Merge 97ba2a5155 into 35819e35a8

* fix(spec): mark DeviceStats.index and NodeInfo.essentials_category as nullable

Two fields in openapi.yaml are declared as required/non-nullable but
the Python implementation legitimately returns `null` for them, so any
client that response-validates against the spec will fail.

`DeviceStats.index` (used by GET /api/system_stats):
- server.py emits `"index": device.index` unconditionally
- For the CPU device (--cpu mode), `torch.device("cpu").index` is `None`
- → JSON response includes `"index": null` for CPU devices

`NodeInfo.essentials_category` (used by GET /api/object_info):
- The V3 schema-based path (comfy_api/latest/_io.py:1654) unconditionally
  passes `essentials_category=self.essentials_category` into NodeInfoV1
  and serializes via dataclasses.asdict(), so the key is always present
- Schema's `essentials_category` defaults to `None` for nodes that
  don't set it in `define_schema` (e.g. the APG node)
- → JSON response includes `"essentials_category": null` for those nodes
- (The V1 path in server.py uses `hasattr` and so omits the key
  entirely when not set, but the V3 path is the one that produces nulls)

Both fields keep their existing `required` status — they're always
present in the response, the value is just nullable. Descriptions
expanded to spell out when `null` is expected.

* docs(spec): clarify essentials_category presence rules

The previous description said "null for nodes that don't set
ESSENTIALS_CATEGORY (V1)" — that's wrong. server.py:739-740 uses
`hasattr` and OMITS the key when the V1 attribute isn't defined; null
only happens if the attribute is explicitly set to None. Spell out
all three legal shapes (string / null / absent) and which path
produces which.

comfy/model_management.py

@@ -721,13 +721,15 @@ 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())
 
-    models_temp = set()
+    # Order-preserving dedup. A plain set() would randomize iteration order across runs
+    models_temp = {}
     for m in models:
-        models_temp.add(m)
+        models_temp[m] = None
         for mm in m.model_patches_models():
-            models_temp.add(mm)
+            models_temp[mm] = None
 
-    models = models_temp
+    models = list(models_temp)
+    models.reverse()
 
     models_to_load = []
 

comfy/model_prefetch.py

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

@@ -253,6 +253,9 @@ 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/sampler_helpers.py

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

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

@@ -199,6 +199,9 @@ 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

@@ -74,6 +74,9 @@ 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_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.ModelPatcher(
+        patcher = comfy.model_patcher.CoreModelPatcher(
             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="image/video",
+            category="video",
             search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
             inputs=[
                 FrameInterpolationModel.Input("interp_model"),
@@ -98,16 +98,13 @@ 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
-
-        # 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)
+        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)
+        H, W = images.shape[1], images.shape[2]
 
         # 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="Image Compare",
+            display_name="Compare Images",
             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="Image Crop (Deprecated)",
+            display_name="Crop Image (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="Image Crop",
+            display_name="Crop Image",
             category="image/transform",
             essentials_category="Image Tools",
             has_intermediate_output=True,
@@ -109,6 +109,7 @@ 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"),
@@ -131,6 +132,7 @@ 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"),
@@ -157,7 +159,8 @@ class ImageAddNoise(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageAddNoise",
             search_aliases=["film grain"],
-            category="image",
+            display_name="Add Noise to Image",
+            category="image/postprocessing",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input(
@@ -259,7 +262,7 @@ class ImageStitch(IO.ComfyNode):
         return IO.Schema(
             node_id="ImageStitch",
             search_aliases=["combine images", "join images", "concatenate images", "side by side"],
-            display_name="Image Stitch",
+            display_name="Stitch Images",
             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.",
@@ -434,6 +437,7 @@ 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"),
@@ -485,6 +489,7 @@ 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=[
@@ -591,7 +596,7 @@ class ImageRotate(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageRotate",
-            display_name="Image Rotate",
+            display_name="Rotate Image",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
             essentials_category="Image Tools",
@@ -624,6 +629,7 @@ 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"),
@@ -650,6 +656,7 @@ 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_logic.py

@@ -91,6 +91,46 @@ class SoftSwitchNode(io.ComfyNode):
         return io.NodeOutput(on_true if switch else on_false)
 
