feat: pass filtering fields to frontend instead of server-side filtering

Remove server-side includeOnDistributions filtering and fileByDistribution
resolution. Instead, forward requiresCustomNodes and includeOnDistributions
in the info object so the frontend handles filtering client-side.

Amp-Thread-ID: https://ampcode.com/threads/T-019c6f43-6212-7308-bea6-bfc35a486cbf

app/subgraph_manager.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 from typing import TypedDict
+import json
+import logging
 import os
 import folder_paths
 import glob
@@ -90,15 +92,58 @@ class SubgraphManager:
         return subgraphs_dict
 
     async def get_blueprint_subgraphs(self, force_reload=False):
-        """Load subgraphs from the blueprints directory."""
+        """Load subgraphs from the blueprints directory using index.json for discovery."""
         if not force_reload and self.cached_blueprint_subgraphs is not None:
             return self.cached_blueprint_subgraphs
 
         subgraphs_dict: dict[SubgraphEntry] = {}
         blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
 
-        if os.path.exists(blueprints_dir):
+        index_path = os.path.join(blueprints_dir, "index.json")
+        if os.path.isfile(index_path):
+            try:
+                with open(index_path, "r", encoding="utf-8") as f:
+                    categories = json.load(f)
+            except (json.JSONDecodeError, OSError) as e:
+                logging.error("Failed to load blueprint index %s: %s", index_path, e)
+                categories = []
+
+            if not isinstance(categories, list):
+                logging.error("Blueprint index.json is not a list: %s", index_path)
+                categories = []
+
+            for category in categories:
+                module_name = category.get("moduleName", "default")
+                for blueprint in category.get("blueprints", []):
+                    name = blueprint.get("name")
+                    if not name:
+                        logging.warning("Blueprint entry missing 'name' in category '%s', skipping", module_name)
+                        continue
+
+                    filename = f"{name}.json"
+                    filepath = os.path.realpath(os.path.join(blueprints_dir, filename))
+                    if not filepath.startswith(os.path.realpath(blueprints_dir) + os.sep):
+                        logging.warning("Blueprint path escapes blueprints directory: %s", filepath)
+                        continue
+
+                    if not os.path.isfile(filepath):
+                        logging.warning("Blueprint file not found: %s", filepath)
+                        continue
+
+                    entry_id, entry = self._create_entry(filepath, Source.templates, module_name)
+                    info = entry["info"]
+                    include_on = blueprint.get("includeOnDistributions")
+                    if include_on is not None:
+                        info["includeOnDistributions"] = include_on
+                    requires = blueprint.get("requiresCustomNodes")
+                    if requires is not None:
+                        info["requiresCustomNodes"] = requires
+                    subgraphs_dict[entry_id] = entry
+        elif os.path.exists(blueprints_dir):
+            logging.warning("No blueprint index.json found at %s, falling back to glob", index_path)
             for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
+                if os.path.basename(file) == "index.json":
+                    continue
                 file = file.replace('\\', '/')
                 entry_id, entry = self._create_entry(file, Source.templates, "comfyui")
                 subgraphs_dict[entry_id] = entry

comfy/ldm/ace/ace_step15.py

@@ -183,7 +183,7 @@ class AceStepAttention(nn.Module):
             else:
                 attn_bias = window_bias
 
-        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True)
+        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False)
         attn_output = self.o_proj(attn_output)
 
         return attn_output
@@ -1035,8 +1035,7 @@ class AceStepConditionGenerationModel(nn.Module):
                     audio_codes = torch.nn.functional.pad(audio_codes, (0, math.ceil(src_latents.shape[1] / 5) - audio_codes.shape[1]), "constant", 35847)
                 lm_hints_5Hz = self.tokenizer.quantizer.get_output_from_indices(audio_codes, dtype=text_hidden_states.dtype)
             else:
-                assert False
-                # TODO ?
+                lm_hints_5Hz, indices = self.tokenizer.tokenize(refer_audio_acoustic_hidden_states_packed)
 
             lm_hints = self.detokenizer(lm_hints_5Hz)
 

