Improve NodeReplace docstring

Merge branch 'master' into jk/node-replace-api
Refactored _node_replace.py InputMap/OutputMap to use a TypedDict instead of objects, simplified the schema sent to the frontend, updated nodes_post_processing.py replacements to use new schema
2026-02-11 18:50:03 +00:00 · 2026-02-11 01:16:39 -08:00 · 2026-02-11 01:06:10 -08:00 · 2026-02-11 01:02:55 -08:00 · 2026-02-10 22:04:32 -05:00 · 2026-02-10 21:45:19 -05:00
39 changed files with 1943 additions and 451 deletions
--- a/comfy/ldm/ace/ace_step15.py
+++ b/comfy/ldm/ace/ace_step15.py
@@ -7,6 +7,67 @@ 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)
@@ -677,7 +738,7 @@ class AttentionPooler(nn.Module):
    def forward(self, x):
        B, T, P, D = x.shape
        x = self.embed_tokens(x)
-        special = self.special_token.expand(B, T, 1, -1)
+        special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)
        x = torch.cat([special, x], dim=2)
        x = x.view(B * T, P + 1, D)

@@ -728,7 +789,7 @@ class FSQ(nn.Module):
        self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)

    def bound(self, z):
-        levels_minus_1 = (self._levels - 1).to(z.dtype)
+        levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)
        scale = 2. / levels_minus_1
        bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5

@@ -743,8 +804,8 @@ 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. / (self._levels.to(zhat.dtype) - 1))
-        return (zhat_normalized * self._basis.to(zhat.dtype)).sum(dim=-1).round().to(torch.int32)
+        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)

    def forward(self, z):
        orig_dtype = z.dtype
@@ -826,7 +887,7 @@ class ResidualFSQ(nn.Module):
        x = self.project_in(x)

        if hasattr(self, 'soft_clamp_input_value'):
-            sc_val = self.soft_clamp_input_value.to(x.dtype)
+            sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)
            x = (x / sc_val).tanh() * sc_val

        quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)
@@ -834,7 +895,7 @@ class ResidualFSQ(nn.Module):
        all_indices = []

        for layer, scale in zip(self.layers, self.scales):
-            scale = scale.to(residual.dtype)
+            scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)

            quantized, indices = layer(residual / scale)
            quantized = quantized * scale
@@ -1040,22 +1101,21 @@ class AceStepConditionGenerationModel(nn.Module):
            lm_hints = self.detokenizer(lm_hints_5Hz)

        lm_hints = lm_hints[:, :src_latents.shape[1], :]
-        if is_covers is None:
+        if is_covers is None or is_covers is True:
            src_latents = lm_hints
-        else:
-            src_latents = torch.where(is_covers.unsqueeze(-1).unsqueeze(-1) > 0, lm_hints, src_latents)
+        elif is_covers is False:
+            src_latents = refer_audio_acoustic_hidden_states_packed

        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, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **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
@@ -1067,7 +1127,7 @@ 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 and is_covers is None:
+        if src_latents is None:
            src_latents = x

        if chunk_masks is None:
@@ -1080,6 +1140,9 @@ 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,
--- a/comfy/ldm/anima/model.py
+++ b/comfy/ldm/anima/model.py
@@ -195,8 +195,20 @@ class Anima(MiniTrainDIT):
        super().__init__(*args, **kwargs)
        self.llm_adapter = LLMAdapter(device=kwargs.get("device"), dtype=kwargs.get("dtype"), operations=kwargs.get("operations"))

-    def preprocess_text_embeds(self, text_embeds, text_ids):
+    def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None):
        if text_ids is not None:
-            return self.llm_adapter(text_embeds, text_ids)
+            out = self.llm_adapter(text_embeds, text_ids)
+            if t5xxl_weights is not None:
+                out = out * t5xxl_weights
+
+            if out.shape[1] < 512:
+                out = torch.nn.functional.pad(out, (0, 0, 0, 512 - out.shape[1]))
+            return out
        else:
            return text_embeds
+
+    def forward(self, x, timesteps, context, **kwargs):
+        t5xxl_ids = kwargs.pop("t5xxl_ids", None)
+        if t5xxl_ids is not None:
+            context = self.preprocess_text_embeds(context, t5xxl_ids, t5xxl_weights=kwargs.pop("t5xxl_weights", None))
+        return super().forward(x, timesteps, context, **kwargs)
--- a/comfy/ldm/cosmos/predict2.py
+++ b/comfy/ldm/cosmos/predict2.py
@@ -335,7 +335,7 @@ class FinalLayer(nn.Module):
        device=None, dtype=None, operations=None
    ):
        super().__init__()
-        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = operations.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
        )
@@ -463,6 +463,8 @@ 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

@@ -512,7 +514,7 @@ 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, "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
                None,
                rope_emb=rope_emb_L_1_1_D,
                transformer_options=transformer_options,
@@ -522,7 +524,7 @@ 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 * result_B_T_H_W_D
+        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)

        def _x_fn(
            _x_B_T_H_W_D: torch.Tensor,
@@ -536,7 +538,7 @@ class Block(nn.Module):
            )
            _result_B_T_H_W_D = rearrange(
                self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
                    crossattn_emb,
                    rope_emb=rope_emb_L_1_1_D,
                    transformer_options=transformer_options,
@@ -555,7 +557,7 @@ 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 * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
+        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

        normalized_x_B_T_H_W_D = _fn(
            x_B_T_H_W_D,
@@ -563,8 +565,8 @@ 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)
-        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
+        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)
        return x_B_T_H_W_D


@@ -876,6 +878,14 @@ 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,
@@ -884,6 +894,6 @@ class MiniTrainDIT(nn.Module):
                **block_kwargs,
            )

-        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_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_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
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -29,19 +29,34 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
    return out.to(dtype=torch.float32, device=pos.device)


+def _apply_rope1(x: Tensor, freqs_cis: Tensor):
+    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+
+    x_out = freqs_cis[..., 0] * x_[..., 0]
+    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
+
+    return x_out.reshape(*x.shape).type_as(x)
+
+
+def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+
+
 try:
    import comfy.quant_ops
-    apply_rope = comfy.quant_ops.ck.apply_rope
-    apply_rope1 = comfy.quant_ops.ck.apply_rope1
+    q_apply_rope = comfy.quant_ops.ck.apply_rope
+    q_apply_rope1 = comfy.quant_ops.ck.apply_rope1
+    def apply_rope(xq, xk, freqs_cis):
+        if comfy.model_management.in_training:
+            return _apply_rope(xq, xk, freqs_cis)
+        else:
+            return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+    def apply_rope1(x, freqs_cis):
+        if comfy.model_management.in_training:
+            return _apply_rope1(x, freqs_cis)
+        else:
+            return q_apply_rope1(x, freqs_cis)
 except:
    logging.warning("No comfy kitchen, using old apply_rope functions.")
-    def apply_rope1(x: Tensor, freqs_cis: Tensor):
-        x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
-
-        x_out = freqs_cis[..., 0] * x_[..., 0]
-        x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
-
-        return x_out.reshape(*x.shape).type_as(x)
-
-    def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-        return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+    apply_rope = _apply_rope
+    apply_rope1 = _apply_rope1
--- a/comfy/model_base.py
+++ b/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
@@ -1160,12 +1160,16 @@ class Anima(BaseModel):
        device = kwargs["device"]
        if cross_attn is not None:
            if t5xxl_ids is not None:
-                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype()), t5xxl_ids.unsqueeze(0).to(device=device))
                if t5xxl_weights is not None:
-                    cross_attn *= t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)
+                    t5xxl_weights = t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)
+                t5xxl_ids = t5xxl_ids.unsqueeze(0)
+
+                if torch.is_inference_mode_enabled():  # if not we are training
+                    cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype()), t5xxl_ids.to(device=device), t5xxl_weights=t5xxl_weights.to(device=device, dtype=self.get_dtype()))
+                else:
+                    out['t5xxl_ids'] = comfy.conds.CONDRegular(t5xxl_ids)
+                    out['t5xxl_weights'] = comfy.conds.CONDRegular(t5xxl_weights)

-                if cross_attn.shape[1] < 512:
-                    cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, 0, 512 - cross_attn.shape[1]))
            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
        return out

@@ -1552,6 +1556,8 @@ 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)
@@ -1560,22 +1566,11 @@ class ACEStep15(BaseModel):

        refer_audio = kwargs.get("reference_audio_timbre_latents", None)
        if refer_audio is None or len(refer_audio) == 0:
-            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])
+            refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
            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:
@@ -1583,6 +1578,12 @@ 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
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -19,7 +19,7 @@
 import psutil
 import logging
 from enum import Enum
-from comfy.cli_args import args, PerformanceFeature, enables_dynamic_vram
+from comfy.cli_args import args, PerformanceFeature
 import threading
 import torch
 import sys
@@ -55,6 +55,11 @@ cpu_state = CPUState.GPU

 total_vram = 0

+
+# Training Related State
+in_training = False
+
+
 def get_supported_float8_types():
    float8_types = []
    try:
@@ -651,7 +656,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
                soft_empty_cache()
    return unloaded_models

-def load_models_gpu_orig(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
    cleanup_models_gc()
    global vram_state

@@ -747,26 +752,6 @@ def load_models_gpu_orig(models, memory_required=0, force_patch_weights=False, m
        current_loaded_models.insert(0, loaded_model)
    return

-def load_models_gpu_thread(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load):
-    with torch.inference_mode():
-        load_models_gpu_orig(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
-        soft_empty_cache()
-
-def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
-    #Deliberately load models outside of the Aimdo mempool so they can be retained accross
-    #nodes. Use a dummy thread to do it as pytorch documents that mempool contexts are
-    #thread local. So exploit that to escape context
-    if enables_dynamic_vram():
-        t = threading.Thread(
-            target=load_models_gpu_thread,
-            args=(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
-        )
-        t.start()
-        t.join()
-    else:
-        load_models_gpu_orig(models, memory_required=memory_required, force_patch_weights=force_patch_weights,
-                             minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
-
 def load_model_gpu(model):
    return load_models_gpu([model])

@@ -1226,21 +1211,16 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
        if dtype is None:
            dtype = weight._model_dtype

