ${bad.length} 根线的端点不在自己节点范围内（悬空）：`, bad);
+        if (!opts || opts.raw !== false) return rows;
+    };
+}
+
+/**
+ * 装配两处钩子：
+ *   ① `drawBackCanvas` **之后** → 线 + 端点圆点（画进 bgcanvas，节点下层）
+ *   ② `draw()` 开头          → 兜底重包 `drawBackCanvas`（前端偶尔会换掉它）
+ *
+ * ⚠️ 为什么不用 `onDrawForeground`（踩过的坑）：
+ *   ComfyUI 前端用 `watch(() => app.canvas, e => e.onDrawForeground = chain(...))`
+ *   维护画布钩子，那个 watch 会在我们之后重新赋值 —— 包装被整个替换掉，
+ *   实测 hookLen 变成了别人的 88 字符函数，绘制一次都没执行。
+ */
+function installPreviewPainter(appRef) {
+    const canvas = appRef.canvas;
+    if (!canvas || typeof canvas.draw !== "function") return;
+    if (canvas.__tpDrawPatched) return;
+    canvas.__tpDrawPatched = true;
+    installAudit();
+
+    /* ⚠️ 为什么挂在 `drawBackCanvas` **之后**（踩过的坑）：
+     *   一开始插在 `drawFrontCanvas` **之前**，结果一个像素都看不到 ——
+     *   drawFrontCanvas 内部会把前景层重绘一遍，我们画的东西被它盖掉了。
+     *   画在 drawBackCanvas 之后：背景和真实连线都画完了，节点还没画 ⇒
+     *   线正好落在「节点下层」，节点一盖就自然遮住，节点之间的空白处保留。
+     *
+     * ⚠️ 只用 `this.ctx` / `this`，**不看入参**：实测 drawBackCanvas 的实参
+     *   既不是 ctx 也不是 canvas（另一个入口报过 `ctx.save is not a function`），
+     *   照它取坐标系会一条线都算不出来。参数原样透传，绝不改写。 */
+    const wrapBack = (cv) => {
+        const inner = cv.drawBackCanvas;
+        if (typeof inner !== "function" || inner.__tpPainter) return;
+        const wrapped = function (...args) {
+            const r = inner.apply(this, args);
+            try {
+                const bg = bgCtx(this);
+                drawBroadcastLines(bg, this);
+                if (!dotsOnFront()) drawBroadcastDots(bg, this);
+            } catch (e) {
+                if (typeof window !== "undefined") window.__tpDrawErr = "line:" + String((e && e.message) || e);
+            }
+            return r;
+        };
+        wrapped.__tpPainter = true;
+        cv.drawBackCanvas = wrapped;
+    };
+
+    wrapBack(canvas);
+
+    const origDraw = canvas.draw;
+    canvas.draw = function (...args) {
+        /* 前端某些路径会重新赋值 drawBackCanvas（拖拽中断时见过），
+         * 每帧开头兜一次，发现被换掉就重包 —— 成本是一次属性读。 */
+        if (!this.drawBackCanvas || !this.drawBackCanvas.__tpPainter) wrapBack(this);
+        let r;
+        try {
+            r = origDraw.apply(this, args);
+        } finally {
+            try {
+                drawBroadcastSlotLit(this.ctx, this);
+                if (dotsOnFront()) drawBroadcastDots(this.ctx, this);
+            } catch (e) {
+                if (typeof window !== "undefined") window.__tpDrawErr = "dot:" + String((e && e.message) || e);
+            }
+        }
+        return r;
+    };
+}
+
+function patchGraphToPrompt(appRef) {
+    if (appRef.__tpBroadcastPatched) return;
+
+    /* ⚠️ 必须**两层都包**，只包 graphToPrompt 会漏：
+     *   cg-use-everywhere 在它自己的 setup() 里就把**当时的** app.graphToPrompt 存成变量，
+     *   点「运行」时它走 queuePrompt → 用存下来的原函数（可能绕过我们这一层）→ 广播静默失效。
+     *   包住 queuePrompt 就能兜住这条路径：不管里面谁调谁，临时线在我们这一层就已经接好了。
+     *   用 depth 计数保证「只广播一次」：外层已经接过，内层直接透传、不要重复接、更不要提前拆。 */
+    let depth = 0;
+
+    function around(fn) {
+        if (depth > 0) return fn();
+        depth += 1;
+        const restore = inflate(appRef);
+        const done = () => {
+            depth -= 1;
+            restore();
+        };
+        let ret;
+        try {
+            ret = fn();
+        } catch (e) {
+            done();
+            throw e;
+        }
+        if (ret && typeof ret.then === "function") {
+            return ret.then(
+                (v) => {
+                    done();
+                    return v;
+                },
+                (e) => {
+                    done();
+                    throw e;
+                },
+            );
+        }
+        done();
+        return ret;
+    }
+
+    const origPrompt = appRef.graphToPrompt;
+    if (typeof origPrompt === "function") {
+        appRef.graphToPrompt = function (...args) {
+            return around(() => origPrompt.apply(this, args));
+        };
+    }
+
+    const origQueue = appRef.queuePrompt;
+    if (typeof origQueue === "function") {
+        appRef.queuePrompt = function (...args) {
+            return around(() => origQueue.apply(this, args));
+        };
+    }
+
+    appRef.__tpBroadcastPatched = true;
+    /* 给自检脚本用：直接在「广播生效」上下文里跑一段同步/异步逻辑 */
+    appRef.__tpBroadcastAround = (fn) => around(fn);
+    /* 给自检脚本用：列出当前会广播哪些线（只算不接） */
+    appRef.__tpBroadcastPlan = () =>
+        planBroadcast(appRef.graph).pairs.map(
+            (p) => `${p.dstNode.id}:${p.dstNode.inputs?.[p.dstSlot]?.name}<-${p.srcNode.id}:${p.srcSlot}`,
+        );
+}
+
+/* ------------------------------------------------------------------ *
+ * 三、注册
+ * ------------------------------------------------------------------ */
+
+app.registerExtension({
+    name: EXT_NAME,
+    async setup() {
+        patchGraphToPrompt(app);
+        installPreviewPainter(app);
+    },
+    beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name !== NODE_NAME) return;
+
+        const origCreated = nodeType.prototype.onNodeCreated;
+        nodeType.prototype.onNodeCreated = function () {
+            const r = origCreated ? origCreated.apply(this, arguments) : undefined;
+            const node = this;
+            /* 加载器自己的 onNodeCreated 末尾会 setSize([270,220])，
+             * 所以这里必须排在它之后加控件、再补高度 */
+            addBroadcastWidget(node);
+            /* 前端重建过画布实例的话，钩子会挂在新实例上缺失——这里顺手兜一次 */
+            try {
+                installPreviewPainter(app);
+            } catch (e) {
+                /* 装不上不影响功能 */
+            }
+            return r;
+        };
+
+        /* 读档时 properties 已经恢复，把开关同步成存档里的状态 */
+        const origConfigure = nodeType.prototype.onConfigure;
+        nodeType.prototype.onConfigure = function () {
+            const r = origConfigure ? origConfigure.apply(this, arguments) : undefined;
+            syncBroadcastWidget(this);
+            return r;
+        };
+    },
+});
diff --git a/js/canvas_menu.js b/js/canvas_menu.js
index 6bee2ed..d8c407c 100644
--- a/js/canvas_menu.js
+++ b/js/canvas_menu.js
@@ -103,7 +103,7 @@ app.registerExtension({
         try {
             const nodes = collectTogglePassNodes();
             if (!nodes.length) return [];
-            return [
+            const items = [
                 {
                     content: MENU_LABEL,
                     has_submenu: true,
@@ -115,6 +115,19 @@ app.registerExtension({
                     },
                 },
             ];
+            // 素材库是运行时可变的（本机目录里丢进新姿势就多出来），给一个手动重扫入口。
+            // 平时不用点：节点自己每 15 秒比对一次指纹，变了就重建下拉。
+            if (nodes.some((n) => n.type === "PoseReference")) {
+                items.push({
+                    content: "🔄 刷新姿势素材库",
+                    callback: () => {
+                        if (typeof window.__tpRefreshPoses === "function") {
+                            window.__tpRefreshPoses();
+                        }
+                    },
+                });
+            }
+            return items;
         } catch (err) {
             console.error("[Toggle-Pass] getCanvasMenuItems failed:", err);
             return [];
diff --git a/js/multi_ref_encode.js b/js/multi_ref_encode.js
index d8c8dd5..f625d6c 100644
--- a/js/multi_ref_encode.js
+++ b/js/multi_ref_encode.js
@@ -89,7 +89,128 @@ function refreshNodeSize(node) {
     });
 }
 