comfy/ldm/modules/attention.py

@@ -524,6 +524,9 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if kwargs.get("low_precision_attention", True) is False:
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
+
     exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape

comfy/model_base.py

@@ -1548,6 +1548,7 @@ class ACEStep15(BaseModel):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
         device = kwargs["device"]
+        noise = kwargs["noise"]
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
@@ -1571,15 +1572,19 @@ class ACEStep15(BaseModel):
                                         1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
                                         -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
                                         -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
-                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, 750)
+                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
+            pass_audio_codes = True
         else:
-            refer_audio = refer_audio[-1]
+            refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
+            pass_audio_codes = False
+
+        if pass_audio_codes:
+            audio_codes = kwargs.get("audio_codes", None)
+            if audio_codes is not None:
+                out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
+                refer_audio = refer_audio[:, :, :750]
+
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
-
-        audio_codes = kwargs.get("audio_codes", None)
-        if audio_codes is not None:
-            out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
-
         return out
 
 class Omnigen2(BaseModel):

comfy/ops.py

@@ -54,6 +54,8 @@ try:
             SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)
 
             def scaled_dot_product_attention(q, k, v, *args, **kwargs):
+                if q.nelement() < 1024 * 128:  # arbitrary number, for small inputs cudnn attention seems slower
+                    return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
                 with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True):
                     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
         else:

comfy/sd.py

@@ -976,7 +976,7 @@ class VAE:
         if overlap is not None:
             args["overlap"] = overlap
 
-        if dims == 1:
+        if dims == 1 or self.extra_1d_channel is not None:
             args.pop("tile_y")
             output = self.decode_tiled_1d(samples, **args)
         elif dims == 2:

comfy/text_encoders/ace15.py

@@ -3,6 +3,7 @@ import comfy.text_encoders.llama
 from comfy import sd1_clip
 import torch
 import math
+import yaml
 import comfy.utils
 
 
@@ -101,9 +102,7 @@ def sample_manual_loop_no_classes(
     return output_audio_codes
 
 
-def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0):
-    cfg_scale = 2.0
-
+def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0):
     positive = [[token for token, _ in inner_list] for inner_list in positive]
     negative = [[token for token, _ in inner_list] for inner_list in negative]
     positive = positive[0]
@@ -120,34 +119,80 @@ def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=102
         positive = [model.special_tokens["pad"]] * pos_pad + positive
 
     paddings = [pos_pad, neg_pad]
-    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
+    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
 
 
 class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_06b", tokenizer=Qwen3Tokenizer)
 
+    def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str:
+        user_metas = {
+            k: kwargs.pop(k)
+            for k in ("bpm", "duration", "keyscale", "timesignature", "language", "caption")
+            if k in kwargs
+        }
+        timesignature = user_metas.get("timesignature")
+        if isinstance(timesignature, str) and timesignature.endswith("/4"):
+            user_metas["timesignature"] = timesignature.rsplit("/", 1)[0]
+        user_metas = {
+            k: v if not isinstance(v, str) or not v.isdigit() else int(v)
+            for k, v in user_metas.items()
+            if v not in {"unspecified", None}
+        }
+        if len(user_metas):
+            meta_yaml = yaml.dump(user_metas, allow_unicode=True, sort_keys=True).strip()
+        else:
+            meta_yaml = ""
+        return f"<think>\n{meta_yaml}\n</think>" if not return_yaml else meta_yaml
+
+    def _metas_to_cap(self, **kwargs) -> str:
+        use_keys = ("bpm", "duration", "keyscale", "timesignature")
+        user_metas = { k: kwargs.pop(k, "N/A") for k in use_keys }
+        duration = user_metas["duration"]
+        if duration == "N/A":
+            user_metas["duration"] = "30 seconds"
+        elif isinstance(duration, (str, int, float)):
+            user_metas["duration"] = f"{math.ceil(float(duration))} seconds"
+        else:
+            raise TypeError("Unexpected type for duration key, must be str, int or float")
+        return "\n".join(f"- {k}: {user_metas[k]}" for k in use_keys)
+
     def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
         out = {}
         lyrics = kwargs.get("lyrics", "")
-        bpm = kwargs.get("bpm", 120)
         duration = kwargs.get("duration", 120)
-        keyscale = kwargs.get("keyscale", "C major")
-        timesignature = kwargs.get("timesignature", 2)
-        language = kwargs.get("language", "en")
+        language = kwargs.get("language")
         seed = kwargs.get("seed", 0)
 
