chore: Update nodes categories (CORE-263) (#14460)

comfy_extras/nodes_ace.py

@@ -11,7 +11,7 @@ class TextEncodeAceStepAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TextEncodeAceStepAudio",
-            category="model/conditioning",
+            category="model/conditioning/ace",
             inputs=[
                 IO.Clip.Input("clip"),
                 IO.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -33,7 +33,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TextEncodeAceStepAudio1.5",
-            category="model/conditioning",
+            category="model/conditioning/ace",
             inputs=[
                 IO.Clip.Input("clip"),
                 IO.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -67,7 +67,7 @@ class EmptyAceStepLatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyAceStepLatentAudio",
             display_name="Empty Ace Step 1.0 Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ace",
             inputs=[
                 IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -90,7 +90,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyAceStep1.5LatentAudio",
             display_name="Empty Ace Step 1.5 Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ace",
             inputs=[
                 IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
                 IO.Int.Input(
@@ -111,8 +111,8 @@ class ReferenceAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ReferenceTimbreAudio",
-            display_name="Reference Audio",
+            display_name="Set Reference Audio",
-            category="advanced/conditioning/audio",
+            category="model/conditioning",
             is_experimental=True,
             description="This node sets the reference audio for ace step 1.5",
             inputs=[

comfy_extras/nodes_apg.py

@@ -16,7 +16,7 @@ class APG(io.ComfyNode):
         return io.Schema(
             node_id="APG",
             display_name="Adaptive Projected Guidance",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(

comfy_extras/nodes_ar_video.py

@@ -19,7 +19,7 @@ class EmptyARVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyARVideoLatent",
-            category="model/latent/video",
+            category="model/latent/autoregressive",
             inputs=[
                 io.Int.Input("width", default=832, min=16, max=8192, step=16),
                 io.Int.Input("height", default=480, min=16, max=8192, step=16),
@@ -85,7 +85,7 @@ class ARVideoI2V(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ARVideoI2V",
-            category="model/conditioning/video_models",
+            category="model/conditioning/autoregressive",
             inputs=[
                 io.Model.Input("model"),
                 io.Vae.Input("vae"),

comfy_extras/nodes_audio.py

@@ -16,7 +16,7 @@ class EmptyLatentAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
-            category="model/latent/audio",
+            category="model/latent",
             essentials_category="Audio",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
@@ -41,7 +41,7 @@ class ConditioningStableAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ConditioningStableAudio",
-            category="model/conditioning",
+            category="model/conditioning/stable audio",
             inputs=[
                 IO.Conditioning.Input("positive"),
                 IO.Conditioning.Input("negative"),
@@ -70,7 +70,7 @@ class VAEEncodeAudio(IO.ComfyNode):
             node_id="VAEEncodeAudio",
             search_aliases=["audio to latent"],
             display_name="VAE Encode Audio",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.Vae.Input("vae"),
@@ -115,7 +115,7 @@ class VAEDecodeAudio(IO.ComfyNode):
             node_id="VAEDecodeAudio",
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
@@ -137,7 +137,7 @@ class VAEDecodeAudioTiled(IO.ComfyNode):
             node_id="VAEDecodeAudioTiled",
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio (Tiled)",
-            category="model/latent/audio",
+            category="model/latent",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),

comfy_extras/nodes_bernini.py

@@ -39,9 +39,9 @@ class BerniniConditioning(io.ComfyNode):
         return io.Schema(
             node_id="BerniniConditioning",
             display_name="Bernini Conditioning",
-            category="conditioning/video_models",
+            category="model/conditioning/bernini",
             description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
-                         "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
+                "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -50,14 +50,11 @@ class BerniniConditioning(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=8192, step=16),
                 io.Int.Input("length", default=81, min=1, max=8192, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Image.Input("source_video", optional=True, tooltip=(
+                io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
-                    "Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
+                io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")),
-                io.Image.Input("reference_video", optional=True, tooltip=(
-                    "Video to insert into the source video (ads2v).")),
                 io.Autogrow.Input("reference_images", optional=True,
                     template=io.Autogrow.TemplatePrefix(
-                        input=io.Image.Input("reference_image", tooltip=(
+                        input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")),
-                            "Reference image injected as an in-context token (r2v, rv2v).")),
                         prefix="reference_image_", min=0, max=8)),
                 io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
                     "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
@@ -70,10 +67,8 @@ class BerniniConditioning(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, positive, negative, vae, width, height, length, batch_size,
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
-                source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
-                             device=comfy.model_management.intermediate_device())
 
         # source_video (1), reference_video (2), reference_images (3, 4, ...).
         context = []
@@ -106,9 +101,7 @@ class BerniniConditioning(io.ComfyNode):
 class BerniniExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
+        return [BerniniConditioning,]
-            BerniniConditioning,
-        ]
 
 
 async def comfy_entrypoint() -> BerniniExtension:

comfy_extras/nodes_camera_trajectory.py

@@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraEmbedding",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/camera",
             inputs=[
                 io.Combo.Input(
                     "camera_pose",

comfy_extras/nodes_chroma_radiance.py

@@ -13,7 +13,7 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="EmptyChromaRadianceLatentImage",
-            category="model/latent/chroma_radiance",
+            category="model/latent/chroma radiance",
             inputs=[
                 io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -33,7 +33,7 @@ class ChromaRadianceOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="ChromaRadianceOptions",
-            category="model/patch/chroma_radiance",
+            category="model/patch/chroma radiance",
             description="Allows setting advanced options for the Chroma Radiance model.",
             inputs=[
                 io.Model.Input(id="model"),

