feat: implement RemoteComboOptions for rich remote-populated combos

Signed-off-by: bigcat88 <bigcat88@icloud.com>

comfy/cli_args.py

@@ -91,7 +91,6 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
-parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/model_management.py

@@ -721,15 +721,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
     else:
         minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())
 
-    # Order-preserving dedup. A plain set() would randomize iteration order across runs
-    models_temp = {}
+    models_temp = set()
     for m in models:
-        models_temp[m] = None
+        models_temp.add(m)
         for mm in m.model_patches_models():
-            models_temp[mm] = None
+            models_temp.add(mm)
 
-    models = list(models_temp)
-    models.reverse()
+    models = models_temp
 
     models_to_load = []
 

comfy/model_prefetch.py

@@ -37,8 +37,7 @@ def prefetch_queue_pop(queue, device, module):
     consumed = queue.pop(0)
     if consumed is not None:
         offload_stream, prefetch_state = consumed
-        if offload_stream is not None:
-            offload_stream.wait_stream(comfy.model_management.current_stream(device))
+        offload_stream.wait_stream(comfy.model_management.current_stream(device))
         _, comfy_modules = prefetch_state
         if comfy_modules is not None:
             cleanup_prefetched_modules(comfy_modules)

comfy/ops.py

@@ -253,9 +253,6 @@ def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, w
     if bias is not None:
         bias = post_cast(s, "bias", bias, bias_dtype, prefetch["resident"], update_weight)
 
-    if prefetch["signature"] is not None:
-        prefetch["resident"] = True
-
     return weight, bias
 
 

comfy/quant_ops.py

@@ -1,8 +1,6 @@
 import torch
 import logging
 
-from comfy.cli_args import args
-
 try:
     import comfy_kitchen as ck
     from comfy_kitchen.tensor import (
@@ -23,15 +21,7 @@ try:
             ck.registry.disable("cuda")
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
-    if args.enable_triton_backend:
-        try:
-            import triton
-            logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
-        except ImportError as e:
-            logging.error(f"Failed to import triton, Error: {e}, the comfy-kitchen triton backend will not be available.")
-            ck.registry.disable("triton")
-    else:
-        ck.registry.disable("triton")
+    ck.registry.disable("triton")
     for k, v in ck.list_backends().items():
         logging.info(f"Found comfy_kitchen backend {k}: {v}")
 except ImportError as e:

comfy/sampler_helpers.py

@@ -89,8 +89,7 @@ def get_additional_models(conds, dtype):
         gligen += get_models_from_cond(conds[k], "gligen")
         add_models += get_models_from_cond(conds[k], "additional_models")
 
-    # Order-preserving dedup. A plain set() would randomize iteration order across runs
-    control_nets = list(dict.fromkeys(cnets))
+    control_nets = set(cnets)
 
     inference_memory = 0
     control_models = []

comfy_api/latest/_io.py

@@ -43,7 +43,67 @@ class UploadType(str, Enum):
     model = "file_upload"
 
 
+class RemoteItemSchema:
+    """Describes how to map API response objects to rich dropdown items.
+
+    All *_field parameters use dot-path notation (e.g. ``"labels.gender"``).
+    ``label_field`` and ``description_field`` additionally support template strings
+    with ``{field}`` placeholders (e.g. ``"{name} ({labels.accent})"``).
+    """
+    def __init__(
+        self,
+        value_field: str,
+        label_field: str,
+        preview_url_field: str | None = None,
+        preview_type: Literal["image", "video", "audio"] = "image",
+        description_field: str | None = None,
+        search_fields: list[str] | None = None,
+    ):
+        if preview_type not in ("image", "video", "audio"):
+            raise ValueError(
+                f"RemoteItemSchema: 'preview_type' must be 'image', 'video', or 'audio'; got {preview_type!r}."
+            )
+        if search_fields is not None:
+            for f in search_fields:
+                if "{" in f or "}" in f:
+                    raise ValueError(
+                        f"RemoteItemSchema: 'search_fields' must be dot-paths, not template strings (got {f!r})."
+                    )
+        self.value_field = value_field
+        """Dot-path to the unique identifier within each item.
+        This value is stored in the widget and passed to execute()."""
+        self.label_field = label_field
+        """Dot-path to the display name, or a template string with {field} placeholders."""
+        self.preview_url_field = preview_url_field
+        """Dot-path to a preview media URL. If None, no preview is shown."""
+        self.preview_type = preview_type
+        """How to render the preview: "image", "video", or "audio"."""
+        self.description_field = description_field
+        """Optional dot-path or template for a subtitle line shown below the label."""
+        self.search_fields = search_fields
+        """Dot-paths to fields included in the search index. When unset, search falls back to
+        the resolved label (i.e. ``label_field`` after template substitution). Note that template
+        label strings (e.g. ``"{first} {last}"``) are not valid path entries here — list the
+        underlying paths (``["first", "last"]``) instead."""
+
+    def as_dict(self):
+        return prune_dict({
+            "value_field": self.value_field,
+            "label_field": self.label_field,
+            "preview_url_field": self.preview_url_field,
+            "preview_type": self.preview_type,
+            "description_field": self.description_field,
+            "search_fields": self.search_fields,
+        })
+
+
 class RemoteOptions:
+    """Plain remote combo: fetches a list of strings/objects and populates a standard dropdown.
+
+    Use this for lightweight lists from endpoints that return a bare array (or an array under
+    ``response_key``). For rich dropdowns with previews, search, filtering, or pagination,
+    use :class:`RemoteComboOptions` and the ``remote_combo=`` parameter on ``Combo.Input``.
+    """
     def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first",
                  timeout: int=None, max_retries: int=None, refresh: int=None):
         self.route = route
@@ -70,6 +130,80 @@ class RemoteOptions:
         })
 
