Merge branch 'comfyanonymous:master' into master

.ci/update_windows/update.py

@@ -66,8 +66,10 @@ if branch is None:
     try:
         ref = repo.lookup_reference('refs/remotes/origin/master')
     except:
-        print("pulling.")  # noqa: T201
-        pull(repo)
+        print("fetching.")  # noqa: T201
+        for remote in repo.remotes:
+            if remote.name == "origin":
+                remote.fetch()
         ref = repo.lookup_reference('refs/remotes/origin/master')
     repo.checkout(ref)
     branch = repo.lookup_branch('master')
@@ -149,3 +151,4 @@ try:
         shutil.copy(stable_update_script, stable_update_script_to)
 except:
     pass
+

comfy/ldm/hunyuan_video/upsampler.py

@@ -1,7 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm, ResnetBlock, VideoConv3d
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
+from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
 import model_management, model_patcher
 
 class SRResidualCausalBlock3D(nn.Module):

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,42 +1,12 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed
 import comfy.ops
 import comfy.ldm.models.autoencoder
 import comfy.model_management
 ops = comfy.ops.disable_weight_init
 
-class NoPadConv3d(nn.Module):
-    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
-        super().__init__()
-        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
-
-    x = xl[0]
-    xl.clear()
-
-    if conv_carry_out is not None:
-        to_push = x[:, :, -2:, :, :].clone()
-        conv_carry_out.append(to_push)
-
-    if isinstance(op, NoPadConv3d):
-        if conv_carry_in is None:
-            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
-        else:
-            carry_len = conv_carry_in[0].shape[2]
-            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
-            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
-
-    out = op(x)
-
-    return out
-
 
 class RMS_norm(nn.Module):
     def __init__(self, dim):
@@ -49,7 +19,7 @@ class RMS_norm(nn.Module):
         return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
 
 class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tds, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tds else 1 * 2 * 2
         assert oc % fct == 0
@@ -109,7 +79,7 @@ class DnSmpl(nn.Module):
 
 
 class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tus, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tus else 1 * 2 * 2
         self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
@@ -163,23 +133,6 @@ class UpSmpl(nn.Module):
 
         return h + x
 
-class HunyuanRefinerResnetBlock(ResnetBlock):
-    def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm):
-        super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-
-    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
-        h = x
-        h = [ self.swish(self.norm1(x)) ]
-        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
-        h = [ self.dropout(self.swish(self.norm2(h))) ]
-        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-
-        return x+h
-
 class Encoder(nn.Module):
     def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
                  ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
@@ -191,7 +144,7 @@ class Encoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = NoPadConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -206,9 +159,10 @@ class Encoder(nn.Module):
 
         for i, tgt in enumerate(block_out_channels):
             stage = nn.Module()
-            stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                                   out_channels=tgt,
-                                                                   conv_op=conv_op, norm_op=norm_op)
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
                                         for j in range(num_res_blocks)])
             ch = tgt
             if i < depth:
@@ -218,9 +172,9 @@ class Encoder(nn.Module):
             self.down.append(stage)
 
         self.mid = nn.Module()
-        self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
 
         self.norm_out = norm_op(ch)
         self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
@@ -246,22 +200,20 @@ class Encoder(nn.Module):
             conv_carry_out = []
             if i == len(x) - 1:
                 conv_carry_out = None
+
             x1 = [ x1 ]
             x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
 
             for stage in self.down:
                 for blk in stage.block:
-                    x1 = blk(x1, conv_carry_in, conv_carry_out)
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
                 if hasattr(stage, 'downsample'):
                     x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
 
             out.append(x1)
             conv_carry_in = conv_carry_out
 
-        if len(out) > 1:
-            out = torch.cat(out, dim=2)
-        else:
-            out = out[0]
+        out = torch_cat_if_needed(out, dim=2)
 
         x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
         del out
@@ -288,7 +240,7 @@ class Decoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = NoPadConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -298,9 +250,9 @@ class Decoder(nn.Module):
         self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
 
         self.mid = nn.Module()
-        self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
 
         self.up = nn.ModuleList()
         depth = (ffactor_spatial >> 1).bit_length()
@@ -308,9 +260,10 @@ class Decoder(nn.Module):
 
         for i, tgt in enumerate(block_out_channels):
             stage = nn.Module()
-            stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                                   out_channels=tgt,
-                                                                   conv_op=conv_op, norm_op=norm_op)
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
                                         for j in range(num_res_blocks + 1)])
             ch = tgt
             if i < depth:
@@ -340,7 +293,7 @@ class Decoder(nn.Module):
                 conv_carry_out = None
             for stage in self.up:
                 for blk in stage.block:
-                    x1 = blk(x1, conv_carry_in, conv_carry_out)
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
                 if hasattr(stage, 'upsample'):
                     x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
 
@@ -350,10 +303,7 @@ class Decoder(nn.Module):
             conv_carry_in = conv_carry_out
         del x
 
-        if len(out) > 1:
-            out = torch.cat(out, dim=2)
-        else:
-            out = out[0]
+        out = torch_cat_if_needed(out, dim=2)
 
         if not self.refiner_vae:
             if z.shape[-3] == 1:

comfy/ldm/lumina/controlnet.py

@@ -0,0 +1,113 @@
+import torch
+from torch import nn
+
+from .model import JointTransformerBlock
+
+class ZImageControlTransformerBlock(JointTransformerBlock):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        block_id=0,
+        operation_settings=None,
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, c, x, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        return c_skip, c
+
+class ZImage_Control(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int = 3840,
+        n_heads: int = 30,
+        n_kv_heads: int = 30,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: float = (8.0 / 3.0),
+        norm_eps: float = 1e-5,
+        qk_norm: bool = True,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+    ):
+        super().__init__()
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.additional_in_dim = 0
+        self.control_in_dim = 16
+        n_refiner_layers = 2
+        self.n_control_layers = 6
+        self.control_layers = nn.ModuleList(
+            [
+                ZImageControlTransformerBlock(
+                    i,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    block_id=i,
+                    operation_settings=operation_settings,
+                )
+                for i in range(self.n_control_layers)
+            ]
+        )
+
+        all_x_embedder = {}
+        patch_size = 2
+        f_patch_size = 1
+        x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * self.control_in_dim, dim, bias=True, device=device, dtype=dtype)
+        all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.control_noise_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                    z_image_modulation=True,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+
+    def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
+        patch_size = 2
+        f_patch_size = 1
+        pH = pW = patch_size
+        B, C, H, W = control_context.shape
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
+
+        x_attn_mask = None
+        for layer in self.control_noise_refiner:
+            control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+        return control_context
+
+    def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+        return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)

comfy/ldm/lumina/model.py

@@ -568,7 +568,7 @@ class NextDiT(nn.Module):
         ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
 