comfy_extras/nodes_clip_sdxl.py

@@ -9,7 +9,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeSDXLRefiner",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SDXL Refiner)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
@@ -30,7 +31,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeSDXL",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SDXL)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),

comfy_extras/nodes_context_windows.py

@@ -66,6 +66,7 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.node_id = "WanContextWindowsManual"
         schema.display_name = "WAN Context Windows (Manual)"
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
+        schema.category="model/patch/wan"
         schema.inputs = [
             io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
                 io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),

comfy_extras/nodes_controlnet.py

@@ -9,6 +9,8 @@ class SetUnionControlNetType(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SetUnionControlNetType",
+            search_aliases=["set controlnet type", "union controlnet type"],
+            display_name="Set Union ControlNet Type",
             category="model/conditioning/controlnet",
             inputs=[
                 io.ControlNet.Input("control_net"),
@@ -39,6 +41,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
         return io.Schema(
             node_id="ControlNetInpaintingAliMamaApply",
             search_aliases=["masked controlnet"],
+            display_name="Apply ControlNet Inpainting (AliMama)",
             category="model/conditioning/controlnet",
             inputs=[
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_cosmos.py

@@ -13,7 +13,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="EmptyCosmosLatentVideo",
-            category="model/latent/video",
+            category="model/latent/cosmos",
             inputs=[
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -45,7 +45,7 @@ class CosmosImageToVideoLatent(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CosmosImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/cosmos",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -88,7 +88,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CosmosPredict2ImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/cosmos",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),

comfy_extras/nodes_custom_sampler.py

@@ -729,7 +729,7 @@ class SamplerCustom(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerCustom",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Model.Input("model"),
                 io.Boolean.Input("add_noise", default=True, advanced=True),
@@ -1015,7 +1015,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerCustomAdvanced",
-            category="model/sampling/custom_sampling",
+            category="model/sampling/custom",
             inputs=[
                 io.Noise.Input("noise"),
                 io.Guider.Input("guider"),
@@ -1143,7 +1143,7 @@ class CFGOverride(io.ComfyNode):
             display_name="CFG Override",
             description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
                         "With multiple overrides, the one nearest the sampler wins on overlap.",
-            category="sampling/custom_sampling",
+            category="model/sampling/guiders",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01),

comfy_extras/nodes_easycache.py

@@ -363,7 +363,7 @@ class EasyCacheNode(io.ComfyNode):
             node_id="EasyCache",
             display_name="EasyCache",
             description="Native EasyCache implementation.",
-            category="advanced/debug/model",
+            category="advanced/debug",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
@@ -496,7 +496,7 @@ class LazyCacheNode(io.ComfyNode):
             node_id="LazyCache",
             display_name="LazyCache",
             description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
-            category="advanced/debug/model",
+            category="advanced/debug",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add LazyCache to."),

comfy_extras/nodes_edit_model.py

@@ -8,7 +8,8 @@ class ReferenceLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ReferenceLatent",
-            category="advanced/conditioning/edit_models",
+            display_name="Set Reference Latent",
+            category="model/conditioning",
             description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
             inputs=[
                 io.Conditioning.Input("conditioning"),

comfy_extras/nodes_flux.py

@@ -13,7 +13,7 @@ class CLIPTextEncodeFlux(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeFlux",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
@@ -40,7 +40,7 @@ class EmptyFlux2LatentImage(io.ComfyNode):
         return io.Schema(
             node_id="EmptyFlux2LatentImage",
             display_name="Empty Flux 2 Latent",
-            category="model/latent",
+            category="model/latent/flux",
             inputs=[
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -61,7 +61,7 @@ class FluxGuidance(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxGuidance",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
@@ -84,7 +84,7 @@ class FluxDisableGuidance(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxDisableGuidance",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             description="This node completely disables the guidance embed on Flux and Flux like models",
             inputs=[
                 io.Conditioning.Input("conditioning"),
@@ -128,7 +128,7 @@ class FluxKontextImageScale(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxKontextImageScale",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             description="This node resizes the image to one that is more optimal for flux kontext.",
             inputs=[
                 io.Image.Input("image"),
@@ -156,7 +156,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
         return io.Schema(
             node_id="FluxKontextMultiReferenceLatentMethod",
             display_name="Edit Model Reference Method",
-            category="advanced/conditioning/flux",
+            category="model/conditioning/flux",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Combo.Input(

comfy_extras/nodes_hidream.py

@@ -11,8 +11,9 @@ class QuadrupleCLIPLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="QuadrupleCLIPLoader",
-            category="advanced/loaders",
+            display_name="Load CLIP (Quadruple)",
-            description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
+            category="model/loaders",
+            description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
             inputs=[
                 io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
@@ -38,8 +39,9 @@ class CLIPTextEncodeHiDream(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeHiDream",
+            display_name="CLIP Text Encode (HiDream)",
             search_aliases=["hidream prompt"],
-            category="advanced/conditioning",
+            category="model/conditioning/hidream",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_hidream_o1.py

