fix: un-mark batch_size and webcam width/height as advanced (should stay basic)

Amp-Thread-ID: https://ampcode.com/threads/T-019c3236-1417-74aa-82a3-bcb365fbe9d1

comfy/ldm/ace/ace_step15.py

@@ -1110,7 +1110,7 @@ class AceStepConditionGenerationModel(nn.Module):
 
         return encoder_hidden, encoder_mask, context_latents
 
-    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, **kwargs):
         text_attention_mask = None
         lyric_attention_mask = None
         refer_audio_order_mask = None
@@ -1140,9 +1140,6 @@ class AceStepConditionGenerationModel(nn.Module):
             src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes
         )
 
-        if replace_with_null_embeds:
-            enc_hidden[:] = self.null_condition_emb.to(enc_hidden)
-
         out = self.decoder(hidden_states=x,
                            timestep=timestep,
                            timestep_r=timestep,

comfy/ldm/cosmos/predict2.py

@@ -335,7 +335,7 @@ class FinalLayer(nn.Module):
         device=None, dtype=None, operations=None
     ):
         super().__init__()
-        self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.linear = operations.Linear(
             hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
         )
@@ -463,8 +463,6 @@ class Block(nn.Module):
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
         transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
-        residual_dtype = x_B_T_H_W_D.dtype
-        compute_dtype = emb_B_T_D.dtype
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
 
@@ -514,7 +512,7 @@ class Block(nn.Module):
         result_B_T_H_W_D = rearrange(
             self.self_attn(
                 # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
                 transformer_options=transformer_options,
@@ -524,7 +522,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -538,7 +536,7 @@ class Block(nn.Module):
             )
             _result_B_T_H_W_D = rearrange(
                 self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
                     transformer_options=transformer_options,
@@ -557,7 +555,7 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
+        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -565,8 +563,8 @@ class Block(nn.Module):
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
         )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
         return x_B_T_H_W_D
 
 
@@ -878,14 +876,6 @@ class MiniTrainDIT(nn.Module):
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
             "transformer_options": kwargs.get("transformer_options", {}),
         }
-
-        # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
-        # in fp32, but run attention and MLP modules in fp16.
-        # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable
-        # quality degradation and visual artifacts.
-        if x_B_T_H_W_D.dtype == torch.float16:
-            x_B_T_H_W_D = x_B_T_H_W_D.float()
-
         for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
@@ -894,6 +884,6 @@ class MiniTrainDIT(nn.Module):
                 **block_kwargs,
             )
 
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
         x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
         return x_B_C_Tt_Hp_Wp

comfy/model_base.py

@@ -1552,8 +1552,6 @@ class ACEStep15(BaseModel):
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            if torch.count_nonzero(cross_attn) == 0:
-                out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
@@ -1577,10 +1575,6 @@ class ACEStep15(BaseModel):
             else:
                 out['is_covers'] = comfy.conds.CONDConstant(False)
 
-        if refer_audio.shape[2] < noise.shape[2]:
-            pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
-            refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)
-
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out
 

comfy/supported_models.py

@@ -993,7 +993,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
@@ -1023,7 +1023,11 @@ class Anima(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Anima(self, device=device)
@@ -1034,12 +1038,6 @@ class Anima(supported_models_base.BASE):
         detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):
-        self.memory_usage_factor = (self.unet_config.get("model_channels", 2048) / 2048) * 0.95
-        if dtype is torch.float16:
-            self.memory_usage_factor *= 1.4
-        return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs)
-
 class CosmosI2VPredict2(CosmosT2IPredict2):
     unet_config = {
         "image_model": "cosmos_predict2",

comfy/text_encoders/anima.py

@@ -23,7 +23,7 @@ class AnimaTokenizer:
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["qwen3_06b"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        out["qwen3_06b"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
         out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 

comfy/text_encoders/lt.py

@@ -25,7 +25,7 @@ def ltxv_te(*args, **kwargs):
 class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}

comfy_api/latest/_io.py

@@ -1430,11 +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."""
-    lazy_outputs: bool=False
-    """When True, cache will invalidate when output connections change, and expected_outputs will be available.
-
-    Use this for nodes that can skip computing outputs that aren't connected downstream.
-    Access via `get_executing_context().expected_outputs` - outputs NOT in the set are definitely unused."""
 
     def validate(self):
         '''Validate the schema:
@@ -1880,14 +1875,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._ACCEPT_ALL_INPUTS
 
-    _LAZY_OUTPUTS = None
-    @final
-    @classproperty
-    def LAZY_OUTPUTS(cls):  # noqa
-        if cls._LAZY_OUTPUTS is None:
-            cls.GET_SCHEMA()
-        return cls._LAZY_OUTPUTS
-
     @final
     @classmethod
     def INPUT_TYPES(cls) -> dict[str, dict]:
@@ -1930,8 +1917,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls._NOT_IDEMPOTENT = schema.not_idempotent
         if cls._ACCEPT_ALL_INPUTS is None:
             cls._ACCEPT_ALL_INPUTS = schema.accept_all_inputs
-        if cls._LAZY_OUTPUTS is None:
-            cls._LAZY_OUTPUTS = schema.lazy_outputs
 
         if cls._RETURN_TYPES is None:
             output = []

comfy_api_nodes/nodes_bfl.py

@@ -57,6 +57,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -200,6 +201,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
                     "prompt_upsampling",
                     default=False,
                     tooltip="Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "input_image",
@@ -296,6 +298,7 @@ class FluxProExpandNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "top",
@@ -433,6 +436,7 @@ class FluxProFillNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "guidance",
@@ -577,6 +581,7 @@ class Flux2ProImageNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation.",
+                    advanced=True,
                 ),
                 IO.Image.Input("images", optional=True, tooltip="Up to 9 images to be used as references."),
             ],

comfy_api_nodes/nodes_bytedance.py

@@ -114,6 +114,7 @@ class ByteDanceImageNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -259,12 +260,14 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "fail_on_partial",
                     default=True,
                     tooltip="If enabled, abort execution if any requested images are missing or return an error.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -432,18 +435,21 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -561,18 +567,21 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -694,18 +703,21 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -834,6 +846,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_gemini.py

@@ -308,6 +308,7 @@ class GeminiNode(IO.ComfyNode):
                     default="",
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -585,6 +586,7 @@ class GeminiImage(IO.ComfyNode):
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "system_prompt",
@@ -592,6 +594,7 @@ class GeminiImage(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -706,6 +709,7 @@ class GeminiImage2(IO.ComfyNode):
                     options=["IMAGE+TEXT", "IMAGE"],
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "images",
@@ -725,6 +729,7 @@ class GeminiImage2(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_ideogram.py

@@ -261,6 +261,7 @@ class IdeogramV1(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -394,6 +395,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -411,6 +413,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="NONE",
                     tooltip="Style type for generation (V2 only)",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "negative_prompt",
@@ -564,6 +567,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -590,6 +594,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="DEFAULT",
                     tooltip="Controls the trade-off between generation speed and quality",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "character_image",

comfy_api_nodes/nodes_kling.py

@@ -2007,6 +2007,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
                     max=2.0,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -2128,6 +2129,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "image_type",
                     options=[i.value for i in KlingImageGenImageReferenceType],
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_fidelity",
@@ -2137,6 +2139,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Reference intensity for user-uploaded images",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "human_fidelity",
@@ -2146,6 +2149,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Subject reference similarity",
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "model_name",
@@ -2260,7 +2264,7 @@ class TextToVideoWithAudio(IO.ComfyNode):
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -2328,7 +2332,7 @@ class ImageToVideoWithAudio(IO.ComfyNode):
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),

comfy_api_nodes/nodes_ltxv.py

@@ -74,6 +74,7 @@ class TextToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -151,6 +152,7 @@ class ImageToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_magnific.py

