Version 3.1.0

API: print filename with stack traces when there is an error during inspection
Support transmitting partial tiles/crops of the canvas in Krita workflows
2026-01-05 10:18:20 +01:00 · 2026-01-05 10:16:54 +01:00 · 2025-12-30 23:47:29 +01:00 · 2025-12-29 21:20:57 +01:00 · 2025-12-29 19:35:06 +01:00 · 2025-12-22 11:25:09 +01:00
21 changed files with 3923 additions and 355 deletions
@@ -11,11 +11,12 @@ jobs:
  publish-node:
    name: Publish Custom Node to registry
    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'Acly' }}
    steps:
      - name: Check out code
        uses: actions/checkout@v4
      - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
        with:
          ## Add your own personal access token to your Github Repository secrets and reference it here.
          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
@@ -1,4 +1,6 @@
 .vscode
 .env
 .dev
-__pycache__
+__pycache__
+
+safetychecker/*.safetensors
@@ -2,12 +2,20 @@

 Provides nodes and API geared towards using ComfyUI as a backend for external tools.

-## Nodes for sending and receiving images
+* <a href="#images">Sending and receiving images</a>
+* <a href="#regions">Regions (Attention Masking)
+* <a href="#tiles">Tiled image processing
+* <a href="#misc">Miscellanious nodes
+* <a href="#api">Http API extensions (Model inspection)
+* <a href="#installation">⭳ Installation</a>
+
+
+## <a id="images" href="#toc">Sending and receiving images</a>

 ComfyUI exchanges images via the filesystem. This requires a
-multi-step process (upload images, prompt, download images), is rather
-inefficient, and invites a whole class of potential issues. It's also unclear
-at which point those images will get cleaned up if ComfyUI is used
+multi-step process (upload images, prompt, download images), which
+invites a whole class of potential issues you might not want to deal with.
+It's also unclear at which point those images will get cleaned up if ComfyUI is used
 via external tools.

 ### Load Image (Base64)
@@ -25,7 +33,7 @@ Loads a mask (single channel) from a PNG embedded into the prompt as base64 stri
 ### Send Image (WebSocket)

 Sends an output image over the client WebSocket connection as PNG binary data.
-* Inputs: the image (RGB or RGBA)
+* Inputs: the image (RGB or RGBA), supports batches

 This will first send one binary message for each image in the batch via WebSocket:
 ```
@@ -36,9 +44,74 @@ That is two 32-bit integers (big endian) with values 1 and 2 followed by the PNG
 {'type': 'executed', 'data': {'node': '<node ID>', 'output': {'images': [{'source': 'websocket', 'content-type': 'image/png', 'type': 'output'}, ...]}, 'prompt_id': '<prompt ID>}}
 ```

-## Nodes for working on regions
+### Load Image from Cache

-When integrating ComfyUI into tools which use layers and compose them on the fly, it is useful to only receive relevant masked regions.
+Loads an image or mask that has been uploaded previously into the workflow.
+Uploaded images are temporarily stored in RAM rather than written to disk. This
+method has less overhead compared to embedding images as base64 into the prompt,
+but is more complex to implement.
+* Inputs: id of an image that was uploaded previously
+* Outputs: image (RGB) and mask (A of RGBA input, or first channel if no alpha present).
+
+To upload an image, upload the _bytes_ of a PNG via a HTTP PUT request to
+`/api/etn/image/{id}`. JPEG or other formats also work. Choose any `id` which
+does not clash with other images you upload, and reference it in the node. The
+request returns `201` if the image was uploaded and `200` if it was already
+cached.
+
+### Save Image to Cache
+
+Stores an output image in RAM temporarily and allows retrieval over HTTP.
+This is typically faster than WebSocket, especially for large images.
+* Inputs: the image (RGB or RGBA). Batches are supported.
+
+This node will send a JSON message over WebSocket when an image is ready:
+```json
+{
+  "type": "executed",
+  "data": {
+    "node": "<node ID>",
+    "output": {
+      "images": [
+        {"source": "http", "id": "<image ID>", "content-type": "image/png", "type": "output"}
+      ]
+    },
+    "prompt_id": "prompt ID"
+  }
+}
+```
+
+To download the images, send a HTTP GET request to `/api/etn/image/{id}` with
+the image IDs from the message. Images will be cached for a few minutes.
+
+
+## <a id="regions" href="#toc">Regions</a>
+
+These nodes implement attention masking for arbitrary number of image regions. Text prompts only apply to the masked area.
+In contrast to condition masking, this method is less "forceful", but leads to more natural image compositions.
+
+![Regions Attention Mask](workflows/region_attention_mask.png)
+[Workflow: region_attention_mask.json](workflows/region_attention_mask.json)
+
+### Background Region
+
+This node starts a list of regions. It takes a prompt, but no mask. The prompt is assigned to all image areas which are _not_
+covered by another region mask in the list.
+
+### Define Region
+
+Appends a new region to a region list (or starts a new list). Takes a prompt, and mask which defines the area in the image
+the prompt will apply to. Masks must be the same size as the image _or_ the latent (which is factor 8 smaller).
+
+### List Region Masks
+
+This node takes a list of regions and outputs all their masks. It can be useful for inspection, debugging or to reuse the
+computed background mask.
+
+### Regions Attention Mask
+
+Patches the model to use the provided list of regions. This replaces the positive text conditioning which is provided
+to the sampler. It's still possible to pass ControlNet and other conditioning to the sampler.

 ### Apply Mask to Image

@@ -46,30 +119,170 @@ Copies a mask into the alpha channel of an image.
 * Inputs: image and mask
 * Outputs: RGBA image with mask used as transparency

-## API for model inspection

-There are various types of models that can be loaded as checkpoint, LoRA, ControlNet, etc. which cannot be used interchangeably. The following API helps to categorize and filter them.
+## <a id="tiles" href="#toc">Tiles</a>

-### /etn/model_info
+Splitting an image into tiles to be processed individually is a useful method to speed up
+diffusion and save VRAM. There are various nodes out there which provide a fixed pipeline.
+In contrast, the following nodes only provide a way to split an image into tiles and merge
+it back together. With tools and scripts it is feasible to generate individual workflows
+for each tile. This allows maximum flexibility (different prompts, regions, control, etc.).

-Lists available models with additional classification info.
-* Paramters: _none_
-* Output: list of model files
-    ```
-    {
-        "checkpoint_file.safetensors": {
-            "base_model": "sd15"|"sd20"|"sd21"|"sdxl"|"ssd1b"|"svd"|"cascade-b"|"cascade-c",
-            "is_inpaint": true|false,
-            "is_refiner": true|false
-        },
-        ...
-    }
-    ```
-    The entry is `{"base_model": "unknown"}` for models which are not in safetensors format or do not match any of the known base models.
+![Image tiles](workflows/image_tiles.png)
+[Workflow: image_tiles.json](workflows/image_tiles.json)

-_Note: currently only supports checkpoints. May add other models in the future._
+### Create Tile Layout

-## Installation
+This node defines the tiling parameters:
+* **min_tile_size**: Minimum resolution of each tile in pixels. Tiles may be larger to fit the image size evenly.
+* **padding**: Padding around each tile in pixels. Overlaps with neighbour tiles. There is no padding at the image borders.
+* **blending**: The part of the padding area which is used for smooth blending to avoid seams. Affects masks which are generated from this layout.
+
+The number of tiles is: `image_size // (min_tile_size + 2 * padding)`
+
+### Extract Image Tile
+
+Splits out part of an image. Tile indices range from 0 to number of tiles and are column-major
+(tile 1 is usually below tile 0).
+
+### Extract Mask Tile
+
+Same as "Extract Image Tile" but for masks.
+
+### Merge Image Tile
+
+Merges a tile into a full image, usually after sampling. Uses a smooth transition overlap
+between neighbouring tiles depending on padding and blending values.
+
+### Generate Tile Mask
+
+Creates a coverage mask for a certain tile. The size of the mask matches the image tile size.
+The image area will be white (1) and the padding area black (0), with a smooth transition
+depending on the chosen blend size. 
+
+This mask is used internally by "Merge Image Tile", but it can also be useful as input for "Set Latent Noise Mask" in upscale workflows.
+
+
+## <a id="misc" href="#toc">Miscellaneous Nodes</a>
+
+<a id="node-translate"></a>
+### Translate Text
+
+Node which translates a string into English. The language to translate from is indicated with a
+_language directive_ of the form `lang:xx` where xx is a 2-letter language code. Multiple
+directives are allowed and change language for any text that comes after, until the next
+directive. `lang:en` (the default) passes through text fragments untouched. Useful
+for keywords, tags and such.
+
+Examples:
+| Input | Output |
+|:-|:-|
+| lang:de eine modische handtasche aus grünem kunstleder | a fashionable handbag made of green suede |
+| origami paperwork, lang:zh 狐狸和鹤, lang:en mountain view | origami paperwork, Fox and crane, mountain view |
+
+Translation happens entirely local, powered by [argosopentech/argos-translate](https://github.com/argosopentech/argos-translate):
+* Install with `pip install argostranslate` or `pip install -r requirements.txt`
+* Models are automatically downloaded on first use.
+
+There is also a [translation API](#api-translation) for immediate feedback in tool UI.
+
+### NSFW Filter
+
+Checks images for NSFW content using [Safety-Checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker). Images which don't pass the check are blurred to
+obfuscate contents. Model is downloaded on first use.
+
+Inputs: image and sensitivity (0.5 for explicit content only, 0.7+ to include partial nudity).
+
+**Important:** the filter isn't perfect. Some explicit content may slip through.
+
+
+## <a id="api" href="#toc">API extensions</a>
+
+### GET /api/etn/model_info/{folder_name}
+
+There are various types of models that can be loaded as checkpoint, LoRA, ControlNet, etc. which cannot be used interchangeably. This endpoint helps to categorize and filter them.
+
+#### Paramters
+* `folder_name`: sub-directory in ComfyUI's models folder.
+  Supported model types: `checkpoints`, `diffusion_models`, `unet`, `unet_gguf`
+* `limit=n`: (query parameter, optional) inspect at `n` models
+* `offset=i`: (query parameter, optional) start with the `i`th model
+
+#### Output
+Lists available models with additional classification info:
+```json
+{
+    "checkpoint_file.safetensors": {
+        "base_model": "sd15",
+        "is_inpaint": false,
+        "type": "eps"
+    },
+    ...
+}
+```
+Possible values for base model: `sd15, sd20, sd21, sd3, sdxl, sdxl-refiner, ssd1b, svd, cascade-b, cascade-c, aura-flow, hunyuan-dit, flux, flux-schnell, flux2, lumina2, z-image, chroma, qwen-image`
+
+If base model is `sdxl`, the `type` attribute is set with possible values: `eps, edm, v-prediction, v-prediction-edm`
+
+Detection supports quantized models:
+* GGUF: if the `gguf` module is installed, .gguf files are detected and will set the `quant` field
+* Nunchaku: SVDQuant models are detected and will set the `quant` field to `svdq`
+
+Returns an entry `{"base_model": "unknown"}` for models with unknown format or which do not match any of the known base models.
+
+
+#### Pagination
+The query parameters limit and offset allow inspecting a subset of models per request.
+Usually inspection is quite fast (it only looks at model headers), but it can be slow
+in some cases due to anti-virus or slow harddrives.
+```
+GET /api/etn/model_info/checkpoints?limit=10&offset=20
+```
+This will return at most 10 models, starting with the 20th model in the list.
+It also returns a special `_meta` entry in the output JSON:
+```json
+{
+    "checkpoint_20.safetensors": { ... },
+    "_meta": { "offset": 20, "count": 1, "total": 21 }
+}
+```
+
+
+### GET /api/etn/languages
+
+Returns a list of available languages for translation.
+```json
+[
+    { "name": "English", "code": "en" },
+    { ... }
+]
+```
+
+<a id="api-translation"></a>
+### GET /api/etn/translate/{lang}/{text}
+
+Translates `text` into English. `lang` is a 2-letter code indicating the language to translate
+from. `text` may also contain _language directives_ to only translate some fragments.
+See the [node documentation](#node-translate) for details.
+
+* Output: JSON string
+* Example: `/api/etn/translate/de/eine%20modische%20Handtasche` -> `"a fashionable handbag"`
+
+### PUT /api/etn/upload/{folder_name}/{filename}
+
+Uploads a model to ComfyUI's local model folder.
+
+#### Parameters
+* `folder_name`: the model type. Must match one of the existing folders in ComfyUI's models folder.
+* `filename`: target filename for the model. Must not contain any (absolute or relative) path. Extension must be .safetensors.
+
+#### Output
+* Code `201` and `{ "status": "success" }` after successful upload.
+* Code `200` and `{ "status": "cached" }` if the file already exists.
+* Code `400` and `{ "error": "..." }` if the parameters are invalid.
+
+
+## <a id="installation" href="#toc">Installation</a>

 Download the repository and unpack into the `custom_nodes` folder in the ComfyUI installation directory.