@@ -14,7 +14,7 @@ class EmptyHiDreamO1LatentImage(io.ComfyNode):
         return io.Schema(
             node_id="EmptyHiDreamO1LatentImage",
             display_name="Empty HiDream-O1 Latent Image",
-            category="model/latent/image",
+            category="model/latent/hidream",
             description=(
                 "Empty pixel-space latent for HiDream-O1-Image. The model was "
                 "trained at ~4 megapixels; lower resolutions go off-distribution "
@@ -47,7 +47,7 @@ class HiDreamO1ReferenceImages(io.ComfyNode):
         return io.Schema(
             node_id="HiDreamO1ReferenceImages",
             display_name="HiDream-O1 Reference Images",
-            category="model/conditioning/image",
+            category="model/conditioning/hidream",
             description=(
                 "Attach 1-10 reference images to conditioning, one for edit instruction"
                 "or multiple for subject-driven personalization."
@@ -117,7 +117,7 @@ class HiDreamO1PatchSeamSmoothing(io.ComfyNode):
         return io.Schema(
             node_id="HiDreamO1PatchSeamSmoothing",
             display_name="HiDream-O1 Patch Seam Smoothing",
-            category="advanced/model",
+            category="model/patch/hidream",
             is_experimental=True,
             description=(
                 "Average the model output across multiple shifted patch-grid "

comfy_extras/nodes_hunyuan.py

@@ -14,7 +14,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeHunyuanDiT",
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (Hunyuan Image)",
+            category="model/conditioning/hunyuan image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("bert", multiline=True, dynamic_prompts=True),
@@ -41,7 +42,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
         return io.Schema(
             node_id="EmptyHunyuanLatentVideo",
             display_name="Empty HunyuanVideo 1.0 Latent",
-            category="model/latent/video",
+            category="model/latent/hunyuan video",
             inputs=[
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -67,6 +68,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
         schema = super().define_schema()
         schema.node_id = "EmptyHunyuanVideo15Latent"
         schema.display_name = "Empty HunyuanVideo 1.5 Latent"
+        schema.category = "model/latent/hunyuan video"
         return schema
 
     @classmethod
@@ -81,7 +83,7 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanVideo15ImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -132,7 +134,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanVideo15SuperResolution",
             display_name="Hunyuan Video 1.5 Super Resolution",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -227,7 +229,7 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanVideo15LatentUpscaleWithModel",
             display_name="Hunyuan Video 15 Latent Upscale With Model",
-            category="model/latent",
+            category="model/latent/hunyhuan video",
             inputs=[
                 io.LatentUpscaleModel.Input("model"),
                 io.Latent.Input("samples"),
@@ -276,7 +278,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeHunyuanVideo_ImageToVideo",
-            category="advanced/conditioning",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Clip.Input("clip"),
                 io.ClipVisionOutput.Input("clip_vision_output"),
@@ -308,7 +310,7 @@ class HunyuanImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Vae.Input("vae"),
@@ -359,7 +361,7 @@ class EmptyHunyuanImageLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyHunyuanImageLatent",
-            category="model/latent",
+            category="model/latent/hunyuan image",
             inputs=[
                 io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
                 io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
@@ -384,7 +386,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanRefinerLatent",
             display_name="Hunyuan Latent Refiner",
-            category="model/conditioning/video_models",
+            category="model/conditioning/hunyuan video",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_hunyuan3d.py

@@ -12,7 +12,7 @@ class EmptyLatentHunyuan3Dv2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="EmptyLatentHunyuan3Dv2",
-            category="model/latent/3d",
+            category="model/latent/hunyuan 3d",
             inputs=[
                 IO.Int.Input("resolution", default=3072, min=1, max=8192),
                 IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
@@ -35,7 +35,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/hunyuan 3d",
             inputs=[
                 IO.ClipVisionOutput.Input("clip_vision_output"),
             ],
@@ -60,7 +60,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2ConditioningMultiView",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/hunyuan 3d",
             inputs=[
                 IO.ClipVisionOutput.Input("front", optional=True),
                 IO.ClipVisionOutput.Input("left", optional=True),
@@ -97,7 +97,7 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VAEDecodeHunyuan3D",
-            category="model/latent/3d",
+            category="model/latent/hunyuan 3d",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),

comfy_extras/nodes_ideogram4.py

@@ -38,7 +38,7 @@ class Ideogram4Scheduler(io.ComfyNode):
         return io.Schema(
             node_id="Ideogram4Scheduler",
             display_name="Ideogram 4 Scheduler",
-            category="sampling/custom_sampling/schedulers",
+            category="model/sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=200),
                 io.Int.Input("width", default=1024, min=256, max=8192, step=16),

comfy_extras/nodes_kandinsky5.py

@@ -13,7 +13,7 @@ class Kandinsky5ImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Kandinsky5ImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/kandinsky",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -71,7 +71,7 @@ class NormalizeVideoLatentStart(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="NormalizeVideoLatentStart",
-            category="model/conditioning/video_models",
+            category="model/conditioning",
             description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
             inputs=[
                 io.Latent.Input("latent"),
@@ -104,8 +104,9 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeKandinsky5",
+            display_name="CLIP Text Encode (Kandinsky 5)",
             search_aliases=["kandinsky prompt"],
-            category="advanced/conditioning/kandinsky5",
+            category="model/conditioning/kandinsky",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_latent.py

