Merge 8ac3e420b6 into 593be209a4

* feat: add essentials_category to nodes and blueprints for Essentials tab

Add ESSENTIALS_CATEGORY or essentials_category to 12 node classes and all
36 blueprint JSONs. Update SubgraphEntry TypedDict and subgraph_manager to
extract and pass through the field.

Fixes COM-15221

Amp-Thread-ID: https://ampcode.com/threads/T-019c83de-f7ab-7779-a451-0ba5940b56a9

* fix: import NotRequired from typing_extensions for Python 3.10 compat

* refactor: keep only node class ESSENTIALS_CATEGORY, remove blueprint/subgraph changes

Frontend will own blueprint categorization separately.

* fix: remove essentials_category from CreateVideo (not in spec)

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>

comfy/model_management.py

@@ -400,7 +400,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):

comfy/ops.py

@@ -336,7 +336,10 @@ class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -357,7 +360,9 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
             disable_weight_init._lazy_load_from_state_dict(
@@ -564,7 +569,10 @@ class disable_weight_init:
         def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
                      norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
                      _freeze=False, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
                 super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
                                  norm_type, scale_grad_by_freq, sparse, _weight,
                                  _freeze, device, dtype)
@@ -590,7 +598,9 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
             disable_weight_init._lazy_load_from_state_dict(

comfy_api_nodes/nodes_kling.py

@@ -1459,6 +1459,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
             node_id="KlingOmniProEditVideoNode",
             display_name="Kling 3.0 Omni Edit Video",
             category="api node/video/Kling",
+            essentials_category="Video Generation",
             description="Edit an existing video with the latest model from Kling.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),

comfy_api_nodes/nodes_recraft.py

@@ -833,6 +833,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
             node_id="RecraftVectorizeImageNode",
             display_name="Recraft Vectorize Image",
             category="api node/image/Recraft",
+            essentials_category="Image Tools",
             description="Generates SVG synchronously from an input image.",
             inputs=[
                 IO.Image.Input("image"),

comfy_extras/nodes_audio.py

@@ -19,6 +19,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
             category="latent/audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -185,6 +186,7 @@ class SaveAudioMP3(IO.ComfyNode):
             search_aliases=["export mp3"],
             display_name="Save Audio (MP3)",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),

comfy_extras/nodes_image_compare.py

@@ -14,6 +14,7 @@ class ImageCompare(IO.ComfyNode):
             display_name="Image Compare",
             description="Compares two images side by side with a slider.",
             category="image",
+            essentials_category="Image Tools",
             is_experimental=True,
             is_output_node=True,
             inputs=[

comfy_extras/nodes_images.py

@@ -58,6 +58,7 @@ class ImageCropV2(IO.ComfyNode):
             search_aliases=["trim"],
             display_name="Image Crop",
             category="image/transform",
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.BoundingBox.Input("crop_region", component="ImageCrop"),

comfy_extras/nodes_post_processing.py

@@ -21,6 +21,7 @@ class Blend(io.ComfyNode):
             node_id="ImageBlend",
             display_name="Image Blend",
             category="image/postprocessing",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image1"),
                 io.Image.Input("image2"),

folder_paths.py

@@ -472,6 +472,26 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
         counter = 1
     return full_output_folder, filename, counter, subfolder, filename_prefix
 
+def get_model_placeholder(folder_name: str) -> str:
+    """Generate placeholder text for empty model dropdowns.
+
+    Args:
+        folder_name: The name of the model folder (e.g., "checkpoints", "loras").
+
+    Returns:
+        A user-friendly placeholder string indicating where models should be placed.
+    """
+    folder_name = map_legacy(folder_name)
+    try:
+        paths = get_folder_paths(folder_name)
+    except KeyError:
+        paths = []
+
+    if paths:
+        return f"No models found — add to: {paths[0]}"
+    return f"No models found for '{folder_name}'..."
+
+
 def get_input_subfolders() -> list[str]:
     """Returns a list of all subfolder paths in the input directory, recursively.
 