+/* ---- 模型类型改名迁移 ----
+ * 2026-11-06：`SD / SDXL` 改名为 `SD / SDXL / Z-Image`（两者 latent 行为完全一样，
+ * Z-Image 就是「不接参考图的通用档」，合进一个名字里省得再开一项）。
+ * 旧工作流的 widgets_values 里存的是旧字面量，不在下拉选项里 → 前端会显示空白。
+ * 这里在加载时直接改值，用户看不到中间态。
+ * ⚠️ 后端 multi_ref_encode.py 有一份同样的表，两层都兜（第三方节点转存 / 手工改 JSON）。
+ */
+const MODEL_TYPE_MIGRATIONS = {
+    "SD / SDXL": "SD / SDXL / Z-Image",
+    "SD/SDXL": "SD / SDXL / Z-Image",
+    "SD / SDXL / z-image": "SD / SDXL / Z-Image",
+    "SD/SDXL/z-image": "SD / SDXL / Z-Image",
+    "SDXL": "SD / SDXL / Z-Image",
+    "SD 1.5": "SD / SDXL / Z-Image",
+    "Z-Image": "SD / SDXL / Z-Image",
+    "z-image": "SD / SDXL / Z-Image",
+    "ZImage": "SD / SDXL / Z-Image",
+};
+
+function migrateModelType(node) {
+    const w = (node.widgets || []).find(x => x.name === "模型类型");
+    if (!w || typeof w.value !== "string") return false;
+    const to = MODEL_TYPE_MIGRATIONS[w.value.trim()];
+    if (!to || to === w.value) return false;
+    console.log(`[Toggle-Pass] 模型类型 已迁移: "${w.value}" -> "${to}"`);
+    w.value = to;
+    return true;
+}
+
+/* ---- Ref Image Encode 输出顺序迁移 ----
+ * 2026-11-06：`negative` 从末尾挪到 `latent` 前面（纯排版，node 上好看）。
+ *   老布局 outputs = [conditioning, latent, negative]
+ *   新布局 outputs = [conditioning, negative, latent]
+ * ⚠️ 索引 1/2 的含义互换了，老工作流存在 JSON 里的 link.origin_slot 会指向错的输出口
+ *    （负面被当 latent、latent 被当负面，类型对不上直接断线）。
+ * 这里按 outputs[i].name 判断布局（不依赖存盘标记，幂等）：
+ *   - 检测到老布局 ⇒ 交换 1/2 号口的整个 output 元素（name/type/links 一起走）
+ *   - 每次都把 graph.links 的 origin_slot 对齐到当前 outputs 位置
+ */
+const OUT_ORDER_TAG = "negative_before_latent";
+
+// ① 只给槽位改名（幂等：新布局下不做任何事）。同步执行，抢在渲染连线之前。
+function renameOutputSlots(node) {
+    const outs = node?.outputs;
+    if (!outs || outs.length < 3) return false;
+    if (outs[1]?.name !== "latent" || outs[2]?.name !== "negative") return false;
+
+    // ⚠️ 不能交换 outputs 数组元素本身 —— ComfyUI 加载完会按 link.origin_slot
+    //    重新往 outputs[i].links 里填，换掉的数组元素会被"按位置"填回旧连线。
+    //    只换 name/type，位置不变，等价于「给槽位改名」。
+    const nm = outs[1].name, tp = outs[1].type;
+    outs[1].name = outs[2].name; outs[1].type = outs[2].type;
+    outs[2].name = nm; outs[2].type = tp;
+    node.properties = node.properties || {};
+    node.properties.tp_outorder = OUT_ORDER_TAG;
+    console.log("[Toggle-Pass] Ref Image Encode 输出顺序已迁移: conditioning / negative / latent");
+    return true;
+}
+
+// ② 把连线按「类型语义」搬回正确槽位。幂等，可无限次重跑。
+//    LATENT → 2 号口(latent)，CONDITIONING → 1 号口(negative)；0 号口不参与。
+//    返回值：-1 = graph.links 还没建好（要继续重试）；>=0 = 本次改动的连线条数。
+function fixLinkSlots(node) {
+    if (!node) return 0;
+    const graph = node.graph || app?.graph;
+    const links = graph?.links;
+    if (!links) return -1;
+
+    // ★ 加载早期 graph.links 是空表（实测 200ms 时仍是空的，几百毫秒后才填满）
+    //   ⇒ 空表一律当「未就绪」，否则会在什么都没做的状态下提前收敛。
+    const empty = Array.isArray(links) ? links.length === 0 : Object.keys(links).length === 0;
+    if (empty) return -1;
+
+    let changed = 0;
+    const fix = (getSlot, setSlot, type, originId) => {
+        if (String(originId) !== String(node.id)) return;
+        const s = getSlot();
+        if (s !== 1 && s !== 2) return;            // 0 号口(conditioning)不参与
+        const want = type === "LATENT" ? 2 : 1;
+        if (s !== want) { setSlot(want); changed++; }
+    };
+
+    if (Array.isArray(links)) {
+        // 老式：[[id, origin_id, origin_slot, target_id, target_slot, type], ...]
+        for (const l of links) {
+            if (!l) continue;
+            fix(() => l[2], (v) => { l[2] = v; }, l[5], l[1]);
+        }
+    } else {
+        // 新版：{[id]: LLink}，origin_slot 是 getter/setter，直接赋值有效
+        for (const lk of Object.values(links)) {
+            if (!lk) continue;
+            fix(() => lk.origin_slot, (v) => { lk.origin_slot = v; }, lk.type, lk.origin_id);
+        }
+    }
+    return changed;
+}
+
+// ③ 阶梯重试直到收敛。graph.links 的就绪时机在不同机器/图规模下差别很大，
+//    单靠一帧 rAF 兜底会漏（这一版就栽在这：name 换了、slot 没换 ⇒ 用户看到「连错了」）。
+const OUT_ORDER_RETRY_MS = [0, 50, 120, 260, 550, 1000, 1800, 3000];
+
+function scheduleOutputOrderFix(node) {
+    let i = 0;
+    const step = () => {
+        if (!node || !node.graph) return;          // 节点已被移除/图已换
+        const r = fixLinkSlots(node);
+        if (r === 0) return;                       // 已收敛，收手，不再干扰用户手动接线
+        i++;
+        if (i < OUT_ORDER_RETRY_MS.length) setTimeout(step, OUT_ORDER_RETRY_MS[i]);
+    };
+    setTimeout(step, OUT_ORDER_RETRY_MS[0]);
+}
+
+// 兼容旧调用点
+function migrateOutputOrder(node) {
+    renameOutputSlots(node);
+    return fixLinkSlots(node);
+}
+
 function applyModelVisibility(node) {
+    migrateModelType(node);              // 先把旧名字换成新名字，再决定显隐
     const get = n => (node.widgets || []).find(w => w.name === n);
     const mw = get("模型类型");
     const model = mw ? mw.value : null;
@@ -137,6 +258,12 @@ app.registerExtension({
         const origConfigure = nodeType.prototype.onConfigure;
         nodeType.prototype.onConfigure = function () {
             origConfigure?.apply(this, arguments);
+            // 输出口改名必须抢在画布渲染连线之前（同步执行）；
+            // 连线归位要等 graph.links 建好，交给阶梯重试（只等一帧太早，实测那时 links 还是空的）。
+            if (nodeData.name === "DynamicRefImageEncode") {
+                renameOutputSlots(this);
+                scheduleOutputOrderFix(this);
+            }
             requestAnimationFrame(() => {
                 const w = this.widgets?.find(w => w.name === cfg.widget);
                 if (w) syncInputs(this, cfg, Math.max(1, Math.min(cfg.max, w.value || 1)));
diff --git a/js/pose_reference.js b/js/pose_reference.js
new file mode 100644
index 0000000..bb2e3ca
--- /dev/null
+++ b/js/pose_reference.js
@@ -0,0 +1,637 @@
+/**
+ * ComfyUI-Toggle-Pass: 参考姿势（PoseReference）— 三级联动 + 节点内缩略图
+ *
+ * Python 端把全部选项平铺给出（大类 4 / 姿势组 26 / 姿势 288 / 视角 5），
+ * 不联动也能用。这里做三件事：
+ *   1. 选了大类就把「姿势组」收窄到该类下的组，选了组再把「姿势」收窄到该组下的，
+ *      切完顺手把画布重绘一次，免得下拉还显示旧选项。
+ *   2. 任何下拉一变（大类/组/姿势/视角/线稿深度/翻转）就去 /toggle_pass/pose_image
+ *      取一张缩略图，直接画在节点下半部分 —— 不用执行工作流就能看到选的是哪张。
+ *   3. 图进来了按比例把节点撑高，保证图不会被挤成一条缝。
+ *
+ * 图片走自绘（onDrawForeground）而不是 node.imgs：后者需要 HTMLImage 对象、
+ * 还依赖前端内部绘制逻辑，跨版本不稳。自绘只依赖 LiteGraph 的公开钩子。
+ */
+
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+const NODE_NAME = "PoseReference";
+const TREE_API = "/toggle_pass/pose_tree";
+const IMAGE_API = "/toggle_pass/pose_image";
+const STAMP_API = "/toggle_pass/pose_stamp";
+
+const PAD = 10;
+const DEBOUNCE_MS = 120;
+// 素材指纹轮询间隔：往本机素材目录里丢新姿势，不用刷新浏览器，最多等这么久自己就出现
+const POLL_MS = 15000;
+// 会改变「该显示哪张图」的六个下拉
+const WATCHED = ["category", "group", "pose", "view", "source", "flip"];
+// 1.57 前端给「有 imgs 的节点」自动挂的官方画布预览 widget
+const PREVIEW_WIDGET = "$$canvas-image-preview";
+
+let treePromise = null;
+
+function fetchTree(force) {
+    if (force) treePromise = null; // 强制重扫：走 ?refresh=1 让后端丢掉缓存
+    if (!treePromise) {
+        treePromise = api
+            .fetchApi(TREE_API + (force ? "?refresh=1" : ""))
+            .then((r) => (r.ok ? r.json() : null))
+            .then((d) => {
+                // 失败/空结果不能留在缓存里：否则后面所有节点都拿这个 null，
+                // 联动永远建不起来，只有刷新页面（清掉模块状态）才恢复。
+                if (!d || !d.tree) treePromise = null;
+                return d;
+            })
+            .catch(() => {
+                treePromise = null;
+                return null;
+            });
+    }
+    return treePromise;
+}
+
+function widget(node, name) {
+    return (node.widgets || []).find((w) => w.name === name);
+}
+
+function val(node, name, fallback) {
+    const w = widget(node, name);
+    return w && w.value !== undefined && w.value !== null ? w.value : fallback;
+}
+
+/**
+ * 换一套选项。keep=true 时保留当前值（加载工作流用，避免把用户存的姿势改掉）。
+ * 返回是否真的变了，用于决定要不要重绘。
+ */
+function setValues(w, values, keep) {
+    if (!w || !Array.isArray(values) || values.length === 0) return false;
+    w.options = w.options || {};
+    const old = w.options.values;
+    const same =
+        Array.isArray(old) &&
+        old.length === values.length &&
+        old.every((v, i) => v === values[i]);
+    if (same) return false;
+
+    w.options.values = values.slice();
+    if (!keep && !values.includes(w.value)) w.value = values[0];
+    return true;
+}
+
+function buildSync(node, tree) {
+    const wCat = widget(node, "category");
+    const wGroup = widget(node, "group");
+    const wPose = widget(node, "pose");
+    if (!wCat || !wGroup || !wPose) return null;
+
+    const groupsOf = (cat) => Object.keys((tree && tree[cat]) || {});
+    const posesOf = (cat, grp) => Object.keys(((tree && tree[cat]) || {})[grp] || {});
+
+    return function sync(keep) {
+        let dirty = false;
+        dirty = setValues(wGroup, groupsOf(wCat.value), keep) || dirty;
+        dirty = setValues(wPose, posesOf(wCat.value, wGroup.value), keep) || dirty;
+        if (dirty) {
+            node.setDirtyCanvas(true, true);
+            app.graph?.setDirtyCanvas?.(true, true);
+        }
+        return dirty;
+    };
+}
+
+// ---------------- 工作流内嵌 ----------------
+// 选中的素材原文件（webp 约 5KB）以 data URI 存进 node.properties.tp_pose，
+// 跟着工作流一起走：别人下载后既能看到图，素材库不在时后端也能拿它出图。
+// 后端从 extra_pnginfo 里按 id 找 properties，所以 key 必须和 Python 的 embed_key 拼法一致。
+
+function embedKey(node) {
+    return [
+        val(node, "category", ""),
+        val(node, "group", ""),
+        val(node, "pose", ""),
+        val(node, "view", ""),
+        val(node, "source", "线稿"),
+    ]
+        .map((x) => String(x))
+        .join("|");
+}
+
+function flipOn(node) {
+    return val(node, "flip", "关") === "开";
+}
+
+function blobToDataUrl(blob) {
+    return new Promise((resolve, reject) => {
+        const fr = new FileReader();
+        fr.onload = () => resolve(String(fr.result));
+        fr.onerror = () => reject(fr.error);
+        fr.readAsDataURL(blob);
+    });
+}
+
+function loadImage(src) {
+    return new Promise((resolve, reject) => {
+        const img = new Image();
+        img.onload = () => resolve(img);
+        img.onerror = () => reject(new Error("image decode failed"));
+        img.src = src;
+    });
+}
+
+function storeEmbed(node, dataUrl) {
+    const key = embedKey(node);
+    node.properties = node.properties || {};
+    const cur = node.properties.tp_pose;
+    if (cur && cur.key === key && cur.img === dataUrl) return;
+    node.properties.tp_pose = { key, img: dataUrl };
+}
+
+/** 兜底显示：素材库取不到时，先用工作流里带的那张。 */
+function showEmbedded(node) {
+    const emb = node.properties && node.properties.tp_pose;
+    if (!emb || !emb.img) return false;
+    loadImage(emb.img)
+        .then((img) => {
+            if (node._tpPoseFromNet) return; // 真图已经到了，不自找麻烦
+            node._tpPoseImg = img;
+            node._tpPoseFlip = flipOn(node);
+            node._tpPoseErr = emb.key === embedKey(node) ? null : "素材库缺失，显示内嵌旧图";
+            fitNode(node, img);
+            node.setDirtyCanvas(true, true);
+        })
+        .catch(() => {});
+    return true;
+}
+
+// ---------------- 缩略图 ----------------
+
+function previewUrl(node) {
+    // raw=1：直接返回素材原文件（webp 约 5KB），不缩放不翻转 —— 既当缩略图又当内嵌源
+    const q = new URLSearchParams({
+        category: String(val(node, "category", "")),
+        group: String(val(node, "group", "")),
+        pose: String(val(node, "pose", "")),
+        view: String(val(node, "view", "")),
+        source: String(val(node, "source", "线稿")),
+        raw: "1",
+    });
+    return `${IMAGE_API}?${q.toString()}`;
+}
+
+/** 所有 widget 画完后的底部 y（节点 body 坐标系，原点在标题栏下沿）。 */
+function widgetsBottom(node) {
+    let bottom = 0;
+    const ws = node.widgets || [];
+    for (let i = 0; i < ws.length; i++) {
+        const w = ws[i];
+        let y = typeof w.last_y === "number" ? w.last_y : 4 + i * 28;
+        let h = 20;
+        if (typeof w.height === "number" && w.height > 0) {
+            h = w.height;
+        } else if (typeof w.computeSize === "function") {
+            try {
+                const s = w.computeSize(node.size[0]);
+                if (Array.isArray(s) && s.length > 1) h = s[1];
+            } catch (e) {
+                /* 忽略：拿不到就用默认行高 */
+            }
+        }
+        bottom = Math.max(bottom, y + h);
+    }
+    return bottom;
+}
+
+function fitNode(node, img) {
+    const top = widgetsBottom(node);
+    const availW = Math.max(80, node.size[0] - PAD * 2);
+    // 按图比例算一个「看得清又不占满屏」的高度，限制在 190~300
+    const wanted = Math.round(Math.min(300, Math.max(190, (availW * img.height) / img.width * 0.72)));
+    const need = Math.ceil(top + 6 + wanted + PAD);
+    if (node.size[1] < need) {
+        node.size[1] = need; // size 是类数组对象，整块赋值会被吞，逐下标写
+        node.setDirtyCanvas(true, true);
+    }
+}
+
+function refreshPreview(node) {
+    const url = previewUrl(node);
+    // flip 不参与 URL（拿的是未翻转的原文件），所以单独同步，不能跟着 URL 早退
+    const f = flipOn(node);
+    if (node._tpPoseFlip !== f) {
+        node._tpPoseFlip = f;
+        node.setDirtyCanvas(true, true);
+    }
+    // 已经有这张图就不重复取；但图对象丢了（出错被清、加载中新节点）还是要重取
+    if (node._tpPoseUrl === url && node._tpPoseImg) return;
+    node._tpPoseUrl = url;
+    const keyAtRequest = embedKey(node);
+
+    (async () => {
+        try {
+            const res = await api.fetchApi(url);
+            if (!res.ok) throw new Error("HTTP " + res.status);
+            const blob = await res.blob();
+            const dataUrl = await blobToDataUrl(blob);
+            if (node._tpPoseUrl !== url) return; // 期间又切了别的，丢弃
+            const img = await loadImage(dataUrl);
+            if (node._tpPoseUrl !== url) return;
+            node._tpPoseImg = img;
+            node._tpPoseFlip = flipOn(node);
+            node._tpPoseErr = null;
+            node._tpPoseFromNet = true;
+            // 只有参数没在旁边被改过才内嵌，避免把 A 的图写进 B 的设置
+            if (embedKey(node) === keyAtRequest) storeEmbed(node, dataUrl);
+            fitNode(node, img);
+            node.setDirtyCanvas(true, true);
+            // litegraph 0.4 里单个节点的 dirty 标记不一定能把画布叫醒，
+            // 图换了却停在旧画面，看着就是「点了没反应」。图是重点，再踹一脚整图。
+            app.graph?.setDirtyCanvas?.(true, true);
+            app.canvas?.setDirty?.(true, true);
+        } catch (e) {
+            if (node._tpPoseUrl !== url) return;
+            // 失败时解锁，否则这个参数组合会被永久判成「已请求过」，
+            // 后端恢复后切回来也不重试。
+            node._tpPoseUrl = null;
+            node._tpPoseFromNet = false;
+            const emb = node.properties && node.properties.tp_pose;
+            const curKey = embedKey(node);
+            // 只有内嵌的那张「正好就是当前选的」才能顶上去。
+            // 以前不管三七二十一都拿内嵌图兜底，结果切到别的姿势时画面还是老图，
+            // 用户看到的是「点了没反应」，还把真正的取图失败藏得干干净净。
+            if (emb && emb.img && emb.key === curKey) {
+                try {
+                    const img = await loadImage(emb.img);
+                    node._tpPoseImg = img;
+                    node._tpPoseFlip = flipOn(node);
+                    node._tpPoseErr = null;
+                    fitNode(node, img);
+                    node.setDirtyCanvas(true, true);
+                    return;
+                } catch (e2) {
+                    /* 解码也失败，走下面统一报错 */
+                }
+            }
+            node._tpPoseImg = null;
+            node._tpPoseErr = emb && emb.img ? "这张没取到（素材库缺失）" : "素材库缺失";
+            node.setDirtyCanvas(true, true);
+            app.graph?.setDirtyCanvas?.(true, true);
+            console.warn("[PoseReference] 取图失败:", url, e && e.message);
+        }
+    })();
+}
+
+function schedulePreview(node) {
+    if (node._tpPoseTimer) clearTimeout(node._tpPoseTimer);
+    node._tpPoseTimer = setTimeout(() => {
+        node._tpPoseTimer = null;
+        try {
+            refreshPreview(node);
+        } catch (e) {
+            console.warn("[PoseReference] 预览刷新失败", e);
+        }
+    }, DEBOUNCE_MS);
+}
+
+/**
+ * 参数自检：当前六个下拉的取值，和「上一次送去取图的那套参数」不一致就重取。
+ *
+ * 这个检查放在绘制里每帧跑，是刻意不依赖 widget.callback 的。实测（1.57 /
+ * litegraph 0.4）下拉选完只有 callback 一条通知路径，而 callback 可能被第三方
+ * 扩展重写、被旧版本闭包吃掉，或者干脆不触发 —— 一旦漏掉，节点就会永远停在
+ * 上一张图上，看起来像「切换不了，得重启」。绘制路径只要画布在动就一定跑，
+ * 拿它当兜底，任何漏通知的情况都能自己补上。
+ */
+function watchParams(node) {
+    const key = embedKey(node);
+    if (node._tpPoseKey === key) return;
+    node._tpPoseKey = key;
+    schedulePreview(node);
+}
+
+function drawPreview(node, ctx) {
+    watchParams(node); // 先自检：参数变了但没人通知，这里补一次
+    const top = widgetsBottom(node);
+    const x0 = PAD;
+    const y0 = top + 6;
+    const w0 = node.size[0] - PAD * 2;
+    const h0 = node.size[1] - y0 - PAD;
+    if (w0 < 40 || h0 < 30) return;
+
+    ctx.save();
+    try {
+        // 底衬：避免透明网格/深色背景上白线稿看不清边界
+        ctx.fillStyle = "#1b1b1b";
+        ctx.fillRect(x0, y0, w0, h0);
+
+        const img = node._tpPoseImg;
+        if (!img) {
+            ctx.fillStyle = "#7a7a7a";
+            ctx.font = "12px sans-serif";
+            ctx.textAlign = "center";
+            ctx.textBaseline = "middle";
+            ctx.fillText(node._tpPoseErr || "加载中…", x0 + w0 / 2, y0 + h0 / 2);
+            return;
+        }
+
+        const scale = Math.min(w0 / img.width, h0 / img.height);
+        const dw = img.width * scale;
+        const dh = img.height * scale;
+        const dx = x0 + (w0 - dw) / 2;
+        const dy = y0 + (h0 - dh) / 2;
+        ctx.imageSmoothingEnabled = true;
+        ctx.imageSmoothingQuality = "high";
+        if (node._tpPoseFlip) {
+            // 缩略图取的是素材原文件（未翻转），翻转在这里镜像
+            ctx.save();
+            try {
+                ctx.translate(dx + dw, dy);
+                ctx.scale(-1, 1);
+                ctx.drawImage(img, 0, 0, dw, dh);
+            } finally {
+                ctx.restore();
+            }
+        } else {
+            ctx.drawImage(img, dx, dy, dw, dh);
+        }
+
+        ctx.strokeStyle = "rgba(255,255,255,0.14)";
+        ctx.lineWidth = 1;
+        ctx.strokeRect(dx + 0.5, dy + 0.5, dw - 1, dh - 1);
+    } finally {
+        ctx.restore();
+    }
+}
+
+function hookCallback(w, node) {
+    if (!w || w._tpPoseHooked) return;
+    w._tpPoseHooked = true;
+    const orig = w.callback;
+    w.callback = function (...args) {
+        const ret = orig ? orig.apply(this, args) : undefined;
+        // 联动和取图分开 try：联动抛异常不能把取图一起带走，
+        // 否则下拉值变了、图却停在上一张。
+        try {
+            // 必须每次现取 node._tpPoseSync：素材库热更新会换上新树的闭包，
+            // 如果在这里闭包捕获旧 sync，下拉永远按旧树联动 —— 新增的类别/组
+            // 切不过去，只有重启（重新挂钩）才恢复。
+            if (typeof node._tpPoseSync === "function") node._tpPoseSync(false);
+        } catch (e) {
+            console.warn("[PoseReference] 联动失败", e);
+        }
+        try {
+            schedulePreview(node);
+        } catch (e) {
+            console.warn("[PoseReference] 预览排队失败", e);
+        }
+        return ret;
+    };
+}
+
+function attach(node, tree) {
+    const sync = buildSync(node, tree);
+    if (sync) node._tpPoseSync = sync;
+    // 六个下拉都要挂：前三个带联动，后三个只触发换图
+    WATCHED.forEach((name) => {
+        hookCallback(widget(node, name), node);
+    });
+    if (sync) sync(true);
+    showEmbedded(node);
+    schedulePreview(node);
+}
+
+// ---------------- 素材库热更新 ----------------
+// 素材分两层：插件自带 pose_assets/（随插件分发、只放非敏感大类）+ 本机外挂
+// （D:\pose 或 TP_POSE_ROOT，敏感/自建素材放这里）。往本机目录里丢新姿势后，
+// 后端指纹会变，这里自动把下拉重建一遍 —— 不用刷新浏览器。
+
+function poseNodes() {
+    return (app.graph?._nodes || []).filter((n) => n.type === NODE_NAME);
+}
+
+async function refreshAll(reason) {
+    const nodes = poseNodes();
+    if (!nodes.length) return 0;
+    const data = await fetchTree(true);
+    if (!data || !data.tree) return 0;
+    for (const n of nodes) {
+        try {
+            attach(n, data.tree); // 换上新的素材树
+            // 新树里可能没有当前选中的组/姿势了（素材被删或改名），这里校正一次，
+            // 否则下拉会显示一个不存在的值、点别的又对不上。
+            if (typeof n._tpPoseSync === "function") n._tpPoseSync(false);
+            // 素材可能被同名替换了内容，URL 却一模一样，不清掉就永远不会重取。
+            n._tpPoseUrl = null;
+            n._tpPoseFromNet = false;
+            schedulePreview(n);
+        } catch (e) {
+            console.warn("[PoseReference] 重建下拉失败", e);
+        }
+    }
+    console.log(`[PoseReference] 素材库已刷新（${reason}）：${nodes.length} 个节点`);
+    return nodes.length;
+}
+
+let lastStamp = null;
+let pollStarted = false;
+
+function startStampPoll() {
+    if (pollStarted) return;
+    pollStarted = true;
+
+    const tick = async () => {
+        if (!poseNodes().length) return; // 画布上没有本节点就不打扰后端
+        try {
+            const r = await api.fetchApi(STAMP_API);
+            if (!r.ok) return;
+            const d = await r.json();
+            if (!d || !d.stamp) return;
+            if (lastStamp === null) {
+                lastStamp = d.stamp; // 首次只记基准，不触发重建
+                return;
+            }
+            if (d.stamp === lastStamp) return;
+            lastStamp = d.stamp;
+            await refreshAll("指纹变化");
+        } catch (e) {
+            /* 网络抖动忽略，下一轮再说 */
+        }
+    };
+
+    // 立刻记一次基准（和画布上有没有节点无关），不能等第一个 interval ——
+    // 否则「打开页面后 15 秒内放进去的素材」会被当成基准吞掉，指纹再也不变，永远不刷新。
+    (async () => {
+        try {
+            const r = await api.fetchApi(STAMP_API);
+            if (!r.ok) return;
+            const d = await r.json();
+            if (d && d.stamp) lastStamp = d.stamp;
+        } catch (e) {
+            /* 拿不到就退化成 tick 里首次记录基准 */
+        }
+    })();
+    setInterval(tick, POLL_MS);
+}
+
+/**
+ * 摘掉 ComfyUI 1.57 自动挂的官方画布预览 widget。
+ *
+ * 前端 `useNodeCanvasImagePreview` 的规则很简单：只要节点有 `node.imgs`，
+ * 就 `addCustomWidget("$$canvas-image-preview")`。这个 widget 会占掉节点底部
+ * 一行，并把执行输出的那张 PNG 画在节点下部 —— 正好压在我们的预览区上面。
+ *
+ * 于是「生图一开始」就等于给节点盖了一张定死的图：下拉照点、我们的图在背面
+ * 其实也换了，但画面上永远停着那张执行结果，看起来就是再也切不动。
+ * 本节点的图完全由 onDrawForeground 自己画，不需要官方这一份，直接摘掉；
+ * 同时清空 `node.imgs`，否则它下次判断又会把 widget 挂回来。
+ */
+function stripOfficialPreview(node) {
+    if (!node || !node.widgets) return false;
+    let hit = false;
+    for (let i = node.widgets.length - 1; i >= 0; i--) {
+        const w = node.widgets[i];
+        if (String((w && w.name) || "") !== PREVIEW_WIDGET) continue;
+        if (typeof w.onRemove === "function") {
+            try {
+                w.onRemove();
+            } catch (e) {
+                /* 清理回调失败也要把 widget 摘掉 */
+            }
+        }
+        node.widgets.splice(i, 1);
+        hit = true;
+    }
+    if (hit) {
+        node.imgs = null;
+        node.setDirtyCanvas(true, true);
+    }
+    return hit;
+}
+
+/**
+ * 参数巡检：300ms 扫一遍画布上的本节点，参数变了就补一次取图。
+ *
+ * 绘制里已经有一道同样的检查，但节点被折叠、画布静止不重绘时绘制不一定会跑。
+ * 这里只读六个 widget 的 value 拼个字符串，画布上没有本节点时直接返回，
+ * 开销可以忽略，换来的是「不管通知从哪条路径丢了，图都能自己跟上」。
+ *
+ * 顺便每轮摘一次官方预览 widget（见 stripOfficialPreview）。
+ */
+function startParamWatch() {
+    setInterval(() => {
+        for (const n of poseNodes()) {
+            try {
+                stripOfficialPreview(n);
+                watchParams(n);
+            } catch (e) {
+                /* 单个节点异常不能拖垮整轮巡检 */
+            }
+        }
+    }, 300);
+}
+
+// 画布右键菜单「刷新姿势素材库」通过这个全局调用（canvas_menu.js 里引用）
+window.__tpRefreshPoses = () => refreshAll("手动");
+
+/**
+ * 排查用：在浏览器控制台敲 __tpPoseDiag()，打印画布上每个本节点的
+ * 选项值 / 已加载图 / 上次请求的 URL / 错误信息。出问题时一眼看出卡在哪一步。
+ */
+window.__tpPoseDiag = () => {
+    const rows = poseNodes().map((n) => ({
+        选中: WATCHED.map((k) => val(n, k, "")).join(" / "),
+        上次请求: decodeURIComponent((n._tpPoseUrl || "").split("?")[1] || "（无）"),
+        已加载图: n._tpPoseImg ? `${n._tpPoseImg.naturalWidth}x${n._tpPoseImg.naturalHeight}` : "（无）",
+        来自网络: !!n._tpPoseFromNet,
+        错误: n._tpPoseErr || "",
+    }));
+    console.table(rows);
+    return rows;
+};
+
+app.registerExtension({
+    name: "TogglePass.PoseReference",
+
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name !== NODE_NAME) return;
+
+        // ---- 节点内缩略图（自绘，链式调用宿主钩子）----
+        const origDraw = nodeType.prototype.onDrawForeground;
+        nodeType.prototype.onDrawForeground = function (ctx) {
+            const ret = origDraw ? origDraw.apply(this, arguments) : undefined;
+            if (ctx) {
+                try {
+                    drawPreview(this, ctx);
+                } catch (e) {
+                    console.warn("[PoseReference] 绘制失败", e);
+                }
+            }
+            return ret;
+        };
+
+        // ---- 创建节点：拉目录树、挂联动、取首张预览 ----
+        const origCreated = nodeType.prototype.onNodeCreated;
+        nodeType.prototype.onNodeCreated = function () {
+            const ret = origCreated ? origCreated.apply(this, arguments) : undefined;
+            const node = this;
+
+            // size 是类数组对象，整块赋值会被忽略，必须逐下标写
+            if (node.size && node.size[0] < 320) {
+                node.size[0] = 340;
+            }
+
+            fetchTree().then((data) => {
+                if (!data || !data.tree) {
+                    schedulePreview(node); // 树拉不到也先把图取上
+                    return;
+                }
+                attach(node, data.tree);
+            });
+            return ret;
+        };
+
+        // ---- 加载工作流：选项可能和存的不一致，刷新一次但保留原值 ----
+        const origConfigure = nodeType.prototype.onConfigure;
+        nodeType.prototype.onConfigure = function () {
+            const ret = origConfigure ? origConfigure.apply(this, arguments) : undefined;
+            const node = this;
+            // 换了工作流：上一张的 URL / 来源标记作废，先把内嵌图顶上去再拉本机的
+            node._tpPoseUrl = null;
+            node._tpPoseFromNet = false;
+            node._tpPoseKey = null; // 让绘制自检也认为「参数刚变过」
+            showEmbedded(node);
+            fetchTree().then((data) => {
+                if (!data || !data.tree) {
+                    schedulePreview(node);
+                    return;
+                }
+                attach(node, data.tree);
+            });
+            return ret;
+        };
+
+        // ---- 执行完：立刻摘掉官方预览 widget，别让它盖住预览区 ----
+        // 这是「生图一开始就再也切不动」的元凶：节点返回 ui.images 后前端会给它
+        // 挂一个固定显示执行结果的预览 widget，正好压在预览区上。
+        const origExecuted = nodeType.prototype.onExecuted;
+        nodeType.prototype.onExecuted = function () {
+            const ret = origExecuted ? origExecuted.apply(this, arguments) : undefined;
+            try {
+                this.imgs = null; // 官方预览靠它判断要不要挂
+                stripOfficialPreview(this);
+                schedulePreview(this); // 执行结束是「该重画」的信号，顺手对齐一次
+            } catch (e) {
+                console.warn("[PoseReference] 清理官方预览失败", e);
+            }
+            return ret;
+        };
+    },
+
+    setup() {
+        // 前端就绪后开始轮询素材指纹（每 POLL_MS 一次，画布没本节点时直接跳过）
+        startStampPoll();
+        // 参数巡检：回调漏通知时靠它把图补上
+        startParamWatch();
+    },
+});
diff --git a/js/sampler_widgets.js b/js/sampler_widgets.js
index 3374329..c0ba95b 100644
--- a/js/sampler_widgets.js
+++ b/js/sampler_widgets.js
@@ -13,14 +13,47 @@
  */
 