@@ -262,6 +262,7 @@ class LatentBatch(io.ComfyNode):
         return io.Schema(
             node_id="LatentBatch",
             search_aliases=["combine latents", "merge latents", "join latents"],
+            display_name="Batch Latents (DEPRECATED)",
             category="model/latent/batch",
             is_deprecated=True,
             inputs=[
@@ -447,6 +448,7 @@ class ReplaceVideoLatentFrames(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ReplaceVideoLatentFrames",
+            display_name="Replace Video Latent Frames",
             category="model/latent/batch",
             inputs=[
                 io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),

comfy_extras/nodes_lt.py

@@ -25,7 +25,7 @@ class GetICLoRAParameters(io.ComfyNode):
             display_name="Get IC-LoRA Parameters",
             description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded "
                         "model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"],
             inputs=[
                 io.Model.Input(
@@ -62,7 +62,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyLTXVLatentVideo",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             inputs=[
                 io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
                 io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
@@ -86,7 +86,7 @@ class LTXVImgToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVImgToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -131,7 +131,7 @@ class LTXVImgToVideoInplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVImgToVideoInplace",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Image.Input("image"),
@@ -251,7 +251,7 @@ class LTXVAddGuide(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVAddGuide",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -498,7 +498,7 @@ class LTXVCropGuides(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVCropGuides",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -542,7 +542,7 @@ class LTXVConditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVConditioning",
-            category="model/conditioning/video_models",
+            category="model/conditioning/ltxv",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -566,7 +566,7 @@ class ModelSamplingLTXV(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ModelSamplingLTXV",
-            category="advanced/model",
+            category="model/patch/ltxv",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
@@ -746,7 +746,7 @@ class LTXVConcatAVLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVConcatAVLatent",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             inputs=[
                 io.Latent.Input("video_latent"),
                 io.Latent.Input("audio_latent"),
@@ -781,7 +781,7 @@ class LTXVSeparateAVLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVSeparateAVLatent",
-            category="model/latent/video/ltxv",
+            category="model/latent/ltxv",
             description="LTXV Separate AV Latent",
             inputs=[
                 io.Latent.Input("av_latent"),
@@ -814,7 +814,7 @@ class LTXVReferenceAudio(io.ComfyNode):
         return io.Schema(
             node_id="LTXVReferenceAudio",
             display_name="LTXV Reference Audio (ID-LoRA)",
-            category="model/conditioning/audio",
+            category="model/conditioning/ltxv",
             description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).",
             inputs=[
                 io.Model.Input("model"),

comfy_extras/nodes_lt_audio.py

@@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio):
         return io.Schema(
             node_id="LTXVAudioVAEEncode",
             display_name="LTXV Audio VAE Encode",
-            category="model/latent/audio",
+            category="model/latent/ltxv",
             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="model/latent/audio",
+            category="model/latent/ltxv",
             inputs=[
                 io.Latent.Input("samples", tooltip="The latent to be decoded."),
                 io.Vae.Input(
@@ -96,7 +96,7 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
         return io.Schema(
             node_id="LTXVEmptyLatentAudio",
             display_name="LTXV Empty Latent Audio",
-            category="model/latent/audio",
+            category="model/latent/ltxv",
             inputs=[
                 io.Int.Input(
                     "frames_number",
@@ -168,9 +168,9 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="LTXAVTextEncoderLoader",
-            display_name="LTXV Audio Text Encoder Loader",
+            display_name="Load LTXV Audio Text Encoder",
-            category="advanced/loaders",
+            category="model/loaders",
-            description="[Recipes]\n\nltxav: gemma 3 12B",
+            description="Recipes:\nltxav: gemma 3 12B",
             inputs=[
                 io.Combo.Input(
                     "text_encoder",

comfy_extras/nodes_lt_upsampler.py

@@ -13,7 +13,7 @@ class LTXVLatentUpsampler(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="LTXVLatentUpsampler",
-            category="model/latent/video",
+            category="model/latent/ltxv",
             is_experimental=True,
             inputs=[
                 IO.Latent.Input("samples"),

comfy_extras/nodes_lumina2.py

@@ -9,7 +9,7 @@ class RenormCFG(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RenormCFG",
-            category="advanced/model",
+            category="model/patch",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
@@ -80,8 +80,8 @@ class CLIPTextEncodeLumina2(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeLumina2",
             search_aliases=["lumina prompt"],
-            display_name="CLIP Text Encode for Lumina2",
+            display_name="CLIP Text Encode (Lumina 2)",
-            category="model/conditioning",
+            category="model/conditioning/lumina",
             description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
                         "that can be used to guide the diffusion model towards generating specific images.",
             inputs=[

comfy_extras/nodes_mask.py

@@ -53,6 +53,7 @@ class LatentCompositeMasked(IO.ComfyNode):
         return IO.Schema(
             node_id="LatentCompositeMasked",
             search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
+            display_name="Latent Composite Masked",
             category="model/latent",
             inputs=[
                 IO.Latent.Input("destination"),

comfy_extras/nodes_mochi.py

@@ -10,7 +10,7 @@ class EmptyMochiLatentVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptyMochiLatentVideo",
-            category="model/latent/video",
+            category="model/latent/mochi",
             inputs=[
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),

comfy_extras/nodes_model_advanced.py

@@ -59,7 +59,7 @@ class ModelSamplingDiscrete:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, zsnr):
         m = model.clone()
@@ -97,7 +97,7 @@ class ModelSamplingStableCascade:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/stable cascade"
 
     def patch(self, model, shift):
         m = model.clone()
@@ -123,7 +123,7 @@ class ModelSamplingSD3:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/stable diffusion"
 
     def patch(self, model, shift, multiplier=1000):
         m = model.clone()
@@ -150,6 +150,7 @@ class ModelSamplingAuraFlow(ModelSamplingSD3):
                               }}
 
