fix: remove whitespace and deprecation cruft

Amp-Thread-ID: https://ampcode.com/threads/T-019c2be8-0b34-747e-b1f7-20a1a1e6c9df

app/node_replace_manager.py

@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from aiohttp import web
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy_api.latest._node_replace import NodeReplace
+
+
+class NodeReplaceManager:
+    """Manages node replacement registrations."""
+
+    def __init__(self):
+        self._replacements: dict[str, list[NodeReplace]] = {}
+
+    def register(self, node_replace: NodeReplace):
+        """Register a node replacement mapping."""
+        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+
+    def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
+        """Get replacements for an old node ID."""
+        return self._replacements.get(old_node_id)
+
+    def has_replacement(self, old_node_id: str) -> bool:
+        """Check if a replacement exists for an old node ID."""
+        return old_node_id in self._replacements
+
+    def as_dict(self):
+        """Serialize all replacements to dict."""
+        return {
+            k: [v.as_dict() for v in v_list]
+            for k, v_list in self._replacements.items()
+        }
+
+    def add_routes(self, routes):
+        @routes.get("/node_replacements")
+        async def get_node_replacements(request):
+            return web.json_response(self.as_dict())

comfy/ldm/ace/ace_step15.py

@@ -7,67 +7,6 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 from comfy.ldm.flux.layers import timestep_embedding
 
-def get_silence_latent(length, device):
-    head = torch.tensor([[[ 0.5707,  0.0982,  0.6909, -0.5658,  0.6266,  0.6996, -0.1365, -0.1291,
-                        -0.0776, -0.1171, -0.2743, -0.8422, -0.1168,  1.5539, -4.6936,  0.7436,
-                        -1.1846, -0.2637,  0.6933, -6.7266,  0.0966, -0.1187, -0.3501, -1.1736,
-                        0.0587, -2.0517, -1.3651,  0.7508, -0.2490, -1.3548, -0.1290, -0.7261,
-                        1.1132, -0.3249,  0.2337,  0.3004,  0.6605, -0.0298, -0.1989, -0.4041,
-                        0.2843, -1.0963, -0.5519,  0.2639, -1.0436, -0.1183,  0.0640,  0.4460,
-                        -1.1001, -0.6172, -1.3241,  1.1379,  0.5623, -0.1507, -0.1963, -0.4742,
-                        -2.4697,  0.5302,  0.5381,  0.4636, -0.1782, -0.0687,  1.0333,  0.4202],
-                        [ 0.3040, -0.1367,  0.6200,  0.0665, -0.0642,  0.4655, -0.1187, -0.0440,
-                        0.2941, -0.2753,  0.0173, -0.2421, -0.0147,  1.5603, -2.7025,  0.7907,
-                        -0.9736, -0.0682,  0.1294, -5.0707, -0.2167,  0.3302, -0.1513, -0.8100,
-                        -0.3894, -0.2884, -0.3149,  0.8660, -0.3817, -1.7061,  0.5824, -0.4840,
-                        0.6938,  0.1859,  0.1753,  0.3081,  0.0195,  0.1403, -0.0754, -0.2091,
-                        0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320,  0.1284,  0.4974,
-                        -1.1889, -0.0344, -0.8313,  0.2953,  0.5445, -0.6249, -0.1595, -0.0682,
-                        -3.1412,  0.0484,  0.4153,  0.8260, -0.1526, -0.0625,  0.5366,  0.8473],
-                        [ 5.3524e-02, -1.7534e-01,  5.4443e-01, -4.3501e-01, -2.1317e-03,
-                        3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01,
-                        -7.7107e-02, -2.0593e-01, -3.2780e-01,  1.5142e+00, -2.6101e+00,
-                        5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00,
-                        1.1601e-01,  4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01,
-                        -6.3474e-01, -1.5893e-01,  8.2745e-01, -2.2992e-01, -1.6816e+00,
-                        5.4440e-01, -4.9579e-01,  5.5128e-01,  3.0477e-01,  8.3052e-02,
-                        -6.1782e-02,  5.9036e-03,  2.9553e-01, -8.0645e-02, -1.0060e-01,
-                        1.9144e-01, -3.8124e-01, -7.2949e-01,  2.4520e-02, -5.0814e-01,
-                        2.3977e-01,  9.2943e-02,  3.9256e-01, -1.1993e+00, -3.2752e-01,
-                        -7.2707e-01,  2.9476e-01,  4.3542e-01, -8.8597e-01, -4.1686e-01,
-                        -8.5390e-02, -2.9018e+00,  6.4988e-02,  5.3945e-01,  9.1988e-01,
-                        5.8762e-02, -7.0098e-02,  6.4772e-01,  8.9118e-01],
-                        [-3.2225e-02, -1.3195e-01,  5.6411e-01, -5.4766e-01, -5.2170e-03,
-                        3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01,
-                        -9.0984e-02, -1.9540e-01, -2.5590e-01,  1.5440e+00, -2.6349e+00,
-                        6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00,
-                        1.8818e-01,  5.0109e-01,  7.3546e-03, -6.8771e-01, -3.0676e-01,
-                        -7.3257e-01, -1.6687e-01,  9.2232e-01, -1.8987e-01, -1.7267e+00,
-                        5.3355e-01, -5.3179e-01,  4.4953e-01,  2.8820e-01,  1.3012e-01,
-                        -2.0943e-01, -1.1348e-01,  3.3929e-01, -1.5069e-01, -1.2919e-01,
-                        1.8929e-01, -3.6166e-01, -8.0756e-01,  6.6387e-02, -5.8867e-01,
-                        1.6978e-01,  1.0134e-01,  3.3877e-01, -1.2133e+00, -3.2492e-01,
-                        -8.1237e-01,  3.8101e-01,  4.3765e-01, -8.0596e-01, -4.4531e-01,
-                        -4.7513e-02, -2.9266e+00,  1.1741e-03,  4.5123e-01,  9.3075e-01,
-                        5.3688e-02, -1.9621e-01,  6.4530e-01,  9.3870e-01]]], device=device).movedim(-1, 1)
-
-    silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
-                                2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
-                                -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
-                                7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
-                                2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
-                                -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
-                                5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
-                                -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
-                                2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
-                                1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
-                                -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
-                                -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
-                                7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length)
-    silence_latent[:, :, :head.shape[-1]] = head
-    return silence_latent
-
-
 def get_layer_class(operations, layer_name):
     if operations is not None and hasattr(operations, layer_name):
         return getattr(operations, layer_name)
@@ -738,7 +677,7 @@ class AttentionPooler(nn.Module):
     def forward(self, x):
         B, T, P, D = x.shape
         x = self.embed_tokens(x)
-        special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)
+        special = self.special_token.expand(B, T, 1, -1)
         x = torch.cat([special, x], dim=2)
         x = x.view(B * T, P + 1, D)
 