@@ -86,11 +86,13 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "engine",
                     options=["automatic", "magnific_illusio", "magnific_sharpy", "magnific_sparkle"],
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum pixel limit.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -242,6 +244,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum resolution.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -392,6 +395,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                         "softy",
                     ],
                     tooltip="Processing engine selection.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "portrait_mode",
@@ -420,6 +424,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                     default=True,
                     tooltip="When disabled, expect each generation to introduce a degree of randomness, "
                     "leading to more diverse outcomes.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -534,16 +539,19 @@ class MagnificImageRelightNode(IO.ComfyNode):
                     "interpolate_from_original",
                     default=False,
                     tooltip="Restricts generation freedom to match original more closely.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "change_background",
                     default=True,
                     tooltip="Modifies background based on prompt/reference.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "preserve_details",
                     default=True,
                     tooltip="Maintains texture and fine details from original.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "advanced_settings",

comfy_api_nodes/nodes_meshy.py

@@ -58,11 +58,12 @@ class MeshyTextToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.Combo.Input(
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -155,6 +156,7 @@ class MeshyRefineNode(IO.ComfyNode):
                     tooltip="Generate PBR Maps (metallic, roughness, normal) in addition to the base color. "
                     "Note: this should be set to false when using Sculpture style, "
                     "as Sculpture style generates its own set of PBR maps.",
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "texture_prompt",
@@ -299,6 +301,7 @@ class MeshyImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -429,7 +432,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.DynamicCombo.Input(
                     "should_texture",
                     options=[
@@ -466,6 +469,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -728,8 +732,9 @@ class MeshyTextureNode(IO.ComfyNode):
                     tooltip="Use the original UV of the model instead of generating new UVs. "
                     "When enabled, Meshy preserves existing textures from the uploaded model. "
                     "If the model has no original UV, the quality of the output might not be as good.",
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pbr", default=False),
+                IO.Boolean.Input("pbr", default=False, advanced=True),
                 IO.String.Input(
                     "text_style_prompt",
                     default="",

comfy_api_nodes/nodes_openai.py

@@ -588,6 +588,7 @@ class OpenAIChatNode(IO.ComfyNode):
                     "persist_context",
                     default=False,
                     tooltip="This parameter is deprecated and has no effect.",
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "model",
@@ -862,6 +863,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     options=["auto", "disabled"],
                     default="auto",
                     tooltip="The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "max_output_tokens",
@@ -870,6 +872,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     max=16384,
                     tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "instructions",

comfy_api_nodes/nodes_rodin.py

@@ -493,7 +493,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
                     default="500K-Triangle",
                     optional=True,
                 ),
-                IO.Boolean.Input("TAPose", default=False),
+                IO.Boolean.Input("TAPose", default=False, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only

comfy_api_nodes/nodes_stability.py

@@ -86,6 +86,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -107,6 +108,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -218,6 +220,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "cfg_scale",
@@ -247,6 +250,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -384,6 +388,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -474,6 +479,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -491,6 +497,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -659,6 +666,7 @@ class StabilityTextToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -736,6 +744,7 @@ class StabilityAudioToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "strength",
@@ -829,6 +838,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_start",
@@ -837,6 +847,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_end",
@@ -845,6 +856,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_topaz.py

@@ -63,12 +63,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     "subject_detection",
                     options=["All", "Foreground", "Background"],
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "face_enhancement",
                     default=True,
                     optional=True,
                     tooltip="Enhance faces (if present) during processing.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_creativity",
@@ -79,6 +81,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Set the creativity level for face enhancement.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_strength",
@@ -89,6 +92,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Controls how sharp enhanced faces are relative to the background.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "crop_to_fill",
@@ -96,6 +100,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     optional=True,
                     tooltip="By default, the image is letterboxed when the output aspect ratio differs. "
                     "Enable to crop the image to fill the output dimensions.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_width",
@@ -106,6 +111,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_height",
@@ -116,6 +122,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to output in the same height as original or output width.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "creativity",
@@ -131,12 +138,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     default=True,
                     optional=True,
                     tooltip="Preserve subjects' facial identity.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "color_preservation",
                     default=True,
                     optional=True,
                     tooltip="Preserve the original colors.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -234,9 +243,10 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="low",
                     tooltip="Creativity level (applies only to Starlight (Astra) Creative).",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("interpolation_enabled", default=False, optional=True),
-                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True),
+                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True, advanced=True),
                 IO.Int.Input(
                     "interpolation_slowmo",
                     default=1,
@@ -246,6 +256,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     tooltip="Slow-motion factor applied to the input video. "
                     "For example, 2 makes the output twice as slow and doubles the duration.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "interpolation_frame_rate",
@@ -261,6 +272,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default=False,
                     tooltip="Analyze the input for duplicate frames and remove them.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "interpolation_duplicate_threshold",
@@ -271,6 +283,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     tooltip="Detection sensitivity for duplicate frames.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "dynamic_compression_level",
@@ -278,6 +291,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="Low",
                     tooltip="CQP level.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_tripo.py

@@ -89,13 +89,13 @@ class TripoTextToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("image_seed", default=42, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("image_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -210,18 +210,18 @@ class TripoImageToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
                 IO.Combo.Input(
-                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True
+                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True, advanced=True
                 ),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -347,18 +347,19 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                     options=TripoOrientation,
                     default=TripoOrientation.DEFAULT,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -474,10 +475,10 @@ class TripoTextureNode(IO.ComfyNode):
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
             ],
             outputs=[
@@ -682,13 +683,14 @@ class TripoConversionNode(IO.ComfyNode):
             inputs=[
                 IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]),
-                IO.Boolean.Input("quad", default=False, optional=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
                 IO.Int.Input(
                     "face_limit",
                     default=-1,
                     min=-1,
                     max=2000000,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "texture_size",
@@ -696,47 +698,53 @@ class TripoConversionNode(IO.ComfyNode):
                     min=128,
                     max=4096,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "texture_format",
                     options=["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"],
                     default="JPEG",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("force_symmetry", default=False, optional=True),
-                IO.Boolean.Input("flatten_bottom", default=False, optional=True),
+                IO.Boolean.Input("force_symmetry", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("flatten_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "flatten_bottom_threshold",
                     default=0.0,
                     min=0.0,
                     max=1.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True),
+                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "scale_factor",
                     default=1.0,
                     min=0.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("with_animation", default=False, optional=True),
-                IO.Boolean.Input("pack_uv", default=False, optional=True),
-                IO.Boolean.Input("bake", default=False, optional=True),
-                IO.String.Input("part_names", default="", optional=True),  # comma-separated list
+                IO.Boolean.Input("with_animation", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("pack_uv", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("bake", default=False, optional=True, advanced=True),
+                IO.String.Input("part_names", default="", optional=True, advanced=True),  # comma-separated list
                 IO.Combo.Input(
                     "fbx_preset",
                     options=["blender", "mixamo", "3dsmax"],
                     default="blender",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("export_vertex_colors", default=False, optional=True),
+                IO.Boolean.Input("export_vertex_colors", default=False, optional=True, advanced=True),
                 IO.Combo.Input(
                     "export_orientation",
                     options=["align_image", "default"],
                     default="default",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("animate_in_place", default=False, optional=True),
+                IO.Boolean.Input("animate_in_place", default=False, optional=True, advanced=True),
             ],
             outputs=[],
             hidden=[

comfy_api_nodes/nodes_veo2.py

@@ -81,6 +81,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -88,6 +89,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -299,6 +301,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default=True,
                     tooltip="This parameter is deprecated and ignored.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -306,6 +309,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",

comfy_api_nodes/nodes_vidu.py

@@ -111,12 +111,14 @@ class ViduTextToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -207,12 +209,14 @@ class ViduImageToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -313,12 +317,14 @@ class ViduReferenceVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -425,12 +431,14 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -510,11 +518,12 @@ class Vidu2TextToVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "3:4", "4:3", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Boolean.Input(
                     "background_music",
                     default=False,
                     tooltip="Whether to add background music to the generated video.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -608,11 +617,13 @@ class Vidu2ImageToVideoNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "resolution",
                     options=["720p", "1080p"],
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -726,6 +737,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     "audio",
                     default=False,
                     tooltip="When enabled video will contain generated speech and background music based on the prompt.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "duration",
@@ -745,11 +757,12 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "4:3", "3:4", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -863,11 +876,12 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
                 ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_wan.py

@@ -227,12 +227,14 @@ class WanTextToImageApi(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -355,6 +357,7 @@ class WanImageToImageApi(IO.ComfyNode):
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -495,18 +498,21 @@ class WanTextToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -515,6 +521,7 @@ class WanTextToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -667,18 +674,21 @@ class WanImageToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -687,6 +697,7 @@ class WanImageToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -839,11 +850,13 @@ class WanReferenceVideoApi(IO.ComfyNode):
                     options=["single", "multi"],
                     tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
                     "single continuous shot or multiple shots with cuts.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_execution/caching.py

