Merge branch 'master' into feat/api-nodes/hunyuan3d

comfy/latent_formats.py

@@ -8,6 +8,7 @@ class LatentFormat:
     latent_rgb_factors_bias = None
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
+    spacial_downscale_ratio = 8
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -181,6 +182,7 @@ class Flux(SD3):
 
 class Flux2(LatentFormat):
     latent_channels = 128
+    spacial_downscale_ratio = 16
 
     def __init__(self):
         self.latent_rgb_factors =[
@@ -592,6 +594,7 @@ class Wan22(Wan21):
 class HunyuanImage21(LatentFormat):
     latent_channels = 64
     latent_dimensions = 2
+    spacial_downscale_ratio = 32
     scale_factor = 0.75289
 
     latent_rgb_factors = [
@@ -725,6 +728,7 @@ class HunyuanVideo15(LatentFormat):
     latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
     latent_channels = 32
     latent_dimensions = 3
+    spacial_downscale_ratio = 16
     scale_factor = 1.03682
     taesd_decoder_name = "lighttaehy1_5"
 
@@ -749,6 +753,7 @@ class ACEAudio(LatentFormat):
 
 class ChromaRadiance(LatentFormat):
     latent_channels = 3
+    spacial_downscale_ratio = 1
 
     def __init__(self):
         self.latent_rgb_factors = [

comfy/ldm/lightricks/av_model.py

@@ -18,12 +18,12 @@ class CompressedTimestep:
     def __init__(self, tensor: torch.Tensor, patches_per_frame: int):
         """
         tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame
-        patches_per_frame: Number of spatial patches per frame (height * width in latent space)
+        patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression
         """
         self.batch_size, num_tokens, self.feature_dim = tensor.shape
 
         # Check if compression is valid (num_tokens must be divisible by patches_per_frame)
-        if num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame:
+        if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame:
             self.patches_per_frame = patches_per_frame
             self.num_frames = num_tokens // patches_per_frame
 
@@ -215,22 +215,9 @@ class BasicAVTransformerBlock(nn.Module):
         return (*scale_shift_ada_values, *gate_ada_values)
 
     def forward(
-        self,
-        x: Tuple[torch.Tensor, torch.Tensor],
-        v_context=None,
-        a_context=None,
-        attention_mask=None,
-        v_timestep=None,
-        a_timestep=None,
-        v_pe=None,
-        a_pe=None,
-        v_cross_pe=None,
-        a_cross_pe=None,
-        v_cross_scale_shift_timestep=None,
-        a_cross_scale_shift_timestep=None,
-        v_cross_gate_timestep=None,
-        a_cross_gate_timestep=None,
-        transformer_options=None,
+        self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,
+        v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
+        v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         run_vx = transformer_options.get("run_vx", True)
         run_ax = transformer_options.get("run_ax", True)
@@ -240,144 +227,102 @@ class BasicAVTransformerBlock(nn.Module):
         run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0
         run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True)
 
+        # video
         if run_vx:
-            vshift_msa, vscale_msa, vgate_msa = (
-                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 3))
-            )
-
+            # video self-attention
+            vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2)))
             norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa
-            vx += self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options) * vgate_msa
-            vx += self.attn2(
-                comfy.ldm.common_dit.rms_norm(vx),
-                context=v_context,
-                mask=attention_mask,
-                transformer_options=transformer_options,
-            )
-
-            del vshift_msa, vscale_msa, vgate_msa
+            del vshift_msa, vscale_msa
+            attn1_out = self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options)
+            del norm_vx
+            # video cross-attention
+            vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
+            vx.addcmul_(attn1_out, vgate_msa)
+            del vgate_msa, attn1_out
+            vx.add_(self.attn2(comfy.ldm.common_dit.rms_norm(vx), context=v_context, mask=attention_mask, transformer_options=transformer_options))
 
+        # audio
         if run_ax:
-            ashift_msa, ascale_msa, agate_msa = (
-                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 3))
-            )
-
+            # audio self-attention
+            ashift_msa, ascale_msa = (self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 2)))
             norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa
-            ax += (
-                self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
-                * agate_msa
-            )
-            ax += self.audio_attn2(
-                comfy.ldm.common_dit.rms_norm(ax),
-                context=a_context,
-                mask=attention_mask,
-                transformer_options=transformer_options,
-            )
+            del ashift_msa, ascale_msa
+            attn1_out = self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
+            del norm_ax
+            # audio cross-attention
+            agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]
+            ax.addcmul_(attn1_out, agate_msa)
+            del agate_msa, attn1_out
+            ax.add_(self.audio_attn2(comfy.ldm.common_dit.rms_norm(ax), context=a_context, mask=attention_mask, transformer_options=transformer_options))
 
-            del ashift_msa, ascale_msa, agate_msa
-
-        # Audio - Video cross attention.
+        # video - audio cross attention.
         if run_a2v or run_v2a:
-            # norm3
             vx_norm3 = comfy.ldm.common_dit.rms_norm(vx)
             ax_norm3 = comfy.ldm.common_dit.rms_norm(ax)
 
-            (
-                scale_ca_audio_hidden_states_a2v,
-                shift_ca_audio_hidden_states_a2v,
-                scale_ca_audio_hidden_states_v2a,
-                shift_ca_audio_hidden_states_v2a,
-                gate_out_v2a,
-            ) = self.get_av_ca_ada_values(
-                self.scale_shift_table_a2v_ca_audio,
-                ax.shape[0],
-                a_cross_scale_shift_timestep,
-                a_cross_gate_timestep,
-            )
-
-            (
-                scale_ca_video_hidden_states_a2v,
-                shift_ca_video_hidden_states_a2v,
-                scale_ca_video_hidden_states_v2a,
-                shift_ca_video_hidden_states_v2a,
-                gate_out_a2v,
-            ) = self.get_av_ca_ada_values(
-                self.scale_shift_table_a2v_ca_video,
-                vx.shape[0],
-                v_cross_scale_shift_timestep,
-                v_cross_gate_timestep,
-            )
-
+            # audio to video cross attention
             if run_a2v:
-                vx_scaled = (
-                    vx_norm3 * (1 + scale_ca_video_hidden_states_a2v)
-                    + shift_ca_video_hidden_states_a2v
-                )
-                ax_scaled = (
-                    ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v)
-                    + shift_ca_audio_hidden_states_a2v
-                )
-                vx += (
-                    self.audio_to_video_attn(
-                        vx_scaled,
-                        context=ax_scaled,
-                        pe=v_cross_pe,
-                        k_pe=a_cross_pe,
-                        transformer_options=transformer_options,
-                    )
-                    * gate_out_a2v
-                )
+                scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v = self.get_ada_values(
+                    self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[:2]
+                scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v = self.get_ada_values(
+                    self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[:2]
 
-                del gate_out_a2v
-                del scale_ca_video_hidden_states_a2v,\
-                    shift_ca_video_hidden_states_a2v,\
-                    scale_ca_audio_hidden_states_a2v,\
-                    shift_ca_audio_hidden_states_a2v,\
+                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v_v) + shift_ca_video_hidden_states_a2v_v
+                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v
+                del scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v, scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v
 
+                a2v_out = self.audio_to_video_attn(vx_scaled, context=ax_scaled, pe=v_cross_pe, k_pe=a_cross_pe, transformer_options=transformer_options)
+                del vx_scaled, ax_scaled
+
+                gate_out_a2v = self.get_ada_values(self.scale_shift_table_a2v_ca_video[4:, :], vx.shape[0], v_cross_gate_timestep)[0]
+                vx.addcmul_(a2v_out, gate_out_a2v)
+                del gate_out_a2v, a2v_out
+
+            # video to audio cross attention
             if run_v2a:
-                ax_scaled = (
-                    ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a)
-                    + shift_ca_audio_hidden_states_v2a
-                )
-                vx_scaled = (
-                    vx_norm3 * (1 + scale_ca_video_hidden_states_v2a)
-                    + shift_ca_video_hidden_states_v2a
-                )
-                ax += (
-                    self.video_to_audio_attn(
-                        ax_scaled,
-                        context=vx_scaled,
-                        pe=a_cross_pe,
-                        k_pe=v_cross_pe,
-                        transformer_options=transformer_options,
-                    )
-                    * gate_out_v2a
-                )
+                scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a = self.get_ada_values(
+                    self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[2:4]
+                scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a = self.get_ada_values(
+                    self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[2:4]
 
-                del gate_out_v2a
-                del scale_ca_video_hidden_states_v2a,\
-                    shift_ca_video_hidden_states_v2a,\
-                    scale_ca_audio_hidden_states_v2a,\
-                    shift_ca_audio_hidden_states_v2a
+                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a
+                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a
+                del scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a, scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a
 