-        r = torch.empty_like(weight, dtype=dtype, device=device)
-
        signature = comfy_aimdo.model_vbar.vbar_fault(weight._v)
        if signature is not None:
-            raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
-            v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
+            v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, weight._v_tensor)[0]
            if not comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
                weight._v_signature = signature
                #Send it over
                v_tensor.copy_(weight, non_blocking=non_blocking)
-            #always take a deep copy even if _v is good, as we have no reasonable point to unpin
-            #a non comfy weight
-            r.copy_(v_tensor)
-            comfy_aimdo.model_vbar.vbar_unpin(weight._v)
-            return r
+            return v_tensor.to(dtype=dtype)
+
+        r = torch.empty_like(weight, dtype=dtype, device=device)

        if weight.dtype != r.dtype and weight.dtype != weight._model_dtype:
            #Offloaded casting could skip this, however it would make the quantizations
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -19,7 +19,6 @@
 from __future__ import annotations

 import collections
-import copy
 import inspect
 import logging
 import math
@@ -317,7 +316,7 @@ class ModelPatcher:

        n.object_patches = self.object_patches.copy()
        n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
-        n.model_options = copy.deepcopy(self.model_options)
+        n.model_options = comfy.utils.deepcopy_list_dict(self.model_options)
        n.backup = self.backup
        n.object_patches_backup = self.object_patches_backup
        n.parent = self
@@ -1492,7 +1491,9 @@ class ModelPatcherDynamic(ModelPatcher):
            if vbar is not None:
                vbar.prioritize()

-            #We have way more tools for acceleration on comfy weight offloading, so always
+            #We force reserve VRAM for the non comfy-weight so we dont have to deal
+            #with pin and unpin syncrhonization which can be expensive for small weights
+            #with a high layer rate (e.g. autoregressive LLMs).
            #prioritize the non-comfy weights (note the order reverse).
            loading = self._load_list(prio_comfy_cast_weights=True)
            loading.sort(reverse=True)
@@ -1541,6 +1542,7 @@ class ModelPatcherDynamic(ModelPatcher):

                    if vbar is not None and not hasattr(m, "_v"):
                        m._v = vbar.alloc(v_weight_size)
+                        m._v_tensor = comfy_aimdo.torch.aimdo_to_tensor(m._v, device_to)
                    allocated_size += v_weight_size

                else:
@@ -1555,8 +1557,10 @@ class ModelPatcherDynamic(ModelPatcher):
                        weight_size = geometry.numel() * geometry.element_size()
                        if vbar is not None and not hasattr(weight, "_v"):
                            weight._v = vbar.alloc(weight_size)
+                            weight._v_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device_to)
                            weight._model_dtype = model_dtype
                        allocated_size += weight_size
+                    vbar.set_watermark_limit(allocated_size)

            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")

--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -87,7 +87,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu

    signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
    if signature is not None:
-        xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
+        xfer_dest = s._v_tensor
    resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)

    if not resident:
@@ -169,8 +169,8 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
                if orig.dtype == dtype and len(fns) == 0:
                    #The layer actually wants our freshly saved QT
                    x = y
-            else:
-                y = x
+            elif update_weight:
+                y = comfy.float.stochastic_rounding(x, orig.dtype, seed = comfy.utils.string_to_seed(s.seed_key))
            if update_weight:
                orig.copy_(y)
        for f in fns:
--- a/comfy/sampler_helpers.py
+++ b/comfy/sampler_helpers.py
@@ -122,20 +122,26 @@ def estimate_memory(model, noise_shape, conds):
    minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
    return memory_required, minimum_memory_required

-def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
    executor = comfy.patcher_extension.WrapperExecutor.new_executor(
        _prepare_sampling,
        comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
    )
-    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load)
+    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload)

-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
+def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
    real_model: BaseModel = None
    models, inference_memory = get_additional_models(conds, model.model_dtype())
    models += get_additional_models_from_model_options(model_options)
    models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
-    memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory, force_full_load=force_full_load)
+    if force_offload: # In training + offload enabled, we want to force prepare sampling to trigger partial load
+        memory_required = 1e20
+        minimum_memory_required = None
+    else:
+        memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
+        memory_required += inference_memory
+        minimum_memory_required += inference_memory
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
    real_model = model.model

    return real_model, conds, models
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -793,8 +793,6 @@ class VAE:
            self.first_stage_model = AutoencoderKL(**(config['params']))
        self.first_stage_model = self.first_stage_model.eval()

-        model_management.archive_model_dtypes(self.first_stage_model)
-
        if device is None:
            device = model_management.vae_device()
        self.device = device
@@ -803,6 +801,7 @@ class VAE:
            dtype = model_management.vae_dtype(self.device, self.working_dtypes)
        self.vae_dtype = dtype
        self.first_stage_model.to(self.vae_dtype)
+        model_management.archive_model_dtypes(self.first_stage_model)
        self.output_device = model_management.intermediate_device()

        mp = comfy.model_patcher.CoreModelPatcher
@@ -976,7 +975,7 @@ class VAE:
        if overlap is not None:
            args["overlap"] = overlap

-        if dims == 1:
+        if dims == 1 or self.extra_1d_channel is not None:
            args.pop("tile_y")
            output = self.decode_tiled_1d(samples, **args)
        elif dims == 2:
--- a/comfy/supported_models.py
+++ b/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.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

    def __init__(self, unet_config):
        super().__init__(unet_config)
@@ -1023,11 +1023,7 @@ class Anima(supported_models_base.BASE):

    memory_usage_factor = 1.0

-    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
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

    def get_model(self, state_dict, prefix="", device=None):
        out = model_base.Anima(self, device=device)
@@ -1038,6 +1034,12 @@ 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",
--- a/comfy/text_encoders/ace15.py
+++ b/comfy/text_encoders/ace15.py
@@ -3,6 +3,8 @@ import comfy.text_encoders.llama
 from comfy import sd1_clip
 import torch
 import math
+from tqdm.auto import trange
+import yaml
 import comfy.utils


@@ -22,6 +24,8 @@ def sample_manual_loop_no_classes(
    audio_end_id: int = 215669,
    eos_token_id: int = 151645,
 ):
+    if ids is None:
+        return []
    device = model.execution_device

    if execution_dtype is None:
@@ -31,6 +35,7 @@ def sample_manual_loop_no_classes(
            execution_dtype = torch.float32

    embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device)
+    embeds_batch = embeds.shape[0]
    for i, t in enumerate(paddings):
        attention_mask[i, :t] = 0
        attention_mask[i, t:] = 1
@@ -40,22 +45,27 @@ def sample_manual_loop_no_classes(
    generator = torch.Generator(device=device)
    generator.manual_seed(seed)
    model_config = model.transformer.model.config
+    past_kv_shape = [embeds_batch, model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim]

    for x in range(model_config.num_hidden_layers):
-        past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+        past_key_values.append((torch.empty(past_kv_shape, device=device, dtype=execution_dtype), torch.empty(past_kv_shape, device=device, dtype=execution_dtype), 0))

    progress_bar = comfy.utils.ProgressBar(max_new_tokens)

-    for step in range(max_new_tokens):
+    for step in trange(max_new_tokens, desc="LM sampling"):
        outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values)
        next_token_logits = model.transformer.logits(outputs[0])[:, -1]
        past_key_values = outputs[2]

-        cond_logits = next_token_logits[0:1]
-        uncond_logits = next_token_logits[1:2]
-        cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits)
+        if cfg_scale != 1.0:
+            cond_logits = next_token_logits[0:1]
+            uncond_logits = next_token_logits[1:2]
+            cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits)
+        else:
+            cfg_logits = next_token_logits[0:1]

-        if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
+        use_eos_score = eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step
+        if use_eos_score:
            eos_score = cfg_logits[:, eos_token_id].clone()

        remove_logit_value = torch.finfo(cfg_logits.dtype).min
@@ -63,7 +73,7 @@ def sample_manual_loop_no_classes(
        cfg_logits[:, :audio_start_id] = remove_logit_value
        cfg_logits[:, audio_end_id:] = remove_logit_value

-        if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
+        if use_eos_score:
            cfg_logits[:, eos_token_id] = eos_score

        if top_k is not None and top_k > 0:
@@ -92,8 +102,8 @@ def sample_manual_loop_no_classes(
            break

        embed, _, _, _ = model.process_tokens([[token]], device)
-        embeds = embed.repeat(2, 1, 1)
-        attention_mask = torch.cat([attention_mask, torch.ones((2, 1), device=device, dtype=attention_mask.dtype)], dim=1)
+        embeds = embed.repeat(embeds_batch, 1, 1)
+        attention_mask = torch.cat([attention_mask, torch.ones((embeds_batch, 1), device=device, dtype=attention_mask.dtype)], dim=1)

        output_audio_codes.append(token - audio_start_id)
        progress_bar.update_absolute(step)
@@ -103,36 +113,79 @@ def sample_manual_loop_no_classes(

 def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0):
    positive = [[token for token, _ in inner_list] for inner_list in positive]
-    negative = [[token for token, _ in inner_list] for inner_list in negative]
    positive = positive[0]
-    negative = negative[0]

-    neg_pad = 0
-    if len(negative) < len(positive):
-        neg_pad = (len(positive) - len(negative))
-        negative = [model.special_tokens["pad"]] * neg_pad + negative
+    if cfg_scale != 1.0:
+        negative = [[token for token, _ in inner_list] for inner_list in negative]
+        negative = negative[0]

-    pos_pad = 0
-    if len(negative) > len(positive):
-        pos_pad = (len(negative) - len(positive))
-        positive = [model.special_tokens["pad"]] * pos_pad + positive
+        neg_pad = 0
+        if len(negative) < len(positive):
+            neg_pad = (len(positive) - len(negative))
+            negative = [model.special_tokens["pad"]] * neg_pad + negative

-    paddings = [pos_pad, neg_pad]
-    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
+        pos_pad = 0
+        if len(negative) > len(positive):
+            pos_pad = (len(negative) - len(positive))
+            positive = [model.special_tokens["pad"]] * pos_pad + positive
+
+        paddings = [pos_pad, neg_pad]
+        ids = [positive, negative]
+    else:
+        paddings = []
+        ids = [positive]
+
+    return sample_manual_loop_no_classes(model, ids, paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)


 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")
+            if k in kwargs
+        }
+        timesignature = user_metas.get("timesignature")
+        if isinstance(timesignature, str) and timesignature.endswith("/4"):
+            user_metas["timesignature"] = timesignature[:-2]
+        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", "timesignature", "keyscale", "duration")
+        user_metas = { k: kwargs.pop(k, "N/A") for k in use_keys }
+        timesignature = user_metas.get("timesignature")
+        if isinstance(timesignature, str) and timesignature.endswith("/4"):
+            user_metas["timesignature"] = timesignature[:-2]
+        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 = {}
+        text = text.strip()
+        text_negative = kwargs.get("caption_negative", text).strip()
        lyrics = kwargs.get("lyrics", "")
-        bpm = kwargs.get("bpm", 120)
+        lyrics_negative = kwargs.get("lyrics_negative", lyrics)
        duration = kwargs.get("duration", 120)
-        keyscale = kwargs.get("keyscale", "C major")
-        timesignature = kwargs.get("timesignature", 2)
-        language = kwargs.get("language", "en")
+        if isinstance(duration, str):
+            duration = float(duration.split(None, 1)[0])
+        language = kwargs.get("language")
        seed = kwargs.get("seed", 0)

        generate_audio_codes = kwargs.get("generate_audio_codes", True)
@@ -142,16 +195,45 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
        top_k = kwargs.get("top_k", 0.0)

        duration = math.ceil(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"
+        kwargs["duration"] = duration
+        tokens_duration = duration * 5
+        min_tokens = int(kwargs.get("min_tokens", tokens_duration))
+        max_tokens = int(kwargs.get("max_tokens", tokens_duration))