 
+class RemoteComboOptions:
+    """Rich remote combo: populates a Vue dropdown with previews, search, and filtering.
+
+    Attached to a :class:`Combo.Input` via ``remote_combo=`` (not ``remote=``). Requires an
+    ``item_schema`` describing how to map API response objects to dropdown items.
+
+    Response-shape contract: the endpoint returns the full items array in a single response
+    (either at the top level, or at the dot-path given by ``response_key``). Backing endpoints
+    that paginate upstream are expected to aggregate and cache server-side.
+    """
+    def __init__(
+        self,
+        route: str,
+        item_schema: RemoteItemSchema,
+        refresh_button: bool = True,
+        auto_select: Literal["first", "last"] | None = None,
+        timeout: int | None = None,
+        max_retries: int | None = None,
+        refresh: int | None = None,
+        response_key: str | None = None,
+    ):
+        if auto_select is not None and auto_select not in ("first", "last"):
+            raise ValueError(
+                f"RemoteComboOptions: 'auto_select' must be 'first', 'last', or None; got {auto_select!r}."
+            )
+        if refresh is not None and 0 < refresh < 128:
+            raise ValueError(
+                f"RemoteComboOptions: 'refresh' must be >= 128 (ms TTL) or <= 0 (cache never expires); got {refresh}."
+            )
+        if timeout is not None and timeout < 0:
+            raise ValueError(
+                f"RemoteComboOptions: 'timeout' must be >= 0 (got {timeout})."
+            )
+        if max_retries is not None and max_retries < 0:
+            raise ValueError(
+                f"RemoteComboOptions: 'max_retries' must be >= 0 (got {max_retries})."
+            )
+        if not route.startswith("/"):
+            raise ValueError(
+                f"RemoteComboOptions: 'route' must be a relative path starting with '/'; got {route!r}."
+            )
+        self.route = route
+        """Relative path to the remote source (must start with ``/``). The frontend resolves this
+        against the comfy-api base URL and injects auth headers; absolute URLs are rejected."""
+        self.item_schema = item_schema
+        """Required: describes how each API response object maps to a dropdown item."""
+        self.refresh_button = refresh_button
+        """Specifies whether to show a refresh button next to the widget."""
+        self.auto_select = auto_select
+        """Fallback item to select when the widget's value is empty. Never overrides an existing
+        selection. Default None means no fallback."""
+        self.timeout = timeout
+        """Maximum time to wait for a response, in milliseconds."""
+        self.max_retries = max_retries
+        """Maximum number of retries before aborting the request. Default None uses the frontend's built-in limit."""
+        self.refresh = refresh
+        """TTL of the cached value in milliseconds. Must be >= 128 (ms TTL) or <= 0 (cache never expires,
+        re-fetched only via the refresh button). Default None uses the frontend's built-in behavior."""
+        self.response_key = response_key
+        """Dot-path to the items array within the response (when not at the top level)."""
+
+    def as_dict(self):
+        return prune_dict({
+            "route": self.route,
+            "item_schema": self.item_schema.as_dict(),
+            "refresh_button": self.refresh_button,
+            "auto_select": self.auto_select,
+            "timeout": self.timeout,
+            "max_retries": self.max_retries,
+            "refresh": self.refresh,
+            "response_key": self.response_key,
+        })
+
+
 class NumberDisplay(str, Enum):
     number = "number"
     slider = "slider"
@@ -359,11 +493,16 @@ class Combo(ComfyTypeIO):
             upload: UploadType=None,
             image_folder: FolderType=None,
             remote: RemoteOptions=None,
+            remote_combo: RemoteComboOptions=None,
             socketless: bool=None,
             extra_dict=None,
             raw_link: bool=None,
             advanced: bool=None,
         ):
+            if remote is not None and remote_combo is not None:
+                raise ValueError("Combo.Input: pass either 'remote' or 'remote_combo', not both.")
+            if options is not None and remote_combo is not None:
+                raise ValueError("Combo.Input: pass either 'options' or 'remote_combo', not both.")
             if isinstance(options, type) and issubclass(options, Enum):
                 options = [v.value for v in options]
             if isinstance(default, Enum):
@@ -375,6 +514,7 @@ class Combo(ComfyTypeIO):
             self.upload = upload
             self.image_folder = image_folder
             self.remote = remote
+            self.remote_combo = remote_combo
             self.default: str
 