+                v2a_out = self.video_to_audio_attn(ax_scaled, context=vx_scaled, pe=a_cross_pe, k_pe=v_cross_pe, transformer_options=transformer_options)
+                del ax_scaled, vx_scaled
+
+                gate_out_v2a = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[4:, :], ax.shape[0], a_cross_gate_timestep)[0]
+                ax.addcmul_(v2a_out, gate_out_v2a)
+                del gate_out_v2a, v2a_out
+
+            del vx_norm3, ax_norm3
+
+        # video feedforward
         if run_vx:
-            vshift_mlp, vscale_mlp, vgate_mlp = (
-                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, None))
-            )
-
+            vshift_mlp, vscale_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, 5))
             vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp
-            vx += self.ff(vx_scaled) * vgate_mlp
-            del vshift_mlp, vscale_mlp, vgate_mlp
+            del vshift_mlp, vscale_mlp
 
+            ff_out = self.ff(vx_scaled)
+            del vx_scaled
+
+            vgate_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(5, 6))[0]
+            vx.addcmul_(ff_out, vgate_mlp)
+            del vgate_mlp, ff_out
+
+        # audio feedforward
         if run_ax:
-            ashift_mlp, ascale_mlp, agate_mlp = (
-                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, None))
-            )
-
+            ashift_mlp, ascale_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, 5))
             ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp
-            ax += self.audio_ff(ax_scaled) * agate_mlp
+            del ashift_mlp, ascale_mlp
 
-            del ashift_mlp, ascale_mlp, agate_mlp
+            ff_out = self.audio_ff(ax_scaled)
+            del ax_scaled
 
+            agate_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(5, 6))[0]
+            ax.addcmul_(ff_out, agate_mlp)
+            del agate_mlp, ff_out
 
         return vx, ax
 
@@ -589,9 +534,20 @@ class LTXAVModel(LTXVModel):
         audio_length = kwargs.get("audio_length", 0)
         # Separate audio and video latents
         vx, ax = self.separate_audio_and_video_latents(x, audio_length)
+
+        has_spatial_mask = False
+        if denoise_mask is not None:
+            # check if any frame has spatial variation (inpainting)
+            for frame_idx in range(denoise_mask.shape[2]):
+                frame_mask = denoise_mask[0, 0, frame_idx]
+                if frame_mask.numel() > 0 and frame_mask.min() != frame_mask.max():
+                    has_spatial_mask = True
+                    break
+
         [vx, v_pixel_coords, additional_args] = super()._process_input(
             vx, keyframe_idxs, denoise_mask, **kwargs
         )
+        additional_args["has_spatial_mask"] = has_spatial_mask
 
         ax, a_latent_coords = self.a_patchifier.patchify(ax)
         ax = self.audio_patchify_proj(ax)
@@ -618,8 +574,9 @@ class LTXAVModel(LTXVModel):
         # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width]
         # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width
         orig_shape = kwargs.get("orig_shape")
+        has_spatial_mask = kwargs.get("has_spatial_mask", None)
         v_patches_per_frame = None
-        if orig_shape is not None and len(orig_shape) == 5:
+        if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5:
             # orig_shape[3] = height, orig_shape[4] = width (in latent space)
             v_patches_per_frame = orig_shape[3] * orig_shape[4]
 
@@ -662,10 +619,11 @@ class LTXAVModel(LTXVModel):
             )
 
             # Compress cross-attention timesteps (only video side, audio is too small to benefit)
+            # v_patches_per_frame is None for spatial masks, set for temporal masks or no mask
             cross_av_timestep_ss = [
                 av_ca_audio_scale_shift_timestep.view(batch_size, -1, av_ca_audio_scale_shift_timestep.shape[-1]),
-                CompressedTimestep(av_ca_video_scale_shift_timestep.view(batch_size, -1, av_ca_video_scale_shift_timestep.shape[-1]), v_patches_per_frame),  # video - compressed
-                CompressedTimestep(av_ca_a2v_gate_noise_timestep.view(batch_size, -1, av_ca_a2v_gate_noise_timestep.shape[-1]), v_patches_per_frame),  # video - compressed
+                CompressedTimestep(av_ca_video_scale_shift_timestep.view(batch_size, -1, av_ca_video_scale_shift_timestep.shape[-1]), v_patches_per_frame),  # video - compressed if possible
+                CompressedTimestep(av_ca_a2v_gate_noise_timestep.view(batch_size, -1, av_ca_a2v_gate_noise_timestep.shape[-1]), v_patches_per_frame),  # video - compressed if possible
                 av_ca_v2a_gate_noise_timestep.view(batch_size, -1, av_ca_v2a_gate_noise_timestep.shape[-1]),
             ]
 

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -1,11 +1,11 @@
 from typing import Tuple, Union
 
+import threading
 import torch
 import torch.nn as nn
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-
 class CausalConv3d(nn.Module):
     def __init__(
         self,
@@ -42,23 +42,34 @@ class CausalConv3d(nn.Module):
             padding_mode=spatial_padding_mode,
             groups=groups,
         )
+        self.temporal_cache_state={}
 
     def forward(self, x, causal: bool = True):
-        if causal:
-            first_frame_pad = x[:, :, :1, :, :].repeat(
-                (1, 1, self.time_kernel_size - 1, 1, 1)
-            )
-            x = torch.concatenate((first_frame_pad, x), dim=2)
-        else:
-            first_frame_pad = x[:, :, :1, :, :].repeat(
-                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
-            )
-            last_frame_pad = x[:, :, -1:, :, :].repeat(
-                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
-            )
-            x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
-        x = self.conv(x)
-        return x
+        tid = threading.get_ident()
+
+        cached, is_end = self.temporal_cache_state.get(tid, (None, False))
+        if cached is None:
+            padding_length = self.time_kernel_size - 1
+            if not causal:
+                padding_length = padding_length // 2
+            if x.shape[2] == 0:
+                return x
+            cached = x[:, :, :1, :, :].repeat((1, 1, padding_length, 1, 1))
+        pieces = [ cached, x ]
+        if is_end and not causal:
+            pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))
+
+        needs_caching = not is_end
+        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
+            needs_caching = False
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+
+        x = torch.cat(pieces, dim=2)
+
+        if needs_caching:
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+
+        return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]
 
     @property
     def weight(self):

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+import threading
 import torch
 from torch import nn
 from functools import partial
@@ -6,12 +7,35 @@ import math
 from einops import rearrange
 from typing import List, Optional, Tuple, Union
 from .conv_nd_factory import make_conv_nd, make_linear_nd
+from .causal_conv3d import CausalConv3d
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
+from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
 
+def mark_conv3d_ended(module):
+    tid = threading.get_ident()
+    for _, m in module.named_modules():
+        if isinstance(m, CausalConv3d):
+            current = m.temporal_cache_state.get(tid, (None, False))
+            m.temporal_cache_state[tid] = (current[0], True)
+
+def split2(tensor, split_point, dim=2):
+    return torch.split(tensor, [split_point, tensor.shape[dim] - split_point], dim=dim)
+
+def add_exchange_cache(dest, cache_in, new_input, dim=2):
+    if dest is not None:
+        if cache_in is not None:
+            cache_to_dest = min(dest.shape[dim], cache_in.shape[dim])
+            lead_in_dest, dest = split2(dest, cache_to_dest, dim=dim)
+            lead_in_source, cache_in = split2(cache_in, cache_to_dest, dim=dim)
+            lead_in_dest.add_(lead_in_source)
+        body, new_input = split2(new_input, dest.shape[dim], dim)
+        dest.add_(body)
+    return torch_cat_if_needed([cache_in, new_input], dim=dim)
+
 class Encoder(nn.Module):
     r"""
     The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
@@ -205,7 +229,7 @@ class Encoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+    def forward_orig(self, sample: torch.FloatTensor) -> torch.FloatTensor:
         r"""The forward method of the `Encoder` class."""
 
         sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
@@ -254,6 +278,22 @@ class Encoder(nn.Module):
 
         return sample
 
+    def forward(self, *args, **kwargs):
+        #No encoder support so just flag the end so it doesnt use the cache.
+        mark_conv3d_ended(self)
+        try:
+            return self.forward_orig(*args, **kwargs)
+        finally:
+            tid = threading.get_ident()
+            for _, module in self.named_modules():
+                # ComfyUI doesn't thread this kind of stuff today, but just in case
+                # we key on the thread to make it thread safe.
+                tid = threading.get_ident()
+                if hasattr(module, "temporal_cache_state"):
+                    module.temporal_cache_state.pop(tid, None)
+
+
+MAX_CHUNK_SIZE=(128 * 1024 ** 2)
 
 class Decoder(nn.Module):
     r"""
@@ -341,18 +381,6 @@ class Decoder(nn.Module):
                     timestep_conditioning=timestep_conditioning,
                     spatial_padding_mode=spatial_padding_mode,
                 )
-            elif block_name == "attn_res_x":
-                block = UNetMidBlock3D(
-                    dims=dims,
-                    in_channels=input_channel,
-                    num_layers=block_params["num_layers"],
-                    resnet_groups=norm_num_groups,
-                    norm_layer=norm_layer,
-                    inject_noise=block_params.get("inject_noise", False),
-                    timestep_conditioning=timestep_conditioning,
-                    attention_head_dim=block_params["attention_head_dim"],
-                    spatial_padding_mode=spatial_padding_mode,
-                )
             elif block_name == "res_x_y":
                 output_channel = output_channel // block_params.get("multiplier", 2)
                 block = ResnetBlock3D(
@@ -428,8 +456,9 @@ class Decoder(nn.Module):
             )
             self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))
 