-        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)
+        metas_negative = {
+            k.rsplit("_", 1)[0]: kwargs.pop(k)
+            for k in ("bpm_negative", "duration_negative", "keyscale_negative", "timesignature_negative", "language_negative", "caption_negative")
+            if k in kwargs
+        }
+        if not kwargs.get("use_negative_caption"):
+            _ = metas_negative.pop("caption", None)

-        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,
+        cot_text = self._metas_to_cot(caption=text, **kwargs)
+        cot_text_negative = "<think>\n\n</think>" if not metas_negative else self._metas_to_cot(**metas_negative)
+        meta_cap = self._metas_to_cap(**kwargs)
+
+        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# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n\n<|im_end|>\n"
+        lyrics_template = "# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>"
+        qwen3_06b_template = "# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>"
+
+        llm_prompts = {
+            "lm_prompt": lm_template.format(text, lyrics.strip(), cot_text),
+            "lm_prompt_negative": lm_template.format(text_negative, lyrics_negative.strip(), cot_text_negative),
+            "lyrics": lyrics_template.format(language if language is not None else "", lyrics),
+            "qwen3_06b": qwen3_06b_template.format(text, meta_cap),
+        }
+
+        out = {
+            prompt_key: self.qwen3_06b.tokenize_with_weights(
+                prompt,
+                prompt_key == "qwen3_06b" and return_word_ids,
+                disable_weights = True,
+                **kwargs,
+            )
+            for prompt_key, prompt in llm_prompts.items()
+        }
+        out["lm_metadata"] = {"min_tokens": min_tokens,
+                              "max_tokens": max_tokens,
                              "seed": seed,
                              "generate_audio_codes": generate_audio_codes,
                              "cfg_scale": cfg_scale,
@@ -218,7 +300,7 @@ class ACE15TEModel(torch.nn.Module):

        lm_metadata = token_weight_pairs["lm_metadata"]
        if lm_metadata["generate_audio_codes"]:
-            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"])
+            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["max_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"])
            out["audio_codes"] = [audio_codes]

        return base_out, None, out
--- a/comfy/text_encoders/anima.py
+++ b/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"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        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["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
        return out

--- a/comfy/text_encoders/lt.py
+++ b/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, 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, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)

    def state_dict(self):
        return {"spiece_model": self.tokenizer.serialize_model()}
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -1376,3 +1376,21 @@ def string_to_seed(data):
            else:
                crc >>= 1
    return crc ^ 0xFFFFFFFF
+
+def deepcopy_list_dict(obj, memo=None):
+    if memo is None:
+        memo = {}
+
+    obj_id = id(obj)
+    if obj_id in memo:
+        return memo[obj_id]
+
+    if isinstance(obj, dict):
+        res = {deepcopy_list_dict(k, memo): deepcopy_list_dict(v, memo) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        res = [deepcopy_list_dict(i, memo) for i in obj]
+    else:
+        res = obj
+
+    memo[obj_id] = res
+    return res
--- a/comfy/weight_adapter/bypass.py
+++ b/comfy/weight_adapter/bypass.py
@@ -21,6 +21,7 @@ from typing import Optional, Union
 import torch
 import torch.nn as nn

+import comfy.model_management
 from .base import WeightAdapterBase, WeightAdapterTrainBase
 from comfy.patcher_extension import PatcherInjection

@@ -181,18 +182,21 @@ class BypassForwardHook:
            )
            return  # Already injected

-        # Move adapter weights to module's device to avoid CPU-GPU transfer on every forward
-        device = None
+        # Move adapter weights to compute device (GPU)
+        # Use get_torch_device() instead of module.weight.device because
+        # with offloading, module weights may be on CPU while compute happens on GPU
+        device = comfy.model_management.get_torch_device()
+
+        # Get dtype from module weight if available
        dtype = None
        if hasattr(self.module, "weight") and self.module.weight is not None:
-            device = self.module.weight.device
            dtype = self.module.weight.dtype
-        elif hasattr(self.module, "W_q"):  # Quantized layers might use different attr
-            device = self.module.W_q.device
-            dtype = self.module.W_q.dtype

-        if device is not None:
-            self._move_adapter_weights_to_device(device, dtype)
+        # Only use dtype if it's a standard float type, not quantized
+        if dtype is not None and dtype not in (torch.float32, torch.float16, torch.bfloat16):
+            dtype = None
+
+        self._move_adapter_weights_to_device(device, dtype)

        self.original_forward = self.module.forward
        self.module.forward = self._bypass_forward
--- a/comfy_api/feature_flags.py
+++ b/comfy_api/feature_flags.py
@@ -14,6 +14,7 @@ SERVER_FEATURE_FLAGS: dict[str, Any] = {
    "supports_preview_metadata": True,
    "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
    "extension": {"manager": {"supports_v4": True}},
+    "node_replacements": True,
 }


--- a/comfy_api/latest/_input/video_types.py
+++ b/comfy_api/latest/_input/video_types.py
@@ -34,6 +34,21 @@ class VideoInput(ABC):
        """
        pass

+    @abstractmethod
+    def as_trimmed(
+        self,
+        start_time: float | None = None,
+        duration: float | None = None,
+        strict_duration: bool = False,
+    ) -> VideoInput | None:
+        """
+        Create a new VideoInput which is trimmed to have the corresponding start_time and duration
+
+        Returns:
+            A new VideoInput, or None if the result would have negative duration
+        """
+        pass
+
    def get_stream_source(self) -> Union[str, io.BytesIO]:
        """
        Get a streamable source for the video. This allows processing without
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -6,6 +6,7 @@ from typing import Optional
 from .._input import AudioInput, VideoInput
 import av
 import io
+import itertools
 import json
 import numpy as np
 import math
@@ -29,7 +30,6 @@ def container_to_output_format(container_format: str | None) -> str | None:
    formats = container_format.split(",")
    return formats[0]

-
 def get_open_write_kwargs(
    dest: str | io.BytesIO, container_format: str, to_format: str | None
 ) -> dict:
@@ -57,12 +57,14 @@ class VideoFromFile(VideoInput):
    Class representing video input from a file.
    """

-    def __init__(self, file: str | io.BytesIO):
+    def __init__(self, file: str | io.BytesIO, *, start_time: float=0, duration: float=0):
        """
        Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object
        containing the file contents.
        """
        self.__file = file
+        self.__start_time = start_time
+        self.__duration = duration

    def get_stream_source(self) -> str | io.BytesIO:
        """
@@ -96,6 +98,16 @@ class VideoFromFile(VideoInput):
        Returns:
            Duration in seconds
        """
+        raw_duration = self._get_raw_duration()
+        if self.__start_time < 0:
+            duration_from_start = min(raw_duration, -self.__start_time)
+        else:
+            duration_from_start = raw_duration - self.__start_time
+        if self.__duration:
+            return min(self.__duration, duration_from_start)
+        return duration_from_start
+
+    def _get_raw_duration(self) -> float:
        if isinstance(self.__file, io.BytesIO):
            self.__file.seek(0)
        with av.open(self.__file, mode="r") as container:
@@ -113,9 +125,13 @@ class VideoFromFile(VideoInput):
            if video_stream and video_stream.average_rate:
                frame_count = 0
                container.seek(0)
-                for packet in container.demux(video_stream):
-                    for _ in packet.decode():
-                        frame_count += 1
+                frame_iterator = (
+                    container.decode(video_stream)
+                    if video_stream.codec.capabilities & 0x100
+                    else container.demux(video_stream)
+                )
+                for packet in frame_iterator:
+                    frame_count += 1
                if frame_count > 0:
                    return float(frame_count / video_stream.average_rate)

@@ -131,36 +147,54 @@ class VideoFromFile(VideoInput):

        with av.open(self.__file, mode="r") as container:
            video_stream = self._get_first_video_stream(container)
-            # 1. Prefer the frames field if available
-            if video_stream.frames and video_stream.frames > 0:
+            # 1. Prefer the frames field if available and usable
+            if (
+                video_stream.frames
+                and video_stream.frames > 0
+                and not self.__start_time
+                and not self.__duration
+            ):
                return int(video_stream.frames)

            # 2. Try to estimate from duration and average_rate using only metadata
-            if container.duration is not None and video_stream.average_rate:
-                duration_seconds = float(container.duration / av.time_base)
-                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
-                if estimated_frames > 0:
-                    return estimated_frames
-
            if (
                getattr(video_stream, "duration", None) is not None
                and getattr(video_stream, "time_base", None) is not None
                and video_stream.average_rate
            ):
-                duration_seconds = float(video_stream.duration * video_stream.time_base)
+                raw_duration = float(video_stream.duration * video_stream.time_base)
+                if self.__start_time < 0:
+                    duration_from_start = min(raw_duration, -self.__start_time)
+                else:
+                    duration_from_start = raw_duration - self.__start_time
+                duration_seconds = min(self.__duration, duration_from_start)
                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
                if estimated_frames > 0:
                    return estimated_frames

            # 3. Last resort: decode frames and count them (streaming)
-            frame_count = 0
-            container.seek(0)
-            for packet in container.demux(video_stream):
-                for _ in packet.decode():
-                    frame_count += 1
-
-            if frame_count == 0:
-                raise ValueError(f"Could not determine frame count for file '{self.__file}'")
+            if self.__start_time < 0:
+                start_time = max(self._get_raw_duration() + self.__start_time, 0)
+            else:
+                start_time = self.__start_time
+            frame_count = 1
+            start_pts = int(start_time / video_stream.time_base)
+            end_pts = int((start_time + self.__duration) / video_stream.time_base)
+            container.seek(start_pts, stream=video_stream)
+            frame_iterator = (
+                container.decode(video_stream)
+                if video_stream.codec.capabilities & 0x100
+                else container.demux(video_stream)
+            )
+            for frame in frame_iterator:
+                if frame.pts >= start_pts:
+                    break
+            else:
+                raise ValueError(f"Could not determine frame count for file '{self.__file}'\nNo frames exist for start_time {self.__start_time}")
+            for frame in frame_iterator:
+                if frame.pts >= end_pts:
+                    break
+                frame_count += 1
            return frame_count

    def get_frame_rate(self) -> Fraction:
@@ -199,9 +233,21 @@ class VideoFromFile(VideoInput):
            return container.format.name

    def get_components_internal(self, container: InputContainer) -> VideoComponents:
+        video_stream = self._get_first_video_stream(container)
+        if self.__start_time < 0:
+            start_time = max(self._get_raw_duration() + self.__start_time, 0)
+        else:
+            start_time = self.__start_time
        # Get video frames
        frames = []
-        for frame in container.decode(video=0):
+        start_pts = int(start_time / video_stream.time_base)
+        end_pts = int((start_time + self.__duration) / video_stream.time_base)
+        container.seek(start_pts, stream=video_stream)
+        for frame in container.decode(video_stream):
+            if frame.pts < start_pts:
+                continue
+            if self.__duration and frame.pts >= end_pts:
+                break
            img = frame.to_ndarray(format='rgb24')  # shape: (H, W, 3)
            img = torch.from_numpy(img) / 255.0  # shape: (H, W, 3)
            frames.append(img)
@@ -209,31 +255,44 @@ class VideoFromFile(VideoInput):
        images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)