         def as_dict(self):
@@ -385,6 +525,7 @@ class Combo(ComfyTypeIO):
                 **({self.upload.value: True} if self.upload is not None else {}),
                 "image_folder": self.image_folder.value if self.image_folder else None,
                 "remote": self.remote.as_dict() if self.remote else None,
+                "remote_combo": self.remote_combo.as_dict() if self.remote_combo else None,
             })
 
     class Output(Output):
@@ -2221,7 +2362,9 @@ class NodeReplace:
 __all__ = [
     "FolderType",
     "UploadType",
+    "RemoteItemSchema",
     "RemoteOptions",
+    "RemoteComboOptions",
     "NumberDisplay",
     "ControlAfterGenerate",
 

comfy_api_nodes/nodes_sora.py

@@ -33,7 +33,7 @@ class OpenAIVideoSora2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="OpenAIVideoSora2",
-            display_name="OpenAI Sora - Video (DEPRECATED)",
+            display_name="OpenAI Sora - Video (Deprecated)",
             category="api node/video/Sora",
             description=(
                 "OpenAI video and audio generation.\n\n"

comfy_extras/frame_interpolation_models/film_net.py

@@ -199,9 +199,6 @@ class FILMNet(nn.Module):
     def get_dtype(self):
         return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype
 
-    def memory_used_forward(self, shape, dtype):
-        return 1700 * shape[1] * shape[2] * dtype.itemsize
-
     def _build_warp_grids(self, H, W, device):
         """Pre-compute warp grids for all pyramid levels."""
         if (H, W) in self._warp_grids:

comfy_extras/frame_interpolation_models/ifnet.py

@@ -74,9 +74,6 @@ class IFNet(nn.Module):
     def get_dtype(self):
         return self.encode.cnn0.weight.dtype
 
-    def memory_used_forward(self, shape, dtype):
-        return 300 * shape[1] * shape[2] * dtype.itemsize
-
     def _build_warp_grids(self, H, W, device):
         if (H, W) in self._warp_grids:
             return

comfy_extras/nodes_compositing.py

@@ -202,11 +202,14 @@ class JoinImageWithAlpha(io.ComfyNode):
 
     @classmethod
     def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
-        batch_size = max(len(image), len(alpha))
+        batch_size = min(len(image), len(alpha))
+        out_images = []
+
         alpha = 1.0 - resize_mask(alpha, image.shape[1:])
-        alpha = comfy.utils.repeat_to_batch_size(alpha, batch_size)
-        image = comfy.utils.repeat_to_batch_size(image, batch_size)
-        return io.NodeOutput(torch.cat((image[..., :3], alpha.unsqueeze(-1)), dim=-1))
+        for i in range(batch_size):
+           out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
+
+        return io.NodeOutput(torch.stack(out_images))
 
 
 class CompositingExtension(ComfyExtension):

comfy_extras/nodes_frame_interpolation.py

@@ -37,7 +37,7 @@ class FrameInterpolationModelLoader(io.ComfyNode):
         model = cls._detect_and_load(sd)
         dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32
         model.eval().to(dtype)
-        patcher = comfy.model_patcher.CoreModelPatcher(
+        patcher = comfy.model_patcher.ModelPatcher(
             model,
             load_device=model_management.get_torch_device(),
             offload_device=model_management.unet_offload_device(),
@@ -78,7 +78,7 @@ class FrameInterpolate(io.ComfyNode):
         return io.Schema(
             node_id="FrameInterpolate",
             display_name="Frame Interpolate",
-            category="video",
+            category="image/video",
             search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
             inputs=[
                 FrameInterpolationModel.Input("interp_model"),
@@ -98,13 +98,16 @@ class FrameInterpolate(io.ComfyNode):
         if num_frames < 2 or multiplier < 2:
             return io.NodeOutput(images)
 
