Merge branch 'master' into matt/oss-spec-fix-assets-import

fix(openapi): correct POST /api/assets/import to importPublishedAssets
The operation at POST /api/assets/import was defined as `importAssets` with a URL-list body shape, but no runtime actually serves that operation at this path. The cloud runtime serves a different operation here — `importPublishedAssets` — which imports published-workflow assets into the caller's library by ID, not by URL. Cloud's URL-based asset ingestion lives at separate paths (POST /assets/download + GET /assets/remote-metadata) tracked elsewhere; nothing in this PR affects that work. Changes: - Replace the operation at POST /api/assets/import with `importPublishedAssets`, taking ImportPublishedAssetsRequest (published_asset_ids + optional share_id) and returning ImportPublishedAssetsResponse (list of AssetInfo). - Remove the unused AssetImportRequest component schema (no other references in the spec). - Operation and schemas tagged x-runtime: [cloud] with [cloud-only] description prefix, matching the existing convention for cloud-runtime-only operations elsewhere in the spec. Spectral lint passes (0 errors); the two hint-level findings on the spec are pre-existing and unrelated. No FE consumer references AssetImportRequest today; this is a pure spec correction to match what the cloud runtime actually serves.
2026-05-21 16:50:07 +08:00 · 2026-05-20 21:23:06 -07:00 · 2026-05-20 21:16:21 -07:00
9 changed files with 91 additions and 167 deletions
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@ -543,7 +543,7 @@ class AudioConcat(IO.ComfyNode):
        return IO.Schema(
            node_id="AudioConcat",
            search_aliases=["join audio", "combine audio", "append audio"],
-            display_name="Concatenate Audio",
+            display_name="Audio Concat",
            description="Concatenates the audio1 to audio2 in the specified direction.",
            category="audio",
            inputs=[
@ -597,7 +597,7 @@ class AudioMerge(IO.ComfyNode):
        return IO.Schema(
            node_id="AudioMerge",
            search_aliases=["mix audio", "overlay audio", "layer audio"],
-            display_name="Merge Audio",
+            display_name="Audio Merge",
            description="Combine two audio tracks by overlaying their waveforms.",
            category="audio",
            inputs=[
@ -667,9 +667,8 @@ class AudioAdjustVolume(IO.ComfyNode):
        return IO.Schema(
            node_id="AudioAdjustVolume",
            search_aliases=["audio gain", "loudness", "audio level"],
-            display_name="Adjust Audio Volume",
+            display_name="Audio Adjust Volume",
            category="audio",
-            description="Adjust the volume of the audio by a specified amount in decibels (dB).",
            inputs=[
                IO.Audio.Input("audio"),
                IO.Int.Input(
--- a/comfy_extras/nodes_dataset.py
+++ b/comfy_extras/nodes_dataset.py
@ -47,10 +47,8 @@ class LoadImageDataSetFromFolderNode(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="LoadImageDataSetFromFolder",
-            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
-            display_name="Load Image (from Folder)",
-            category="image",
-            description="Load a dataset of images from a specified folder and return a list of images. Supported formats: PNG, JPG, JPEG, WEBP.",
+            display_name="Load Image Dataset from Folder",
+            category="dataset",
            is_experimental=True,
            inputs=[
                io.Combo.Input(
@ -86,16 +84,14 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="LoadImageTextDataSetFromFolder",
-            search_aliases=["load folder", "load from folder", "load dataset", "load images", "import dataset"],
-            display_name="Load Image-Text (from Folder)",
-            category="image",
-            description="Load a dataset of pairs of images and text captions from a specified folder and return them as a list. Supported formats: PNG, JPG, JPEG, WEBP.",
+            display_name="Load Image and Text Dataset from Folder",
+            category="dataset",
            is_experimental=True,
            inputs=[
                io.Combo.Input(
                    "folder",
                    options=folder_paths.get_input_subfolders(),
-                    tooltip="The folder to load images and text captions from.",
+                    tooltip="The folder to load images from.",
                )
            ],
            outputs=[
@ -210,10 +206,8 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="SaveImageDataSetToFolder",
-            search_aliases=["save folder", "save to folder", "save dataset", "save images", "export dataset"],
-            display_name="Save Image (to Folder) (DEPRECATED)",
-            category="image",
-            description="Save a dataset of images to a specified folder. Supported formats: PNG.",
+            display_name="Save Image Dataset to Folder",
+            category="dataset",
            is_experimental=True,
            is_output_node=True,
            is_input_list=True,  # Receive images as list
@ -232,7 +226,6 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                ),
            ],
            outputs=[],
-            is_deprecated=True,  # This node is redundant and superseded by existing Save Image nodes where the target folder can be specified in the filename_prefix
        )

