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

README.md

@@ -33,7 +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:
 ```
@@ -44,6 +44,47 @@ 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>}}
 ```
 
+### Load Image from Cache
+
+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.

__init__.py

@@ -1,59 +1,42 @@
+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_ReferenceImage": nodes.ReferenceImage,
-    "ETN_ApplyReferenceImages": nodes.ApplyReferenceImages,
-    "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,
-    "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)",
-    "ETN_LoadMaskBase64": "Load Mask (Base64)",
-    "ETN_SendImageWebSocket": "Send Image (WebSocket)",
-    "ETN_CropImage": "Crop Image",
-    "ETN_ApplyMaskToImage": "Apply Mask to Image",
-    "ETN_ReferenceImage": "Reference Image",
-    "ETN_ApplyReferenceImages": "Apply Reference Images",
-    "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",
-    "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",
-}
+
+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"

api.py

@@ -15,6 +15,7 @@ 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"
 
@@ -243,6 +244,13 @@ def has_invalid_filename(filename: str):
     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)
@@ -277,6 +285,39 @@ if _server is not None:
         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", "")

krita.py

@@ -8,6 +8,7 @@ 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
 
 
@@ -72,37 +73,39 @@ class _BasicTypes(str):
 BasicTypes = _BasicTypes("BASIC")
 
 
-class KritaOutput:
+class KritaOutput(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"images": ("IMAGE",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaOutput",
+            display_name="Krita Output",
+            category="krita",
+            inputs=[io.Image.Input("images")],
+            is_output_node=True,
+        )
 
-    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"}),
-            }
-        }
+    def execute(cls, images: torch.Tensor):
+        return SendImageWebSocket.execute(images, "PNG")
 
-    RETURN_TYPES = ()
-    FUNCTION = "send"
-    OUTPUT_NODE = True
-    CATEGORY = "krita"
 
-    def send(self, value: Any, name: str, type: str):
+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",
@@ -115,72 +118,79 @@ class KritaSendText:
             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}]}}
+        return io.NodeOutput(ui={"text": [{"name": name, "text": text, "content-type": mime}]})
 
 
-class KritaCanvas:
+class KritaCanvas(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {}
+    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"),
+            ],
+        )
 
-    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)
+    def execute(cls):
+        return io.NodeOutput(_placeholder_image(), 512, 512, 0)
 
 
-class KritaImageLayer:
+class KritaSelection(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "name": ("STRING", {"default": "Image"}),
-            }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaSelection",
+            display_name="Krita Selection",
+            category="krita",
+            outputs=[io.Mask.Output(display_name="mask"), io.Boolean.Output(display_name="active")],
+        )
 
-    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"}),
-            }
-        }
+    def execute(cls):
+        return io.NodeOutput(torch.ones(1, 512, 512), False)
 
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "placeholder"
-    CATEGORY = "krita"
 
-    def placeholder(self, name: str):
-        return (torch.ones(1, 512, 512),)
+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 = [
@@ -193,71 +203,63 @@ _param_types = [
     "prompt (positive)",
     "prompt (negative)",
 ]
-_any_float = {"default": 0.0, "min": -sys.float_info.max, "max": sys.float_info.max}
+_fmax = sys.float_info.max
 
 
-class Parameter:
+class Parameter(io.ComfyNode):
     @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),
-            },
-        }
+    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=0.0, min=-_fmax, max=_fmax, optional=True),
+                io.Float.Input("max", default=1.0, min=-_fmax, max=_fmax, optional=True),
+            ],
+            outputs=[io.AnyType.Output(display_name="value")],
+        )
 
-    RETURN_TYPES = (BasicTypes,)
-    RETURN_NAMES = ("value",)
-    FUNCTION = "placeholder"
-    CATEGORY = "krita"
-
-    def placeholder(self, name: str, type: str, default, min=0.0, max=1.0):
+    @classmethod
+    def execute(cls, name: str, type: str, default, min=0.0, max=1.0):
         if type == "number":
-            return (float(default),)
+            return io.NodeOutput(float(default))
         elif type == "number (integer)":
-            return (int(default),)
-        return (default,)
+            return io.NodeOutput(int(default))
+        return io.NodeOutput(default)
 