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1b4
+comfyui_manager==4.1b5

nodes.py

@@ -81,6 +81,7 @@ class CLIPTextEncode(ComfyNodeABC):
 
 
 class ConditioningCombine:
+    ESSENTIALS_CATEGORY = "Image Generation"
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
@@ -589,7 +590,10 @@ class CheckpointLoaderSimple:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {
+                    "tooltip": "The name of the checkpoint (model) to load.",
+                    "placeholder": folder_paths.get_model_placeholder("checkpoints")
+                }),
             }
         }
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
@@ -639,7 +643,9 @@ class DiffusersLoader:
 class unCLIPCheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {
+                                "placeholder": folder_paths.get_model_placeholder("checkpoints")
+                             }),
                              }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
     FUNCTION = "load_checkpoint"
@@ -679,7 +685,10 @@ class LoraLoader:
             "required": {
                 "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
                 "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
-                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                "lora_name": (folder_paths.get_filename_list("loras"), {
+                    "tooltip": "The name of the LoRA.",
+                    "placeholder": folder_paths.get_model_placeholder("loras")
+                }),
                 "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
                 "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
             }
@@ -716,7 +725,9 @@ class LoraLoaderModelOnly(LoraLoader):
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "lora_name": (folder_paths.get_filename_list("loras"), {
+                                  "placeholder": folder_paths.get_model_placeholder("loras")
+                              }),
                               "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
                               }}
     RETURN_TYPES = ("MODEL",)
@@ -806,7 +817,9 @@ class VAELoader:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "vae_name": (s.vae_list(s), )}}
+        return {"required": { "vae_name": (s.vae_list(s), {
+            "placeholder": folder_paths.get_model_placeholder("vae")
+        })}}
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
@@ -833,7 +846,9 @@ class VAELoader:
 class ControlNetLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+        return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), {
+            "placeholder": folder_paths.get_model_placeholder("controlnet")
+        })}}
 
     RETURN_TYPES = ("CONTROL_NET",)
     FUNCTION = "load_controlnet"
@@ -852,7 +867,9 @@ class DiffControlNetLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+                              "control_net_name": (folder_paths.get_filename_list("controlnet"), {
+                                  "placeholder": folder_paths.get_model_placeholder("controlnet")
+                              })}}
 
     RETURN_TYPES = ("CONTROL_NET",)
     FUNCTION = "load_controlnet"
@@ -950,7 +967,9 @@ class ControlNetApplyAdvanced:
 class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
+        return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), {
+                                  "placeholder": folder_paths.get_model_placeholder("diffusion_models")
+                              }),
                               "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
@@ -975,7 +994,9 @@ class UNETLoader:
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), {
+                                  "placeholder": folder_paths.get_model_placeholder("text_encoders")
+                              }),
                               "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image"], ),
                               },
                 "optional": {
@@ -1002,8 +1023,12 @@ class CLIPLoader:
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), {
+                                  "placeholder": folder_paths.get_model_placeholder("text_encoders")
+                              }),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), {
+                                  "placeholder": folder_paths.get_model_placeholder("text_encoders")
+                              }),
                               "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15", "kandinsky5", "kandinsky5_image", "ltxv", "newbie", "ace"], ),
                               },
                 "optional": {
@@ -1032,7 +1057,9 @@ class DualCLIPLoader:
 class CLIPVisionLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), {
+                                  "placeholder": folder_paths.get_model_placeholder("clip_vision")
+                             }),
                              }}
     RETURN_TYPES = ("CLIP_VISION",)
     FUNCTION = "load_clip"
@@ -1068,7 +1095,9 @@ class CLIPVisionEncode:
 class StyleModelLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}
+        return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), {
+            "placeholder": folder_paths.get_model_placeholder("style_models")
+        })}}
 
     RETURN_TYPES = ("STYLE_MODEL",)
     FUNCTION = "load_style_model"
@@ -1167,7 +1196,9 @@ class unCLIPConditioning:
 class GLIGENLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}
+        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), {
+            "placeholder": folder_paths.get_model_placeholder("gligen")
+        })}}
 
     RETURN_TYPES = ("GLIGEN",)
     FUNCTION = "load_gligen"
@@ -1211,9 +1242,6 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
-
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -1232,7 +1260,7 @@ class EmptyLatentImage:
     SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return ({"samples": latent, "downscale_ratio_spacial": 8}, )
 
 
@@ -1781,6 +1809,7 @@ class LoadImage:
         return True
 
 class LoadImageMask:
+    ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
 
     _color_channels = ["alpha", "red", "green", "blue"]
@@ -1889,6 +1918,7 @@ class ImageScale:
         return (s,)
 
 class ImageScaleBy:
+    ESSENTIALS_CATEGORY = "Image Tools"
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
 
     @classmethod