@@ -789,7 +728,7 @@ class FSQ(nn.Module):
         self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)
 
     def bound(self, z):
-        levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)
+        levels_minus_1 = (self._levels - 1).to(z.dtype)
         scale = 2. / levels_minus_1
         bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5
 
@@ -804,8 +743,8 @@ class FSQ(nn.Module):
         return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1.
 
     def codes_to_indices(self, zhat):
-        zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1))
-        return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32)
+        zhat_normalized = (zhat + 1.) / (2. / (self._levels.to(zhat.dtype) - 1))
+        return (zhat_normalized * self._basis.to(zhat.dtype)).sum(dim=-1).round().to(torch.int32)
 
     def forward(self, z):
         orig_dtype = z.dtype
@@ -887,7 +826,7 @@ class ResidualFSQ(nn.Module):
         x = self.project_in(x)
 
         if hasattr(self, 'soft_clamp_input_value'):
-            sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)
+            sc_val = self.soft_clamp_input_value.to(x.dtype)
             x = (x / sc_val).tanh() * sc_val
 
         quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)
@@ -895,7 +834,7 @@ class ResidualFSQ(nn.Module):
         all_indices = []
 
         for layer, scale in zip(self.layers, self.scales):
-            scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)
+            scale = scale.to(residual.dtype)
 
             quantized, indices = layer(residual / scale)
             quantized = quantized * scale
@@ -1101,21 +1040,22 @@ class AceStepConditionGenerationModel(nn.Module):
             lm_hints = self.detokenizer(lm_hints_5Hz)
 