+        generate_audio_codes = kwargs.get("generate_audio_codes", True)
+        cfg_scale = kwargs.get("cfg_scale", 2.0)
+        temperature = kwargs.get("temperature", 0.85)
+        top_p = kwargs.get("top_p", 0.9)
+        top_k = kwargs.get("top_k", 0.0)
+
+
         duration = math.ceil(duration)
-        meta_lm = 'bpm: {}\nduration: {}\nkeyscale: {}\ntimesignature: {}'.format(bpm, duration, keyscale, timesignature)
-        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n{}\n<|im_end|>\n<|im_start|>assistant\n<think>\n{}\n</think>\n\n<|im_end|>\n"
+        kwargs["duration"] = duration
 
-        meta_cap = '- bpm: {}\n- timesignature: {}\n- keyscale: {}\n- duration: {}\n'.format(bpm, timesignature, keyscale, duration)
-        out["lm_prompt"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, meta_lm), disable_weights=True)
-        out["lm_prompt_negative"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, ""), disable_weights=True)
+        cot_text = self._metas_to_cot(caption = text, **kwargs)
+        meta_cap = self._metas_to_cap(**kwargs)
 
-        out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric{}<|endoftext|><|endoftext|>".format(language, lyrics), return_word_ids, disable_weights=True, **kwargs)
-        out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}# Metas\n{}<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
-        out["lm_metadata"] = {"min_tokens": duration * 5, "seed": seed}
+        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n<|im_end|>\n"
+
+        out["lm_prompt"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, cot_text), disable_weights=True)
+        out["lm_prompt_negative"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, "<think>\n</think>"), disable_weights=True)
+
+        out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>".format(language if language is not None else "", lyrics), return_word_ids, disable_weights=True, **kwargs)
+        out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
+        out["lm_metadata"] = {"min_tokens": duration * 5,
+                              "seed": seed,
+                              "generate_audio_codes": generate_audio_codes,
+                              "cfg_scale": cfg_scale,
+                              "temperature": temperature,
+                              "top_p": top_p,
+                              "top_k": top_k,
+                              }
         return out
 
 
@@ -203,10 +248,14 @@ class ACE15TEModel(torch.nn.Module):
         self.qwen3_06b.set_clip_options({"layer": [0]})
         lyrics_embeds, _, extra_l = self.qwen3_06b.encode_token_weights(token_weight_pairs_lyrics)
 
-        lm_metadata = token_weight_pairs["lm_metadata"]
-        audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"])
+        out = {"conditioning_lyrics": lyrics_embeds[:, 0]}
 
-        return base_out, None, {"conditioning_lyrics": lyrics_embeds[:, 0], "audio_codes": [audio_codes]}
+        lm_metadata = token_weight_pairs["lm_metadata"]
+        if lm_metadata["generate_audio_codes"]:
+            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"])
+            out["audio_codes"] = [audio_codes]
+
+        return base_out, None, out
 
     def set_clip_options(self, options):
         self.qwen3_06b.set_clip_options(options)

comfy_api/latest/_io.py

@@ -1309,7 +1309,6 @@ class NodeInfoV1:
     api_node: bool=None
     price_badge: dict | None = None
     search_aliases: list[str]=None
-    main_category: str=None
 
 
 @dataclass
@@ -1431,8 +1430,6 @@ class Schema:
     """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
     accept_all_inputs: bool=False
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
-    main_category: str | None = None
-    """Optional main category for top-level tabs in the node library (e.g., 'Basic', 'Image Tools', 'Partner Nodes')."""
 