 import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
 
 const NODE_NAME = "ToggleSampler";
 const SCHEME_AUTO = "自动（按步数 + 调度器）";
 const SCHEME_CUSTOM = "自定义表";
 
-/* 新建节点时的初始宽度（高度用 computeSize 的默认值，不加高）。
-   仅在 onNodeCreated 生效，不影响已保存的工作流尺寸。 */
-const INITIAL_WIDTH = 300;
+/* 节点宽度。预设用的是完整模型名，最长一条实测 240px（"Qwen-Image Layered（9步·CFG 2.5）"），
+   加上左侧 47px 的「⚡预设」和两侧留白，340 就会开始重叠 ⇒ 取 350。
+
+   ⚠️ 宽度不够时 combo 的值是右对齐画的，会把左边的标题整个盖住，
+      看起来就成了「一个没有名字的下拉框」—— 这正是之前被误认成"重复控件"的原因。
+      ⇒ 老工作流里存的是 300/340，加载时若不够就补到 PRESET_FIT_WIDTH（只加不缩）。 */
+const INITIAL_WIDTH = 350;
+const PRESET_FIT_WIDTH = 350;
+
+/* node.size 是"类数组对象"（Array.isArray 恒为 false），取值必须逐下标。 */
+function sizeAt(node, i) {
+    const s = node && node.size;
+    if (!s) return 0;
+    const v = typeof s[i] === "number" ? s[i] : s[String(i)];
+    return typeof v === "number" ? v : 0;
+}
+
+/** 节点太窄就把标题挤没了，补到能放下最长预设名的宽度。只加宽，不缩窄。 */
+function ensureWidthForPreset(node) {
+    const w = sizeAt(node, 0);
+    if (!w || w >= PRESET_FIT_WIDTH) return false;
+    try {
+        node.size[0] = PRESET_FIT_WIDTH;      // 逐下标赋值，整体赋值会被类数组吞掉
+        node.setDirtyCanvas?.(true, true);
+        app.canvas?.setDirty?.(true, true);
+        return true;
+    } catch (e) {
+        return false;
+    }
+}
+
+/* 控件上显示的名字。**内部 name 一律不改** —— widgets_values_named 和 /prompt 的
+   inputs 都按 name 走，改 name 等于让所有已存工作流失联。改 label 才是安全的。 */
+const LABEL_SCHEME = "Sigma 曲线";   // 原名「采样方案」：它只管噪声曲线，容易和「预设」混
+const LABEL_PRESET = "⚡预设";       // 原名「模型预设」：四个字太长，窄节点上会没地方显示
 
 /* 各预置表对应的真实步数（= 表点数 - 1）。
    与 sampler.py 的 SIGMA_PRESETS 保持一致；只用于把「步数」显示得跟实际一致，
@@ -32,9 +65,197 @@ const PRESET_STEPS = {
 };
 
 const W_SCHEME = "采样方案";
+const W_SAMPLER = "采样器";
 const W_SCHEDULER = "调度器";
 const W_STEPS = "步数";
+const W_CFG = "CFG";
 const W_CUSTOM = "自定义Sigma表";
+const W_MODEL_PRESET = "模型预设";
+
+/* ==================== 模型预设 ====================
+ *
+ * 预设表只存在后端（sampler.py 的 MODEL_PRESETS），这里通过
+ * /toggle_pass/sampler_presets 拉回来，**不在 JS 里再抄一份**，
+ * 免得以后加模型时两边对不上。
+ *
+ * 语义是「一键填充」而不是「持续锁定」：选完预设后，只要用户手动改了
+ * 采样方案/采样器/调度器/步数/CFG 中的任何一个，下拉就自己退回「手动」——
+ * 这样控件显示的永远是真实状态，不会出现「写着 Qwen 预设、其实在跑 40 步」。
+ */
+
+const PRESET_NONE = "手动（不套用）";
+const PRESET_API = "/toggle_pass/sampler_presets";
+// 预设会写到的五个参数（降噪和随机种不归预设管，改它们不该影响预设显示）
+const PRESET_KEYS = [W_SCHEME, W_SAMPLER, W_SCHEDULER, W_STEPS, W_CFG];
+
+/* 「模型预设」挪到最前面之前的控件顺序（与 sampler.py 的 LEGACY_WIDGET_ORDER 同步）。
+   1.57 的存档会额外写一份 widgets_values_named（按名字恢复，位置无关），但更早的
+   纯位置存档只能按顺序对号；把这张表交给 nodeData.fallbackWidgetsValuesNames，
+   老存档就会按名字重新落到正确控件上，而不是整体错一位。 */
+const LEGACY_WIDGET_ORDER = [
+    W_SCHEME, W_SAMPLER, W_SCHEDULER, W_STEPS, W_CFG, "降噪",
+    "噪波随机种", "control_after_generate", W_CUSTOM, W_MODEL_PRESET,
+];
+
+/* 旧预设名 → 当前预设名。权威副本在 sampler.py 的 PRESET_ALIASES，由
+   /toggle_pass/sampler_presets 下发；这里初始只放一份兜底，fetch 成功后会被覆盖。
+
+   为什么需要兜底：加载工作流（onConfigure）可能发生在接口返回之前，那一刻
+   如果表是空的，旧短名就翻译不出来，下拉会停在 `Layered · 9步` 这种已不存在的值上。
+   fetch 完成后 retroFixPresets() 会对画布上已有节点再补一次。 */
+const PRESET_ALIASES = {
+    "Qwen 2.1 · 25步": "Qwen-Image 2.1（25步）",
+    "Qwen 2.1 turbo · 8步表": "Qwen-Image 2.1 turbo（8步·固定表）",
+    "Krea2 turbo · 8步": "Krea 2 turbo（8步）",
+    "Krea2 细节 · 12步": "Krea 2 turbo・细节增强（12步）",
+    "FLUX2 klein · 4步": "FLUX.2 klein（4步·beta）",
+    "FLUX2 高清 · 4步": "FLUX.2 klein・高清修复（4步）",
+    "Z-Image turbo · 8步": "Z-Image turbo（8步）",
+    "Layered · 9步": "Qwen-Image Layered（9步·CFG2.5）",
+    "2511 AIO · 4步": "Qwen-Image Edit 2511 AIO（4步）",
+};
+
+let presetPromise = null;
+
+/** 别名到齐后，把画布上已经加载完的节点再修一遍（加载早于接口返回的情况）。 */
+function retroFixPresets() {
+    const nodes = (app.graph && app.graph._nodes) || [];
+    for (const n of nodes) {
+        if (!n || n.type !== NODE_NAME) continue;
+        try {
+            if (fixPresetValue(n)) n.setDirtyCanvas?.(true, true);
+        } catch (e) {
+            /* 单个节点异常不影响其余 */
+        }
+    }
+}
+
+function fetchPresets() {
+    if (!presetPromise) {
+        presetPromise = api
+            .fetchApi(PRESET_API)
+            .then((r) => (r.ok ? r.json() : null))
+            .then((d) => {
+                // 失败不要缓存 null，否则这个页面里永远拿不到预设
+                if (!d || !d.presets) {
+                    presetPromise = null;
+                    return d;
+                }
+                if (d.aliases) {
+                    Object.assign(PRESET_ALIASES, d.aliases);
+                    retroFixPresets();
+                }
+                return d;
+            })
+            .catch(() => {
+                presetPromise = null;
+                return null;
+            });
+    }
+    return presetPromise;
+}
+
+const findW = (node, name) => node.widgets && node.widgets.find((w) => w.name === name);
+
+/** 只在值真的不同、且该值确实在下拉可选项里时才写 —— 免得把 widget 写成一个非法值。 */
+function setValue(w, v) {
+    if (!w || v === undefined || v === null) return false;
+    const vals = w.options && w.options.values;
+    if (Array.isArray(vals) && !vals.includes(v)) {
+        console.warn("[Toggle-Pass] 预设值不在可选项里，跳过:", w.name, v);
+        return false;
+    }
+    if (w.value === v) return false;
+    w.value = v; // 直接赋值不触发 callback，正是我们要的
+    return true;
+}
+
+/**
+ * 把某个预设套到节点上。
+ *
+ * 顺序很关键：先换「采样方案」并立刻跑一次 applySchemeState —— 因为调度器/步数
+ * 会随方案被解锁或锁死（锁死时 options.min=max=当前值，这时再写就写不进去了）。
+ * 解锁之后再填其余四项，最后再跑一次让锁定状态和步数显示对齐。
+ */
+async function applyPreset(node, name) {
+    if (!name || name === PRESET_NONE) return;
+    if (!node.__tpPresets) {
+        const d = await fetchPresets();
+        if (d && d.presets) node.__tpPresets = d.presets;
+    }
+    const conf = node.__tpPresets && node.__tpPresets[name];
+    if (!conf) {
+        console.warn("[Toggle-Pass] 找不到预设:", name);
+        return;
+    }
+    if (node.__tpApplying) return;
+    node.__tpApplying = true;
+    try {
+        setValue(findW(node, W_SCHEME), conf.scheme);
+        applySchemeState(node);
+        setValue(findW(node, W_SAMPLER), conf.sampler);
+        setValue(findW(node, W_SCHEDULER), conf.scheduler);
+        setValue(findW(node, W_STEPS), conf.steps);
+        setValue(findW(node, W_CFG), conf.cfg);
+    } finally {
+        node.__tpApplying = false;
+    }
+    applySchemeState(node);
+    node.setDirtyCanvas(true, true);
+    if (node.graph && node.graph.setDirtyCanvas) node.graph.setDirtyCanvas(true, false);
+}
+
+/** 用户手改了预设管辖的参数 —— 把下拉退回「手动」，别让它说谎。 */
+function markManual(node) {
+    if (node.__tpApplying) return; // 预设自己填的值不算
+    const w = findW(node, W_MODEL_PRESET);
+    if (!w || w.value === PRESET_NONE) return;
+    w.value = PRESET_NONE;
+    node.setDirtyCanvas(true, true);
+}
+
+/**
+ * 这个值是不是「采样方案」的取值？
+ *
+ * 用来识别一种错位：老工作流没有 widgets_values_named，只能按位置恢复，而
+ * 「模型预设」被挪到了第一位 —— 于是它拿到的其实是老位置 0 的采样方案值。
+ * 采样方案和模型预设的取值集合完全不重叠，所以这里可以可靠地区分。
+ */
+function looksLikeScheme(v) {
+    if (typeof v !== "string") return false;
+    return v === SCHEME_AUTO || v === SCHEME_CUSTOM
+        || Object.prototype.hasOwnProperty.call(PRESET_STEPS, v);
+}
+
+/**
+ * 把「模型预设」的值修成合法的：
+ *   · 旧长名 -> 新短名（改过名的老存档）
+ *   · 拿到采样方案的值 -> 退回「手动」（纯位置存档的错位）
+ * 返回是否改动过。
+ */
+function fixPresetValue(node) {
+    const w = findW(node, W_MODEL_PRESET);
+    if (!w || typeof w.value !== "string") return false;
+    if (Object.prototype.hasOwnProperty.call(PRESET_ALIASES, w.value)) {
+        w.value = PRESET_ALIASES[w.value];
+        return true;
+    }
+    if (looksLikeScheme(w.value)) {
+        // 位置错位：这一格拿到的是「采样方案」的值，退回手动
+        w.value = PRESET_NONE;
+        return true;
+    }
+    // 剩下的可能是个已废弃的旧名：别名表没覆盖时留在控件上会显示成一个不存在的值，
+    // 退回「手动」更诚实 —— 真正的采样参数还在，只是不再声称套着某个预设。
+    const vals = w.options && w.options.values;
+    if (Array.isArray(vals) && vals.length && !vals.includes(w.value)) {
+        console.warn("[Toggle-Pass] 未知预设名，已退回手动:", w.value);
+        w.value = PRESET_NONE;
+        return true;
+    }
+    return false;
+}
+
 
 /** 从手填的 sigma 表里数步数（N 个点 = N-1 步；解析不出来就返回 0） */
 function countSigmaSteps(text) {
@@ -172,6 +393,12 @@ app.registerExtension({
     async beforeRegisterNodeDef(nodeType, nodeData) {
         if (nodeData.name !== NODE_NAME) return;
 
+        /* 老存档兜底：1.57 之前的存档只有按位置的 widgets_values。
+           「模型预设」从末尾挪到了第一位，纯位置恢复会整体错一位 —— 把老顺序
+           交给前端，它就会按 widget 名字重新对号。（新版存档有 widgets_values_named，
+           本来就不受位置影响，这里只是补上更早的那批。） */
+        nodeData.fallbackWidgetsValuesNames = LEGACY_WIDGET_ORDER;
+
         // ---- 新建节点 ----
         const onNodeCreated = nodeType.prototype.onNodeCreated;
         nodeType.prototype.onNodeCreated = function () {
@@ -181,14 +408,61 @@ app.registerExtension({
             const wScheme = node.widgets && node.widgets.find((w) => w.name === W_SCHEME);
             if (!wScheme) return r;
 
+            wScheme.label = LABEL_SCHEME;
+            const wPresetInit = node.widgets.find((w) => w.name === W_MODEL_PRESET);
+            if (wPresetInit) wPresetInit.label = LABEL_PRESET;
+
             // 拦截方案切换（用户点下拉时走 callback）
             const origCb = wScheme.callback;
             wScheme.callback = function () {
                 const out = origCb ? origCb.apply(this, arguments) : undefined;
-                applySchemeState(node);
+                try {
+                    applySchemeState(node);
+                    markManual(node);
+                } catch (e) {
+                    console.warn("[Toggle-Pass] 采样方案联动失败:", e);
+                }
                 return out;
             };
 
+            // 预设表提前拉好，等用户点下拉时基本已经到位
+            fetchPresets().then((d) => {
+                if (d && d.presets) node.__tpPresets = d.presets;
+            });
+
+            // 「模型预设」：选中就把五项参数一次性填好
+            const wPreset = node.widgets.find((w) => w.name === W_MODEL_PRESET);
+            if (wPreset) {
+                const origPresetCb = wPreset.callback;
+                wPreset.callback = function () {
+                    const out = origPresetCb ? origPresetCb.apply(this, arguments) : undefined;
+                    try {
+                        applyPreset(node, wPreset.value);
+                    } catch (e) {
+                        console.warn("[Toggle-Pass] 套用模型预设失败:", e);
+                    }
+                    return out;
+                };
+            }
+
+            // 其余四个参数：用户一动就说明偏离预设了，把下拉退回「手动」
+            PRESET_KEYS.forEach((name) => {
+                if (name === W_SCHEME) return; // 方案上面已经包过
+                const w = node.widgets.find((x) => x.name === name);
+                if (!w || w.__tpManualHooked) return;
+                w.__tpManualHooked = true;
+                const orig = w.callback;
+                w.callback = function () {
+                    const out = orig ? orig.apply(this, arguments) : undefined;
+                    try {
+                        markManual(node);
+                    } catch (e) {
+                        /* 标记失败不影响参数本身 */
+                    }
+                    return out;
+                };
+            });
+
             applySchemeState(node);
 
