Version 2.0.6

- this fixes sdxl-vpred diffusion models being detected as eps

.github/workflows/publish.yml

@@ -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 }}

.gitignore

@@ -1,4 +1,6 @@
 .vscode
 .env
 .dev
-__pycache__
+__pycache__
+
+safetychecker/*.safetensors

README.md

@@ -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)
@@ -36,9 +44,33 @@ 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
+## <a id="regions" href="#toc">Regions</a>
 
-When integrating ComfyUI into tools which use layers and compose them on the fly, it is useful to only receive relevant masked regions.
+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 +78,150 @@ 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`
+
+#### 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, lumina2, 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.
+
+### 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.
 

__init__.py

@@ -1,4 +1,4 @@
-from . import api, nodes, tile, region
+from . import api as api, nodes, tile, region, nsfw, translation, krita
 
 NODE_CLASS_MAPPINGS = {
     "ETN_LoadImageBase64": nodes.LoadImageBase64,
@@ -6,6 +6,8 @@ NODE_CLASS_MAPPINGS = {
     "ETN_SendImageWebSocket": nodes.SendImageWebSocket,
     "ETN_CropImage": nodes.CropImage,
     "ETN_ApplyMaskToImage": nodes.ApplyMaskToImage,
+    "ETN_ReferenceImage": nodes.ReferenceImage,
+    "ETN_ApplyReferenceImages": nodes.ApplyReferenceImages,
     "ETN_TileLayout": tile.TileLayout,
     "ETN_ExtractImageTile": tile.ExtractImageTile,
     "ETN_ExtractMaskTile": tile.ExtractMaskTile,
@@ -15,6 +17,16 @@ NODE_CLASS_MAPPINGS = {
     "ETN_DefineRegion": region.DefineRegion,
     "ETN_ListRegionMasks": region.ListRegionMasks,
     "ETN_AttentionMask": region.AttentionMask,
+    "ETN_NSFWFilter": nsfw.NSFWFilter,
+    "ETN_Translate": translation.Translate,
+    "ETN_KritaOutput": krita.KritaOutput,
+    "ETN_KritaSendText": krita.KritaSendText,
+    "ETN_KritaCanvas": krita.KritaCanvas,
+    "ETN_KritaSelection": krita.KritaSelection,
+    "ETN_KritaImageLayer": krita.KritaImageLayer,
+    "ETN_KritaMaskLayer": krita.KritaMaskLayer,
+    "ETN_Parameter": krita.Parameter,
+    "ETN_KritaStyle": krita.KritaStyle,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "ETN_LoadImageBase64": "Load Image (Base64)",
@@ -22,8 +34,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "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_ReferenceImage": "Reference Image",
+    "ETN_ApplyReferenceImages": "Apply Reference Images",
     "ETN_TileLayout": "Create Tile Layout",
     "ETN_ExtractImageTile": "Extract Image Tile",
     "ETN_ExtractMaskTile": "Extract Mask Tile",
@@ -33,4 +45,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ETN_DefineRegion": "Define Region",
     "ETN_ListRegionMasks": "List Region Masks",
     "ETN_AttentionMask": "Regions Attention Mask",
+    "ETN_NSFWFilter": "NSFW Filter",
+    "ETN_Translate": "Translate Text",
+    "ETN_KritaOutput": "Krita Output",
+    "ETN_KritaSendText": "Send Text",
+    "ETN_KritaCanvas": "Krita Canvas",
+    "ETN_KritaSelection": "Krita Selection",
+    "ETN_KritaImageLayer": "Krita Image Layer",
+    "ETN_KritaMaskLayer": "Krita Mask Layer",
+    "ETN_Parameter": "Parameter",
+    "ETN_KritaStyle": "Krita Style",
 }
+WEB_DIRECTORY = "./js"

api.py

@@ -1,13 +1,21 @@
+from __future__ import annotations
 from aiohttp import web
 from typing import 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
+
 input_block_name = "model.diffusion_model.input_blocks.0.0.weight"
 
 model_names = {
@@ -15,12 +23,41 @@ 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",
+    "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",
+}
+
+gguf_architectures = {
+    "sd1": "sd15",
+    "qwen_image": "qwen-image",
 }
 
 
@@ -35,10 +72,10 @@ 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 +86,14 @@ 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 +102,232 @@ 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 = {"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:
+        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)
+            return comfy_config.get("model_class")
+        model_class = md.get("model_class")
+        if model_class == "NunchakuFluxTransformer2dModel":
+            return "Flux"
+        if model_class == "NunchakuQwenImageTransformer2DModel":
+            return "QwenImage"
+    return None
 
-    @_server.routes.get("/etn/model_info")
-    async def model_info(request):
+
+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)
+        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}
+        if arch_str == "flux" and any(
+            t.name.startswith("distilled_guidance_layer")
+            for t in itertools.islice(reader.tensors, 5)
+        ):
+            arch_str = "chroma"
+
+        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)
+            result["quant"] = reader.get_field("general.file_type").lower()
+        except Exception as e:
+            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):
+    try:
+        try:
+            files = folder_paths.get_filename_list(model_type)
+        except KeyError:
+            return web.json_response({"error": f"Model folder not found: {model_type}"})
+        is_checkpoint = model_type == "checkpoints"
+        info = {
+            filename: inspect_diffusion_model(filename, model_type, is_checkpoint)
+            for filename in 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
+
+
+_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)
+
+    @_server.routes.get("/api/etn/model_info")
+    async def api_model_info(request):
+        return inspect_models("checkpoints")
+
+    @_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.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")

js/external_tooling_nodes.js

@@ -0,0 +1,237 @@
+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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")
+const outputIcon = 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)"
+    }
+    if (connectedWidget.options?.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 === 0 ? 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_KritaOutput") {
+            setIconImage(nodeType, outputIcon, [200, 100], 1, 0)
+        } 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)
+        }
+    },
+});
+
+})();

krita.py

@@ -0,0 +1,263 @@
+import sys
+import torch
+import numpy as np
+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 .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 KritaOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"images": ("IMAGE",)}}
+
+    RETURN_TYPES = ()
+    FUNCTION = "send_images"
+    OUTPUT_NODE = True
+    CATEGORY = "krita"
+
+    def send_images(self, images):
+        return SendImageWebSocket().send_images(images, "PNG")
+
+
+class KritaSendText:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": (IO.ANY, {}),
+                "name": ("STRING", {"default": "Output"}),
+                "type": (["text", "markdown", "html"], {"default": "text"}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "send"
+    OUTPUT_NODE = True
+    CATEGORY = "krita"
+
+    def send(self, 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}"
+
+        print(f"Sending text: {name} = {text}")
+        return {"ui": {"text": [{"name": name, "text": text, "content-type": mime}]}}
+
+
+class KritaCanvas:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {}
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT", "INT")
+    RETURN_NAMES = ("image", "width", "height", "seed")
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self):
+        return (_placeholder_image(), 512, 512, 0)
+
+
+class KritaSelection:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {}
+
+    RETURN_TYPES = (IO.MASK, IO.BOOLEAN)
+    RETURN_NAMES = ("mask", "active")
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self):
+        return (torch.ones(1, 512, 512), False)
+
+
+class KritaImageLayer:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Image"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask")
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self, name: str):
+        return (_placeholder_image(), torch.ones(1, 512, 512))
+
+
+class KritaMaskLayer:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Mask"}),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self, name: str):
+        return (torch.ones(1, 512, 512),)
+
+
+_param_types = [
+    "auto",
+    "number",
+    "number (integer)",
+    "toggle",
+    "choice",
+    "text",
+    "prompt (positive)",
+    "prompt (negative)",
+]
+_any_float = {"default": 0.0, "min": -sys.float_info.max, "max": sys.float_info.max}
+
+
+class Parameter:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Parameter"}),
+                "type": (_param_types, {"default": "auto"}),
+                "default": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "min": ("FLOAT", _any_float),
+                "max": ("FLOAT", _any_float),
+            },
+        }
+
+    RETURN_TYPES = (BasicTypes,)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self, name: str, type: str, default, min=0.0, max=1.0):
+        if type == "number":
+            return (float(default),)
+        elif type == "number (integer)":
+            return (int(default),)
+        return (default,)
+
+
+class KritaStyle:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "name": ("STRING", {"default": "Style"}),
+                "sampler_preset": (["auto", "regular", "live"],),
+            }
+        }
+
+    RETURN_TYPES = (
+        "MODEL",
+        "CLIP",
+        "VAE",
+        "STRING",
+        "STRING",
+        comfy.samplers.KSampler.SAMPLERS,
+        comfy.samplers.KSampler.SCHEDULERS,
+        "INT",
+        "FLOAT",
+    )
+    RETURN_NAMES = (
+        "model",
+        "clip",
+        "vae",
+        "positive prompt",
+        "negative prompt",
+        "sampler name",
+        "scheduler",
+        "steps",
+        "guidance",
+    )
+    FUNCTION = "placeholder"
+    CATEGORY = "krita"
+
+    def placeholder(self, name: str, sampler_preset: str):
+        raise NotImplementedError("This workflow must be started from Krita!")

nodes.py

@@ -1,11 +1,17 @@
 from __future__ import annotations
+from copy import copy
+from typing import NamedTuple
 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
+
 
 class LoadImageBase64:
     @classmethod
@@ -16,15 +22,16 @@ class LoadImageBase64:
     CATEGORY = "external_tooling"
     FUNCTION = "load_image"
 
-    def load_image(self, image):
+    def load_image(self, 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
@@ -42,7 +49,8 @@ class LoadMaskBase64:
     CATEGORY = "external_tooling"
     FUNCTION = "load_mask"
 
-    def load_mask(self, mask):
+    def load_mask(self, 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
@@ -55,14 +63,19 @@ class LoadMaskBase64:
 class SendImageWebSocket:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {"images": ("IMAGE",)}}
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "format": (["PNG", "JPEG"], {"default": "PNG"}),
+            }
+        }
 
     RETURN_TYPES = ()
     FUNCTION = "send_images"
     OUTPUT_NODE = True
     CATEGORY = "external_tooling"
 
-    def send_images(self, images):
+    def send_images(self, images, format):
         results = []
         for tensor in images:
             array = 255.0 * tensor.cpu().numpy()
@@ -71,13 +84,14 @@ 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}}
 
@@ -118,6 +132,22 @@ class CropImage:
         return (out,)
 
 
+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:
     @classmethod
     def INPUT_TYPES(cls):
@@ -133,29 +163,146 @@ class ApplyMaskToImage:
     FUNCTION = "apply_mask"
 
     def apply_mask(self, image: torch.Tensor, mask: torch.Tensor):
-        # Move the channel to the second dimension for processing
-        out = image.movedim(-1, 1)
-
-        # Check if the images are RGB, and if so, add an alpha channel initialized to 1
+        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:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "range_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
+                "range_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
+            },
+            "optional": {
+                "reference_images": ("REFERENCE_IMAGE",),
+            },
+        }
+
+    CATEGORY = "external_tooling"
+    RETURN_TYPES = ("REFERENCE_IMAGE",)
+    RETURN_NAMES = ("reference_images",)
+    FUNCTION = "append"
+
+    def append(
+        self,
+        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:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "conditioning": ("CONDITIONING",),
+                "clip_vision": ("CLIP_VISION",),
+                "style_model": ("STYLE_MODEL",),
+                "references": ("REFERENCE_IMAGE",),
+            }
+        }
+
+    CATEGORY = "external_tooling"
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply"
+
+    def apply(
+        self,
+        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

nsfw.py

@@ -0,0 +1,146 @@
+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 .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: WeakRef | 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):
+        models = cls._instance and cls._instance()
+        if models is None:
+            models = cls()
+            cls._instance = WeakRef(models)
+        return models
+
+    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:
+    models: CachedModels
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "sensitivity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.10}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "check"
+    CATEGORY = "external_tooling"
+
+    def __init__(self):
+        self.models = CachedModels.load()
+
+    def check(self, image, sensitivity):
+        image = to_bchw(image)
+        input = self.models.feature_extractor(image, do_rescale=False, return_tensors="pt")
+        filtered = self.models.safety_checker(
+            images=image, clip_input=input.pixel_values, sensitivity=sensitivity
+        )
+        return (to_bhwc(filtered),)

pyproject.toml

@@ -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 = "2.0.6"
+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

region.py

@@ -96,6 +96,8 @@ class DefineRegion:
     FUNCTION = "define"
 
     def define(self, mask: Tensor, conditioning: list, regions: Region | None = None):
+        if mask.dim() < 3:
+            mask = mask.unsqueeze(0)
         return (Region(regions, mask, conditioning),)
 
 
@@ -113,7 +115,6 @@ class ListRegionMasks:
 
 
 class AttentionMask:
-
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -148,9 +149,9 @@ class AttentionMask:
             assert k.mean() == v.mean(), "k and v must be the same."
             device, dtype = q.device, q.dtype
 
-            if self.conds[0].device != device:
+            if self.conds[0].device != device or self.conds[0].dtype != dtype:
                 self.conds = [cond.to(device, dtype=dtype) for cond in self.conds]
-            if self.mask.device != device:
+            if self.mask.device != device or self.mask.dtype != dtype:
                 self.mask = self.mask.to(device, dtype=dtype)
 
             cond_or_unconds = extra_options["cond_or_uncond"]

requirements.txt

@@ -0,0 +1,2 @@
+# Optional, only required for Translate node:
+argostranslate

safetychecker/config.json

@@ -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
+  }
+}

safetychecker/preprocessor_config.json

@@ -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
+}

tile.py

@@ -36,12 +36,12 @@ class TileLayout:
     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.tile_count = 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)
@@ -85,7 +85,7 @@ 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):
@@ -93,7 +93,7 @@ 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:

translation.py

@@ -0,0 +1,115 @@
+"""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
+
+
+@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)
+            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:
+    @staticmethod
+    def INPUT_TYPES():
+        return {"required": {"text": ("STRING", {"multiline": True})}}
+
+    CATEGORY = "external_tooling"
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "translate"
+
+    def translate(self, text: str):
+        return (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

workflows/image_tiles.json

@@ -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,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MaskToImage"
+      }
+    },
+    {
+      "id": 78,
+      "type": "PreviewImage",
+      "pos": [
+        629,
+        -140
+      ],
+      "size": {
+        "0": 227.79318237304688,
+        "1": 201.64602661132812
+      },
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 126
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 71,
+      "type": "EmptyImage",
+      "pos": [
+        -681,
+        -247
+      ],
+      "size": {
+        "0": 315,
+        "1": 130
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            120
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyImage"
+      },
+      "widgets_values": [
+        2304,
+        1728,
+        1,
+        0
+      ]
+    },
+    {
+      "id": 67,
+      "type": "PreviewImage",
+      "pos": [
+        380,
+        -470
+      ],
+      "size": {
+        "0": 222.79318237304688,
+        "1": 197.94602966308594
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 107
+        }
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