         lm_hints = lm_hints[:, :src_latents.shape[1], :]
-        if is_covers is None or is_covers is True:
+        if is_covers is None:
             src_latents = lm_hints
-        elif is_covers is False:
-            src_latents = refer_audio_acoustic_hidden_states_packed
+        else:
+            src_latents = torch.where(is_covers.unsqueeze(-1).unsqueeze(-1) > 0, lm_hints, src_latents)
 
         context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1)
 
         return encoder_hidden, encoder_mask, context_latents
 
-    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, **kwargs):
         text_attention_mask = None
         lyric_attention_mask = None
         refer_audio_order_mask = None
         attention_mask = None
         chunk_masks = None
+        is_covers = None
         src_latents = None
         precomputed_lm_hints_25Hz = None
         lyric_hidden_states = lyric_embed
@@ -1127,7 +1067,7 @@ class AceStepConditionGenerationModel(nn.Module):
         if refer_audio_order_mask is None:
             refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long)
 
-        if src_latents is None:
+        if src_latents is None and is_covers is None:
             src_latents = x
 
         if chunk_masks is None:
@@ -1140,9 +1080,6 @@ class AceStepConditionGenerationModel(nn.Module):
             src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes
         )
 
-        if replace_with_null_embeds:
-            enc_hidden[:] = self.null_condition_emb.to(enc_hidden)
-
         out = self.decoder(hidden_states=x,
                            timestep=timestep,
                            timestep_r=timestep,

comfy/ldm/cosmos/predict2.py

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

comfy/model_base.py

@@ -147,11 +147,11 @@ class BaseModel(torch.nn.Module):
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
             logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
-            comfy.model_management.archive_model_dtypes(self.diffusion_model)
-
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
+        comfy.model_management.archive_model_dtypes(self.diffusion_model)
+
         self.adm_channels = unet_config.get("adm_in_channels", None)
         if self.adm_channels is None:
             self.adm_channels = 0
@@ -1552,8 +1552,6 @@ class ACEStep15(BaseModel):
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            if torch.count_nonzero(cross_attn) == 0:
-                out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
@@ -1562,11 +1560,22 @@ class ACEStep15(BaseModel):
 
         refer_audio = kwargs.get("reference_audio_timbre_latents", None)
         if refer_audio is None or len(refer_audio) == 0:
-            refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
+            refer_audio = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
+                                        2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
+                                        -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
+                                        7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
+                                        2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
+                                        -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
+                                        5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
+                                        -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
+                                        2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
+                                        1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
+                                        -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
+                                        -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
+                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
             pass_audio_codes = True
         else:
             refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
-            out['is_covers'] = comfy.conds.CONDConstant(True)
             pass_audio_codes = False
 
         if pass_audio_codes:
@@ -1574,12 +1583,6 @@ class ACEStep15(BaseModel):
             if audio_codes is not None:
                 out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
                 refer_audio = refer_audio[:, :, :750]
-            else:
-                out['is_covers'] = comfy.conds.CONDConstant(False)
-
-        if refer_audio.shape[2] < noise.shape[2]:
-            pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
-            refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)
 
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out

comfy/sd.py

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

comfy/supported_models.py

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

comfy/text_encoders/ace15.py

@@ -3,7 +3,6 @@ import comfy.text_encoders.llama
 from comfy import sd1_clip
 import torch
 import math
-import yaml
 import comfy.utils
 
 
@@ -126,43 +125,14 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_06b", tokenizer=Qwen3Tokenizer)
 