+
     # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
-    def forward(
+    def forward_orig(
         self,
         sample: torch.FloatTensor,
         timestep: Optional[torch.Tensor] = None,
@@ -437,6 +466,7 @@ class Decoder(nn.Module):
         r"""The forward method of the `Decoder` class."""
         batch_size = sample.shape[0]
 
+        mark_conv3d_ended(self.conv_in)
         sample = self.conv_in(sample, causal=self.causal)
 
         checkpoint_fn = (
@@ -445,24 +475,12 @@ class Decoder(nn.Module):
             else lambda x: x
         )
 
-        scaled_timestep = None
+        timestep_shift_scale = None
         if self.timestep_conditioning:
             assert (
                 timestep is not None
             ), "should pass timestep with timestep_conditioning=True"
             scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
-
-        for up_block in self.up_blocks:
-            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
-                sample = checkpoint_fn(up_block)(
-                    sample, causal=self.causal, timestep=scaled_timestep
-                )
-            else:
-                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
-
-        sample = self.conv_norm_out(sample)
-
-        if self.timestep_conditioning:
             embedded_timestep = self.last_time_embedder(
                 timestep=scaled_timestep.flatten(),
                 resolution=None,
@@ -483,16 +501,62 @@ class Decoder(nn.Module):
                 embedded_timestep.shape[-2],
                 embedded_timestep.shape[-1],
             )
-            shift, scale = ada_values.unbind(dim=1)
-            sample = sample * (1 + scale) + shift
+            timestep_shift_scale = ada_values.unbind(dim=1)
 
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample, causal=self.causal)
+        output = []
+
+        def run_up(idx, sample, ended):
+            if idx >= len(self.up_blocks):
+                sample = self.conv_norm_out(sample)
+                if timestep_shift_scale is not None:
+                    shift, scale = timestep_shift_scale
+                    sample = sample * (1 + scale) + shift
+                sample = self.conv_act(sample)
+                if ended:
+                    mark_conv3d_ended(self.conv_out)
+                sample = self.conv_out(sample, causal=self.causal)
+                if sample is not None and sample.shape[2] > 0:
+                    output.append(sample)
+                return
+
+            up_block = self.up_blocks[idx]
+            if (ended):
+                mark_conv3d_ended(up_block)
+            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
+                sample = checkpoint_fn(up_block)(
+                    sample, causal=self.causal, timestep=scaled_timestep
+                )
+            else:
+                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+            if sample is None or sample.shape[2] == 0:
+                return
+
+            total_bytes = sample.numel() * sample.element_size()
+            num_chunks = (total_bytes + MAX_CHUNK_SIZE - 1) // MAX_CHUNK_SIZE
+            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
+
+            for chunk_idx, sample1 in enumerate(samples):
+                run_up(idx + 1, sample1, ended and chunk_idx == len(samples) - 1)
+
+        run_up(0, sample, True)
+        sample = torch.cat(output, dim=2)
 
         sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
 
         return sample
 
+    def forward(self, *args, **kwargs):
+        try:
+            return self.forward_orig(*args, **kwargs)
+        finally:
+            for _, module in self.named_modules():
+                #ComfyUI doesn't thread this kind of stuff today, but just incase
+                #we key on the thread to make it thread safe.
+                tid = threading.get_ident()
+                if hasattr(module, "temporal_cache_state"):
+                    module.temporal_cache_state.pop(tid, None)
+
 
 class UNetMidBlock3D(nn.Module):
     """
@@ -663,8 +727,22 @@ class DepthToSpaceUpsample(nn.Module):
         )
         self.residual = residual
         self.out_channels_reduction_factor = out_channels_reduction_factor
+        self.temporal_cache_state = {}
 
     def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None):
+        tid = threading.get_ident()
+        cached, drop_first_conv, drop_first_res = self.temporal_cache_state.get(tid, (None, True, True))
+        y = self.conv(x, causal=causal)
+        y = rearrange(
+            y,
+            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+        if self.stride[0] == 2 and y.shape[2] > 0 and drop_first_conv:
+            y = y[:, :, 1:, :, :]
+            drop_first_conv = False
         if self.residual:
             # Reshape and duplicate the input to match the output shape
             x_in = rearrange(
@@ -676,21 +754,20 @@ class DepthToSpaceUpsample(nn.Module):
             )
             num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor
             x_in = x_in.repeat(1, num_repeat, 1, 1, 1)
-            if self.stride[0] == 2:
+            if self.stride[0] == 2 and x_in.shape[2] > 0 and drop_first_res:
                 x_in = x_in[:, :, 1:, :, :]
-        x = self.conv(x, causal=causal)
-        x = rearrange(
-            x,
-            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
-            p1=self.stride[0],
-            p2=self.stride[1],
-            p3=self.stride[2],
-        )
-        if self.stride[0] == 2:
-            x = x[:, :, 1:, :, :]
-        if self.residual:
-            x = x + x_in
-        return x
+                drop_first_res = False
+
+            if y.shape[2] == 0:
+                y = None
+
+            cached = add_exchange_cache(y, cached, x_in, dim=2)
+            self.temporal_cache_state[tid] = (cached, drop_first_conv, drop_first_res)
+
+        else:
+            self.temporal_cache_state[tid] = (None, drop_first_conv, False)
+
+        return y
 
 class LayerNorm(nn.Module):
     def __init__(self, dim, eps, elementwise_affine=True) -> None:
@@ -807,6 +884,8 @@ class ResnetBlock3D(nn.Module):
                 torch.randn(4, in_channels) / in_channels**0.5
             )
 
+        self.temporal_cache_state={}
+
     def _feed_spatial_noise(
         self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor
     ) -> torch.FloatTensor:
@@ -880,9 +959,12 @@ class ResnetBlock3D(nn.Module):
 
         input_tensor = self.conv_shortcut(input_tensor)
 
-        output_tensor = input_tensor + hidden_states
+        tid = threading.get_ident()
+        cached = self.temporal_cache_state.get(tid, None)
+        cached = add_exchange_cache(hidden_states, cached, input_tensor, dim=2)
+        self.temporal_cache_state[tid] = cached
 
-        return output_tensor
+        return hidden_states
 
 
 def patchify(x, patch_size_hw, patch_size_t=1):

comfy/ldm/modules/diffusionmodules/model.py

@@ -14,10 +14,13 @@ if model_management.xformers_enabled_vae():
     import xformers.ops
 
 def torch_cat_if_needed(xl, dim):
+    xl = [x for x in xl if x is not None and x.shape[dim] > 0]
     if len(xl) > 1:
         return torch.cat(xl, dim)
-    else:
+    elif len(xl) == 1:
         return xl[0]
+    else:
+        return None
 
 def get_timestep_embedding(timesteps, embedding_dim):
     """

comfy/ldm/qwen_image/model.py

@@ -170,8 +170,14 @@ class Attention(nn.Module):
         joint_query = apply_rope1(joint_query, image_rotary_emb)
         joint_key = apply_rope1(joint_key, image_rotary_emb)
 
+        if encoder_hidden_states_mask is not None:
+            attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)
+            attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask
+        else:
+            attn_mask = None
+
         joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
-                                                         attention_mask, transformer_options=transformer_options,
+                                                         attn_mask, transformer_options=transformer_options,
                                                          skip_reshape=True)
 
         txt_attn_output = joint_hidden_states[:, :seq_txt, :]
@@ -430,6 +436,9 @@ class QwenImageTransformer2DModel(nn.Module):
         encoder_hidden_states = context
         encoder_hidden_states_mask = attention_mask
 
+        if encoder_hidden_states_mask is not None and not torch.is_floating_point(encoder_hidden_states_mask):
+            encoder_hidden_states_mask = (encoder_hidden_states_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max
+
         hidden_states, img_ids, orig_shape = self.process_img(x)
         num_embeds = hidden_states.shape[1]
 

comfy/ldm/wan/vae.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
-from comfy.ldm.modules.diffusionmodules.model import vae_attention
+from comfy.ldm.modules.diffusionmodules.model import vae_attention, torch_cat_if_needed
 