        # Get frame rate
-        video_stream = next(s for s in container.streams if s.type == 'video')
-        frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1)
+        frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)

        # Get audio if available
        audio = None
-        try:
-            container.seek(0)  # Reset the container to the beginning
-            for stream in container.streams:
-                if stream.type != 'audio':
-                    continue
-                assert isinstance(stream, av.AudioStream)
-                audio_frames = []
-                for packet in container.demux(stream):
-                    for frame in packet.decode():
-                        assert isinstance(frame, av.AudioFrame)
-                        audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
-                if len(audio_frames) > 0:
-                    audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
-                    audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
-                    audio = AudioInput({
-                        "waveform": audio_tensor,
-                        "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1,
-                    })
-        except StopIteration:
-            pass  # No audio stream
+        container.seek(start_pts, stream=video_stream)
+        # Use last stream for consistency
+        if len(container.streams.audio):
+            audio_stream = container.streams.audio[-1]
+            audio_frames = []
+            resample = av.audio.resampler.AudioResampler(format='fltp').resample
+            frames = itertools.chain.from_iterable(
+                map(resample, container.decode(audio_stream))
+            )
+
+            has_first_frame = False
+            for frame in frames:
+                offset_seconds = start_time - frame.pts * audio_stream.time_base
+                to_skip = int(offset_seconds * audio_stream.sample_rate)
+                if to_skip < frame.samples:
+                    has_first_frame = True
+                    break
+            if has_first_frame:
+                audio_frames.append(frame.to_ndarray()[..., to_skip:])
+
+            for frame in frames:
+                if frame.time > start_time + self.__duration:
+                    break
+                audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
+            if len(audio_frames) > 0:
+                audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
+                if self.__duration:
+                    audio_data = audio_data[..., :int(self.__duration * audio_stream.sample_rate)]
+
+                audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
+                audio = AudioInput({
+                    "waveform": audio_tensor,
+                    "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1,
+                })

        metadata = container.metadata
        return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
@@ -250,7 +309,7 @@ class VideoFromFile(VideoInput):
        path: str | io.BytesIO,
        format: VideoContainer = VideoContainer.AUTO,
        codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None
+        metadata: Optional[dict] = None,
    ):
        if isinstance(self.__file, io.BytesIO):
            self.__file.seek(0)  # Reset the BytesIO object to the beginning
@@ -262,15 +321,14 @@ class VideoFromFile(VideoInput):
                reuse_streams = False
            if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None:
                reuse_streams = False
+            if self.__start_time or self.__duration:
+                reuse_streams = False

            if not reuse_streams:
                components = self.get_components_internal(container)
                video = VideoFromComponents(components)
                return video.save_to(
-                    path,
-                    format=format,
-                    codec=codec,
-                    metadata=metadata
+                    path, format=format, codec=codec, metadata=metadata
                )

            streams = container.streams
@@ -304,10 +362,21 @@ class VideoFromFile(VideoInput):
                        output_container.mux(packet)

    def _get_first_video_stream(self, container: InputContainer):
-        video_stream = next((s for s in container.streams if s.type == "video"), None)
-        if video_stream is None:
-            raise ValueError(f"No video stream found in file '{self.__file}'")
-        return video_stream
+        if len(container.streams.video):
+            return container.streams.video[0]
+        raise ValueError(f"No video stream found in file '{self.__file}'")
+
+    def as_trimmed(
+        self, start_time: float = 0, duration: float = 0, strict_duration: bool = True
+    ) -> VideoInput | None:
+        trimmed = VideoFromFile(
+            self.get_stream_source(),
+            start_time=start_time + self.__start_time,
+            duration=duration,
+        )
+        if trimmed.get_duration() < duration and strict_duration:
+            return None
+        return trimmed


 class VideoFromComponents(VideoInput):
@@ -322,7 +391,7 @@ class VideoFromComponents(VideoInput):
        return VideoComponents(
            images=self.__components.images,
            audio=self.__components.audio,
-            frame_rate=self.__components.frame_rate
+            frame_rate=self.__components.frame_rate,
        )

    def save_to(
@@ -330,7 +399,7 @@ class VideoFromComponents(VideoInput):
        path: str,
        format: VideoContainer = VideoContainer.AUTO,
        codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None
+        metadata: Optional[dict] = None,
    ):
        if format != VideoContainer.AUTO and format != VideoContainer.MP4:
            raise ValueError("Only MP4 format is supported for now")
@@ -357,7 +426,10 @@ class VideoFromComponents(VideoInput):
            audio_stream: Optional[av.AudioStream] = None
            if self.__components.audio:
                audio_sample_rate = int(self.__components.audio['sample_rate'])
-                audio_stream = output.add_stream('aac', rate=audio_sample_rate)
+                waveform = self.__components.audio['waveform']
+                waveform = waveform[0, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
+                layout = {1: 'mono', 2: 'stereo', 6: '5.1'}.get(waveform.shape[0], 'stereo')
+                audio_stream = output.add_stream('aac', rate=audio_sample_rate, layout=layout)

            # Encode video
            for i, frame in enumerate(self.__components.images):
@@ -372,12 +444,21 @@ class VideoFromComponents(VideoInput):
            output.mux(packet)

            if audio_stream and self.__components.audio:
-                waveform = self.__components.audio['waveform']
-                waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
-                frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().cpu().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo')
+                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().numpy(), format='fltp', layout=layout)
                frame.sample_rate = audio_sample_rate
                frame.pts = 0
                output.mux(audio_stream.encode(frame))

                # Flush encoder
                output.mux(audio_stream.encode(None))
+
+    def as_trimmed(
+        self,
+        start_time: float | None = None,
+        duration: float | None = None,
+        strict_duration: bool = True,
+    ) -> VideoInput | None:
+        if self.get_duration() < start_time + duration:
+            return None
+        #TODO Consider tracking duration and trimming at time of save?
+        return VideoFromFile(self.get_stream_source(), start_time=start_time, duration=duration)
--- a/comfy_api/latest/_node_replace.py
+++ b/comfy_api/latest/_node_replace.py
@@ -1,6 +1,32 @@
 from __future__ import annotations

-from typing import Any
+from typing import Any, TypedDict
+
+
+class InputMapOldId(TypedDict):
+    """Map an old node input to a new node input by ID."""
+    new_id: str
+    old_id: str
+
+
+class InputMapSetValue(TypedDict):
+    """Set a specific value for a new node input."""
+    new_id: str
+    set_value: Any
+
+
+InputMap = InputMapOldId | InputMapSetValue
+"""
+Input mapping for node replacement. Type is inferred by dictionary keys:
+- {"new_id": str, "old_id": str} - maps old input to new input
+- {"new_id": str, "set_value": Any} - sets a specific value for new input
+"""
+
+
+class OutputMap(TypedDict):
+    """Map outputs of node replacement via indexes."""
+    new_idx: int
+    old_idx: int


 class NodeReplace:
@@ -8,6 +34,16 @@ 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.
+
+    Args:
+        new_node_id: The class name of the new replacement node.
+        old_node_id: The class name of the deprecated node.
+        old_widget_ids: Ordered list of input IDs for widgets that may not have an input slot
+            connected. The workflow JSON stores widget values by their relative position index,
+            not by ID. This list maps those positional indexes to input IDs, enabling the
+            replacement system to correctly identify widget values during node migration.
+        input_mapping: List of input mappings from old node to new node.
+        output_mapping: List of output mappings from old node to new node.
    """
    def __init__(self,
        new_node_id: str,
@@ -28,67 +64,6 @@ class NodeReplace:
            "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,
+            "input_mapping": list(self.input_mapping) if self.input_mapping else None,
+            "output_mapping": list(self.output_mapping) if self.output_mapping else None,
        }
--- a/comfy_api_nodes/apis/init.py
+++ b/comfy_api_nodes/apis/init.py
@@ -1197,12 +1197,6 @@ class KlingImageGenImageReferenceType(str, Enum):
    face = 'face'


-class KlingImageGenModelName(str, Enum):
-    kling_v1 = 'kling-v1'
-    kling_v1_5 = 'kling-v1-5'
-    kling_v2 = 'kling-v2'
-
-
 class KlingImageGenerationsRequest(BaseModel):
    aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9'
    callback_url: Optional[AnyUrl] = Field(
@@ -1218,7 +1212,7 @@ class KlingImageGenerationsRequest(BaseModel):
        0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0
    )
    image_reference: Optional[KlingImageGenImageReferenceType] = None
-    model_name: Optional[KlingImageGenModelName] = 'kling-v1'
+    model_name: str = Field(...)
    n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9)
    negative_prompt: Optional[str] = Field(
        None, description='Negative text prompt', max_length=200
--- a/comfy_api_nodes/apis/kling.py
+++ b/comfy_api_nodes/apis/kling.py
@@ -1,12 +1,22 @@
 from pydantic import BaseModel, Field