+        model_management.load_model_gpu(interp_model)
         device = interp_model.load_device
         dtype = interp_model.model_dtype()
         inference_model = interp_model.model
-        activation_mem = inference_model.memory_used_forward(images.shape, dtype)
-        model_management.load_models_gpu([interp_model], memory_required=activation_mem)
-        align = getattr(inference_model, "pad_align", 1)
+
+        # Free VRAM for inference activations (model weights + ~20x a single frame's worth)
         H, W = images.shape[1], images.shape[2]
+        activation_mem = H * W * 3 * images.element_size() * 20
+        model_management.free_memory(activation_mem, device)
+        align = getattr(inference_model, "pad_align", 1)
 
         # Prepare a single padded frame on device for determining output dimensions
         def prepare_frame(idx):

comfy_extras/nodes_image_compare.py

@@ -11,7 +11,7 @@ class ImageCompare(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCompare",
-            display_name="Compare Images",
+            display_name="Image Compare",
             description="Compares two images side by side with a slider.",
             category="image",
             essentials_category="Image Tools",

comfy_extras/nodes_images.py

@@ -24,7 +24,7 @@ class ImageCrop(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageCrop",
             search_aliases=["trim"],
-            display_name="Crop Image (DEPRECATED)",
+            display_name="Image Crop (Deprecated)",
             category="image/transform",
             is_deprecated=True,
             essentials_category="Image Tools",
@@ -56,7 +56,7 @@ class ImageCropV2(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageCropV2",
             search_aliases=["trim"],
-            display_name="Crop Image",
+            display_name="Image Crop",
             category="image/transform",
             essentials_category="Image Tools",
             has_intermediate_output=True,
@@ -109,7 +109,6 @@ class RepeatImageBatch(IO.ComfyNode):
         return IO.Schema(
             node_id="RepeatImageBatch",
             search_aliases=["duplicate image", "clone image"],
-            display_name="Repeat Image Batch",
             category="image/batch",
             inputs=[
                 IO.Image.Input("image"),
@@ -132,7 +131,6 @@ class ImageFromBatch(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageFromBatch",
             search_aliases=["select image", "pick from batch", "extract image"],
-            display_name="Get Image from Batch",
             category="image/batch",
             inputs=[
                 IO.Image.Input("image"),
@@ -159,8 +157,7 @@ class ImageAddNoise(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageAddNoise",
             search_aliases=["film grain"],
-            display_name="Add Noise to Image",
-            category="image/postprocessing",
+            category="image",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input(
@@ -262,7 +259,7 @@ class ImageStitch(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageStitch",
             search_aliases=["combine images", "join images", "concatenate images", "side by side"],
-            display_name="Stitch Images",
+            display_name="Image Stitch",
             description="Stitches image2 to image1 in the specified direction.\n"
             "If image2 is not provided, returns image1 unchanged.\n"
             "Optional spacing can be added between images.",
@@ -437,7 +434,6 @@ class ResizeAndPadImage(IO.ComfyNode):
         return IO.Schema(
             node_id="ResizeAndPadImage",
             search_aliases=["fit to size"],
-            display_name="Resize And Pad Image",
             category="image/transform",
             inputs=[
                 IO.Image.Input("image"),
@@ -489,7 +485,6 @@ class SaveSVGNode(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveSVGNode",
             search_aliases=["export vector", "save vector graphics"],
-            display_name="Save SVG",
             description="Save SVG files on disk.",
             category="image/save",
             inputs=[
@@ -596,7 +591,7 @@ class ImageRotate(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageRotate",
-            display_name="Rotate Image",
+            display_name="Image Rotate",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
             essentials_category="Image Tools",
@@ -629,7 +624,6 @@ class ImageFlip(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageFlip",
             search_aliases=["mirror", "reflect"],
-            display_name="Flip Image",
             category="image/transform",
             inputs=[
                 IO.Image.Input("image"),
@@ -656,7 +650,6 @@ class ImageScaleToMaxDimension(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageScaleToMaxDimension",
-            display_name="Scale Image to Max Dimension",
             category="image/upscaling",
             inputs=[
                 IO.Image.Input("image"),