    @classmethod
@ -253,20 +246,14 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="SaveImageTextDataSetToFolder",
-            search_aliases=["save folder", "save to folder", "save dataset", "save images", "save text", "export dataset"],
-            display_name="Save Image-Text (to Folder)",
-            category="image",
-            description="Save a dataset of pairs of images and text captions to a specified folder. Images are saved as PNG files and captions are saved as TXT files with the same filename_prefix.",
+            display_name="Save Image and Text Dataset to Folder",
+            category="dataset",
            is_experimental=True,
            is_output_node=True,
            is_input_list=True,  # Receive both images and texts as lists
            inputs=[
                io.Image.Input("images", tooltip="List of images to save."),
-                io.String.Input("texts",
-                    optional=True,
-                    force_input=True,
-                    tooltip="List of text captions to save."
-                ),
+                io.String.Input("texts", tooltip="List of text captions to save."),
                io.String.Input(
                    "folder_name",
                    default="dataset",
@ -283,7 +270,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
        )

    @classmethod
-    def execute(cls, images, folder_name, filename_prefix, texts=None):
+    def execute(cls, images, texts, folder_name, filename_prefix):
        # Extract scalar values
        folder_name = folder_name[0]
        filename_prefix = filename_prefix[0]
@ -292,12 +279,11 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
        saved_files = save_images_to_folder(images, output_dir, filename_prefix)

        # Save captions
-        if texts:
-            for idx, (filename, caption) in enumerate(zip(saved_files, texts)):
-                caption_filename = filename.replace(".png", ".txt")
-                caption_path = os.path.join(output_dir, caption_filename)
-                with open(caption_path, "w", encoding="utf-8") as f:
-                    f.write(caption)
+        for idx, (filename, caption) in enumerate(zip(saved_files, texts)):
+            caption_filename = filename.replace(".png", ".txt")
+            caption_path = os.path.join(output_dir, caption_filename)
+            with open(caption_path, "w", encoding="utf-8") as f:
+                f.write(caption)

        logging.info(f"Saved {len(saved_files)} images and captions to {output_dir}.")
        return io.NodeOutput()
@ -328,13 +314,11 @@ class ImageProcessingNode(io.ComfyNode):

    Child classes should set:
        node_id: Unique node identifier (required)
-        search_aliases: List of search aliases (optional)
        display_name: Display name (optional, defaults to node_id)
        description: Node description (optional)
        extra_inputs: List of additional io.Input objects beyond "images" (optional)
        is_group_process: None (auto-detect), True (group), or False (individual) (optional)
        is_output_list: True (list output) or False (single output) (optional, default True)
-        is_deprecated: True if the node is deprecated (optional, default False)