-    def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str:
-        user_metas = {
-            k: kwargs.pop(k)
-            for k in ("bpm", "duration", "keyscale", "timesignature", "language", "caption")
-            if k in kwargs
-        }
-        timesignature = user_metas.get("timesignature")
-        if isinstance(timesignature, str) and timesignature.endswith("/4"):
-            user_metas["timesignature"] = timesignature.rsplit("/", 1)[0]
-        user_metas = {
-            k: v if not isinstance(v, str) or not v.isdigit() else int(v)
-            for k, v in user_metas.items()
-            if v not in {"unspecified", None}
-        }
-        if len(user_metas):
-            meta_yaml = yaml.dump(user_metas, allow_unicode=True, sort_keys=True).strip()
-        else:
-            meta_yaml = ""
-        return f"<think>\n{meta_yaml}\n</think>" if not return_yaml else meta_yaml
-
-    def _metas_to_cap(self, **kwargs) -> str:
-        use_keys = ("bpm", "duration", "keyscale", "timesignature")
-        user_metas = { k: kwargs.pop(k, "N/A") for k in use_keys }
-        duration = user_metas["duration"]
-        if duration == "N/A":
-            user_metas["duration"] = "30 seconds"
-        elif isinstance(duration, (str, int, float)):
-            user_metas["duration"] = f"{math.ceil(float(duration))} seconds"
-        else:
-            raise TypeError("Unexpected type for duration key, must be str, int or float")
-        return "\n".join(f"- {k}: {user_metas[k]}" for k in use_keys)
-
     def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
         out = {}
         lyrics = kwargs.get("lyrics", "")
+        bpm = kwargs.get("bpm", 120)
         duration = kwargs.get("duration", 120)
-        language = kwargs.get("language")
+        keyscale = kwargs.get("keyscale", "C major")
+        timesignature = kwargs.get("timesignature", 2)
+        language = kwargs.get("language", "en")
         seed = kwargs.get("seed", 0)
 
         generate_audio_codes = kwargs.get("generate_audio_codes", True)
@@ -171,20 +141,16 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
         top_p = kwargs.get("top_p", 0.9)
         top_k = kwargs.get("top_k", 0.0)
 
-
         duration = math.ceil(duration)
-        kwargs["duration"] = duration
+        meta_lm = 'bpm: {}\nduration: {}\nkeyscale: {}\ntimesignature: {}'.format(bpm, duration, keyscale, timesignature)
+        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n{}\n<|im_end|>\n<|im_start|>assistant\n<think>\n{}\n</think>\n\n<|im_end|>\n"
 
-        cot_text = self._metas_to_cot(caption = text, **kwargs)
-        meta_cap = self._metas_to_cap(**kwargs)
+        meta_cap = '- bpm: {}\n- timesignature: {}\n- keyscale: {}\n- duration: {}\n'.format(bpm, timesignature, keyscale, duration)
+        out["lm_prompt"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, meta_lm), disable_weights=True)
+        out["lm_prompt_negative"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, ""), disable_weights=True)
 
-        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n<|im_end|>\n"
-
-        out["lm_prompt"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, cot_text), disable_weights=True)
-        out["lm_prompt_negative"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, "<think>\n</think>"), disable_weights=True)
-
-        out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>".format(language if language is not None else "", lyrics), return_word_ids, disable_weights=True, **kwargs)
-        out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
+        out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric{}<|endoftext|><|endoftext|>".format(language, lyrics), return_word_ids, disable_weights=True, **kwargs)
+        out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}# Metas\n{}<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
         out["lm_metadata"] = {"min_tokens": duration * 5,
                               "seed": seed,
                               "generate_audio_codes": generate_audio_codes,

comfy/text_encoders/anima.py

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

comfy/text_encoders/lt.py

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

comfy_api/latest/__init__.py

@@ -10,6 +10,7 @@ from ._input_impl import VideoFromFile, VideoFromComponents
 from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL, File3D
 from . import _io_public as io
 from . import _ui_public as ui
+from . import _node_replace_public as node_replace
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
 from PIL import Image
@@ -21,6 +22,14 @@ class ComfyAPI_latest(ComfyAPIBase):
     VERSION = "latest"
     STABLE = False
 