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -585,16 +585,24 @@ class NextDiT(nn.Module):
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
+        patches = transformer_options.get("patches", {})
         transformer_options = kwargs.get("transformer_options", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
-        freqs_cis = freqs_cis.to(x.device)
+        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+        freqs_cis = freqs_cis.to(img.device)
 
-        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+        for i, layer in enumerate(self.layers):
+            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
 
-        x = self.final_layer(x, adaln_input)
-        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+        img = self.final_layer(img, adaln_input)
+        img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
 
-        return -x
+        return -img
 

comfy/ldm/modules/diffusionmodules/model.py

@@ -13,6 +13,12 @@ if model_management.xformers_enabled_vae():
     import xformers
     import xformers.ops
 
+def torch_cat_if_needed(xl, dim):
+    if len(xl) > 1:
+        return torch.cat(xl, dim)
+    else:
+        return xl[0]
+
 def get_timestep_embedding(timesteps, embedding_dim):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
@@ -43,6 +49,37 @@ def Normalize(in_channels, num_groups=32):
     return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
 
+class CarriedConv3d(nn.Module):
+    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
+        super().__init__()
+        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
+
+    x = xl[0]
+    xl.clear()
+
+    if isinstance(op, CarriedConv3d):
+        if conv_carry_in is None:
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
+        else:
+            carry_len = conv_carry_in[0].shape[2]
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
+            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
+
+        if conv_carry_out is not None:
+            to_push = x[:, :, -2:, :, :].clone()
+            conv_carry_out.append(to_push)
+
+    out = op(x)
+
+    return out
+
+
 class VideoConv3d(nn.Module):
     def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):
         super().__init__()
@@ -89,29 +126,24 @@ class Upsample(nn.Module):
                                         stride=1,
                                         padding=1)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         scale_factor = self.scale_factor
         if isinstance(scale_factor, (int, float)):
             scale_factor = (scale_factor,) * (x.ndim - 2)
 
         if x.ndim == 5 and scale_factor[0] > 1.0:
-            t = x.shape[2]
-            if t > 1:
-                a, b = x.split((1, t - 1), dim=2)
-                del x
-                b = interpolate_up(b, scale_factor)
-            else:
-                a = x
-
-            a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)
-            if t > 1:
-                x = torch.cat((a, b), dim=2)
-            else:
-                x = a
+            results = []
+            if conv_carry_in is None:
+                first = x[:, :, :1, :, :]
+                results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))
+                x = x[:, :, 1:, :, :]
+            if x.shape[2] > 0:
+                results.append(interpolate_up(x, scale_factor))
+            x = torch_cat_if_needed(results, dim=2)
         else:
             x = interpolate_up(x, scale_factor)
         if self.with_conv:
-            x = self.conv(x)
+            x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
         return x
 
 
@@ -127,17 +159,20 @@ class Downsample(nn.Module):
                                         stride=stride,
                                         padding=0)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         if self.with_conv:
-            if x.ndim == 4:
+            if isinstance(self.conv, CarriedConv3d):
+                x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
+            elif x.ndim == 4:
                 pad = (0, 1, 0, 1)
                 mode = "constant"
                 x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
+                x = self.conv(x)
             elif x.ndim == 5:
                 pad = (1, 1, 1, 1, 2, 0)
                 mode = "replicate"
                 x = torch.nn.functional.pad(x, pad, mode=mode)
-            x = self.conv(x)
+                x = self.conv(x)
         else:
             x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
         return x
@@ -183,23 +218,23 @@ class ResnetBlock(nn.Module):
                                                     stride=1,
                                                     padding=0)
 
-    def forward(self, x, temb=None):
+    def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):
         h = x
         h = self.norm1(h)
-        h = self.swish(h)
-        h = self.conv1(h)
+        h = [ self.swish(h) ]
+        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if temb is not None:
             h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
 
         h = self.norm2(h)
         h = self.swish(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
+        h = [ self.dropout(h) ]
+        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
+                x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
             else:
                 x = self.nin_shortcut(x)
 
@@ -520,9 +555,14 @@ class Encoder(nn.Module):
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
+            if not attn_resolutions:
+                conv_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -535,6 +575,7 @@ class Encoder(nn.Module):
                                        stride=1,
                                        padding=1)
 
+        self.time_compress = 1
         curr_res = resolution
         in_ch_mult = (1,)+tuple(ch_mult)
         self.in_ch_mult = in_ch_mult
@@ -561,10 +602,15 @@ class Encoder(nn.Module):
                 if time_compress is not None:
                     if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):
                         stride = (1, 2, 2)
+                else:
+                    self.time_compress *= 2
                 down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
                 curr_res = curr_res // 2
             self.down.append(down)
 
+        if time_compress is not None:
+            self.time_compress = time_compress
+
         # middle
         self.mid = nn.Module()
         self.mid.block_1 = ResnetBlock(in_channels=block_in,
@@ -590,15 +636,42 @@ class Encoder(nn.Module):
     def forward(self, x):
         # timestep embedding
         temb = None
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions-1:
-                h = self.down[i_level].downsample(h)
+
+        if self.carried:
+            xl = [x[:, :, :1, :, :]]
+            if x.shape[2] > self.time_compress:
+                tc = self.time_compress
+                xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)
+            x = xl
+        else:
+            x = [x]
+        out = []
+
+        conv_carry_in = None
+
+        for i, x1 in enumerate(x):
+            conv_carry_out = []
+            if i == len(x) - 1:
+                conv_carry_out = None
+
+            # downsampling
+            x1 = [ x1 ]
+            h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
+
+            for i_level in range(self.num_resolutions):
+                for i_block in range(self.num_res_blocks):
+                    h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)
+                    if len(self.down[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.down[i_level].attn[i_block](h1)
+                if i_level != self.num_resolutions-1:
+                    h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)
+
+            out.append(h1)
+            conv_carry_in = conv_carry_out
+
+        h = torch_cat_if_needed(out, dim=2)
+        del out
 