     FUNCTION = "patch_aura"
+    CATEGORY = "model/patch"
 
     def patch_aura(self, model, shift):
         return self.patch(model, shift, multiplier=1.0)
@@ -167,7 +168,7 @@ class ModelSamplingFlux:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch/flux"
 
     def patch(self, model, max_shift, base_shift, width, height):
         m = model.clone()
@@ -202,7 +203,7 @@ class ModelSamplingContinuousEDM:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
@@ -247,7 +248,7 @@ class ModelSamplingContinuousV:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
@@ -273,7 +274,7 @@ class RescaleCFG:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, multiplier):
         def rescale_cfg(args):
@@ -314,7 +315,7 @@ class ModelNoiseScale:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/model"
+    CATEGORY = "model/patch"
 
     def patch(self, model, noise_scale):
         m = model.clone()
@@ -337,7 +338,7 @@ class ModelComputeDtype:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "advanced/debug/model"
+    CATEGORY = "advanced/debug"
 
     def patch(self, model, dtype):
         m = model.clone()

comfy_extras/nodes_model_merging.py

@@ -21,7 +21,7 @@ class ModelMergeSimple:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, ratio):
         m = model1.clone()
@@ -40,7 +40,7 @@ class ModelSubtract:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, multiplier):
         m = model1.clone()
@@ -58,7 +58,7 @@ class ModelAdd:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2):
         m = model1.clone()
@@ -78,7 +78,7 @@ class CLIPMergeSimple:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2, ratio):
         m = clip1.clone()
@@ -101,7 +101,7 @@ class CLIPSubtract:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2, multiplier):
         m = clip1.clone()
@@ -123,7 +123,7 @@ class CLIPAdd:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, clip1, clip2):
         m = clip1.clone()
@@ -147,7 +147,7 @@ class ModelMergeBlocks:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "merge"
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def merge(self, model1, model2, **kwargs):
         m = model1.clone()
@@ -242,7 +242,7 @@ class CheckpointSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
         save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
@@ -261,7 +261,7 @@ class CLIPSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
         prompt_info = ""
@@ -318,7 +318,7 @@ class VAESave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -353,7 +353,7 @@ class ModelSave:
     FUNCTION = "save"
     OUTPUT_NODE = True
 
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None):
         save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)

comfy_extras/nodes_model_merging_model_specific.py

@@ -1,7 +1,7 @@
 import comfy_extras.nodes_model_merging
 
 class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
     @classmethod
     def INPUT_TYPES(s):
         arg_dict = { "model1": ("MODEL",),
@@ -27,7 +27,7 @@ class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
 
 class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -53,7 +53,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -77,7 +77,7 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
 
 class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -104,7 +104,7 @@ class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -130,7 +130,7 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -153,7 +153,7 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -175,7 +175,7 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -197,7 +197,7 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -221,7 +221,7 @@ class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -245,7 +245,7 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
     DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb."
 
     @classmethod
@@ -269,7 +269,7 @@ class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -292,7 +292,7 @@ class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlo
         return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -315,7 +315,7 @@ class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBl
         return {"required": arg_dict}
 
 class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
+    CATEGORY = "model/merging/model specific"
 
     @classmethod
     def INPUT_TYPES(s):

comfy_extras/nodes_model_patch.py

@@ -232,7 +232,7 @@ class ModelPatchLoader:
     FUNCTION = "load_model_patch"
     EXPERIMENTAL = True
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
     def load_model_patch(self, name):
         model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name)
@@ -479,7 +479,7 @@ class QwenImageDiffsynthControlnet:
     FUNCTION = "diffsynth_controlnet"
     EXPERIMENTAL = True
 
-    CATEGORY = "advanced/loaders/qwen"
+    CATEGORY = "model/patch/qwen"
 
     def diffsynth_controlnet(self, model, model_patch, vae, image=None, strength=1.0, inpaint_image=None, mask=None):
         model_patched = model.clone()
@@ -512,7 +512,7 @@ class ZImageFunControlnet(QwenImageDiffsynthControlnet):
                               },
                 "optional": {"image": ("IMAGE",), "inpaint_image": ("IMAGE",), "mask": ("MASK",)}}
 
-    CATEGORY = "advanced/loaders/zimage"
+    CATEGORY = "model/patch/z-image"
 
 class UsoStyleProjectorPatch:
     def __init__(self, model_patch, encoded_image):
@@ -675,3 +675,11 @@ NODE_CLASS_MAPPINGS = {
     "USOStyleReference": USOStyleReference,
     "SUPIRApply": SUPIRApply,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ModelPatchLoader": "Load Model Patch",
+    "QwenImageDiffsynthControlnet": "Apply Qwen Image DiffSynth ControlNet",
+    "ZImageFunControlnet": "Apply Z-Image Fun ControlNet",
+    "USOStyleReference": "Apply USO Style Reference",
+    "SUPIRApply": "Apply SUPIR Patch",
+}

comfy_extras/nodes_pid.py

@@ -14,10 +14,8 @@ class PiDConditioning(io.ComfyNode):
         return io.Schema(
             node_id="PiDConditioning",
             display_name="PiD Conditioning",
-            category="advanced/conditioning",
+            category="model/conditioning",
-            description=(
+            description=("Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"),
-                "Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"
-            ),
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),