@@ -1,36 +1,42 @@
-from . import api, nodes, tile, region
+from comfy_api.latest import ComfyExtension, io
+from . import api as api, nodes, tile, region, nsfw, translation, krita

-NODE_CLASS_MAPPINGS = {
-    "ETN_LoadImageBase64": nodes.LoadImageBase64,
-    "ETN_LoadMaskBase64": nodes.LoadMaskBase64,
-    "ETN_SendImageWebSocket": nodes.SendImageWebSocket,
-    "ETN_CropImage": nodes.CropImage,
-    "ETN_ApplyMaskToImage": nodes.ApplyMaskToImage,
-    "ETN_TileLayout": tile.TileLayout,
-    "ETN_ExtractImageTile": tile.ExtractImageTile,
-    "ETN_ExtractMaskTile": tile.ExtractMaskTile,
-    "ETN_GenerateTileMask": tile.GenerateTileMask,
-    "ETN_MergeImageTile": tile.MergeImageTile,
-    "ETN_BackgroundRegion": region.BackgroundRegion,
-    "ETN_DefineRegion": region.DefineRegion,
-    "ETN_ListRegionMasks": region.ListRegionMasks,
-    "ETN_AttentionMask": region.AttentionMask,
-}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ETN_LoadImageBase64": "Load Image (Base64)",
-    "ETN_LoadMaskBase64": "Load Mask (Base64)",
-    "ETN_SendImageWebSocket": "Send Image (WebSocket)",
-    "ETN_CropImage": "Crop Image",
-    "ETN_ApplyMaskToImage": "Apply Mask to Image",
-    "ETN_ListAppend": "List 🢒 Append",
-    "ETN_ListElement": "List 🢒 Get Element",
-    "ETN_TileLayout": "Create Tile Layout",
-    "ETN_ExtractImageTile": "Extract Image Tile",
-    "ETN_ExtractMaskTile": "Extract Mask Tile",
-    "ETN_MergeImageTile": "Merge Image Tile",
-    "ETN_GenerateTileMask": "Generate Tile Mask",
-    "ETN_BackgroundRegion": "Background Region",
-    "ETN_DefineRegion": "Define Region",
-    "ETN_ListRegionMasks": "List Region Masks",
-    "ETN_AttentionMask": "Regions Attention Mask",
-}
+
+class ExternalToolingNodes(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            nodes.LoadImageCache,
+            nodes.SaveImageCache,
+            nodes.LoadImageBase64,
+            nodes.LoadMaskBase64,
+            nodes.SendImageWebSocket,
+            nodes.ApplyMaskToImage,
+            nodes.ReferenceImage,
+            nodes.ApplyReferenceImages,
+            tile.CreateTileLayout,
+            tile.ExtractImageTile,
+            tile.ExtractMaskTile,
+            tile.GenerateTileMask,
+            tile.MergeImageTile,
+            region.BackgroundRegion,
+            region.DefineRegion,
+            region.ListRegionMasks,
+            region.AttentionMask,
+            nsfw.NSFWFilter,
+            translation.Translate,
+            krita.KritaOutput,
+            krita.KritaSendText,
+            krita.KritaCanvas,
+            krita.KritaSelection,
+            krita.KritaImageLayer,
+            krita.KritaMaskLayer,
+            krita.Parameter,
+            krita.KritaStyle,
+        ]
+
+
+async def comfy_entrypoint():
+    return ExternalToolingNodes()
+
+
+WEB_DIRECTORY = "./js"
@@ -1,13 +1,22 @@
+from __future__ import annotations
 from aiohttp import web
-from typing import NamedTuple
+from typing import Any, NamedTuple
+from pathlib import Path
 import json
+import traceback
+import re
+import logging
+import itertools

-import comfy.utils
-from comfy import supported_models
 from comfy import model_detection
+import comfy.utils
 import folder_paths
 import server

+from .translation import available_languages, translate
+from .krita import WorkflowExchange
+from .nodes import image_cache
+
 input_block_name = "model.diffusion_model.input_blocks.0.0.weight"

 model_names = {
@@ -15,12 +24,44 @@ model_names = {
    "SD20": "sd20",
    "SD21UnclipL": "sd21",
    "SD21UnclipH": "sd21",
-    "SDXLRefiner": "sdxl",
+    "SDXLRefiner": "sdxl-refiner",
    "SDXL": "sdxl",
    "SSD1B": "ssd1b",
    "SVD_img2vid": "svd",
    "Stable_Cascade_B": "cascade-b",
    "Stable_Cascade_C": "cascade-c",
+    "SD3": "sd3",
+    "AuraFlow": "aura-flow",
+    "HunyuanDiT": "hunyuan-dit",
+    "HunyuanDiT1": "hunyuan-dit",
+    "Flux": "flux",
+    "FluxInpaint": "flux",
+    "FluxSchnell": "flux-schnell",
+    "GenmoMochi": "mochi",
+    "LTXV": "ltxv",
+    "HunyuanVideo": "hunyuan-video",
+    "CosmosT2V": "cosmos",
+    "CosmosI2V": "cosmos",
+    "CosmosT2IPredict2": "cosmos-predict2",
+    "CosmosI2VPredict2": "cosmos-predict2",
+    "ZImage": "z-image",
+    "Lumina2": "lumina2",
+    "WAN21_T2V": "wan21",
+    "WAN21_I2V": "wan21",
+    "WAN21_FunControl2V": "wan21-fun",
+    "WAN21_Vace": "wan21-vace",
+    "WAN21_Camera": "wan21-camera",
+    "HiDream": "hi-dream",
+    "Chroma": "chroma",
+    "ACEStep": "ace-step",
+    "Omnigen2": "omnigen2",
+    "QwenImage": "qwen-image",
+    "Flux2": "flux2",
+}
+
+gguf_architectures = {
+    "sd1": "sd15",
+    "qwen_image": "qwen-image",
 }


@@ -35,10 +76,10 @@ class FakeTensor(NamedTuple):
            return d


-def inspect_checkpoint(filename):
+def inspect_safetensors(filename: str, model_type: str, is_checkpoint: bool):
    try:
        # Read header of safetensors file
-        path = folder_paths.get_full_path("checkpoints", filename)
+        path = folder_paths.get_full_path(model_type, filename)
        header = comfy.utils.safetensors_header(path)
        if header:
            cfg = json.loads(header.decode("utf-8"))
@@ -49,11 +90,14 @@ def inspect_checkpoint(filename):
                    cfg[key] = FakeTensor.from_dict(cfg[key])

            # Reuse Comfy's model detection
-            unet_args = [cfg, "model.diffusion_model.", "F32"]
+            prefix = model_detection.unet_prefix_from_state_dict(cfg)
+            if not is_checkpoint:
+                cfg = comfy.utils.state_dict_prefix_replace(cfg, {prefix: ""}, filter_keys=False)
+                prefix = ""
            try:  # latest ComfyUI takes 2 args
-                unet_config = model_detection.detect_unet_config(*unet_args[:-1])
+                unet_config = model_detection.detect_unet_config(cfg, prefix)
            except TypeError as e:  # older ComfyUI versions take 3 args
-                unet_config = model_detection.detect_unet_config(*unet_args)
+                raise TypeError(f"{e} when calling detect_unet_config - old version of ComfyUI?")

            # Get input count to detect inpaint models
            if input_block := cfg.get(input_block_name, None):
@@ -62,31 +106,295 @@ def inspect_checkpoint(filename):
                input_count = 4

            # Find a matching base model depending on unet config
-            base_model = model_detection.model_config_from_unet_config(unet_config)
-            if base_model is None:
+            base_model = None
+            model_type = None
+            model_quant = None
+
+            # Check if it's a Nunchaku SVDQ model by inspecting metadata
+            raw_name = detect_svdq(cfg)
+            if raw_name:
+                model_quant = "svdq"
+            # Otherwise try ComfyUI's model detection
+            elif unet_config is not None:
+                base_model = model_detection.model_config_from_unet_config(unet_config)
+                if base_model:
+                    raw_name = base_model.__class__.__name__
+                    if raw_name == "SDXL":
+                        model_type = base_model.model_type(cfg).name.lower().replace("_", "-")
+
+            if not raw_name:
                return {"base_model": "unknown"}

-            base_model_class = base_model.__class__
-            base_model_name = model_names.get(base_model_class.__name__, "unknown")
-            return {
-                "base_model": base_model_name,
-                "is_inpaint": base_model_name in ["sd15", "sdxl"] and input_count > 4,
-                "is_refiner": base_model_class is supported_models.SDXLRefiner,
-            }
+            base_model_name = model_names.get(raw_name, "unknown")
+            result: dict[str, Any] = {"base_model": base_model_name}
+            result["is_inpaint"] = (
+                base_model_name in ["sd15", "sdxl"] and input_count > 4
+            ) or raw_name == "FluxInpaint"
+            if model_quant:
+                result["quant"] = model_quant
+            if model_type:
+                result["type"] = model_type
+            elif "T2I" in raw_name:
+                result["type"] = "t2i"
+            elif "I2V" in raw_name:
+                result["type"] = "i2v"
+            elif "T2V" in raw_name:
+                result["type"] = "t2v"
+            elif "Control2V" in raw_name:
+                result["type"] = "control2v"
+            return result
        return {"base_model": "unknown"}
    except Exception as e:
+        print("[comfyui-tooling-nodes] Error inspecting file", filename)
+        traceback.print_exc()
        return {"base_model": "unknown", "error": f"Failed to detect base model: {e}"}


-if _server := getattr(server.PromptServer, "instance", None):
+def detect_svdq(cfg: dict) -> str | None:
+    if md := cfg.get("__metadata__"):
+        if comfy_config := md.get("comfy_config"):
+            if isinstance(comfy_config, str):
+                comfy_config = json.loads(comfy_config)
+            if model_class := comfy_config.get("model_class"):
+                return model_class