    Child classes must implement ONE of:
        _process(cls, image, **kwargs) -> tensor  (for single-item processing)
@ -342,13 +326,12 @@ class ImageProcessingNode(io.ComfyNode):
    """

    node_id = None
-    search_aliases = []
    display_name = None
    description = None
    extra_inputs = []
    is_group_process = None  # None = auto-detect, True/False = explicit
    is_output_list = None  # None = auto-detect based on processing mode
-    is_deprecated = False
+
    @classmethod
    def _detect_processing_mode(cls):
        """Detect whether this node uses group or individual processing.
@ -419,10 +402,8 @@ class ImageProcessingNode(io.ComfyNode):

        return io.Schema(
            node_id=cls.node_id,
-            search_aliases=cls.search_aliases,
            display_name=cls.display_name or cls.node_id,
-            category=cls.category,
-            description=cls.description,
+            category="dataset/image",
            is_experimental=True,
            is_input_list=is_group,  # True for group, False for individual
            inputs=inputs,
@ -491,13 +472,11 @@ class TextProcessingNode(io.ComfyNode):

    Child classes should set:
        node_id: Unique node identifier (required)
-        search_aliases: List of search aliases (optional)
        display_name: Display name (optional, defaults to node_id)
        description: Node description (optional)
        extra_inputs: List of additional io.Input objects beyond "texts" (optional)
        is_group_process: None (auto-detect), True (group), or False (individual) (optional)
        is_output_list: True (list output) or False (single output) (optional, default True)
-        is_deprecated: True if the node is deprecated (optional, default False)

    Child classes must implement ONE of:
        _process(cls, text, **kwargs) -> str  (for single-item processing)
@ -505,13 +484,12 @@ class TextProcessingNode(io.ComfyNode):
    """

    node_id = None
-    search_aliases = []
    display_name = None
    description = None
    extra_inputs = []
    is_group_process = None  # None = auto-detect, True/False = explicit
    is_output_list = None  # None = auto-detect based on processing mode
-    is_deprecated = False
+
    @classmethod
    def _detect_processing_mode(cls):
        """Detect whether this node uses group or individual processing.
@ -649,17 +627,15 @@ class TextProcessingNode(io.ComfyNode):

 class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
    node_id = "ResizeImagesByShorterEdge"
-    display_name = "Resize Images by Shorter Edge (DEPRECATED)"
-    category = "image/transform"
-    description = "Resize images so that the shorter edge matches the specified dimension while preserving aspect ratio."
-    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale shorter dimension
+    display_name = "Resize Images by Shorter Edge"
+    description = "Resize images so that the shorter edge matches the specified length while preserving aspect ratio."
    extra_inputs = [
        io.Int.Input(
            "shorter_edge",
            default=512,
            min=1,
            max=8192,
-            tooltip="Target dimension for the shorter edge.",
+            tooltip="Target length for the shorter edge.",
        ),
    ]

@ -679,17 +655,15 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):

 class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
    node_id = "ResizeImagesByLongerEdge"
-    display_name = "Resize Images by Longer Edge (DEPRECATED)"
-    category = "image/transform"
-    description = "Resize images so that the longer edge matches the specified dimension while preserving aspect ratio."
-    is_deprecated = True  # This node is superseded by Resize Image/Mask with resize_type = scale longer dimension
+    display_name = "Resize Images by Longer Edge"
+    description = "Resize images so that the longer edge matches the specified length while preserving aspect ratio."
    extra_inputs = [
        io.Int.Input(
            "longer_edge",
            default=1024,
            min=1,
            max=8192,
-            tooltip="Target dimension for the longer edge.",
+            tooltip="Target length for the longer edge.",
        ),
    ]

@ -712,10 +686,8 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):

 class CenterCropImagesNode(ImageProcessingNode):
    node_id = "CenterCropImages"
-    search_aliases=["crop", "cut", "trim"]
-    display_name="Crop Image (Center)"
-    category="image/transform"
-    description = "Center crop an image to the specified dimensions."
+    display_name = "Center Crop Images"
+    description = "Center crop all images to the specified dimensions."
    extra_inputs = [
        io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
        io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@ -734,11 +706,10 @@ class CenterCropImagesNode(ImageProcessingNode):