 
+class SwitchInverseNode(SwitchNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySwitchInverseNode",
+            display_name="Switch (Inverse)",
+            category="logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_true", template=template, lazy=True),
+                io.MatchType.Input("on_false", template=template, lazy=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+
+class SoftSwitchInverseNode(SoftSwitchNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySoftSwitchInverseNode",
+            display_name="Soft Switch (Inverse)",
+            category="logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
+                io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+
 class CustomComboNode(io.ComfyNode):
     """
     Frontend node that allows user to write their own options for a combo.
@@ -260,8 +300,10 @@ class LogicExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             SwitchNode,
+            SwitchInverseNode,
             CustomComboNode,
             # SoftSwitchNode,
+            # SoftSwitchInverseNode,
             # ConvertStringToComboNode,
             # DCTestNode,
             # AutogrowNamesTestNode,

comfy_extras/nodes_mask.py

@@ -80,7 +80,8 @@ class ImageCompositeMasked(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCompositeMasked",
-            search_aliases=["paste image", "overlay", "layer"],
+            search_aliases=["overlay", "layer", "paste image", "images composition"],
+            display_name="Image Composite Masked",
             category="image",
             inputs=[
                 IO.Image.Input("destination"),
@@ -201,6 +202,7 @@ 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"),
@@ -222,6 +224,7 @@ 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"),
@@ -247,7 +250,8 @@ class MaskComposite(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="MaskComposite",
-            search_aliases=["combine masks", "blend masks", "layer masks"],
+            search_aliases=["combine masks", "blend masks", "layer masks", "masks composition"],
+            display_name="Combine Masks",
             category="mask",
             inputs=[
                 IO.Mask.Input("destination"),
@@ -298,6 +302,7 @@ 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

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

comfy_extras/nodes_post_processing.py

@@ -20,7 +20,8 @@ class Blend(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageBlend",
-            display_name="Image Blend",
+            search_aliases=["mix images"],
+            display_name="Blend Images",
             category="image/postprocessing",
             essentials_category="Image Tools",
             inputs=[
@@ -224,6 +225,7 @@ 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"),
@@ -568,7 +570,7 @@ class BatchImagesNode(io.ComfyNode):
         return io.Schema(
             node_id="BatchImagesNode",
             display_name="Batch Images",
-            category="image",
+            category="image/batch",
             essentials_category="Image Tools",
             search_aliases=["batch", "image batch", "batch images", "combine images", "merge images", "stack images"],
             inputs=[

comfy_extras/nodes_video.py

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

nodes.py

@@ -1887,7 +1887,7 @@ class ImageInvert:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "invert"
 
-    CATEGORY = "image"
+    CATEGORY = "image/color"
 
     def invert(self, image):
         s = 1.0 - image
@@ -1903,7 +1903,7 @@ class ImageBatch:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "batch"
 
-    CATEGORY = "image"
+    CATEGORY = "image/batch"
     DEPRECATED = True
 
     def batch(self, image1, image2):
@@ -1960,7 +1960,7 @@ class ImagePadForOutpaint:
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "expand_image"
 
-    CATEGORY = "image"
+    CATEGORY = "image/transform"
 
     def expand_image(self, image, left, top, right, bottom, feathering):
         d1, d2, d3, d4 = image.size()
@@ -2103,7 +2103,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
-    "ControlNetApply": "Apply ControlNet (OLD)",
+    "ControlNetApply": "Apply ControlNet (DEPRECATED)",
     "ControlNetApplyAdvanced": "Apply ControlNet",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
@@ -2121,6 +2121,7 @@ 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",
@@ -2128,15 +2129,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LoadImageOutput": "Load Image (from Outputs)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",
-    "ImageInvert": "Invert Image",
+    "ImageInvert": "Invert Image Colors",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
-    "ImageBatch": "Batch Images",
-    "ImageCrop": "Image Crop",
-    "ImageStitch": "Image Stitch",
-    "ImageBlend": "Image Blend",
-    "ImageBlur": "Image Blur",
-    "ImageQuantize": "Image Quantize",
-    "ImageSharpen": "Image Sharpen",
+    "ImageBatch": "Batch Images (DEPRECATED)",
+    "ImageCrop": "Crop Image",
+    "ImageStitch": "Stitch Images",
+    "ImageBlend": "Blend Images",
+    "ImageBlur": "Blur Image",
+    "ImageQuantize": "Quantize Image",
+    "ImageSharpen": "Sharpen Image",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
     # _for_testing

openapi.yaml

@@ -2347,7 +2347,12 @@ components:
           description: Device type (cuda, mps, cpu, etc.)
         index:
           type: number
-          description: Device index
+          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`).
         vram_total:
           type: number
           description: Total VRAM in bytes
@@ -2503,7 +2508,18 @@ components:
           description: Alternative search terms for finding this node
         essentials_category:
           type: string
-          description: Category override used by the essentials pack
+          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.
 
     # -------------------------------------------------------------------
     # Models