-    @_server.routes.get("/etn/model_info")
-    async def model_info(request):
+        match md.get("model_class"):
+            case "NunchakuFluxTransformer2dModel":
+                return "Flux"
+            case "NunchakuQwenImageTransformer2DModel":
+                return "QwenImage"
+            case "NunchakuZImageTransformer2DModel":
+                return "ZImage"
+    return None
+
+
+def inspect_gguf(filename: str, model_type: str):
+    try:
+        import gguf
+    except ImportError:
+        return {"base_model": "unknown", "error": "GGUF module not found"}
+
+    try:
+        path = folder_paths.get_full_path(model_type, filename)
+        if path is None:
+            raise Exception(f"Could not find full path for {model_type}/{filename}")
+
+        reader = gguf.GGUFReader(path)
+        arch_field = reader.get_field("general.architecture")
+        if arch_field is not None:
+            if len(arch_field.types) != 1 or arch_field.types[0] != gguf.GGUFValueType.STRING:
+                raise TypeError(
+                    f"Bad type for GGUF general.architecture key: expected string, got {arch_field.types!r}"
+                )
+            arch_str = str(arch_field.parts[arch_field.data[-1]], encoding="utf-8")
+        else:  # stable-diffusion.cpp, requires conversion. not handled for now
+            return {"base_model": "flux", "is_inpaint": False}
+
+        # Detect Chroma (modified Flux)
+        if arch_str == "flux" and any(
+            t.name.startswith("distilled_guidance_layer")
+            for t in itertools.islice(reader.tensors, 5)
+        ):
+            arch_str = "chroma"
+
+        # Detect Z-Image (modified Lumina2)
+        if arch_str == "lumina2":
+            for t in reader.tensors:
+                if t.name == "cap_embedder.1.bias" and t.shape[0] == 3840:
+                    arch_str = "z-image"
+                    break
+
+        result = {
+            "base_model": gguf_architectures.get(arch_str, arch_str),
+            "is_inpaint": False,
+        }
        try:
-            info = {
-                filename: inspect_checkpoint(filename)
-                for filename in folder_paths.get_filename_list("checkpoints")
-            }
-            return web.json_response(info)
+            if file_type := reader.get_field("general.file_type"):
+                result["quant"] = file_type.contents().lower()
+        except Exception:
+            result["quant"] = "gguf"
+        return result
+
+    except Exception as e:
+        # traceback.print_exc()
+        return {"base_model": "unknown", "error": f"Failed to detect base model: {e}"}
+
+
+def inspect_diffusion_model(filename: str, model_type: str, is_checkpoint: bool):
+    if filename.endswith(".gguf"):
+        return inspect_gguf(filename, model_type)
+    return inspect_safetensors(filename, model_type, is_checkpoint)
+
+
+def inspect_models(model_type: str, params: dict[str, str]):
+    try:
+        try:
+            files = folder_paths.get_filename_list(model_type)
+        except KeyError:
+            return web.json_response({"error": f"Model folder not found: {model_type}"})
+        limit = int(params.get("limit", "1000"))
+        offset = int(params.get("offset", "0"))
+        files_range = files[offset : offset + limit]
+        is_checkpoint = model_type == "checkpoints"
+        info = {
+            filename: inspect_diffusion_model(filename, model_type, is_checkpoint)
+            for filename in files_range
+        }
+        if "limit" in params:
+            info["_meta"] = dict(offset=offset, count=len(files_range), total=len(files))
+        return web.json_response(info)
+    except Exception as e:
+        traceback.print_exc()
+        return web.json_response(dict(error=str(e)), status=500)
+
+
+def has_invalid_folder_name(folder_name: str):
+    valid_names = list(folder_paths.folder_names_and_paths.keys())
+    if folder_name not in valid_names:
+        return web.json_response(
+            dict(error=f"Invalid folder path, must be one of {', '.join(valid_names)}"),
+            status=400,
+        )
+    return None
+
+
+def has_invalid_filename(filename: str):
+    if not filename.lower().endswith((".sft", ".safetensors")):
+        return web.json_response(dict(error="File extension must be .safetensors"), status=400)
+    if not filename or not filename.strip() or len(filename) > 255:
+        return web.json_response(dict(error="Invalid filename"), status=400)
+    if any(char in filename for char in ["..", "/", "\\", "\n", "\r", "\t", "\0"]):
+        return web.json_response(dict(error="Invalid filename"), status=400)
+    if filename.startswith(".") or not re.match(r"^[a-zA-Z0-9_\-. ]+$", filename):
+        return web.json_response(dict(error="Invalid filename"), status=400)
+    return None
+
+
+async def image_sender(data: bytes):
+    mem = memoryview(data)
+    csize = 2**14
+    for i in range(0, len(mem), csize):
+        yield mem[i : i + csize]
+
+
+_server: server.PromptServer | None = getattr(server.PromptServer, "instance", None)
+if _server is not None:
+    _workflow_exchange = WorkflowExchange(_server)
+
+    @_server.routes.get("/api/etn/model_info/{folder_name}")
+    async def model_info(request: web.Request):
+        folder_name = request.match_info.get("folder_name", "checkpoints")
+        error = has_invalid_folder_name(folder_name)
+        if error is not None:
+            return error
+        return inspect_models(folder_name, request.rel_url.query)
+
+    @_server.routes.get("/api/etn/model_info")
+    async def api_model_info(request):
+        return inspect_models("checkpoints", request.rel_url.query)
+
+    @_server.routes.get("/api/etn/languages")
+    async def languages(request):
+        try:
+            result = [dict(name=name, code=code) for code, name in available_languages()]
+            return web.json_response(result)
        except Exception as e:
            return web.json_response(dict(error=str(e)), status=500)
+
+    @_server.routes.get("/api/etn/translate/{lang}/{text}")
+    async def translate_text(request):
+        try:
+            language = request.match_info.get("lang", "en")
+            text = request.match_info.get("text", "")
+            result = translate(f"lang:{language} {text}")
+            return web.json_response(result)
+        except Exception as e:
+            return web.json_response(dict(error=str(e)), status=500)
+
+    @_server.routes.get("/api/etn/image/{id}")
+    async def get_image(request: web.Request):
+        try:
+            id = request.match_info.get("id", "")
+            data, content_type = image_cache.get(id)
+            if data is None or content_type is None:
+                return web.json_response(dict(error="Image not found"), status=404)
+            response = web.Response(
+                body=image_sender(data),
+                content_type=content_type,
+                headers={"Content-Length": str(len(data))},
+            )
+            return response
+        except Exception as e:
+            return web.json_response(dict(error=str(e)), status=500)
+
+    @_server.routes.put("/api/etn/image/{id}")
+    async def put_image(request: web.Request):
+        try:
+            id = request.match_info.get("id", "")
+            if id in image_cache:
+                return web.json_response(dict(status="cached"), status=200)
+
+            content_type = request.headers.get("Content-Type", "application/octet-stream")
+            data = bytearray()
+            async for chunk, _ in request.content.iter_chunks():
+                data.extend(chunk)
+
+            image_cache.insert(id, bytes(data), content_type)
+            return web.json_response(dict(status="success"), status=201)
+        except Exception as e:
+            return web.json_response(dict(error=str(e)), status=500)
+
+    @_server.routes.put("/api/etn/upload/{folder_name}/{filename}")
+    async def upload(request: web.Request):
+        folder_name = request.match_info.get("folder_name", "")
+        error = has_invalid_folder_name(folder_name)
+        if error is not None:
+            return error
+
+        filename = request.match_info.get("filename", "")
+        error = has_invalid_filename(filename)
+        if error is not None:
+            return error
+
+        try:
+            if folder_paths.get_full_path(folder_name, filename) is not None:
+                return web.json_response(dict(status="cached"), status=200)
+
+            folder = Path(folder_paths.folder_names_and_paths[folder_name][0][0])
+            total_size = int(request.headers.get("Content-Length", "0"))
+            logging.info(
+                f"Uploading {filename} ({total_size / (1024**2):.1f} MB) to {folder} folder"
+            )
+
+            with open(folder / filename, "wb") as f:
+                async for chunk, _ in request.content.iter_chunks():
+                    f.write(chunk)
+
+            return web.json_response(dict(status="success"), status=201)
+        except Exception as e:
+            return web.json_response(dict(error=str(e)), status=500)
+
+    async def _handle_workflow_request(request: web.Request, handler, *arg_keys):
+        try:
+            data = await request.json()
+            args = [data[key] for key in arg_keys]
+            await handler(*args)
+            return web.json_response(dict(status="success"), status=200)
+        except KeyError as e:
+            return web.json_response(dict(error=str(e)), status=400)
+        except Exception as e:
+            return web.json_response(dict(error=str(e)), status=500)
+
+    @_server.routes.post("/api/etn/workflow/publish")
+    async def publish_workflow(request: web.Request):
+        return await _handle_workflow_request(
+            request, _workflow_exchange.publish, "name", "client_id", "workflow"
+        )
+
+    @_server.routes.post("/api/etn/workflow/subscribe")
+    async def subscribe_workflow(request: web.Request):
+        return await _handle_workflow_request(request, _workflow_exchange.subscribe, "client_id")
+
+    @_server.routes.post("/api/etn/workflow/unsubscribe")
+    async def unsubscribe_workflow(request: web.Request):
+        return await _handle_workflow_request(request, _workflow_exchange.unsubscribe, "client_id")
@@ -0,0 +1,235 @@
+import { app } from "/scripts/app.js"
+import { api } from "/scripts/api.js"
+
+(function() {
+
+// Workflow publishing
+// - done whenever the graph changes, as long as there is a KritaOutput node
+
+let publisherRegistered = false
+
+async function publishWorkflow(e) {
+    const prompt = await app.graphToPrompt()
+    await api.fetchApi("/api/etn/workflow/publish", {
+        method: "POST",
+        body: JSON.stringify({
+            name: "ComfyUI Web",
+            client_id: api.clientId,
+            workflow: prompt["output"]
+        }, null, 2)
+    })
+}
+
+
+// Image background for nodes
+// - this is just for visuals
+
+function loadImage(base64) {
+    const image = new Image()
+    image.src = base64
+    // image.onerror = () => console.error("Failed to load image");
+    return image
+}
+
+const canvasIcon = loadImage("data:image/webp;base64,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")
+
+function setIconImage(nodeType, image, size, padRows, padCols) {
+    const onAdded = nodeType.prototype.onAdded
+    nodeType.prototype.onAdded = function () {
+        onAdded?.apply(this, arguments)
+        this.size = size
+    }
+
+    const onDrawBackground = nodeType.prototype.onDrawBackground
+    nodeType.prototype.onDrawBackground = function(ctx) {
+        onDrawBackground?.apply(this, arguments)
+
+        const pad = [padCols * 20, LiteGraph.NODE_SLOT_HEIGHT * padRows + 8];
+        if(this.flags.collapsed || pad[1] + 32 > this.size[1] || image.width === 0) {
+            return
+        }
+        const avail = [this.size[0] - pad[0], this.size[1] - pad[1]]
+        const scale = Math.min(1.0, avail[0] / image.width, avail[1] / image.height)
+        const size = [Math.floor(image.width * scale), Math.floor(image.height * scale)]
+        const offset = [Math.max(0, (avail[0] - size[0]) / 2), Math.max(0, (avail[1] - size[1]) / 2)]
+        ctx.drawImage(image, offset[0], pad[1] + offset[1], size[0], size[1])
+    }
+}
+
+
+// Parameter node
+// - represents a customizable parameter that should be exposed in external tools
+// - adapts to whichever node it is connected to, similar to the built-in "Primitive" node
+// - can only be connected to slots which are converted widgets
+
+const replaceableWidgets = ["INT", "FLOAT", "BOOLEAN", "STRING", "COMBO", "INT:seed"]
+
+const parameterTypes = {
+    "combo": ["choice"],
+    "number": ["number", "number (integer)"],
+    "toggle": ["toggle"],
+    "text": ["text", "prompt (positive)", "prompt (negative)"],
+}
+
+function defaultParameterType(widgetType, connectedNode, connectedWidget) {
+    let paramType = parameterTypes[widgetType][0]
+    if (connectedNode.comfyClass === "CLIPTextEncode") {
+        paramType = "prompt (positive)"
+    }
+    const round = connectedWidget.options?.round
+    if ((paramType == "number" && round === undefined) || round === 1) {
+        paramType = "number (integer)"
+    }
+    return paramType
+}
+
+function valueMatchesType(value, type, options) {
+    if (type === "number") {
+        return typeof value === "number"
+    } else if (type === "combo") {
+        return options?.values?.includes(value)
+    } else if (type === "toggle") {
+        return typeof value === "boolean"
+    }
+    return typeof value === "string"
+}
+
+function optionalWidgetValue(widgets, index, fallback) {
+    const result = widgets.length > index ? widgets[index].value : null
+    return result === null || result === -1e10 || result === 1e10 ? fallback : result
+}
+
+function changeWidgets(node, type, connectedNode, connectedWidget) {
+    if (type === "customtext") {
+        type = "text"
+    }
+    const options = connectedWidget.options
+
+    const parameterTypeHint = node.widgets[1].value
+    const notSpecialized = node.widgets[1].options.values.includes("auto")
+    const parameterTypeMismatch = !parameterTypes[type].includes(parameterTypeHint)
+    if (notSpecialized || parameterTypeMismatch) {
+        node.widgets[1].options = {values: parameterTypes[type]}
+    }
+    if (parameterTypeMismatch) {
+        node.widgets[1].value = defaultParameterType(type, connectedNode, connectedWidget)
+    }
+    const oldDefault = node.widgets.length > 2 ? node.widgets[2].value : connectedWidget.value
+    const oldMin = optionalWidgetValue(node.widgets, 3, options?.min ?? 0)
+    const oldMax = optionalWidgetValue(node.widgets, 4, options?.max ?? 100)
+    const isDefaultValid = valueMatchesType(oldDefault, type, connectedWidget.options)
+    while (node.widgets.length > 2) {
+        node.widgets.pop()
+    }
+    const value = isDefaultValid && oldDefault !== "" ? oldDefault : connectedWidget.value
+    node.addWidget(type, "default", value, null, options)
+    if (type === "number") {
+        node.addWidget("number", "min", oldMin, null, options)
+        node.addWidget("number", "max", oldMax, null, options)
+    }
+}
+
+function adaptWidgetsToConnection(node) {
+    if (!node.outputs || node.outputs.length === 0) {
+        return
+    }
+    const links = node.outputs[0].links
+    if (links && links.length === 1) {
+        const link = node.graph.links[links[0]]
+        if (!link) return
+
+        const theirNode = node.graph.getNodeById(link.target_id)
+        if (!theirNode || !theirNode.inputs) return
+    
+        const input = theirNode.inputs[link.target_slot]
+        if (!input || !input.widget || theirNode.widgets === undefined) return
+
+        node.outputs[0].type = input.type
+
+        if (node.widgets[0].value === "Parameter") {
+            node.widgets[0].value = input.name
+        }
+
+        const widgetName = input.widget.name
+        const theirWidget = theirNode.widgets.find((w) => w.name === widgetName)
+        if (!theirWidget) return // connected to a custom node that isn't installed
+
+        const widgetType = theirWidget.origType ?? theirWidget.type
+        changeWidgets(node, widgetType, theirNode, theirWidget)
+
+    } else if (!links || links.length === 0) {
+        node.outputs[0].type = "*"
+        node.widgets[1].value = "auto"
+        node.widgets[1].options = {values: ["auto"]}
+    }
+}
+
+function setupParameterNode(nodeType) {
+    const onAdded = nodeType.prototype.onAdded
+    nodeType.prototype.onAdded = function() {
+        onAdded?.apply(this, arguments)
+        adaptWidgetsToConnection(this)
+    }
+
+    const onAfterGraphConfigured = nodeType.prototype.onAfterGraphConfigured
+    nodeType.prototype.onAfterGraphConfigured = function() {
+        onAfterGraphConfigured?.apply(this, arguments)
+        adaptWidgetsToConnection(this)
+    }
+
+    const onConnectOutput = nodeType.prototype.onConnectOutput
+    nodeType.prototype.onConnectOutput = function(slot, type, input, target_node, target_slot) {
+        if (!input.widget && !(input.type in replaceableWidgets)) {
+            return false
+        } else if (onConnectOutput) {
+            result = onConnectOutput.apply(this, arguments)
+            return result
+        }
+        return true
+    }
+
+    const onConnectionsChange = nodeType.prototype.onConnectionsChange
+    nodeType.prototype.onConnectionsChange = function(_, index, connected) {
+        if (!app.configuringGraph) {
+            adaptWidgetsToConnection(this)
+        }
+        onConnectionsChange?.apply(this, arguments)
+    }
+}
+
+
+// Register the extension
+
+app.registerExtension({
+	name: "external_tooling_nodes",
+
+    beforeRegisterNodeDef(nodeType /*typeof LGraphNode*/, nodeData /*ComfyObjectInfo*/, app) {
+        if (nodeData.name === "ETN_KritaCanvas") {
+            setIconImage(nodeType, canvasIcon, [200, 100], 0, 2)
+        } else if (nodeData.name === "ETN_Parameter") {
+            setupParameterNode(nodeType)
+        } else if (nodeData.name === "ETN_SendText") {
+            const onAdded = nodeType.prototype.onAdded
+            nodeType.prototype.onAdded = function() {
+                onAdded?.apply(this, arguments)
+                this.inputs[0].type = "*"
+            }
+        }
+    },
+
+    nodeCreated(node /*ComfyNode*/, app) {
+        if (publisherRegistered || node.comfyClass !== "ETN_KritaOutput") {
+            return
+        }
+        api.addEventListener('graphChanged', publishWorkflow)
+        publisherRegistered = true
+    },
+
+    setup(app) {
+        if (publisherRegistered) {
+            publishWorkflow(null)
+        }
+    },
+});
+
+})();
@@ -0,0 +1,330 @@
+import sys
+import torch
+import numpy as np
+from enum import Enum
+from pathlib import Path
+from typing import Any, NamedTuple
+from PIL import Image
+
+import server
+import comfy.samplers
+from comfy.comfy_types.node_typing import IO
+from comfy_api.latest import io
+from .nodes import SendImageWebSocket
+
+
+class Publisher(NamedTuple):
+    name: str
+    id: str
+    workflow: dict
+
+
+class WorkflowExchange:
+    def __init__(self, server: server.PromptServer):
+        self._server = server
+        self._publishers: dict[str, Publisher] = {}
+        self._subscribers: list[str] = []
+
+    async def publish(self, publisher_name: str, publisher_id: str, workflow: dict):
+        publisher = Publisher(publisher_name, publisher_id, workflow)
+        for client_id in self._subscribers:
+            await self._notify(client_id, publisher)
+        self._publishers[publisher_id] = publisher
+
+    async def subscribe(self, client_id: str):
+        if client_id in self._subscribers:
+            raise KeyError("Already subscribed")
+        self._subscribers.append(client_id)
+        for publisher in self._publishers.values():
+            await self._notify(client_id, publisher)
+
+    async def unsubscribe(self, client_id: str):
+        if client_id in self._subscribers:
+            self._subscribers.remove(client_id)
+        else:
+            raise KeyError("No subscriber found with id " + client_id)
+
+    async def _notify(self, client_id: str, publisher: Publisher):
+        data = {
+            "publisher": {"name": publisher.name, "id": publisher.id},
+            "workflow": publisher.workflow,
+        }
+        await self._server.send_json("etn_workflow_published", data, client_id)
+
+
+def _placeholder_image():
+    path = Path(__file__).parent / "data" / "external-image-placeholder.webp"
+    image = Image.open(path).convert("RGB")
+    image = np.array(image).astype(np.float32) / 255.0
+    return torch.from_numpy(image)[None,]
+
+
+class _BasicTypes(str):
+    """Matches IO.PRIMITIVE, but also any list of choices"""
+
+    basic_types = IO.PRIMITIVE.split(",")  # STRING, FLOAT, INT, BOOLEAN
+
+    def __eq__(self, other):
+        return other in self.basic_types or isinstance(other, (list, _BasicTypes))
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+BasicTypes = _BasicTypes("BASIC")
+
+
+class OutputBatchMode(Enum):
+    default = "default"
+    images = "images"
+    animation = "animation"
+    layers = "layers"
+
+
+class KritaOutput(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaOutput",
+            display_name="Krita Output",
+            category="krita",
+            inputs=[
+                io.Image.Input("images"),
+                io.Int.Input("x", "offset x", default=0),
+                io.Int.Input("y", "offset y", default=0),
+                io.String.Input("name", default=""),
+                io.Combo.Input(
+                    "batch_mode", OutputBatchMode, "batch mode", default=OutputBatchMode.default
+                ),
+                io.Boolean.Input("resize_canvas", "resize canvas", default=False),
+            ],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        images: torch.Tensor,
+        x: int = 0,
+        y: int = 0,
+        name="",
+        batch_mode: OutputBatchMode | str = OutputBatchMode.default,
+        resize_canvas=False,
+    ):
+        batch_mode = batch_mode.value if isinstance(batch_mode, OutputBatchMode) else batch_mode
+        info = {
+            "name": name,
+            "offset_x": x,
+            "offset_y": y,
+            "batch_mode": batch_mode,
+            "resize_canvas": resize_canvas,
+        }
+        output = SendImageWebSocket.execute(images, "PNG")
+        assert isinstance(output.ui, dict)
+        output.ui["info"] = [info]
+        return output
+
+
+class KritaSendText(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaSendText",
+            display_name="Send Text",
+            category="krita",
+            inputs=[
+                io.AnyType.Input("value"),
+                io.String.Input("name", default="Output"),
+                io.Combo.Input("type", options=["text", "markdown", "html"], default="text"),
+            ],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, value: Any, name: str, type: str):
+        mime = {
+            "text": "text/plain",
+            "markdown": "text/markdown",
+            "html": "text/html",
+        }[type]
+        text = "None"
+        if value is not None:
+            try:
+                text = str(value)
+            except Exception as e:
+                text = f"Could not convert to text: {e}"
+
+        return io.NodeOutput(ui={"text": [{"name": name, "text": text, "content-type": mime}]})
+
+
+class KritaCanvas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaCanvas",
+            display_name="Krita Canvas",
+            category="krita",
+            outputs=[
+                io.Image.Output(display_name="image"),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+                io.Int.Output(display_name="seed"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls):
+        return io.NodeOutput(_placeholder_image(), 512, 512, 0)
+
+
+class SelectionContext(Enum):
+    automatic = "automatic"
+    entire_image = "entire image"
+    mask_bounds = "mask bounds"
+
+
+_selection_context_help = """
+Determines the section (crop bounding box) of the image and mask to transmit:
+- automatic: area around the selection determined by Krita settings
+- entire image: always use the entire canvas area
+- mask bounds: tight bounding box of the current selection
+
+This affects the Selection and Canvas nodes. The offset x/y outputs indicate the top-left corner of the context area relative to the full canvas."""
+
+
+class KritaSelection(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaSelection",
+            display_name="Krita Selection",
+            category="krita",
+            inputs=[
+                io.Combo.Input(
+                    "context",
+                    options=SelectionContext,
+                    default=SelectionContext.entire_image,
+                    tooltip=_selection_context_help,
+                ),
+                io.Int.Input("padding", "padding", default=0, min=0),
+            ],
+            outputs=[
+                io.Mask.Output("mask", "mask"),
+                io.Boolean.Output("active", "active"),
+                io.Int.Output("x", "offset x"),
+                io.Int.Output("y", "offset y"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, **kwargs):
+        return io.NodeOutput(torch.ones(1, 512, 512), False, 0, 0)
+
+
+class KritaImageLayer(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaImageLayer",
+            display_name="Krita Image Layer",
+            category="krita",
+            inputs=[io.String.Input("name", default="Image")],
+            outputs=[
+                io.Image.Output(display_name="image"),
+                io.Mask.Output(display_name="mask"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, name: str):
+        return io.NodeOutput(_placeholder_image(), torch.ones(1, 512, 512))
+
+
+class KritaMaskLayer(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaMaskLayer",
+            display_name="Krita Mask Layer",
+            category="krita",
+            inputs=[io.String.Input("name", default="Mask")],
+            outputs=[
+                io.Mask.Output(display_name="mask"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, name: str):
+        return io.NodeOutput(torch.ones(1, 512, 512))
+
+
+_param_types = [
+    "auto",
+    "number",
+    "number (integer)",
+    "toggle",
+    "choice",
+    "text",
+    "prompt (positive)",
+    "prompt (negative)",
+]
+_fmax = sys.float_info.max
+
+
+class Parameter(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_Parameter",
+            display_name="Parameter",
+            category="krita",
+            inputs=[
+                io.String.Input("name", default="Parameter"),
+                io.Combo.Input("type", options=_param_types, default="auto"),
+                io.String.Input("default", default=""),
+                io.Float.Input("min", default=-1e10, min=-_fmax, max=_fmax, optional=True),
+                io.Float.Input("max", default=1e10, min=-_fmax, max=_fmax, optional=True),
+            ],
+            outputs=[io.AnyType.Output(display_name="value")],
+        )
+
+    @classmethod
+    def execute(cls, name: str, type: str, default, min=0.0, max=1.0):
+        if type == "number":
+            return io.NodeOutput(float(default))
+        elif type == "number (integer)":
+            return io.NodeOutput(int(default))
+        return io.NodeOutput(default)
+
+
+class KritaStyle(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaStyle",
+            display_name="Krita Style",
+            category="krita",
+            inputs=[
+                io.String.Input("name", default="Style"),
+                io.Combo.Input("sampler_preset", options=["auto", "regular", "live"]),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+                io.Clip.Output(display_name="clip"),
+                io.Vae.Output(display_name="vae"),
+                io.String.Output(display_name="positive prompt"),
+                io.String.Output(display_name="negative prompt"),
+                io.Combo.Output(
+                    display_name="sampler name", options=comfy.samplers.KSampler.SAMPLERS
+                ),
+                io.Combo.Output(
+                    display_name="scheduler", options=comfy.samplers.KSampler.SCHEDULERS
+                ),
+                io.Int.Output(display_name="steps"),
+                io.Float.Output(display_name="guidance"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, name: str, sampler_preset: str):
+        raise NotImplementedError("This workflow must be started from Krita!")
@@ -1,30 +1,44 @@
 from __future__ import annotations
+from copy import copy
+from dataclasses import dataclass
+import time
+from typing import NamedTuple
+from uuid import uuid4
 from PIL import Image
 import numpy as np
 import base64
 import torch
+import torch.nn.functional as F
 from io import BytesIO
 from server import PromptServer, BinaryEventTypes