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -20,22 +20,29 @@ class CausalConv3d(ops.Conv3d):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._padding = (self.padding[2], self.padding[2], self.padding[1],
-                         self.padding[1], 2 * self.padding[0], 0)
-        self.padding = (0, 0, 0)
+        self._padding = 2 * self.padding[0]
+        self.padding = (0, self.padding[1], self.padding[2])
 
     def forward(self, x, cache_x=None, cache_list=None, cache_idx=None):
         if cache_list is not None:
             cache_x = cache_list[cache_idx]
             cache_list[cache_idx] = None
 
-        padding = list(self._padding)
-        if cache_x is not None and self._padding[4] > 0:
-            cache_x = cache_x.to(x.device)
-            x = torch.cat([cache_x, x], dim=2)
-            padding[4] -= cache_x.shape[2]
+        if cache_x is None and x.shape[2] == 1:
+            #Fast path - the op will pad for use by truncating the weight
+            #and save math on a pile of zeros.
+            return super().forward(x, autopad="causal_zero")
+
+        if self._padding > 0:
+            padding_needed = self._padding
+            if cache_x is not None:
+                cache_x = cache_x.to(x.device)
+                padding_needed = max(0, padding_needed - cache_x.shape[2])
+            padding_shape = list(x.shape)
+            padding_shape[2] = padding_needed
+            padding = torch.zeros(padding_shape, device=x.device, dtype=x.dtype)
+            x = torch_cat_if_needed([padding, cache_x, x], dim=2)
             del cache_x
-        x = F.pad(x, padding)
 
         return super().forward(x)
 

comfy/lora.py

@@ -260,6 +260,7 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
                 key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
+                key_map[k[:-len(".weight")]] = to #DiffSynth lora format
         for k in sdk:
             hidden_size = model.model_config.unet_config.get("hidden_size", 0)
             if k.endswith(".weight") and ".linear1." in k:

comfy/model_base.py

@@ -1578,6 +1578,9 @@ class QwenImage(BaseModel):
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)

comfy/ops.py

@@ -203,7 +203,9 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def _conv_forward(self, input, weight, bias, *args, **kwargs):
+        def _conv_forward(self, input, weight, bias, autopad=None, *args, **kwargs):
+            if autopad == "causal_zero":
+                weight = weight[:, :, -input.shape[2]:, :, :]
             if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
                 out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
                 if bias is not None:
@@ -212,15 +214,15 @@ class disable_weight_init:
             else:
                 return super()._conv_forward(input, weight, bias, *args, **kwargs)
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_comfy_cast_weights(self, input, autopad=None):
             weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-            x = self._conv_forward(input, weight, bias)
+            x = self._conv_forward(input, weight, bias, autopad=autopad)
             uncast_bias_weight(self, weight, bias, offload_stream)
             return x
 
         def forward(self, *args, **kwargs):
             run_every_op()
-            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0 or "autopad" in kwargs:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)

comfy/sample.py

@@ -37,12 +37,18 @@ def prepare_noise(latent_image, seed, noise_inds=None):
 
     return noises
 
-def fix_empty_latent_channels(model, latent_image):
+def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):
     if latent_image.is_nested:
         return latent_image
     latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
-    if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
-        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
+    if torch.count_nonzero(latent_image) == 0:
+        if latent_format.latent_channels != latent_image.shape[1]:
+            latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
+        if downscale_ratio_spacial is not None:
+            if downscale_ratio_spacial != latent_format.spacial_downscale_ratio:
+                ratio = downscale_ratio_spacial / latent_format.spacial_downscale_ratio
+                latent_image = comfy.utils.common_upscale(latent_image, round(latent_image.shape[-1] * ratio), round(latent_image.shape[-2] * ratio), "nearest-exact", crop="disabled")
+
     if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
         latent_image = latent_image.unsqueeze(2)
     return latent_image

comfy/supported_models.py

@@ -771,10 +771,24 @@ class Flux2(Flux):
         return out
 
     def clip_target(self, state_dict={}):
-        return None # TODO
         pref = self.text_encoder_key_prefix[0]
-        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
+        if len(detect) > 0:
+            detect["model_type"] = "qwen3_4b"
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer, comfy.text_encoders.flux.klein_te(**detect))
+
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_8b.transformer.".format(pref))
+        if len(detect) > 0:
+            detect["model_type"] = "qwen3_8b"
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer8B, comfy.text_encoders.flux.klein_te(**detect))
+
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}mistral3_24b.transformer.".format(pref))
+        if len(detect) > 0:
+            if "{}mistral3_24b.transformer.model.layers.39.post_attention_layernorm.weight".format(pref) not in state_dict:
+                detect["pruned"] = True
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.Flux2Tokenizer, comfy.text_encoders.flux.flux2_te(**detect))
+
+        return None
 
 class GenmoMochi(supported_models_base.BASE):
     unet_config = {

comfy/text_encoders/hunyuan_video.py

@@ -10,9 +10,11 @@ import comfy.utils
 
 def llama_detect(state_dict, prefix=""):
     out = {}
-    t5_key = "{}model.norm.weight".format(prefix)
-    if t5_key in state_dict:
-        out["dtype_llama"] = state_dict[t5_key].dtype
+    norm_keys = ["{}model.norm.weight".format(prefix), "{}model.layers.0.input_layernorm.weight".format(prefix)]
+    for norm_key in norm_keys:
+        if norm_key in state_dict:
+            out["dtype_llama"] = state_dict[norm_key].dtype
+            break
 
     quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
     if quant is not None:

comfy_extras/nodes_align_your_steps.py

@@ -28,6 +28,7 @@ class AlignYourStepsScheduler(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="AlignYourStepsScheduler",
+            search_aliases=["AYS scheduler"],
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),

comfy_extras/nodes_attention_multiply.py

@@ -71,6 +71,7 @@ class CLIPAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CLIPAttentionMultiply",
+            search_aliases=["clip attention scale", "text encoder attention"],
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),

comfy_extras/nodes_audio.py

@@ -69,6 +69,7 @@ class VAEEncodeAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VAEEncodeAudio",
+            search_aliases=["audio to latent"],
             display_name="VAE Encode Audio",
             category="latent/audio",
             inputs=[
@@ -97,6 +98,7 @@ class VAEDecodeAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VAEDecodeAudio",
+            search_aliases=["latent to audio"],
             display_name="VAE Decode Audio",
             category="latent/audio",
             inputs=[
@@ -122,6 +124,7 @@ class SaveAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="SaveAudio",
+            search_aliases=["export flac"],
             display_name="Save Audio (FLAC)",
             category="audio",
             inputs=[
@@ -146,6 +149,7 @@ class SaveAudioMP3(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="SaveAudioMP3",
+            search_aliases=["export mp3"],
             display_name="Save Audio (MP3)",
             category="audio",
             inputs=[
@@ -173,6 +177,7 @@ class SaveAudioOpus(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="SaveAudioOpus",
+            search_aliases=["export opus"],
             display_name="Save Audio (Opus)",
             category="audio",
             inputs=[
@@ -200,6 +205,7 @@ class PreviewAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="PreviewAudio",
+            search_aliases=["play audio"],
             display_name="Preview Audio",
             category="audio",
             inputs=[
@@ -259,6 +265,7 @@ class LoadAudio(IO.ComfyNode):
         files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
         return IO.Schema(
             node_id="LoadAudio",
+            search_aliases=["import audio", "open audio", "audio file"],
             display_name="Load Audio",
             category="audio",
             inputs=[
@@ -296,6 +303,7 @@ class RecordAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="RecordAudio",
+            search_aliases=["microphone input", "audio capture", "voice input"],
             display_name="Record Audio",
             category="audio",
             inputs=[
@@ -320,6 +328,7 @@ class TrimAudioDuration(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TrimAudioDuration",
+            search_aliases=["cut audio", "audio clip", "shorten audio"],
             display_name="Trim Audio Duration",
             description="Trim audio tensor into chosen time range.",
             category="audio",
@@ -372,6 +381,7 @@ class SplitAudioChannels(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="SplitAudioChannels",
+            search_aliases=["stereo to mono"],
             display_name="Split Audio Channels",
             description="Separates the audio into left and right channels.",
             category="audio",
@@ -472,6 +482,7 @@ class AudioConcat(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="AudioConcat",
+            search_aliases=["join audio", "combine audio", "append audio"],
             display_name="Audio Concat",
             description="Concatenates the audio1 to audio2 in the specified direction.",
             category="audio",
@@ -519,6 +530,7 @@ class AudioMerge(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="AudioMerge",
+            search_aliases=["mix audio", "overlay audio", "layer audio"],
             display_name="Audio Merge",
             description="Combine two audio tracks by overlaying their waveforms.",
             category="audio",
@@ -579,6 +591,7 @@ class AudioAdjustVolume(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="AudioAdjustVolume",
+            search_aliases=["audio gain", "loudness", "audio level"],
             display_name="Audio Adjust Volume",
             category="audio",
             inputs=[
@@ -614,6 +627,7 @@ class EmptyAudio(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="EmptyAudio",
+            search_aliases=["blank audio"],
             display_name="Empty Audio",
             category="audio",
             inputs=[

comfy_extras/nodes_canny.py

@@ -10,6 +10,7 @@ class Canny(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Canny",
+            search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
             inputs=[
                 io.Image.Input("image"),

comfy_extras/nodes_compositing.py

@@ -109,6 +109,7 @@ class PorterDuffImageComposite(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PorterDuffImageComposite",
+            search_aliases=["alpha composite", "blend modes", "layer blend", "transparency blend"],
             display_name="Porter-Duff Image Composite",
             category="mask/compositing",
             inputs=[
@@ -165,6 +166,7 @@ class SplitImageWithAlpha(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SplitImageWithAlpha",
+            search_aliases=["extract alpha", "separate transparency", "remove alpha"],
             display_name="Split Image with Alpha",
             category="mask/compositing",
             inputs=[
@@ -188,6 +190,7 @@ class JoinImageWithAlpha(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="JoinImageWithAlpha",
+            search_aliases=["add transparency", "apply alpha", "composite alpha", "RGBA"],
             display_name="Join Image with Alpha",
             category="mask/compositing",
             inputs=[

comfy_extras/nodes_controlnet.py

@@ -38,6 +38,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ControlNetInpaintingAliMamaApply",
+            search_aliases=["masked controlnet"],
             category="conditioning/controlnet",
             inputs=[
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_custom_sampler.py