         # middle
         h = self.mid.block_1(h, temb)
@@ -607,15 +680,15 @@ class Encoder(nn.Module):
 
         # end
         h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
+        h = [ nonlinearity(h) ]
+        h = conv_carry_causal_3d(h, self.conv_out)
         return h
 
 
 class Decoder(nn.Module):
     def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+                 resolution, z_channels, tanh_out=False, use_linear_attn=False,
                  conv_out_op=ops.Conv2d,
                  resnet_op=ResnetBlock,
                  attn_op=AttnBlock,
@@ -629,12 +702,18 @@ class Decoder(nn.Module):
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
         self.tanh_out = tanh_out
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
-            conv_out_op = VideoConv3d
+            if not attn_resolutions and resnet_op == ResnetBlock:
+                conv_op = CarriedConv3d
+                conv_out_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
+                conv_out_op = VideoConv3d
+
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -709,29 +788,43 @@ class Decoder(nn.Module):
         temb = None
 
         # z to block_in
-        h = self.conv_in(z)
+        h = conv_carry_causal_3d([z], self.conv_in)
 
         # middle
         h = self.mid.block_1(h, temb, **kwargs)
         h = self.mid.attn_1(h, **kwargs)
         h = self.mid.block_2(h, temb, **kwargs)
 
+        if self.carried:
+            h = torch.split(h, 2, dim=2)
+        else:
+            h = [ h ]
+        out = []
+
+        conv_carry_in = None
+
         # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb, **kwargs)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h, **kwargs)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
+        for i, h1 in enumerate(h):
+            conv_carry_out = []
+            if i == len(h) - 1:
+                conv_carry_out = None
+            for i_level in reversed(range(self.num_resolutions)):
+                for i_block in range(self.num_res_blocks+1):
+                    h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)
+                    if len(self.up[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.up[i_level].attn[i_block](h1, **kwargs)
+                if i_level != 0:
+                    h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)
 
-        # end
-        if self.give_pre_end:
-            return h
+            h1 = self.norm_out(h1)
+            h1 = [ nonlinearity(h1) ]
+            h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)
+            if self.tanh_out:
+                h1 = torch.tanh(h1)
+            out.append(h1)
+            conv_carry_in = conv_carry_out
 
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h, **kwargs)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
+        out = torch_cat_if_needed(out, dim=2)
+
+        return out

comfy/model_patcher.py

@@ -704,7 +704,7 @@ class ModelPatcher:
 
                 lowvram_weight = False
 
-                potential_offload = max(offload_buffer, module_offload_mem * (comfy.model_management.NUM_STREAMS + 1))
+                potential_offload = max(offload_buffer, module_offload_mem + (comfy.model_management.NUM_STREAMS * module_mem))
                 lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory
 
                 weight_key = "{}.weight".format(n)
@@ -883,7 +883,7 @@ class ModelPatcher:
                     break
                 module_offload_mem, module_mem, n, m, params = unload
 
-                potential_offload = (comfy.model_management.NUM_STREAMS + 1) * module_offload_mem
+                potential_offload = module_offload_mem + (comfy.model_management.NUM_STREAMS * module_mem)
 
                 lowvram_possible = hasattr(m, "comfy_cast_weights")
                 if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:

comfy/ops.py

@@ -111,22 +111,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     if s.bias is not None:
         bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
 
-        if bias_has_function:
-            with wf_context:
-                for f in s.bias_function:
-                    bias = f(bias)
+    comfy.model_management.sync_stream(device, offload_stream)
+
+    bias_a = bias
+    weight_a = weight
+
+    if s.bias is not None:
+        for f in s.bias_function:
+            bias = f(bias)
 
     if weight_has_function or weight.dtype != dtype:
-        with wf_context:
-            weight = weight.to(dtype=dtype)
-            if isinstance(weight, QuantizedTensor):
-                weight = weight.dequantize()
-            for f in s.weight_function:
-                weight = f(weight)
+        weight = weight.to(dtype=dtype)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        for f in s.weight_function:
+            weight = f(weight)
 
-    comfy.model_management.sync_stream(device, offload_stream)
     if offloadable:
-        return weight, bias, offload_stream
+        return weight, bias, (offload_stream, weight_a, bias_a)
     else:
         #Legacy function signature
         return weight, bias
@@ -135,13 +137,16 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
 def uncast_bias_weight(s, weight, bias, offload_stream):
     if offload_stream is None:
         return
-    if weight is not None:
-        device = weight.device
+    os, weight_a, bias_a = offload_stream
+    if os is None:
+        return
+    if weight_a is not None:
+        device = weight_a.device
     else:
-        if bias is None:
+        if bias_a is None:
             return
-        device = bias.device
-    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+        device = bias_a.device
+    os.wait_stream(comfy.model_management.current_stream(device))
 
 
 class CastWeightBiasOp:

comfy_api/latest/__init__.py

@@ -8,8 +8,8 @@ from comfy_api.internal.async_to_sync import create_sync_class
 from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
 from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
 from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
-from . import _io as io
-from . import _ui as ui
+from . import _io_public as io
+from . import _ui_public as ui
 # from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple

comfy_api/latest/_io.py

@@ -4,6 +4,7 @@ import copy
 import inspect
 from abc import ABC, abstractmethod
 from collections import Counter
+from collections.abc import Iterable
 from dataclasses import asdict, dataclass
 from enum import Enum
 from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING
@@ -150,6 +151,9 @@ class _IO_V3:
     def __init__(self):
         pass
 
+    def validate(self):
+        pass
+
     @property
     def io_type(self):
         return self.Parent.io_type
@@ -182,6 +186,9 @@ class Input(_IO_V3):
     def get_io_type(self):
         return _StringIOType(self.io_type)
 
+    def get_all(self) -> list[Input]:
+        return [self]
+
 class WidgetInput(Input):
     '''
     Base class for a V3 Input with widget.
@@ -814,13 +821,61 @@ class MultiType:
             else:
                 return super().as_dict()
 