 class RandomCropImagesNode(ImageProcessingNode):
    node_id = "RandomCropImages"
-    search_aliases=["crop", "cut", "trim"]
-    display_name = "Crop Image (Random)"
-    category="image/transform"
-    description = "Randomly crop an image to the specified dimensions."
-
+    display_name = "Random Crop Images"
+    description = (
+        "Randomly crop all images to the specified dimensions (for data augmentation)."
+    )
    extra_inputs = [
        io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
        io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@ -763,9 +734,7 @@ class RandomCropImagesNode(ImageProcessingNode):

 class NormalizeImagesNode(ImageProcessingNode):
    node_id = "NormalizeImages"
-    search_aliases=["normalize", "normalize colors"]
-    display_name = "Normalize Image Colors"
-    category = "image/color"
+    display_name = "Normalize Images"
    description = "Normalize images using mean and standard deviation."
    extra_inputs = [
        io.Float.Input(
@ -793,10 +762,8 @@ class NormalizeImagesNode(ImageProcessingNode):

 class AdjustBrightnessNode(ImageProcessingNode):
    node_id = "AdjustBrightness"
-    search_aliases=["brightness"]
    display_name = "Adjust Brightness"
-    category="image/adjustments"
-    description = "Adjust the brightness of an image."
+    description = "Adjust brightness of all images."
    extra_inputs = [
        io.Float.Input(
            "factor",
@ -814,10 +781,8 @@ class AdjustBrightnessNode(ImageProcessingNode):

 class AdjustContrastNode(ImageProcessingNode):
    node_id = "AdjustContrast"
-    search_aliases=["contrast"]
    display_name = "Adjust Contrast"
-    category="image/adjustments"
-    description = "Adjust the contrast of an image."
+    description = "Adjust contrast of all images."
    extra_inputs = [
        io.Float.Input(
            "factor",
@ -835,10 +800,8 @@ class AdjustContrastNode(ImageProcessingNode):

 class ShuffleDatasetNode(ImageProcessingNode):
    node_id = "ShuffleDataset"
-    search_aliases=["shuffle", "randomize", "mix"]
-    display_name = "Shuffle Images List"
-    category = "image/batch"
-    description = "Randomly shuffle the order of images in a list."
+    display_name = "Shuffle Image Dataset"
+    description = "Randomly shuffle the order of images in the dataset."
    is_group_process = True  # Requires full list to shuffle
    extra_inputs = [
        io.Int.Input(
@ -860,15 +823,13 @@ class ShuffleImageTextDatasetNode(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="ShuffleImageTextDataset",
-            search_aliases=["shuffle", "randomize", "mix"],
-            display_name = "Shuffle Pairs of Image-Text",
-            category = "image/batch",
-            description = "Randomly shuffle the order of pairs of image-text in a list.",
+            display_name="Shuffle Image-Text Dataset",
+            category="dataset/image",
            is_experimental=True,
            is_input_list=True,
            inputs=[
                io.Image.Input("images", tooltip="List of images to shuffle."),
-                io.String.Input("texts", tooltip="List of texts to shuffle.", force_input=True),
+                io.String.Input("texts", tooltip="List of texts to shuffle."),
                io.Int.Input(
                    "seed",
                    default=0,
@ -904,11 +865,8 @@ class ShuffleImageTextDatasetNode(io.ComfyNode):

 class TextToLowercaseNode(TextProcessingNode):
    node_id = "TextToLowercase"
-    search_aliases=["lowercase"]
-    display_name = "Convert Text to Lowercase (DEPRECATED)"
-    category = "text"
-    description = "Convert text to lowercase."
-    is_deprecated = True  # This node is superseded by the Convert Text Case node
+    display_name = "Text to Lowercase"
+    description = "Convert all texts to lowercase."