 
-class KritaStyle:
+class KritaStyle(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "name": ("STRING", {"default": "Style"}),
-                "sampler_preset": (["auto", "regular", "live"],),
-            }
-        }
+    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"),
+            ],
+        )
 
-    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):
+    @classmethod
+    def execute(cls, name: str, sampler_preset: str):
         raise NotImplementedError("This workflow must be started from Krita!")

nodes.py

@@ -1,6 +1,9 @@
 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
@@ -11,18 +14,22 @@ 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: str):
+    @classmethod
+    def execute(cls, image: str):
         _strip_prefix(image, "data:image/png;base64,")
         imgdata = base64.b64decode(image)
         img = Image.open(BytesIO(imgdata))
@@ -40,16 +47,19 @@ 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: str):
+    @classmethod
+    def execute(cls, mask: str):
         _strip_prefix(mask, "data:image/png;base64,")
         imgdata = base64.b64decode(mask)
         img = Image.open(BytesIO(imgdata))
@@ -60,22 +70,22 @@ class LoadMaskBase64:
         return (img.unsqueeze(0),)
 
 
-class SendImageWebSocket:
+class SendImageWebSocket(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "format": (["PNG", "JPEG"], {"default": "PNG"}),
-            }
-        }
+    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, format):
+    @classmethod
+    def execute(cls, images: torch.Tensor, format: str):
         results = []
         for tensor in images:
             array = 255.0 * tensor.cpu().numpy()
@@ -93,43 +103,129 @@ class SendImageWebSocket:
                 "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:
+    @dataclass
+    class Entry:
+        data: bytes
+        content_type: str
+        timestamp: float
+        retrieved: int
+
+    def __init__(self):
+        self.images: dict[str, ImageCache.Entry] = {}
+
+    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:
+            return None, None
+        entry.retrieved += 1
+        if extend:
+            entry.timestamp = time.time()
+        self.prune()
+        return entry.data, entry.content_type
+
+    def prune(self):
+        now = time.time()
+        keys_to_delete = []
+        for key, entry in self.images.items():
+            d = now - entry.timestamp
+            if (d > 60 and entry.retrieved > 1) or d > 600:
+                keys_to_delete.append(key)
+        for key in keys_to_delete:
+            del self.images[key]
+
+    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 SaveImageCache(io.ComfyNode):
+    @classmethod
+    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,
+        )
+
+    @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)
+
+            results.append({
+                "source": "http",
+                "id": key,
+                "content-type": f"image/{format.lower()}",
+                "type": "output",
+            })
+        return io.NodeOutput(ui={"images": results})
 
 
 def to_bchw(image: torch.Tensor):
@@ -148,21 +244,22 @@ def mask_batch(mask: torch.Tensor):
     return mask
 
 
-class ApplyMaskToImage:
+class ApplyMaskToImage(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "mask": ("MASK",),
-            }
-        }
+    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")],
+        )
 
-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "apply_mask"
-
-    def apply_mask(self, image: torch.Tensor, mask: torch.Tensor):
+    @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)
@@ -189,28 +286,26 @@ class _ReferenceImageData(NamedTuple):
     range: tuple[float, float]
 
 
-class ReferenceImage:
+class ReferenceImage(io.ComfyNode):
     @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",),
-            },
-        }
+    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")],
+        )
 
-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("REFERENCE_IMAGE",)
-    RETURN_NAMES = ("reference_images",)
-    FUNCTION = "append"
-
-    def append(
-        self,
+    @classmethod
+    def execute(
+        cls,
         image: torch.Tensor,
         weight: float,
         range_start: float,
@@ -222,24 +317,25 @@ class ReferenceImage:
         return (imgs,)
 
 
-class ApplyReferenceImages:
+class ApplyReferenceImages(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "conditioning": ("CONDITIONING",),
-                "clip_vision": ("CLIP_VISION",),
-                "style_model": ("STYLE_MODEL",),
-                "references": ("REFERENCE_IMAGE",),
-            }
-        }
+    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")],
+        )
 