comfy_extras/nodes_mask.py

@@ -80,8 +80,7 @@ class ImageCompositeMasked(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCompositeMasked",
-            search_aliases=["overlay", "layer", "paste image", "images composition"],
-            display_name="Image Composite Masked",
+            search_aliases=["paste image", "overlay", "layer"],
             category="image",
             inputs=[
                 IO.Image.Input("destination"),
@@ -202,7 +201,6 @@ class InvertMask(IO.ComfyNode):
         return IO.Schema(
             node_id="InvertMask",
             search_aliases=["reverse mask", "flip mask"],
-            display_name="Invert Mask",
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -224,7 +222,6 @@ class CropMask(IO.ComfyNode):
         return IO.Schema(
             node_id="CropMask",
             search_aliases=["cut mask", "extract mask region", "mask slice"],
-            display_name="Crop Mask",
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -250,8 +247,7 @@ class MaskComposite(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="MaskComposite",
-            search_aliases=["combine masks", "blend masks", "layer masks", "masks composition"],
-            display_name="Combine Masks",
+            search_aliases=["combine masks", "blend masks", "layer masks"],
             category="mask",
             inputs=[
                 IO.Mask.Input("destination"),
@@ -302,7 +298,6 @@ class FeatherMask(IO.ComfyNode):
         return IO.Schema(
             node_id="FeatherMask",
             search_aliases=["soft edge mask", "blur mask edges", "gradient mask edge"],
-            display_name="Feather Mask",
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),

comfy_extras/nodes_morphology.py

@@ -59,8 +59,7 @@ class ImageRGBToYUV(io.ComfyNode):
         return io.Schema(
             node_id="ImageRGBToYUV",
             search_aliases=["color space conversion"],
-            display_name="Image RGB to YUV",
-            category="image/color",
+            category="image/batch",
             inputs=[
                 io.Image.Input("image"),
             ],
@@ -82,8 +81,7 @@ class ImageYUVToRGB(io.ComfyNode):
         return io.Schema(
             node_id="ImageYUVToRGB",
             search_aliases=["color space conversion"],
-            display_name="Image YUV to RGB",
-            category="image/color",
+            category="image/batch",
             inputs=[
                 io.Image.Input("Y"),
                 io.Image.Input("U"),

comfy_extras/nodes_post_processing.py

@@ -20,8 +20,7 @@ class Blend(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageBlend",
-            search_aliases=["mix images"],
-            display_name="Blend Images",
+            display_name="Image Blend",
             category="image/postprocessing",
             essentials_category="Image Tools",
             inputs=[
@@ -225,7 +224,6 @@ class ImageScaleToTotalPixels(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageScaleToTotalPixels",
-            display_name="Scale Image to Total Pixels",
             category="image/upscaling",
             inputs=[
                 io.Image.Input("image"),
@@ -570,7 +568,7 @@ class BatchImagesNode(io.ComfyNode):
         return io.Schema(
             node_id="BatchImagesNode",
             display_name="Batch Images",
-            category="image/batch",
+            category="image",
             essentials_category="Image Tools",
             search_aliases=["batch", "image batch", "batch images", "combine images", "merge images", "stack images"],
             inputs=[
@@ -668,13 +666,12 @@ class ColorTransfer(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ColorTransfer",
-            display_name="Color Transfer",
             category="image/postprocessing",
             description="Match the colors of one image to another using various algorithms.",
             search_aliases=["color match", "color grading", "color correction", "match colors", "color transform", "mkl", "reinhard", "histogram"],
             inputs=[
                 io.Image.Input("image_target", tooltip="Image(s) to apply the color transform to."),
-                io.Image.Input("image_ref", tooltip="Reference image(s) to match colors to."),
+                io.Image.Input("image_ref", optional=True, tooltip="Reference image(s) to match colors to. If not provided, processing is skipped"),
                 io.Combo.Input("method", options=['reinhard_lab', 'mkl_lab', 'histogram'],),
                 io.DynamicCombo.Input("source_stats",
                     tooltip="per_frame: each frame matched to image_ref individually. uniform: pool stats across all source frames as baseline, match to image_ref. target_frame: use one chosen frame as the baseline for the transform to image_ref, applied uniformly to all frames (preserves relative differences)",

comfy_extras/nodes_primitive.py

@@ -49,7 +49,7 @@ class Int(io.ComfyNode):
             display_name="Int",
             category="utils/primitive",
             inputs=[
-                io.Int.Input("value", min=-sys.maxsize, max=sys.maxsize, control_after_generate=io.ControlAfterGenerate.fixed),
+                io.Int.Input("value", min=-sys.maxsize, max=sys.maxsize, control_after_generate=True),
             ],
             outputs=[io.Int.Output()],
         )