             /* 初始尺寸：只加宽，高度沿用 computeSize 算出的默认值。
@@ -206,9 +480,21 @@ app.registerExtension({
         nodeType.prototype.onConfigure = function () {
             const r = onConfigure ? onConfigure.apply(this, arguments) : undefined;
             try {
+                // label 不是存档内容，但某些路径会重建 widget，这里补一遍最稳
+                const ws = findW(this, W_SCHEME);
+                if (ws) ws.label = LABEL_SCHEME;
+                const wp = findW(this, W_MODEL_PRESET);
+                if (wp) wp.label = LABEL_PRESET;
+
+                // 先把预设值修成合法的（旧长名 / 位置错位），再对方案做联动
+                fixPresetValue(this);
+
                 this.__tpPrevScheme = undefined;   // 加载时不还原历史步数，按存档来
                 applySchemeState(this);
                 this.__tpPrevScheme = this.widgets?.find((w) => w.name === W_SCHEME)?.value;
+
+                // 老存档里节点可能只有 300 宽，全名会把「⚡预设」压在下面看不见
+                ensureWidthForPreset(this);
             } catch (e) {
                 console.warn("[Toggle-Pass] 采样器参数联动失败:", e);
             }
diff --git a/multi_ref_encode.py b/multi_ref_encode.py
index fbab937..6b63e57 100644
--- a/multi_ref_encode.py
+++ b/multi_ref_encode.py
@@ -8,6 +8,7 @@ Version: 3.0 - Multiple node classes (3/5/10 images) + Custom output size for im
 
 import math
 import torch
+import torch.nn.functional as F
 import comfy.utils
 import node_helpers
 import comfy.model_management
@@ -154,6 +155,10 @@ KREA2_VISION_TOKEN = "<|vision_start|><|image_pad|><|vision_end|>"
 def _krea2_ref_preprocess(samples, vae):
     """Krea2 参考图预处理，返回 (ref_latent, vl_image)。
 
+    ⚠️ 2026-11-05 起三个编码节点的 Krea2 分支都改用 `_krea2_fit_ref`（参考图必须先
+    fit 到出图网格，否则 ref latent 与采样起点的 h/w 不同 ⇒ 线稿/边缘渗进画面），
+    本函数已无调用者，保留作参考（它编码的是「约 1 MP、保持原比例」的 latent）。
+
     samples : [B, C, H, W]（已 movedim(-1, 1)）
     · ref_latent —— VAE 编码，缩到约 1024x1024 并对齐 8 的倍数，只吃 RGB
                    （qwen_image_vae 输入 3 通道，Krea2 不吃 alpha）
@@ -187,6 +192,35 @@ def _krea2_ref_preprocess(samples, vae):
     return ref_latent, vl_image
 
 
+def _krea2_fit_ref(samples, out_w, out_h, mode="参考图保比例"):
+    """把参考图 fit 到 Krea2 采样的**空间网格**（= krea2_edit LoRA 的训练几何）。
+
+    原生 krea2 forward 给每张参考图用它**自己的网格**造位置编码：
+        ref    = (frame=i, h=0..h_ref-1, w=0..w_ref-1)
+        target = (frame=0, h=0..h_tgt-1, w=0..w_tgt-1)
+    ref 与 target 的空间尺寸不一致时，模型把它当成「另一块画布」——坐标对不上，
+    实测症状是参考图的线条被贴进成图（脸上出现轮廓线）。krea2_edit 训练时源图是
+    先 fit 到 target 网格再 VAE 编码的。这里照做：ref latent 与采样起点在 h/w 上
+    逐维相等，位置编码只差 frame 轴。
+
+    mode 沿用节点上的「尺寸适配」：
+      参考图保比例 → 中心裁到出图宽高比，再缩到出图像素尺寸（默认，几乎无损）
+      参考图裁剪   → 直接中心裁到出图尺寸
+      参考图拉伸   → 直接缩到出图尺寸（会变形）
+    """
+    tw = max(8, int(round(out_w / 8.0)) * 8)
+    th = max(8, int(round(out_h / 8.0)) * 8)
+    if mode != "参考图拉伸":
+        samples = _center_crop(samples, tw / th)
+    if (samples.shape[3], samples.shape[2]) == (tw, th):
+        return samples
+    # ★★ antialias 不能省：缩小一张锐利线稿时，无 antialias 的双三次会产生振铃/摩尔纹，
+    #    黑白边界的潜空间特征被放大，模型会照着把线条画进成图（实测：脸上出现轮廓线）。
+    #    krea2Edit 的像素路径同样是 bicubic + antialias。
+    return F.interpolate(samples.float(), size=(th, tw), mode="bicubic",
+                         antialias=True).to(samples.dtype)
+
+
 def _krea2_empty_latent(width, height):
     """Krea2 采样起点：全零 16 通道 / 8x latent。
 
@@ -205,6 +239,97 @@ def _krea2_align8(v):
     return max(8, int(round(v / 8.0)) * 8)
 
 
+def _empty_negative(clip):
+    """真正的空文本负条件（不带参考图）。
+
+    ⚠️ 不要用 `ConditioningZeroOut` 把正面条件清零当负面：
+       CFG == 1.0 时负面根本不参与计算，看着没问题；
+       一旦 CFG > 1，负面真的参与计算了，清零的张量会让整张图花屏（绿紫斑块）。
+       实测 Qwen 2.1 fp8 / int8 20 步 CFG 2.5 都崩，换成本函数这种空文本就正常。
+    """
+    tokens = clip.tokenize("")
+    return clip.encode_from_tokens_scheduled(tokens)
+
+
+# 模型类型改名兼容：老工作流里存的是旧字面量，后端收到旧值时归一化到新名字。
+# 前端 js/multi_ref_encode.js 里有一份同样的表（加载时直接把 widget 值改掉），
+# 这里再兜一层是为了「手工改 JSON / 第三方节点转存」也能走对分支。
+_MODEL_TYPE_ALIASES = {
+    "SD / SDXL": "SD / SDXL / Z-Image",
+    "SD/SDXL": "SD / SDXL / Z-Image",
+    "SD/SDXL/z-image": "SD / SDXL / Z-Image",
+    "SDXL": "SD / SDXL / Z-Image",
+    "SD 1.5": "SD / SDXL / Z-Image",
+    "Z-Image": "SD / SDXL / Z-Image",
+    "z-image": "SD / SDXL / Z-Image",
+    "ZImage": "SD / SDXL / Z-Image",
+}
+
+
+def _norm_model_type(v):
+    """把旧/别名写法的模型类型归一成当前下拉里的正式值。"""
+    if not isinstance(v, str):
+        return v
+    return _MODEL_TYPE_ALIASES.get(v.strip(), v)
+
+
+def _te_class_name(clip):
+    """取文本编码器的实际类名（如 ZImageTEModel_）。取不到返回 None。
+
+    ⚠️ 必须显式判 None：`type(None).__name__` 是字符串 "NoneType"（truthy），
+    直接拿去做 `in` 判断会误命中。
+    """
+    try:
+        csm = getattr(clip, "cond_stage_model", None)
+        if csm is None:
+            return None
+        return type(csm).__name__
+    except Exception:
+        return None
+
+
+# 底座没有 in-context 参考图通路的模型：一旦接了参考图，出图必然异常。
+# 只登记**实测确认过**的，不按型号名字猜。
+_NO_REF_CAPABILITY = {
+    "ZImage": (
+        "Z-Image（Lumina2 / NextDiT 系）是纯文生图模型，训练时没有参考图 token；"
+        "官方 5 个 z_image 模板里也没有任何「参考图编辑」接法"
+        "（唯一带图输入的是 fun_union_controlnet，走 ControlNet，机制完全不同）。"
+        "接上 image1 后参考图 token 进不了模型（缺配套的 reference_latents_text_embeds），"
+        "实测出图是满屏方块马赛克。\n"
+        "    → 纯文生图：断开 image1 即可，模型类型选「SD / SDXL / Z-Image」"
+        "（空 latent 会自动补到 16 通道）；\n"
+        "    → 要「看图改图」：换 Qwen-Image 2.1 / Krea2 / Flux2 系；\n"
+        "    → 要 Z-Image 的图生图：用 VAE Encode + 采样器 denoise < 1 的普通 img2img。"
+    ),
+}
+
+
+_WARNED_NO_REF = set()
+
+
+def _warn_no_ref_capability(clip, n_refs, tag="Ref Image Encode"):
+    """接了参考图、但底座不支持时打一条明确警告。只打印，不改变任何行为。
+
+    同一个（节点, 编码器类）只打一次：`Ref Independent` 是按图循环调用的，
+    不去重会刷满日志。
+    """
+    if n_refs <= 0:
+        return
+    te = _te_class_name(clip) or ""
+    for key, msg in _NO_REF_CAPABILITY.items():
+        if key in te:
+            if (tag, te) in _WARNED_NO_REF:
+                return
+            _WARNED_NO_REF.add((tag, te))
+            print(
+                f"\n[Toggle-Pass][{tag}] ⚠️ 文本编码器 = {te}，已接入 {n_refs} 张参考图，"
+                f"但这个底座没有参考图通路。\n    {msg}\n"
+                f"    （本次仍按你的接线执行，以上仅为提示，不修改任何参数）\n"
+            )
+            return
+
+
 class DynamicRefImageEncode:
     """
     动态 Ref Image Encode 节点，合并原 III/V/X 三个节点。
@@ -212,6 +337,20 @@ class DynamicRefImageEncode:
     所有参考图共享同一个 prompt，输出 1 个 conditioning + 1 个 latent。
     """
 
+    DESCRIPTION = (
+        "一个 prompt + 最多 10 张参考图，输出 CONDITIONING + LATENT + NEGATIVE。"
+        "第三个输出 negative 是空文本负条件，直接接采样器「负面条件」即可，"
+        "CFG 调多高都不会花屏（别再用 ConditioningZeroOut 清零正面当负面）。"
+        "MOSS-Image 与 FIBO 2.0 的 latent 与 Flux2 完全同族（128 通道 / 16x，都用 flux2-vae），"
+        "「模型类型」请直接选 Flux2。若需要 MOSS 的 edit_format（必须与编辑 LoRA 的训练格式配对）"
+        "或 image_strength、FIBO 的 negative_prompt，请改用官方编码器 "
+        "TextEncodeMOSSImageEdit / TextEncodeFibo2。"
+        "—— Krea 2：「模型类型」选 Krea2 就能做编辑（保身份 / 换姿势），不需要 krea2Edit 插件。"
+        "参考图会先 fit 到出图网格再编码，并以 reference_latents_method=index 注入。"
+        "⚠️ 顺序是硬约定：最后一个参考图 = 主体（姿势线稿放 image1、原照片放 image2），"
+        "反过来会渲成线稿人偶。"
+    )
+
     @classmethod
     def INPUT_TYPES(cls):
         opt = {}
@@ -236,7 +375,7 @@ class DynamicRefImageEncode:
             },
             "optional": {
                 **opt,
-                "模型类型": (["Flux2", "Qwen Layered", "Qwen Image 2.1", "SD / SDXL", "Krea2"], {"default": "Flux2"}),
+                "模型类型": (["Flux2", "Qwen Layered", "Qwen Image 2.1", "SD / SDXL / Z-Image", "Krea2"], {"default": "Flux2"}),
                 "匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}),
                 "输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
                 "输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
@@ -251,8 +390,14 @@ class DynamicRefImageEncode:
             },
         }
 
-    RETURN_TYPES = ("CONDITIONING", "LATENT")
-    RETURN_NAMES = ("conditioning", "latent")
+    # ↓ 第三个输出是后加的，追加在末尾 ⇒ 旧工作流的 0/1 号口不受影响
+    # 输出顺序：conditioning / negative / latent
+    # ⚠️ 2026-11-06 调整：`negative` 从末尾挪到 `latent` 前面（纯粹为了节点上好看）。
+    #   ⇒ 索引变了（latent: 1 → 2，negative: 2 → 1），所有老工作流的连线索引都失效。
+    #   前端 js/multi_ref_encode.js 的 `migrateOutputOrder()` 会在加载时按
+    #   outputs[i].name 判断布局并交换 1/2 号口的连线，两层都改才一致。
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("conditioning", "negative", "latent")
     FUNCTION = "encode"
     CATEGORY = "Toggle-Pass"
 
@@ -303,9 +448,18 @@ class DynamicRefImageEncode:
                 device=comfy.model_management.intermediate_device(),
             )
         elif 模型类型 == "Flux2":
+            # Flux2 latent：128 通道 / 16x 空间压缩（flux2-vae）。
+            # MOSS-Image 与 FIBO 2.0 用的是同一套 latent，这两个模型请直接选 Flux2。
+            # ⚠️ 这里原来写的是 4 通道（SD 的 latent）。只有「一张参考图都不接、
+            #    纯把它当空 latent 源」时才会走到这个分支（接图时走 ref_latents[0]），
+            #    所以一直没暴露；接上 Flux2/moss/fibo 采样会直接通道不匹配。
+            channels = 128
             h = height // 16
             w = width // 16
-            latent = torch.zeros(1, 4, h, w)
+            latent = torch.zeros(
+                [1, channels, h, w],
+                device=comfy.model_management.intermediate_device(),
+            )
         else:
             h = height // 8
             w = width // 8
@@ -320,6 +474,7 @@ class DynamicRefImageEncode:
 
         n = max(1, min(10, int(num_images)))
         llama_template = self._get_system_prompt(instruction)
+        模型类型 = _norm_model_type(模型类型)          # 老存档兼容
         is_krea2 = (模型类型 == "Krea2")
 
         # 出图分辨率：2.1 用官方 ResolutionSelector 同款（宽高比 + 百万像素 + 倍数）；
@@ -375,21 +530,18 @@ class DynamicRefImageEncode:
                 vl_images.append(rgb)
                 # 2.1 不加 "Picture N: " 文本前缀（Qwen3-VL 靠占位符，不靠文本）
             elif is_krea2:
-                # ---- Krea2：照抄官方 TextEncodeQwenImageEditPlus ----
-                # 参考图 VAE 缩放到约 1024x1024、对齐 8 的倍数；VAE 只吃 RGB
-                # （qwen_image_vae 输入是 3 通道，Krea2 不吃 alpha）
-                total = 1024 * 1024
-                scale_by = math.sqrt(total / current_total)
-                kw = max(8, round(samples.shape[3] * scale_by / 8.0) * 8)
-                kh = max(8, round(samples.shape[2] * scale_by / 8.0) * 8)
-                if (kw, kh) == (samples.shape[3], samples.shape[2]):
-                    s = samples
-                else:
-                    s = comfy.utils.common_upscale(samples, kw, kh, "area", "disabled")
+                # ---- Krea2：参考图先 fit 到「出图网格」再 VAE 编码 ----
+                # 原生 krea2 forward 用每张参考图**自己的网格**造位置编码，尺寸与 target
+                # 不一致时模型把它当另一块画布 ⇒ 参考图的线条会被贴进成图（实测症状：
+                # 脸上出现轮廓线）。krea2_edit LoRA 训练时源图就是先 fit 到 target 网格
+                # 再编码的，这里对齐它。VAE 只吃 RGB（qwen_image_vae 3 通道）。
+                if first_tw is None:
+                    first_tw, first_th = _krea2_align8(pixel_width), _krea2_align8(pixel_height)
+                s = _krea2_fit_ref(samples, first_tw, first_th, 尺寸适配)
                 if vae is not None:
                     ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
 
-                # 视觉塔：384x384 附近
+                # 视觉塔：384x384 附近（语义 grounding，与潜空间几何无关）
                 vl_total = 384 * 384
                 vl_scale = math.sqrt(vl_total / current_total)
                 s_vl = comfy.utils.common_upscale(
@@ -441,14 +593,23 @@ class DynamicRefImageEncode:
             tokens = clip.tokenize(full_prompt, images=vl_images, llama_template=llama_template)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
 
+        _warn_no_ref_capability(clip, len(ref_latents), "Ref Image Encode")
+
         if len(ref_latents) > 0:
             ref_vals = {"reference_latents": ref_latents}
             if is_krea2:
                 # ⚠️ Krea2 的 default_ref_method 实测是 None，而模型侧写的是
                 #     if ref_method is not None and ref_latents is not None and len(...)
                 # ⇒ 不显式给方法时参考图会被**静默忽略**（不报错、只是没效果）。
-                #   官方风格参考模板用 FluxKontextMultiReferenceLatentMethod 设成 index_timestep_zero。
-                ref_vals["reference_latents_method"] = "index_timestep_zero"
+                #
+                # ★★ 方法必须用 "index"，不能照官方模板用 "index_timestep_zero"：
+                #   "index_timestep_zero" 会让参考图 token 走 **t=0 的 adaLN 调制**
+                #   （见 comfy/ldm/krea2/model.py SingleStreamBlock.forward 的
+                #     timestep_zero_index 分支），target 走当前 t —— 两套调制。
+                #   而 krea2_edit LoRA（ai-toolkit predict_velocity_edit）训练时
+                #   全序列共用**同一个 tvec**，正好对应原生的 "index"（不分裂 tvec）。
+                #   实测：用 timestep_zero 时参考线稿会被"描"进成图（脸上出轮廓线）。
+                ref_vals["reference_latents_method"] = "index"
             conditioning = node_helpers.conditioning_set_values(
                 conditioning,
                 ref_vals,
@@ -486,7 +647,11 @@ class DynamicRefImageEncode:
         else:
             latent_out = self._make_empty_latent(模型类型, final_w, final_h, 图层数)
 
-        return (conditioning, latent_out)
+        # 第二/三个输出：空文本负条件 + 采样起点 latent。
+        # 顺序见 RETURN_NAMES —— negative 在前、latent 在后。
+        # CFG=1.0 时负面不参与计算（等价于原来的 ConditioningZeroOut），
+        # CFG>1 时它是唯一正确的接法。
+        return (conditioning, _empty_negative(clip), latent_out)
 
 
 NODE_CLASS_MAPPINGS = {
@@ -648,7 +813,7 @@ class OneImageMultiPrompt:
                         "with the original input where appropriate."
                     ),
                 }),
-                "模型类型": (["Flux2", "Qwen Image 2.1", "SD / SDXL", "Krea2"], {"default": "Flux2"}),
+                "模型类型": (["Flux2", "Qwen Image 2.1", "SD / SDXL / Z-Image", "Krea2"], {"default": "Flux2"}),
                 "匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}),
                 "输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
                 "输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
@@ -701,6 +866,7 @@ class OneImageMultiPrompt:
 
         n = max(2, min(8, int(prompt数量)))
         llama_template = self._get_system_prompt(instruction)
+        模型类型 = _norm_model_type(模型类型)          # 老存档兼容
         is_21 = (模型类型 == "Qwen Image 2.1")
         is_krea2 = (模型类型 == "Krea2")
 