+class MultiPromptEntry(BaseModel):
+    index: int = Field(...)
+    prompt: str = Field(...)
+    duration: str = Field(...)
+
+
 class OmniProText2VideoRequest(BaseModel):
    model_name: str = Field(..., description="kling-video-o1")
    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
    duration: str = Field(..., description="'5' or '10'")
    prompt: str = Field(...)
    mode: str = Field("pro")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)
+    sound: str = Field(..., description="'on' or 'off'")


 class OmniParamImage(BaseModel):
@@ -26,6 +36,10 @@ class OmniProFirstLastFrameRequest(BaseModel):
    duration: str = Field(..., description="'5' or '10'")
    prompt: str = Field(...)
    mode: str = Field("pro")
+    sound: str | None = Field(None, description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)


 class OmniProReferences2VideoRequest(BaseModel):
@@ -38,6 +52,10 @@ class OmniProReferences2VideoRequest(BaseModel):
    duration: str | None = Field(..., description="From 3 to 10.")
    prompt: str = Field(...)
    mode: str = Field("pro")
+    sound: str | None = Field(None, description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)


 class TaskStatusVideoResult(BaseModel):
@@ -54,6 +72,7 @@ class TaskStatusImageResult(BaseModel):
 class TaskStatusResults(BaseModel):
    videos: list[TaskStatusVideoResult] | None = Field(None)
    images: list[TaskStatusImageResult] | None = Field(None)
+    series_images: list[TaskStatusImageResult] | None = Field(None)


 class TaskStatusResponseData(BaseModel):
@@ -77,31 +96,42 @@ class OmniImageParamImage(BaseModel):


 class OmniProImageRequest(BaseModel):
-    model_name: str = Field(..., description="kling-image-o1")
-    resolution: str = Field(..., description="'1k' or '2k'")
+    model_name: str = Field(...)
+    resolution: str = Field(...)
    aspect_ratio: str | None = Field(...)
    prompt: str = Field(...)
    mode: str = Field("pro")
    n: int | None = Field(1, le=9)
    image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
+    result_type: str | None = Field(None, description="Set to 'series' for series generation")
+    series_amount: int | None = Field(None, ge=2, le=9, description="Number of images in a series")


 class TextToVideoWithAudioRequest(BaseModel):
-    model_name: str = Field(..., description="kling-v2-6")
+    model_name: str = Field(...)
    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
-    duration: str = Field(..., description="'5' or '10'")
-    prompt: str = Field(...)
+    duration: str = Field(...)
+    prompt: str | None = Field(...)
+    negative_prompt: str | None = Field(None)
    mode: str = Field("pro")
    sound: str = Field(..., description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)


 class ImageToVideoWithAudioRequest(BaseModel):
-    model_name: str = Field(..., description="kling-v2-6")
+    model_name: str = Field(...)
    image: str = Field(...)
-    duration: str = Field(..., description="'5' or '10'")
-    prompt: str = Field(...)
+    image_tail: str | None = Field(None)
+    duration: str = Field(...)
+    prompt: str | None = Field(...)
+    negative_prompt: str | None = Field(None)
    mode: str = Field("pro")
    sound: str = Field(..., description="'on' or 'off'")
+    multi_shot: bool | None = Field(None)
+    multi_prompt: list[MultiPromptEntry] | None = Field(None)
+    shot_type: str | None = Field(None)


 class MotionControlRequest(BaseModel):
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
--- a/comfy_api_nodes/nodes_moonvalley.py
+++ b/comfy_api_nodes/nodes_moonvalley.py
@@ -219,8 +219,8 @@ class MoonvalleyImg2VideoNode(IO.ComfyNode):
                ),
                IO.Int.Input(
                    "steps",
-                    default=33,
-                    min=1,
+                    default=80,
+                    min=75,  # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
                    max=100,
                    step=1,
                    tooltip="Number of denoising steps",
@@ -340,8 +340,8 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
                ),
                IO.Int.Input(
                    "steps",
-                    default=33,
-                    min=1,
+                    default=60,
+                    min=60,  # steps should be greater or equal to cooldown_steps(36) + warmup_steps(24)
                    max=100,
                    step=1,
                    display_mode=IO.NumberDisplay.number,
@@ -370,7 +370,7 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
        video: Input.Video | None = None,
        control_type: str = "Motion Transfer",
        motion_intensity: int | None = 100,
-        steps=33,
+        steps=60,
        prompt_adherence=4.5,
    ) -> IO.NodeOutput:
        validated_video = validate_video_to_video_input(video)
@@ -465,8 +465,8 @@ class MoonvalleyTxt2VideoNode(IO.ComfyNode):
                ),
                IO.Int.Input(
                    "steps",
-                    default=33,
-                    min=1,
+                    default=80,
+                    min=75,  # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
                    max=100,
                    step=1,
                    tooltip="Inference steps",
--- a/comfy_execution/jobs.py
+++ b/comfy_execution/jobs.py
@@ -20,10 +20,60 @@ class JobStatus:


 # Media types that can be previewed in the frontend
-PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio'})
+PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d'})

 # 3D file extensions for preview fallback (no dedicated media_type exists)
-THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb'})
+THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
+
+
+def has_3d_extension(filename: str) -> bool:
+    lower = filename.lower()
+    return any(lower.endswith(ext) for ext in THREE_D_EXTENSIONS)
+
+
+def normalize_output_item(item):
+    """Normalize a single output list item for the jobs API.
+
+    Returns the normalized item, or None to exclude it.
+    String items with 3D extensions become {filename, type, subfolder} dicts.
+    """
+    if item is None:
+        return None
+    if isinstance(item, str):
+        if has_3d_extension(item):
+            return {'filename': item, 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
+        return None
+    if isinstance(item, dict):
+        return item
+    return None
+
+
+def normalize_outputs(outputs: dict) -> dict:
+    """Normalize raw node outputs for the jobs API.
+
+    Transforms string 3D filenames into file output dicts and removes
+    None items. All other items (non-3D strings, dicts, etc.) are
+    preserved as-is.
+    """
+    normalized = {}
+    for node_id, node_outputs in outputs.items():
+        if not isinstance(node_outputs, dict):
+            normalized[node_id] = node_outputs
+            continue
+        normalized_node = {}
+        for media_type, items in node_outputs.items():
+            if media_type == 'animated' or not isinstance(items, list):
+                normalized_node[media_type] = items
+                continue
+            normalized_items = []
+            for item in items:
+                if item is None:
+                    continue
+                norm = normalize_output_item(item)
+                normalized_items.append(norm if norm is not None else item)
+            normalized_node[media_type] = normalized_items
+        normalized[node_id] = normalized_node
+    return normalized


 def _extract_job_metadata(extra_data: dict) -> tuple[Optional[int], Optional[str]]:
@@ -45,9 +95,9 @@ def is_previewable(media_type: str, item: dict) -> bool:
    Maintains backwards compatibility with existing logic.

    Priority:
-    1. media_type is 'images', 'video', or 'audio'
+    1. media_type is 'images', 'video', 'audio', or '3d'
    2. format field starts with 'video/' or 'audio/'
-    3. filename has a 3D extension (.obj, .fbx, .gltf, .glb)
+    3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
    """
    if media_type in PREVIEWABLE_MEDIA_TYPES:
        return True
@@ -139,7 +189,7 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
    })

    if include_outputs:
-        job['outputs'] = outputs
+        job['outputs'] = normalize_outputs(outputs)
        job['execution_status'] = status_info
        job['workflow'] = {
            'prompt': prompt,
@@ -171,18 +221,23 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                continue

            for item in items:
-                count += 1
-
-                if not isinstance(item, dict):
+                normalized = normalize_output_item(item)
+                if normalized is None:
                    continue

-                if preview_output is None and is_previewable(media_type, item):
+                count += 1
+
+                if preview_output is not None:
+                    continue
+
+                if isinstance(normalized, dict) and is_previewable(media_type, normalized):
                    enriched = {
-                        **item,
+                        **normalized,
                        'nodeId': node_id,
-                        'mediaType': media_type
                    }
-                    if item.get('type') == 'output':
+                    if 'mediaType' not in normalized:
+                        enriched['mediaType'] = media_type
+                    if normalized.get('type') == 'output':
                        preview_output = enriched
                    elif fallback_preview is None:
                        fallback_preview = enriched
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -94,6 +94,19 @@ 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):
@@ -111,16 +124,33 @@ class VAEDecodeAudio(IO.ComfyNode):

    @classmethod
    def execute(cls, vae, samples) -> IO.NodeOutput:
-        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"]})
+        return IO.NodeOutput(vae_decode_audio(vae, samples))

    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):
@@ -675,6 +705,7 @@ class AudioExtension(ComfyExtension):
            EmptyLatentAudio,
            VAEEncodeAudio,
            VAEDecodeAudio,
+            VAEDecodeAudioTiled,
            SaveAudio,
            SaveAudioMP3,
            SaveAudioOpus,
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -622,6 +622,7 @@ 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"),
@@ -666,6 +667,7 @@ 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"]),
--- a/comfy_extras/nodes_easycache.py
+++ b/comfy_extras/nodes_easycache.py
@@ -9,6 +9,14 @@ 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]
@@ -17,7 +25,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
        if not transformer_options:
            transformer_options = args[-2]
    easycache: EasyCacheHolder = transformer_options["easycache"]
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    x, ax = _extract_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):
@@ -35,7 +43,11 @@ 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}")
-            return easycache.apply_cache_diff(x, 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
        if easycache.initial_step:
            easycache.first_cond_uuid = uuids[0]
            has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
@@ -51,13 +63,18 @@ 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
-                    return easycache.apply_cache_diff(x, 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
                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

-    output: torch.Tensor = executor(*args, **kwargs)
+    full_output: torch.Tensor = executor(*args, **kwargs)
+    output, audio_output = _extract_tensor(full_output, easycache.output_channels)
    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:
@@ -74,13 +91,15 @@ 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 output
+    return full_output

 def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
    # get values from args
@@ -89,8 +108,8 @@ 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)
-    # prepare next x_prev
    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    # prepare next x_prev
    next_x_prev = x
    input_change = None
    do_easycache = easycache.should_do_easycache(timestep)
@@ -197,6 +216,7 @@ 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
@@ -245,20 +265,21 @@ 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]):
-        if self.first_cond_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:
            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:] != self.uuid_cache_diffs[uuid].shape[1:]:
+            if x.shape[1:] != 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(self.uuid_cache_diffs[uuid].shape, x.shape):
+                for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
                    if skip_this_dim:
                        skip_this_dim = False
                        continue
@@ -270,10 +291,11 @@ class EasyCacheHolder:
                    else:
                        slicing.append(slice(None))
                batch_slice = batch_slice + slicing
-            x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
+            x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
        return x

-    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
+    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
        # 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:
@@ -293,7 +315,7 @@ class EasyCacheHolder:
        diff = output - x
        batch_offset = diff.shape[0] // len(uuids)
        for i, uuid in enumerate(uuids):
-            self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
+            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
@@ -324,6 +346,8 @@ 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
--- a/comfy_extras/nodes_latent.py
+++ b/comfy_extras/nodes_latent.py
@@ -391,8 +391,9 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
            normalized_latent = latent / latent_vector_magnitude

-            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            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)

            top = (std * 5 + mean) * multiplier

--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -656,93 +656,98 @@ class BatchImagesMasksLatentsNode(io.ComfyNode):
        return io.NodeOutput(batched)


-from comfy_api.latest import ComfyAPI, node_replace
+from comfy_api.latest import node_replace
+from server import PromptServer
+
+def _register(nr: node_replace.NodeReplace):
+    """Helper to register replacements via PromptServer."""
+    PromptServer.instance.node_replace_manager.register(nr)

 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()
+    """Register all built-in node replacements."""
+    register_replacements_longeredge()
+    register_replacements_batchimages()
+    register_replacements_upscaleimage()
+    register_replacements_controlnet()
+    register_replacements_load3d()
+    register_replacements_preview3d()
+    register_replacements_svdimg2vid()
+    register_replacements_conditioningavg()