     def validate(self):
         '''Validate the schema:
@@ -1539,7 +1536,6 @@ class Schema:
             python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
             price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
             search_aliases=self.search_aliases if self.search_aliases else None,
-            main_category=self.main_category,
         )
         return info
 

comfy_api_nodes/nodes_hunyuan3d.py

@@ -37,7 +37,6 @@ class TencentTextToModelNode(IO.ComfyNode):
             node_id="TencentTextToModelNode",
             display_name="Hunyuan3D: Text to Model (Pro)",
             category="api node/3d/Tencent",
-            main_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -148,7 +147,6 @@ class TencentImageToModelNode(IO.ComfyNode):
             node_id="TencentImageToModelNode",
             display_name="Hunyuan3D: Image(s) to Model (Pro)",
             category="api node/3d/Tencent",
-            main_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_kling.py

@@ -1936,7 +1936,6 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
             node_id="KlingLipSyncAudioToVideoNode",
             display_name="Kling Lip Sync Video with Audio",
             category="api node/video/Kling",
-            main_category="Video Generation",
             description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
             inputs=[
                 IO.Video.Input("video"),

comfy_api_nodes/nodes_openai.py

@@ -576,7 +576,6 @@ class OpenAIChatNode(IO.ComfyNode):
             node_id="OpenAIChatNode",
             display_name="OpenAI ChatGPT",
             category="api node/text/OpenAI",
-            main_category="Text Generation",
             description="Generate text responses from an OpenAI model.",
             inputs=[
                 IO.String.Input(

comfy_api_nodes/nodes_recraft.py

@@ -963,7 +963,6 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
             node_id="RecraftRemoveBackgroundNode",
             display_name="Recraft Remove Background",
             category="api node/image/Recraft",
-            main_category="Image Tools",
             description="Remove background from image, and return processed image and mask.",
             inputs=[
                 IO.Image.Input("image"),

comfy_api_nodes/nodes_stability.py

@@ -624,7 +624,6 @@ class StabilityTextToAudio(IO.ComfyNode):
             node_id="StabilityTextToAudio",
             display_name="Stability AI Text To Audio",
             category="api node/audio/Stability AI",
-            main_category="Audio",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Combo.Input(

comfy_extras/nodes_ace.py

@@ -44,13 +44,18 @@ class TextEncodeAceStepAudio15(io.ComfyNode):
                 io.Combo.Input("timesignature", options=['2', '3', '4', '6']),
                 io.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
                 io.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
+                io.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
+                io.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
+                io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
+                io.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
             ],
             outputs=[io.Conditioning.Output()],
         )
 
     @classmethod
-    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale) -> io.NodeOutput:
-        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed)
+    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k) -> io.NodeOutput:
+        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         return io.NodeOutput(conditioning)
 
@@ -100,14 +105,15 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
         latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
         return io.NodeOutput({"samples": latent, "type": "audio"})
 
-class ReferenceTimbreAudio(io.ComfyNode):
+class ReferenceAudio(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id="ReferenceTimbreAudio",
+            display_name="Reference Audio",
             category="advanced/conditioning/audio",
             is_experimental=True,
-            description="This node sets the reference audio for timbre (for ace step 1.5)",
+            description="This node sets the reference audio for ace step 1.5",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("latent", optional=True),
@@ -131,7 +137,7 @@ class AceExtension(ComfyExtension):
             EmptyAceStepLatentAudio,
             TextEncodeAceStepAudio15,
             EmptyAceStep15LatentAudio,
-            ReferenceTimbreAudio,
+            ReferenceAudio,
         ]
 
 async def comfy_entrypoint() -> AceExtension:

comfy_extras/nodes_audio.py

@@ -94,6 +94,19 @@ class VAEEncodeAudio(IO.ComfyNode):
     encode = execute  # TODO: remove
 
 
+def vae_decode_audio(vae, samples, tile=None, overlap=None):
+    if tile is not None:
+        audio = vae.decode_tiled(samples["samples"], tile_y=tile, overlap=overlap).movedim(-1, 1)
+    else:
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+
+    std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
+    std[std < 1.0] = 1.0
+    audio /= std
+    vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
+    return {"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]}
+
+
 class VAEDecodeAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -111,16 +124,33 @@ class VAEDecodeAudio(IO.ComfyNode):
 