@@ -750,7 +916,19 @@ class OneImageMultiPrompt:
             # ⚠️ 传了 llama_template 时 Qwen3VL tokenizer 不会自动插视觉占位符
             #    （只有用内置 llama_template_images 时才自动），必须自己写
             #    <|vision_start|><|image_pad|><|vision_end|>，否则参考图对模型完全不可见。
-            ref_latent, vl_image = _krea2_ref_preprocess(samples, vae)
+            # ★ 参考图先 fit 到出图网格（krea2_edit 训练几何），理由见 _krea2_fit_ref。
+            kw_, kh_ = (_krea2_align8(pixel_width), _krea2_align8(pixel_height))
+            s = _krea2_fit_ref(samples, kw_, kh_, 尺寸适配)
+            ref_latent = vae.encode(s.movedim(1, -1)[:, :, :, :3]) if vae is not None else None
+            vl_total = 384 * 384
+            vl_scale = math.sqrt(vl_total / current_total)
+            s_vl = comfy.utils.common_upscale(
+                samples,
+                max(8, round(samples.shape[3] * vl_scale)),
+                max(8, round(samples.shape[2] * vl_scale)),
+                "area", "disabled",
+            )
+            vl_image = s_vl.movedim(1, -1)
             image_prompt = "Picture 1: " + KREA2_VISION_TOKEN
         else:
             # VAE 编码参考图（图生图用）
@@ -789,12 +967,17 @@ class OneImageMultiPrompt:
                 tokens = clip.tokenize(image_prompt + p, images=[vl_image], llama_template=llama_template)
             cond = clip.encode_from_tokens_scheduled(tokens)
 
+            _warn_no_ref_capability(clip, 0 if ref_latent is None else 1, "一图多Prompt")
+
             if ref_latent is not None:
                 ref_vals = {"reference_latents": [ref_latent]}
                 if is_krea2:
                     # Krea2 的 default_ref_method 是 None ⇒ 不显式给方法时参考图会被
-                    # 静默忽略（不报错、只是没效果）。官方风格模板用 index_timestep_zero。
-                    ref_vals["reference_latents_method"] = "index_timestep_zero"
+                    # 静默忽略（不报错、只是没效果）。
+                    # ★★ 必须用 "index" 而非 "index_timestep_zero"：后者给参考图单独
+                    #    一套 t=0 的 adaLN 调制，与 krea2_edit LoRA 的训练（全序列同一
+                    #    tvec）不符，实测会把参考线稿"描"进成图。详见 DynamicRefImageEncode。
+                    ref_vals["reference_latents_method"] = "index"
                 cond = node_helpers.conditioning_set_values(
                     cond,
                     ref_vals,
@@ -829,11 +1012,19 @@ class OneImageMultiPrompt:
         elif ref_latent is not None:
             latent_out = {"samples": ref_latent}
         else:
-            # 纯文生图
-            scale = 16 if 模型类型 == "Flux2" else 8
+            # 纯文生图：通道数必须跟模型一致。
+            # Flux2（以及同族的 MOSS-Image / FIBO 2.0）是 128 通道 / 16x，SD 是 4 通道 / 8x。
+            # 原来不分类型一律 4 通道，接 Flux2 采样会直接通道不匹配。
+            if 模型类型 == "Flux2":
+                scale, channels = 16, 128
+            else:
+                scale, channels = 8, 4
             lw = 输出宽度 // scale
             lh = 输出高度 // scale
-            latent_out = {"samples": torch.zeros(1, 4, lh, lw)}
+            latent_out = {"samples": torch.zeros(
+                [1, channels, lh, lw],
+                device=comfy.model_management.intermediate_device(),
+            )}
 
         # 补齐到 8 个 conditioning（未使用的填空 conditioning）
         while len(results) < 8:
@@ -892,7 +1083,7 @@ class DynamicRefIndependent:
             },
             "optional": {
                 **opt,
-                "模型类型": (["Flux2", "Qwen Layered", "Qwen Image 2.1", "SD / SDXL", "Krea2"], {"default": "Flux2"}),
+                "模型类型": (["Flux2", "Qwen Layered", "Qwen Image 2.1", "SD / SDXL / Z-Image", "Krea2"], {"default": "Flux2"}),
                 "匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}),
                 "输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
                 "输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
@@ -944,8 +1135,17 @@ class DynamicRefIndependent:
                尺寸适配="参考图保比例", **kwargs):
         n = max(1, min(10, int(num_images)))
         llama_template = self._get_system_prompt(instruction)
+        模型类型 = _norm_model_type(模型类型)          # 老存档兼容
         is_21 = (模型类型 == "Qwen Image 2.1")
         is_krea2 = (模型类型 == "Krea2")
+        if is_krea2 and n > 1:
+            # 本节点把每张图编进各自独立的 conditioning（Qwen 2.1 的每图独立 index 平面那套设计）。
+            # Krea2 需要的是「一条连贯的参考序列」：合并多个独立 conditioning 后，
+            # reference_latents 的 frame 语义会乱 —— 实测线稿残留 + 姿势跑偏，而且不报错。
+            print("[Toggle-Pass] ⚠️ DynamicRefIndependent 在 Krea2 多图下不可靠：多图请改用 "
+                  "Ref Image Encode（DynamicRefImageEncode），一个节点就能带上多张参考图。",
+                  flush=True)
+
         conds = []
         latents = []          # 每张图自己的 latent
         first_latent = None   # 第一张非空图的 latent，用于共享 latent_out
@@ -1012,7 +1212,18 @@ class DynamicRefIndependent:
                     final_w = _krea2_align8(samples.shape[3])
                     final_h = _krea2_align8(samples.shape[2])
                     size_locked = True
-                img_latent, vl_image = _krea2_ref_preprocess(samples, vae)
+                # ★ 参考图先 fit 到出图网格（krea2_edit 训练几何），理由见 _krea2_fit_ref
+                s_k = _krea2_fit_ref(samples, final_w, final_h, 尺寸适配)
+                img_latent = vae.encode(s_k.movedim(1, -1)[:, :, :, :3]) if vae is not None else None
+                vl_total = 384 * 384
+                vl_scale = math.sqrt(vl_total / current_total)
+                s_vl = comfy.utils.common_upscale(
+                    samples,
+                    max(8, round(samples.shape[3] * vl_scale)),
+                    max(8, round(samples.shape[2] * vl_scale)),
+                    "area", "disabled",
+                )
+                vl_image = s_vl.movedim(1, -1)
                 if img_latent is not None and first_latent is None:
                     first_latent = img_latent
             else:
@@ -1051,12 +1262,17 @@ class DynamicRefIndependent:
             cond = clip.encode_from_tokens_scheduled(tokens)
 
             # ---- 每张图的 conditioning 绑定自己的 latent ----
+            _warn_no_ref_capability(clip, 0 if img_latent is None else 1, "Ref Independent")
+
             if img_latent is not None:
                 ref_vals = {"reference_latents": [img_latent]}
                 if is_krea2:
                     # Krea2 的 default_ref_method 是 None ⇒ 不显式给方法时参考图会被
-                    # 静默忽略（不报错、只是没效果）。官方风格模板用 index_timestep_zero。
-                    ref_vals["reference_latents_method"] = "index_timestep_zero"
+                    # 静默忽略（不报错、只是没效果）。
+                    # ★★ 必须用 "index" 而非 "index_timestep_zero"：后者给参考图单独
+                    #    一套 t=0 的 adaLN 调制，与 krea2_edit LoRA 的训练（全序列同一
+                    #    tvec）不符，实测会把参考线稿"描"进成图。
+                    ref_vals["reference_latents_method"] = "index"
                 cond = node_helpers.conditioning_set_values(
                     cond,
                     ref_vals,
@@ -1094,7 +1310,17 @@ class DynamicRefIndependent:
         elif is_krea2:
             latent_out = _krea2_empty_latent(final_w, final_h)
         elif first_latent is None:
-            latent_out = {"samples": torch.zeros(1, 4, 1, 1)}
+            # 一张图都没接：给一个与模型匹配的空 latent。
+            # 原来固定 4 通道 1×1 —— KSampler 的 fix_empty_latent_channels 会把它补成
+            # (1, 128, 1, 1) 并照常采样，最后解码出一张 16×16 的废图（不报错，很难查）。
+            if 模型类型 == "Flux2":
+                scale, channels = 16, 128
+            else:
+                scale, channels = 8, 4
+            latent_out = {"samples": torch.zeros(
+                [1, channels, max(1, 输出高度 // scale), max(1, 输出宽度 // scale)],
+                device=comfy.model_management.intermediate_device(),
+            )}
         else:
             latent_out = {"samples": first_latent}
 