@@ -5,7 +5,7 @@ import psutil
 import time
 import torch
 from typing import Sequence, Mapping, Dict
-from comfy_execution.graph import DynamicPrompt, get_expected_outputs_for_node
+from comfy_execution.graph import DynamicPrompt
 from abc import ABC, abstractmethod
 
 import nodes
@@ -115,10 +115,6 @@ class CacheKeySetInputSignature(CacheKeySet):
         signature = [class_type, await self.is_changed_cache.get(node_id)]
         if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
-        # Include expected_outputs in cache key for nodes that opt in via LAZY_OUTPUTS
-        if hasattr(class_def, 'LAZY_OUTPUTS') and class_def.LAZY_OUTPUTS:
-            expected = get_expected_outputs_for_node(dynprompt, node_id)
-            signature.append(("expected_outputs", tuple(sorted(expected))))
         inputs = node["inputs"]
         for key in sorted(inputs.keys()):
             if is_link(inputs[key]):

comfy_execution/graph.py

@@ -19,15 +19,6 @@ class NodeInputError(Exception):
 class NodeNotFoundError(Exception):
     pass
 
-
-def get_expected_outputs_for_node(dynprompt, node_id: str) -> frozenset:
-    """Get the set of output indices that are connected downstream.
-    Returns outputs that MIGHT be used.
-    Outputs NOT in this set are DEFINITELY not used and safe to skip.
-    """
-    return dynprompt.get_expected_outputs_map().get(node_id, frozenset())
-
-
 class DynamicPrompt:
     def __init__(self, original_prompt):
         # The original prompt provided by the user
@@ -36,7 +27,6 @@ class DynamicPrompt:
         self.ephemeral_prompt = {}
         self.ephemeral_parents = {}
         self.ephemeral_display = {}
-        self._expected_outputs_map = None
 
     def get_node(self, node_id):
         if node_id in self.ephemeral_prompt:
@@ -52,7 +42,6 @@ class DynamicPrompt:
         self.ephemeral_prompt[node_id] = node_info
         self.ephemeral_parents[node_id] = parent_id
         self.ephemeral_display[node_id] = display_id
-        self._expected_outputs_map = None
 
     def get_real_node_id(self, node_id):
         while node_id in self.ephemeral_parents:
@@ -70,26 +59,6 @@ class DynamicPrompt:
     def all_node_ids(self):
         return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
 
-    def _build_expected_outputs_map(self):
-        result = {}
-        for node_id in self.all_node_ids():
-            try:
-                node_data = self.get_node(node_id)
-            except NodeNotFoundError:
-                continue
-            for value in node_data.get("inputs", {}).values():
-                if is_link(value):
-                    from_node_id, from_socket = value
-                    if from_node_id not in result:
-                        result[from_node_id] = set()
-                    result[from_node_id].add(from_socket)
-        self._expected_outputs_map = {k: frozenset(v) for k, v in result.items()}
-
-    def get_expected_outputs_map(self):
-        if self._expected_outputs_map is None:
-            self._build_expected_outputs_map()
-        return self._expected_outputs_map
-
     def get_original_prompt(self):
         return self.original_prompt
 

comfy_execution/utils.py

@@ -1,41 +1,23 @@
 import contextvars
-from typing import NamedTuple, FrozenSet
+from typing import Optional, NamedTuple
 
 class ExecutionContext(NamedTuple):
     """
     Context information about the currently executing node.
 
     Attributes:
-        prompt_id: The ID of the current prompt execution
         node_id: The ID of the currently executing node
         list_index: The index in a list being processed (for operations on batches/lists)
-        expected_outputs: Set of output indices that might be used downstream.
-                         Outputs NOT in this set are definitely unused (safe to skip).
-                         None means the information is not available.
     """
     prompt_id: str
     node_id: str
-    list_index: int | None
-    expected_outputs: FrozenSet[int] | None = None
+    list_index: Optional[int]
 
-current_executing_context: contextvars.ContextVar[ExecutionContext | None] = contextvars.ContextVar("current_executing_context", default=None)
+current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
 
-def get_executing_context() -> ExecutionContext | None:
+def get_executing_context() -> Optional[ExecutionContext]:
     return current_executing_context.get(None)
 
-
-def is_output_needed(output_index: int) -> bool:
-    """Check if an output at the given index is connected downstream.
-
-    Returns True if the output might be used (should be computed).
-    Returns False if the output is definitely not connected (safe to skip).
-    """
-    ctx = get_executing_context()
-    if ctx is None or ctx.expected_outputs is None:
-        return True
-    return output_index in ctx.expected_outputs
-
-
 class CurrentNodeContext:
     """
     Context manager for setting the current executing node context.
@@ -43,22 +25,15 @@ class CurrentNodeContext:
     Sets the current_executing_context on enter and resets it on exit.
 
     Example:
-        with CurrentNodeContext(prompt_id="abc", node_id="123", list_index=0):
+        with CurrentNodeContext(node_id="123", list_index=0):
             # Code that should run with the current node context set
             process_image()
     """
-    def __init__(
-        self,
-        prompt_id: str,
-        node_id: str,
-        list_index: int | None = None,
-        expected_outputs: FrozenSet[int] | None = None,
-    ):
+    def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
         self.context = ExecutionContext(
-            prompt_id=prompt_id,
-            node_id=node_id,
-            list_index=list_index,
-            expected_outputs=expected_outputs,
+            prompt_id= prompt_id,
+            node_id= node_id,
+            list_index= list_index
         )
         self.token = None
 

comfy_extras/nodes_advanced_samplers.py

@@ -47,8 +47,8 @@ class SamplerLCMUpscale(io.ComfyNode):
             node_id="SamplerLCMUpscale",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
-                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
+                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01, advanced=True),
+                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1, advanced=True),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],
             outputs=[io.Sampler.Output()],
@@ -94,7 +94,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
             display_name="SamplerEulerCFG++",
             category="_for_testing",  # "sampling/custom_sampling/samplers"
             inputs=[
-                io.Combo.Input("version", options=["regular", "alternative"]),
+                io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             is_experimental=True,

comfy_extras/nodes_apg.py

@@ -26,6 +26,7 @@ class APG(io.ComfyNode):
                     max=10.0,
                     step=0.01,
                     tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "norm_threshold",
@@ -34,6 +35,7 @@ class APG(io.ComfyNode):
                     max=50.0,
                     step=0.1,
                     tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "momentum",
@@ -42,6 +44,7 @@ class APG(io.ComfyNode):
                     max=1.0,
                     step=0.01,
                     tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],

comfy_extras/nodes_attention_multiply.py

@@ -28,10 +28,10 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -51,10 +51,10 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -75,10 +75,10 @@ class CLIPAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,
@@ -109,10 +109,10 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,

comfy_extras/nodes_audio.py

@@ -22,7 +22,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
-                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.",
                 ),
             ],
             outputs=[IO.Latent.Output()],
@@ -677,6 +677,7 @@ class EmptyAudio(IO.ComfyNode):
                     tooltip="Sample rate of the empty audio clip.",
                     min=1,
                     max=192000,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "channels",
@@ -684,6 +685,7 @@ class EmptyAudio(IO.ComfyNode):
                     min=1,
                     max=2,
                     tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+                    advanced=True,
                 ),
             ],
             outputs=[IO.Audio.Output()],

comfy_extras/nodes_camera_trajectory.py

@@ -174,10 +174,10 @@ class WanCameraEmbedding(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
-                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
-                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
+                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
+                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
             ],
             outputs=[
                 io.WanCameraEmbedding.Output(display_name="camera_embedding"),

comfy_extras/nodes_chroma_radiance.py

@@ -48,6 +48,7 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="First sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="end_sigma",
@@ -55,12 +56,14 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="Last sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Int.Input(
                     id="nerf_tile_size",
                     default=-1,
                     min=-1,
                     tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],

comfy_extras/nodes_clip_sdxl.py

@@ -35,8 +35,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
                 io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.String.Input("text_g", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_cond.py

@@ -38,8 +38,8 @@ class T5TokenizerOptions(io.ComfyNode):
             category="_for_testing/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
-                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,

comfy_extras/nodes_context_windows.py

@@ -14,15 +14,15 @@ class ContextWindowsManualNode(io.ComfyNode):
             description="Manually set context windows.",
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
-                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
-                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
@@ -67,15 +67,15 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
         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."),
-                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),

comfy_extras/nodes_controlnet.py

@@ -48,8 +48,8 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Mask.Input("mask"),
                 io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_custom_sampler.py

