[pull] master from Comfy-Org:master

comfy_extras/nodes_audio.py

@@ -543,7 +543,7 @@ def define_schema(cls):
         return IO.Schema(
             node_id="AudioConcat",
             search_aliases=["join audio", "combine audio", "append audio"],
-            display_name="Audio Concat",
+            display_name="Concatenate Audio",
             description="Concatenates the audio1 to audio2 in the specified direction.",
             category="audio",
             inputs=[
@@ -597,7 +597,7 @@ def define_schema(cls):
         return IO.Schema(
             node_id="AudioMerge",
             search_aliases=["mix audio", "overlay audio", "layer audio"],
-            display_name="Audio Merge",
+            display_name="Merge Audio",
             description="Combine two audio tracks by overlaying their waveforms.",
             category="audio",
             inputs=[
@@ -667,8 +667,9 @@ def define_schema(cls):
         return IO.Schema(
             node_id="AudioAdjustVolume",
             search_aliases=["audio gain", "loudness", "audio level"],
-            display_name="Audio Adjust Volume",
+            display_name="Adjust Audio 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(

comfy_extras/nodes_hunyuan3d.py

@@ -419,15 +419,17 @@ class VoxelToMeshBasic(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMeshBasic",
-            display_name="Voxel to Mesh (Basic)",
+            display_name="Voxel to Mesh (Basic) (DEPRECATED)",
             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+            is_deprecated=True, # This node is superseded by the Voxel To Mesh node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
@@ -453,9 +455,10 @@ def define_schema(cls):
             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"], advanced=True),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[

comfy_extras/nodes_images.py

@@ -55,9 +55,10 @@ class ImageCropV2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCropV2",
-            search_aliases=["trim"],
+            search_aliases=["crop", "cut", "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=[

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="LTXV Audio VAE Loader",
-            category="audio",
+            display_name="Load LTXV Audio VAE",
+            category="loaders",
             inputs=[
                 io.Combo.Input(
                     "ckpt_name",
@@ -40,7 +40,7 @@ def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="LTXVAudioVAEEncode",
             display_name="LTXV Audio VAE Encode",
-            category="audio",
+            category="latent/audio",
             inputs=[
                 io.Audio.Input("audio", tooltip="The audio to be encoded."),
                 io.Vae.Input(
@@ -63,7 +63,7 @@ def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="LTXVAudioVAEDecode",
             display_name="LTXV Audio VAE Decode",
-            category="audio",
+            category="latent/audio",
             inputs=[
                 io.Latent.Input("samples", tooltip="The latent to be decoded."),
                 io.Vae.Input(

comfy_extras/nodes_mediapipe.py

@@ -28,7 +28,7 @@
 from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection
 
 
-FaceLandmarkerType = io.Custom("FACE_LANDMARKER")
+FaceDetectionType = io.Custom("FACE_DETECTION_MODEL")
 FaceLandmarksType = io.Custom("FACE_LANDMARKS")
 
 _CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
@@ -204,18 +204,19 @@ class LoadMediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoadMediaPipeFaceLandmarker",
-            display_name="Load MediaPipe Face Landmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Load Face Detection Model (MediaPipe)",
             category="loaders",
             inputs=[
-                io.Combo.Input("model_name", options=folder_paths.get_filename_list("mediapipe"),
-                               tooltip="Face Landmarker safetensors from models/mediapipe/."),
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("detection"),
+                               tooltip="Face detection model from models/detection/."),
             ],
-            outputs=[FaceLandmarkerType.Output()],
+            outputs=[FaceDetectionType.Output()],
         )
 
     @classmethod
     def execute(cls, model_name) -> io.NodeOutput:
-        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("mediapipe", model_name), safe_load=True)
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("detection", model_name), safe_load=True)
         wrapper = FaceLandmarkerModel(sd)
         return io.NodeOutput(wrapper)
 
@@ -234,10 +235,12 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceLandmarker",
-            display_name="MediaPipe Face Landmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Detect Face Landmarks (MediaPipe)",
             category="image/detection",
+            description="Detects facial landmarks using MediaPipe model.",
             inputs=[
-                FaceLandmarkerType.Input("face_landmarker"),
+                FaceDetectionType.Input("face_detection_model"),
                 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 "
@@ -261,9 +264,9 @@ def define_schema(cls):
         )
 
     @classmethod
-    def execute(cls, face_landmarker, image, detector_variant, num_faces, min_confidence,
+    def execute(cls, face_detection_model, image, detector_variant, num_faces, min_confidence,
                 missing_frame_fallback) -> io.NodeOutput:
-        canonical = face_landmarker.canonical_data
+        canonical = face_detection_model.canonical_data
         img_np = _image_to_uint8(image)
         B, H, W = img_np.shape[:3]
         chunk = 16
@@ -276,7 +279,7 @@ def _run(variant: str) -> list[list[dict]]:
             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_landmarker.detect_batch(
+                    res.extend(face_detection_model.detect_batch(
                         [img_np[bi] for bi in range(i, end)],
                         num_faces=int(num_faces),
                         score_thresh=float(min_confidence),
@@ -306,7 +309,7 @@ def _run(variant: str) -> list[list[dict]]:
                 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_landmarker.connection_sets}, bboxes)
+                              "connection_sets": face_detection_model.connection_sets}, bboxes)
 