+from comfy.clip_vision import ClipVisionModel
+from comfy.sd import StyleModel
+from comfy_api.latest import io

-class LoadImageBase64:
+
+class LoadImageBase64(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image": ("STRING", {"multiline": False})}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_LoadImageBase64",
+            display_name="Load Image (Base64)",
+            category="external_tooling",
+            inputs=[io.String.Input("image", multiline=False)],
+            outputs=[io.Image.Output(display_name="image"), io.Mask.Output(display_name="mask")],
+        )

-    RETURN_TYPES = ("IMAGE", "MASK")
-    CATEGORY = "external_tooling"
-    FUNCTION = "load_image"
-
-    def load_image(self, image):
+    @classmethod
+    def execute(cls, image: str):
+        _strip_prefix(image, "data:image/png;base64,")
        imgdata = base64.b64decode(image)
        img = Image.open(BytesIO(imgdata))

        if "A" in img.getbands():
            mask = np.array(img.getchannel("A")).astype(np.float32) / 255.0
-            mask = 1.0 - torch.from_numpy(mask)
+            mask = torch.from_numpy(mask)
        else:
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            mask = None

        img = img.convert("RGB")
        img = np.array(img).astype(np.float32) / 255.0
@@ -33,16 +47,20 @@ class LoadImageBase64:
        return (img, mask)


-class LoadMaskBase64:
+class LoadMaskBase64(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"mask": ("STRING", {"multiline": False})}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_LoadMaskBase64",
+            display_name="Load Mask (Base64)",
+            category="external_tooling",
+            inputs=[io.String.Input("mask", multiline=False)],
+            outputs=[io.Mask.Output(display_name="mask")],
+        )

-    RETURN_TYPES = ("MASK",)
-    CATEGORY = "external_tooling"
-    FUNCTION = "load_mask"
-
-    def load_mask(self, mask):
+    @classmethod
+    def execute(cls, mask: str):
+        _strip_prefix(mask, "data:image/png;base64,")
        imgdata = base64.b64decode(mask)
        img = Image.open(BytesIO(imgdata))
        img = np.array(img).astype(np.float32) / 255.0
@@ -52,17 +70,22 @@ class LoadMaskBase64:
        return (img.unsqueeze(0),)


-class SendImageWebSocket:
+class SendImageWebSocket(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"images": ("IMAGE",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_SendImageWebSocket",
+            display_name="Send Image (WebSocket)",
+            category="external_tooling",
+            inputs=[
+                io.Image.Input("images"),
+                io.Combo.Input("format", options=["PNG", "JPEG"], default="PNG"),
+            ],
+            is_output_node=True,
+        )