@@ -297,6 +297,7 @@ class ExtendIntermediateSigmas(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ExtendIntermediateSigmas",
+            search_aliases=["interpolate sigmas"],
             category="sampling/custom_sampling/sigmas",
             inputs=[
                 io.Sigmas.Input("sigmas"),
@@ -740,7 +741,7 @@ class SamplerCustom(io.ComfyNode):
         latent = latent_image
         latent_image = latent["samples"]
         latent = latent.copy()
-        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
         latent["samples"] = latent_image
 
         if not add_noise:
@@ -759,6 +760,7 @@ class SamplerCustom(io.ComfyNode):
         samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
 
         out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
         out["samples"] = samples
         if "x0" in x0_output:
             x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
@@ -856,6 +858,7 @@ class DualCFGGuider(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="DualCFGGuider",
+            search_aliases=["dual prompt guidance"],
             category="sampling/custom_sampling/guiders",
             inputs=[
                 io.Model.Input("model"),
@@ -883,6 +886,7 @@ class DisableNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="DisableNoise",
+            search_aliases=["zero noise"],
             category="sampling/custom_sampling/noise",
             inputs=[],
             outputs=[io.Noise.Output()]
@@ -936,7 +940,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
         latent = latent_image
         latent_image = latent["samples"]
         latent = latent.copy()
-        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None))
         latent["samples"] = latent_image
 
         noise_mask = None
@@ -951,6 +955,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
         samples = samples.to(comfy.model_management.intermediate_device())
 
         out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
         out["samples"] = samples
         if "x0" in x0_output:
             x0_out = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
@@ -1019,6 +1024,7 @@ class ManualSigmas(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ManualSigmas",
+            search_aliases=["custom noise schedule", "define sigmas"],
             category="_for_testing/custom_sampling",
             is_experimental=True,
             inputs=[

comfy_extras/nodes_dataset.py

@@ -1223,11 +1223,11 @@ class ResolutionBucket(io.ComfyNode):
 
 class MakeTrainingDataset(io.ComfyNode):
     """Encode images with VAE and texts with CLIP to create a training dataset."""
-
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id="MakeTrainingDataset",
+            search_aliases=["encode dataset"],
             display_name="Make Training Dataset",
             category="dataset",
             is_experimental=True,
@@ -1309,11 +1309,11 @@ class MakeTrainingDataset(io.ComfyNode):
 
 class SaveTrainingDataset(io.ComfyNode):
     """Save encoded training dataset (latents + conditioning) to disk."""
-
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id="SaveTrainingDataset",
+            search_aliases=["export training data"],
             display_name="Save Training Dataset",
             category="dataset",
             is_experimental=True,
@@ -1410,11 +1410,11 @@ class SaveTrainingDataset(io.ComfyNode):
 
 class LoadTrainingDataset(io.ComfyNode):
     """Load encoded training dataset from disk."""
-
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id="LoadTrainingDataset",
+            search_aliases=["import dataset", "training data"],
             display_name="Load Training Dataset",
             category="dataset",
             is_experimental=True,

comfy_extras/nodes_differential_diffusion.py

@@ -11,6 +11,7 @@ class DifferentialDiffusion(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="DifferentialDiffusion",
+            search_aliases=["inpaint gradient", "variable denoise strength"],
             display_name="Differential Diffusion",
             category="_for_testing",
             inputs=[

comfy_extras/nodes_fresca.py

@@ -58,6 +58,7 @@ class FreSca(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FreSca",
+            search_aliases=["frequency guidance"],
             display_name="FreSca",
             category="_for_testing",
             description="Applies frequency-dependent scaling to the guidance",

comfy_extras/nodes_hidream.py

@@ -38,6 +38,7 @@ class CLIPTextEncodeHiDream(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeHiDream",
+            search_aliases=["hidream prompt"],
             category="advanced/conditioning",
             inputs=[
                 io.Clip.Input("clip"),

comfy_extras/nodes_hooks.py

@@ -259,6 +259,7 @@ class SetClipHooks:
         return (clip,)
 
 class ConditioningTimestepsRange:
+    SEARCH_ALIASES = ["prompt scheduling", "timestep segments", "conditioning phases"]
     NodeId = 'ConditioningTimestepsRange'
     NodeName = 'Timesteps Range'
     @classmethod
@@ -468,6 +469,7 @@ class SetHookKeyframes:
         return (hooks,)
 
 class CreateHookKeyframe:
+    SEARCH_ALIASES = ["hook scheduling", "strength animation", "timed hook"]
     NodeId = 'CreateHookKeyframe'
     NodeName = 'Create Hook Keyframe'
     @classmethod
@@ -497,6 +499,7 @@ class CreateHookKeyframe:
         return (prev_hook_kf,)
 
 class CreateHookKeyframesInterpolated:
+    SEARCH_ALIASES = ["ease hook strength", "smooth hook transition", "interpolate keyframes"]
     NodeId = 'CreateHookKeyframesInterpolated'
     NodeName = 'Create Hook Keyframes Interp.'
     @classmethod
@@ -544,6 +547,7 @@ class CreateHookKeyframesInterpolated:
         return (prev_hook_kf,)
 
 class CreateHookKeyframesFromFloats:
+    SEARCH_ALIASES = ["batch keyframes", "strength list to keyframes"]
     NodeId = 'CreateHookKeyframesFromFloats'
     NodeName = 'Create Hook Keyframes From Floats'
     @classmethod
@@ -618,6 +622,7 @@ class SetModelHooksOnCond:
 # Combine Hooks
 #------------------------------------------
 class CombineHooks:
+    SEARCH_ALIASES = ["merge hooks"]
     NodeId = 'CombineHooks2'
     NodeName = 'Combine Hooks [2]'
     @classmethod

comfy_extras/nodes_hunyuan3d.py

@@ -618,6 +618,7 @@ class SaveGLB(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="SaveGLB",
+            search_aliases=["export 3d model", "save mesh"],
             category="3d",
             is_output_node=True,
             inputs=[

comfy_extras/nodes_images.py

@@ -22,6 +22,7 @@ class ImageCrop(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCrop",
+            search_aliases=["trim"],
             display_name="Image Crop",
             category="image/transform",
             inputs=[
@@ -51,6 +52,7 @@ class RepeatImageBatch(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="RepeatImageBatch",
+            search_aliases=["duplicate image", "clone image"],
             category="image/batch",
             inputs=[
                 IO.Image.Input("image"),
@@ -72,6 +74,7 @@ class ImageFromBatch(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageFromBatch",
+            search_aliases=["select image", "pick from batch", "extract image"],
             category="image/batch",
             inputs=[
                 IO.Image.Input("image"),
@@ -97,6 +100,7 @@ class ImageAddNoise(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageAddNoise",
+            search_aliases=["film grain"],
             category="image",
             inputs=[
                 IO.Image.Input("image"),
@@ -194,11 +198,11 @@ class SaveAnimatedPNG(IO.ComfyNode):
 
 class ImageStitch(IO.ComfyNode):
     """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageStitch",
+            search_aliases=["combine images", "join images", "concatenate images", "side by side"],
             display_name="Image Stitch",
             description="Stitches image2 to image1 in the specified direction.\n"
             "If image2 is not provided, returns image1 unchanged.\n"
@@ -369,11 +373,11 @@ class ImageStitch(IO.ComfyNode):
 
 
 class ResizeAndPadImage(IO.ComfyNode):
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="ResizeAndPadImage",
+            search_aliases=["fit to size"],
             category="image/transform",
             inputs=[
                 IO.Image.Input("image"),
@@ -420,11 +424,11 @@ class ResizeAndPadImage(IO.ComfyNode):
 