+@comfytype(io_type="COMFY_MATCHTYPE_V3")
+class MatchType(ComfyTypeIO):
+    class Template:
+        def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType] = AnyType):
+            self.template_id = template_id
+            # account for syntactic sugar
+            if not isinstance(allowed_types, Iterable):
+                allowed_types = [allowed_types]
+            for t in allowed_types:
+                if not isinstance(t, type):
+                    if not isinstance(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__class__.__name__}")
+                else:
+                    if not issubclass(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__name__}")
+            self.allowed_types = allowed_types
+
+        def as_dict(self):
+            return {
+                "template_id": self.template_id,
+                "allowed_types": ",".join([t.io_type for t in self.allowed_types]),
+            }
+
+    class Input(Input):
+        def __init__(self, id: str, template: MatchType.Template,
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
+    class Output(Output):
+        def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
+                     is_output_list=False):
+            super().__init__(id, display_name, tooltip, is_output_list)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
 class DynamicInput(Input, ABC):
     '''
     Abstract class for dynamic input registration.
     '''
-    @abstractmethod
     def get_dynamic(self) -> list[Input]:
-        ...
+        return []
+
+    def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+        pass
+
 
 class DynamicOutput(Output, ABC):
     '''
@@ -830,99 +885,223 @@ class DynamicOutput(Output, ABC):
                  is_output_list=False):
         super().__init__(id, display_name, tooltip, is_output_list)
 
-    @abstractmethod
     def get_dynamic(self) -> list[Output]:
-        ...
+        return []
 
 
 @comfytype(io_type="COMFY_AUTOGROW_V3")
-class AutogrowDynamic(ComfyTypeI):
-    Type = list[Any]
-    class Input(DynamicInput):
-        def __init__(self, id: str, template_input: Input, min: int=1, max: int=None,
-                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
-            self.template_input = template_input
-            if min is not None:
-                assert(min >= 1)
-            if max is not None:
-                assert(max >= 1)
+class Autogrow(ComfyTypeI):
+    Type = dict[str, Any]
+    _MaxNames = 100  # NOTE: max 100 names for sanity
+
+    class _AutogrowTemplate:
+        def __init__(self, input: Input):
+            # dynamic inputs are not allowed as the template input
+            assert(not isinstance(input, DynamicInput))
+            self.input = copy.copy(input)
+            if isinstance(self.input, WidgetInput):
+                self.input.force_input = True
+            self.names: list[str] = []
+            self.cached_inputs = {}
+
+        def _create_input(self, input: Input, name: str):
+            new_input = copy.copy(self.input)
+            new_input.id = name
+            return new_input
+
+        def _create_cached_inputs(self):
+            for name in self.names:
+                self.cached_inputs[name] = self._create_input(self.input, name)
+
+        def get_all(self) -> list[Input]:
+            return list(self.cached_inputs.values())
+
+        def as_dict(self):
+            return prune_dict({
+                "input": create_input_dict_v1([self.input]),
+            })
+
+        def validate(self):
+            self.input.validate()
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            real_inputs = []
+            for name, input in self.cached_inputs.items():
+                if name in live_inputs:
+                    real_inputs.append(input)
+            add_to_input_dict_v1(d, real_inputs, live_inputs, curr_prefix)
+            add_dynamic_id_mapping(d, real_inputs, curr_prefix)
+
+    class TemplatePrefix(_AutogrowTemplate):
+        def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
+            super().__init__(input)
+            self.prefix = prefix
+            assert(min >= 0)
+            assert(max >= 1)
+            assert(max <= Autogrow._MaxNames)
             self.min = min
             self.max = max
+            self.names = [f"{self.prefix}{i}" for i in range(self.max)]
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "prefix": self.prefix,
+                "min": self.min,
+                "max": self.max,
+            })
+
+    class TemplateNames(_AutogrowTemplate):
+        def __init__(self, input: Input, names: list[str], min: int=1):
+            super().__init__(input)
+            self.names = names[:Autogrow._MaxNames]
+            assert(min >= 0)
+            self.min = min
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "names": self.names,
+                "min": self.min,
+            })
+
+    class Input(DynamicInput):
+        def __init__(self, id: str, template: Autogrow.TemplatePrefix | Autogrow.TemplateNames,
+                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
 
         def get_dynamic(self) -> list[Input]:
-            curr_count = 1
-            new_inputs = []
-            for i in range(self.min):
-                new_input = copy.copy(self.template_input)
-                new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
-                if new_input.display_name is not None:
-                    new_input.display_name = f"{new_input.display_name}{curr_count}"
-                new_input.optional = self.optional or new_input.optional
-                if isinstance(self.template_input, WidgetInput):
-                    new_input.force_input = True
-                new_inputs.append(new_input)
-                curr_count += 1
-            # pretend to expand up to max
-            for i in range(curr_count-1, self.max):
-                new_input = copy.copy(self.template_input)
-                new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
-                if new_input.display_name is not None:
-                    new_input.display_name = f"{new_input.display_name}{curr_count}"
-                new_input.optional = True
-                if isinstance(self.template_input, WidgetInput):
-                    new_input.force_input = True
-                new_inputs.append(new_input)
-                curr_count += 1
-            return new_inputs
+            return self.template.get_all()
 
-@comfytype(io_type="COMFY_COMBODYNAMIC_V3")
-class ComboDynamic(ComfyTypeI):
-    class Input(DynamicInput):
-        def __init__(self, id: str):
-            pass
+        def get_all(self) -> list[Input]:
+            return [self] + self.template.get_all()
 
-@comfytype(io_type="COMFY_MATCHTYPE_V3")
-class MatchType(ComfyTypeIO):
-    class Template:
-        def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType]):
-            self.template_id = template_id
-            self.allowed_types = [allowed_types] if isinstance(allowed_types, _ComfyType) else allowed_types
+        def validate(self):
+            self.template.validate()
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            curr_prefix = f"{curr_prefix}{self.id}."
+            # need to remove self from expected inputs dictionary; replaced by template inputs in frontend
+            for inner_dict in d.values():
+                if self.id in inner_dict:
+                    del inner_dict[self.id]
+            self.template.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+
+@comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
+class DynamicCombo(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Option:
+        def __init__(self, key: str, inputs: list[Input]):
+            self.key = key
+            self.inputs = inputs
 
         def as_dict(self):
             return {
-                "template_id": self.template_id,
-                "allowed_types": "".join(t.io_type for t in self.allowed_types),
+                "key": self.key,
+                "inputs": create_input_dict_v1(self.inputs),
             }
 
     class Input(DynamicInput):
-        def __init__(self, id: str, template: MatchType.Template,
+        def __init__(self, id: str, options: list[DynamicCombo.Option],
                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
             super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
-            self.template = template
+            self.options = options
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            # check if dynamic input's id is in live_inputs
+            if self.id in live_inputs:
+                curr_prefix = f"{curr_prefix}{self.id}."
+                key = live_inputs[self.id]
+                selected_option = None
+                for option in self.options:
+                    if option.key == key:
+                        selected_option = option
+                        break
+                if selected_option is not None:
+                    add_to_input_dict_v1(d, selected_option.inputs, live_inputs, curr_prefix)
+                    add_dynamic_id_mapping(d, selected_option.inputs, curr_prefix, self)
 
         def get_dynamic(self) -> list[Input]:
-            return [self]
+            return [input for option in self.options for input in option.inputs]
+
+        def get_all(self) -> list[Input]:
+            return [self] + [input for option in self.options for input in option.inputs]
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
-                "template": self.template.as_dict(),
+                "options": [o.as_dict() for o in self.options],
             })
 