-    RETURN_TYPES = ()
-    FUNCTION = "send_images"
-    OUTPUT_NODE = True
-    CATEGORY = "external_tooling"
-
-    def send_images(self, images):
+    @classmethod
+    def execute(cls, images: torch.Tensor, format: str):
        results = []
        for tensor in images:
            array = 255.0 * tensor.cpu().numpy()
@@ -71,91 +94,336 @@ class SendImageWebSocket:
            server = PromptServer.instance
            server.send_sync(
                BinaryEventTypes.UNENCODED_PREVIEW_IMAGE,
-                ["PNG", image, None],
+                [format, image, None],
                server.client_id,
            )
-            results.append(
-                # Could put some kind of ID here, but for now just match them by index
-                {"source": "websocket", "content-type": "image/png", "type": "output"}
-            )
+            results.append({
+                "source": "websocket",
+                "content-type": f"image/{format.lower()}",
+                "type": "output",
+            })

-        return {"ui": {"images": results}}
+        return io.NodeOutput(ui={"images": results})


-class CropImage:
-    """Deprecated, ComfyUI has an ImageCrop node now which does the same."""
+class ImageCache:
+    timeout = 600  # 10 minutes
+    max_size = 100 * 1024 * 1024  # 100 MB
+
+    @dataclass
+    class Entry:
+        data: bytes
+        content_type: str
+        timestamp: float
+        retrieved: int
+
+    class OldEntry(NamedTuple):
+        last_used: float
+        deleted: float
+        size: int
+        retrieved: int
+
+    def __init__(self):
+        self.images: dict[str, ImageCache.Entry] = {}
+        self.old: dict[str, ImageCache.OldEntry] = {}
+
+    def add(self, image: Image.Image, format: str):
+        key = uuid4().hex
+        with BytesIO() as output:
+            image.save(output, format=format, quality=95, compress_level=1)
+            image_data = output.getvalue()
+
+        self.insert(key, image_data, f"image/{format.lower()}")
+        return key
+
+    def insert(self, key: str, data: bytes, content_type: str):
+        self.images[key] = ImageCache.Entry(
+            data=data,
+            content_type=content_type,
+            timestamp=time.time(),
+            retrieved=0,
+        )
+
+    def get(self, key: str, extend: bool = False):
+        entry = self.images.get(key)
+        if entry is None:
+            if old := self.old.get(key):
+                now = time.time()
+                print(
+                    f"[comfyui-tooling-nodes] requested image {key} has been deleted ",
+                    f"(last used {now - old.last_used:.0f}s ago, deleted {now - old.deleted:.0f}s ago, "
+                    f"size {old.size / 1024**2:.1f}MB, retrieved {old.retrieved} times)",
+                )
+            return None, None
+        entry.retrieved += 1
+        if extend:
+            entry.timestamp = time.time()
+        self.prune()
+        return entry.data, entry.content_type
+
+    def prune(self):
+        total_size = sum(len(entry.data) for entry in self.images.values())
+        if total_size <= self.max_size:
+            return
+        # Remove least recently used entries until under max size
+        sorted_entries = sorted(self.images.items(), key=lambda item: item[1].timestamp)
+        now = time.time()
+        for key, entry in sorted_entries:
+            age = now - entry.timestamp
+            if age > self.timeout or (age > 60 and entry.retrieved > 0):
+                self.old[key] = ImageCache.OldEntry(
+                    entry.timestamp, now, len(entry.data), entry.retrieved
+                )
+                del self.images[key]
+                total_size -= len(entry.data)
+                if total_size <= self.max_size:
+                    break
+
+    def __contains__(self, key: str):
+        return key in self.images
+
+
+image_cache = ImageCache()
+
+
+class LoadImageCache(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_LoadImageCache",
+            display_name="Load Image from Cache",
+            category="external_tooling",
+            inputs=[io.String.Input("id", multiline=False)],
+            outputs=[io.Image.Output(display_name="image"), io.Mask.Output(display_name="mask")],
+        )

    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "x": (
-                    "INT",
-                    {"default": 0, "min": 0, "max": 8192, "step": 1},
-                ),
-                "y": (
-                    "INT",
-                    {"default": 0, "min": 0, "max": 8192, "step": 1},
-                ),
-                "width": (
-                    "INT",
-                    {"default": 512, "min": 1, "max": 8192, "step": 1},
-                ),
-                "height": (
-                    "INT",
-                    {"default": 512, "min": 1, "max": 8192, "step": 1},
-                ),
-            }
-        }
+    def execute(cls, id: str):
+        image_data, content_type = image_cache.get(id, extend=True)
+        if image_data is None:
+            raise ValueError(f"Image with ID {id} not found in cache.")

-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "crop"
+        img = Image.open(BytesIO(image_data))
+        w, h = img.size
+        c = len(img.getbands())
+        normalized = np.array(img).astype(np.float32) / 255.0
+        tensor = torch.from_numpy(normalized).reshape(1, h, w, c)
+        match c:
+            case 1:
+                image = tensor.expand(1, h, w, 3)
+                mask = tensor.reshape(1, h, w)
+            case 3:
+                image = tensor
+                mask = tensor[..., 0]
+            case 4:
+                image = tensor[..., :3]
+                mask = tensor[..., 3]

-    def crop(self, image, x, y, width, height):
-        out = image[:, y : y + height, x : x + width, :]
-        return (out,)
+        return io.NodeOutput(image, mask)


-class ApplyMaskToImage:
+class SaveImageCache(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "mask": ("MASK",),
-            }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_SaveImageCache",
+            display_name="Save Image to Cache",
+            category="external_tooling",
+            inputs=[
+                io.Image.Input("images"),
+                io.Combo.Input("format", options=["PNG", "JPEG"], default="PNG"),
+            ],
+            is_output_node=True,
+        )

-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "apply_mask"
+    @classmethod
+    def execute(cls, images: torch.Tensor, format: str):
+        results = []
+        for tensor in images:
+            array = 255.0 * tensor.cpu().numpy()
+            image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
+            key = image_cache.add(image, format)

-    def apply_mask(self, image: torch.Tensor, mask: torch.Tensor):
-        # Move the channel to the second dimension for processing
-        out = image.movedim(-1, 1)
+            results.append({
+                "source": "http",
+                "id": key,
+                "content-type": f"image/{format.lower()}",
+                "type": "output",
+            })
+        return io.NodeOutput(ui={"images": results})

-        # Check if the images are RGB, and if so, add an alpha channel initialized to 1
+
+def to_bchw(image: torch.Tensor):
+    if image.ndim == 3:
+        image = image.unsqueeze(0)
+    return image.movedim(-1, 1)
+
+
+def to_bhwc(image: torch.Tensor):
+    return image.movedim(1, -1)
+
+
+def mask_batch(mask: torch.Tensor):
+    if mask.ndim == 2:
+        mask = mask.unsqueeze(0)
+    return mask
+
+
+class ApplyMaskToImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ApplyMaskToImage",
+            display_name="Apply Mask to Image",
+            category="external_tooling",
+            inputs=[
+                io.Image.Input("image"),
+                io.Mask.Input("mask"),
+            ],
+            outputs=[io.Image.Output(display_name="masked")],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, mask: torch.Tensor):
+        out = to_bchw(image)
        if out.shape[1] == 3:  # Assuming RGB images
            out = torch.cat([out, torch.ones_like(out[:, :1, :, :])], dim=1)
-
-        # Ensure masks are unsqueezed to match the alpha channel dimension if needed
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0)  # Add a batch dimension to masks
-        # For single mask, expand it to match size of image batch size.
-        if mask.shape[0] == 1:
-            mask = mask.repeat(out.shape[0], 1, 1)
+        mask = mask_batch(mask)

        assert mask.ndim == 3, f"Mask should have shape [B, H, W]. {mask.shape}"
-        assert out.ndim == 4, f"Image should have shsape [B, C, H, W]. {out.shape}"
-        assert out.shape[-2:] == mask.shape[-2:], f"{out.shape[-2:]} != {mask.shape[-2:]}"
-        assert out.shape[0] == mask.shape[0], f"{out.shape[0]} != {mask.shape[0]}"
+        assert out.ndim == 4, f"Image should have shape [B, C, H, W]. {out.shape}"
+        assert out.shape[-2:] == mask.shape[-2:], (
+            f"Image size {out.shape[-2:]} must match mask size {mask.shape[-2:]}"
+        )
+        is_mask_batch = mask.shape[0] == out.shape[0]
+
        # Apply each mask in the batch to its corresponding image's alpha channel
        for i in range(out.shape[0]):
-            out[i, 3, :, :] = mask[i]
+            alpha = mask[i] if is_mask_batch else mask[0]
+            out[i, 3, :, :] = alpha

-        # Move the channel back to its original dimension
-        out = out.movedim(1, -1)
+        return (to_bhwc(out),)

-        return (out,)
+
+class _ReferenceImageData(NamedTuple):
+    image: torch.Tensor
+    weight: float
+    range: tuple[float, float]
+
+
+class ReferenceImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ReferenceImage",
+            display_name="Reference Image",
+            category="external_tooling",
+            inputs=[
+                io.Image.Input("image"),
+                io.Float.Input("weight", default=1.0, min=0.0, max=10.0),
+                io.Float.Input("range_start", default=0.0, min=0.0, max=1.0),
+                io.Float.Input("range_end", default=1.0, min=0.0, max=1.0),
+                io.Custom("ReferenceImage").Input("reference_images", optional=True),
+            ],
+            outputs=[io.Custom("ReferenceImage").Output(display_name="reference_images")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image: torch.Tensor,
+        weight: float,
+        range_start: float,
+        range_end: float,
+        reference_images: list[_ReferenceImageData] | None = None,
+    ):
+        imgs = copy(reference_images) if reference_images is not None else []
+        imgs.append(_ReferenceImageData(image, weight, (range_start, range_end)))
+        return (imgs,)
+
+
+class ApplyReferenceImages(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ApplyReferenceImages",
+            display_name="Apply Reference Images",
+            category="external_tooling",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.ClipVision.Input("clip_vision"),
+                io.StyleModel.Input("style_model"),
+                io.Custom("ReferenceImage").Input("references"),
+            ],
+            outputs=[io.Conditioning.Output(display_name="conditioning")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        conditioning: list[list],
+        clip_vision: ClipVisionModel,
+        style_model: StyleModel,
+        references: list[_ReferenceImageData],
+    ):
+        delimiters = {0.0, 1.0}
+        delimiters |= set(r.range[0] for r in references)
+        delimiters |= set(r.range[1] for r in references)
+        delimiters = sorted(delimiters)
+        ranges = [(delimiters[i], delimiters[i + 1]) for i in range(len(delimiters) - 1)]
+
+        embeds = [_encode_image(r.image, clip_vision, style_model, r.weight) for r in references]
+        base = conditioning[0][0]
+        result = []
+        for start, end in ranges:
+            e = [
+                embeds[i]
+                for i, r in enumerate(references)
+                if r.range[0] <= start and r.range[1] >= end
+            ]
+            options = conditioning[0][1].copy()
+            options["start_percent"] = start
+            options["end_percent"] = end
+            result.append((torch.cat([base] + e, dim=1), options))
+
+        return (result,)
+
+
+def _encode_image(
+    image: torch.Tensor, clip_vision: ClipVisionModel, style_model: StyleModel, weight: float
+):
+    e = clip_vision.encode_image(image)
+    e = style_model.get_cond(e).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+    e = _downsample_image_cond(e, weight)
+    return e
+
+
+def _downsample_image_cond(cond: torch.Tensor, weight: float):
+    if weight >= 1.0:
+        return cond
+    elif weight <= 0.0:
+        return torch.zeros_like(cond)
+    elif weight >= 0.6:
+        factor = 2
+    elif weight >= 0.3:
+        factor = 3
+    else:
+        factor = 4
+
+    # Downsample the clip vision embedding to make it smaller, resulting in less impact
+    # compared to other conditioning.
+    # See https://github.com/kaibioinfo/ComfyUI_AdvancedRefluxControl
+    (b, t, h) = cond.shape
+    m = int(np.sqrt(t))
+    cond = F.interpolate(
+        cond.view(b, m, m, h).transpose(1, -1),
+        size=(m // factor, m // factor),
+        mode="area",
+    )
+    return cond.transpose(1, -1).reshape(b, -1, h)
+
+
+def _strip_prefix(s: str, prefix: str) -> str:
+    if s.startswith(prefix):
+        return s[len(prefix) :]
+    return s
@@ -0,0 +1,142 @@
+from __future__ import annotations
+from weakref import ref as WeakRef
+from pathlib import Path
+from tqdm import tqdm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import CLIPImageProcessor, CLIPConfig, CLIPVisionModel, PreTrainedModel
+from kornia.filters import box_blur
+from comfy_api.latest import io
+
+from .nodes import to_bchw, to_bhwc
+
+
+def cosine_similarity(image_embeds: Tensor, text_embeds: Tensor):
+    if image_embeds.dim() == 2 and text_embeds.dim() == 2:
+        image_embeds = image_embeds.unsqueeze(1)
+    return F.cosine_similarity(image_embeds, text_embeds, dim=-1)
+
+
+class CLIPSafetyChecker(PreTrainedModel):
+    # https://huggingface.co/CompVis/stable-diffusion-safety-checker
+    # Adapted from:
+    # https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
+
+    config_class = CLIPConfig
+    _no_split_modules = ["CLIPEncoderLayer"]
+
+    def __init__(self, config: CLIPConfig):
+        super().__init__(config)
+        projdim = config.projection_dim
+
+        self.vision_model = CLIPVisionModel(config.vision_config)
+        self.visual_projection = nn.Linear(config.vision_config.hidden_size, projdim, bias=False)
+
+        self.concept_embeds = nn.Parameter(torch.ones(17, projdim), requires_grad=False)
+        self.special_care_embeds = nn.Parameter(torch.ones(3, projdim), requires_grad=False)
+        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
+        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
+
+    def forward(self, clip_input, images: Tensor, sensitivity: float):
+        with torch.no_grad():
+            image_batch = self.vision_model(clip_input)[1]
+            image_embeds = self.visual_projection(image_batch)
+            sensitivity = -0.1 + 0.14 * sensitivity
+
+            special_cos_dist = cosine_similarity(image_embeds, self.special_care_embeds)
+            special_scores_threshold = self.special_care_embeds_weights.unsqueeze(0)
+            special_scores = special_cos_dist - special_scores_threshold + sensitivity
+
+            if torch.any(special_scores > 0):
+                sensitivity = sensitivity + 0.01
+
+            cos_dist = cosine_similarity(image_embeds, self.concept_embeds)
+            concept_threshold = self.concept_embeds_weights.unsqueeze(0)
+            concept_scores = cos_dist - concept_threshold + sensitivity
+
+            is_nsfw = [torch.any(concept_scores[i] > 0) for i in range(concept_scores.shape[0])]
+            is_nsfw = [x.item() for x in is_nsfw]
+            return self.filter_images(images, is_nsfw)
+
+    def filter_images(self, images: Tensor, is_nsfw: list[bool]):
+        if not any(is_nsfw):
+            return images
+
+        images = images.clone()
+        images_to_filter = (i for i, nsfw in enumerate(is_nsfw) if nsfw)
+        orig_size = images.shape[-2:]
+        for idx in images_to_filter:
+            filtered = images[idx].unsqueeze(0)
+            filtered = F.interpolate(filtered, size=64, mode="nearest")
+            filtered = box_blur(filtered, 11, separable=True)
+            filtered = F.interpolate(filtered, size=orig_size, mode="bilinear")
+            images[idx] = filtered.squeeze(0)
+        return images
+
+
+class CachedModels:
+    _instance: CachedModels | None = None
+
+    def __init__(self):
+        model_dir = Path(__file__).parent / "safetychecker"
+        model_file = model_dir / "model.safetensors"
+        if not model_file.exists():
+            self.download(
+                "https://huggingface.co/CompVis/stable-diffusion-safety-checker/resolve/refs%2Fpr%2F41/model.safetensors",
+                target=model_file,
+            )
+        self.feature_extractor = CLIPImageProcessor.from_pretrained(model_dir)
+        self.safety_checker = CLIPSafetyChecker.from_pretrained(model_dir)
+
+    @classmethod
+    def load(cls):
+        if cls._instance is None:
+            cls._instance = CachedModels()
+        return cls._instance
+
+    def download(self, url: str, target: Path):
+        import requests
+
+        try:
+            target_temp = target.with_suffix(".download")
+            with requests.get(url, stream=True) as response:
+                text = "NSFWFilter model download"
+                total = int(response.headers.get("content-length", 0))
+                pbar = tqdm(None, total=total, unit="b", unit_scale=True, desc=text)
+                with open(target_temp, "wb") as f:
+                    for chunk in response.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                        pbar.update(len(chunk))
+                pbar.close()
+            target_temp.rename(target)
+        except Exception as e:
+            raise RuntimeError(
+                f"NSFWFilter: Failed to download safety-checker model from {url} to target location {target}: {e}"
+            ) from e
+
+
+class NSFWFilter(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_NSFWFilter",
+            display_name="NSFW Filter",
+            category="external_tooling",
+            inputs=[
+                io.Image.Input("image"),
+                io.Float.Input("sensitivity", default=0.5, min=0.0, max=1.0, step=0.1),
+            ],
+            outputs=[io.Image.Output(display_name="image")],
+        )
+
+    @classmethod
+    def execute(cls, image: Tensor, sensitivity: float):
+        models = CachedModels.load()
+        image = to_bchw(image)
+        input = models.feature_extractor(image, do_rescale=False, return_tensors="pt")
+        filtered = models.safety_checker(
+            images=image, clip_input=input.pixel_values, sensitivity=sensitivity
+        )
+        return io.NodeOutput(to_bhwc(filtered))
@@ -1,12 +1,20 @@
 [project]
 name = "comfyui-tooling-nodes"
 description = "Provides nodes and server API extensions geared towards using ComfyUI as a backend for external tools."
-version = "1.0.0"
-license = "LICENSE"
+version = "3.1.0"
+license = { file = "LICENSE" }