@@ -50,9 +50,9 @@ class KarrasScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -72,8 +72,8 @@ class ExponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -93,9 +93,9 @@ class PolyexponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -115,10 +115,10 @@ class LaplaceScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False),
-                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -164,8 +164,8 @@ class BetaSamplingScheduler(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
-                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
+                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -185,9 +185,9 @@ class VPScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values
-                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False),
+                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False, advanced=True), #TODO: fix default values
+                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -398,9 +398,9 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_3M_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -424,9 +424,9 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=['midpoint', 'heun']),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -450,10 +450,10 @@ class SamplerDPMPP_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -496,8 +496,8 @@ class SamplerEulerAncestral(io.ComfyNode):
             node_id="SamplerEulerAncestral",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -538,7 +538,7 @@ class SamplerLMS(io.ComfyNode):
         return io.Schema(
             node_id="SamplerLMS",
             category="sampling/custom_sampling/samplers",
-            inputs=[io.Int.Input("order", default=4, min=1, max=100)],
+            inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
             outputs=[io.Sampler.Output()]
         )
 
@@ -556,16 +556,16 @@ class SamplerDPMAdaptative(io.ComfyNode):
             node_id="SamplerDPMAdaptative",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Int.Input("order", default=3, min=2, max=3),
-                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("order", default=3, min=2, max=3, advanced=True),
+                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -588,9 +588,9 @@ class SamplerER_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
-                io.Int.Input("max_stage", default=3, min=1, max=3),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type.", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -622,18 +622,17 @@ class SamplerSASolver(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSASolver",
-            search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
-                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Int.Input("predictor_order", default=3, min=1, max=6),
-                io.Int.Input("corrector_order", default=4, min=0, max=6),
-                io.Boolean.Input("use_pece"),
-                io.Boolean.Input("simple_order_2"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
+                io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
+                io.Boolean.Input("use_pece", advanced=True),
+                io.Boolean.Input("simple_order_2", advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -667,13 +666,12 @@ class SamplerSEEDS2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSEEDS2",
-            search_aliases=["sde", "exp heun"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength"),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
-                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier", advanced=True),
+                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)", advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             description=(
@@ -730,7 +728,7 @@ class SamplerCustom(io.ComfyNode):
             category="sampling/custom_sampling",
             inputs=[
                 io.Model.Input("model"),
-                io.Boolean.Input("add_noise", default=True),
+                io.Boolean.Input("add_noise", default=True, advanced=True),
                 io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_dataset.py

@@ -222,6 +222,7 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -262,6 +263,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -741,6 +743,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Mean value for normalization.",
+            advanced=True,
         ),
         io.Float.Input(
             "std",
@@ -748,6 +751,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.001,
             max=1.0,
             tooltip="Standard deviation for normalization.",
+            advanced=True,
         ),
     ]
 
@@ -961,6 +965,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.",
+            advanced=True,
         ),
     ]
 
@@ -1039,6 +1044,7 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Width of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
             "cell_height",
@@ -1046,9 +1052,10 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Height of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
-            "padding", default=4, min=0, max=50, tooltip="Padding between images."
+            "padding", default=4, min=0, max=50, tooltip="Padding between images.", advanced=True
         ),
     ]
 
@@ -1339,6 +1346,7 @@ class SaveTrainingDataset(io.ComfyNode):
                     min=1,
                     max=100000,
                     tooltip="Number of samples per shard file.",
+                    advanced=True,
                 ),
             ],
             outputs=[],

comfy_extras/nodes_easycache.py

@@ -108,7 +108,7 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    x: torch.Tensor = _extract_tensor(args[0], easycache.output_channels)
     # prepare next x_prev
     next_x_prev = x
     input_change = None
@@ -367,10 +367,10 @@ class EasyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with EasyCache."),
@@ -500,10 +500,10 @@ class LazyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add LazyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with LazyCache."),

comfy_extras/nodes_eps.py

@@ -28,6 +28,7 @@ class EpsilonScaling(io.ComfyNode):
                     max=1.5,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -97,6 +98,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "tsr_sigma",
@@ -109,6 +111,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_extras/nodes_flux.py

@@ -161,6 +161,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
                 io.Combo.Input(
                     "reference_latents_method",
                     options=["offset", "index", "uxo/uno", "index_timestep_zero"],
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_extras/nodes_freelunch.py

@@ -32,10 +32,10 @@ class FreeU(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),
@@ -79,10 +79,10 @@ class FreeU_V2(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),

comfy_extras/nodes_fresca.py

@@ -65,11 +65,11 @@ class FreSca(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for low-frequency components"),
+                               tooltip="Scaling factor for low-frequency components", advanced=True),
                 io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for high-frequency components"),
+                               tooltip="Scaling factor for high-frequency components", advanced=True),
                 io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
-                             tooltip="Number of frequency indices around center to consider as low-frequency"),
+                             tooltip="Number of frequency indices around center to consider as low-frequency", advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_gits.py

@@ -342,7 +342,7 @@ class GITSScheduler(io.ComfyNode):
             node_id="GITSScheduler",
             category="sampling/custom_sampling/schedulers",
             inputs=[
-                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05, advanced=True),
                 io.Int.Input("steps", default=10, min=2, max=1000),
                 io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
             ],

comfy_extras/nodes_hooks.py

@@ -233,8 +233,8 @@ class SetClipHooks:
         return {
             "required": {
                 "clip": ("CLIP",),
-                "apply_to_conds": ("BOOLEAN", {"default": True}),
-                "schedule_clip": ("BOOLEAN", {"default": False})
+                "apply_to_conds": ("BOOLEAN", {"default": True, "advanced": True}),
+                "schedule_clip": ("BOOLEAN", {"default": False, "advanced": True})
             },
             "optional": {
                 "hooks": ("HOOKS",)
@@ -512,7 +512,7 @@ class CreateHookKeyframesInterpolated:
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
@@ -557,7 +557,7 @@ class CreateHookKeyframesFromFloats:
                 "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),

comfy_extras/nodes_hunyuan.py

@@ -138,7 +138,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
                 io.Image.Input("start_image", optional=True),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[
@@ -285,6 +285,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
                     min=1,
                     max=512,
                     tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -313,7 +314,7 @@ class HunyuanImageToVideo(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
+                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"], advanced=True),
                 io.Image.Input("start_image", optional=True),
             ],
             outputs=[
@@ -384,7 +385,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[

comfy_extras/nodes_hunyuan3d.py

@@ -106,8 +106,8 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
-                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000),
-                IO.Int.Input("octree_resolution", default=256, min=16, max=512),
+                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000, advanced=True),
+                IO.Int.Input("octree_resolution", default=256, min=16, max=512, advanced=True),
             ],
             outputs=[
                 IO.Voxel.Output(),
@@ -456,7 +456,7 @@ class VoxelToMesh(IO.ComfyNode):
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),
-                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[

comfy_extras/nodes_hypertile.py

@@ -30,10 +30,10 @@ class HyperTile(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("tile_size", default=256, min=1, max=2048),
-                io.Int.Input("swap_size", default=2, min=1, max=128),
-                io.Int.Input("max_depth", default=0, min=0, max=10),
-                io.Boolean.Input("scale_depth", default=False),
+                io.Int.Input("tile_size", default=256, min=1, max=2048, advanced=True),
+                io.Int.Input("swap_size", default=2, min=1, max=128, advanced=True),
+                io.Int.Input("max_depth", default=0, min=0, max=10, advanced=True),
+                io.Boolean.Input("scale_depth", default=False, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_images.py

@@ -175,7 +175,7 @@ class SaveAnimatedPNG(IO.ComfyNode):
                 IO.Image.Input("images"),
                 IO.String.Input("filename_prefix", default="ComfyUI"),
                 IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
-                IO.Int.Input("compress_level", default=4, min=0, max=9),
+                IO.Int.Input("compress_level", default=4, min=0, max=9, advanced=True),
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
@@ -212,8 +212,8 @@ class ImageStitch(IO.ComfyNode):
                 IO.Image.Input("image1"),
                 IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
                 IO.Boolean.Input("match_image_size", default=True),
-                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
-                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
+                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2, advanced=True),
+                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white", advanced=True),
                 IO.Image.Input("image2", optional=True),
             ],
             outputs=[IO.Image.Output()],
@@ -383,8 +383,8 @@ class ResizeAndPadImage(IO.ComfyNode):
                 IO.Image.Input("image"),
                 IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Combo.Input("padding_color", options=["white", "black"]),
-                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
+                IO.Combo.Input("padding_color", options=["white", "black"], advanced=True),
+                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"], advanced=True),
             ],
             outputs=[IO.Image.Output()],
         )

comfy_extras/nodes_latent.py

@@ -391,9 +391,8 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
             latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
             normalized_latent = latent / latent_vector_magnitude
 