-    class Output(DynamicOutput):
-        def __init__(self, id: str, template: MatchType.Template, display_name: str=None, tooltip: str=None,
-                     is_output_list=False):
-            super().__init__(id, display_name, tooltip, is_output_list)
-            self.template = template
+        def validate(self):
+            # make sure all nested inputs are validated
+            for option in self.options:
+                for input in option.inputs:
+                    input.validate()
 
-        def get_dynamic(self) -> list[Output]:
-            return [self]
+@comfytype(io_type="COMFY_DYNAMICSLOT_V3")
+class DynamicSlot(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Input(DynamicInput):
+        def __init__(self, slot: Input, inputs: list[Input],
+                    display_name: str=None, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            assert(not isinstance(slot, DynamicInput))
+            self.slot = copy.copy(slot)
+            self.slot.display_name = slot.display_name if slot.display_name is not None else display_name
+            optional = True
+            self.slot.tooltip = slot.tooltip if slot.tooltip is not None else tooltip
+            self.slot.lazy = slot.lazy if slot.lazy is not None else lazy
+            self.slot.extra_dict = slot.extra_dict if slot.extra_dict is not None else extra_dict
+            super().__init__(slot.id, self.slot.display_name, optional, self.slot.tooltip, self.slot.lazy, self.slot.extra_dict)
+            self.inputs = inputs
+            self.force_input = None
+            # force widget inputs to have no widgets, otherwise this would be awkward
+            if isinstance(self.slot, WidgetInput):
+                self.force_input = True
+                self.slot.force_input = True
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            if self.id in live_inputs:
+                curr_prefix = f"{curr_prefix}{self.id}."
+                add_to_input_dict_v1(d, self.inputs, live_inputs, curr_prefix)
+                add_dynamic_id_mapping(d, [self.slot] + self.inputs, curr_prefix)
+
+        def get_dynamic(self) -> list[Input]:
+            return [self.slot] + self.inputs
+
+        def get_all(self) -> list[Input]:
+            return [self] + [self.slot] + self.inputs
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
-                "template": self.template.as_dict(),
+                "slotType": str(self.slot.get_io_type()),
+                "inputs": create_input_dict_v1(self.inputs),
+                "forceInput": self.force_input,
             })
 
+        def validate(self):
+            self.slot.validate()
+            for input in self.inputs:
+                input.validate()
+
+def add_dynamic_id_mapping(d: dict[str, Any], inputs: list[Input], curr_prefix: str, self: DynamicInput=None):
+    dynamic = d.setdefault("dynamic_paths", {})
+    if self is not None:
+        dynamic[self.id] = f"{curr_prefix}{self.id}"
+    for i in inputs:
+        if not isinstance(i, DynamicInput):
+            dynamic[f"{i.id}"] = f"{curr_prefix}{i.id}"
+
+class V3Data(TypedDict):
+    hidden_inputs: dict[str, Any]
+    dynamic_paths: dict[str, Any]
 
 class HiddenHolder:
     def __init__(self, unique_id: str, prompt: Any,
@@ -984,6 +1163,7 @@ class NodeInfoV1:
     output_is_list: list[bool]=None
     output_name: list[str]=None
     output_tooltips: list[str]=None
+    output_matchtypes: list[str]=None
     name: str=None
     display_name: str=None
     description: str=None
@@ -1061,7 +1241,11 @@ class Schema:
         '''Validate the schema:
         - verify ids on inputs and outputs are unique - both internally and in relation to each other
         '''
-        input_ids = [i.id for i in self.inputs] if self.inputs is not None else []
+        nested_inputs: list[Input] = []
+        if self.inputs is not None:
+            for input in self.inputs:
+                nested_inputs.extend(input.get_all())
+        input_ids = [i.id for i in nested_inputs] if nested_inputs is not None else []
         output_ids = [o.id for o in self.outputs] if self.outputs is not None else []
         input_set = set(input_ids)
         output_set = set(output_ids)
@@ -1077,6 +1261,13 @@ class Schema:
             issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.")
         if len(issues) > 0:
             raise ValueError("\n".join(issues))
+        # validate inputs and outputs
+        if self.inputs is not None:
+            for input in self.inputs:
+                input.validate()
+        if self.outputs is not None:
+            for output in self.outputs:
+                output.validate()
 
     def finalize(self):
         """Add hidden based on selected schema options, and give outputs without ids default ids."""
@@ -1102,19 +1293,10 @@ class Schema:
                 if output.id is None:
                     output.id = f"_{i}_{output.io_type}_"
 
-    def get_v1_info(self, cls) -> NodeInfoV1:
+    def get_v1_info(self, cls, live_inputs: dict[str, Any]=None) -> NodeInfoV1:
+        # NOTE: live_inputs will not be used anymore very soon and this will be done another way
         # get V1 inputs
-        input = {
-            "required": {}
-        }
-        if self.inputs:
-            for i in self.inputs:
-                if isinstance(i, DynamicInput):
-                    dynamic_inputs = i.get_dynamic()
-                    for d in dynamic_inputs:
-                        add_to_dict_v1(d, input)
-                else:
-                    add_to_dict_v1(i, input)
+        input = create_input_dict_v1(self.inputs, live_inputs)
         if self.hidden:
             for hidden in self.hidden:
                 input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
@@ -1123,12 +1305,24 @@ class Schema:
         output_is_list = []
         output_name = []
         output_tooltips = []
+        output_matchtypes = []
+        any_matchtypes = False
         if self.outputs:
             for o in self.outputs:
                 output.append(o.io_type)
                 output_is_list.append(o.is_output_list)
                 output_name.append(o.display_name if o.display_name else o.io_type)
                 output_tooltips.append(o.tooltip if o.tooltip else None)
+                # special handling for MatchType
+                if isinstance(o, MatchType.Output):
+                    output_matchtypes.append(o.template.template_id)
+                    any_matchtypes = True
+                else:
+                    output_matchtypes.append(None)
+
+        # clear out lists that are all None
+        if not any_matchtypes:
+            output_matchtypes = None
 
         info = NodeInfoV1(
             input=input,
@@ -1137,6 +1331,7 @@ class Schema:
             output_is_list=output_is_list,
             output_name=output_name,
             output_tooltips=output_tooltips,
+            output_matchtypes=output_matchtypes,
             name=self.node_id,
             display_name=self.display_name,
             category=self.category,
@@ -1182,16 +1377,57 @@ class Schema:
         return info
 