 [project.urls]
 Repository = "https://github.com/Acly/comfyui-tooling-nodes"

+[tool.ruff]
+target-version = "py311"
+line-length = 100
+preview = true
+
+[tool.ruff.lint]
+ignore = ["E741"]
+
 [tool.black]
 line-length = 100
 preview = true
@@ -8,6 +8,7 @@ import torch.nn.functional as F
 import math
 from torch import Tensor, Size
 from comfy.model_patcher import ModelPatcher
+from comfy_api.latest import io


 def downsample_mask(mask: Tensor, batch: int, target_size: int, original_shape: Size) -> Tensor:
@@ -65,103 +66,112 @@ class Region(NamedTuple):
        return result


-class BackgroundRegion:
+Regions = io.Custom("Regions")
+
+
+class BackgroundRegion(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"conditioning": ("CONDITIONING",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_BackgroundRegion",
+            display_name="Background Region",
+            category="external_tooling/regions",
+            inputs=[io.Conditioning.Input("conditioning")],
+            outputs=[Regions.Output(display_name="regions")],
+        )

-    CATEGORY = "external_tooling/regions"
-    RETURN_TYPES = ("REGIONS",)
-    FUNCTION = "define"
-
-    def define(self, conditioning: list):
+    @classmethod
+    def execute(cls, conditioning: list):
        return (Region(None, None, conditioning),)


-class DefineRegion:
+class DefineRegion(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "conditioning": ("CONDITIONING",),
-            },
-            "optional": {
-                "regions": ("REGIONS",),
-            },
-        }
-
-    CATEGORY = "external_tooling/regions"
-    RETURN_TYPES = ("REGIONS",)
-    FUNCTION = "define"
-
-    def define(self, mask: Tensor, conditioning: list, regions: Region | None = None):
-        return (Region(regions, mask, conditioning),)
-
-
-class ListRegionMasks:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"regions": ("REGIONS",)}}
-
-    CATEGORY = "external_tooling/regions"
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "get_masks"
-
-    def get_masks(self, regions: Region):
-        return (torch.stack([r.mask for r in regions.preprocess()], dim=0),)
-
-
-class AttentionMask:
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_DefineRegion",
+            display_name="Define Region",
+            category="external_tooling/regions",
+            inputs=[
+                io.Mask.Input("mask"),
+                io.Conditioning.Input("conditioning"),
+                Regions.Input("regions", optional=True),
+            ],
+            outputs=[Regions.Output(display_name="regions")],
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "regions": ("REGIONS",),
-            }
-        }
+    def execute(cls, mask: Tensor, conditioning: list, regions: Region | None = None):
+        if mask.dim() < 3:
+            mask = mask.unsqueeze(0)
+        return io.NodeOutput(Region(regions, mask, conditioning))

-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "attention_mask"
-    CATEGORY = "external_tooling/regions"

-    mask: Tensor
-    conds: list[Tensor]
-    batch_size: int
+class ListRegionMasks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ListRegionMasks",
+            display_name="List Region Masks",
+            category="external_tooling/regions",
+            inputs=[Regions.Input("regions")],
+            outputs=[io.Mask.Output(display_name="masks")],
+        )

-    def attention_mask(self, model: ModelPatcher, regions: Region):
-        new_model = model.clone()
-        region_list = regions.preprocess()
-        num_conds = len(region_list)
+    @classmethod
+    def execute(cls, regions: Region):
+        return io.NodeOutput(torch.stack([r.mask for r in regions.preprocess()], dim=0))

+
+class AttentionMask(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_AttentionMask",
+            display_name="Regions Attention Mask",
+            category="external_tooling/regions",
+            inputs=[io.Model.Input("model"), Regions.Input("regions")],
+            outputs=[io.Model.Output(display_name="model")],
+        )
+
+    @classmethod
+    def execute(cls, model: ModelPatcher, regions: Region):
+        return io.NodeOutput(AttentionMaskPatch.apply(model, regions))
+
+
+class AttentionMaskPatch:
+    def __init__(self, region_list: list[Region]):
        mask = torch.stack([r.mask for r in region_list], dim=0)
        mask_sum = mask.sum(dim=0, keepdim=True)
        assert mask_sum.sum() > 0, "There are areas that are zero in all masks."
        self.mask = mask / mask_sum
-
        self.conds = [r.conditioning[0][0] for r in region_list]
-        num_tokens = [cond.shape[1] for cond in self.conds]
+        self.num_tokens = [cond.shape[1] for cond in self.conds]
+        self.num_conds = len(region_list)
+        self.batch_size = 0
+
+    @staticmethod
+    def apply(model: ModelPatcher, regions: Region):
+        patch = AttentionMaskPatch(regions.preprocess())

        def attn2_patch(q: Tensor, k: Tensor, v: Tensor, extra_options: dict):
            assert k.mean() == v.mean(), "k and v must be the same."
            device, dtype = q.device, q.dtype

-            if self.conds[0].device != device:
-                self.conds = [cond.to(device, dtype=dtype) for cond in self.conds]
-            if self.mask.device != device:
-                self.mask = self.mask.to(device, dtype=dtype)
+            if patch.conds[0].device != device or patch.conds[0].dtype != dtype:
+                patch.conds = [cond.to(device, dtype=dtype) for cond in patch.conds]
+            if patch.mask.device != device or patch.mask.dtype != dtype:
+                patch.mask = patch.mask.to(device, dtype=dtype)

            cond_or_unconds = extra_options["cond_or_uncond"]
            num_chunks = len(cond_or_unconds)
-            self.batch_size = q.shape[0] // num_chunks
+            patch.batch_size = q.shape[0] // num_chunks
            q_chunks = q.chunk(num_chunks, dim=0)
            k_chunks = k.chunk(num_chunks, dim=0)
-            lcm_tokens = lcm_for_list(num_tokens + [k.shape[1]])
+            lcm_tokens = lcm_for_list(patch.num_tokens + [k.shape[1]])
            conds_tensor = [
-                cond.repeat(self.batch_size, lcm_tokens // num_tokens[i], 1)
-                for i, cond in enumerate(self.conds)
+                cond.repeat(patch.batch_size, lcm_tokens // patch.num_tokens[i], 1)
+                for i, cond in enumerate(patch.conds)
            ]
            conds_tensor = torch.cat(conds_tensor, dim=0)

@@ -172,9 +182,9 @@ class AttentionMask:
                    qs.insert(0, q_chunks[i])
                    ks.insert(0, k_target)
                else:
-                    qs.insert(0, q_chunks[i].repeat(num_conds, 1, 1))
+                    qs.insert(0, q_chunks[i].repeat(patch.num_conds, 1, 1))
                    ks.insert(0, conds_tensor)
-                    for _ in range(num_conds - 1):
+                    for _ in range(patch.num_conds - 1):
                        cond_or_unconds.insert(i, 0)

            qs = torch.cat(qs, dim=0)
@@ -182,29 +192,32 @@ class AttentionMask:
            return qs, ks, ks

        def attn2_output_patch(out: Tensor, extra_options: dict):
+            num_conds = patch.num_conds
            cond_or_unconds = extra_options["cond_or_uncond"]
            mask_downsample = downsample_mask(
-                self.mask, self.batch_size, out.shape[1], extra_options["original_shape"]
+                patch.mask, patch.batch_size, out.shape[1], extra_options["original_shape"]
            )
            outputs: list[Tensor] = []
            pos = 0
            i = 0
            while i < len(cond_or_unconds):
                if cond_or_unconds[i] == 1:  # uncond
-                    outputs.append(out[pos : pos + self.batch_size])
-                    pos += self.batch_size
+                    outputs.append(out[pos : pos + patch.batch_size])
+                    pos += patch.batch_size
                else:
-                    masked = out[pos : pos + num_conds * self.batch_size] * mask_downsample
-                    masked = masked.view(num_conds, self.batch_size, out.shape[1], out.shape[2])
+                    masked = out[pos : pos + num_conds * patch.batch_size] * mask_downsample
+                    masked = masked.view(num_conds, patch.batch_size, out.shape[1], out.shape[2])
                    masked = masked.sum(dim=0)
                    outputs.append(masked)
-                    pos += num_conds * self.batch_size
+                    pos += num_conds * patch.batch_size
                    for _ in range(num_conds - 1):
                        cond_or_unconds.pop(i)
                i += 1

            return torch.cat(outputs, dim=0)