+    class NodeReplacement(ProxiedSingleton):
+        async def register(self, node_replace: 'node_replace.NodeReplace') -> None:
+            """Register a node replacement mapping."""
+            from server import PromptServer
+            PromptServer.instance.node_replace_manager.register(node_replace)
+
+    node_replacement: NodeReplacement
+
     class Execution(ProxiedSingleton):
         async def set_progress(
             self,
@@ -131,4 +140,5 @@ __all__ = [
     "IO",
     "ui",
     "UI",
+    "node_replace",
 ]

comfy_api/latest/_node_replace.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import Any
+
+
+class NodeReplace:
+    """
+    Defines a possible node replacement, mapping inputs and outputs of the old node to the new node.
+
+    Also supports assigning specific values to the input widgets of the new node.
+    """
+    def __init__(self,
+        new_node_id: str,
+        old_node_id: str,
+        old_widget_ids: list[str] | None=None,
+        input_mapping: list[InputMap] | None=None,
+        output_mapping: list[OutputMap] | None=None,
+    ):
+        self.new_node_id = new_node_id
+        self.old_node_id = old_node_id
+        self.old_widget_ids = old_widget_ids
+        self.input_mapping = input_mapping
+        self.output_mapping = output_mapping
+
+    def as_dict(self):
+        """Create serializable representation of the node replacement."""
+        return {
+            "new_node_id": self.new_node_id,
+            "old_node_id": self.old_node_id,
+            "old_widget_ids": self.old_widget_ids,
+            "input_mapping": [m.as_dict() for m in self.input_mapping] if self.input_mapping else None,
+            "output_mapping": [m.as_dict() for m in self.output_mapping] if self.output_mapping else None,
+        }
+
+
+class InputMap:
+    """
+    Map inputs of node replacement.
+
+    Use InputMap.OldId or InputMap.SetValue for mapping purposes.
+    """
+    class _Assign:
+        def __init__(self, assign_type: str):
+            self.assign_type = assign_type
+
+        def as_dict(self):
+            return {
+                "assign_type": self.assign_type,
+            }
+
+    class OldId(_Assign):
+        """Connect the input of the old node with given id to new node when replacing."""
+        def __init__(self, old_id: str):
+            super().__init__("old_id")
+            self.old_id = old_id
+
+        def as_dict(self):
+            return super().as_dict() | {
+                "old_id": self.old_id,
+            }
+
+    class SetValue(_Assign):
+        """Use the given value for the input of the new node when replacing; assumes input is a widget."""
+        def __init__(self, value: Any):
+            super().__init__("set_value")
+            self.value = value
+
+        def as_dict(self):
+            return super().as_dict() | {
+                "value": self.value,
+            }
+
+    def __init__(self, new_id: str, assign: OldId | SetValue):
+        self.new_id = new_id
+        self.assign = assign
+
+    def as_dict(self):
+        return {
+            "new_id": self.new_id,
+            "assign": self.assign.as_dict(),
+        }
+
+
+class OutputMap:
+    """Map outputs of node replacement via indexes, as that's how outputs are stored."""
+    def __init__(self, new_idx: int, old_idx: int):
+        self.new_idx = new_idx
+        self.old_idx = old_idx
+
+    def as_dict(self):
+        return {
+            "new_idx": self.new_idx,
+            "old_idx": self.old_idx,
+        }

comfy_api/latest/_node_replace_public.py

@@ -0,0 +1 @@
+from ._node_replace 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, IO, UI, ComfyExtension  #noqa: F401
+from comfy_api.latest import io, ui, IO, UI, ComfyExtension, node_replace  #noqa: F401
 
 
 class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
@@ -46,4 +46,5 @@ __all__ = [
     "IO",
     "ui",
     "UI",
+    "node_replace",
 ]

comfy_extras/nodes_audio.py

@@ -94,19 +94,6 @@ class VAEEncodeAudio(IO.ComfyNode):
     encode = execute  # TODO: remove
 
 
-def vae_decode_audio(vae, samples, tile=None, overlap=None):
-    if tile is not None:
-        audio = vae.decode_tiled(samples["samples"], tile_y=tile, overlap=overlap).movedim(-1, 1)
-    else:
-        audio = vae.decode(samples["samples"]).movedim(-1, 1)
-
-    std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
-    std[std < 1.0] = 1.0
-    audio /= std
-    vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
-    return {"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]}
-
-
 class VAEDecodeAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -124,33 +111,16 @@ class VAEDecodeAudio(IO.ComfyNode):
 