comfy_extras/nodes_video.py

@@ -17,8 +17,7 @@ class SaveWEBM(io.ComfyNode):
         return io.Schema(
             node_id="SaveWEBM",
             search_aliases=["export webm"],
-            display_name="Save WEBM",
-            category="video",
+            category="image/video",
             is_experimental=True,
             inputs=[
                 io.Image.Input("images"),
@@ -73,7 +72,7 @@ class SaveVideo(io.ComfyNode):
             node_id="SaveVideo",
             search_aliases=["export video"],
             display_name="Save Video",
-            category="video",
+            category="image/video",
             essentials_category="Basics",
             description="Saves the input images to your ComfyUI output directory.",
             inputs=[
@@ -122,7 +121,7 @@ class CreateVideo(io.ComfyNode):
             node_id="CreateVideo",
             search_aliases=["images to video"],
             display_name="Create Video",
-            category="video",
+            category="image/video",
             description="Create a video from images.",
             inputs=[
                 io.Image.Input("images", tooltip="The images to create a video from."),
@@ -147,7 +146,7 @@ class GetVideoComponents(io.ComfyNode):
             node_id="GetVideoComponents",
             search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
-            category="video",
+            category="image/video",
             description="Extracts all components from a video: frames, audio, and framerate.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to extract components from."),
@@ -175,7 +174,7 @@ class LoadVideo(io.ComfyNode):
             node_id="LoadVideo",
             search_aliases=["import video", "open video", "video file"],
             display_name="Load Video",
-            category="video",
+            category="image/video",
             essentials_category="Basics",
             inputs=[
                 io.Combo.Input("file", options=sorted(files), upload=io.UploadType.video),
@@ -217,7 +216,7 @@ class VideoSlice(io.ComfyNode):
                 "frame load cap",
                 "start time",
             ],
-            category="video",
+            category="image/video",
             essentials_category="Video Tools",
             inputs=[
                 io.Video.Input("video"),

execution.py

@@ -1016,6 +1016,10 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
 
                 if isinstance(input_type, list) or input_type == io.Combo.io_type:
                     if input_type == io.Combo.io_type:
+                        # Skip validation for combos with remote options — options
+                        # are fetched client-side and not available on the server.
+                        if extra_info.get("remote_combo"):
+                            continue
                         combo_options = extra_info.get("options", [])
                     else:
                         combo_options = input_type

extra_model_paths.yaml.example

@@ -28,7 +28,7 @@
 #config for a1111 ui
 #all you have to do is uncomment this (remove the #) and change the base_path to where yours is installed
 
-#a1111:
+#a111:
 #     base_path: path/to/stable-diffusion-webui/
 #     checkpoints: models/Stable-diffusion
 #     configs: models/Stable-diffusion

nodes.py

@@ -1754,49 +1754,57 @@ class LoadImage:
 
         return True
 
-
-class LoadImageMask(LoadImage):
+class LoadImageMask:
     ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
 
     _color_channels = ["alpha", "red", "green", "blue"]
-
     @classmethod
     def INPUT_TYPES(s):
-        types = super().INPUT_TYPES()
-        return {
-            "required": {
-                **types["required"],
-                "channel": (s._color_channels, )
-            }
-        }
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True}),
+                     "channel": (s._color_channels, ), }
+                }
 
     CATEGORY = "mask"
+
     RETURN_TYPES = ("MASK",)
-    FUNCTION = "load_image_mask"
-
-    def load_image_mask(self, image, channel):
-        image_tensor, mask_tensor = super().load_image(image)
+    FUNCTION = "load_image"
+    def load_image(self, image, channel):
+        image_path = folder_paths.get_annotated_filepath(image)
+        i = node_helpers.pillow(Image.open, image_path)
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+        if i.getbands() != ("R", "G", "B", "A"):
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            i = i.convert("RGBA")
+        mask = None
         c = channel[0].upper()
-
-        if c == 'A':
-            return (mask_tensor,)
-
-        channel_idx = {'R': 0, 'G': 1, 'B': 2}.get(c, 0)
-
-        if channel_idx < image_tensor.shape[-1]:
-            return (image_tensor[..., channel_idx].clone(),)
+        if c in i.getbands():
+            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
+            mask = torch.from_numpy(mask)
+            if c == 'A':
+                mask = 1. - mask
         else:
-            empty_mask = torch.zeros(
-                image_tensor.shape[:-1],
-                dtype=image_tensor.dtype,
-                device=image_tensor.device
-            )
-            return (empty_mask,)
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (mask.unsqueeze(0),)
 