comfy_extras/nodes_pixart.py

@@ -7,8 +7,9 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodePixArtAlpha",
+            display_name="CLIP Text Encode (PixArt Alpha)",
             search_aliases=["pixart prompt"],
-            category="advanced/conditioning",
+            category="model/conditioning/pixart",
             description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
             inputs=[
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),

comfy_extras/nodes_post_processing.py

@@ -616,7 +616,7 @@ class BatchLatentsNode(io.ComfyNode):
             node_id="BatchLatentsNode",
             search_aliases=["combine latents", "stack latents", "merge latents"],
             display_name="Batch Latents",
-            category="model/latent",
+            category="model/latent/batch",
             inputs=[
                 io.Autogrow.Input("latents", template=autogrow_template)
             ],

comfy_extras/nodes_qwen.py

@@ -12,7 +12,7 @@ class TextEncodeQwenImageEdit(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeQwenImageEdit",
-            category="advanced/conditioning",
+            category="model/conditioning/qwen image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
@@ -55,7 +55,7 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeQwenImageEditPlus",
-            category="advanced/conditioning",
+            category="model/conditioning/qwen image",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_scail.py

@@ -123,7 +123,7 @@ class WanSCAILToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSCAILToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/scail",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -257,18 +257,16 @@ class SCAIL2ColoredMask(io.ComfyNode):
         return io.Schema(
             node_id="SCAIL2ColoredMask",
             display_name="Create SCAIL-2 Colored Mask",
-            category="conditioning/video_models/scail",
+            category="model/conditioning/wan/scail",
             inputs=[
                 SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
-                SAM3TrackData.Input("ref_track_data", optional=True,
+                SAM3TrackData.Input("ref_track_data", optional=True, tooltip="SAM3 track of the reference image."),
-                                    tooltip="SAM3 track of the reference image."),
+                io.String.Input("object_indices", default="", tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
-                io.String.Input("object_indices", default="",
-                                tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
                 io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
-                               tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
+                    tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
                 io.Boolean.Input("replacement_mode", default=False,
-                                 tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
+                    tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
-                                         "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
+                    "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
             ],
             outputs=[
                 io.Image.Output("pose_video_mask"),

comfy_extras/nodes_sd3.py

@@ -13,8 +13,9 @@ class TripleCLIPLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TripleCLIPLoader",
-            category="advanced/loaders",
+            display_name="Load CLIP (Triple)",
-            description="[Recipes]\n\nsd3: clip-l, clip-g, t5",
+            category="model/loaders",
+            description="Recipes:\nsd3: clip-l, clip-g, t5",
             inputs=[
                 io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
@@ -41,7 +42,7 @@ class EmptySD3LatentImage(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="EmptySD3LatentImage",
-            category="model/latent/sd3",
+            category="model/latent/stable diffusion",
             inputs=[
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -66,7 +67,8 @@ class CLIPTextEncodeSD3(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeSD3",
             search_aliases=["sd3 prompt"],
-            category="advanced/conditioning",
+            display_name="CLIP Text Encode (SD3)",
+            category="model/conditioning/stable diffusion",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_sdupscale.py

@@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SD_4XUpscale_Conditioning",
-            category="model/conditioning/upscale_diffusion",
+            category="model/conditioning/stable diffusion upscaler",
             inputs=[
                 io.Image.Input("images"),
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_stable3d.py

@@ -27,7 +27,7 @@ class StableZero123_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableZero123_Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable zero123",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),
@@ -65,7 +65,7 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableZero123_Conditioning_Batched",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable zero123",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),
@@ -112,7 +112,7 @@ class SV3D_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SV3D_Conditioning",
-            category="model/conditioning/3d_models",
+            category="model/conditioning/stable video 3d",
             inputs=[
                 io.ClipVision.Input("clip_vision"),
                 io.Image.Input("init_image"),

comfy_extras/nodes_stable_cascade.py

@@ -29,7 +29,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_EmptyLatentImage",
-            category="model/latent/stable_cascade",
+            category="model/latent/stable cascade",
             inputs=[
                 io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
                 io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
@@ -58,7 +58,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_StageC_VAEEncode",
-            category="model/latent/stable_cascade",
+            category="model/latent/stable cascade",
             inputs=[
                 io.Image.Input("image"),
                 io.Vae.Input("vae"),
@@ -93,7 +93,7 @@ class StableCascade_StageB_Conditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_StageB_Conditioning",
-            category="model/conditioning/stable_cascade",
+            category="model/conditioning/stable cascade",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("stage_c"),

comfy_extras/nodes_train.py

@@ -1367,7 +1367,7 @@ class SaveLoRA(io.ComfyNode):
             node_id="SaveLoRA",
             search_aliases=["export lora"],
             display_name="Save LoRA Weights",
-            category="advanced/model_merging",
+            category="model/merging",
             is_experimental=True,
             is_output_node=True,
             inputs=[

comfy_extras/nodes_video_model.py

@@ -41,7 +41,7 @@ class SVD_img2vid_Conditioning:
 
     FUNCTION = "encode"
 