+        new_model = model.clone()
        new_model.set_model_attn2_patch(attn2_patch)
        new_model.set_model_attn2_output_patch(attn2_output_patch)
-        return (new_model,)
+        new_model.set_attachments("etn_attention_mask", patch)
+        return new_model
@@ -0,0 +1,2 @@
+# Optional, only required for Translate node:
+argostranslate
@@ -0,0 +1,171 @@
+{
+  "_name_or_path": "clip-vit-large-patch14/",
+  "architectures": [
+    "SafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+	"id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.21.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}
@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}
@@ -3,49 +3,25 @@ import numpy as np
 import numpy.typing as npt
 import torch
 from torch import Tensor
+from comfy_api.latest import io

 IntArray = npt.NDArray[np.int_]


 class TileLayout:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "min_tile_size": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
-                "padding": ("INT", {"default": 32, "min": 0, "max": 8192, "step": 8}),
-                "blending": ("INT", {"default": 8, "min": 0, "max": 256, "step": 8}),
-            }
-        }
-
-    CATEGORY = "external_tooling/tiles"
-    RETURN_TYPES = ("TILE_LAYOUT",)
-    FUNCTION = "node"
-
-    image_size: IntArray
-    tile_size: IntArray
-    padding: int
-    blending: int
-    tile_count: IntArray
-
-    def node(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
-        self.init(image, min_tile_size, padding, blending)
-        return (self,)
-
-    def init(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
+    def __init__(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
        assert all([x % 8 == 0 for x in image.shape[-3:-1]]), "Image size must be divisible by 8"
        assert min_tile_size % 8 == 0, "Tile size must be divisible by 8"
-        assert blending < padding, "Blending must be smaller than padding"
+        assert blending <= padding, "Blending must be smaller than padding"

-        self.image_size = np.array(image.shape[-3:-1])
-        self.padding = padding
-        self.blending = blending
-        self.tile_count = self.image_size // (min_tile_size - 2 * padding)
+        self.image_size: IntArray = np.array(image.shape[-3:-1])
+        self.padding: int = padding
+        self.blending: int = blending
+        self.tile_count: IntArray = np.maximum(1, self.image_size // (min_tile_size - 2 * padding))

        image_size_with_overlap = self.image_size + (self.tile_count - 1) * 2 * padding
        tile_size = np.ceil(image_size_with_overlap / self.tile_count)
-        self.tile_size = (np.ceil(tile_size / 8) * 8).astype(int)
+        self.tile_size: IntArray = (np.ceil(tile_size / 8) * 8).astype(int)

    def size(self, coord: IntArray):
        return self.end(coord) - self.start(coord)
@@ -85,7 +61,7 @@ class TileLayout:
        mask = torch.zeros((1, 1, size[0], size[1]), dtype=torch.float)
        mask[:, :, s[0] : e[0], s[1] : e[1]] = 1.0
        if blend and self.blending > 0:
-            mask = box_blur(mask, (self.blending, self.blending), separable=True)
+            mask = box_blur(mask, (self.blending, self.blending))
        return mask.squeeze(0)

    def merge(self, image: Tensor, index: int, tile: Tensor):
@@ -93,83 +69,111 @@ class TileLayout:
        rect = self.rect(coord)
        mask = self.mask(coord, blend=True)
        mask = mask.reshape(*mask.shape, 1).repeat(1, 1, 1, image.shape[-1])
-        image[*rect] = (1 - mask) * image[*rect] + mask * tile
+        image[rect] = (1 - mask) * image[rect] + mask * tile


-class ExtractImageTile:
+class CreateTileLayout(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "layout": ("TILE_LAYOUT",),
-                "index": ("INT", {"min": 0}),
-            }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_TileLayout",
+            display_name="Create Tile Layout",
+            category="external_tooling/tiles",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("min_tile_size", default=512, min=64, max=8192, step=8),
+                io.Int.Input("padding", default=32, min=0, max=8192, step=8),
+                io.Int.Input("blending", default=8, min=0, max=256, step=8),
+            ],
+            outputs=[io.Custom("TileLayout").Output(display_name="layout")],
+        )

-    CATEGORY = "external_tooling/tiles"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "slice"
-
-    def slice(self, image: Tensor, layout: TileLayout, index: int):
-        return (layout.tile(image, index),)
-
-
-class ExtractMaskTile:
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "layout": ("TILE_LAYOUT",),
-                "index": ("INT", {"min": 0}),
-            }
-        }
+    def execute(cls, image: Tensor, min_tile_size: int, padding: int, blending: int):
+        return io.NodeOutput(TileLayout(image, min_tile_size, padding, blending))

-    CATEGORY = "external_tooling/tiles"
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "slice"

-    def slice(self, mask: Tensor, layout: TileLayout, index: int):
+class ExtractImageTile(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ExtractImageTile",
+            display_name="Extract Image Tile",
+            category="external_tooling/tiles",
+            inputs=[
+                io.Image.Input("image"),
+                io.Custom("TileLayout").Input("layout"),
+                io.Int.Input("index", default=0, min=0),
+            ],
+            outputs=[io.Image.Output(display_name="tile")],
+        )
+
+    @classmethod
+    def execute(cls, image: Tensor, layout: TileLayout, index: int):
+        return io.NodeOutput(layout.tile(image, index))
+
+
+class ExtractMaskTile(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_ExtractMaskTile",
+            display_name="Extract Mask Tile",
+            category="external_tooling/tiles",
+            inputs=[
+                io.Mask.Input("mask"),
+                io.Custom("TileLayout").Input("layout"),
+                io.Int.Input("index", default=0, min=0),
+            ],
+            outputs=[io.Mask.Output(display_name="tile")],
+        )
+
+    @classmethod
+    def execute(cls, mask: Tensor, layout: TileLayout, index: int):
        tile = layout.tile(mask.unsqueeze(3), index)
-        return (tile.squeeze(3),)
+        return io.NodeOutput(tile.squeeze(3))


-class GenerateTileMask:
+class GenerateTileMask(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {"layout": ("TILE_LAYOUT",), "index": ("INT", {"min": 0})},
-            "optional": {"blend": ("BOOLEAN",)},
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_GenerateTileMask",
+            display_name="Generate Tile Mask",
+            category="external_tooling/tiles",
+            inputs=[
+                io.Custom("TileLayout").Input("layout"),
+                io.Int.Input("index", default=0, min=0),
+                io.Boolean.Input("blend", default=False, optional=True),
+            ],
+            outputs=[io.Mask.Output(display_name="mask")],
+        )

-    CATEGORY = "external_tooling/tiles"
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "generate"
-
-    def generate(self, layout: TileLayout, index: int, blend: bool = False):
-        return (layout.mask(layout.coord(index), blend=blend),)
-
-
-class MergeImageTile:
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "layout": ("TILE_LAYOUT",),
-                "index": ("INT", {"min": 0}),
-                "tile": ("IMAGE",),
-            }
-        }
+    def execute(cls, layout: TileLayout, index: int, blend: bool = False):
+        return io.NodeOutput(layout.mask(layout.coord(index), blend=blend))

-    CATEGORY = "external_tooling/tiles"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "merge"