 
-def add_to_dict_v1(i: Input, input: dict):
+def create_input_dict_v1(inputs: list[Input], live_inputs: dict[str, Any]=None) -> dict:
+    input = {
+        "required": {}
+    }
+    add_to_input_dict_v1(input, inputs, live_inputs)
+    return input
+
+def add_to_input_dict_v1(d: dict[str, Any], inputs: list[Input], live_inputs: dict[str, Any]=None, curr_prefix=''):
+    for i in inputs:
+        if isinstance(i, DynamicInput):
+            add_to_dict_v1(i, d)
+            if live_inputs is not None:
+                i.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+        else:
+            add_to_dict_v1(i, d)
+
+def add_to_dict_v1(i: Input, d: dict, dynamic_dict: dict=None):
     key = "optional" if i.optional else "required"
     as_dict = i.as_dict()
     # for v1, we don't want to include the optional key
     as_dict.pop("optional", None)
-    input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
+    if dynamic_dict is None:
+        value = (i.get_io_type(), as_dict)
+    else:
+        value = (i.get_io_type(), as_dict, dynamic_dict)
+    d.setdefault(key, {})[i.id] = value
 
 def add_to_dict_v3(io: Input | Output, d: dict):
     d[io.id] = (io.get_io_type(), io.as_dict())
 
+def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
+    paths = v3_data.get("dynamic_paths", None)
+    if paths is None:
+        return values
+    values = values.copy()
+    result = {}
+
+    for key, path in paths.items():
+        parts = path.split(".")
+        current = result
+
+        for i, p in enumerate(parts):
+            is_last = (i == len(parts) - 1)
+
+            if is_last:
+                current[p] = values.pop(key, None)
+            else:
+                current = current.setdefault(p, {})
+
+    values.update(result)
+    return values
 
 
 class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@@ -1311,12 +1547,12 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNode]:
+    def PREPARE_CLASS_CLONE(cls, v3_data: V3Data) -> type[ComfyNode]:
         """Creates clone of real node class to prevent monkey-patching."""
         c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
         type_clone: type[ComfyNode] = shallow_clone_class(c_type)
         # set hidden
-        type_clone.hidden = HiddenHolder.from_dict(hidden_inputs)
+        type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"])
         return type_clone
 
     @final
@@ -1433,14 +1669,18 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], Schema]:
+    def INPUT_TYPES(cls, include_hidden=True, return_schema=False, live_inputs=None) -> dict[str, dict] | tuple[dict[str, dict], Schema, V3Data]:
         schema = cls.FINALIZE_SCHEMA()
-        info = schema.get_v1_info(cls)
+        info = schema.get_v1_info(cls, live_inputs)
         input = info.input
         if not include_hidden:
             input.pop("hidden", None)
         if return_schema:
-            return input, schema
+            v3_data: V3Data = {}
+            dynamic = input.pop("dynamic_paths", None)
+            if dynamic is not None:
+                v3_data["dynamic_paths"] = dynamic
+            return input, schema, v3_data
         return input
 
     @final
@@ -1513,7 +1753,7 @@ class ComfyNode(_ComfyNodeBaseInternal):
         raise NotImplementedError
 
     @classmethod
-    def validate_inputs(cls, **kwargs) -> bool:
+    def validate_inputs(cls, **kwargs) -> bool | str:
         """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS."""
         raise NotImplementedError
 
@@ -1628,6 +1868,7 @@ __all__ = [
     "StyleModel",
     "Gligen",
     "UpscaleModel",
+    "LatentUpscaleModel",
     "Audio",
     "Video",
     "SVG",
@@ -1651,6 +1892,10 @@ __all__ = [
     "SEGS",
     "AnyType",
     "MultiType",
+    # Dynamic Types
+    "MatchType",
+    # "DynamicCombo",
+    # "Autogrow",
     # Other classes
     "HiddenHolder",
     "Hidden",
@@ -1661,4 +1906,5 @@ __all__ = [
     "NodeOutput",
     "add_to_dict_v1",
     "add_to_dict_v3",
+    "V3Data",
 ]

comfy_api/latest/_io_public.py

@@ -0,0 +1 @@
+from ._io import *  # noqa: F403

comfy_api/latest/_ui_public.py

@@ -0,0 +1 @@
+from ._ui import *  # noqa: F403

comfy_api/v0_0_2/__init__.py

@@ -6,7 +6,7 @@ from comfy_api.latest import (
 )
 from typing import Type, TYPE_CHECKING
 from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest import io, ui, ComfyExtension  #noqa: F401
+from comfy_api.latest import io, ui, IO, UI, ComfyExtension  #noqa: F401
 
 
 class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
@@ -42,4 +42,8 @@ __all__ = [
     "InputImpl",
     "Types",
     "ComfyExtension",
+    "io",
+    "IO",
+    "ui",
+    "UI",
 ]

comfy_execution/validation.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+from comfy_api.latest import IO
 
 
 def validate_node_input(
@@ -23,6 +24,11 @@ def validate_node_input(
     if not received_type != input_type:
         return True
 