     @classmethod
     def execute(cls, vae, samples) -> IO.NodeOutput:
-        audio = vae.decode(samples["samples"]).movedim(-1, 1)
-        std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
-        std[std < 1.0] = 1.0
-        audio /= std
-        vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
-        return IO.NodeOutput({"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]})
+        return IO.NodeOutput(vae_decode_audio(vae, samples))
 
     decode = execute  # TODO: remove
 
 
+class VAEDecodeAudioTiled(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeAudioTiled",
+            search_aliases=["latent to audio"],
+            display_name="VAE Decode Audio (Tiled)",
+            category="latent/audio",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+                IO.Int.Input("tile_size", default=512, min=32, max=8192, step=8),
+                IO.Int.Input("overlap", default=64, min=0, max=1024, step=8),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, samples, tile_size, overlap) -> IO.NodeOutput:
+        return IO.NodeOutput(vae_decode_audio(vae, samples, tile_size, overlap))
+
+
 class SaveAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -129,7 +159,6 @@ class SaveAudio(IO.ComfyNode):
             search_aliases=["export flac"],
             display_name="Save Audio (FLAC)",
             category="audio",
-            main_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -271,7 +300,6 @@ class LoadAudio(IO.ComfyNode):
             search_aliases=["import audio", "open audio", "audio file"],
             display_name="Load Audio",
             category="audio",
-            main_category="Audio",
             inputs=[
                 IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
             ],
@@ -677,6 +705,7 @@ class AudioExtension(ComfyExtension):
             EmptyLatentAudio,
             VAEEncodeAudio,
             VAEDecodeAudio,
+            VAEDecodeAudioTiled,
             SaveAudio,
             SaveAudioMP3,
             SaveAudioOpus,

comfy_extras/nodes_canny.py

@@ -12,7 +12,6 @@ class Canny(io.ComfyNode):
             node_id="Canny",
             search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
-            main_category="Image Tools/Preprocessing",
             inputs=[
                 io.Image.Input("image"),
                 io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),

comfy_extras/nodes_hunyuan3d.py

@@ -621,7 +621,6 @@ class SaveGLB(IO.ComfyNode):
             display_name="Save 3D Model",
             search_aliases=["export 3d model", "save mesh"],
             category="3d",
-            main_category="Basic",
             is_output_node=True,
             inputs=[
                 IO.MultiType.Input(

comfy_extras/nodes_images.py

@@ -25,7 +25,6 @@ class ImageCrop(IO.ComfyNode):
             search_aliases=["trim"],
             display_name="Image Crop",
             category="image/transform",
-            main_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
@@ -538,7 +537,6 @@ class ImageRotate(IO.ComfyNode):
             node_id="ImageRotate",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
-            main_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),

comfy_extras/nodes_load_3d.py

@@ -31,7 +31,6 @@ class Load3D(IO.ComfyNode):
             node_id="Load3D",
             display_name="Load 3D & Animation",
             category="3d",
-            main_category="Basic",
             is_experimental=True,
             inputs=[
                 IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),

comfy_extras/nodes_post_processing.py

@@ -77,7 +77,6 @@ class Blur(io.ComfyNode):
         return io.Schema(
             node_id="ImageBlur",
             category="image/postprocessing",
-            main_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),

comfy_extras/nodes_toolkit.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class CreateList(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template_matchtype = io.MatchType.Template("type")
+        template_autogrow = io.Autogrow.TemplatePrefix(
+            input=io.MatchType.Input("input", template=template_matchtype),
+            prefix="input",
+        )
+        return io.Schema(
+            node_id="CreateList",
+            display_name="Create List",
+            category="logic",
+            is_input_list=True,
+            search_aliases=["Image Iterator", "Text Iterator", "Iterator"],
+            inputs=[io.Autogrow.Input("inputs", template=template_autogrow)],
+            outputs=[
+                io.MatchType.Output(
+                    template=template_matchtype,
+                    is_output_list=True,
+                    display_name="list",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, inputs: io.Autogrow.Type) -> io.NodeOutput:
+        output_list = []
+        for input in inputs.values():
+            output_list += input
+        return io.NodeOutput(output_list)
+
+
+class ToolkitExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CreateList,
+        ]
+
+
+async def comfy_entrypoint() -> ToolkitExtension:
+    return ToolkitExtension()