-            dims = list(range(1, latent_vector_magnitude.ndim))
-            mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
 
             top = (std * 5 + mean) * multiplier
 
@@ -413,9 +412,9 @@ class LatentOperationSharpen(io.ComfyNode):
             category="latent/advanced/operations",
             is_experimental=True,
             inputs=[
-                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
-                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
+                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.LatentOperation.Output(),

comfy_extras/nodes_load_3d.py

@@ -97,8 +97,8 @@ class Preview3D(IO.ComfyNode):
                     ],
                     tooltip="3D model file or path string",
                 ),
-                IO.Load3DCamera.Input("camera_info", optional=True),
-                IO.Image.Input("bg_image", optional=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Image.Input("bg_image", optional=True, advanced=True),
             ],
             outputs=[],
         )

comfy_extras/nodes_lora_extract.py

@@ -83,9 +83,9 @@ class LoraSave(io.ComfyNode):
             category="_for_testing",
             inputs=[
                 io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
-                io.Int.Input("rank", default=8, min=1, max=4096, step=1),
-                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())),
-                io.Boolean.Input("bias_diff", default=True),
+                io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),
+                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys()), advanced=True),
+                io.Boolean.Input("bias_diff", default=True, advanced=True),
                 io.Model.Input(
                     "model_diff",
                     tooltip="The ModelSubtract output to be converted to a lora.",

comfy_extras/nodes_lt.py

@@ -450,6 +450,7 @@ class LTXVScheduler(io.ComfyNode):
                     id="stretch",
                     default=True,
                     tooltip="Stretch the sigmas to be in the range [terminal, 1].",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="terminal",
@@ -458,6 +459,7 @@ class LTXVScheduler(io.ComfyNode):
                     max=0.99,
                     step=0.01,
                     tooltip="The terminal value of the sigmas after stretching.",
+                    advanced=True,
                 ),
                 io.Latent.Input("latent", optional=True),
             ],

comfy_extras/nodes_lt_audio.py

@@ -189,6 +189,7 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
                 io.Combo.Input(
                     "device",
                     options=["default", "cpu"],
+                    advanced=True,
                 )
             ],
             outputs=[io.Clip.Output()],

comfy_extras/nodes_lumina2.py

@@ -12,8 +12,8 @@ class RenormCFG(io.ComfyNode):
             category="advanced/model",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
-                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
+                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_mask.py

@@ -348,7 +348,7 @@ class GrowMask(IO.ComfyNode):
             inputs=[
                 IO.Mask.Input("mask"),
                 IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Boolean.Input("tapered_corners", default=True),
+                IO.Boolean.Input("tapered_corners", default=True, advanced=True),
             ],
             outputs=[IO.Mask.Output()],
         )

comfy_extras/nodes_model_advanced.py

@@ -53,7 +53,7 @@ class ModelSamplingDiscrete:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
-                              "zsnr": ("BOOLEAN", {"default": False}),
+                              "zsnr": ("BOOLEAN", {"default": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -153,8 +153,8 @@ class ModelSamplingFlux:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
-                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
+                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
                               "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               }}
@@ -190,8 +190,8 @@ class ModelSamplingContinuousEDM:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],),
-                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -235,8 +235,8 @@ class ModelSamplingContinuousV:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction"],),
-                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -303,7 +303,7 @@ class ModelComputeDtype:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "dtype": (["default", "fp32", "fp16", "bf16"],),
+                              "dtype": (["default", "fp32", "fp16", "bf16"], {"advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)

comfy_extras/nodes_model_downscale.py

@@ -13,11 +13,11 @@ class PatchModelAddDownscale(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("block_number", default=3, min=1, max=32, step=1),
+                io.Int.Input("block_number", default=3, min=1, max=32, step=1, advanced=True),
                 io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001),
-                io.Boolean.Input("downscale_after_skip", default=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Boolean.Input("downscale_after_skip", default=True, advanced=True),
                 io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],

comfy_extras/nodes_perpneg.py

@@ -29,7 +29,7 @@ class PerpNeg(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("empty_conditioning"),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
@@ -134,7 +134,7 @@ class PerpNegGuider(io.ComfyNode):
                 io.Conditioning.Input("negative"),
                 io.Conditioning.Input("empty_conditioning"),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Guider.Output(),

comfy_extras/nodes_post_processing.py

@@ -179,9 +179,9 @@ class Sharpen(io.ComfyNode):
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
-                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
-                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),
@@ -225,7 +225,7 @@ class ImageScaleToTotalPixels(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Combo.Input("upscale_method", options=cls.upscale_methods),
                 io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
-                io.Int.Input("resolution_steps", default=1, min=1, max=256),
+                io.Int.Input("resolution_steps", default=1, min=1, max=256, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),

comfy_extras/nodes_qwen.py

@@ -116,7 +116,7 @@ class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
             inputs=[
                 io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
-                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[

comfy_extras/nodes_rope.py

@@ -12,14 +12,14 @@ class ScaleROPE(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
 
             ],

comfy_extras/nodes_sag.py

@@ -117,7 +117,7 @@ class SelfAttentionGuidance(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
-                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
+                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_sd3.py

@@ -72,7 +72,7 @@ class CLIPTextEncodeSD3(io.ComfyNode):
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
                 io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
                 io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
-                io.Combo.Input("empty_padding", options=["none", "empty_prompt"]),
+                io.Combo.Input("empty_padding", options=["none", "empty_prompt"], advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(),
@@ -179,10 +179,10 @@ class SkipLayerGuidanceSD3(io.ComfyNode):
             description="Generic version of SkipLayerGuidance node that can be used on every DiT model.",
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("layers", default="7, 8, 9", multiline=False),
+                io.String.Input("layers", default="7, 8, 9", multiline=False, advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_sdupscale.py

@@ -15,7 +15,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_slg.py

@@ -21,11 +21,11 @@ class SkipLayerGuidanceDiT(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
                 io.Float.Input("rescaling_scale", default=0.0, min=0.0, max=10.0, step=0.01),
             ],
             outputs=[
@@ -101,10 +101,10 @@ class SkipLayerGuidanceDiTSimple(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_stable3d.py

@@ -75,8 +75,8 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
                 io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
                 io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
+                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True)
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_stable_cascade.py

@@ -33,7 +33,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
             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),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[
@@ -62,7 +62,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
             inputs=[
                 io.Image.Input("image"),
                 io.Vae.Input("vae"),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
             ],
             outputs=[
                 io.Latent.Output(display_name="stage_c"),

comfy_extras/nodes_string.py

@@ -169,7 +169,7 @@ class StringContains(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("substring", multiline=True),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="contains"),
@@ -198,7 +198,7 @@ class StringCompare(io.ComfyNode):
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
                 io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(),
@@ -233,9 +233,9 @@ class RegexMatch(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="matches"),
@@ -275,10 +275,10 @@ class RegexExtract(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
-                io.Int.Input("group_index", default=1, min=0, max=100),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
+                io.Int.Input("group_index", default=1, min=0, max=100, advanced=True),
             ],
             outputs=[
                 io.String.Output(),
@@ -351,10 +351,10 @@ class RegexReplace(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.String.Input("replace", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True, optional=True),
-                io.Boolean.Input("multiline", default=False, optional=True),
-                io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
-                io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
+                io.Boolean.Input("case_insensitive", default=True, optional=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, optional=True, advanced=True),
+                io.Boolean.Input("dotall", default=False, optional=True, advanced=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
+                io.Int.Input("count", default=0, min=0, max=100, optional=True, advanced=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
             ],
             outputs=[
                 io.String.Output(),

comfy_extras/nodes_torch_compile.py

@@ -16,6 +16,7 @@ class TorchCompileModel(io.ComfyNode):
                 io.Combo.Input(
                     "backend",
                     options=["inductor", "cudagraphs"],
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],