 
 class SaveSVGNode(IO.ComfyNode):
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="SaveSVGNode",
+            search_aliases=["export vector", "save vector graphics"],
             description="Save SVG files on disk.",
             category="image/save",
             inputs=[
@@ -492,11 +496,11 @@ class SaveSVGNode(IO.ComfyNode):
 
 
 class GetImageSize(IO.ComfyNode):
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="GetImageSize",
+            search_aliases=["dimensions", "resolution", "image info"],
             display_name="Get Image Size",
             description="Returns width and height of the image, and passes it through unchanged.",
             category="image",
@@ -527,11 +531,11 @@ class GetImageSize(IO.ComfyNode):
 
 
 class ImageRotate(IO.ComfyNode):
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageRotate",
+            search_aliases=["turn", "flip orientation"],
             category="image/transform",
             inputs=[
                 IO.Image.Input("image"),
@@ -557,11 +561,11 @@ class ImageRotate(IO.ComfyNode):
 
 
 class ImageFlip(IO.ComfyNode):
-
     @classmethod
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageFlip",
+            search_aliases=["mirror", "reflect"],
             category="image/transform",
             inputs=[
                 IO.Image.Input("image"),

comfy_extras/nodes_kandinsky5.py

@@ -104,6 +104,7 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeKandinsky5",
+            search_aliases=["kandinsky prompt"],
             category="advanced/conditioning/kandinsky5",
             inputs=[
                 io.Clip.Input("clip"),

comfy_extras/nodes_latent.py

@@ -21,6 +21,7 @@ class LatentAdd(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentAdd",
+            search_aliases=["combine latents", "sum latents"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples1"),
@@ -47,6 +48,7 @@ class LatentSubtract(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentSubtract",
+            search_aliases=["difference latent", "remove features"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples1"),
@@ -73,6 +75,7 @@ class LatentMultiply(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentMultiply",
+            search_aliases=["scale latent", "amplify latent", "latent gain"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples"),
@@ -96,6 +99,7 @@ class LatentInterpolate(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentInterpolate",
+            search_aliases=["blend latent", "mix latent", "lerp latent", "transition"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples1"),
@@ -134,6 +138,7 @@ class LatentConcat(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentConcat",
+            search_aliases=["join latents", "stitch latents"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples1"),
@@ -173,6 +178,7 @@ class LatentCut(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentCut",
+            search_aliases=["crop latent", "slice latent", "extract region"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples"),
@@ -213,6 +219,7 @@ class LatentCutToBatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentCutToBatch",
+            search_aliases=["slice to batch", "split latent", "tile latent"],
             category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples"),
@@ -254,6 +261,7 @@ class LatentBatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentBatch",
+            search_aliases=["combine latents", "merge latents", "join latents"],
             category="latent/batch",
             is_deprecated=True,
             inputs=[
@@ -310,6 +318,7 @@ class LatentApplyOperation(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentApplyOperation",
+            search_aliases=["transform latent"],
             category="latent/advanced/operations",
             is_experimental=True,
             inputs=[
@@ -365,6 +374,7 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LatentOperationTonemapReinhard",
+            search_aliases=["hdr latent"],
             category="latent/advanced/operations",
             is_experimental=True,
             inputs=[

comfy_extras/nodes_load_3d.py

@@ -24,7 +24,7 @@ class Load3D(IO.ComfyNode):
         files = [
             normalize_path(str(file_path.relative_to(base_path)))
             for file_path in input_path.rglob("*")
-            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
+            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl', '.spz', '.splat', '.ply', '.ksplat'}
         ]
         return IO.Schema(
             node_id="Load3D",
@@ -75,6 +75,7 @@ class Preview3D(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Preview3D",
+            search_aliases=["view mesh", "3d viewer"],
             display_name="Preview 3D & Animation",
             category="3d",
             is_experimental=True,

comfy_extras/nodes_logic.py

@@ -224,6 +224,7 @@ class ConvertStringToComboNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ConvertStringToComboNode",
+            search_aliases=["string to dropdown", "text to combo"],
             display_name="Convert String to Combo",
             category="logic",
             inputs=[io.String.Input("string")],
@@ -239,6 +240,7 @@ class InvertBooleanNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="InvertBooleanNode",
+            search_aliases=["not", "toggle", "negate", "flip boolean"],
             display_name="Invert Boolean",
             category="logic",
             inputs=[io.Boolean.Input("boolean")],

comfy_extras/nodes_lora_extract.py

@@ -78,6 +78,7 @@ class LoraSave(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoraSave",
+            search_aliases=["export lora"],
             display_name="Extract and Save Lora",
             category="_for_testing",
             inputs=[

comfy_extras/nodes_lumina2.py

@@ -79,6 +79,7 @@ class CLIPTextEncodeLumina2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeLumina2",
+            search_aliases=["lumina prompt"],
             display_name="CLIP Text Encode for Lumina2",
             category="conditioning",
             description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "

comfy_extras/nodes_mask.py

@@ -50,6 +50,7 @@ class LatentCompositeMasked(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="LatentCompositeMasked",
+            search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
             category="latent",
             inputs=[
                 IO.Latent.Input("destination"),
@@ -78,6 +79,7 @@ class ImageCompositeMasked(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageCompositeMasked",
+            search_aliases=["paste image", "overlay", "layer"],
             category="image",
             inputs=[
                 IO.Image.Input("destination"),
@@ -105,6 +107,7 @@ class MaskToImage(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="MaskToImage",
+            search_aliases=["convert mask"],
             display_name="Convert Mask to Image",
             category="mask",
             inputs=[
@@ -126,6 +129,7 @@ class ImageToMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageToMask",
+            search_aliases=["extract channel", "channel to mask"],
             display_name="Convert Image to Mask",
             category="mask",
             inputs=[
@@ -149,6 +153,7 @@ class ImageColorToMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ImageColorToMask",
+            search_aliases=["color keying", "chroma key"],
             category="mask",
             inputs=[
                 IO.Image.Input("image"),
@@ -194,6 +199,7 @@ class InvertMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="InvertMask",
+            search_aliases=["reverse mask", "flip mask"],
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -214,6 +220,7 @@ class CropMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="CropMask",
+            search_aliases=["cut mask", "extract mask region", "mask slice"],
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -239,6 +246,7 @@ class MaskComposite(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="MaskComposite",
+            search_aliases=["combine masks", "blend masks", "layer masks"],
             category="mask",
             inputs=[
                 IO.Mask.Input("destination"),
@@ -287,6 +295,7 @@ class FeatherMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="FeatherMask",
+            search_aliases=["soft edge mask", "blur mask edges", "gradient mask edge"],
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -333,6 +342,7 @@ class GrowMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="GrowMask",
+            search_aliases=["expand mask", "shrink mask"],
             display_name="Grow Mask",
             category="mask",
             inputs=[
@@ -370,6 +380,7 @@ class ThresholdMask(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ThresholdMask",
+            search_aliases=["binary mask"],
             category="mask",
             inputs=[
                 IO.Mask.Input("mask"),
@@ -394,6 +405,7 @@ class MaskPreview(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="MaskPreview",
+            search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
             display_name="Preview Mask",
             category="mask",
             description="Saves the input images to your ComfyUI output directory.",

comfy_extras/nodes_model_advanced.py

@@ -299,6 +299,7 @@ class RescaleCFG:
         return (m, )
 
 class ModelComputeDtype:
+    SEARCH_ALIASES = ["model precision", "change dtype"]
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),

comfy_extras/nodes_model_merging.py

@@ -91,6 +91,7 @@ class CLIPMergeSimple:
 
 
 class CLIPSubtract:
+    SEARCH_ALIASES = ["clip difference", "text encoder subtract"]
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip1": ("CLIP",),
@@ -113,6 +114,7 @@ class CLIPSubtract:
 
 
 class CLIPAdd:
+    SEARCH_ALIASES = ["combine clip"]
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip1": ("CLIP",),
@@ -225,6 +227,7 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
     comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
 
 class CheckpointSave:
+    SEARCH_ALIASES = ["save model", "export checkpoint", "merge save"]
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
 
@@ -337,6 +340,7 @@ class VAESave:
         return {}
 
 class ModelSave:
+    SEARCH_ALIASES = ["export model", "checkpoint save"]
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
 

comfy_extras/nodes_morphology.py

@@ -12,6 +12,7 @@ class Morphology(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Morphology",
+            search_aliases=["erode", "dilate"],
             display_name="ImageMorphology",
             category="image/postprocessing",
             inputs=[
@@ -57,6 +58,7 @@ class ImageRGBToYUV(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageRGBToYUV",
+            search_aliases=["color space conversion"],
             category="image/batch",
             inputs=[
                 io.Image.Input("image"),
@@ -78,6 +80,7 @@ class ImageYUVToRGB(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageYUVToRGB",
+            search_aliases=["color space conversion"],
             category="image/batch",
             inputs=[
                 io.Image.Input("Y"),

comfy_extras/nodes_pixart.py

@@ -7,6 +7,7 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodePixArtAlpha",
+            search_aliases=["pixart prompt"],
             category="advanced/conditioning",
             description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
             inputs=[

comfy_extras/nodes_post_processing.py

@@ -402,7 +402,6 @@ def scale_to_multiple_cover(input: torch.Tensor, multiple: int, scale_method: st
     return input[:, y0:y1, x0:x1]
 
 class ResizeImageMaskNode(io.ComfyNode):
-
     scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
 