comfy_extras/nodes_video.py

@@ -73,7 +73,6 @@ class SaveVideo(io.ComfyNode):
             search_aliases=["export video"],
             display_name="Save Video",
             category="image/video",
-            main_category="Basic",
             description="Saves the input images to your ComfyUI output directory.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to save."),
@@ -147,7 +146,6 @@ class GetVideoComponents(io.ComfyNode):
             search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
             category="image/video",
-            main_category="Video Tools",
             description="Extracts all components from a video: frames, audio, and framerate.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to extract components from."),
@@ -176,7 +174,6 @@ class LoadVideo(io.ComfyNode):
             search_aliases=["import video", "open video", "video file"],
             display_name="Load Video",
             category="image/video",
-            main_category="Basic",
             inputs=[
                 io.Combo.Input("file", options=sorted(files), upload=io.UploadType.video),
             ],

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.12.2"
+__version__ = "0.12.3"

nodes.py

@@ -69,7 +69,6 @@ class CLIPTextEncode(ComfyNodeABC):
     FUNCTION = "encode"
 
     CATEGORY = "conditioning"
-    MAIN_CATEGORY = "Basic"
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
     SEARCH_ALIASES = ["text", "prompt", "text prompt", "positive prompt", "negative prompt", "encode text", "text encoder", "encode prompt"]
 
@@ -668,8 +667,6 @@ class CLIPSetLastLayer:
         return (clip,)
 
 class LoraLoader:
-    MAIN_CATEGORY = "Image Generation"
-
     def __init__(self):
         self.loaded_lora = None
 
@@ -1651,7 +1648,6 @@ class SaveImage:
     OUTPUT_NODE = True
 
     CATEGORY = "image"
-    MAIN_CATEGORY = "Basic"
     DESCRIPTION = "Saves the input images to your ComfyUI output directory."
     SEARCH_ALIASES = ["save", "save image", "export image", "output image", "write image", "download"]
 
@@ -1710,7 +1706,6 @@ class LoadImage:
                 }
 
     CATEGORY = "image"
-    MAIN_CATEGORY = "Basic"
     SEARCH_ALIASES = ["load image", "open image", "import image", "image input", "upload image", "read image", "image loader"]
 
     RETURN_TYPES = ("IMAGE", "MASK")
@@ -1868,7 +1863,6 @@ class ImageScale:
     FUNCTION = "upscale"
 
     CATEGORY = "image/upscaling"
-    MAIN_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["resize", "resize image", "scale image", "image resize", "zoom", "zoom in", "change size"]
 
     def upscale(self, image, upscale_method, width, height, crop):
@@ -1908,7 +1902,6 @@ class ImageScaleBy:
 
 class ImageInvert:
     SEARCH_ALIASES = ["reverse colors"]
-    MAIN_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -1925,7 +1918,6 @@ class ImageInvert:
 
 class ImageBatch:
     SEARCH_ALIASES = ["combine images", "merge images", "stack images"]
-    MAIN_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -2441,7 +2433,8 @@ async def init_builtin_extra_nodes():
         "nodes_image_compare.py",
         "nodes_zimage.py",
         "nodes_lora_debug.py",
-        "nodes_color.py"
+        "nodes_color.py",
+        "nodes_toolkit.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.12.2"
+version = "0.12.3"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.37.11
+comfyui-frontend-package==1.38.13
 comfyui-workflow-templates==0.8.31
 comfyui-embedded-docs==0.4.0
 torch

server.py

@@ -687,10 +687,6 @@ class PromptServer():
                 info['api_node'] = obj_class.API_NODE
 
             info['search_aliases'] = getattr(obj_class, 'SEARCH_ALIASES', [])
-
-            if hasattr(obj_class, 'MAIN_CATEGORY'):
-                info['main_category'] = obj_class.MAIN_CATEGORY
-
             return info
 
         @routes.get("/object_info")