-async def register_replacements_longeredge():
+def register_replacements_longeredge():
    # No dynamic inputs here
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _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")),
+                {"new_id": "image", "old_id": "images"},
+                {"new_id": "largest_size", "old_id": "longer_edge"},
+                {"new_id": "upscale_method", "set_value": "lanczos"},
            ],
            # just to test the frontend output_mapping code, does nothing really here
-            output_mapping=[node_replace.OutputMap(new_idx=0, old_idx=0)],
+            output_mapping=[{"new_idx": 0, "old_idx": 0}],
        ))

-async def register_replacements_batchimages():
+def register_replacements_batchimages():
    # BatchImages node uses Autogrow
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _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")),
+                {"new_id": "images.image0", "old_id": "image1"},
+                {"new_id": "images.image1", "old_id": "image2"},
            ],
        ))

-async def register_replacements_upscaleimage():
+def register_replacements_upscaleimage():
    # ResizeImageMaskNode uses DynamicCombo
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _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")),
+                {"new_id": "input", "old_id": "image"},
+                {"new_id": "resize_type", "set_value": "scale by multiplier"},
+                {"new_id": "resize_type.multiplier", "old_id": "scale_by"},
+                {"new_id": "scale_method", "old_id": "upscale_method"},
            ],
        ))

-async def register_replacements_controlnet():
+def register_replacements_controlnet():
    # T2IAdapterLoader → ControlNetLoader
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _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")),
+                {"new_id": "control_net_name", "old_id": "t2i_adapter_name"},
            ],
        ))

-async def register_replacements_load3d():
+def register_replacements_load3d():
    # Load3DAnimation merged into Load3D
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _register(node_replace.NodeReplace(
            new_node_id="Load3D",
            old_node_id="Load3DAnimation",
        ))

-async def register_replacements_preview3d():
+def register_replacements_preview3d():
    # Preview3DAnimation merged into Preview3D
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _register(node_replace.NodeReplace(
            new_node_id="Preview3D",
            old_node_id="Preview3DAnimation",
        ))

-async def register_replacements_svdimg2vid():
+def register_replacements_svdimg2vid():
    # Typo fix: SDV → SVD
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _register(node_replace.NodeReplace(
            new_node_id="SVD_img2vid_Conditioning",
            old_node_id="SDV_img2vid_Conditioning",
        ))

-async def register_replacements_conditioningavg():
+def register_replacements_conditioningavg():
    # Typo fix: trailing space in node name
-    await ComfyAPI.node_replacement.register(node_replace.NodeReplace(
+    _register(node_replace.NodeReplace(
            new_node_id="ConditioningAverage",
            old_node_id="ConditioningAverage ",
        ))
--- a/comfy_extras/nodes_toolkit.py
+++ b/comfy_extras/nodes_toolkit.py
@@ -0,0 +1,47 @@
+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()
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -4,6 +4,7 @@ import os
 import numpy as np
 import safetensors
 import torch
+import torch.nn as nn
 import torch.utils.checkpoint
 from tqdm.auto import trange
 from PIL import Image, ImageDraw, ImageFont
@@ -27,6 +28,11 @@ class TrainGuider(comfy_extras.nodes_custom_sampler.Guider_Basic):
    """
    CFGGuider with modifications for training specific logic
    """
+
+    def __init__(self, *args, offloading=False, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.offloading = offloading
+
    def outer_sample(
        self,
        noise,
@@ -45,9 +51,11 @@ class TrainGuider(comfy_extras.nodes_custom_sampler.Guider_Basic):
                noise.shape,
                self.conds,
                self.model_options,
-                force_full_load=True,  # mirror behavior in TrainLoraNode.execute() to keep model loaded
+                force_full_load=not self.offloading,
+                force_offload=self.offloading,
            )
        )
+        torch.cuda.empty_cache()
        device = self.model_patcher.load_device

        if denoise_mask is not None:
@@ -404,16 +412,97 @@ def find_all_highest_child_module_with_forward(
    return result


-def patch(m):
+def find_modules_at_depth(
+    model: nn.Module, depth: int = 1, result=None, current_depth=0, name=None
+) -> list[nn.Module]:
+    """
+    Find modules at a specific depth level for gradient checkpointing.
+
+    Args:
+        model: The model to search
+        depth: Target depth level (1 = top-level blocks, 2 = their children, etc.)
+        result: Accumulator for results
+        current_depth: Current recursion depth
+        name: Current module name for logging
+
+    Returns:
+        List of modules at the target depth
+    """
+    if result is None:
+        result = []
+    name = name or "root"
+
+    # Skip container modules (they don't have meaningful forward)
+    is_container = isinstance(model, (nn.ModuleList, nn.Sequential, nn.ModuleDict))
+    has_forward = hasattr(model, "forward") and not is_container
+
+    if has_forward:
+        current_depth += 1
+        if current_depth == depth:
+            result.append(model)
+            logging.debug(f"Found module at depth {depth}: {name} ({model.__class__.__name__})")
+            return result
+
+    # Recurse into children
+    for next_name, child in model.named_children():
+        find_modules_at_depth(child, depth, result, current_depth, f"{name}.{next_name}")
+
+    return result
+
+
+class OffloadCheckpointFunction(torch.autograd.Function):
+    """
+    Gradient checkpointing that works with weight offloading.
+
+    Forward: no_grad -> compute -> weights can be freed
+    Backward: enable_grad -> recompute -> backward -> weights can be freed
+
+    For single input, single output modules (Linear, Conv*).
+    """
+
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, forward_fn):
+        ctx.save_for_backward(x)
+        ctx.forward_fn = forward_fn
+        with torch.no_grad():
+            return forward_fn(x)
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor):
+        x, = ctx.saved_tensors
+        forward_fn = ctx.forward_fn
+
+        # Clear context early
+        ctx.forward_fn = None
+
+        with torch.enable_grad():
+            x_detached = x.detach().requires_grad_(True)
+            y = forward_fn(x_detached)
+            y.backward(grad_out)
+            grad_x = x_detached.grad
+
+        # Explicit cleanup
+        del y, x_detached, forward_fn
+
+        return grad_x, None
+
+
+def patch(m, offloading=False):
    if not hasattr(m, "forward"):
        return
    org_forward = m.forward

-    def fwd(args, kwargs):
-        return org_forward(*args, **kwargs)
+    # Branch 1: Linear/Conv* -> offload-compatible checkpoint (single input/output)
+    if offloading and isinstance(m, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        def checkpointing_fwd(x):
+            return OffloadCheckpointFunction.apply(x, org_forward)
+    # Branch 2: Others -> standard checkpoint
+    else:
+        def fwd(args, kwargs):
+            return org_forward(*args, **kwargs)

-    def checkpointing_fwd(*args, **kwargs):
-        return torch.utils.checkpoint.checkpoint(fwd, args, kwargs, use_reentrant=False)
+        def checkpointing_fwd(*args, **kwargs):
+            return torch.utils.checkpoint.checkpoint(fwd, args, kwargs, use_reentrant=False)

    m.org_forward = org_forward
    m.forward = checkpointing_fwd
@@ -936,6 +1025,18 @@ class TrainLoraNode(io.ComfyNode):
                    default=True,
                    tooltip="Use gradient checkpointing for training.",
                ),
+                io.Int.Input(
+                    "checkpoint_depth",
+                    default=1,
+                    min=1,
+                    max=5,
+                    tooltip="Depth level for gradient checkpointing.",
+                ),
+                io.Boolean.Input(
+                    "offloading",
+                    default=False,
+                    tooltip="Depth level for gradient checkpointing.",
+                ),
                io.Combo.Input(
                    "existing_lora",
                    options=folder_paths.get_filename_list("loras") + ["[None]"],
@@ -982,6 +1083,8 @@ class TrainLoraNode(io.ComfyNode):
        lora_dtype,
        algorithm,
        gradient_checkpointing,
+        checkpoint_depth,
+        offloading,
        existing_lora,
        bucket_mode,
        bypass_mode,
@@ -1000,6 +1103,8 @@ class TrainLoraNode(io.ComfyNode):
        lora_dtype = lora_dtype[0]
        algorithm = algorithm[0]
        gradient_checkpointing = gradient_checkpointing[0]
+        offloading = offloading[0]
+        checkpoint_depth = checkpoint_depth[0]
        existing_lora = existing_lora[0]
        bucket_mode = bucket_mode[0]
        bypass_mode = bypass_mode[0]
@@ -1054,16 +1159,18 @@ class TrainLoraNode(io.ComfyNode):

            # Setup gradient checkpointing
            if gradient_checkpointing:
-                for m in find_all_highest_child_module_with_forward(
-                    mp.model.diffusion_model
-                ):
-                    patch(m)
+                modules_to_patch = find_modules_at_depth(
+                    mp.model.diffusion_model, depth=checkpoint_depth
+                )
+                logging.info(f"Gradient checkpointing: patching {len(modules_to_patch)} modules at depth {checkpoint_depth}")
+                for m in modules_to_patch:
+                    patch(m, offloading=offloading)

            torch.cuda.empty_cache()
            # With force_full_load=False we should be able to have offloading
            # But for offloading in training we need custom AutoGrad hooks for fwd/bwd
            comfy.model_management.load_models_gpu(
-                [mp], memory_required=1e20, force_full_load=True
+                [mp], memory_required=1e20, force_full_load=not offloading
            )
            torch.cuda.empty_cache()

@@ -1100,7 +1207,7 @@ class TrainLoraNode(io.ComfyNode):
                )

            # Setup guider
-            guider = TrainGuider(mp)
+            guider = TrainGuider(mp, offloading=offloading)
            guider.set_conds(positive)

            # Inject bypass hooks if bypass mode is enabled
@@ -1113,6 +1220,7 @@ class TrainLoraNode(io.ComfyNode):

            # Run training loop
            try:
+                comfy.model_management.in_training = True
                _run_training_loop(
                    guider,
                    train_sampler,
@@ -1123,6 +1231,7 @@ class TrainLoraNode(io.ComfyNode):
                    multi_res,
                )
            finally:
+                comfy.model_management.in_training = False
                # Eject bypass hooks if they were injected
                if bypass_injections is not None:
                    for injection in bypass_injections:
@@ -1132,19 +1241,20 @@ class TrainLoraNode(io.ComfyNode):
                    unpatch(m)
            del train_sampler, optimizer

-            # Finalize adapters
+            for param in lora_sd:
+                lora_sd[param] = lora_sd[param].to(lora_dtype).detach()
+
            for adapter in all_weight_adapters:
                adapter.requires_grad_(False)
-
-            for param in lora_sd:
-                lora_sd[param] = lora_sd[param].to(lora_dtype)
+                del adapter
+            del all_weight_adapters

            # mp in train node is highly specialized for training
            # use it in inference will result in bad behavior so we don't return it
            return io.NodeOutput(lora_sd, loss_map, steps + existing_steps)