comfy_extras/nodes_video_model.py

@@ -32,9 +32,9 @@ class SVD_img2vid_Conditioning:
                               "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
-                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
+                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023, "advanced": True}),
                               "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
-                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
+                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01, "advanced": True})
                              }}
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
     RETURN_NAMES = ("positive", "negative", "latent")
@@ -60,7 +60,7 @@ class VideoLinearCFGGuidance:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
@@ -84,7 +84,7 @@ class VideoTriangleCFGGuidance:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"

comfy_extras/nodes_wan.py

@@ -717,8 +717,8 @@ class WanTrackToVideo(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1),
-                io.Int.Input("topk", default=2, min=1, max=10),
+                io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1, advanced=True),
+                io.Int.Input("topk", default=2, min=1, max=10, advanced=True),
                 io.Image.Input("start_image"),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
             ],
@@ -1323,7 +1323,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
                 io.Image.Input("start_image", optional=True),
                 io.AudioEncoderOutput.Input("audio_encoder_output_1"),
-                io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context."),
+                io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context.", advanced=True),
                 io.Float.Input("audio_scale", default=1.0, min=-10.0, max=10.0, step=0.01),
                 io.Image.Input("previous_frames", optional=True),
             ],

comfy_extras/nodes_wanmove.py

@@ -252,9 +252,9 @@ class WanMoveVisualizeTracks(io.ComfyNode):
                 io.Image.Input("images"),
                 io.Tracks.Input("tracks", optional=True),
                 io.Int.Input("line_resolution", default=24, min=1, max=1024),
-                io.Int.Input("circle_size", default=12, min=1, max=128),
+                io.Int.Input("circle_size", default=12, min=1, max=128, advanced=True),
                 io.Float.Input("opacity", default=0.75, min=0.0, max=1.0, step=0.01),
-                io.Int.Input("line_width", default=16, min=1, max=128),
+                io.Int.Input("line_width", default=16, min=1, max=128, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),

comfy_extras/nodes_zimage.py

@@ -16,7 +16,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
                 io.Clip.Input("clip"),
                 io.ClipVision.Input("image_encoder", optional=True),
                 io.String.Input("prompt", multiline=True, dynamic_prompts=True),
-                io.Boolean.Input("auto_resize_images", default=True),
+                io.Boolean.Input("auto_resize_images", default=True, advanced=True),
                 io.Vae.Input("vae", optional=True),
                 io.Image.Input("image1", optional=True),
                 io.Image.Input("image2", optional=True),

execution.py

@@ -31,7 +31,6 @@ from comfy_execution.graph import (
     ExecutionBlocker,
     ExecutionList,
     get_input_info,
-    get_expected_outputs_for_node,
 )
 from comfy_execution.graph_utils import GraphBuilder, is_link
 from comfy_execution.validation import validate_node_input
@@ -228,18 +227,7 @@ async def resolve_map_node_over_list_results(results):
                 raise exc
         return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
 
-async def _async_map_node_over_list(
-    prompt_id,
-    unique_id,
-    obj,
-    input_data_all,
-    func,
-    allow_interrupt=False,
-    execution_block_cb=None,
-    pre_execute_cb=None,
-    v3_data=None,
-    expected_outputs=None,
-):
+async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
     # check if node wants the lists
     input_is_list = getattr(obj, "INPUT_IS_LIST", False)
 
@@ -289,12 +277,10 @@ async def _async_map_node_over_list(
             else:
                 f = getattr(obj, func)
             if inspect.iscoroutinefunction(f):
-                async def async_wrapper(f, prompt_id, unique_id, list_index, args, expected_outputs):
-                    with CurrentNodeContext(prompt_id, unique_id, list_index, expected_outputs):
+                async def async_wrapper(f, prompt_id, unique_id, list_index, args):
+                    with CurrentNodeContext(prompt_id, unique_id, list_index):
                         return await f(**args)
-                task = asyncio.create_task(
-                    async_wrapper(f, prompt_id, unique_id, index, args=inputs, expected_outputs=expected_outputs)
-                )
+                task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs))
                 # Give the task a chance to execute without yielding
                 await asyncio.sleep(0)
                 if task.done():
@@ -303,7 +289,7 @@ async def _async_map_node_over_list(
                 else:
                     results.append(task)
             else:
-                with CurrentNodeContext(prompt_id, unique_id, index, expected_outputs):
+                with CurrentNodeContext(prompt_id, unique_id, index):
                     result = f(**inputs)
                 results.append(result)
         else:
@@ -341,17 +327,8 @@ def merge_result_data(results, obj):
             output.append([o[i] for o in results])
     return output
 
-async def get_output_data(
-    prompt_id,
-    unique_id,
-    obj,
-    input_data_all,
-    execution_block_cb=None,
-    pre_execute_cb=None,
-    v3_data=None,
-    expected_outputs=None,
-):
-    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data, expected_outputs=expected_outputs)
+async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
+    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
     has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values)
     if has_pending_task:
         return return_values, {}, False, has_pending_task
@@ -545,10 +522,9 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             #will cause all sorts of incompatible memory shapes to fragment the pytorch alloc
             #that we just want to cull out each model run.
             allocator = comfy.memory_management.aimdo_allocator
-            expected_outputs = get_expected_outputs_for_node(dynprompt, unique_id)
             with nullcontext() if allocator is None else torch.cuda.use_mem_pool(torch.cuda.MemPool(allocator.allocator())):
                 try:
-                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data, expected_outputs=expected_outputs)
+                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                 finally:
                     if allocator is not None:
                         comfy.model_management.reset_cast_buffers()

nodes.py

@@ -320,10 +320,10 @@ class VAEDecodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}),
-                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
-                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time."}),
-                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32, "advanced": True}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "advanced": True}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time.", "advanced": True}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap.", "advanced": True}),
                             }}
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
@@ -367,10 +367,10 @@ class VAEEncodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
-                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
-                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
-                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64, "advanced": True}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "advanced": True}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time.", "advanced": True}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap.", "advanced": True}),
                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
@@ -654,7 +654,7 @@ class CLIPSetLastLayer:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip": ("CLIP", ),
-                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1, "advanced": True}),
                               }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "set_last_layer"
@@ -1594,7 +1594,7 @@ class KSamplerAdvanced:
     def INPUT_TYPES(s):
         return {"required":
                     {"model": ("MODEL",),
-                    "add_noise": (["enable", "disable"], ),
+                    "add_noise": (["enable", "disable"], {"advanced": True}),
                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
@@ -1603,9 +1603,9 @@ class KSamplerAdvanced:
                     "positive": ("CONDITIONING", ),
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
-                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                    "return_with_leftover_noise": (["disable", "enable"], ),
+                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "advanced": True}),
+                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "advanced": True}),
+                    "return_with_leftover_noise": (["disable", "enable"], {"advanced": True}),
                      }
                 }
 
@@ -1974,7 +1974,7 @@ class ImagePadForOutpaint:
                 "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                 "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                 "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1, "advanced": True}),
             }
         }
 

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.38.13
 comfyui-workflow-templates==0.8.31
-comfyui-embedded-docs==0.4.1
+comfyui-embedded-docs==0.4.0
 torch
 torchsde
 torchvision