@@ -421,46 +420,62 @@ class ResizeImageMaskNode(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         template = io.MatchType.Template("input_type", [io.Image, io.Mask])
-        crop_combo = io.Combo.Input("crop", options=cls.crop_methods, default="center")
+        crop_combo = io.Combo.Input(
+            "crop",
+            options=cls.crop_methods,
+            default="center",
+            tooltip="How to handle aspect ratio mismatch: 'disabled' stretches to fit, 'center' crops to maintain aspect ratio.",
+        )
         return io.Schema(
             node_id="ResizeImageMaskNode",
             display_name="Resize Image/Mask",
+            description="Resize an image or mask using various scaling methods.",
             category="transform",
+            search_aliases=["resize", "resize image", "resize mask", "scale", "scale image", "scale mask", "image resize", "change size", "dimensions", "shrink", "enlarge"],
             inputs=[
                 io.MatchType.Input("input", template=template),
-                io.DynamicCombo.Input("resize_type", options=[
-                    io.DynamicCombo.Option(ResizeType.SCALE_BY, [
-                        io.Float.Input("multiplier", default=1.00, min=0.01, max=8.0, step=0.01),
+                io.DynamicCombo.Input(
+                    "resize_type",
+                    tooltip="Select how to resize: by exact dimensions, scale factor, matching another image, etc.",
+                    options=[
+                        io.DynamicCombo.Option(ResizeType.SCALE_DIMENSIONS, [
+                            io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target width in pixels. Set to 0 to auto-calculate from height while preserving aspect ratio."),
+                            io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target height in pixels. Set to 0 to auto-calculate from width while preserving aspect ratio."),
+                            crop_combo,
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_DIMENSIONS, [
-                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
-                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
-                        crop_combo,
+                        io.DynamicCombo.Option(ResizeType.SCALE_BY, [
+                            io.Float.Input("multiplier", default=1.00, min=0.01, max=8.0, step=0.01, tooltip="Scale factor (e.g., 2.0 doubles size, 0.5 halves size)."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_LONGER_DIMENSION, [
-                        io.Int.Input("longer_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.DynamicCombo.Option(ResizeType.SCALE_LONGER_DIMENSION, [
+                            io.Int.Input("longer_size", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="The longer edge will be resized to this value. Aspect ratio is preserved."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_SHORTER_DIMENSION, [
-                        io.Int.Input("shorter_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.DynamicCombo.Option(ResizeType.SCALE_SHORTER_DIMENSION, [
+                            io.Int.Input("shorter_size", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="The shorter edge will be resized to this value. Aspect ratio is preserved."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_WIDTH, [
-                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.DynamicCombo.Option(ResizeType.SCALE_WIDTH, [
+                            io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target width in pixels. Height auto-adjusts to preserve aspect ratio."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_HEIGHT, [
-                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.DynamicCombo.Option(ResizeType.SCALE_HEIGHT, [
+                            io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target height in pixels. Width auto-adjusts to preserve aspect ratio."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_TOTAL_PIXELS, [
-                        io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+                        io.DynamicCombo.Option(ResizeType.SCALE_TOTAL_PIXELS, [
+                            io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01, tooltip="Target total megapixels (e.g., 1.0 ≈ 1024×1024). Aspect ratio is preserved."),
                         ]),
-                    io.DynamicCombo.Option(ResizeType.MATCH_SIZE, [
-                        io.MultiType.Input("match", [io.Image, io.Mask]),
-                        crop_combo,
+                        io.DynamicCombo.Option(ResizeType.MATCH_SIZE, [
+                            io.MultiType.Input("match", [io.Image, io.Mask], tooltip="Resize input to match the dimensions of this reference image or mask."),
+                            crop_combo,
                         ]),
-                    io.DynamicCombo.Option(ResizeType.SCALE_TO_MULTIPLE, [
-                        io.Int.Input("multiple", default=8, min=1, max=MAX_RESOLUTION, step=1),
+                        io.DynamicCombo.Option(ResizeType.SCALE_TO_MULTIPLE, [
+                            io.Int.Input("multiple", default=8, min=1, max=MAX_RESOLUTION, step=1, tooltip="Resize so width and height are divisible by this number. Useful for latent alignment (e.g., 8 or 64)."),
                         ]),
-                ]),
-                io.Combo.Input("scale_method", options=cls.scale_methods, default="area"),
+                    ],
+                ),
+                io.Combo.Input(
+                    "scale_method",
+                    options=cls.scale_methods,
+                    default="area",
+                    tooltip="Interpolation algorithm. 'area' is best for downscaling, 'lanczos' for upscaling, 'nearest-exact' for pixel art.",
+                ),
             ],
             outputs=[io.MatchType.Output(template=template, display_name="resized")]
         )
@@ -569,6 +584,7 @@ class BatchMasksNode(io.ComfyNode):
         autogrow_template = io.Autogrow.TemplatePrefix(io.Mask.Input("mask"), prefix="mask", min=2, max=50)
         return io.Schema(
             node_id="BatchMasksNode",
+            search_aliases=["combine masks", "stack masks", "merge masks"],
             display_name="Batch Masks",
             category="mask",
             inputs=[
@@ -589,6 +605,7 @@ class BatchLatentsNode(io.ComfyNode):
         autogrow_template = io.Autogrow.TemplatePrefix(io.Latent.Input("latent"), prefix="latent", min=2, max=50)
         return io.Schema(
             node_id="BatchLatentsNode",
+            search_aliases=["combine latents", "stack latents", "merge latents"],
             display_name="Batch Latents",
             category="latent",
             inputs=[
@@ -612,6 +629,7 @@ class BatchImagesMasksLatentsNode(io.ComfyNode):
                 prefix="input", min=1, max=50)
         return io.Schema(
             node_id="BatchImagesMasksLatentsNode",
+            search_aliases=["combine batch", "merge batch", "stack inputs"],
             display_name="Batch Images/Masks/Latents",
             category="util",
             inputs=[

comfy_extras/nodes_preview_any.py

@@ -16,7 +16,7 @@ class PreviewAny():
     OUTPUT_NODE = True
 
     CATEGORY = "utils"
-    SEARCH_ALIASES = ["preview", "show", "display", "view", "show text", "display text", "preview text", "show output", "inspect", "debug"]
+    SEARCH_ALIASES = ["show output", "inspect", "debug", "print value", "show text"]
 
     def main(self, source=None):
         value = 'None'

comfy_extras/nodes_sd3.py

@@ -55,7 +55,7 @@ class EmptySD3LatentImage(io.ComfyNode):
     @classmethod
     def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples":latent})
+        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
 
     generate = execute  # TODO: remove
 
@@ -65,6 +65,7 @@ class CLIPTextEncodeSD3(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CLIPTextEncodeSD3",
+            search_aliases=["sd3 prompt"],
             category="advanced/conditioning",
             inputs=[
                 io.Clip.Input("clip"),

comfy_extras/nodes_string.py

@@ -32,6 +32,7 @@ class StringSubstring(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringSubstring",
+            search_aliases=["extract text", "text portion"],
             display_name="Substring",
             category="utils/string",
             inputs=[
@@ -54,6 +55,7 @@ class StringLength(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringLength",
+            search_aliases=["character count", "text size"],
             display_name="Length",
             category="utils/string",
             inputs=[
@@ -74,6 +76,7 @@ class CaseConverter(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CaseConverter",
+            search_aliases=["text case", "uppercase", "lowercase", "capitalize"],
             display_name="Case Converter",
             category="utils/string",
             inputs=[
@@ -106,6 +109,7 @@ class StringTrim(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringTrim",
+            search_aliases=["clean whitespace", "remove whitespace"],
             display_name="Trim",
             category="utils/string",
             inputs=[
@@ -136,6 +140,7 @@ class StringReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringReplace",
+            search_aliases=["find and replace", "substitute", "swap text"],
             display_name="Replace",
             category="utils/string",
             inputs=[
@@ -158,6 +163,7 @@ class StringContains(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringContains",
+            search_aliases=["text includes", "string includes"],
             display_name="Contains",
             category="utils/string",
             inputs=[
@@ -185,6 +191,7 @@ class StringCompare(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringCompare",
+            search_aliases=["text match", "string equals", "starts with", "ends with"],
             display_name="Compare",
             category="utils/string",
             inputs=[
@@ -220,6 +227,7 @@ class RegexMatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexMatch",
+            search_aliases=["pattern match", "text contains", "string match"],
             display_name="Regex Match",
             category="utils/string",
             inputs=[
@@ -260,6 +268,7 @@ class RegexExtract(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexExtract",
+            search_aliases=["pattern extract", "text parser", "parse text"],
             display_name="Regex Extract",
             category="utils/string",
             inputs=[
@@ -334,6 +343,7 @@ class RegexReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexReplace",
+            search_aliases=["pattern replace", "find and replace", "substitution"],
             display_name="Regex Replace",
             category="utils/string",
             description="Find and replace text using regex patterns.",