+    # If the received type or input_type is a MatchType, we can return True immediately;
+    # validation for this is handled by the frontend
+    if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
+        return True
+
     # Not equal, and not strings
     if not isinstance(received_type, str) or not isinstance(input_type, str):
         return False

comfy_extras/nodes_logic.py

@@ -0,0 +1,155 @@
+from typing import TypedDict
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.latest import _io
+
+
+
+class SwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySwitchNode",
+            display_name="Switch",
+            category="logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
+                io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=..., on_true=...):
+        # We use ... instead of None, as None is passed for connected-but-unevaluated inputs.
+        # This trick allows us to ignore the value of the switch and still be able to run execute().
+
+        # One of the inputs may be missing, in which case we need to evaluate the other input
+        if on_false is ...:
+            return ["on_true"]
+        if on_true is ...:
+            return ["on_false"]
+        # Normal lazy switch operation
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def validate_inputs(cls, switch, on_false=..., on_true=...):
+        # This check happens before check_lazy_status(), so we can eliminate the case where
+        # both inputs are missing.
+        if on_false is ... and on_true is ...:
+            return "At least one of on_false or on_true must be connected to Switch node"
+        return True
+
+    @classmethod
+    def execute(cls, switch, on_true=..., on_false=...) -> io.NodeOutput:
+        if on_true is ...:
+            return io.NodeOutput(on_false)
+        if on_false is ...:
+            return io.NodeOutput(on_true)
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class DCTestNode(io.ComfyNode):
+    class DCValues(TypedDict):
+        combo: str
+        string: str
+        integer: int
+        image: io.Image.Type
+        subcombo: dict[str]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DCTestNode",
+            display_name="DCTest",
+            category="logic",
+            is_output_node=True,
+            inputs=[_io.DynamicCombo.Input("combo", options=[
+                _io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+                _io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+                _io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+                _io.DynamicCombo.Option("option4", [
+                    _io.DynamicCombo.Input("subcombo", options=[
+                        _io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+                        _io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+                    ])
+                ])]
+            )],
+            outputs=[io.AnyType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: DCValues) -> io.NodeOutput:
+        combo_val = combo["combo"]
+        if combo_val == "option1":
+            return io.NodeOutput(combo["string"])
+        elif combo_val == "option2":
+            return io.NodeOutput(combo["integer"])
+        elif combo_val == "option3":
+            return io.NodeOutput(combo["image"])
+        elif combo_val == "option4":
+            return io.NodeOutput(f"{combo['subcombo']}")
+        else:
+            raise ValueError(f"Invalid combo: {combo_val}")
+
+
+class AutogrowNamesTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplateNames(input=io.Float.Input("float"), names=["a", "b", "c"])
+        return io.Schema(
+            node_id="AutogrowNamesTestNode",
+            display_name="AutogrowNamesTest",
+            category="logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class AutogrowPrefixTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplatePrefix(input=io.Float.Input("float"), prefix="float", min=1, max=10)
+        return io.Schema(
+            node_id="AutogrowPrefixTestNode",
+            display_name="AutogrowPrefixTest",
+            category="logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class LogicExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            # SwitchNode,
+            # DCTestNode,
+            # AutogrowNamesTestNode,
+            # AutogrowPrefixTestNode,
+        ]
+
+async def comfy_entrypoint() -> LogicExtension:
+    return LogicExtension()

comfy_extras/nodes_model_patch.py

@@ -6,6 +6,7 @@ import comfy.ops
 import comfy.model_management
 import comfy.ldm.common_dit
 import comfy.latent_formats
+import comfy.ldm.lumina.controlnet
 
 
 class BlockWiseControlBlock(torch.nn.Module):
@@ -189,6 +190,35 @@ class SigLIPMultiFeatProjModel(torch.nn.Module):
 
         return embedding
 
+def z_image_convert(sd):
+    replace_keys = {".attention.to_out.0.bias": ".attention.out.bias",
+                    ".attention.norm_k.weight": ".attention.k_norm.weight",
+                    ".attention.norm_q.weight": ".attention.q_norm.weight",
+                    ".attention.to_out.0.weight": ".attention.out.weight"
+                    }
+
+    out_sd = {}
+    for k in sorted(sd.keys()):
+        w = sd[k]
+
+        k_out = k
+        if k_out.endswith(".attention.to_k.weight"):
+            cc = [w]
+            continue
+        if k_out.endswith(".attention.to_q.weight"):
+            cc = [w] + cc
+            continue
+        if k_out.endswith(".attention.to_v.weight"):
+            cc = cc + [w]
+            w = torch.cat(cc, dim=0)
+            k_out = k_out.replace(".attention.to_v.weight", ".attention.qkv.weight")
+
+        for r, rr in replace_keys.items():
+            k_out = k_out.replace(r, rr)
+        out_sd[k_out] = w
+
+    return out_sd
+
 class ModelPatchLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -211,6 +241,9 @@ class ModelPatchLoader:
         elif 'feature_embedder.mid_layer_norm.bias' in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True)
             model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+        elif 'control_all_x_embedder.2-1.weight' in sd: # alipai z image fun controlnet
+            sd = z_image_convert(sd)
+            model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
 
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -263,6 +296,69 @@ class DiffSynthCnetPatch:
     def models(self):
         return [self.model_patch]
 
+class ZImageControlPatch:
+    def __init__(self, model_patch, vae, image, strength):
+        self.model_patch = model_patch
+        self.vae = vae
+        self.image = image
+        self.strength = strength
+        self.encoded_image = self.encode_latent_cond(image)
+        self.encoded_image_size = (image.shape[1], image.shape[2])
+        self.temp_data = None
+
+    def encode_latent_cond(self, image):
+        latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(image))
+        return latent_image
+
+    def __call__(self, kwargs):
+        x = kwargs.get("x")
+        img = kwargs.get("img")
+        txt = kwargs.get("txt")
+        pe = kwargs.get("pe")
+        vec = kwargs.get("vec")
+        block_index = kwargs.get("block_index")
+        spacial_compression = self.vae.spacial_compression_encode()
+        if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
+            image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
+            loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+            self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1))
+            self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
+            comfy.model_management.load_models_gpu(loaded_models)
+
+        cnet_index = (block_index // 5)
+        cnet_index_float = (block_index / 5)
+
+        kwargs.pop("img")  # we do ops in place
+        kwargs.pop("txt")
+
+        cnet_blocks = self.model_patch.model.n_control_layers
+        if cnet_index_float > (cnet_blocks - 1):
+            self.temp_data = None
+            return kwargs
+
+        if self.temp_data is None or self.temp_data[0] > cnet_index:
+            self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+
+        while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+            next_layer = self.temp_data[0] + 1
+            self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+
+        if cnet_index_float == self.temp_data[0]:
+            img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+            if cnet_blocks == self.temp_data[0] + 1:
+                self.temp_data = None
+
+        return kwargs
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            self.encoded_image = self.encoded_image.to(device_or_dtype)
+            self.temp_data = None
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
 class QwenImageDiffsynthControlnet:
     @classmethod
     def INPUT_TYPES(s):
@@ -289,7 +385,10 @@ class QwenImageDiffsynthControlnet:
                 mask = mask.unsqueeze(2)
             mask = 1.0 - mask
 