-class LoraModelLoader(io.ComfyNode):#
+class LoraModelLoader(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
@@ -1166,6 +1276,11 @@ class LoraModelLoader(io.ComfyNode):#
                    max=100.0,
                    tooltip="How strongly to modify the diffusion model. This value can be negative.",
                ),
+                io.Boolean.Input(
+                    "bypass",
+                    default=False,
+                    tooltip="When enabled, applies LoRA in bypass mode without modifying base model weights. Useful for training and when model weights are offloaded.",
+                ),
            ],
            outputs=[
                io.Model.Output(
@@ -1175,13 +1290,18 @@ class LoraModelLoader(io.ComfyNode):#
        )

    @classmethod
-    def execute(cls, model, lora, strength_model):
+    def execute(cls, model, lora, strength_model, bypass=False):
        if strength_model == 0:
            return io.NodeOutput(model)

-        model_lora, _ = comfy.sd.load_lora_for_models(
-            model, None, lora, strength_model, 0
-        )
+        if bypass:
+            model_lora, _ = comfy.sd.load_bypass_lora_for_models(
+                model, None, lora, strength_model, 0
+            )
+        else:
+            model_lora, _ = comfy.sd.load_lora_for_models(
+                model, None, lora, strength_model, 0
+            )
        return io.NodeOutput(model_lora)


--- a/comfy_extras/nodes_video.py
+++ b/comfy_extras/nodes_video.py
@@ -202,6 +202,56 @@ class LoadVideo(io.ComfyNode):

        return True

+class VideoSlice(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Video Slice",
+            display_name="Video Slice",
+            search_aliases=[
+                "trim video duration",
+                "skip first frames",
+                "frame load cap",
+                "start time",
+            ],
+            category="image/video",
+            inputs=[
+                io.Video.Input("video"),
+                io.Float.Input(
+                    "start_time",
+                    default=0.0,
+                    max=1e5,
+                    min=-1e5,
+                    step=0.001,
+                    tooltip="Start time in seconds",
+                ),
+                io.Float.Input(
+                    "duration",
+                    default=0.0,
+                    min=0.0,
+                    step=0.001,
+                    tooltip="Duration in seconds, or 0 for unlimited duration",
+                ),
+                io.Boolean.Input(
+                    "strict_duration",
+                    default=False,
+                    tooltip="If True, when the specified duration is not possible, an error will be raised.",
+                ),
+            ],
+            outputs=[
+                io.Video.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video: io.Video.Type, start_time: float, duration: float, strict_duration: bool) -> io.NodeOutput:
+        trimmed = video.as_trimmed(start_time, duration, strict_duration=strict_duration)
+        if trimmed is not None:
+            return io.NodeOutput(trimmed)
+        raise ValueError(
+            f"Failed to slice video:\nSource duration: {video.get_duration()}\nStart time: {start_time}\nTarget duration: {duration}"
+        )
+

 class VideoExtension(ComfyExtension):
    @override
@@ -212,6 +262,7 @@ class VideoExtension(ComfyExtension):
            CreateVideo,
            GetVideoComponents,
            LoadVideo,
+            VideoSlice,
        ]

 async def comfy_entrypoint() -> VideoExtension:
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.12.2"
+__version__ = "0.13.0"
--- a/execution.py
+++ b/execution.py
@@ -13,8 +13,11 @@ from contextlib import nullcontext

 import torch

+from comfy.cli_args import args
 import comfy.memory_management
 import comfy.model_management
+import comfy_aimdo.model_vbar
+
 from latent_preview import set_preview_method
 import nodes
 from comfy_execution.caching import (
@@ -527,8 +530,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                finally:
                    if allocator is not None:
+                        if args.verbose == "DEBUG":
+                            comfy_aimdo.model_vbar.vbars_analyze()
                        comfy.model_management.reset_cast_buffers()
-                        torch.cuda.synchronize()
+                        comfy_aimdo.model_vbar.vbars_reset_watermark_limits()

            if has_pending_tasks:
                pending_async_nodes[unique_id] = output_data
--- a/nodes.py
+++ b/nodes.py
@@ -2433,7 +2433,8 @@ async def init_builtin_extra_nodes():
        "nodes_image_compare.py",
        "nodes_zimage.py",
        "nodes_lora_debug.py",
-        "nodes_color.py"
+        "nodes_color.py",
+        "nodes_toolkit.py",
    ]

    import_failed = []
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.12.2"
+version = "0.13.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.37.11
-comfyui-workflow-templates==0.8.31
-comfyui-embedded-docs==0.4.0
+comfyui-frontend-package==1.38.13
+comfyui-workflow-templates==0.8.38
+comfyui-embedded-docs==0.4.1
 torch
 torchsde
 torchvision
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.1.7
+comfy-aimdo>=0.1.8
 requests

 #non essential dependencies:
--- a/tests/execution/test_jobs.py
+++ b/tests/execution/test_jobs.py
@@ -5,8 +5,11 @@ from comfy_execution.jobs import (
    is_previewable,
    normalize_queue_item,
    normalize_history_item,
+    normalize_output_item,
+    normalize_outputs,
    get_outputs_summary,
    apply_sorting,
+    has_3d_extension,
 )


@@ -35,8 +38,8 @@ class TestIsPreviewable:
    """Unit tests for is_previewable()"""

    def test_previewable_media_types(self):
-        """Images, video, audio media types should be previewable."""
-        for media_type in ['images', 'video', 'audio']:
+        """Images, video, audio, 3d media types should be previewable."""
+        for media_type in ['images', 'video', 'audio', '3d']:
            assert is_previewable(media_type, {}) is True

    def test_non_previewable_media_types(self):
@@ -46,7 +49,7 @@ class TestIsPreviewable:

    def test_3d_extensions_previewable(self):
        """3D file extensions should be previewable regardless of media_type."""
-        for ext in ['.obj', '.fbx', '.gltf', '.glb']:
+        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
            item = {'filename': f'model{ext}'}
            assert is_previewable('files', item) is True

@@ -160,7 +163,7 @@ class TestGetOutputsSummary:

    def test_3d_files_previewable(self):
        """3D file extensions should be previewable."""
-        for ext in ['.obj', '.fbx', '.gltf', '.glb']:
+        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
            outputs = {
                'node1': {
                    'files': [{'filename': f'model{ext}', 'type': 'output'}]
@@ -192,6 +195,64 @@ class TestGetOutputsSummary:
        assert preview['mediaType'] == 'images'
        assert preview['subfolder'] == 'outputs'

+    def test_string_3d_filename_creates_preview(self):
+        """String items with 3D extensions should synthesize a preview (Preview3D node output).
+        Only the .glb counts — nulls and non-file strings are excluded."""
+        outputs = {
+            'node1': {
+                'result': ['preview3d_abc123.glb', None, None]
+            }
+        }
+        count, preview = get_outputs_summary(outputs)
+        assert count == 1
+        assert preview is not None
+        assert preview['filename'] == 'preview3d_abc123.glb'
+        assert preview['mediaType'] == '3d'
+        assert preview['nodeId'] == 'node1'
+        assert preview['type'] == 'output'
+
+    def test_string_non_3d_filename_no_preview(self):
+        """String items without 3D extensions should not create a preview."""
+        outputs = {
+            'node1': {
+                'result': ['data.json', None]
+            }
+        }
+        count, preview = get_outputs_summary(outputs)
+        assert count == 0
+        assert preview is None
+
+    def test_string_3d_filename_used_as_fallback(self):
+        """String 3D preview should be used when no dict items are previewable."""
+        outputs = {
+            'node1': {
+                'latents': [{'filename': 'latent.safetensors'}],
+            },
+            'node2': {
+                'result': ['model.glb', None]
+            }
+        }
+        count, preview = get_outputs_summary(outputs)
+        assert preview is not None
+        assert preview['filename'] == 'model.glb'
+        assert preview['mediaType'] == '3d'
+
+
+class TestHas3DExtension:
+    """Unit tests for has_3d_extension()"""
+
+    def test_recognized_extensions(self):
+        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
+            assert has_3d_extension(f'model{ext}') is True
+
+    def test_case_insensitive(self):
+        assert has_3d_extension('MODEL.GLB') is True
+        assert has_3d_extension('Scene.GLTF') is True
+
+    def test_non_3d_extensions(self):
+        for name in ['photo.png', 'video.mp4', 'data.json', 'model']:
+            assert has_3d_extension(name) is False
+