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diff --git a/pose_reference.py b/pose_reference.py
new file mode 100644
index 0000000..228597d
--- /dev/null
+++ b/pose_reference.py
@@ -0,0 +1,531 @@
+"""参考姿势（Pose Reference）—— 从姿势素材库里取一张参考图，喂给编辑模型。
+
+素材分两层，运行时合并成一个索引：
+
+1. **插件自带** `pose_assets/` —— 随插件分发，只放非敏感大类（上半身单人、
+   全身单人・标准系），别人装完节点就能用。
+2. **本机外挂** —— 默认 `D:\\pose`，可用环境变量 `TP_POSE_ROOT` 改（也可放
+   `ComfyUI/models/pose_reference`）。敏感或自建素材丢进去，刷新即出现，不进 git。
+
+查找顺序是「本机外挂 → models → 插件自带」，所以同名素材以本机为准。
+
+素材目录结构（每一层根目录都一样）：
+    <root>/01_AI线稿姿势/<大类>/<姿势组>/<编号_姿势名>/<编号_视角>.webp
+    <root>/02_AI深度姿势库/<同上>/
+
+线稿与深度两套的文件名、目录结构一一对应，所以只需扫一套来建索引，
+取图时按 source 决定去哪个目录拿。
+
+选线稿还是深度：实测（FIBO 2.0 / Qwen-Image-2.1 各两组对照）深度图因为
+没有边界线，脚底和地面的亮度几乎相同，模型会把亮部补成不存在的鞋或护具
+（3/3 复现）；线稿有明确黑线，1/3 才出现小瑕疵。默认用线稿。
+
+深度的唯一优势是遮挡关系（手臂在腿前还是后），复杂交叠姿势可以切过去。
+"""
+
+import base64
+import hashlib
+import io
+import json
+import mimetypes
+import os
+import re
+
+import numpy as np
+import torch
+from PIL import Image, ImageFile, ImageOps
+
+import folder_paths
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+DEFAULT_ROOT = r"D:\pose"
+BUILTIN_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "pose_assets")
+IMAGE_EXT = (".webp", ".png", ".jpg", ".jpeg", ".bmp")
+# 视角按常见构图顺序排，不跟着中文编码字典序走
+VIEW_ORDER = ["正面", "侧面", "斜侧", "俯视", "仰视"]
+
+_cache = {"stamp": None, "tree": None}
+
+
+def _roots():
+    """按优先级返回可用的素材根目录（前面的优先）。
+
+    顺序：TP_POSE_ROOT（或默认 D:\\pose）→ models/pose_reference → 插件自带。
+    本机目录在前，所以自己放的素材会覆盖插件自带里的同名额。
+    """
+    cands = []
+    env = os.environ.get("TP_POSE_ROOT")
+    if env:
+        cands.append(env)
+    elif DEFAULT_ROOT:
+        cands.append(DEFAULT_ROOT)
+    try:
+        cands.append(os.path.join(folder_paths.models_dir, "pose_reference"))
+    except Exception:
+        pass
+    cands.append(BUILTIN_ROOT)
+
+    out = []
+    for d in cands:
+        if d and os.path.isdir(d) and os.path.abspath(d) not in [os.path.abspath(x) for x in out]:
+            out.append(d)
+    return out
+
+
+def _root():
+    """兼容旧调用：返回主素材目录（没有可用的就返回默认路径，方便报错信息）。"""
+    roots = _roots()
+    return roots[0] if roots else DEFAULT_ROOT
+
+
+def _mtime(path):
+    try:
+        return os.stat(path).st_mtime
+    except OSError:
+        return 0.0
+
+
+def _stamp(root):
+    """素材指纹，扫到「大类/姿势组」这一层。
+
+    只看根目录一层不够：往已有大类里新放一个姿势组，根目录 mtime 不会变，
+    缓存就永远不刷新。所以按 套/大类/组 逐层取 mtime，外加每组里的条目数。
+    """
+    if not os.path.isdir(root):
+        return None
+    parts = []
+    for set_name in sorted(os.listdir(root)):
+        set_path = os.path.join(root, set_name)
+        if not os.path.isdir(set_path):
+            continue
+        parts.append("{0}:{1:.0f}".format(set_name, _mtime(set_path)))
+        for cat in sorted(os.listdir(set_path)):
+            cat_path = os.path.join(set_path, cat)
+            if not os.path.isdir(cat_path):
+                continue
+            parts.append("  {0}:{1:.0f}".format(cat, _mtime(cat_path)))
+            for grp in sorted(os.listdir(cat_path)):
+                grp_path = os.path.join(cat_path, grp)
+                if not os.path.isdir(grp_path):
+                    continue
+                try:
+                    n = len(os.listdir(grp_path))
+                except OSError:
+                    n = -1
+                parts.append("    {0}:{1:.0f}:{2}".format(grp, _mtime(grp_path), n))
+    return "|".join(parts)
+
+
+def _stamp_all():
+    """所有根目录的总指纹（前端轮询它判断要不要重建下拉）。"""
+    roots = _roots()
+    return "||".join("{0}#{1}".format(r, _stamp(r) or "-") for r in roots), roots
+
+
+def _pick_sets(root):
+    """按目录名里的关键词区分线稿套与深度套，认不出就退回「前两个目录」。"""
+    lineart = depth = None
+    subs = []
+    if os.path.isdir(root):
+        subs = [d for d in sorted(os.listdir(root)) if os.path.isdir(os.path.join(root, d))]
+    for d in subs:
+        low = d.lower()
+        if lineart is None and ("线稿" in d or "lineart" in low or low.startswith("01")):
+            lineart = os.path.join(root, d)
+        elif depth is None and ("深度" in d or "depth" in low or low.startswith("02")):
+            depth = os.path.join(root, d)
+    if lineart is None and subs:
+        lineart = os.path.join(root, subs[0])
+    if depth is None and len(subs) > 1:
+        depth = os.path.join(root, subs[1])
+    return lineart, depth
+
+
+def _view_of(filename):
+    stem = os.path.splitext(filename)[0]
+    return re.sub(r"^P\d+_", "", stem)
+
+
+def _view_key(view):
+    try:
+        return (VIEW_ORDER.index(view), "")
+    except ValueError:
+        return (len(VIEW_ORDER), view)
+
+
+def _scan(base):
+    """扫一套素材，返回 {大类: {姿势组: {姿势目录: [视角...]}}}。"""
+    tree = {}
+    if not base or not os.path.isdir(base):
+        return tree
+    for cat in sorted(os.listdir(base)):
+        cat_path = os.path.join(base, cat)
+        if not os.path.isdir(cat_path):
+            continue
+        groups = {}
+        for group in sorted(os.listdir(cat_path)):
+            group_path = os.path.join(cat_path, group)
+            if not os.path.isdir(group_path):
+                continue
+            poses = {}
+            for pose in sorted(os.listdir(group_path)):
+                pose_path = os.path.join(group_path, pose)
+                if not os.path.isdir(pose_path):
+                    continue
+                try:
+                    names = os.listdir(pose_path)
+                except OSError:
+                    continue
+                views = sorted(
+                    {_view_of(f) for f in names if f.lower().endswith(IMAGE_EXT)},
+                    key=_view_key,
+                )
+                if views:
+                    poses[pose] = views
+            if poses:
+                groups[group] = poses
+        if groups:
+            tree[cat] = groups
+    return tree
+
+
+def _merge_into(dst, src):
+    """把 src 的 大类/组/姿势/视角 并进 dst：取并集，已有顺序不动。"""
+    for cat, group_map in src.items():
+        dcat = dst.setdefault(cat, {})
+        for group, pose_map in group_map.items():
+            dgrp = dcat.setdefault(group, {})
+            for pose, views in pose_map.items():
+                dviews = dgrp.setdefault(pose, [])
+                for v in views:
+                    if v not in dviews:
+                        dviews.append(v)
+                dviews.sort(key=_view_key)
+
+
+def pose_tree(force=False):
+    """把各根目录的线稿套合并成一份带缓存的索引（本机目录在前）。"""
+    stamp, roots = _stamp_all()
+    if not force and _cache["stamp"] == stamp and _cache["tree"] is not None:
+        return _cache["tree"]
+    tree = {}
+    for root in roots:
+        lineart, _depth = _pick_sets(root)
+        if lineart and os.path.isdir(lineart):
+            _merge_into(tree, _scan(lineart))
+    _cache["stamp"] = stamp
+    _cache["tree"] = tree
+    return tree
+
+
+def flatten(tree):
+    """下拉框用的三个平铺清单：大类 / 姿势组 / 姿势全名。"""
+    cats, groups, poses, views = [], [], [], []
+    for cat, group_map in tree.items():
+        cats.append(cat)
+        for group, pose_map in group_map.items():
+            if group not in groups:
+                groups.append(group)
+            for pose, view_list in pose_map.items():
+                if pose not in poses:
+                    poses.append(pose)
+                for v in view_list:
+                    if v not in views:
+                        views.append(v)
+    views.sort(key=_view_key)
+    return cats, groups, poses, views
+
+
+def locate(category, group, pose, view):
+    """定位到具体文件用的四元组，容错到「只要姿势名对」也能找到。"""
+    tree = pose_tree()
+    if category in tree and group in tree[category] and pose in tree[category][group]:
+        views = tree[category][group][pose]
+        if view in views:
+            return category, group, pose, view
+        if views:
+            return category, group, pose, views[0]
+    for cat, group_map in tree.items():
+        for grp, pose_map in group_map.items():
+            if pose in pose_map:
+                views = pose_map[pose]
+                return cat, grp, pose, (view if view in views else views[0])
+    raise ValueError(
+        "在姿势素材库里找不到 {0} / {1} / {2}。已搜索：{3}".format(
+            category, group, pose, "；".join(_roots()) or "无可用素材目录")
+    )
+
+
+def build_path(category, group, pose, view, source):
+    """按根目录优先级找文件：本机放的同名素材会盖过插件自带的。"""
+    tried = []
+    for root in _roots():
+        lineart, depth = _pick_sets(root)
+        base = depth if source == "深度" else lineart
+        if not base or not os.path.isdir(base):
+            tried.append("{0}（没有{1}套）".format(root, source))
+            continue
+        folder = os.path.join(base, category, group, pose)
+        if not os.path.isdir(folder):
+            tried.append(folder)
+            continue
+        for fn in sorted(os.listdir(folder)):
+            if fn.lower().endswith(IMAGE_EXT) and _view_of(fn) == view:
+                return os.path.join(folder, fn)
+        tried.append("{0}（无 {1} 视角）".format(folder, view))
+    raise ValueError(
+        "找不到 {0} / {1} / {2} / {3}。已搜索：{4}".format(
+            category, group, pose, view, "；".join(tried) or "无可用素材目录")
+    )
+
+
+# 视觉编码器 / VAE 的下采样倍数是 8，加上 patch 2 ⇒ 实际要求 16 的倍数。
+# 不满足时 VAEEncode 会自己 pad 或 crop，边缘发生轻微位移 —— 线稿/深度图对位移特别敏感，
+# 会表现成「人物整体歪了一点点」。
+ALIGN = 16
+
+
+def _align_down(v, m=ALIGN):
+    """向下取到 m 的倍数。比 m 还小就不动 —— 把 8px 硬拉到 16px 只会更糊。"""
+    if v < m:
+        return max(1, v)
+    return v - (v % m)
+
+
+def _resize(img, long_edge):
+    """按长边等比缩放，再把宽高对齐到 ALIGN 的倍数。"""
+    if long_edge and long_edge > 0:
+        w, h = img.size
+        scale = float(long_edge) / max(w, h)
+        if abs(scale - 1.0) > 1e-3:
+            img = img.resize((max(1, round(w * scale)), max(1, round(h * scale))), Image.LANCZOS)
+
+    w, h = img.size
+    aw, ah = _align_down(w), _align_down(h)
+    if (aw, ah) != (w, h):
+        # 用缩放而不是裁切：1% 以内的形变看不出来，裁切却可能切掉脚尖或手指
+        img = img.resize((aw, ah), Image.LANCZOS)
+    return img
+
+
+def _load(path, long_edge, flip):
+    img = Image.open(path)
+    img = ImageOps.exif_transpose(img).convert("RGB")
+    if flip:
+        img = ImageOps.mirror(img)
+    return _resize(img, long_edge)
+
+
+# ---- 工作流内嵌图：让别人下载工作流后，没有素材库也能看见图、也能出图 ----
+EMBED_PROP = "tp_pose"
+EMBED_MAX_BYTES = 2 * 1024 * 1024  # 防御：内嵌字段异常大就不解析
+
+
+def embed_key(category, group, pose, view, source):
+    """前端与后端必须用同一套拼法，回退时才对得上。"""
+    return "|".join(str(x) for x in (category, group, pose, view, source))
+
+
+def _node_properties(extra_pnginfo, unique_id):
+    """从工作流 JSON 里捞出本节点的 properties（properties 不会进 prompt inputs）。"""
+    try:
+        wf = (extra_pnginfo or {}).get("workflow") or {}
+        for node in wf.get("nodes") or []:
+            if str(node.get("id")) == str(unique_id):
+                return node.get("properties") or {}
+    except Exception:
+        pass
+    return {}
+
+
+def _decode_data_uri(data_uri):
+    """把前端存进 properties 的 data URI 解回 PIL 图。"""
+    if not isinstance(data_uri, str) or "," not in data_uri:
+        return None
+    head, _, payload = data_uri.partition(",")
+    if "base64" not in head or len(payload) > EMBED_MAX_BYTES:
+        return None
+    try:
+        raw = base64.b64decode(payload)
+        img = Image.open(io.BytesIO(raw))
+        return ImageOps.exif_transpose(img).convert("RGB")
+    except Exception:
+        return None
+
+
+def _png_bytes(img):
+    buf = io.BytesIO()
+    img.save(buf, format="PNG", compress_level=1)
+    return buf.getvalue()
+
+
+class PoseReference:
+    """从本地姿势素材库取一张参考图。"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        tree = pose_tree()
+        cats, groups, poses, views = flatten(tree)
+        if not cats:
+            cats, groups, poses, views = ["（未找到素材）"], ["—"], ["—"], ["—"]
+        return {
+            "required": {
+                "category": (cats, {"tooltip": "素材大类"}),
+                "group": (groups, {"tooltip": "姿势组，如 站姿 / 坐姿"}),
+                "pose": (poses, {"tooltip": "具体姿势，编号_名称"}),
+                "view": (views, {"tooltip": "机位视角"}),
+            },
+            "optional": {
+                "source": (["线稿", "深度"], {"default": "线稿", "tooltip": "线稿更稳；深度适合肢体交叠多、需要遮挡关系的姿势"}),
+                "long_edge": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64,
+                                      "tooltip": "输出图长边像素，素材原始只有 300x400"}),
+                # 用下拉而不是 BOOL：1.57 会把 BOOL 渲染成可接线插槽，节点上看不到勾选框
+                "flip": (["关", "开"], {"default": "关", "tooltip": "水平翻转，左右镜像的姿势不用另找"}),
+            },
+            # properties 不会进 prompt inputs，只能靠 extra_pnginfo 把整份工作流带过来，
+            # 从里面按 unique_id 找到本节点，读内嵌图
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "load"
+    CATEGORY = "Toggle-Pass"
+    DESCRIPTION = ("从本地姿势素材库取参考图，输出 IMAGE。接 Qwen-Image-2.1 / MOSS / FIBO 的 images 输入；"
+                   "接 2511 / FLUX.2 这类编辑模型时必须多图：原图接 image1，本节点接 image2，"
+                   "否则模型会把线稿当成待修改的原图，出图但姿势不变。"
+                   "Krea 2 只有 Ostris 节点（TextEncodeKrea2OstrisEdit）能吃骨架图，接 image2；"
+                   "krea2Edit 那套的 image_b 是「第二个主体」，接进来线稿会被画进画面。")
+
+    def load(self, category, group, pose, view, source="线稿", long_edge=1024, flip="关",
+             unique_id=None, extra_pnginfo=None):
+        want = embed_key(category, group, pose, view, source)
+        embed = (_node_properties(extra_pnginfo, unique_id) or {}).get(EMBED_PROP) or {}
+        origin = "素材库"
+        try:
+            cat, grp, ps, vw = locate(category, group, pose, view)
+            path = build_path(cat, grp, ps, vw, source)
+            img = _load(path, long_edge, flip in (True, "开"))
+        except Exception as exc:
+            # 素材库里没有 → 用工作流内嵌的那张。别人下载工作流就是走这条路
+            img0 = _decode_data_uri(embed.get("img"))
+            if img0 is None:
+                raise
+            print("[PoseReference] 素材库取图失败（{0}），改用工作流内嵌图".format(str(exc)[:140]))
+            if embed.get("key") != want:
+                print("[PoseReference] 提示：内嵌图对应「{0}」，与当前所选「{1}」不同".format(
+                    embed.get("key"), want))
+            if flip in (True, "开"):
+                img0 = ImageOps.mirror(img0)
+            img = _resize(img0, long_edge)
+            cat, grp, ps, vw = category, group, pose, view
+            origin = "内嵌"
+
+        arr = np.array(img).astype(np.float32) / 255.0
+        tensor = torch.from_numpy(arr)[None,]
+        # 这里**故意不返回 ui.images**。一旦返回，ComfyUI 1.57 前端就会给节点设
+        # node.imgs，进而挂一个 `$$canvas-image-preview` 的官方预览 widget：它占掉
+        # 节点底部一行，并把这张执行结果画在预览区上面。于是「生图一开始」画面就被
+        # 一张定死的图盖住 —— 下拉照点、节点自己画的预览在背面其实也换了，但看起来
+        # 就是再也切不动。本节点的预览由前端 onDrawForeground 自绘，不需要这一份。
+        return {"ui": {"text": ["{0}/{1}/{2}/{3} · {4} · {5}".format(
+            cat, grp, ps, vw, source, origin)]},
+            "result": (tensor,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "PoseReference": PoseReference,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PoseReference": "参考姿势 Pose Reference",
+}
+
+
+# ---- 给前端联动用的目录树接口 ----
+def _register_routes():
+    try:
+        from aiohttp import web
+        from server import PromptServer
+
+        instance = getattr(PromptServer, "instance", None)
+        routes = getattr(instance, "routes", None) if instance is not None else None
+        if routes is None:
+            return
+
+        @routes.get("/toggle_pass/pose_tree")
+        async def _pose_tree(request):
+            try:
+                force = request.rel_url.query.get("refresh") in ("1", "true")
+                tree = pose_tree(force=force)
+                cats, groups, poses, views = flatten(tree)
+                return web.json_response({"tree": tree, "categories": cats,
+                                          "groups": groups, "poses": poses,
+                                          "views": views, "root": _root(),
+                                          "roots": _roots()})
+            except Exception as exc:
+                return web.json_response({"error": str(exc), "tree": {}}, status=500)
+
+        @routes.get("/toggle_pass/pose_stamp")
+        async def _pose_stamp(request):
+            """轻量指纹：前端每隔十几秒问一次，变了才重新拉整个 tree。
+
+            往素材目录里丢新文件（尤其是敏感那几类放本机）后，不用刷新浏览器，
+            下拉自己就会长出来。
+            """
+            try:
+                stamp, roots = _stamp_all()
+                return web.json_response({"stamp": hashlib.md5(stamp.encode("utf-8")).hexdigest(),
+                                          "roots": roots, "count": len(roots)})
+            except Exception as exc:
+                return web.json_response({"error": str(exc)}, status=500)
+
+        @routes.get("/toggle_pass/pose_image")
+        async def _pose_image(request):
+            """给前端节点内缩略图 + 内嵌用：按参数把选中的那张姿势吐回去。
+
+            参数全在 query 里，同一个 URL 永远对应同一张图，所以放开浏览器缓存，
+            来回切下拉不重复解码。
+
+            `raw=1` 时直接返回素材原文件（webp，约 5KB），不缩放不翻转 —— 前端拿它
+            当缩略图显示、同时 base64 内嵌进工作流；翻转由前端绘制时镜像。
+            """
+            try:
+                q = request.rel_url.query
+                source = q.get("source", "线稿")
+                try:
+                    long_edge = int(q.get("long_edge", "0") or 0)
+                except ValueError:
+                    long_edge = 0
+                flip = q.get("flip") in ("开", "1", "true", "True")
+                cat, grp, ps, vw = locate(q.get("category", ""), q.get("group", ""),
+                                          q.get("pose", ""), q.get("view", ""))
+                path = build_path(cat, grp, ps, vw, source)
+                # 不能强缓存（max-age）：同一个 URL 对应的素材文件可能被同名替换，
+                # 强缓存会让浏览器 24 小时都拿旧图，换了素材看起来像"切换不了"。
+                # 改用 ETag 逐次校验：没变返回 304（几乎零成本），变了立刻给新内容。
+                mtime_ms = int(os.path.getmtime(path) * 1000)
+                size = os.path.getsize(path)
+                etag = '"{0}-{1}-{2}-{3}"'.format(mtime_ms, size, long_edge, int(flip))
+                headers = {"Cache-Control": "no-cache", "ETag": etag}
+                if request.headers.get("If-None-Match") == etag:
+                    return web.Response(status=304, headers=headers)
+                if q.get("raw") in ("1", "true", "开"):
+                    with open(path, "rb") as fh:
+                        body = fh.read()
+                    mime = mimetypes.guess_type(path)[0] or "image/webp"
+                    return web.Response(body=body, content_type=mime, headers=headers)
+                img = _load(path, long_edge, flip)
+                return web.Response(body=_png_bytes(img), content_type="image/png", headers=headers)
+            except Exception as exc:
+                return web.json_response({"error": str(exc)}, status=404)
+    except Exception:
+        # 独立导入 / 无 server 模块时静默跳过，不影响节点本身
+        return
+
+
+_register_routes()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3d781bf
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,25 @@
+[project]
+name = "comfyui-toggle-pass"
+version = "1.0.0"
+description = "Toggle-switch ComfyUI nodes: bypass any node without rewiring, multi-reference image editing, unified model loader, one-piece sampler with presets, and a built-in pose reference library."
+readme = "README.md"
+requires-python = ">=3.9"
+license = { text = "MIT" }
+authors = [{ name = "Clever", url = "https://github.com/electricty00" }]
+keywords = ["comfyui", "comfyui-custom-node", "toggle", "image-editing", "reference-image", "pose"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+    "Topic :: Multimedia :: Graphics",
+]
+
+# 运行时依赖由 ComfyUI 本体提供，不在这里重复声明，避免版本冲突。
+dependencies = []
+
+[project.urls]
+Homepage = "https://github.com/electricty00/ComfyUI-Toggle-Pass"
+Issues = "https://github.com/electricty00/ComfyUI-Toggle-Pass/issues"
+
+[tool.comfy]
+# 留在 git 安装模式（不发布到 Comfy Registry），所以不写 PublisherId / DisplayName。
+# 想让 ComfyUI-Manager 把它当成可更新节点而不报错，这一节留空即可。
diff --git a/sampler.py b/sampler.py
index 88aee65..1438af7 100644
--- a/sampler.py
+++ b/sampler.py
@@ -1,232 +1,377 @@
-"""
-ComfyUI-Toggle-Pass: 采样器（一体）
-
-把「随机噪波 + CFG引导器 + K采样器选择 + Sigmas」四件套收进一个节点。
-等价于以下官方链路：
-    RandomNoise + CFGGuider + KSamplerSelect + (BasicScheduler | ManualSigmas)
-        -> SamplerCustomAdvanced
-
-设计要点
-  · 模型 / 条件 / Latent 仍是输入插槽，所以换任何模型都能直接用；
-    真正随模型变的只有「采样方案（sigma 表）」+ 步数 + CFG，做成参数即可。
-  · 「采样方案」分三类：
-      1) 自动：按 步数 + 调度器 现算 sigma（= BasicScheduler，Krea2 turbo 走这条）
-      2) 预置表：蒸馏模型专用的固定 sigma 轨迹（Qwen-Image-2.1 turbo 走这条）
-      3) 自定义：手填一串 sigma，方便接新模型而不用改代码
-  · 预置表不能靠 steps 推算 —— 那是 8 步蒸馏轨迹，改了就偏离训练分布。
-
-Author: Clever
-"""
-
-import torch
-
-import comfy.model_management
-import comfy.sample
-import comfy.samplers
-import comfy.utils
-import latent_preview
-
-
-# ==================== 采样方案 ====================
-
-SCHEME_AUTO = "自动（按步数 + 调度器）"
-SCHEME_CUSTOM = "自定义表"
-
-# 蒸馏模型专用的固定 sigma 轨迹（来自官方模板，改一个数字就会跑偏）
-# 规则：逗号分隔、严格递减、末值必须 0、N 个点 = N-1 步
-SIGMA_PRESETS = {
-    "Qwen-Image-2.1 turbo 8步":
-        "1.0, 0.978453, 0.95418, 0.926626, 0.89508, 0.845148, 0.704534, 0.414568, 0",
-    "LTX-2 视频 8步（蒸馏）":
-        "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0",
-    "LTX-2 视频 3步（低噪）":
-        "0.909375, 0.725, 0.421875, 0.0",
-}
-
-SCHEME_LIST = [SCHEME_AUTO] + list(SIGMA_PRESETS.keys()) + [SCHEME_CUSTOM]
-
-# 「自定义表」输入框的默认示例：用户点开就能看到该填什么格式
-SCHEMA_SAMPLE = SIGMA_PRESETS["Qwen-Image-2.1 turbo 8步"]
-
-
-# ==================== 工具 ====================
-
-class _RandomNoise:
-    """等价于官方的 Noise_RandomNoise。"""
-
-    def __init__(self, seed):
-        self.seed = seed
-
-    def generate_noise(self, input_latent):
-        latent_image = input_latent["samples"]
-        batch_inds = input_latent.get("batch_index")
-        return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
-
-
-def parse_sigmas(text):
-    """把 "1.0, 0.9, 0" 解析成 tensor，并做基本校验。"""
-    if text is None:
-        raise ValueError("Sigma 表为空")
-    raw = str(text).replace("\n", ",").replace(";", ",").replace("\t", ",")
-    values = []
-    for part in raw.split(","):
-        part = part.strip()
-        if not part:
-            continue
-        try:
-            values.append(float(part))
-        except ValueError:
-            raise ValueError(f"Sigma 表里有无法解析的值：{part!r}（只能是数字和逗号）")
-
-    if len(values) < 2:
-        raise ValueError("Sigma 表至少需要 2 个值（N 个点 = N-1 步）")
-    if abs(values[-1]) > 1e-6:
-        raise ValueError(f"Sigma 表末值必须是 0，现在是 {values[-1]}（否则采样收不干净）")
-    for i in range(len(values) - 1):
-        if values[i + 1] > values[i] + 1e-6:
-            raise ValueError(
-                f"Sigma 表必须严格递减，第 {i + 1} 个值 {values[i]} -> {values[i + 1]} 是上涨的"
-            )
-    return torch.FloatTensor(values)
-
-
-def truncate_sigmas(sigmas, denoise):
-    """按降噪比例从表尾截取 —— 与 BasicScheduler 的 denoise 语义一致。
-
-    img2img / 细化时从较低噪声处起步，所以砍掉表头的高噪声段。
-    """
-    if denoise >= 0.999:
-        return sigmas
-    if denoise <= 0.0:
-        raise ValueError("降噪为 0 时没有可采样的步数")
-
-    total = sigmas.shape[-1] - 1          # 总步数
-    keep = max(1, min(total, int(round(total * denoise))))
-    return sigmas[-(keep + 1):]
-
-
-# ==================== 节点 ====================
-
-class ToggleSampler:
-    """一体化采样器：噪波 + CFG引导器 + 采样器 + Sigmas => 出一个 Latent。"""
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "模型": ("MODEL",),
-                "正面条件": ("CONDITIONING",),
-                "负面条件": ("CONDITIONING",),
-                "Latent图像": ("LATENT",),
-                "采样方案": (SCHEME_LIST, {"default": SCHEME_AUTO}),
-                "采样器": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler"}),
-                "调度器": (comfy.samplers.KSampler.SCHEDULERS, {"default": "simple"}),
-                "步数": ("INT", {
-                    "default": 20, "min": 1, "max": 10000, "step": 1,
-                    "tooltip": "仅「自动」方案生效；选预置表时步数由表的点数决定",
-                }),
-                "CFG": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "降噪": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "噪波随机种": ("INT", {
-                    "default": 0, "min": 0, "max": 0xffffffffffffffff,
-                    "control_after_generate": True,
-                }),
-                "自定义Sigma表": ("STRING", {
-                    "default": SCHEMA_SAMPLE,
-                    "multiline": True,
-                    "tooltip": "仅「自定义表」方案生效。逗号分隔、严格递减、末值必须是 0",
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("LATENT", "LATENT")
-    RETURN_NAMES = ("Latent", "降噪Latent")
-    FUNCTION = "sample"
-    CATEGORY = "Toggle-Pass"
-
-    # ---------- sigma ----------
-
-    @staticmethod
-    def build_sigmas(model, scheme, scheduler, steps, denoise, custom_text):
-        if scheme == SCHEME_AUTO:
-            total = steps
-            if denoise < 1.0:
-                if denoise <= 0.0:
-                    raise ValueError("降噪为 0 时没有可采样的步数")
-                total = int(steps / denoise)
-            sigmas = comfy.samplers.calculate_sigmas(
-                model.get_model_object("model_sampling"), scheduler, total
-            ).cpu()
-            return sigmas[-(steps + 1):]
-
-        text = custom_text if scheme == SCHEME_CUSTOM else SIGMA_PRESETS.get(scheme)
-        if text is None:
-            raise ValueError(f"未知采样方案：{scheme}")
-        return truncate_sigmas(parse_sigmas(text), denoise)
-
-    # ---------- 主流程 ----------
-
-    def sample(self, 模型, 正面条件, 负面条件, Latent图像, 采样方案, 采样器,
-               调度器, 步数, CFG, 降噪, 噪波随机种, 自定义Sigma表):
-        model = 模型
-        latent = Latent图像
-        work = latent.copy()
-
-        samples = comfy.sample.fix_empty_latent_channels(
-            model,
-            latent["samples"],
-            work.get("downscale_ratio_spacial"),
-            work.get("downscale_ratio_temporal"),
-        )
-        work["samples"] = samples
-        noise_mask = work.get("noise_mask")
-
-        sigmas = self.build_sigmas(model, 采样方案, 调度器, 步数, 降噪, 自定义Sigma表)
-        if sigmas.shape[-1] < 2:
-            raise ValueError("Sigma 表换算后不足 2 个点，请加大步数或减小降噪")
-
-        guider = comfy.samplers.CFGGuider(model)
-        guider.set_conds(正面条件, 负面条件)
-        guider.set_cfg(CFG)
-
-        sampler = comfy.samplers.sampler_object(采样器)
-        noise = _RandomNoise(噪波随机种)
-
-        x0_output = {}
-        callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
-        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
-
-        out_samples = guider.sample(
-            noise.generate_noise(work),
-            samples,
-            sampler,
-            sigmas,
-            denoise_mask=noise_mask,
-            callback=callback,
-            disable_pbar=disable_pbar,
-            seed=噪波随机种,
-        )
-        out_samples = out_samples.to(comfy.model_management.intermediate_device())
-
-        out = latent.copy()
-        out.pop("downscale_ratio_spacial", None)
-        out.pop("downscale_ratio_temporal", None)
-        out["samples"] = out_samples
-
-        if "x0" in x0_output:
-            x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
-            out_denoised = latent.copy()
-            out_denoised["samples"] = x0_out
-        else:
-            out_denoised = out
-
-        return (out, out_denoised)
-
-
-NODE_CLASS_MAPPINGS = {
-    "ToggleSampler": ToggleSampler,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ToggleSampler": "采样器（一体）",
-}
+"""
+ComfyUI-Toggle-Pass: 采样器（一体）
+
+把「随机噪波 + CFG引导器 + K采样器选择 + Sigmas」四件套收进一个节点。
+等价于以下官方链路：
+    RandomNoise + CFGGuider + KSamplerSelect + (BasicScheduler | ManualSigmas)
+        -> SamplerCustomAdvanced
+
+设计要点
+  · 模型 / 条件 / Latent 仍是输入插槽，所以换任何模型都能直接用；
+    真正随模型变的只有「采样方案（sigma 表）」+ 步数 + CFG，做成参数即可。
+  · 「采样方案」分三类：
+      1) 自动：按 步数 + 调度器 现算 sigma（= BasicScheduler，Krea2 turbo 走这条）
+      2) 预置表：蒸馏模型专用的固定 sigma 轨迹（Qwen-Image-2.1 turbo 走这条）
+      3) 自定义：手填一串 sigma，方便接新模型而不用改代码
+  · 预置表不能靠 steps 推算 —— 那是 8 步蒸馏轨迹，改了就偏离训练分布。
+
+Author: Clever
+"""
+
+import torch
+
+import comfy.model_management
+import comfy.sample
+import comfy.samplers
+import comfy.utils
+import latent_preview
+
+
+# ==================== 采样方案 ====================
+
+SCHEME_AUTO = "自动（按步数 + 调度器）"
+SCHEME_CUSTOM = "自定义表"
+
+# 蒸馏模型专用的固定 sigma 轨迹（来自官方模板，改一个数字就会跑偏）
+# 规则：逗号分隔、严格递减、末值必须 0、N 个点 = N-1 步
+SIGMA_PRESETS = {
+    "Qwen-Image-2.1 turbo 8步":
+        "1.0, 0.978453, 0.95418, 0.926626, 0.89508, 0.845148, 0.704534, 0.414568, 0",
+    "LTX-2 视频 8步（蒸馏）":
+        "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0",
+    "LTX-2 视频 3步（低噪）":
+        "0.909375, 0.725, 0.421875, 0.0",
+}
+
+SCHEME_LIST = [SCHEME_AUTO] + list(SIGMA_PRESETS.keys()) + [SCHEME_CUSTOM]
+
+# 「自定义表」输入框的默认示例：用户点开就能看到该填什么格式
+SCHEMA_SAMPLE = SIGMA_PRESETS["Qwen-Image-2.1 turbo 8步"]
+
+
+# ==================== 模型预设 ====================
+# 一键把「采样方案 + 采样器 + 调度器 + 步数 + CFG」设成该模型的常用组合。
+#
+# 数值不是凭印象写的，全部来自本机 `ComfyUI/user/default/workflows/` 里
+# 实际跑通的工作流（括号内是来源文件），改之前请先跑一遍再到这里调：
+#   · Qwen-Image 2.1        25 步 euler simple CFG 1   ← qwen-iamge-2.1-Full / qwen-image-2.1-aio
+#   · Qwen-Image 2.1 turbo  8 步 固定 sigma 表          ← Qwen21_turbo_TogglePass
+#   · Krea 2 turbo          8 步 euler simple CFG 1    ← Krea2_Ostris_Edit_outfit
+#   · Krea 2 细节增强       12 步 euler simple CFG 1    ← krea2_turbo_edit
+#   · FLUX.2 klein          4 步 euler beta CFG 1      ← Flux2/Flux2图文合一、画质提升
+#   · FLUX.2 klein 高清修复 4 步 euler_ancestral simple ← Flux2/flux2-klein-高清修复
+#   · Z-Image turbo         8 步 euler_ancestral simple ← z-image/z-image-turbo
+#   · Qwen-Image Layered    9 步 euler simple CFG 2.5   ← Qwen-image/image_qwen_layered
+#   · Qwen-Image Edit 2511  4 步 euler_ancestral beta   ← Qwen-image/qwen_Image-AIO-2511-*
+#   · FIBO 2.0 turbo        4 步 euler simple CFG 1    ← FIBO2-文生图+图编辑
+#   · MOSS-Image            30 步 euler normal CFG 4    ← image_moss_image_edit
+#
+# 前端会读 /toggle_pass/sampler_presets 拿这张表（单一数据源，别在前端再抄一份）。
+# Python 本身**不套用**预设：它只是前端的一键填充，参数最终以 widget 上的值为准，
+# 这样用户选完预设再手改任何一个值都不会被后端偷偷改回去。
+PRESET_NONE = "手动（不套用）"
+
+# 下拉里只显示短名 —— 节点宽度有限，名字太长会把左边的标题「预设」整个挤掉。
+# 完整信息都在括注里，而 CFG 之类真正会变的参数选完就能在旁边看到。
+MODEL_PRESETS = {
+    PRESET_NONE: None,
+    "Qwen-Image 2.1（25步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "simple",
+        "steps": 25, "cfg": 1.0,
+    },
+    "Qwen-Image 2.1 turbo（8步·固定表）": {
+        "scheme": "Qwen-Image-2.1 turbo 8步", "sampler": "euler", "scheduler": "simple",
+        "steps": 8, "cfg": 1.0,
+    },
+    "Krea 2 turbo（8步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "simple",
+        "steps": 8, "cfg": 1.0,
+    },
+    "Krea 2 turbo・细节增强（12步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "simple",
+        "steps": 12, "cfg": 1.0,
+    },
+    "FLUX.2 klein（4步·beta）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "beta",
+        "steps": 4, "cfg": 1.0,
+    },
+    "FLUX.2 klein・高清修复（4步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler_ancestral", "scheduler": "simple",
+        "steps": 4, "cfg": 1.0,
+    },
+    "Z-Image turbo（8步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler_ancestral", "scheduler": "simple",
+        "steps": 8, "cfg": 1.0,
+    },
+    "Qwen-Image Layered（9步·CFG2.5）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "simple",
+        "steps": 9, "cfg": 2.5,
+    },
+    "Qwen-Image Edit 2511 AIO（4步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler_ancestral", "scheduler": "beta",
+        "steps": 4, "cfg": 1.0,
+    },
+    # 下面两个是 11-04 新加：FIBO 是 4 步蒸馏（steps 给大反而崩），MOSS 是 SFT 不是蒸馏，
+    # 30 步 + CFG 4 才是它训练时的配置，降到 4 步会糊成一片。
+    "FIBO 2.0 turbo（4步）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "simple",
+        "steps": 4, "cfg": 1.0,
+    },
+    "MOSS-Image（30步·CFG4）": {
+        "scheme": SCHEME_AUTO, "sampler": "euler", "scheduler": "normal",
+        "steps": 30, "cfg": 4.0,
+    },
+}
+
+# 中途有过一版用短名（"Qwen 2.1 · 25步" 这种），已存盘的工作流里可能是它。
+# 加载时由前端按这张表把旧值翻译成当前全名，否则下拉会显示成空值。
+PRESET_ALIASES = {
+    "Qwen 2.1 · 25步": "Qwen-Image 2.1（25步）",
+    "Qwen 2.1 turbo · 8步表": "Qwen-Image 2.1 turbo（8步·固定表）",
+    "Krea2 turbo · 8步": "Krea 2 turbo（8步）",
+    "Krea2 细节 · 12步": "Krea 2 turbo・细节增强（12步）",
+    "FLUX2 klein · 4步": "FLUX.2 klein（4步·beta）",
+    "FLUX2 高清 · 4步": "FLUX.2 klein・高清修复（4步）",
+    "Z-Image turbo · 8步": "Z-Image turbo（8步）",
+    "Layered · 9步": "Qwen-Image Layered（9步·CFG2.5）",
+    "2511 AIO · 4步": "Qwen-Image Edit 2511 AIO（4步）",
+}
+
+PRESET_LIST = [PRESET_NONE] + [k for k in MODEL_PRESETS if k != PRESET_NONE]
+
+# widgets_values 是「按顺序」存的，而 1.57 之前的老存档没有 widgets_values_named，
+# 只能靠位置对上号。把「模型预设」挪到最前面之后，这份「老顺序」就是给前端
+# 填 nodeData.fallbackWidgetsValuesNames 用的 —— 老存档照这张表按名字重新对应。
+LEGACY_WIDGET_ORDER = [
+    "采样方案", "采样器", "调度器", "步数", "CFG", "降噪",
+    "噪波随机种", "control_after_generate", "自定义Sigma表", "模型预设",
+]
+
+
+# ==================== 工具 ====================
+
+class _RandomNoise:
+    """等价于官方的 Noise_RandomNoise。"""
+
+    def __init__(self, seed):
+        self.seed = seed
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        batch_inds = input_latent.get("batch_index")
+        return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
+
+
+def parse_sigmas(text):
+    """把 "1.0, 0.9, 0" 解析成 tensor，并做基本校验。"""
+    if text is None:
+        raise ValueError("Sigma 表为空")
+    raw = str(text).replace("\n", ",").replace(";", ",").replace("\t", ",")
+    values = []
+    for part in raw.split(","):
+        part = part.strip()
+        if not part:
+            continue
+        try:
+            values.append(float(part))
+        except ValueError:
+            raise ValueError(f"Sigma 表里有无法解析的值：{part!r}（只能是数字和逗号）")
+
+    if len(values) < 2:
+        raise ValueError("Sigma 表至少需要 2 个值（N 个点 = N-1 步）")
+    if abs(values[-1]) > 1e-6:
+        raise ValueError(f"Sigma 表末值必须是 0，现在是 {values[-1]}（否则采样收不干净）")
+    for i in range(len(values) - 1):
+        if values[i + 1] > values[i] + 1e-6:
+            raise ValueError(
+                f"Sigma 表必须严格递减，第 {i + 1} 个值 {values[i]} -> {values[i + 1]} 是上涨的"
+            )
+    return torch.FloatTensor(values)
+
+
+def truncate_sigmas(sigmas, denoise):
+    """按降噪比例从表尾截取 —— 与 BasicScheduler 的 denoise 语义一致。
+
+    img2img / 细化时从较低噪声处起步，所以砍掉表头的高噪声段。
+    """
+    if denoise >= 0.999:
+        return sigmas
+    if denoise <= 0.0:
+        raise ValueError("降噪为 0 时没有可采样的步数")
+
+    total = sigmas.shape[-1] - 1          # 总步数
+    keep = max(1, min(total, int(round(total * denoise))))
+    return sigmas[-(keep + 1):]
+
+
+# ==================== 节点 ====================
+
+class ToggleSampler:
+    """一体化采样器：噪波 + CFG引导器 + 采样器 + Sigmas => 出一个 Latent。"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "模型": ("MODEL",),
+                "正面条件": ("CONDITIONING",),
+                "负面条件": ("CONDITIONING",),
+                "Latent图像": ("LATENT",),
+                # 「模型预设」放在所有参数控件的最前面：先选模型、再微调，才是真实顺序。
+                # 它只是前端的一键填充按钮，后端不参与计算。
+                "模型预设": (PRESET_LIST, {
+                    "default": PRESET_NONE,
+                    "tooltip": "选一个模型，自动把下面的采样方案/采样器/调度器/步数/CFG 填好；"
+                               "之后手动改其中任何一个会自动回到「手动」",
+                }),
+                "采样方案": (SCHEME_LIST, {"default": SCHEME_AUTO}),
+                "采样器": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler"}),
+                "调度器": (comfy.samplers.KSampler.SCHEDULERS, {"default": "simple"}),
+                "步数": ("INT", {
+                    "default": 20, "min": 1, "max": 10000, "step": 1,
+                    "tooltip": "仅「自动」方案生效；选预置表时步数由表的点数决定",
+                }),
+                "CFG": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "降噪": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "噪波随机种": ("INT", {
+                    "default": 0, "min": 0, "max": 0xffffffffffffffff,
+                    "control_after_generate": True,
+                }),
+                "自定义Sigma表": ("STRING", {
+                    "default": SCHEMA_SAMPLE,
+                    "multiline": True,
+                    "tooltip": "仅「自定义表」方案生效。逗号分隔、严格递减、末值必须是 0",
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("Latent", "降噪Latent")
+    FUNCTION = "sample"
+    CATEGORY = "Toggle-Pass"
+
+    # ---------- sigma ----------
+
+    @staticmethod
+    def build_sigmas(model, scheme, scheduler, steps, denoise, custom_text):
+        if scheme == SCHEME_AUTO:
+            total = steps
+            if denoise < 1.0:
+                if denoise <= 0.0:
+                    raise ValueError("降噪为 0 时没有可采样的步数")
+                total = int(steps / denoise)
+            sigmas = comfy.samplers.calculate_sigmas(
+                model.get_model_object("model_sampling"), scheduler, total
+            ).cpu()
+            return sigmas[-(steps + 1):]
+
+        text = custom_text if scheme == SCHEME_CUSTOM else SIGMA_PRESETS.get(scheme)
+        if text is None:
+            raise ValueError(f"未知采样方案：{scheme}")
+        return truncate_sigmas(parse_sigmas(text), denoise)
+
+    # ---------- 主流程 ----------
+
+    def sample(self, 模型, 正面条件, 负面条件, Latent图像, 采样方案, 采样器,
+               调度器, 步数, CFG, 降噪, 噪波随机种, 自定义Sigma表,
+               模型预设=PRESET_NONE):
+        # ⚠️ 这里的参数顺序和 INPUT_TYPES 不一样是故意的：`模型预设` 在 INPUT_TYPES 里排第一
+        #    （决定前端控件的显示顺序），但这里必须留在末尾 —— Python 不允许有默认值的参数
+        #    排在无默认值的前面。ComfyUI 用关键字传参（getattr(obj, FUNCTION)(**inputs)），
+        #    所以顺序差异不影响调用。
+        # 模型预设只是个前端快捷填充：选中时前端已经把下面几个参数改好了，
+        # 这里不能再套一次 —— 否则用户选完预设又手改了步数，会被后端悄悄改回去。
+        model = 模型
+        latent = Latent图像
+        work = latent.copy()
+
+        samples = comfy.sample.fix_empty_latent_channels(
+            model,
+            latent["samples"],
+            work.get("downscale_ratio_spacial"),
+            work.get("downscale_ratio_temporal"),
+        )
+        work["samples"] = samples
+        noise_mask = work.get("noise_mask")
+
+        sigmas = self.build_sigmas(model, 采样方案, 调度器, 步数, 降噪, 自定义Sigma表)
+        if sigmas.shape[-1] < 2:
+            raise ValueError("Sigma 表换算后不足 2 个点，请加大步数或减小降噪")
+
+        guider = comfy.samplers.CFGGuider(model)
+        guider.set_conds(正面条件, 负面条件)
+        guider.set_cfg(CFG)
+
+        sampler = comfy.samplers.sampler_object(采样器)
+        noise = _RandomNoise(噪波随机种)
+
+        x0_output = {}
+        callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+        out_samples = guider.sample(
+            noise.generate_noise(work),
+            samples,
+            sampler,
+            sigmas,
+            denoise_mask=noise_mask,
+            callback=callback,
+            disable_pbar=disable_pbar,
+            seed=噪波随机种,
+        )
+        out_samples = out_samples.to(comfy.model_management.intermediate_device())
+
+        out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
+        out.pop("downscale_ratio_temporal", None)
+        out["samples"] = out_samples
+
+        if "x0" in x0_output:
+            x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
+            out_denoised = latent.copy()
+            out_denoised["samples"] = x0_out
+        else:
+            out_denoised = out
+
+        return (out, out_denoised)
+
+
+NODE_CLASS_MAPPINGS = {
+    "ToggleSampler": ToggleSampler,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ToggleSampler": "采样器（一体）",
+}
+
+
+# ---- 给前端用的预设表接口 ----
+def _register_routes():
+    """把 MODEL_PRESETS 原样吐给前端，避免 JS 里再抄一份导致两边不同步。"""
+    try:
+        from aiohttp import web
+        from server import PromptServer
+
+        instance = getattr(PromptServer, "instance", None)
+        routes = getattr(instance, "routes", None) if instance is not None else None
+        if routes is None:
+            return
+
+        @routes.get("/toggle_pass/sampler_presets")
+        async def _sampler_presets(request):
+            return web.json_response({
+                "presets": MODEL_PRESETS,
+                "list": PRESET_LIST,
+                "default": PRESET_NONE,
+                "schemes": SCHEME_LIST,
+                # 老存档里存的是长名，前端加载时按这张表换成新名
+                "aliases": PRESET_ALIASES,
+                # 1.57 之前的老存档只有按位置的 widgets_values，用这张表按名字重新对应
+                "legacyWidgetOrder": LEGACY_WIDGET_ORDER,
+            })
+    except Exception:
+        # 独立导入 / 无 server 模块时静默跳过，不影响节点本身
+        return
+
+
+_register_routes()
diff --git a/unified_loader.py b/unified_loader.py
index f280995..ce45b04 100644
--- a/unified_loader.py
+++ b/unified_loader.py
@@ -35,8 +35,55 @@ CLIP_TYPE_MAP = {
     # Krea-2 专用：TE 是 Qwen3-VL-4B（12 层 tap），与 Qwen-Image 的 8B 不是一回事，
     # 选错会建出通用 qwen3vl，conditioning 宽度对不上
     "krea2": comfy.sd.CLIPType.KREA2,
+    # ★ 下面两个和 krea2 共用 Qwen3-VL-4B 的权重文件，但**取层方式完全不同**，
+    #   选错不是效果差，而是 conditioning 维度直接对不上（跑不起来或全废）：
+    #     krea2      → [2,5,8,...,35] 每 3 层取满全程 → 12 层 × 2560
+    #     fibo2      → [5,7,9,...,35] 每 2 层只截中段 → 6 组 × 7680
+    #     moss_image → TE 是 Qwen3.5-**9B**（不是 VL-4B），无 tap，取 last hidden state
+    "fibo2": comfy.sd.CLIPType.FIBO2,
+    "moss_image": comfy.sd.CLIPType.MOSS_IMAGE,
 }
 