    @classmethod
    def _process(cls, text):
@ -917,11 +875,8 @@ class TextToLowercaseNode(TextProcessingNode):

 class TextToUppercaseNode(TextProcessingNode):
    node_id = "TextToUppercase"
-    search_aliases=["uppercase"]
-    display_name = "Convert Text to Uppercase (DEPRECATED)"
-    category = "text"
-    description = "Convert text to uppercase."
-    is_deprecated = True  # This node is superseded by the Convert Text Case node
+    display_name = "Text to Uppercase"
+    description = "Convert all texts to uppercase."

    @classmethod
    def _process(cls, text):
@ -930,10 +885,8 @@ class TextToUppercaseNode(TextProcessingNode):

 class TruncateTextNode(TextProcessingNode):
    node_id = "TruncateText"
-    search_aliases=["truncate", "cut", "shorten"]
    display_name = "Truncate Text"
-    category = "text"
-    description = "Truncate text to a maximum length."
+    description = "Truncate all texts to a maximum length."
    extra_inputs = [
        io.Int.Input(
            "max_length", default=77, min=1, max=10000, tooltip="Maximum text length."
@ -947,10 +900,8 @@ class TruncateTextNode(TextProcessingNode):

 class AddTextPrefixNode(TextProcessingNode):
    node_id = "AddTextPrefix"
-    display_name = "Add Text Prefix (DEPRECATED)"
-    category = "text"
+    display_name = "Add Text Prefix"
    description = "Add a prefix to all texts."
-    is_deprecated = True  # This node is superseded by the Concatenate Text node
    extra_inputs = [
        io.String.Input("prefix", default="", tooltip="Prefix to add."),
    ]
@ -962,10 +913,8 @@ class AddTextPrefixNode(TextProcessingNode):

 class AddTextSuffixNode(TextProcessingNode):
    node_id = "AddTextSuffix"
-    display_name = "Add Text Suffix (DEPRECATED)"
-    category = "text"
+    display_name = "Add Text Suffix"
    description = "Add a suffix to all texts."
-    is_deprecated = True  # This node is superseded by the Concatenate Text node
    extra_inputs = [
        io.String.Input("suffix", default="", tooltip="Suffix to add."),
    ]
@ -977,10 +926,8 @@ class AddTextSuffixNode(TextProcessingNode):

 class ReplaceTextNode(TextProcessingNode):
    node_id = "ReplaceText"
-    display_name = "Replace Text (DEPRECATED)"
-    category = "text"
+    display_name = "Replace Text"
    description = "Replace text in all texts."
-    is_deprecated = True  # This node is superseded by the other Replace Text node
    extra_inputs = [
        io.String.Input("find", default="", tooltip="Text to find."),
        io.String.Input("replace", default="", tooltip="Text to replace with."),
@ -993,10 +940,8 @@ class ReplaceTextNode(TextProcessingNode):

 class StripWhitespaceNode(TextProcessingNode):
    node_id = "StripWhitespace"
-    display_name = "Strip Whitespace (DEPRECATED)"
-    category = "text"
+    display_name = "Strip Whitespace"
    description = "Strip leading and trailing whitespace from all texts."
-    is_deprecated = True  # This node is superseded by the Trim Text node

    @classmethod
    def _process(cls, text):
@ -1007,13 +952,11 @@ class StripWhitespaceNode(TextProcessingNode):


 class ImageDeduplicationNode(ImageProcessingNode):
-    """Remove duplicate or very similar images from a list using perceptual hashing."""
+    """Remove duplicate or very similar images from the dataset using perceptual hashing."""

    node_id = "ImageDeduplication"
-    search_aliases=["deduplicate", "remove duplicates", "similarity filter"]
-    display_name = "Deduplicate Images"
-    category = "image/batch"
-    description = "Remove duplicate or very similar images from a list."
+    display_name = "Image Deduplication"
+    description = "Remove duplicate or very similar images from the dataset."
    is_group_process = True  # Requires full list to compare images
    extra_inputs = [
        io.Float.Input(
@ -1083,9 +1026,7 @@ class ImageGridNode(ImageProcessingNode):
    """Combine multiple images into a single grid/collage."""