 class TestApplySorting:
    """Unit tests for apply_sorting()"""
@@ -395,3 +456,142 @@ class TestNormalizeHistoryItem:
            'prompt': {'nodes': {'1': {}}},
            'extra_data': {'create_time': 1234567890, 'client_id': 'abc'},
        }
+
+    def test_include_outputs_normalizes_3d_strings(self):
+        """Detail view should transform string 3D filenames into file output dicts."""
+        history_item = {
+            'prompt': (
+                5,
+                'prompt-3d',
+                {'nodes': {}},
+                {'create_time': 1234567890},
+                ['node1'],
+            ),
+            'status': {'status_str': 'success', 'completed': True, 'messages': []},
+            'outputs': {
+                'node1': {
+                    'result': ['preview3d_abc123.glb', None, None]
+                }
+            },
+        }
+        job = normalize_history_item('prompt-3d', history_item, include_outputs=True)
+
+        assert job['outputs_count'] == 1
+        result_items = job['outputs']['node1']['result']
+        assert len(result_items) == 1
+        assert result_items[0] == {
+            'filename': 'preview3d_abc123.glb',
+            'type': 'output',
+            'subfolder': '',
+            'mediaType': '3d',
+        }
+
+    def test_include_outputs_preserves_dict_items(self):
+        """Detail view normalization should pass dict items through unchanged."""
+        history_item = {
+            'prompt': (
+                5,
+                'prompt-img',
+                {'nodes': {}},
+                {'create_time': 1234567890},
+                ['node1'],
+            ),
+            'status': {'status_str': 'success', 'completed': True, 'messages': []},
+            'outputs': {
+                'node1': {
+                    'images': [
+                        {'filename': 'photo.png', 'type': 'output', 'subfolder': ''},
+                    ]
+                }
+            },
+        }
+        job = normalize_history_item('prompt-img', history_item, include_outputs=True)
+
+        assert job['outputs_count'] == 1
+        assert job['outputs']['node1']['images'] == [
+            {'filename': 'photo.png', 'type': 'output', 'subfolder': ''},
+        ]
+
+
+class TestNormalizeOutputItem:
+    """Unit tests for normalize_output_item()"""
+
+    def test_none_returns_none(self):
+        assert normalize_output_item(None) is None
+
+    def test_string_3d_extension_synthesizes_dict(self):
+        result = normalize_output_item('model.glb')
+        assert result == {'filename': 'model.glb', 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
+
+    def test_string_non_3d_extension_returns_none(self):
+        assert normalize_output_item('data.json') is None
+
+    def test_string_no_extension_returns_none(self):
+        assert normalize_output_item('camera_info_string') is None
+
+    def test_dict_passes_through(self):
+        item = {'filename': 'test.png', 'type': 'output'}
+        assert normalize_output_item(item) is item
+
+    def test_other_types_return_none(self):
+        assert normalize_output_item(42) is None
+        assert normalize_output_item(True) is None
+
+
+class TestNormalizeOutputs:
+    """Unit tests for normalize_outputs()"""
+
+    def test_empty_outputs(self):
+        assert normalize_outputs({}) == {}
+
+    def test_dict_items_pass_through(self):
+        outputs = {
+            'node1': {
+                'images': [{'filename': 'a.png', 'type': 'output'}],
+            }
+        }
+        result = normalize_outputs(outputs)
+        assert result == outputs
+
+    def test_3d_string_synthesized(self):
+        outputs = {
+            'node1': {
+                'result': ['model.glb', None, None],
+            }
+        }
+        result = normalize_outputs(outputs)
+        assert result == {
+            'node1': {
+                'result': [
+                    {'filename': 'model.glb', 'type': 'output', 'subfolder': '', 'mediaType': '3d'},
+                ],
+            }
+        }
+
+    def test_animated_key_preserved(self):
+        outputs = {
+            'node1': {
+                'images': [{'filename': 'a.png', 'type': 'output'}],
+                'animated': [True],
+            }
+        }
+        result = normalize_outputs(outputs)
+        assert result['node1']['animated'] == [True]
+
+    def test_non_dict_node_outputs_preserved(self):
+        outputs = {'node1': 'unexpected_value'}
+        result = normalize_outputs(outputs)
+        assert result == {'node1': 'unexpected_value'}
+
+    def test_none_items_filtered_but_other_types_preserved(self):
+        outputs = {
+            'node1': {
+                'result': ['data.json', None, [1, 2, 3]],
+            }
+        }
+        result = normalize_outputs(outputs)
+        assert result == {
+            'node1': {
+                'result': ['data.json', [1, 2, 3]],
+            }
+        }
Author	SHA1	Message	Date
Jedrzej Kosinski	b1c69ed6f6	Improve NodeReplace docstring	2026-02-11 01:16:39 -08:00
Jedrzej Kosinski	1ef4c6e529	Merge branch 'master' into jk/node-replace-api	2026-02-11 01:06:10 -08:00
Jedrzej Kosinski	9e758b5b0c	Refactored _node_replace.py InputMap/OutputMap to use a TypedDict instead of objects, simplified the schema sent to the frontend, updated nodes_post_processing.py replacements to use new schema	2026-02-11 01:02:55 -08:00
comfyanonymous	76a7fa96db	Make built in lora training work on anima. (#12402 )	2026-02-10 22:04:32 -05:00
Kohaku-Blueleaf	cdcf4119b3	[Trainer] training with proper offloading (#12189 ) * Fix bypass dtype/device moving * Force offloading mode for training * training context var * offloading implementation in training node * fix wrong input type * Support bypass load lora model, correct adapter/offloading handling	2026-02-10 21:45:19 -05:00
Jedrzej Kosinski	a6d691dc45	Merge branch 'master' into jk/node-replace-api	2026-02-10 16:54:35 -06:00
Christian Byrne	8d0da49499	feat: add node_replacements server feature flag (#12362 ) Amp-Thread-ID: https://ampcode.com/threads/T-019c3f3d-e208-704f-bf25-4f643c1e0059	2026-02-10 14:53:28 -08:00
AustinMroz	dbe70b6821	Add a VideoSlice node (#12107 ) * Base TrimVideo implementation * Raise error if as_trimmed call fails * Bigger max start_time, tooltips, and formatting * Count packets unless codec has subframes * Remove incorrect nested decode * Add null check for audio streams * Support non-strict duration * Added strict_duration bool to node definition * Empty commit for approval * Fix duration * Support 5.1 audio layout on save --------- Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>	2026-02-10 14:42:21 -08:00
guill	00fff6019e	feat(jobs): add 3d to PREVIEWABLE_MEDIA_TYPES for first-class 3D output support (#12381 ) Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>	2026-02-10 14:37:14 -08:00
rattus	123a7874a9	ops: Fix vanilla-fp8 loaded lora quality (#12390 ) This was missing the stochastic rounding required for fp8 downcast to be consistent with model_patcher.patch_weight_to_device. Missed in testing as I spend too much time with quantized tensors and overlooked the simpler ones.	2026-02-10 13:38:28 -05:00
rattus	f719f9c062	sd: delay VAE dtype archive until after override (#12388 ) VAEs have host specific dtype logic that should override the dynamic _model_dtype. Defer the archiving of model dtypes until after.	2026-02-10 13:37:46 -05:00
rattus	fe053ba5eb	mp: dont deep-clone objects from model_options (#12382 ) If there are non-trivial python objects nested in the model_options, this causes all sorts of issues. Traverse lists and dicts so clones can safely overide settings and BYO objects but stop there on the deepclone.	2026-02-10 13:37:17 -05:00
comfyanonymous	6648ab68bc	ComfyUI v0.13.0	2026-02-10 13:26:29 -05:00
ComfyUI Wiki	6615db925c	chore: update workflow templates to v0.8.38 (#12394 )	2026-02-10 13:24:56 -05:00
Alexander Piskun	8ca842a8ed	feat(api-nodes-Kling): add new models (V3, O3) (#12389 ) * feat(api-nodes-Kling): add new models (V3, O3) * remove storyboard from VideoToVideo node * added check for total duration of storyboards * fixed other small things * updated display name for nodes * added "fake" seed	2026-02-10 09:34:54 -08:00
Alexander Piskun	c1b63a7e78	fix(Moonvalley-API-Nodes): adjust "steps" parameter to not raise exception (#12370 )	2026-02-09 21:58:27 -05:00
ComfyUI Wiki	349a636a2b	chore: update workflow templates to v0.8.37 (#12377 )	2026-02-09 21:25:34 -05:00
comfyanonymous	a4be04c5d7	Ace step prompts match now. (#12376 )	2026-02-09 19:45:56 -05:00
blepping	baf8c87455	Iimprovements to ACE-Steps 1.5 text encoding (part 2) (#12350 )	2026-02-09 19:41:49 -05:00
rattus	62315fbb15	Dynamic VRAM fixes - Ace 1.5 performance + a VRAM leak (#12368 ) * revert threaded model loader change This change was only needed to get around the pytorch 2.7 mempool bugs, and should have been reverted along with #12260. This fixes a different memory leak where pytorch gets confused about cache emptying. * load non comfy weights * MPDynamic: Pre-generate the tensors for vbars Apparently this is an expensive operation that slows down things. * bump to aimdo 1.8 New features: watermark limit feature logging enhancements -O2 build on linux	2026-02-09 16:16:08 -05:00
comfyanonymous	a0302cc6a8	Make tonemap latent work on any dim latents. (#12363 )	2026-02-08 21:16:40 -05:00
comfyanonymous	f350a84261	Disable prompt weights for ltxv2. (#12354 )	2026-02-07 19:16:28 -05:00
ComfyUI Wiki	3760d74005	chore: update embedded docs to v0.4.1 (#12346 )	2026-02-07 18:34:52 -05:00
chaObserv	9bf5aa54db	Add search_aliases to sa-solver and seeds-2 node (#12327 )	2026-02-07 17:38:51 -05:00
Jukka Seppänen	5ff4fdedba	Fix LazyCache (#12344 )	2026-02-07 11:25:30 -08:00
comfyanonymous	17e7df43d1	Pad ace step 1.5 ref audio if not long enough. (#12341 )	2026-02-07 00:02:11 -05:00
comfyanonymous	039955c527	Some fixes to previous pr. (#12339 )	2026-02-06 20:14:52 -05:00
tdrussell	6a26328842	Support fp16 for Cosmos-Predict2 and Anima (#12249 )	2026-02-06 20:12:15 -05:00
comfyanonymous	204e65b8dc	Fix bug with last pr (#12338 )	2026-02-06 19:48:20 -05:00
asagi4	a831c19b70	Fix return_word_ids=True with Anima tokenizer (#12328 )	2026-02-06 19:38:04 -05:00
comfyanonymous	eba6c940fd	Make ace step 1.5 base model work properly with default workflow. (#12337 )	2026-02-06 19:14:56 -05:00
Jukka Seppänen	a1c101f861	EasyCache: Support LTX2 (#12231 )	2026-02-06 00:43:09 -05:00
comfyanonymous	c2d7f07dbf	Fix issue when using disable_unet_model_creation (#12315 )	2026-02-05 19:24:09 -05:00
comfyanonymous	458292fef0	Fix some lowvram stuff with ace step 1.5 (#12312 )	2026-02-05 19:15:04 -05:00
bymyself	739ed21714	fix: use direct PromptServer registration instead of ComfyAPI class Amp-Thread-ID: https://ampcode.com/threads/T-019c2be8-0b34-747e-b1f7-20a1a1e6c9df	2026-02-05 15:52:21 -08:00
comfyanonymous	6555dc65b8	Make ace step 1.5 work without the llm. (#12311 )	2026-02-05 16:43:45 -05:00
Christian Byrne	a2d4c0f98b	refactor: process isolation support for node replacement API (#12298 ) * refactor: process isolation support for node replacement API - Move REGISTERED_NODE_REPLACEMENTS global to NodeReplaceManager instance state - Add NodeReplacement class to ComfyAPI_latest with async register() method - Deprecate module-level register_node_replacement() function - Call register_replacements() from comfy_entrypoint() This enables pyisolate compatibility where extensions run in separate processes and communicate via RPC. The async API allows registration calls to cross process boundaries. Refs: TDD-002 Amp-Thread-ID: https://ampcode.com/threads/T-019c2b33-ac55-76a9-9c6b-0246a8625f21 * fix: remove whitespace and deprecation cruft Amp-Thread-ID: https://ampcode.com/threads/T-019c2be8-0b34-747e-b1f7-20a1a1e6c9df	2026-02-05 12:21:03 -08:00
AustinMroz	2b70ab9ad0	Add a Create List node (#12173 )	2026-02-05 01:18:21 -05:00
Comfy Org PR Bot	00efcc6cd0	Bump comfyui-frontend-package to 1.38.13 (#12238 )	2026-02-05 01:17:37 -05:00
comfyanonymous	cb459573c8	ComfyUI v0.12.3	2026-02-05 01:13:35 -05:00
comfyanonymous	35183543e0	Add VAE tiled decode node for audio. (#12299 )	2026-02-05 01:12:04 -05:00
blepping	a246cc02b2	Improvements to ACE-Steps 1.5 text encoding (#12283 )	2026-02-05 00:17:37 -05:00