     @classmethod
     def execute(cls, vae, samples) -> IO.NodeOutput:
-        return IO.NodeOutput(vae_decode_audio(vae, samples))
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
+        std[std < 1.0] = 1.0
+        audio /= std
+        vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
+        return IO.NodeOutput({"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]})
 
     decode = execute  # TODO: remove
 
 
-class VAEDecodeAudioTiled(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="VAEDecodeAudioTiled",
-            search_aliases=["latent to audio"],
-            display_name="VAE Decode Audio (Tiled)",
-            category="latent/audio",
-            inputs=[
-                IO.Latent.Input("samples"),
-                IO.Vae.Input("vae"),
-                IO.Int.Input("tile_size", default=512, min=32, max=8192, step=8),
-                IO.Int.Input("overlap", default=64, min=0, max=1024, step=8),
-            ],
-            outputs=[IO.Audio.Output()],
-        )
-
-    @classmethod
-    def execute(cls, vae, samples, tile_size, overlap) -> IO.NodeOutput:
-        return IO.NodeOutput(vae_decode_audio(vae, samples, tile_size, overlap))
-
-
 class SaveAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -705,7 +675,6 @@ class AudioExtension(ComfyExtension):
             EmptyLatentAudio,
             VAEEncodeAudio,
             VAEDecodeAudio,
-            VAEDecodeAudioTiled,
             SaveAudio,
             SaveAudioMP3,
             SaveAudioOpus,

comfy_extras/nodes_custom_sampler.py

@@ -622,7 +622,6 @@ class SamplerSASolver(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSASolver",
-            search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
@@ -667,7 +666,6 @@ class SamplerSEEDS2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSEEDS2",
-            search_aliases=["sde", "exp heun"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),

comfy_extras/nodes_easycache.py

@@ -9,14 +9,6 @@ if TYPE_CHECKING:
     from uuid import UUID
 
 
-def _extract_tensor(data, output_channels):
-    """Extract tensor from data, handling both single tensors and lists."""
-    if isinstance(data, list):
-        # LTX2 AV tensors: [video, audio]
-        return data[0][:, :output_channels], data[1][:, :output_channels]
-    return data[:, :output_channels], None
-
-
 def easycache_forward_wrapper(executor, *args, **kwargs):
     # get values from args
     transformer_options: dict[str] = args[-1]
@@ -25,7 +17,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if not transformer_options:
             transformer_options = args[-2]
     easycache: EasyCacheHolder = transformer_options["easycache"]
-    x, ax = _extract_tensor(args[0], easycache.output_channels)
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     sigmas = transformer_options["sigmas"]
     uuids = transformer_options["uuids"]
     if sigmas is not None and easycache.is_past_end_timestep(sigmas):
@@ -43,11 +35,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if easycache.skip_current_step and can_apply_cache_diff:
             if easycache.verbose:
                 logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
-            result = easycache.apply_cache_diff(x, uuids)
-            if ax is not None:
-                result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
-                return [result, result_audio]
-            return result
+            return easycache.apply_cache_diff(x, uuids)
         if easycache.initial_step:
             easycache.first_cond_uuid = uuids[0]
             has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
@@ -63,18 +51,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
                         logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     # other conds should also skip this step, and instead use their cached values
                     easycache.skip_current_step = True
-                    result = easycache.apply_cache_diff(x, uuids)
-                    if ax is not None:
-                        result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
-                        return [result, result_audio]
-                    return result
+                    return easycache.apply_cache_diff(x, uuids)
                 else:
                     if easycache.verbose:
                         logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     easycache.cumulative_change_rate = 0.0
 
-    full_output: torch.Tensor = executor(*args, **kwargs)
-    output, audio_output = _extract_tensor(full_output, easycache.output_channels)
+    output: torch.Tensor = executor(*args, **kwargs)
     if has_first_cond_uuid and easycache.has_output_prev_norm():
         output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
         if easycache.verbose:
@@ -91,15 +74,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
             logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
     # TODO: allow cache_diff to be offloaded
     easycache.update_cache_diff(output, next_x_prev, uuids)
-    if audio_output is not None:
-        easycache.update_cache_diff(audio_output, ax, uuids, is_audio=True)
     if has_first_cond_uuid:
         easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
         easycache.output_prev_subsampled = easycache.subsample(output, uuids)
         easycache.output_prev_norm = output.flatten().abs().mean()
         if easycache.verbose:
             logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
-    return full_output
+    return output
 