tests-unit/execution_test/expected_outputs_test.py

@@ -1,322 +0,0 @@
-"""Unit tests for the expected_outputs feature.
-
-This feature allows nodes to know at runtime which outputs are connected downstream,
-enabling them to skip computing outputs that aren't needed.
-"""
-
-from comfy_api.latest import IO
-from comfy_execution.graph import DynamicPrompt, get_expected_outputs_for_node
-from comfy_execution.utils import (
-    CurrentNodeContext,
-    ExecutionContext,
-    get_executing_context,
-    is_output_needed,
-)
-
-
-class TestGetExpectedOutputsForNode:
-    """Tests for get_expected_outputs_for_node() function."""
-
-    def test_single_output_connected(self):
-        """Test node with single output connected to one downstream node."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {"class_type": "ConsumerNode", "inputs": {"image": ["1", 0]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0})
-
-    def test_multiple_outputs_partial_connected(self):
-        """Test node with multiple outputs, only some connected."""
-        prompt = {
-            "1": {"class_type": "MultiOutputNode", "inputs": {}},
-            "2": {"class_type": "ConsumerA", "inputs": {"input": ["1", 0]}},
-            # Output 1 is not connected
-            "3": {"class_type": "ConsumerC", "inputs": {"input": ["1", 2]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0, 2})
-        assert 1 not in expected  # Output 1 is definitely unused
-
-    def test_no_outputs_connected(self):
-        """Test node with no outputs connected."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {"class_type": "OtherNode", "inputs": {}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset()
-
-    def test_same_output_connected_multiple_times(self):
-        """Test same output connected to multiple downstream nodes."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {"class_type": "ConsumerA", "inputs": {"input": ["1", 0]}},
-            "3": {"class_type": "ConsumerB", "inputs": {"input": ["1", 0]}},
-            "4": {"class_type": "ConsumerC", "inputs": {"input": ["1", 0]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0})
-
-    def test_node_not_in_prompt(self):
-        """Test getting expected outputs for a node not in the prompt."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "999")
-        assert expected == frozenset()
-
-    def test_chained_nodes(self):
-        """Test expected outputs in a chain of nodes."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {"class_type": "MiddleNode", "inputs": {"input": ["1", 0]}},
-            "3": {"class_type": "EndNode", "inputs": {"input": ["2", 0]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-
-        # Node 1's output 0 is connected to node 2
-        expected_1 = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected_1 == frozenset({0})
-
-        # Node 2's output 0 is connected to node 3
-        expected_2 = get_expected_outputs_for_node(dynprompt, "2")
-        assert expected_2 == frozenset({0})
-
-        # Node 3 has no downstream connections
-        expected_3 = get_expected_outputs_for_node(dynprompt, "3")
-        assert expected_3 == frozenset()
-
-    def test_complex_graph(self):
-        """Test expected outputs in a complex graph with multiple connections."""
-        prompt = {
-            "1": {"class_type": "MultiOutputNode", "inputs": {}},
-            "2": {"class_type": "ProcessorA", "inputs": {"image": ["1", 0], "mask": ["1", 1]}},
-            "3": {"class_type": "ProcessorB", "inputs": {"data": ["1", 2]}},
-            "4": {"class_type": "Combiner", "inputs": {"a": ["2", 0], "b": ["3", 0]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-
-        # Node 1 has outputs 0, 1, 2 all connected
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0, 1, 2})
-
-    def test_constant_inputs_ignored(self):
-        """Test that constant (non-link) inputs don't affect expected outputs."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {
-                "class_type": "ConsumerNode",
-                "inputs": {
-                    "image": ["1", 0],
-                    "value": 42,
-                    "name": "test",
-                },
-            },
-        }
-        dynprompt = DynamicPrompt(prompt)
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0})
-
-    def test_ephemeral_node_invalidates_cache(self):
-        """Test that adding ephemeral nodes updates expected outputs."""
-        prompt = {
-            "1": {"class_type": "SourceNode", "inputs": {}},
-            "2": {"class_type": "ConsumerNode", "inputs": {"image": ["1", 0]}},
-        }
-        dynprompt = DynamicPrompt(prompt)
-
-        # Initially only output 0 is connected
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0})
-
-        # Add an ephemeral node that connects to output 1
-        dynprompt.add_ephemeral_node(
-            "eph_1",
-            {"class_type": "EphemeralNode", "inputs": {"data": ["1", 1]}},
-            parent_id="2",
-            display_id="2",
-        )
-
-        # Now both outputs 0 and 1 should be expected
-        expected = get_expected_outputs_for_node(dynprompt, "1")
-        assert expected == frozenset({0, 1})
-
-
-class TestExecutionContext:
-    """Tests for ExecutionContext with expected_outputs field."""
-
-    def test_context_with_expected_outputs(self):
-        """Test creating ExecutionContext with expected_outputs."""
-        ctx = ExecutionContext(
-            prompt_id="prompt-123", node_id="node-456", list_index=0, expected_outputs=frozenset({0, 2})
-        )
-        assert ctx.prompt_id == "prompt-123"
-        assert ctx.node_id == "node-456"
-        assert ctx.list_index == 0
-        assert ctx.expected_outputs == frozenset({0, 2})
-
-    def test_context_without_expected_outputs(self):
-        """Test ExecutionContext defaults to None for expected_outputs."""
-        ctx = ExecutionContext(prompt_id="prompt-123", node_id="node-456", list_index=0)
-        assert ctx.expected_outputs is None
-
-    def test_context_empty_expected_outputs(self):
-        """Test ExecutionContext with empty expected_outputs set."""
-        ctx = ExecutionContext(
-            prompt_id="prompt-123", node_id="node-456", list_index=None, expected_outputs=frozenset()
-        )
-        assert ctx.expected_outputs == frozenset()
-        assert len(ctx.expected_outputs) == 0
-
-
-class TestCurrentNodeContext:
-    """Tests for CurrentNodeContext context manager with expected_outputs."""
-
-    def test_context_manager_with_expected_outputs(self):
-        """Test CurrentNodeContext sets and resets context correctly."""
-        assert get_executing_context() is None
-
-        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 1})):
-            ctx = get_executing_context()
-            assert ctx is not None
-            assert ctx.prompt_id == "prompt-1"
-            assert ctx.node_id == "node-1"
-            assert ctx.list_index == 0
-            assert ctx.expected_outputs == frozenset({0, 1})
-
-        assert get_executing_context() is None
-
-    def test_context_manager_without_expected_outputs(self):
-        """Test CurrentNodeContext works without expected_outputs (backwards compatible)."""
-        with CurrentNodeContext("prompt-1", "node-1"):
-            ctx = get_executing_context()
-            assert ctx is not None
-            assert ctx.expected_outputs is None
-
-    def test_nested_context_managers(self):
-        """Test nested CurrentNodeContext managers."""
-        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0})):
-            ctx1 = get_executing_context()
-            assert ctx1.expected_outputs == frozenset({0})
-
-            with CurrentNodeContext("prompt-1", "node-2", 0, frozenset({1, 2})):
-                ctx2 = get_executing_context()
-                assert ctx2.expected_outputs == frozenset({1, 2})
-                assert ctx2.node_id == "node-2"
-
-            # After inner context exits, should be back to outer context
-            ctx1_again = get_executing_context()
-            assert ctx1_again.expected_outputs == frozenset({0})
-            assert ctx1_again.node_id == "node-1"
-
-    def test_output_check_pattern(self):
-        """Test the typical pattern nodes will use to check expected outputs."""
-        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
-            ctx = get_executing_context()
-
-            # Typical usage pattern
-            if ctx and ctx.expected_outputs is not None:
-                should_compute_0 = 0 in ctx.expected_outputs
-                should_compute_1 = 1 in ctx.expected_outputs
-                should_compute_2 = 2 in ctx.expected_outputs
-            else:
-                # Fallback when info not available
-                should_compute_0 = should_compute_1 = should_compute_2 = True
-
-            assert should_compute_0 is True
-            assert should_compute_1 is False  # Not in expected_outputs
-            assert should_compute_2 is True
-
-
-class TestSchemaLazyOutputs:
-    """Tests for lazy_outputs in V3 Schema."""
-
-    def test_schema_lazy_outputs_default(self):
-        """Test that lazy_outputs defaults to False."""
-        schema = IO.Schema(
-            node_id="TestNode",
-            inputs=[],
-            outputs=[IO.Float.Output()],
-        )
-        assert schema.lazy_outputs is False
-
-    def test_schema_lazy_outputs_true(self):
-        """Test setting lazy_outputs to True."""
-        schema = IO.Schema(
-            node_id="TestNode",
-            lazy_outputs=True,
-            inputs=[],
-            outputs=[IO.Float.Output()],
-        )
-        assert schema.lazy_outputs is True
-
-    def test_v3_node_lazy_outputs_property(self):
-        """Test that LAZY_OUTPUTS property works on V3 nodes."""
-
-        class TestNodeWithLazyOutputs(IO.ComfyNode):
-            @classmethod
-            def define_schema(cls):
-                return IO.Schema(
-                    node_id="TestNodeWithLazyOutputs",
-                    lazy_outputs=True,
-                    inputs=[],
-                    outputs=[IO.Float.Output()],
-                )
-
-            @classmethod
-            def execute(cls):
-                return IO.NodeOutput(1.0)
-
-        assert TestNodeWithLazyOutputs.LAZY_OUTPUTS is True
-
-    def test_v3_node_lazy_outputs_default(self):
-        """Test that LAZY_OUTPUTS defaults to False on V3 nodes."""
-
-        class TestNodeWithoutLazyOutputs(IO.ComfyNode):
-            @classmethod
-            def define_schema(cls):
-                return IO.Schema(
-                    node_id="TestNodeWithoutLazyOutputs",
-                    inputs=[],
-                    outputs=[IO.Float.Output()],
-                )
-
-            @classmethod
-            def execute(cls):
-                return IO.NodeOutput(1.0)
-
-        assert TestNodeWithoutLazyOutputs.LAZY_OUTPUTS is False
-
-
-class TestIsOutputNeeded:
-    """Tests for is_output_needed() helper function."""
-
-    def test_output_needed_when_in_expected(self):
-        """Test that output is needed when in expected_outputs."""
-        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
-            assert is_output_needed(0) is True
-            assert is_output_needed(2) is True
-
-    def test_output_not_needed_when_not_in_expected(self):
-        """Test that output is not needed when not in expected_outputs."""
-        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
-            assert is_output_needed(1) is False
-            assert is_output_needed(3) is False
-
-    def test_output_needed_when_no_context(self):
-        """Test that output is needed when no context."""
-        assert get_executing_context() is None
-        assert is_output_needed(0) is True
-        assert is_output_needed(1) is True
-
-    def test_output_needed_when_expected_outputs_is_none(self):
-        """Test that output is needed when expected_outputs is None."""
-        with CurrentNodeContext("prompt-1", "node-1", 0, None):
-            assert is_output_needed(0) is True
-            assert is_output_needed(1) is True