-    CATEGORY = "model/conditioning/video_models"
+    CATEGORY = "model/conditioning/stable video"
 
     def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
         output = clip_vision.encode_image(init_image)
@@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance:
         return (m, )
 
 class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
-    CATEGORY = "advanced/model_merging"
+    CATEGORY = "model/merging"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -138,7 +138,7 @@ class ConditioningSetAreaPercentageVideo:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, temporal, x, y, z, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
@@ -160,4 +160,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)",
     "VideoLinearCFGGuidance": "Video Linear CFG Guidance",
     "VideoTriangleCFGGuidance": "Video Triangle CFG Guidance",
+    "ConditioningSetAreaPercentageVideo": "Conditioning (Set Area with Percentage for Video)",
 }

comfy_extras/nodes_void.py

@@ -175,7 +175,7 @@ class VOIDInpaintConditioning(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDInpaintConditioning",
-            category="model/conditioning/video_models",
+            category="model/conditioning/void",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -288,7 +288,7 @@ class VOIDWarpedNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDWarpedNoise",
-            category="model/latent/video",
+            category="model/latent/void",
             inputs=[
                 OpticalFlow.Input(
                     "optical_flow",
@@ -393,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="VOIDWarpedNoiseSource",
-            category="model/sampling/noise",
+            category="model/latent/void",
             inputs=[
                 io.Latent.Input("warped_noise",
                     tooltip="Warped noise latent from VOIDWarpedNoise"),

comfy_extras/nodes_wan.py

@@ -18,7 +18,7 @@ class WanImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -66,7 +66,7 @@ class WanFunControlToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFunControlToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun control",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -119,7 +119,7 @@ class Wan22FunControlToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Wan22FunControlToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun control",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -184,7 +184,7 @@ class WanFirstLastFrameToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFirstLastFrameToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -256,7 +256,7 @@ class WanFunInpaintToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanFunInpaintToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/fun inpaint",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -288,7 +288,7 @@ class WanVaceToVideo(io.ComfyNode):
         return io.Schema(
             node_id="WanVaceToVideo",
             search_aliases=["video conditioning", "video control"],
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/vace",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -375,7 +375,8 @@ class TrimVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TrimVideoLatent",
-            category="model/latent/video",
+            display_name="Trim Video Latent",
+            category="model/latent",
             inputs=[
                 io.Latent.Input("samples"),
                 io.Int.Input("trim_amount", default=0, min=0, max=99999),
@@ -398,7 +399,7 @@ class WanCameraImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/camera",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -452,7 +453,7 @@ class WanPhantomSubjectToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanPhantomSubjectToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/phantom subject",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -707,7 +708,7 @@ class WanTrackToVideo(io.ComfyNode):
         return io.Schema(
             node_id="WanTrackToVideo",
             search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"],
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -951,7 +952,7 @@ class WanSoundImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSoundImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/sound",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -984,7 +985,7 @@ class WanSoundImageToVideoExtend(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanSoundImageToVideoExtend",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/sound",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1046,7 +1047,7 @@ class WanHuMoImageToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanHuMoImageToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/humo",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1112,7 +1113,7 @@ class WanAnimateToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanAnimateToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/animate",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -1252,7 +1253,7 @@ class Wan22ImageToVideoLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Wan22ImageToVideoLatent",
-            category="model/conditioning/inpaint",
+            category="model/conditioning/wan",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32),
@@ -1302,7 +1303,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanInfiniteTalkToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/infinite talk",
             inputs=[
                 io.DynamicCombo.Input("mode", options=[
                 io.DynamicCombo.Option("single_speaker", []),

comfy_extras/nodes_wandancer.py

@@ -713,7 +713,7 @@ class WanDancerEncodeAudio(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanDancerEncodeAudio",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/dancer",
             inputs=[
                 io.Audio.Input("audio"),
                 io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4),
@@ -787,7 +787,7 @@ class WanDancerVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanDancerVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/dancer",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_wanmove.py

@@ -247,7 +247,7 @@ class WanMoveVisualizeTracks(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveVisualizeTracks",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Image.Input("images"),
                 io.Tracks.Input("tracks", optional=True),
@@ -283,7 +283,7 @@ class WanMoveTracksFromCoords(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveTracksFromCoords",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.String.Input("track_coords", force_input=True, default="[]", optional=True),
                 io.Mask.Input("track_mask", optional=True),
@@ -325,7 +325,8 @@ class GenerateTracks(io.ComfyNode):
         return io.Schema(
             node_id="GenerateTracks",
             search_aliases=["motion paths", "camera movement", "trajectory"],
-            category="model/conditioning/video_models",
+            display_name="Generate Video Tracks",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Int.Input("width", default=832, min=16, max=4096, step=16),
                 io.Int.Input("height", default=480, min=16, max=4096, step=16),
@@ -434,7 +435,7 @@ class WanMoveConcatTrack(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveConcatTrack",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Tracks.Input("tracks_1"),
                 io.Tracks.Input("tracks_2", optional=True),
@@ -463,7 +464,7 @@ class WanMoveTrackToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanMoveTrackToVideo",
-            category="model/conditioning/video_models",
+            category="model/conditioning/wan/move",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_zimage.py

@@ -10,7 +10,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="TextEncodeZImageOmni",
-            category="advanced/conditioning",
+            category="model/conditioning/z-image",
             is_experimental=True,
             inputs=[
                 io.Clip.Input("clip"),

nodes.py

@@ -87,7 +87,7 @@ class ConditioningCombine:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "combine"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
     SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"]
 
     def combine(self, conditioning_1, conditioning_2):
@@ -104,7 +104,7 @@ class ConditioningAverage :
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "addWeighted"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
         out = []
@@ -143,7 +143,7 @@ class ConditioningConcat:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "concat"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def concat(self, conditioning_to, conditioning_from):
         out = []
@@ -176,7 +176,7 @@ class ConditioningSetArea:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, x, y, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
@@ -197,7 +197,7 @@ class ConditioningSetAreaPercentage:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, width, height, x, y, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
@@ -214,7 +214,7 @@ class ConditioningSetAreaStrength:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, strength):
         c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
@@ -234,7 +234,7 @@ class ConditioningSetMask:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "append"
 