-    def merge(self, image: Tensor, layout: TileLayout, index: int, tile: Tensor):
+class MergeImageTile(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_MergeImageTile",
+            display_name="Merge Image Tile",
+            category="external_tooling/tiles",
+            inputs=[
+                io.Image.Input("image"),
+                io.Custom("TileLayout").Input("layout"),
+                io.Int.Input("index", default=0, min=0),
+                io.Image.Input("tile"),
+            ],
+            outputs=[io.Image.Output(display_name="image")],
+        )
+
+    @classmethod
+    def execute(cls, image: Tensor, layout: TileLayout, index: int, tile: Tensor):
        assert index < layout.total_count, f"Index {index} out of range"
        if index == 0:
            image = image.clone()
        layout.merge(image, index, tile)
-        return (image,)
+        return io.NodeOutput(image)
@@ -0,0 +1,119 @@
+"""Text translation using Argos Translate.
+
+The node takes text input and translates it to English. The text may contain any
+number of language directives in the form `lang:xx` where `xx` is a two-letter
+language code. Text fragments after a language directives are translated.
+If the language is `en` text is passed through unmodified.
+"""
+
+from __future__ import annotations
+import re
+from functools import cache
+from typing import NamedTuple
+from comfy_api.latest import io
+
+
+@cache
+def available_languages():
+    try:
+        from argostranslate.package import update_package_index, get_available_packages
+
+        update_package_index()
+        list = get_available_packages()
+        return [(l.from_code, l.from_name) for l in list if l.to_code == "en"]
+    except ImportError:
+        return [("NOT INSTALLED", "NOT INSTALLED")]
+
+
+def translate_chunk(text: str, language: str):
+    if text.strip() == "":
+        return text
+
+    target = "en"
+    if language == target:
+        return text
+
+    try:
+        from argostranslate.package import get_installed_packages, get_available_packages
+        from argostranslate.translate import translate
+
+        installed = get_installed_packages()
+        if not any(p.from_code == language and p.to_code == target for p in installed):
+            available = get_available_packages()
+            pkg = next(
+                (p for p in available if p.from_code == language and p.to_code == target), None
+            )
+            assert pkg, f"Couldn't find package for translation from {language}"
+            # print("Downloading and installing translation package", pkg)  # this will cause encoding errors
+            pkg.install()
+
+        text, embeddings = _extract_embeddings(text)
+        translation = translate(text, language, target)
+        return embeddings + translation
+
+    except ImportError:
+        raise ImportError(
+            "Argos Translate is not installed. Please install it with `pip install argostranslate`"
+        )
+
+
+def translate(text: str):
+    chunks = Chunk.parse(text)
+    return " ".join(translate_chunk(c.text, c.lang) for c in chunks)
+
+
+class Translate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_Translate",
+            display_name="Translate Text",
+            category="external_tooling",
+            inputs=[io.String.Input("text", multiline=True)],
+            outputs=[io.String.Output(display_name="translation")],
+        )
+
+    @classmethod
+    def execute(cls, text: str):
+        return io.NodeOutput(translate(text))
+
+
+_lang_regex = re.compile(r"(lang:\w\w)")
+
+
+class Chunk(NamedTuple):
+    text: str
+    lang: str
+
+    @staticmethod
+    def parse(text: str):
+        languages = [code for code, name in available_languages()] + ["en"]
+        chunks: list[Chunk] = []
+        lang = "en"
+        last = 0
+        for m in _lang_regex.finditer(text):
+            if m.start() > 0:
+                chunks.append(Chunk(text[last : m.start()].strip(), lang))
+            last = m.end()
+            lang = m.group(0)[5:]
+            if lang not in languages:
+                raise ValueError(
+                    f"Invalid language directive {m.group(0)} - {lang} is not a known language code."
+                    f" Available languages: {', '.join(languages)}"
+                )
+        if last < len(text):
+            chunks.append(Chunk(text[last:].strip(), lang))
+        return [c for c in chunks if c.text != ""]
+
+
+_embedding_regex = re.compile(r"(embedding:[^\s,]+)")
+
+
+def _extract_embeddings(text: str):
+    matches = _embedding_regex.findall(text)
+    embeddings = " ".join(matches)
+    if matches:
+        embeddings += " "
+    for m in matches:
+        text = text.replace(m, "")
+    return text, embeddings
@@ -0,0 +1,771 @@
+{
+  "last_node_id": 78,
+  "last_link_id": 126,
+  "nodes": [
+    {
+      "id": 60,
+      "type": "LoadImage",
+      "pos": [
+        -692,
+        -758
+      ],
+      "size": {
+        "0": 310.5925598144531,
+        "1": 335.9309997558594
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            101,
+            110,
+            111,
+            112,
+            113
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "photo.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 74,
+      "type": "ETN_MergeImageTile",
+      "pos": [
+        -326,
+        -250
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 120
+        },
+        {
+          "name": "layout",
+          "type": "TILE_LAYOUT",
+          "link": 122,
+          "slot_index": 1
+        },
+        {
+          "name": "tile",
+          "type": "IMAGE",
+          "link": 121
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            123
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ETN_MergeImageTile"
+      },
+      "widgets_values": [
+        3
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 61,
+      "type": "ETN_TileLayout",
+      "pos": [
+        -333,
+        -639
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 101
+        }
+      ],
+      "outputs": [
+        {
+          "name": "TILE_LAYOUT",
+          "type": "TILE_LAYOUT",
+          "links": [
+            102,
+            104,
+            105,
+            106,
+            122,
+            124
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ETN_TileLayout"
+      },
+      "widgets_values": [
+        880,
+        48,
+        16
+      ]
+    },
+    {
+      "id": 63,
+      "type": "PreviewImage",
+      "pos": [
+        380,
+        -700
+      ],
+      "size": {
+        "0": 221.69317626953125,
+        "1": 191.44602966308594
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 103
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 69,
+      "type": "PreviewImage",
+      "pos": [
+        620,
+        -470
+      ],
+      "size": {
+        "0": 231.39317321777344,
+        "1": 200.04603576660156
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 109
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 75,
+      "type": "PreviewImage",
+      "pos": [
+        25,
+        -220
+      ],
+      "size": {
+        "0": 321.5931701660156,
+        "1": 266.5460205078125
+      },
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 123
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 76,
+      "type": "ETN_GenerateTileMask",
+      "pos": [
+        376,
+        -207
+      ],
+      "size": {
+        "0": 210,
+        "1": 85.74603271484375
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "layout",
+          "type": "TILE_LAYOUT",
+          "link": 124,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            125
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ETN_GenerateTileMask"
+      },
+      "widgets_values": [
+        3,
+        true
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 77,
+      "type": "MaskToImage",
+      "pos": [
+        620,
+        -210
+      ],
+      "size": {
+        "0": 210,
+        "1": 26
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 125
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            126
+          ],
+          "shape": 3,
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Author	SHA1	Message	Date
Acly	9d2e03e8d5	Version 3.1.0	2026-01-05 10:18:20 +01:00
Acly	c5606f8e8f	API: print filename with stack traces when there is an error during inspection	2026-01-05 10:16:54 +01:00
Acly	79e9b6426f	Support transmitting partial tiles/crops of the canvas in Krita workflows	2025-12-30 23:47:29 +01:00
Acly	ad320a218c	Support additional output info for Krita workflows: name, animation, layers	2025-12-29 21:20:57 +01:00
Jax	a310f4593b	Transmit request to resize the canvas with Krita Output node (#52 ) * Added Krita Resize node for plugin * Registered canvas resize node to the __init__.py * Removed resizenode and integrated it into the "Krita output" * Quick update to simplify the node to return the re-sized image to krita instead of a json	2025-12-29 19:35:06 +01:00
Acly	7d957dcfa7	Model inspection: support Z-Image SVDQ (Nunchaku) files	2025-12-22 11:25:09 +01:00
Acly	22cfd71f95	Fix detection of integer widget for Parameter node	2025-12-17 11:05:00 +01:00
Acly	21a2f44d4c	Fix Parameter node min/max being reset to default when it's set to 0 #53 * Use a different default than 0 as workaround * Don't want to change type of min/max as that would break workflows	2025-12-17 10:40:58 +01:00
Acly	0220252912	Version 3.0.1	2025-12-01 09:36:18 +01:00
Acly	f447ef70fa	Model inspection: support Z-Image GGUFs	2025-11-29 20:26:00 +01:00
Acly	fb27a5bda8	Model inspection: support Lumina2, Z-Image, Flux2	2025-11-28 20:00:00 +01:00
Acly	aa83259e66	Change image cache to take size into account	2025-11-09 15:01:52 +01:00
Acly	75c632df4b	Version 3.0.0	2025-11-03 10:57:01 +01:00
Acly	a088a2dde2	API: support pagination for /api/etn/model_info	2025-10-23 14:41:24 +02:00
Acly	fbf99f2a08	Add LoadImageCached and SaveImageCached (renamed from SendImageHTTP) * short-lived in-memory cache for image transfers * upload images via HTTP to cache and load/reference them in workflows * save/store images in workflows to cache and download them via HTTP	2025-10-20 14:39:42 +02:00
Acly	dfe014ae88	Fix region attention mask not being applied	2025-10-19 12:45:21 +02:00
Acly	6a7ae5ab70	Add SendImageHTTP node * alternative to SendImageWebSocket * requires an extra step, but transfers are much faster for large images * doesn't involve saving files to disk	2025-10-19 12:06:19 +02:00
Acly	d4cac6ac95	Change node definitions to "V3" schema, remove CropImage node	2025-10-18 23:59:05 +02:00
Aoi	929fdfcc13	Comment out translation package download print statement Comment out print statement to prevent encoding errors.	2025-10-18 10:55:56 +02:00
Acly	20f8d8ecc9	Version 2.0.6	2025-10-11 19:17:02 +02:00
Acly	f555efb71b	API: fix qwen svdq models not having the quant field set	2025-10-05 12:54:04 +02:00
Acly	db4f296533	API: map GGUF "qwen_image" arch to "qwen-image" to be consistent with safetensors models	2025-10-05 12:41:06 +02:00
Acly	17c36ebc70	Parameter node: avoid unhandled exception when input is not a widget	2025-10-05 11:15:12 +02:00
Acly	0697a3ac1f	Add active output to KritaSelection node (true if there is a selection, false otherwise)	2025-09-06 23:33:07 +02:00
Acly	fa46b93329	Model inspection: support Qwen and Nunchaku quants	2025-08-20 21:30:16 +02:00
Acly	fa84eec8fc	Model inspection: support more base models	2025-08-09 10:40:43 +02:00
Acly	5ef2fddc1b	Version 2.0.3	2025-06-15 12:32:46 +02:00
Acly	bff22b8351	Model inspection: don't filter keys for diffusion models (strips things like vpred and zsnr keys) - this fixes sdxl-vpred diffusion models being detected as eps	2025-05-28 20:45:58 +02:00
Acly	ca2b59248e	Fix error when importing workflows that contain Parameter nodes connected to nodes that aren't installed	2025-05-22 15:46:42 +02:00
Acly	696899a5fc	Allow to run workflows with parameter nodes - fix validation error due to missing min/max - fix errors due to numbers being passed as string	2025-05-12 15:51:42 +02:00
Acly	5f4373d71a	Version 2.0.2	2025-04-28 10:11:42 +02:00
Acly	61d2a19120	Remove `data:image/png;base64,` prefix in base64 strings if present #39	2025-04-27 19:44:17 +02:00
Acly	a6af76ac39	Fix default values of Parameter node not being editable #38	2025-04-27 17:21:13 +02:00
Acly	6a5c8e02e5	Fix error response when a model folder cannot be found	2025-04-27 09:51:59 +02:00
Acly	c2308a0762	Don't run publish action on forks, close #114	2025-03-31 11:49:17 +02:00
Acly	b8e4659a10	Add optional mask output for Krita Image Layer node	2025-03-01 20:20:52 +01:00
Acly	93e1932456	Don't return alpha channel from LoadImageBase64 inverted ... why was it ever inverted?	2025-03-01 20:20:24 +01:00
Acly	ea755151fe	Add Lumina 2 to known base models	2025-02-13 16:48:23 +01:00
Acly	facd65995a	Version 2.0.1, remove some unused code and imports	2025-02-02 20:17:51 +01:00
Acly	d7b18203a4	Use comfy built-in any type (*) matching	2025-02-01 00:03:33 +01:00
Acly	1839c099ad	Fix type matching BOOL -> BOOLEAN	2025-01-31 23:44:51 +01:00
Acly	bed8b36705	Fix assertion when using tiling with padding=blending=0	2025-01-26 11:57:11 +01:00
Acly	8ed5591574	API breaking: removed is_refiner attribute from model inspection - sdxl refiner is reported with base model "sdxl-refiner" - added type attribute for sdxl model, allows to detect eps/v-prediction	2025-01-12 17:53:47 +01:00
Acly	fe39d22eb9	Return a more specific error when inspect model folder doesn't exist	2024-12-07 19:49:00 +01:00
Acly	50d3479fba	Code compatibility (match was no longer useful anyway) #31	2024-11-30 00:29:00 +01:00
Acly	d7d421baaa	Model detection: add support for (some) GGUF and Flux Inpaint models - GGUF detection only works for converted models	2024-11-29 09:46:45 +01:00
Acly	e10daee9ed	Nodes for stacking and weighting reference images with flux redux model	2024-11-24 22:51:42 +01:00
Acly	50c3ffdf64	Parameter node: Fix type reset to default for connected widget on reload #29	2024-11-15 15:52:50 +01:00
Acly	517790d1d6	Parameter node: fix not being able to enter negative numbers for min/max	2024-11-11 11:47:53 +01:00
Acly	e2bd09d7e9	Parameter node: Restrict initial type choice to avoid mismatch between type and default value before connecting the output	2024-10-29 13:03:53 +01:00
Acly	035c68c629	Parameter node: keep configured default values when reloading #25 - make sure default is changed if the node is reconnected to a non-matching type	2024-10-29 12:13:42 +01:00
Acly	e86973fedf	Remove image format parameter from Krita Output node	2024-10-28 14:56:01 +01:00
Acly	19337dcc0e	Fix parameter node not being connectable #23	2024-10-28 13:11:33 +01:00
Acly	1d4ffe14bb	Add a Send Text node - converts any input to string and sends it as output (websocket message)	2024-10-27 10:43:43 +01:00
Acly	20c8039a98	Detect some diffusion models which have prefix like checkpoints	2024-10-25 13:08:41 +02:00
Acly	fcf678735c	New package version	2024-10-23 13:05:24 +02:00
Acly	63ab33800e	Fix Parameter node type comparison for workflow validation	2024-10-21 15:11:01 +02:00
Acly	ef5ccfa98f	Fix Parameter node widget values being reset when switching or reloading workflows	2024-10-18 16:38:01 +02:00
Acly	0b01696f5b	Fix workflow/unsubscribe endpoint	2024-10-14 13:20:41 +02:00
Acly	2fce4c56d5	Fix "Object of type _BasicTypes is not JSON serializable"	2024-10-12 15:37:38 +02:00
Acly	a7f77032ec	Make parameter nodes validate when executed	2024-10-11 21:46:49 +02:00
Acly	72335898cb	Detect better parameter type defaults	2024-10-11 21:20:31 +02:00
Acly	9a9cbe78a5	High level krita parameters (style, control-net, ip-adapter)	2024-10-11 09:30:12 +02:00
Acly	9a8d90dd95	Replace typed parameter nodes with a universal parameter node which adapts to the first widget it is connected to	2024-10-06 23:58:25 +02:00
Acly	24b7aabf8b	Add placeholder image when running external nodes from web ui	2024-10-04 21:27:19 +02:00
Acly	81f944f119	Add custom icons to some of the krita interop nodes	2024-10-04 20:46:56 +02:00
Acly	327b2a1fe3	More parameter nodes for shared workflows	2024-10-04 11:04:35 +02:00
Acly	fb847a5225	Publish workflows only if there's a related sink node in the graph	2024-10-02 17:56:19 +02:00
Acly	5a45172d02	API to exchange workflows between multiple connected clients Placeholder nodes to parametrize and run custom workflows from Krita	2024-10-02 11:07:02 +02:00
Acly	29e24ec52c	Initial API for workflow exchange between ComfyUI clients	2024-09-23 20:59:57 +02:00
Marco Tundo	e5e62a4a79	Added filetype selector to SendImageWebSocket	2024-09-23 20:59:19 +02:00
Acly	1a24975f99	Don't print trace if a model can't be detected (leads to more confusion than it helps)	2024-09-20 09:53:12 +02:00
Acly	61fa161c34	Regions: also check if dtype matches	2024-09-12 10:17:37 +02:00
Acly	f986f6a442	Document upload api	2024-08-30 12:10:16 +02:00
Acly	e0d0c3cc2c	Add model upload API endpoint - folder must match existing model folder - file must be safetensors	2024-08-26 23:05:17 +02:00
Acly	f5ec9d830c	Make /api/etn/model_info work with diffusion_models folder (formerly unet) - endpoint is now `/api/etn/model_info/{folder_name}` - old endpoints are still available - also works with unet folder (deprecated)	2024-08-20 16:17:38 +02:00
Acly	d1dcf12f10	Fix detection for HunyuanDit #17	2024-08-09 18:54:05 +02:00
Acly	cb92e547c6	Version 1.4.0, fix toml license directive	2024-08-09 10:08:30 +02:00
Acly	b5fec4a062	Model info api: add aura-flow, hunyuan-dit, flux	2024-08-05 00:25:26 +02:00
Acly	42965013f9	Add __future__ imports for older python	2024-07-28 13:14:29 +02:00
Acly	d20615fb48	Support language directives in translate api, add documentation	2024-07-27 15:37:20 +02:00
Acly	5bad00f72f	Remove debug prints	2024-07-24 14:38:51 +02:00
Acly	df54344077	Translate: parse language directives included in the text	2024-07-24 12:00:07 +02:00
Acly	f42c0f29b6	Don't translate embeddings	2024-07-22 18:48:02 +02:00
Acly	547c3d5c97	Add text translation node & API	2024-07-22 16:53:09 +02:00
Acly	73babbd00e	Add NSFWFilter node	2024-07-21 20:47:18 +02:00
Acly	cac32fe37c	Move image channel permutation to separate functions	2024-07-21 18:31:42 +02:00
Acly	5620b5c6e2	Document tiling nodes	2024-07-20 23:52:09 +02:00
Acly	3d4a960982	Document region nodes	2024-07-20 23:12:46 +02:00
Acly	9d533984c2	Version 1.2.0	2024-06-24 17:55:55 +02:00
Acly	715a41e04f	Prefix server API route with api/	2024-06-20 16:39:15 +02:00
Acly	aff32e8da6	Tiles: fix div by zero when image is smaller than tile size	2024-06-20 11:26:00 +02:00
Acly	e46123612d	Model info API: support SD3	2024-06-12 16:56:25 +02:00
Acly	6e7b2445db	Remove seperable=True for box_blur - not supported by older kornia versions, and probably no actual speed up at typical tile sizes	2024-06-12 09:51:03 +02:00
Acly	2f39365248	Bump version to 1.1.0	2024-06-11 12:14:23 +02:00
Acly	c324f6741d	Expand mask batch dimension if it doesn't exist	2024-06-08 09:31:29 +02:00
Acly	c27b662fd8	Don't unpack tuple within index operation (not supported by older Python)	2024-06-07 17:25:52 +02:00