+# ↑ 以上是「常用 + 有专门说明」的项，顺序不能动（widgets_values 按位置序列化）。
+# ↓ 以下是「兜底全量」：把 ComfyUI 当前支持、但上面没登记的类型补齐，
+#   免得遇到少见模型时下拉里找不到、只能去翻 comfy/sd.py。
+#   实现要点：用 getattr 取枚举 —— 旧版 ComfyUI 少了某个成员时只是跳过该项，
+#   不会在 import 阶段抛 AttributeError 把整个插件带崩。
+#   全部**追加在末尾**，已有项的位置不变，老工作流照旧。
+_CLIP_TYPE_EXTRA = [
+    ("stable_audio", "STABLE_AUDIO"),
+    ("hunyuan_dit", "HUNYUAN_DIT"),
+    ("hunyuan_video", "HUNYUAN_VIDEO"),
+    ("hunyuan_video_15", "HUNYUAN_VIDEO_15"),
+    ("hunyuan_image", "HUNYUAN_IMAGE"),
+    ("pixart", "PIXART"),
+    ("cosmos", "COSMOS"),
+    ("lumina2", "LUMINA2"),
+    ("chroma", "CHROMA"),
+    ("ace", "ACE"),
+    ("omnigen2", "OMNIGEN2"),
+    ("ovis", "OVIS"),
+    ("kandinsky5", "KANDINSKY5"),
+    ("kandinsky5_image", "KANDINSKY5_IMAGE"),
+    ("newbie", "NEWBIE"),
+    ("longcat_image", "LONGCAT_IMAGE"),
+    ("cogvideox", "COGVIDEOX"),
+    ("lens", "LENS"),
+    ("pixeldit", "PIXELDIT"),
+    ("ideogram4", "IDEOGRAM4"),
+    ("boogu", "BOOGU"),
+    ("joyimage", "JOYIMAGE"),
+    ("mage", "MAGE"),
+    ("minimax", "MINIMAX"),
+    ("yue2", "YUE2"),
+    ("flux3", "FLUX3"),
+]
+for _key, _enum_name in _CLIP_TYPE_EXTRA:
+    _v = getattr(comfy.sd.CLIPType, _enum_name, None)
+    if _v is not None and _key not in CLIP_TYPE_MAP:
+        CLIP_TYPE_MAP[_key] = _v
+del _key, _enum_name, _v
+
 # auto 检测用：supported_models 检测出的模型类 -> 对应的 CLIPType 枚举。
 # 注意 comfy.sd.load_clip 的 clip_type 参数是与 CLIPType 枚举比较的
 # （见 comfy/sd.py load_text_encoder_state_dicts），所以这里必须返回枚举，
@@ -46,8 +93,22 @@ _MODEL_CLASS_TO_CLIPTYPE = {
     "QwenImage21": comfy.sd.CLIPType.QWEN_IMAGE,
     "QwenImage": comfy.sd.CLIPType.QWEN_IMAGE,
     "Krea2": comfy.sd.CLIPType.KREA2,
+    "Fibo2": comfy.sd.CLIPType.FIBO2,
+    "MOSSImage": comfy.sd.CLIPType.MOSS_IMAGE,
     "Flux2": comfy.sd.CLIPType.FLUX2,
     "Flux": comfy.sd.CLIPType.FLUX,
+    # Z-Image（Lumina2 / NextDiT）的 TE 是 qwen_3_4b，ComfyUI 按「TE 类型」自动选
+    # comfy.text_encoders.z_image.te（见 comfy/sd.py 的 TEModel.QWEN3_4B 分支：
+    # 只要 clip_type 不是 flux/flux2 就走 z_image.te）。这里登记 stable_diffusion
+    # 只为让 auto 检测有个确定值、不打印「未登记映射」的告警。
+    # ⚠️ 手工选的时候**别选 flux / flux2**：2026-11-06 实测，同样 qwen_3_4b + 同样 unet，
+    #    flux → 文本编码器建成 Flux2TEModel_（Flux 系的 Klein 编码器），
+    #    stable_diffusion / qwen_image / auto → 都是 ZImageTEModel_（正确）。
+    #    建错类不报错，提示词编码全变，出图「能跑但不像 prompt」。
+    "ZImage": comfy.sd.CLIPType.STABLE_DIFFUSION,
+    # 实测 model_detection 对 Z-Image 权重返回的类名是父类 Lumina2（不是 ZImage），
+    # 所以这里两条都要登记，否则 auto 会打「未登记映射」并静默回落。
+    "Lumina2": comfy.sd.CLIPType.STABLE_DIFFUSION,
     "SD3": comfy.sd.CLIPType.SD3,
     "SDXL": comfy.sd.CLIPType.STABLE_DIFFUSION,
     "SD15": comfy.sd.CLIPType.STABLE_DIFFUSION,
@@ -61,6 +122,56 @@ _MODEL_CLASS_TO_CLIPTYPE = {
     "HiDream": comfy.sd.CLIPType.HIDREAM,
 }
 