 def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     # get values from args
@@ -108,8 +89,8 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
     # prepare next x_prev
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     next_x_prev = x
     input_change = None
     do_easycache = easycache.should_do_easycache(timestep)
@@ -216,7 +197,6 @@ class EasyCacheHolder:
         self.output_prev_subsampled: torch.Tensor = None
         self.output_prev_norm: torch.Tensor = None
         self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
-        self.uuid_cache_diffs_audio: dict[UUID, torch.Tensor] = {}
         self.output_change_rates = []
         self.approx_output_change_rates = []
         self.total_steps_skipped = 0
@@ -265,21 +245,20 @@ class EasyCacheHolder:
     def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
         return all(uuid in self.uuid_cache_diffs for uuid in uuids)
 
-    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
-        if self.first_cond_uuid in uuids and not is_audio:
+    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]):
+        if self.first_cond_uuid in uuids:
             self.total_steps_skipped += 1
-        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
         batch_offset = x.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
             # slice out only what is relevant to this cond
             batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
             # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
-            if x.shape[1:] != cache_diffs[uuid].shape[1:]:
+            if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]:
                 if not self.allow_mismatch:
                     raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
                 slicing = []
                 skip_this_dim = True
-                for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
+                for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape):
                     if skip_this_dim:
                         skip_this_dim = False
                         continue
@@ -291,11 +270,10 @@ class EasyCacheHolder:
                     else:
                         slicing.append(slice(None))
                 batch_slice = batch_slice + slicing
-            x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
+            x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
         return x
 
-    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
-        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
+    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
         # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
         if output.shape[1:] != x.shape[1:]:
             if not self.allow_mismatch:
@@ -315,7 +293,7 @@ class EasyCacheHolder:
         diff = output - x
         batch_offset = diff.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
-            cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
+            self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
 
     def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
         return self.first_cond_uuid in uuids
@@ -346,8 +324,6 @@ class EasyCacheHolder:
         self.output_prev_norm = None
         del self.uuid_cache_diffs
         self.uuid_cache_diffs = {}
-        del self.uuid_cache_diffs_audio
-        self.uuid_cache_diffs_audio = {}
         self.total_steps_skipped = 0
         self.state_metadata = None
         return self

comfy_extras/nodes_latent.py

@@ -391,9 +391,8 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
             latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
             normalized_latent = latent / latent_vector_magnitude
 
-            dims = list(range(1, latent_vector_magnitude.ndim))
-            mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
 
             top = (std * 5 + mean) * multiplier
 

comfy_extras/nodes_post_processing.py

@@ -655,6 +655,98 @@ class BatchImagesMasksLatentsNode(io.ComfyNode):
             batched = batch_masks(values)
         return io.NodeOutput(batched)
 