     @classmethod
     def IS_CHANGED(s, image, channel):
-        return super().IS_CHANGED(image)
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True
 
 
 class LoadImageOutput(LoadImage):
@@ -1887,7 +1895,7 @@ class ImageInvert:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "invert"
 
-    CATEGORY = "image/color"
+    CATEGORY = "image"
 
     def invert(self, image):
         s = 1.0 - image
@@ -1903,7 +1911,7 @@ class ImageBatch:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "batch"
 
-    CATEGORY = "image/batch"
+    CATEGORY = "image"
     DEPRECATED = True
 
     def batch(self, image1, image2):
@@ -1960,7 +1968,7 @@ class ImagePadForOutpaint:
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "expand_image"
 
-    CATEGORY = "image/transform"
+    CATEGORY = "image"
 
     def expand_image(self, image, left, top, right, bottom, feathering):
         d1, d2, d3, d4 = image.size()
@@ -2103,7 +2111,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
-    "ControlNetApply": "Apply ControlNet (DEPRECATED)",
+    "ControlNetApply": "Apply ControlNet (OLD)",
     "ControlNetApplyAdvanced": "Apply ControlNet",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
@@ -2121,7 +2129,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LatentFromBatch" : "Latent From Batch",
     "RepeatLatentBatch": "Repeat Latent Batch",
     # Image
-    "EmptyImage": "Empty Image",
     "SaveImage": "Save Image",
     "PreviewImage": "Preview Image",
     "LoadImage": "Load Image",
@@ -2129,15 +2136,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LoadImageOutput": "Load Image (from Outputs)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",
-    "ImageInvert": "Invert Image Colors",
+    "ImageInvert": "Invert Image",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
-    "ImageBatch": "Batch Images (DEPRECATED)",
-    "ImageCrop": "Crop Image",
-    "ImageStitch": "Stitch Images",
-    "ImageBlend": "Blend Images",
-    "ImageBlur": "Blur Image",
-    "ImageQuantize": "Quantize Image",
-    "ImageSharpen": "Sharpen Image",
+    "ImageBatch": "Batch Images",
+    "ImageCrop": "Image Crop",
+    "ImageStitch": "Image Stitch",
+    "ImageBlend": "Image Blend",
+    "ImageBlur": "Image Blur",
+    "ImageQuantize": "Image Quantize",
+    "ImageSharpen": "Image Sharpen",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
     # _for_testing

openapi.yaml

@@ -2347,12 +2347,7 @@ components:
           description: Device type (cuda, mps, cpu, etc.)
         index:
           type: number
-          nullable: true
-          description: |
-            Device index within its type (e.g. CUDA ordinal for `cuda:0`,
-            `cuda:1`). `null` for devices with no index, including the CPU
-            device returned in `--cpu` mode (PyTorch's `torch.device('cpu').index`
-            is `None`).
+          description: Device index
         vram_total:
           type: number
           description: Total VRAM in bytes
@@ -2508,18 +2503,7 @@ components:
           description: Alternative search terms for finding this node
         essentials_category:
           type: string
-          nullable: true
-          description: |
-            Category override used by the essentials pack. The
-            `essentials_category` key may be present with a string value,
-            present and `null`, or absent entirely:
-
-            - V1 nodes: `essentials_category` is **omitted** when the node
-              class doesn't define an `ESSENTIALS_CATEGORY` attribute, and
-              **`null`** if the attribute is explicitly set to `None`.
-            - V3 nodes (`comfy_api.latest.io`): `essentials_category` is
-              **always present**, and **`null`** for nodes whose `Schema`
-              doesn't populate it.
+          description: Category override used by the essentials pack
 
     # -------------------------------------------------------------------
     # Models

server.py

@@ -1,4 +1,3 @@
-import errno
 import os
 import sys
 import asyncio
@@ -1246,13 +1245,7 @@ class PromptServer():
             address = addr[0]
             port = addr[1]
             site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
-            try:
-                await site.start()
-            except OSError as e:
-                if e.errno == errno.EADDRINUSE:
-                    logging.error(f"Port {port} is already in use on address {address}. Please close the other application or use a different port with --port.")
-                    raise SystemExit(1)
-                raise
+            await site.start()
 
             if not hasattr(self, 'address'):
                 self.address = address #TODO: remove this

tests-unit/comfy_api_test/remote_combo_options_test.py

@@ -0,0 +1,139 @@
+import pytest
+
+from comfy_api.latest._io import (
+    Combo,
+    RemoteComboOptions,
+    RemoteItemSchema,
+    RemoteOptions,
+)
+
+
+def _schema(**overrides):
+    defaults = dict(value_field="id", label_field="name")
+    return RemoteItemSchema(**{**defaults, **overrides})
+
+
+def _combo(**overrides):
+    defaults = dict(route="/proxy/foo", item_schema=_schema())
+    return RemoteComboOptions(**{**defaults, **overrides})
+
+
+def test_item_schema_defaults_accepted():
+    d = _schema().as_dict()
+    assert d == {"value_field": "id", "label_field": "name", "preview_type": "image"}
+
+
+def test_item_schema_full_config_accepted():
+    d = _schema(
+        preview_url_field="preview",
+        preview_type="audio",
+        description_field="desc",
+        search_fields=["first", "last", "profile.email"],
+    ).as_dict()
+    assert d["preview_type"] == "audio"
+    assert d["search_fields"] == ["first", "last", "profile.email"]
+
+
+@pytest.mark.parametrize(
+    "bad_fields",
+    [
+        ["{first} {last}"],
+        ["name", "{age}"],
+        ["leading{"],
+        ["trailing}"],
+    ],
+)
+def test_item_schema_rejects_template_strings_in_search_fields(bad_fields):
+    with pytest.raises(ValueError, match="search_fields"):
+        _schema(search_fields=bad_fields)
+
+
+@pytest.mark.parametrize("bad_preview_type", ["middle", "IMAGE", "", "gif"])
+def test_item_schema_rejects_unknown_preview_type(bad_preview_type):
+    with pytest.raises(ValueError, match="preview_type"):
+        _schema(preview_type=bad_preview_type)
+
+
+def test_combo_options_minimal_accepted():
+    d = _combo().as_dict()
+    assert d["route"] == "/proxy/foo"
+    assert d["refresh_button"] is True
+    assert "item_schema" in d
+
+
+@pytest.mark.parametrize(
+    "route",
+    [
+        "/proxy/foo",
+        "/voices",
+    ],
+)
+def test_combo_options_accepts_valid_routes(route):
+    _combo(route=route)
+
+
+@pytest.mark.parametrize(
+    "route",
+    [
+        "",
+        "api.example.com/voices",
+        "voices",
+        "ftp-no-scheme",
+        "http://localhost:9000/voices",
+        "https://api.example.com/v1/voices",
+    ],
+)
+def test_combo_options_rejects_non_relative_routes(route):
+    with pytest.raises(ValueError, match="'route'"):
+        _combo(route=route)
+
+
+@pytest.mark.parametrize("bad_auto_select", ["middle", "FIRST", "", "firstlast"])
+def test_combo_options_rejects_unknown_auto_select(bad_auto_select):
+    with pytest.raises(ValueError, match="auto_select"):
+        _combo(auto_select=bad_auto_select)
+
+
+@pytest.mark.parametrize("bad_refresh", [1, 127])
+def test_combo_options_refresh_in_forbidden_range_rejected(bad_refresh):
+    with pytest.raises(ValueError, match="refresh"):
+        _combo(refresh=bad_refresh)
+
+
+@pytest.mark.parametrize("ok_refresh", [0, -1, 128])
+def test_combo_options_refresh_valid_values_accepted(ok_refresh):
+    _combo(refresh=ok_refresh)
+
+
+def test_combo_options_timeout_negative_rejected():
+    with pytest.raises(ValueError, match="timeout"):
+        _combo(timeout=-1)
+
+
+def test_combo_options_max_retries_negative_rejected():
+    with pytest.raises(ValueError, match="max_retries"):
+        _combo(max_retries=-1)
+
+
+def test_combo_options_as_dict_prunes_none_fields():
+    d = _combo().as_dict()
+    for pruned in ("response_key", "refresh", "timeout", "max_retries", "auto_select"):
+        assert pruned not in d
+
+
+def test_combo_input_accepts_remote_combo_alone():
+    Combo.Input("voice", remote_combo=_combo())
+
+
+def test_combo_input_rejects_remote_plus_remote_combo():
+    with pytest.raises(ValueError, match="remote.*remote_combo"):
+        Combo.Input(
+            "voice",
+            remote=RemoteOptions(route="/r", refresh_button=True),
+            remote_combo=_combo(),
+        )
+
+
+def test_combo_input_rejects_options_plus_remote_combo():
+    with pytest.raises(ValueError, match="options.*remote_combo"):
+        Combo.Input("voice", options=["a", "b"], remote_combo=_combo())