    node_id = "ImageGrid"
-    search_aliases=["grid", "collage", "combine"]
-    display_name = "Make Image Grid"
-    category="image/batch"
+    display_name = "Image Grid"
    description = "Arrange multiple images into a grid layout."
    is_group_process = True  # Requires full list to create grid
    is_output_list = False  # Outputs single grid image
@ -1161,12 +1102,9 @@ class MergeImageListsNode(ImageProcessingNode):
    """Merge multiple image lists into a single list."""

    node_id = "MergeImageLists"
-    search_aliases=["list", "merge list", "make list"]
-    display_name = "Merge Image Lists (DEPRECATED)"
-    category = "image/batch"
+    display_name = "Merge Image Lists"
    description = "Concatenate multiple image lists into one."
    is_group_process = True  # Receives images as list
-    is_deprecated = True  # This node is superseded by the Create List node

    @classmethod
    def _group_process(cls, images):
@ -1181,11 +1119,9 @@ class MergeTextListsNode(TextProcessingNode):
    """Merge multiple text lists into a single list."""

    node_id = "MergeTextLists"
-    display_name = "Merge Text Lists (DEPRECATED)"
-    category = "text"
+    display_name = "Merge Text Lists"
    description = "Concatenate multiple text lists into one."
    is_group_process = True  # Receives texts as list
-    is_deprecated = True  # This node is superseded by the Create List node

    @classmethod
    def _group_process(cls, texts):
@ -1206,10 +1142,8 @@ class ResolutionBucket(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="ResolutionBucket",
-            search_aliases=["bucket by resolution", "group by resolution", "batch by resolution"],
            display_name="Resolution Bucket",
-            category="training",
-            description="Group latents and conditionings into buckets",
+            category="dataset",
            is_experimental=True,
            is_input_list=True,
            inputs=[
@ -1302,8 +1236,7 @@ class MakeTrainingDataset(io.ComfyNode):
            node_id="MakeTrainingDataset",
            search_aliases=["encode dataset"],
            display_name="Make Training Dataset",
-            category="training",
-            description="Encode images with VAE and texts with CLIP to create a training dataset of latents and conditionings.",
+            category="dataset",
            is_experimental=True,
            is_input_list=True,  # images and texts as lists
            inputs=[
@ -1318,7 +1251,6 @@ class MakeTrainingDataset(io.ComfyNode):
                    "texts",
                    optional=True,
                    tooltip="List of text captions. Can be length n (matching images), 1 (repeated for all), or omitted (uses empty string).",
-                    force_input=True
                ),
            ],
            outputs=[
@ -1388,10 +1320,9 @@ class SaveTrainingDataset(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="SaveTrainingDataset",
-            search_aliases=["export dataset", "save dataset"],
+            search_aliases=["export training data"],
            display_name="Save Training Dataset",
-            category="training",
-            description="Save encoded training dataset (latents + conditioning) to disk for efficient loading during training.",
+            category="dataset",
            is_experimental=True,
            is_output_node=True,
            is_input_list=True,  # Receive lists
@ -1493,8 +1424,7 @@ class LoadTrainingDataset(io.ComfyNode):
            node_id="LoadTrainingDataset",
            search_aliases=["import dataset", "training data"],
            display_name="Load Training Dataset",
-            category="training",
-            description="Load encoded training dataset (latents + conditioning) from disk for use in training.",
+            category="dataset",
            is_experimental=True,
            inputs=[
                io.String.Input(
--- a/comfy_extras/nodes_hunyuan3d.py
+++ b/comfy_extras/nodes_hunyuan3d.py
@ -419,17 +419,15 @@ class VoxelToMeshBasic(IO.ComfyNode):
    def define_schema(cls):
        return IO.Schema(
            node_id="VoxelToMeshBasic",
-            display_name="Voxel to Mesh (Basic) (DEPRECATED)",
+            display_name="Voxel to Mesh (Basic)",
            category="3d",
-            description="Converts a voxel grid to a mesh.",
-            is_deprecated=True, # This node is superseded by the Voxel To Mesh node
            inputs=[
                IO.Voxel.Input("voxel"),
                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
            ],
            outputs=[
                IO.Mesh.Output(),
-            ],
+            ]
        )