comfy_extras/nodes_train.py

@@ -1101,6 +1101,7 @@ class SaveLoRA(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveLoRA",
+            search_aliases=["export lora"],
             display_name="Save LoRA Weights",
             category="loaders",
             is_experimental=True,
@@ -1144,6 +1145,7 @@ class LossGraphNode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LossGraphNode",
+            search_aliases=["training chart", "training visualization", "plot loss"],
             display_name="Plot Loss Graph",
             category="training",
             is_experimental=True,

comfy_extras/nodes_video.py

@@ -16,6 +16,7 @@ class SaveWEBM(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveWEBM",
+            search_aliases=["export webm"],
             category="image/video",
             is_experimental=True,
             inputs=[
@@ -69,6 +70,7 @@ class SaveVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SaveVideo",
+            search_aliases=["export video"],
             display_name="Save Video",
             category="image/video",
             description="Saves the input images to your ComfyUI output directory.",
@@ -116,6 +118,7 @@ class CreateVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CreateVideo",
+            search_aliases=["images to video"],
             display_name="Create Video",
             category="image/video",
             description="Create a video from images.",
@@ -140,6 +143,7 @@ class GetVideoComponents(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="GetVideoComponents",
+            search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
             category="image/video",
             description="Extracts all components from a video: frames, audio, and framerate.",
@@ -167,6 +171,7 @@ class LoadVideo(io.ComfyNode):
         files = folder_paths.filter_files_content_types(files, ["video"])
         return io.Schema(
             node_id="LoadVideo",
+            search_aliases=["import video", "open video", "video file"],
             display_name="Load Video",
             category="image/video",
             inputs=[

comfy_extras/nodes_wan.py

@@ -287,6 +287,7 @@ class WanVaceToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanVaceToVideo",
+            search_aliases=["video conditioning", "video control"],
             category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),
@@ -705,6 +706,7 @@ class WanTrackToVideo(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanTrackToVideo",
+            search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"],
             category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_wanmove.py

@@ -324,6 +324,7 @@ class GenerateTracks(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="GenerateTracks",
+            search_aliases=["motion paths", "camera movement", "trajectory"],
             category="conditioning/video_models",
             inputs=[
                 io.Int.Input("width", default=832, min=16, max=4096, step=16),

comfy_extras/nodes_webcam.py

@@ -5,6 +5,7 @@ MAX_RESOLUTION = nodes.MAX_RESOLUTION
 
 
 class WebcamCapture(nodes.LoadImage):
+    SEARCH_ALIASES = ["camera input", "live capture", "camera feed", "snapshot"]
     @classmethod
     def INPUT_TYPES(s):
         return {

nodes.py

@@ -93,6 +93,8 @@ class ConditioningCombine:
         return (conditioning_1 + conditioning_2, )
 
 class ConditioningAverage :
+    SEARCH_ALIASES = ["blend prompts", "interpolate conditioning", "mix prompts", "style fusion", "weighted blend"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
@@ -159,6 +161,8 @@ class ConditioningConcat:
         return (out, )
 
 class ConditioningSetArea:
+    SEARCH_ALIASES = ["regional prompt", "area prompt", "spatial conditioning", "localized prompt"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning": ("CONDITIONING", ),
@@ -217,6 +221,8 @@ class ConditioningSetAreaStrength:
 
 
 class ConditioningSetMask:
+    SEARCH_ALIASES = ["masked prompt", "regional inpaint conditioning", "mask conditioning"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning": ("CONDITIONING", ),
@@ -242,6 +248,8 @@ class ConditioningSetMask:
         return (c, )
 
 class ConditioningZeroOut:
+    SEARCH_ALIASES = ["null conditioning", "clear conditioning"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning": ("CONDITIONING", )}}
@@ -467,6 +475,8 @@ class InpaintModelConditioning:
 
 
 class SaveLatent:
+    SEARCH_ALIASES = ["export latent"]
+
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
 
@@ -518,6 +528,8 @@ class SaveLatent:
 
 
 class LoadLatent:
+    SEARCH_ALIASES = ["import latent", "open latent"]
+
     @classmethod
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
@@ -554,6 +566,8 @@ class LoadLatent:
 
 
 class CheckpointLoader:
+    SEARCH_ALIASES = ["load model", "model loader"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
@@ -593,6 +607,8 @@ class CheckpointLoaderSimple:
         return out[:3]
 
 class DiffusersLoader:
+    SEARCH_ALIASES = ["load diffusers model"]
+
     @classmethod
     def INPUT_TYPES(cls):
         paths = []
@@ -1063,6 +1079,8 @@ class StyleModelLoader:
 
 
 class StyleModelApply:
+    SEARCH_ALIASES = ["style transfer"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning": ("CONDITIONING", ),
@@ -1212,10 +1230,12 @@ class EmptyLatentImage:
 
     def generate(self, width, height, batch_size=1):
         latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
-        return ({"samples":latent}, )
+        return ({"samples": latent, "downscale_ratio_spacial": 8}, )
 
 
 class LatentFromBatch:
+    SEARCH_ALIASES = ["select from batch", "pick latent", "batch subset"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
@@ -1248,6 +1268,8 @@ class LatentFromBatch:
         return (s,)
 
 class RepeatLatentBatch:
+    SEARCH_ALIASES = ["duplicate latent", "clone latent"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
@@ -1274,6 +1296,8 @@ class RepeatLatentBatch:
         return (s,)
 
 class LatentUpscale:
+    SEARCH_ALIASES = ["enlarge latent", "resize latent"]
+
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
     crop_methods = ["disabled", "center"]
 
@@ -1308,6 +1332,8 @@ class LatentUpscale:
         return (s,)
 
 class LatentUpscaleBy:
+    SEARCH_ALIASES = ["enlarge latent", "resize latent", "scale latent"]
+
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
 
     @classmethod
@@ -1351,6 +1377,8 @@ class LatentRotate:
         return (s,)
 
 class LatentFlip:
+    SEARCH_ALIASES = ["mirror latent"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
@@ -1371,6 +1399,8 @@ class LatentFlip:
         return (s,)
 
 class LatentComposite:
+    SEARCH_ALIASES = ["overlay latent", "layer latent", "paste latent"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples_to": ("LATENT",),
@@ -1413,6 +1443,8 @@ class LatentComposite:
         return (samples_out,)
 
 class LatentBlend:
+    SEARCH_ALIASES = ["mix latents", "interpolate latents"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
@@ -1454,6 +1486,8 @@ class LatentBlend:
             raise ValueError(f"Unsupported blend mode: {mode}")
 
 class LatentCrop:
+    SEARCH_ALIASES = ["trim latent", "cut latent"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
@@ -1504,7 +1538,7 @@ class SetLatentNoiseMask:
 
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
     latent_image = latent["samples"]
-    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
 
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@@ -1522,6 +1556,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                   force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
     out = latent.copy()
+    out.pop("downscale_ratio_spacial", None)
     out["samples"] = samples
     return (out, )
 
@@ -1739,6 +1774,8 @@ class LoadImage:
         return True
 
 class LoadImageMask:
+    SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
+
     _color_channels = ["alpha", "red", "green", "blue"]
     @classmethod
     def INPUT_TYPES(s):
@@ -1789,6 +1826,8 @@ class LoadImageMask:
 
 
 class LoadImageOutput(LoadImage):
+    SEARCH_ALIASES = ["output image", "previous generation"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -1862,6 +1901,7 @@ class ImageScaleBy:
         return (s,)
 
 class ImageInvert:
+    SEARCH_ALIASES = ["reverse colors"]
 
     @classmethod
     def INPUT_TYPES(s):
@@ -1877,6 +1917,7 @@ class ImageInvert:
         return (s,)
 
 class ImageBatch:
+    SEARCH_ALIASES = ["combine images", "merge images", "stack images"]
 
     @classmethod
     def INPUT_TYPES(s):
@@ -1922,6 +1963,7 @@ class EmptyImage:
         return (torch.cat((r, g, b), dim=-1), )
 
 class ImagePadForOutpaint:
+    SEARCH_ALIASES = ["extend canvas", "expand image"]
 
     @classmethod
     def INPUT_TYPES(s):