+
+from comfy_api.latest import ComfyAPI, node_replace
+
+async def register_replacements():
+    """Register all built-in node replacements using the async API."""
+    await register_replacements_longeredge()
+    await register_replacements_batchimages()
+    await register_replacements_upscaleimage()
+    await register_replacements_controlnet()
+    await register_replacements_load3d()
+    await register_replacements_preview3d()
+    await register_replacements_svdimg2vid()
+    await register_replacements_conditioningavg()
+
+async def register_replacements_longeredge():
+    # No dynamic inputs here
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="ImageScaleToMaxDimension",
+            old_node_id="ResizeImagesByLongerEdge",
+            old_widget_ids=["longer_edge"],
+            input_mapping=[
+                node_replace.InputMap(new_id="image", assign=node_replace.InputMap.OldId("images")),
+                node_replace.InputMap(new_id="largest_size", assign=node_replace.InputMap.OldId("longer_edge")),
+                node_replace.InputMap(new_id="upscale_method", assign=node_replace.InputMap.SetValue("lanczos")),
+            ],
+            # just to test the frontend output_mapping code, does nothing really here
+            output_mapping=[node_replace.OutputMap(new_idx=0, old_idx=0)],
+        ))
+
+async def register_replacements_batchimages():
+    # BatchImages node uses Autogrow
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="BatchImagesNode",
+            old_node_id="ImageBatch",
+            input_mapping=[
+                node_replace.InputMap(new_id="images.image0", assign=node_replace.InputMap.OldId("image1")),
+                node_replace.InputMap(new_id="images.image1", assign=node_replace.InputMap.OldId("image2")),
+            ],
+        ))
+
+async def register_replacements_upscaleimage():
+    # ResizeImageMaskNode uses DynamicCombo
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="ResizeImageMaskNode",
+            old_node_id="ImageScaleBy",
+            old_widget_ids=["upscale_method", "scale_by"],
+            input_mapping=[
+                node_replace.InputMap(new_id="input", assign=node_replace.InputMap.OldId("image")),
+                node_replace.InputMap(new_id="resize_type", assign=node_replace.InputMap.SetValue("scale by multiplier")),
+                node_replace.InputMap(new_id="resize_type.multiplier", assign=node_replace.InputMap.OldId("scale_by")),
+                node_replace.InputMap(new_id="scale_method", assign=node_replace.InputMap.OldId("upscale_method")),
+            ],
+        ))
+
+async def register_replacements_controlnet():
+    # T2IAdapterLoader → ControlNetLoader
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="ControlNetLoader",
+            old_node_id="T2IAdapterLoader",
+            input_mapping=[
+                node_replace.InputMap(new_id="control_net_name", assign=node_replace.InputMap.OldId("t2i_adapter_name")),
+            ],
+        ))
+
+async def register_replacements_load3d():
+    # Load3DAnimation merged into Load3D
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="Load3D",
+            old_node_id="Load3DAnimation",
+        ))
+
+async def register_replacements_preview3d():
+    # Preview3DAnimation merged into Preview3D
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="Preview3D",
+            old_node_id="Preview3DAnimation",
+        ))
+
+async def register_replacements_svdimg2vid():
+    # Typo fix: SDV → SVD
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="SVD_img2vid_Conditioning",
+            old_node_id="SDV_img2vid_Conditioning",
+        ))
+
+async def register_replacements_conditioningavg():
+    # Typo fix: trailing space in node name
+    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+            new_node_id="ConditioningAverage",
+            old_node_id="ConditioningAverage ",
+        ))
+
 class PostProcessingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -672,4 +764,5 @@ class PostProcessingExtension(ComfyExtension):
         ]
 
 async def comfy_entrypoint() -> PostProcessingExtension:
+    await register_replacements()
     return PostProcessingExtension()

comfy_extras/nodes_toolkit.py

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

comfyui_version.py

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

nodes.py

@@ -2433,8 +2433,7 @@ async def init_builtin_extra_nodes():
         "nodes_image_compare.py",
         "nodes_zimage.py",
         "nodes_lora_debug.py",
-        "nodes_color.py",
-        "nodes_toolkit.py",
+        "nodes_color.py"
     ]
 
     import_failed = []

pyproject.toml

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

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.38.13
+comfyui-frontend-package==1.37.11
 comfyui-workflow-templates==0.8.31
-comfyui-embedded-docs==0.4.1
+comfyui-embedded-docs==0.4.0
 torch
 torchsde
 torchvision

server.py

@@ -40,6 +40,7 @@ from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
 from app.custom_node_manager import CustomNodeManager
 from app.subgraph_manager import SubgraphManager
+from app.node_replace_manager import NodeReplaceManager
 from typing import Optional, Union
 from api_server.routes.internal.internal_routes import InternalRoutes
 from protocol import BinaryEventTypes
@@ -204,6 +205,7 @@ class PromptServer():
         self.model_file_manager = ModelFileManager()
         self.custom_node_manager = CustomNodeManager()
         self.subgraph_manager = SubgraphManager()
+        self.node_replace_manager = NodeReplaceManager()
         self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = execution.PromptQueue(self)
@@ -995,6 +997,7 @@ class PromptServer():
         self.model_file_manager.add_routes(self.routes)
         self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items())
         self.subgraph_manager.add_routes(self.routes, nodes.LOADED_MODULE_DIRS.items())
+        self.node_replace_manager.add_routes(self.routes)
         self.app.add_subapp('/internal', self.internal_routes.get_app())
 
         # Prefix every route with /api for easier matching for delegation.