 
 # Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
@@ -332,8 +335,10 @@ class MediaPipeFaceMeshVisualize(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMeshVisualize",
-            display_name="MediaPipe Face Mesh Visualize",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection", "visualize"],
+            display_name="Visualize Face Landmarks (MediaPipe)",
             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."),
@@ -443,8 +448,10 @@ class MediaPipeFaceMask(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMask",
-            display_name="MediaPipe Face Mask",
+            search_aliases=["face", "facial", "mediapipe", "face mask", "blazeface", "face detection", "visualize"],
+            display_name="Draw Face Mask (MediaPipe)",
             category="image/detection",
+            description="Draws a mask from face landmarks.",
             inputs=[
                 FaceLandmarksType.Input("face_landmarks"),
                 io.DynamicCombo.Input(

comfy_extras/nodes_moge.py

@@ -103,8 +103,10 @@ class MoGePanoramaInference(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGePanoramaInference",
-            display_name="MoGe Panorama Inference",
+            search_aliases=["moge", "panorama", "depth", "geometry", "depth estimation", "geometry estimation"],
+            display_name="Run MoGe Panorama Inference",
             category="image/geometry_estimation",
+            description="Run MoGe on an equirectangular panorama by splitting it into 12 perspective views, running inference on each, and merging the results into a single depth map.",
             inputs=[
                 MoGeModelType.Input("moge_model"),
                 io.Image.Input("image", tooltip="Equirectangular panorama (any aspect)."),
@@ -222,7 +224,9 @@ class MoGeInference(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGeInference",
-            display_name="MoGe Inference",
+            search_aliases=["moge", "depth", "geometry", "depth estimation", "geometry estimation"],
+            display_name="Run MoGe Inference",
+            description="Run MoGe on a single image to estimate depth and geometry.",
             category="image/geometry_estimation",
             inputs=[
                 MoGeModelType.Input("moge_model"),
@@ -277,7 +281,9 @@ class MoGeRender(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGeRender",
-            display_name="MoGe Render",
+            search_aliases=["moge", "render", "geometry", "depth", "normal"],
+            display_name="Render MoGe Geometry",
+            description="Render a depth map or normal map from geometry data",
             category="image/geometry_estimation",
             inputs=[
                 MoGeGeometry.Input("moge_geometry"),
@@ -342,7 +348,9 @@ class MoGePointMapToMesh(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MoGePointMapToMesh",
-            display_name="MoGe Point Map to Mesh",
+            search_aliases=["moge", "mesh", "geometry", "point map"],
+            display_name="Convert MoGe Point Map to Mesh",
+            description="Convert a MoGe point map into a 3D mesh.",
             category="image/geometry_estimation",
             inputs=[
                 MoGeGeometry.Input("moge_geometry"),

folder_paths.py

@@ -60,7 +60,7 @@
 
 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)
 
-folder_names_and_paths["mediapipe"] = ([os.path.join(models_dir, "mediapipe")], supported_pt_extensions)
+folder_names_and_paths["detection"] = ([os.path.join(models_dir, "detection")], supported_pt_extensions)
 
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")

openapi.yaml

@@ -1556,12 +1556,6 @@ 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:
@@ -2514,37 +2508,25 @@ paths:
 
   /api/assets/import:
     post:
-      operationId: importAssets
+      operationId: importPublishedAssets
       tags: [assets]
-      summary: Import assets from external URLs
-      description: "[cloud-only] Imports one or more assets from external URLs into the cloud asset store."
+      summary: "[cloud-only] Import published assets into the caller's library"
+      description: |
+        [cloud-only] Imports the specified published assets into the caller's asset library. New DB records reference the same storage objects; no file copying occurs. Assets the caller already owns (by hash) are deduplicated. The `id` field on each returned `AssetInfo` is the caller's newly-created private asset ID, not the published asset ID supplied in the request.
       x-runtime: [cloud]
       requestBody:
         required: true
         content:
           application/json:
             schema:
-              type: object
-              required:
-                - imports
-              properties:
-                imports:
-                  type: array
-                  items:
-                    $ref: "#/components/schemas/AssetImportRequest"
-                  description: Assets to import
+              $ref: "#/components/schemas/ImportPublishedAssetsRequest"
       responses:
         "200":
-          description: Import initiated
+          description: Successfully imported assets
           content:
             application/json:
               schema:
-                type: object
-                properties:
-                  assets:
-                    type: array
-                    items:
-                      $ref: "#/components/schemas/Asset"
+                $ref: "#/components/schemas/ImportPublishedAssetsResponse"
         "400":
           description: Bad request
           content:
@@ -7379,24 +7361,35 @@ components:
           type: string
           description: Target path on the runtime filesystem
 
-    AssetImportRequest:
+    ImportPublishedAssetsRequest:
       type: object
       x-runtime: [cloud]
-      description: "[cloud-only] A single asset to import from an external URL."
+      description: "[cloud-only] Request body for importing published assets into the caller's library."
       required:
-        - url
+        - published_asset_ids
       properties:
-        url:
-          type: string
-          format: uri
-          description: URL of the asset to import
-        name:
-          type: string
-          description: Display name for the imported asset
-        tags:
+        published_asset_ids:
           type: array
+          description: IDs of published assets (inputs and models) to import.
           items:
             type: string
+        share_id:
+          type: string
+          nullable: true
+          description: |
+            Optional. Share ID of the published workflow these assets belong to. When provided (non-null, non-empty): all `published_asset_ids` must belong to this share's workflow version; returns 400 if the share is not found or any asset does not belong to it. When omitted, null, or empty string: no share-scoped validation is performed and the assets are validated only against global rules (preserved for clients that have not yet adopted `share_id`).
+
+    ImportPublishedAssetsResponse:
+      type: object
+      x-runtime: [cloud]
+      description: "[cloud-only] Response after importing published assets. Each returned `AssetInfo.id` is the caller's newly-created private asset ID, not the published asset ID supplied in the request."
+      required:
+        - assets
+      properties:
+        assets:
+          type: array
+          items:
+            $ref: "#/components/schemas/AssetInfo"
 
     RemoteAssetMetadata:
       type: object