-    CATEGORY = "model/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def append(self, conditioning, mask, set_cond_area, strength):
         set_area_to_bounds = False
@@ -257,7 +257,7 @@ class ConditioningZeroOut:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "zero_out"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def zero_out(self, conditioning):
         c = []
@@ -283,11 +283,10 @@ class ConditioningSetTimestepRange:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "set_range"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "model/conditioning/transform"
 
     def set_range(self, conditioning, start, end):
-        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
+        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end})
-                                                                "end_percent": end})
         return (c, )
 
 class VAEDecode:
@@ -389,7 +388,7 @@ class VAEEncodeForInpaint:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "model/latent/inpaint"
+    CATEGORY = "model/latent"
 
     def encode(self, vae, pixels, mask, grow_mask_by=6):
         downscale_ratio = vae.spacial_compression_encode()
@@ -438,7 +437,7 @@ class InpaintModelConditioning:
     RETURN_NAMES = ("positive", "negative", "latent")
     FUNCTION = "encode"
 
-    CATEGORY = "model/conditioning/inpaint"
+    CATEGORY = "model/conditioning"
 
     def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
         x = (pixels.shape[1] // 8) * 8
@@ -576,7 +575,7 @@ class CheckpointLoader:
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
     DEPRECATED = True
 
     def load_checkpoint(self, config_name, ckpt_name):
@@ -622,8 +621,9 @@ class DiffusersLoader:
         return {"required": {"model_path": (paths,), }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
+    DEPRECATED = True
 
-    CATEGORY = "advanced/loaders/deprecated"
+    CATEGORY = "model/loaders"
 
     def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
         for search_path in folder_paths.get_folder_paths("diffusers"):
@@ -949,7 +949,7 @@ class UNETLoader:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
     def load_unet(self, unet_name, weight_dtype):
         model_options = {}
@@ -977,9 +977,9 @@ class CLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm"
+    DESCRIPTION = "Recipes:\nsd: clip-l\nstable cascade: clip-g\nsd3: t5 xxl / clip-g / clip-l\nstable audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\nhidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\npixeldit: gemma 2 2B elm"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1005,9 +1005,9 @@ class DualCLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "model/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
+    DESCRIPTION = "Recipes:\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
 
     def load_clip(self, clip_name1, clip_name2, type, device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1088,7 +1088,7 @@ class StyleModelApply:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_stylemodel"
 
-    CATEGORY = "model/conditioning/style_model"
+    CATEGORY = "model/conditioning"
 
     def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type):
         cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
@@ -1518,13 +1518,11 @@ class LatentCrop:
 class SetLatentNoiseMask:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
+        return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }}
-                              "mask": ("MASK",),
-                              }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "set_mask"
 
-    CATEGORY = "model/latent/inpaint"
+    CATEGORY = "model/latent"
 
     def set_mask(self, samples, mask):
         s = samples.copy()
@@ -2045,7 +2043,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageBatch": ImageBatch,
     "ImagePadForOutpaint": ImagePadForOutpaint,
     "EmptyImage": EmptyImage,
-    "ConditioningAverage": ConditioningAverage ,
+    "ConditioningAverage": ConditioningAverage,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
     "ConditioningSetArea": ConditioningSetArea,
@@ -2101,6 +2099,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LoraLoader": "Load LoRA (Model and CLIP)",
     "LoraLoaderModelOnly": "Load LoRA",
     "CLIPLoader": "Load CLIP",
+    "DualCLIPLoader": "Load CLIP (Dual)",
     "ControlNetLoader": "Load ControlNet Model",
     "DiffControlNetLoader": "Load ControlNet Model (diff)",
     "StyleModelLoader": "Load Style Model",
@@ -2108,6 +2107,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "UNETLoader": "Load Diffusion Model",
     "unCLIPCheckpointLoader": "Load unCLIP Checkpoint",
     "GLIGENLoader": "Load GLIGEN Model",
+    "DiffusersLoader": "Load Diffusers Model (DEPRECATED)",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",
@@ -2115,12 +2115,16 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CLIPSetLastLayer": "CLIP Set Last Layer",
     "ConditioningCombine": "Conditioning (Combine)",
     "ConditioningAverage ": "Conditioning (Average)",
+    "ConditioningAverage": "Conditioning (Average)",
     "ConditioningConcat": "Conditioning (Concat)",
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
+    "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
     "ControlNetApply": "Apply ControlNet (DEPRECATED)",
     "ControlNetApplyAdvanced": "Apply ControlNet",
+    "GLIGENTextBoxApply": "Apply GLIGEN Text Box",
+    "ConditioningZeroOut": "Conditioning Zero Out",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
@@ -2134,7 +2138,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LatentUpscaleBy": "Upscale Latent By",
     "LatentComposite": "Latent Composite",
     "LatentBlend": "Latent Blend",
-    "LatentFromBatch" : "Latent From Batch",
+    "LatentFromBatch" : "Get Latent From Batch",
     "RepeatLatentBatch": "Repeat Latent Batch",
     # Image
     "EmptyImage": "Empty Image",