    @classmethod
@ -455,10 +453,9 @@ class VoxelToMesh(IO.ComfyNode):
            node_id="VoxelToMesh",
            display_name="Voxel to Mesh",
            category="3d",
-            description="Converts a voxel grid to a mesh.",
            inputs=[
                IO.Voxel.Input("voxel"),
-                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
            ],
            outputs=[
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@ -55,10 +55,9 @@ class ImageCropV2(IO.ComfyNode):
    def define_schema(cls):
        return IO.Schema(
            node_id="ImageCropV2",
-            search_aliases=["crop", "cut", "trim"],
+            search_aliases=["trim"],
            display_name="Crop Image",
            category="image/transform",
-            description = "Crop an image to the specified dimensions.",
            essentials_category="Image Tools",
            has_intermediate_output=True,
            inputs=[
--- a/comfy_extras/nodes_lt_audio.py
+++ b/comfy_extras/nodes_lt_audio.py
@ -11,8 +11,8 @@ class LTXVAudioVAELoader(io.ComfyNode):
    def define_schema(cls) -> io.Schema:
        return io.Schema(
            node_id="LTXVAudioVAELoader",
-            display_name="Load LTXV Audio VAE",
-            category="loaders",
+            display_name="LTXV Audio VAE Loader",
+            category="audio",
            inputs=[
                io.Combo.Input(
                    "ckpt_name",
@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio):
        return io.Schema(
            node_id="LTXVAudioVAEEncode",
            display_name="LTXV Audio VAE Encode",
-            category="latent/audio",
+            category="audio",
            inputs=[
                io.Audio.Input("audio", tooltip="The audio to be encoded."),
                io.Vae.Input(
@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode):
        return io.Schema(
            node_id="LTXVAudioVAEDecode",
            display_name="LTXV Audio VAE Decode",
-            category="latent/audio",
+            category="audio",
            inputs=[
                io.Latent.Input("samples", tooltip="The latent to be decoded."),
                io.Vae.Input(
--- a/comfy_extras/nodes_mediapipe.py
+++ b/comfy_extras/nodes_mediapipe.py
@ -28,7 +28,7 @@ from comfy_extras.mediapipe.face_landmarker import FaceLandmarker
 from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection


-FaceDetectionType = io.Custom("FACE_DETECTION_MODEL")
+FaceLandmarkerType = io.Custom("FACE_LANDMARKER")
 FaceLandmarksType = io.Custom("FACE_LANDMARKS")

 _CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
@ -204,19 +204,18 @@ class LoadMediaPipeFaceLandmarker(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="LoadMediaPipeFaceLandmarker",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
-            display_name="Load Face Detection Model (MediaPipe)",
+            display_name="Load MediaPipe Face Landmarker",
            category="loaders",
            inputs=[
-                io.Combo.Input("model_name", options=folder_paths.get_filename_list("detection"),
-                               tooltip="Face detection model from models/detection/."),
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("mediapipe"),
+                               tooltip="Face Landmarker safetensors from models/mediapipe/."),
            ],
-            outputs=[FaceDetectionType.Output()],
+            outputs=[FaceLandmarkerType.Output()],
        )

    @classmethod
    def execute(cls, model_name) -> io.NodeOutput:
-        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("detection", model_name), safe_load=True)
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("mediapipe", model_name), safe_load=True)
        wrapper = FaceLandmarkerModel(sd)
        return io.NodeOutput(wrapper)