-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "apply"
-
-    def apply(
-        self,
+    @classmethod
+    def execute(
+        cls,
         conditioning: list[list],
         clip_vision: ClipVisionModel,
         style_model: StyleModel,

nsfw.py

@@ -8,6 +8,7 @@ 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
 
@@ -76,7 +77,7 @@ class CLIPSafetyChecker(PreTrainedModel):
 
 
 class CachedModels:
-    _instance: WeakRef | None = None
+    _instance: CachedModels | None = None
 
     def __init__(self):
         model_dir = Path(__file__).parent / "safetychecker"
@@ -91,11 +92,9 @@ class CachedModels:
 
     @classmethod
     def load(cls):
-        models = cls._instance and cls._instance()
-        if models is None:
-            models = cls()
-            cls._instance = WeakRef(models)
-        return models
+        if cls._instance is None:
+            cls._instance = CachedModels()
+        return cls._instance
 
     def download(self, url: str, target: Path):
         import requests
@@ -118,29 +117,26 @@ class CachedModels:
             ) from e
 
 
-class NSFWFilter:
-    models: CachedModels
+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 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):
+    def execute(cls, image: Tensor, sensitivity: float):
+        models = CachedModels.load()
         image = to_bchw(image)
-        input = self.models.feature_extractor(image, do_rescale=False, return_tensors="pt")
-        filtered = self.models.safety_checker(
+        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 (to_bhwc(filtered),)
+        return io.NodeOutput(to_bhwc(filtered))

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-tooling-nodes"
 description = "Provides nodes and server API extensions geared towards using ComfyUI as a backend for external tools."
-version = "2.0.6"
+version = "3.0.0"
 license = { file = "LICENSE" }
 
 [project.urls]

region.py

@@ -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:
@@ -65,104 +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",),
-            },
-        }
+    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")],
+        )
 
-    CATEGORY = "external_tooling/regions"
-    RETURN_TYPES = ("REGIONS",)
-    FUNCTION = "define"
-
-    def define(self, mask: Tensor, conditioning: list, regions: Region | None = None):
+    @classmethod
+    def execute(cls, mask: Tensor, conditioning: list, regions: Region | None = None):
         if mask.dim() < 3:
             mask = mask.unsqueeze(0)
-        return (Region(regions, mask, conditioning),)
+        return io.NodeOutput(Region(regions, mask, conditioning))
 
 
-class ListRegionMasks:
+class ListRegionMasks(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"regions": ("REGIONS",)}}
+    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")],
+        )
 
-    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:
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "regions": ("REGIONS",),
-            }
-        }
+    def execute(cls, regions: Region):
+        return io.NodeOutput(torch.stack([r.mask for r in regions.preprocess()], dim=0))
 
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "attention_mask"
-    CATEGORY = "external_tooling/regions"
 
-    mask: Tensor
-    conds: list[Tensor]
-    batch_size: int
+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")],
+        )
 
-    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, 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 or self.conds[0].dtype != dtype:
-                self.conds = [cond.to(device, dtype=dtype) for cond in self.conds]
-            if self.mask.device != device or self.mask.dtype != dtype:
-                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)
 
@@ -173,9 +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)
@@ -183,29 +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

tile.py

@@ -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"
 
-        self.image_size = np.array(image.shape[-3:-1])
-        self.padding = padding
-        self.blending = blending
-        self.tile_count = np.maximum(1, 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)
@@ -96,80 +72,108 @@ class TileLayout:
         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)

translation.py

@@ -10,6 +10,7 @@ from __future__ import annotations
 import re
 from functools import cache
 from typing import NamedTuple
+from comfy_api.latest import io
 
 
 @cache
@@ -43,7 +44,7 @@ def translate_chunk(text: str, language: str):
                 (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)
+            # print("Downloading and installing translation package", pkg)  # this will cause encoding errors
             pkg.install()
 
         text, embeddings = _extract_embeddings(text)
@@ -61,17 +62,20 @@ def translate(text: str):
     return " ".join(translate_chunk(c.text, c.lang) for c in chunks)
 
 
-class Translate:
-    @staticmethod
-    def INPUT_TYPES():
-        return {"required": {"text": ("STRING", {"multiline": True})}}
+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")],
+        )
 
-    CATEGORY = "external_tooling"
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "translate"
-
-    def translate(self, text: str):
-        return (translate(text),)
+    @classmethod
+    def execute(cls, text: str):
+        return io.NodeOutput(translate(text))
 
 
 _lang_regex = re.compile(r"(lang:\w\w)")