-        model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
+        if isinstance(model_patch.model, comfy.ldm.lumina.controlnet.ZImage_Control):
+            model_patched.set_model_double_block_patch(ZImageControlPatch(model_patch, vae, image, strength))
+        else:
+            model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
         return (model_patched,)
 
 

execution.py

@@ -34,7 +34,7 @@ from comfy_execution.validation import validate_node_input
 from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler
 from comfy_execution.utils import CurrentNodeContext
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
-from comfy_api.latest import io
+from comfy_api.latest import io, _io
 
 
 class ExecutionResult(Enum):
@@ -76,7 +76,7 @@ class IsChangedCache:
             return self.is_changed[node_id]
 
         # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
-        input_data_all, _, hidden_inputs = get_input_data(node["inputs"], class_def, node_id, None)
+        input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
         try:
             is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name)
             is_changed = await resolve_map_node_over_list_results(is_changed)
@@ -146,8 +146,9 @@ SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 
 def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
+    v3_data: io.V3Data = {}
     if is_v3:
-        valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True)
+        valid_inputs, schema, v3_data = class_def.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
     else:
         valid_inputs = class_def.INPUT_TYPES()
     input_data_all = {}
@@ -207,7 +208,8 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
                     input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)]
                 if h[x] == "API_KEY_COMFY_ORG":
                     input_data_all[x] = [extra_data.get("api_key_comfy_org", None)]
-    return input_data_all, missing_keys, hidden_inputs_v3
+    v3_data["hidden_inputs"] = hidden_inputs_v3
+    return input_data_all, missing_keys, v3_data
 
 map_node_over_list = None #Don't hook this please
 
@@ -223,7 +225,7 @@ async def resolve_map_node_over_list_results(results):
                 raise exc
         return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
 
-async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None):
+async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
     # check if node wants the lists
     input_is_list = getattr(obj, "INPUT_IS_LIST", False)
 
@@ -259,13 +261,16 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                 if is_class(obj):
                     type_obj = obj
                     obj.VALIDATE_CLASS()
-                    class_clone = obj.PREPARE_CLASS_CLONE(hidden_inputs)
+                    class_clone = obj.PREPARE_CLASS_CLONE(v3_data)
                 # otherwise, use class instance to populate/reuse some fields
                 else:
                     type_obj = type(obj)
                     type_obj.VALIDATE_CLASS()
-                    class_clone = type_obj.PREPARE_CLASS_CLONE(hidden_inputs)
+                    class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data)
                 f = make_locked_method_func(type_obj, func, class_clone)
+                # in case of dynamic inputs, restructure inputs to expected nested dict
+                if v3_data is not None:
+                    inputs = _io.build_nested_inputs(inputs, v3_data)
             # V1
             else:
                 f = getattr(obj, func)
@@ -320,8 +325,8 @@ def merge_result_data(results, obj):
             output.append([o[i] for o in results])
     return output
 
-async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None):
-    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
+async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
+    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
     has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values)
     if has_pending_task:
         return return_values, {}, False, has_pending_task
@@ -460,7 +465,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             has_subgraph = False
         else:
             get_progress_state().start_progress(unique_id)
-            input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
+            input_data_all, missing_keys, v3_data = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
             if server.client_id is not None:
                 server.last_node_id = display_node_id
                 server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
@@ -475,7 +480,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             else:
                 lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
             if lazy_status_present:
-                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, hidden_inputs=hidden_inputs)
+                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data)
                 required_inputs = await resolve_map_node_over_list_results(required_inputs)
                 required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
                 required_inputs = [x for x in required_inputs if isinstance(x,str) and (
@@ -507,7 +512,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             def pre_execute_cb(call_index):
                 # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)?
                 GraphBuilder.set_default_prefix(unique_id, call_index, 0)
-            output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
+            output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
             if has_pending_tasks:
                 pending_async_nodes[unique_id] = output_data
                 unblock = execution_list.add_external_block(unique_id)
@@ -745,18 +750,17 @@ async def validate_inputs(prompt_id, prompt, item, validated):
     class_type = prompt[unique_id]['class_type']
     obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
 
-    class_inputs = obj_class.INPUT_TYPES()
-    valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
-
     errors = []
     valid = True
 
     validate_function_inputs = []
     validate_has_kwargs = False
     if issubclass(obj_class, _ComfyNodeInternal):
+        class_inputs, _, _ = obj_class.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
         validate_function_name = "validate_inputs"
         validate_function = first_real_override(obj_class, validate_function_name)
     else:
+        class_inputs = obj_class.INPUT_TYPES()
         validate_function_name = "VALIDATE_INPUTS"
         validate_function = getattr(obj_class, validate_function_name, None)
     if validate_function is not None:
@@ -765,6 +769,8 @@ async def validate_inputs(prompt_id, prompt, item, validated):
         validate_has_kwargs = argspec.varkw is not None
     received_types = {}
 
+    valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
+
     for x in valid_inputs:
         input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
         assert extra_info is not None
@@ -935,7 +941,7 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                         continue
 
     if len(validate_function_inputs) > 0 or validate_has_kwargs:
-        input_data_all, _, hidden_inputs = get_input_data(inputs, obj_class, unique_id)
+        input_data_all, _, v3_data = get_input_data(inputs, obj_class, unique_id)
         input_filtered = {}
         for x in input_data_all:
             if x in validate_function_inputs or validate_has_kwargs:
@@ -943,7 +949,7 @@ async def validate_inputs(prompt_id, prompt, item, validated):
         if 'input_types' in validate_function_inputs:
             input_filtered['input_types'] = [received_types]
 
-        ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, hidden_inputs=hidden_inputs)
+        ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, v3_data=v3_data)
         ret = await resolve_map_node_over_list_results(ret)
         for x in input_filtered:
             for i, r in enumerate(ret):

nodes.py

@@ -2355,6 +2355,7 @@ async def init_builtin_extra_nodes():
         "nodes_easycache.py",
         "nodes_audio_encoder.py",
         "nodes_rope.py",
+        "nodes_logic.py",
         "nodes_nop.py",
     ]
 

server.py

@@ -98,7 +98,7 @@ def create_cors_middleware(allowed_origin: str):
             response = await handler(request)
 
         response.headers['Access-Control-Allow-Origin'] = allowed_origin
-        response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS'
+        response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS, PATCH'
         response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
         response.headers['Access-Control-Allow-Credentials'] = 'true'
         return response