@ -235,12 +234,10 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="MediaPipeFaceLandmarker",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
-            display_name="Detect Face Landmarks (MediaPipe)",
+            display_name="MediaPipe Face Landmarker",
            category="image/detection",
-            description="Detects facial landmarks using MediaPipe model.",
            inputs=[
-                FaceDetectionType.Input("face_detection_model"),
+                FaceLandmarkerType.Input("face_landmarker"),
                io.Image.Input("image"),
                io.Combo.Input("detector_variant", options=["short", "full", "both"], default="short",
                               tooltip="Face detector range. 'short' is tuned for close-up faces "
@ -264,9 +261,9 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
        )

    @classmethod
-    def execute(cls, face_detection_model, image, detector_variant, num_faces, min_confidence,
+    def execute(cls, face_landmarker, image, detector_variant, num_faces, min_confidence,
                missing_frame_fallback) -> io.NodeOutput:
-        canonical = face_detection_model.canonical_data
+        canonical = face_landmarker.canonical_data
        img_np = _image_to_uint8(image)
        B, H, W = img_np.shape[:3]
        chunk = 16
@ -279,7 +276,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
            with tqdm(total=B, desc=f"MediaPipe Face Landmarker ({variant})") as tq:
                for i in range(0, B, chunk):
                    end = min(i + chunk, B)
-                    res.extend(face_detection_model.detect_batch(
+                    res.extend(face_landmarker.detect_batch(
                        [img_np[bi] for bi in range(i, end)],
                        num_faces=int(num_faces),
                        score_thresh=float(min_confidence),
@ -309,7 +306,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
                per_bb.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1, "label": "face", "score": float(f["score"])})
            bboxes.append(per_bb)
        return io.NodeOutput({"frames": frames, "image_size": (H, W),
-                              "connection_sets": face_detection_model.connection_sets}, bboxes)
+                              "connection_sets": face_landmarker.connection_sets}, bboxes)


 # Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
@ -335,10 +332,8 @@ class MediaPipeFaceMeshVisualize(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="MediaPipeFaceMeshVisualize",
-            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection", "visualize"],
-            display_name="Visualize Face Landmarks (MediaPipe)",
+            display_name="MediaPipe Face Mesh Visualize",
            category="image/detection",
-            description="Draws face landmarks mesh on the input image.",
            inputs=[
                FaceLandmarksType.Input("face_landmarks"),
                io.Image.Input("image", optional=True, tooltip="If not connected, a black canvas will be used."),
@ -448,10 +443,8 @@ class MediaPipeFaceMask(io.ComfyNode):
    def define_schema(cls):
        return io.Schema(
            node_id="MediaPipeFaceMask",
-            search_aliases=["face", "facial", "mediapipe", "face mask", "blazeface", "face detection", "visualize"],
-            display_name="Draw Face Mask (MediaPipe)",
+            display_name="MediaPipe Face Mask",
            category="image/detection",
-            description="Draws a mask from face landmarks.",
            inputs=[
                FaceLandmarksType.Input("face_landmarks"),
                io.DynamicCombo.Input(
--- a/folder_paths.py
+++ b/folder_paths.py
@ -60,7 +60,7 @@ folder_names_and_paths["geometry_estimation"] = ([os.path.join(models_dir, "geom

 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)

-folder_names_and_paths["detection"] = ([os.path.join(models_dir, "detection")], supported_pt_extensions)
+folder_names_and_paths["mediapipe"] = ([os.path.join(models_dir, "mediapipe")], supported_pt_extensions)

 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
--- a/models/mediapipe/put_mediapipe_models_here
+++ b/models/mediapipe/put_mediapipe_models_here
--- a/openapi.yaml
+++ b/openapi.yaml
@ -1556,6 +1556,12 @@ paths:
            type: string
            enum: [asc, desc]
          description: Sort direction
+        - name: job_ids
+          in: query
+          schema:
+            type: string
+          x-runtime: [cloud]
+          description: "[cloud-only] Comma-separated UUIDs to filter assets by associated job."
        - name: include_public
          in: query
          schema: