Merge c03a90ecfc into ed6002cb60

used to output selected combo index

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>

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 =[
@@ -593,6 +595,7 @@ class Wan22(Wan21):
 class HunyuanImage21(LatentFormat):
     latent_channels = 64
     latent_dimensions = 2
+    spacial_downscale_ratio = 32
     scale_factor = 0.75289
 
     latent_rgb_factors = [
@@ -726,6 +729,7 @@ 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"
 
@@ -750,6 +754,7 @@ 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/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/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_api/latest/_io.py

@@ -1383,6 +1383,8 @@ class Schema:
     """Flags a node as not idempotent; when True, the node will run and not reuse the cached outputs when identical inputs are provided on a different node in the graph."""
     enable_expand: bool=False
     """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
+    accept_all_inputs: bool=False
+    """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
 
     def validate(self):
         '''Validate the schema:
@@ -1853,6 +1855,14 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._NOT_IDEMPOTENT
 
+    _ACCEPT_ALL_INPUTS = None
+    @final
+    @classproperty
+    def ACCEPT_ALL_INPUTS(cls):  # noqa
+        if cls._ACCEPT_ALL_INPUTS is None:
+            cls.GET_SCHEMA()
+        return cls._ACCEPT_ALL_INPUTS
+
     @final
     @classmethod
     def INPUT_TYPES(cls) -> dict[str, dict]:
@@ -1891,6 +1901,8 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls._INPUT_IS_LIST = schema.is_input_list
         if cls._NOT_IDEMPOTENT is None:
             cls._NOT_IDEMPOTENT = schema.not_idempotent
+        if cls._ACCEPT_ALL_INPUTS is None:
+            cls._ACCEPT_ALL_INPUTS = schema.accept_all_inputs
 
         if cls._RETURN_TYPES is None:
             output = []

comfy_api_nodes/apis/hunyuan3d.py

@@ -0,0 +1,66 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class InputGenerateType(TypedDict):
+    generate_type: str
+    polygon_type: str
+    pbr: bool
+
+
+class Hunyuan3DViewImage(BaseModel):
+    ViewType: str = Field(..., description="Valid values: back, left, right.")
+    ViewImageUrl: str = Field(...)
+
+
+class To3DProTaskRequest(BaseModel):
+    Model: str = Field(...)
+    Prompt: str | None = Field(None)
+    ImageUrl: str | None = Field(None)
+    MultiViewImages: list[Hunyuan3DViewImage] | None = Field(None)
+    EnablePBR: bool | None = Field(...)
+    FaceCount: int | None = Field(...)
+    GenerateType: str | None = Field(...)
+    PolygonType: str | None = Field(...)
+
+
+class RequestError(BaseModel):
+    Code: str = Field("")
+    Message: str = Field("")
+
+
+class To3DProTaskCreateResponse(BaseModel):
+    JobId: str | None = Field(None)
+    Error: RequestError | None = Field(None)
+
+    @model_validator(mode="before")
+    @classmethod
+    def unwrap_data(cls, values: dict) -> dict:
+        if "Response" in values and isinstance(values["Response"], dict):
+            return values["Response"]
+        return values
+
+
+class ResultFile3D(BaseModel):
+    Type: str = Field(...)
+    Url: str = Field(...)
+    PreviewImageUrl: str = Field("")
+
+
+class To3DProTaskResultResponse(BaseModel):
+    ErrorCode: str = Field("")
+    ErrorMessage: str = Field("")
+    ResultFile3Ds: list[ResultFile3D] = Field([])
+    Status: str = Field(...)
+
+    @model_validator(mode="before")
+    @classmethod
+    def unwrap_data(cls, values: dict) -> dict:
+        if "Response" in values and isinstance(values["Response"], dict):
+            return values["Response"]
+        return values
+
+
+class To3DProTaskQueryRequest(BaseModel):
+    JobId: str = Field(...)

comfy_api_nodes/nodes_hunyuan3d.py

@@ -0,0 +1,297 @@
+import os
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.hunyuan3d import (
+    Hunyuan3DViewImage,
+    InputGenerateType,
+    ResultFile3D,
+    To3DProTaskCreateResponse,
+    To3DProTaskQueryRequest,
+    To3DProTaskRequest,
+    To3DProTaskResultResponse,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_bytesio,
+    downscale_image_tensor_by_max_side,
+    poll_op,
+    sync_op,
+    upload_image_to_comfyapi,
+    validate_image_dimensions,
+    validate_string,
+)
+from folder_paths import get_output_directory
+
+
+def get_glb_obj_from_response(response_objs: list[ResultFile3D]) -> ResultFile3D:
+    for i in response_objs:
+        if i.Type.lower() == "glb":
+            return i
+    raise ValueError("No GLB file found in response. Please report this to the developers.")
+
+
+class TencentTextToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentTextToModelNode",
+            display_name="Hunyuan3D: Text to Model (Pro)",
+            category="api node/3d/Tencent",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Supports up to 1024 characters."),
+                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["generate_type", "generate_type.pbr", "face_count"]),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                Prompt=prompt,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        model_file = f"hunyuan_model_{response.JobId}.glb"
+        await download_url_to_bytesio(
+            get_glb_obj_from_response(result.ResultFile3Ds).Url,
+            os.path.join(get_output_directory(), model_file),
+        )
+        return IO.NodeOutput(model_file)
+
+
+class TencentImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentImageToModelNode",
+            display_name="Hunyuan3D: Image(s) to Model (Pro)",
+            category="api node/3d/Tencent",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.Image.Input("image"),
+                IO.Image.Input("image_left", optional=True),
+                IO.Image.Input("image_right", optional=True),
+                IO.Image.Input("image_back", optional=True),
+                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["generate_type", "generate_type.pbr", "face_count"],
+                    inputs=["image_left", "image_right", "image_back"],
+                ),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $multiview := (
+                    inputs.image_left.connected or inputs.image_right.connected or inputs.image_back.connected
+                  ) ? 10 : 0;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $multiview + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+        image_left: Input.Image | None = None,
+        image_right: Input.Image | None = None,
+        image_back: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        validate_image_dimensions(image, min_width=128, min_height=128)
+        multiview_images = []
+        for k, v in {
+            "left": image_left,
+            "right": image_right,
+            "back": image_back,
+        }.items():
+            if v is None:
+                continue
+            validate_image_dimensions(v, min_width=128, min_height=128)
+            multiview_images.append(
+                Hunyuan3DViewImage(
+                    ViewType=k,
+                    ViewImageUrl=await upload_image_to_comfyapi(
+                        cls,
+                        downscale_image_tensor_by_max_side(v, max_side=4900),
+                        mime_type="image/webp",
+                        total_pixels=24_010_000,
+                    ),
+                )
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                ImageUrl=await upload_image_to_comfyapi(
+                    cls,
+                    downscale_image_tensor_by_max_side(image, max_side=4900),
+                    mime_type="image/webp",
+                    total_pixels=24_010_000,
+                ),
+                MultiViewImages=multiview_images if multiview_images else None,
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        model_file = f"hunyuan_model_{response.JobId}.glb"
+        await download_url_to_bytesio(
+            get_glb_obj_from_response(result.ResultFile3Ds).Url,
+            os.path.join(get_output_directory(), model_file),
+        )
+        return IO.NodeOutput(model_file)
+
+
+class TencentHunyuan3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TencentTextToModelNode,
+            TencentImageToModelNode,
+        ]
+
+
+async def comfy_entrypoint() -> TencentHunyuan3DExtension:
+    return TencentHunyuan3DExtension()

comfy_api_nodes/nodes_kling.py

@@ -249,7 +249,6 @@ async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusRe
         ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
         response_model=TaskStatusResponse,
         status_extractor=lambda r: (r.data.task_status if r.data else None),
-        max_poll_attempts=160,
     )
     return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 

comfy_api_nodes/nodes_sora.py

@@ -149,7 +149,6 @@ class OpenAIVideoSora2(IO.ComfyNode):
             response_model=Sora2GenerationResponse,
             status_extractor=lambda x: x.status,
             poll_interval=8.0,
-            max_poll_attempts=160,
             estimated_duration=int(45 * (duration / 4) * model_time_multiplier),
         )
         return IO.NodeOutput(

comfy_api_nodes/nodes_topaz.py

@@ -203,7 +203,6 @@ class TopazImageEnhance(IO.ComfyNode):
             progress_extractor=lambda x: getattr(x, "progress", 0),
             price_extractor=lambda x: x.credits * 0.08,
             poll_interval=8.0,
-            max_poll_attempts=160,
             estimated_duration=60,
         )
 

comfy_api_nodes/util/__init__.py

@@ -13,6 +13,7 @@ from .conversions import (
     bytesio_to_image_tensor,
     convert_mask_to_image,
     downscale_image_tensor,
+    downscale_image_tensor_by_max_side,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
@@ -33,6 +34,7 @@ from .download_helpers import (
 from .upload_helpers import (
     upload_audio_to_comfyapi,
     upload_file_to_comfyapi,
+    upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
 )
@@ -61,6 +63,7 @@ __all__ = [
     # Upload helpers
     "upload_audio_to_comfyapi",
     "upload_file_to_comfyapi",
+    "upload_image_to_comfyapi",
     "upload_images_to_comfyapi",
     "upload_video_to_comfyapi",
     # Download helpers
@@ -75,6 +78,7 @@ __all__ = [
     "bytesio_to_image_tensor",
     "convert_mask_to_image",
     "downscale_image_tensor",
+    "downscale_image_tensor_by_max_side",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",

comfy_api_nodes/util/client.py

@@ -141,7 +141,7 @@ async def poll_op(
     queued_statuses: list[str | int] | None = None,
     data: BaseModel | None = None,
     poll_interval: float = 5.0,
-    max_poll_attempts: int = 120,
+    max_poll_attempts: int = 160,
     timeout_per_poll: float = 120.0,
     max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,
@@ -238,7 +238,7 @@ async def poll_op_raw(
     queued_statuses: list[str | int] | None = None,
     data: dict[str, Any] | BaseModel | None = None,
     poll_interval: float = 5.0,
-    max_poll_attempts: int = 120,
+    max_poll_attempts: int = 160,
     timeout_per_poll: float = 120.0,
     max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,

comfy_api_nodes/util/conversions.py

@@ -144,6 +144,21 @@ def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024)
     return s
 
 
+def downscale_image_tensor_by_max_side(image: torch.Tensor, *,  max_side: int) -> torch.Tensor:
+    """Downscale input image tensor so the largest dimension is at most max_side pixels."""
+    samples = image.movedim(-1, 1)
+    height, width = samples.shape[2], samples.shape[3]
+    max_dim = max(width, height)
+    if max_dim <= max_side:
+        return image
+    scale_by = max_side / max_dim
+    new_width = round(width * scale_by)
+    new_height = round(height * scale_by)
+    s = common_upscale(samples, new_width, new_height, "lanczos", "disabled")
+    s = s.movedim(1, -1)
+    return s
+
+
 def tensor_to_data_uri(
     image_tensor: torch.Tensor,
     total_pixels: int = 2048 * 2048,

comfy_api_nodes/util/upload_helpers.py

@@ -88,6 +88,28 @@ async def upload_images_to_comfyapi(
     return download_urls
 
 
+async def upload_image_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    image: torch.Tensor,
+    *,
+    mime_type: str | None = None,
+    wait_label: str | None = "Uploading",
+    total_pixels: int = 2048 * 2048,
+) -> str:
+    """Uploads a single image to ComfyUI API and returns its download URL."""
+    return (
+        await upload_images_to_comfyapi(
+            cls,
+            image,
+            max_images=1,
+            mime_type=mime_type,
+            wait_label=wait_label,
+            show_batch_index=False,
+            total_pixels=total_pixels,
+        )
+    )[0]
+
+
 async def upload_audio_to_comfyapi(
     cls: type[IO.ComfyNode],
     audio: Input.Audio,

comfy_extras/nodes_custom_sampler.py

@@ -741,7 +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:
@@ -760,6 +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())
@@ -939,7 +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
@@ -954,6 +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())

comfy_extras/nodes_logic.py

@@ -104,19 +104,23 @@ class CustomComboNode(io.ComfyNode):
             category="utils",
             is_experimental=True,
             inputs=[io.Combo.Input("choice", options=[])],
-            outputs=[io.String.Output()]
+            outputs=[
+                io.String.Output(display_name="STRING"),
+                io.Int.Output(display_name="INDEX"),
+            ],
+            accept_all_inputs=True,
         )
 
     @classmethod
-    def validate_inputs(cls, choice: io.Combo.Type) -> bool:
+    def validate_inputs(cls, choice: io.Combo.Type, index: int = 0, **kwargs) -> bool:
         # NOTE: DO NOT DO THIS unless you want to skip validation entirely on the node's inputs.
         # I am doing that here because the widgets (besides the combo dropdown) on this node are fully frontend defined.
         # I need to skip checking that the chosen combo option is in the options list, since those are defined by the user.
         return True
 
     @classmethod
-    def execute(cls, choice: io.Combo.Type) -> io.NodeOutput:
-        return io.NodeOutput(choice)
+    def execute(cls, choice: io.Combo.Type, index: int = 0, **kwargs) -> io.NodeOutput:
+        return io.NodeOutput(choice, index)
 
 
 class DCTestNode(io.ComfyNode):

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
 

execution.py

@@ -175,7 +175,7 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
                 continue
             obj = cached.outputs[output_index]
             input_data_all[x] = obj
-        elif input_category is not None:
+        elif input_category is not None or (is_v3 and class_def.ACCEPT_ALL_INPUTS):
             input_data_all[x] = [input_data]
 
     if is_v3:

nodes.py

@@ -1241,7 +1241,7 @@ 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:
@@ -1549,7 +1549,7 @@ 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")
@@ -1567,6 +1567,7 @@ 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, )