+# 只登记「clip_type 真的改变结果」、且继承链救不回来的类。
+# 判断依据是 comfy/sd.py 的 load_text_encoder_state_dicts：clip_type 只在
+# **同一个 TE 权重能走多条路**的分支里起作用，其余情况它被忽略 ——
+# 所以不必给 108 个类都登记，只补歧义区。
+# 典型歧义：Qwen3-VL-8B（QWEN_IMAGE / IDEOGRAM4 / BOOGU / JOYIMAGE）、
+#           Qwen3-VL-4B（KREA2 / FIBO2 / FLUX3 / MAGE / MINIMAX / FLUX）、
+#           T5-XXL（SD3 / LTXV / WAN / CHROMA / PIXART / COGVIDEOX / MOCHI）、
+#           CLIP-G（SD3 / STABLE_CASCADE / HIDREAM）。
+# 用 getattr：旧版 ComfyUI 少成员时跳过该项，不会在 import 阶段把插件带崩。
+_EXTRA_MODEL_CLASS_TO_CLIPTYPE = [
+    ("Flux3", "FLUX3"),
+    ("HunyuanVideo15", "HUNYUAN_VIDEO_15"),
+    ("HunyuanVideo15_SR_Distilled", "HUNYUAN_VIDEO_15"),
+    ("HunyuanImage21", "HUNYUAN_IMAGE"),
+    ("HunyuanImage21Refiner", "HUNYUAN_VIDEO"),
+    ("HunyuanDiT", "HUNYUAN_DIT"),
+    ("HunyuanDiT1", "HUNYUAN_DIT"),
+    ("Chroma", "CHROMA"),
+    ("ChromaRadiance", "CHROMA"),
+    ("CogVideoX_T2V", "COGVIDEOX"),
+    ("CogVideoX_I2V", "COGVIDEOX"),
+    ("CogVideoX_Inpaint", "COGVIDEOX"),
+    ("GenmoMochi", "MOCHI"),
+    ("Ideogram4", "IDEOGRAM4"),
+    ("Boogu", "BOOGU"),
+    ("JoyImage", "JOYIMAGE"),
+    ("MageFlow", "MAGE"),
+    ("MiniMaxH3", "MINIMAX"),
+    ("LongCatImage", "LONGCAT_IMAGE"),
+    ("PixelDiTT2I", "PIXELDIT"),
+    ("PiD", "PIXELDIT"),
+    ("PixArtAlpha", "PIXART"),
+    ("PixArtSigma", "PIXART"),
+    ("Stable_Cascade_B", "STABLE_CASCADE"),
+    ("Stable_Cascade_C", "STABLE_CASCADE"),
+    ("SDXLRefiner", "STABLE_DIFFUSION"),
+]
+for _mc, _enum_name in _EXTRA_MODEL_CLASS_TO_CLIPTYPE:
+    _v = getattr(comfy.sd.CLIPType, _enum_name, None)
+    if _v is not None and _mc not in _MODEL_CLASS_TO_CLIPTYPE:
+        _MODEL_CLASS_TO_CLIPTYPE[_mc] = _v
+del _mc, _enum_name, _v
+
+# WAN 有 17 个类（WAN21_T2V / WAN22_S2V / WAN_Animate2 …），逐个登记会随上游发版漂移。
+# 它们都是 T5-XXL，且 clip_type 不选 wan 就会掉进 else 建成 **Mochi** 的 TE
+# （comfy/sd.py: TEModel.T5_XXL 分支的 else 就是 mochi_te），所以按前缀兜住。
+_CLIP_TYPE_PREFIX_RULES = [
+    ("WAN", comfy.sd.CLIPType.WAN),
+]
+
 # 模型类 -> 文本编码器类名前缀（用于分体模式的 CLIP 校验）。
 # 只登记「实测确认过类名」的几类；没登记的模型不做校验，宁可漏报也不误报。
 # ⚠️ 这不是等价关系：QwenImage(v1, Qwen2.5-VL) 与 QwenImage21(Qwen3-VL-8B) 的 TE 类
@@ -69,11 +180,44 @@ _MODEL_CLASS_TO_TE_PREFIX = {
     "QwenImage21": "QwenImage21TEModel",
     "QwenImage": "QwenImageTEModel",
     "Krea2": "Krea2TEModel",
+    # fibo2 用 Qwen3-VL-4B 的 15 层中段，类名 Fibo2TEModel_；
+    # moss_image 用 Qwen3.5-9B，类名 MOSSImageTEModel_ —— 两者都不能用 Krea2TEModel_ 代替
+    "Fibo2": "Fibo2TEModel",
+    "MOSSImage": "MOSSImageTEModel",
     "Flux2": "Flux2TEModel",
     "Flux": "FluxTEModel",
+    "ZImage": "ZImageTEModel",
 }
 
 
+def _resolve_clip_type(model):
+    """按「已加载的模型对象」推导 CLIPType（auto 用）。返回 (枚举|None, 命中方式)。
+
+    三层查找：精确表 → 继承链 → 前缀规则。
+    之所以不用一张大表覆盖全部 108 个 supported_models 类：comfy/sd.py 里
+    clip_type 只在「同一个 TE 权重能走多条路」的分支才起作用（T5-XXL 的
+    SD3/LTXV/WAN/CHROMA/PIXART/COGVIDEOX/MOCHI、CLIP-G 的 SD3/CASCADE/HIDREAM、
+    Qwen3-VL 的 QWEN_IMAGE/KREA2/FIBO2/…），其余模型它根本不参与判断。
+    所以精确表只登记歧义项，子类靠继承链（FluxSchnell→Flux）、变体靠前缀
+    （WAN21_I2V→WAN*）兜住，上游加新类也不会突然全错。
+    """
+    # ⚠️ load_unet 返回的是 ModelPatcher，真正的模型对象在 .model 里。
+    #    直接 type(model) 会永远拿到 ModelPatcher —— 实测会让所有模型都推不出来。
+    inner = getattr(model, "model", None) or model
+    cls = type(inner)
+    ct = _MODEL_CLASS_TO_CLIPTYPE.get(cls.__name__)
+    if ct is not None:
+        return ct, f"精确匹配 {cls.__name__}"
+    for base in cls.__mro__[1:]:
+        ct = _MODEL_CLASS_TO_CLIPTYPE.get(base.__name__)
+        if ct is not None:
+            return ct, f"继承链 {cls.__name__} → {base.__name__}"
+    for prefix, ct in _CLIP_TYPE_PREFIX_RULES:
+        if cls.__name__.startswith(prefix):
+            return ct, f"前缀规则 {cls.__name__} → {prefix}*"
+    return None, ""
+
+
 def _detect_clip_type(unet_name):
     """【已弃用，保留备查】从 UNet 文件猜 CLIPType 枚举。
 
@@ -229,20 +373,43 @@ def _te_class_name(clip):
         return None
 
 
-def _check_clip_match(model_cls, clip):
+def _expected_te_from_model(model):
+    """向模型自己询问「我该配哪个文本编码器」，拿不到返回 None。
+
+    comfy 的模型 config 自带 clip_target()，返回的 (tokenizer, clip) 就是这套
+    权重官方配对的**正确答案**，与我们这张手工表无关。实测 supported_models
+    里 106/108 个类能这样拿到类名（Flux2 / HiDream 等返回 None，退回手工表）。
+    只用作「手工表没登记时」的补充，不覆盖已实测过的条目。
+    """
+    try:
+        cfg = getattr(getattr(model, "model", None), "model_config", None)
+        if cfg is None:
+            return None
+        clip_cls = getattr(cfg.clip_target(), "clip", None)
+        return clip_cls.__name__ if isinstance(clip_cls, type) else None
+    except Exception:
+        return None
+
+
+def _check_clip_match(model_cls, clip, model=None):
     """校验分体模式下 CLIP 文件是否与模型配套。
 
     背景：换 CLIP 文件会改变 ComfyUI 建出的文本编码器类，日志实测出现过
     QwenImage21TEModel_(6018MB，正确) / QwenImageTEModel_(7910MB，v1) /
     Flux2TEModel_(8262MB，完全不对)。建错了 conditioning 就是废的，
     表现却是「跑得通但什么都不生效」，极难自查 —— 所以这里主动报出来。
-    返回 {"te":..., "expect":..., "ok": True/False/None}（None = 该模型不做校验）。
+    返回 {"te":..., "expect":..., "ok": True/False/None, "src":...}
+    （ok=None = 该模型不做校验）。
     """
     te = _te_class_name(clip)
     expect = _MODEL_CLASS_TO_TE_PREFIX.get(model_cls)
+    src = "手工表"
+    if expect is None and model is not None:
+        expect = _expected_te_from_model(model)
+        src = "模型自述"
     if not te or not expect:
-        return {"te": te, "expect": expect, "ok": None}
-    return {"te": te, "expect": expect, "ok": te.startswith(expect)}
+        return {"te": te, "expect": expect, "ok": None, "src": src}
+    return {"te": te, "expect": expect, "ok": te.startswith(expect), "src": src}
 
 
 # 模型类 -> 换 VAE 的具体建议。只登记实测确认过的场景，避免给出误导性推荐。
@@ -251,6 +418,16 @@ _VAE_HINT = {
                    "qwen_image_vae.safetensors 是 Qwen-Image 1.0 的（16 通道 / 3 维），两者不通用。",
     "Krea2": "Krea-2 用的是 qwen_image_vae.safetensors（16 通道 / 3 维 / 8x）；"
              "别选 qwen_image_2.1_vae_bf16（64 通道 / 2 维），那个是 Qwen-Image-2.1 的。",
+    # Fibo2 / MOSS-Image 共用 Flux2 的 latent（32ch + bn -> 128ch / 16x），
+    # VAE 必须是 flux2-vae 系列，换成 qwen/其它系列解码就会炸
+    "Fibo2": "FIBO 2.0 用的是 flux2-vae.safetensors（128 通道 / 16x）；"
+             "别选 qwen_image_vae 或 qwen_image_2.1_vae_bf16，通道数完全不同。",
+    "MOSSImage": "MOSS-Image 同样走 Flux2 latent，用 flux2-vae_small.safetensors"
+                 "（官方编辑模板）或 flux2-vae.safetensors 都可以；别选 qwen 系列 VAE。",
+    # Z-Image 的 latent_format = Flux（16 通道 / 8x），VAE 就是 Flux 的 ae.safetensors。
+    # qwen 系列 VAE 通道数碰巧也是 16，光看通道校验不出来，但两者不是同一套，别混用。
+    "ZImage": "Z-Image 的 latent_format 是 Flux（16 通道 / 8×），VAE 用 ae.safetensors；"
+              "qwen_image_vae 通道数碰巧也是 16，校验查不出来，但别拿它顶替。",
 }
 
 
@@ -397,7 +574,7 @@ class ToggleUnifiedLoader:
                   "（实测与官方节点逐像素一致），只是效果会比全精度底座更『糊』一些；"
                   "风格特别细腻的 LoRA 可能不易看出，想要更干净可换 bf16/fp16 一体包。")
         # 一体包也会自动猜 CLIP，同样校验一下（猜错时 conditioning 会整体失效）
-        self._clip_check = _check_clip_match(type(getattr(model, "model", None)).__name__, clip)
+        self._clip_check = _check_clip_match(type(getattr(model, "model", None)).__name__, clip, model)
         if self._clip_check.get("ok") is False:
             print(f"[ToggleUnifiedLoader] ⚠️ CLIP 校验：文本编码器建成 {self._clip_check['te']}"
                   f"（预期 {self._clip_check['expect']}），该 checkpoint 的 CLIP 可能与模型不配套")
@@ -423,13 +600,17 @@ class ToggleUnifiedLoader:
             clip_type_enum = CLIP_TYPE_MAP[clip_type]
             print(f"[ToggleUnifiedLoader] CLIP 类型: 手动指定 {clip_type}")
         else:
-            clip_type_enum = _MODEL_CLASS_TO_CLIPTYPE.get(model_cls)
+            clip_type_enum, how = _resolve_clip_type(model)
             if clip_type_enum is None:
-                print(f"[ToggleUnifiedLoader] 模型类 {model_cls} 未登记映射，"
-                      f"CLIP 类型默认使用 STABLE_DIFFUSION")
+                # 兜底。多数情况下无害：TE 权重唯一确定时 ComfyUI 根本不看 clip_type
+                # （这类模型占 supported_models 的三分之一，如 Anima/AuraFlow/Cosmos/Lens）。
+                # 真正会因此建错 TE 的只有 T5-XXL / Qwen3-VL / CLIP-G 那几族，
+                # 而它们都已在前两层命中；万一是上游新加的类，由下一步的 TE 校验兜住报出来。
+                print(f"[ToggleUnifiedLoader] 自动检测：模型类 {model_cls} 没有专门登记，"
+                      f"使用通用 CLIP 类型 STABLE_DIFFUSION（多数模型下这个值不参与判断）")
                 clip_type_enum = comfy.sd.CLIPType.STABLE_DIFFUSION
             else:
-                print(f"[ToggleUnifiedLoader] 自动检测 CLIP 类型: {model_cls} -> {clip_type_enum}")
+                print(f"[ToggleUnifiedLoader] 自动检测 CLIP 类型: {how} → {clip_type_enum.name}")
 
         # 3. 加载 CLIP
         clip_path = folder_paths.get_full_path("clip", clip_name)
@@ -437,11 +618,11 @@ class ToggleUnifiedLoader:
 
         # 3.1 校验文本编码器是否与模型配套。
         #     选错 CLIP 文件时 ComfyUI 会静静建出另一个 TE 类，跑得通但 conditioning 全废。
-        self._clip_check = _check_clip_match(model_cls, clip)
+        self._clip_check = _check_clip_match(model_cls, clip, model)
         if self._clip_check.get("ok") is False:
             print(f"[ToggleUnifiedLoader] ⚠️ CLIP 校验失败：模型是 {model_cls}，"
                   f"但文本编码器建成了 {self._clip_check['te']}"
-                  f"（预期 {self._clip_check['expect']}）。"
+                  f"（预期 {self._clip_check['expect']}，依据 {self._clip_check.get('src')}）。"
                   f"这个 CLIP 文件与模型不配套 → 提示词会被错误编码（跑得通但什么都不生效）。"
                   f"请更换 CLIP 文件，或检查『CLIP 类型』是否选对。")
         elif self._clip_check.get("te"):