tests/execution/test_execution.py

@@ -574,104 +574,6 @@ class TestExecution:
         else:
             assert result.did_run(test_node), "The execution should have been re-run"
 
-    def test_expected_outputs_all_connected(self, client: ComfyClient, builder: GraphBuilder):
-        """Test that expected_outputs contains all connected outputs."""
-        g = builder
-        # Create a node with 3 outputs, all connected
-        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
-
-        # Connect all 3 outputs to preview nodes
-        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
-        output1 = g.node("PreviewImage", images=expected_outputs_node.out(1))
-        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
-
-        result = client.run(g)
-
-        # All outputs should be white (255) since all are connected
-        images0 = result.get_images(output0)
-        images1 = result.get_images(output1)
-        images2 = result.get_images(output2)
-
-        assert len(images0) == 1, "Should have 1 image for output0"
-        assert len(images1) == 1, "Should have 1 image for output1"
-        assert len(images2) == 1, "Should have 1 image for output2"
-
-        # White pixels = 255, meaning output was in expected_outputs
-        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white (was expected)"
-        assert numpy.array(images1[0]).min() == 255, "Output 1 should be white (was expected)"
-        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white (was expected)"
-
-    def test_expected_outputs_partial_connected(self, client: ComfyClient, builder: GraphBuilder):
-        """Test that expected_outputs only contains connected outputs."""
-        g = builder
-        # Create a node with 3 outputs, only some connected
-        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
-
-        # Only connect outputs 0 and 2, leave output 1 disconnected
-        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
-        # output1 is intentionally not connected
-        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
-
-        result = client.run(g)
-
-        # Connected outputs should be white (255)
-        images0 = result.get_images(output0)
-        images2 = result.get_images(output2)
-
-        assert len(images0) == 1, "Should have 1 image for output0"
-        assert len(images2) == 1, "Should have 1 image for output2"
-
-        # White = expected, output 1 is not connected so we can't verify it directly but outputs 0 and 2 should be white
-        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white (was expected)"
-        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white (was expected)"
-
-    def test_expected_outputs_single_connected(self, client: ComfyClient, builder: GraphBuilder):
-        """Test that expected_outputs works with single connected output."""
-        g = builder
-        # Create a node with 3 outputs, only one connected
-        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
-
-        # Only connect output 1
-        output1 = g.node("PreviewImage", images=expected_outputs_node.out(1))
-
-        result = client.run(g)
-
-        images1 = result.get_images(output1)
-        assert len(images1) == 1, "Should have 1 image for output1"
-
-        # Output 1 should be white (connected), others are not visible in this test
-        assert numpy.array(images1[0]).min() == 255, "Output 1 should be white (was expected)"
-
-    def test_expected_outputs_cache_invalidation(self, client: ComfyClient, builder: GraphBuilder, server):
-        """Test that cache invalidates when output connections change."""
-        g = builder
-        # Use unique dimensions to avoid cache collision with other expected_outputs tests
-        expected_outputs_node = g.node("TestExpectedOutputs", height=32, width=32)
-
-        # First run: only connect output 0
-        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
-
-        result1 = client.run(g)
-        assert result1.did_run(expected_outputs_node), "First run should execute the node"
-
-        # Second run: same connections, should be cached
-        result2 = client.run(g)
-        if server["should_cache_results"]:
-            assert not result2.did_run(expected_outputs_node), "Second run should be cached"
-
-        # Third run: add connection to output 2
-        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
-
-        result3 = client.run(g)
-        # Because LAZY_OUTPUTS=True, changing connections should invalidate cache
-        if server["should_cache_results"]:
-            assert result3.did_run(expected_outputs_node), "Adding output connection should invalidate cache"
-
-        # Verify both outputs are now white
-        images0 = result3.get_images(output0)
-        images2 = result3.get_images(output2)
-        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white"
-        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white"
 
     def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
         # Warmup execution to ensure server is fully initialized

tests/execution/testing_nodes/testing-pack/specific_tests.py

@@ -6,7 +6,6 @@ from .tools import VariantSupport
 from comfy_execution.graph_utils import GraphBuilder
 from comfy.comfy_types.node_typing import ComfyNodeABC
 from comfy.comfy_types import IO
-from comfy_execution.utils import get_executing_context
 
 class TestLazyMixImages:
     @classmethod
@@ -483,57 +482,6 @@ class TestOutputNodeWithSocketOutput:
         result = image * value
         return (result,)
 
-
-class TestExpectedOutputs:
-    """Test node for the expected_outputs feature.
-
-    This node has 3 IMAGE outputs that encode which outputs were expected:
-    - White image (255) if the output was in expected_outputs
-    - Black image (0) if the output was NOT in expected_outputs
-
-    This allows integration tests to verify which outputs were expected by checking pixel values.
-    """
-    LAZY_OUTPUTS = True  # Opt into cache invalidation on output connection changes
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "height": ("INT", {"default": 64, "min": 1, "max": 1024}),
-                "width": ("INT", {"default": 64, "min": 1, "max": 1024}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
-    RETURN_NAMES = ("output0", "output1", "output2")
-    FUNCTION = "execute"
-    CATEGORY = "_for_testing"
-
-    def execute(self, height, width):
-        ctx = get_executing_context()
-
-        # Default: assume all outputs are expected (backwards compatibility)
-        output0_expected = True
-        output1_expected = True
-        output2_expected = True
-
-        if ctx is not None and ctx.expected_outputs is not None:
-            output0_expected = 0 in ctx.expected_outputs
-            output1_expected = 1 in ctx.expected_outputs
-            output2_expected = 2 in ctx.expected_outputs
-
-        # Return white image if expected, black if not
-        # This allows tests to verify which outputs were expected via pixel values
-        white = torch.ones(1, height, width, 3)
-        black = torch.zeros(1, height, width, 3)
-
-        return (
-            white if output0_expected else black,
-            white if output1_expected else black,
-            white if output2_expected else black,
-        )
-
-
 TEST_NODE_CLASS_MAPPINGS = {
     "TestLazyMixImages": TestLazyMixImages,
     "TestVariadicAverage": TestVariadicAverage,
@@ -550,7 +498,6 @@ TEST_NODE_CLASS_MAPPINGS = {
     "TestSleep": TestSleep,
     "TestParallelSleep": TestParallelSleep,
     "TestOutputNodeWithSocketOutput": TestOutputNodeWithSocketOutput,
-    "TestExpectedOutputs": TestExpectedOutputs,
 }
 
 TEST_NODE_DISPLAY_NAME_MAPPINGS = {
@@ -569,5 +516,4 @@ TEST_NODE_DISPLAY_NAME_MAPPINGS = {
     "TestSleep": "Test Sleep",
     "TestParallelSleep": "Test Parallel Sleep",
     "TestOutputNodeWithSocketOutput": "Test Output Node With Socket Output",
-    "TestExpectedOutputs": "Test Expected Outputs",
 }