Add Number Convert node (#13041)

* Add Number Convert node for unified numeric type conversion

Consolidates fragmented IntToFloat/FloatToInt nodes (previously only
available via third-party packs like ComfyMath, FillNodes, etc.) into
a single core node.

- Single input accepting INT, FLOAT, STRING, and BOOL types
- Two outputs: FLOAT and INT
- Conversion: bool→0/1, string→parsed number, float↔int standard cast
- Follows Math Expression node patterns (comfy_api, io.Schema, etc.)

Refs: COM-16925

* Register nodes_number_convert.py in extras_files list

Without this entry in nodes.py, the Number Convert node file
would not be discovered and loaded at startup.

* Add isfinite guard, exception chaining, and unit tests for Number Convert node

- Add math.isfinite() check to prevent int() crash on inf/nan string inputs
- Use 'from None' for cleaner exception chaining on string parse failure
- Add 21 unit tests covering all input types and error paths

comfy/context_windows.py

@@ -93,6 +93,50 @@ class IndexListCallbacks:
         return {}
 
 
+def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, device, temporal_dim: int, temporal_scale: int=1, temporal_offset: int=0, retain_index_list: list[int]=[]):
+    if not (hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor)):
+        return None
+    cond_tensor = cond_value.cond
+    if temporal_dim >= cond_tensor.ndim:
+        return None
+
+    cond_size = cond_tensor.size(temporal_dim)
+
+    if temporal_scale == 1:
+        expected_size = x_in.size(window.dim) - temporal_offset
+        if cond_size != expected_size:
+            return None
+
+    if temporal_offset == 0 and temporal_scale == 1:
+        sliced = window.get_tensor(cond_tensor, device, dim=temporal_dim, retain_index_list=retain_index_list)
+        return cond_value._copy_with(sliced)
+
+    # skip leading latent positions that have no corresponding conditioning (e.g. reference frames)
+    if temporal_offset > 0:
+        indices = [i - temporal_offset for i in window.index_list[temporal_offset:]]
+        indices = [i for i in indices if 0 <= i]
+    else:
+        indices = list(window.index_list)
+
+    if not indices:
+        return None
+
+    if temporal_scale > 1:
+        scaled = []
+        for i in indices:
+            for k in range(temporal_scale):
+                si = i * temporal_scale + k
+                if si < cond_size:
+                    scaled.append(si)
+        indices = scaled
+        if not indices:
+            return None
+
+    idx = tuple([slice(None)] * temporal_dim + [indices])
+    sliced = cond_tensor[idx].to(device)
+    return cond_value._copy_with(sliced)
+
+
 @dataclass
 class ContextSchedule:
     name: str
@@ -177,10 +221,17 @@ class IndexListContextHandler(ContextHandlerABC):
                                     new_cond_item[cond_key] = result
                                     handled = True
                                     break
+                            if not handled and self._model is not None:
+                                result = self._model.resize_cond_for_context_window(
+                                    cond_key, cond_value, window, x_in, device,
+                                    retain_index_list=self.cond_retain_index_list)
+                                if result is not None:
+                                    new_cond_item[cond_key] = result
+                                    handled = True
                             if handled:
                                 continue
                             if isinstance(cond_value, torch.Tensor):
-                                if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
+                                if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \
                                    (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
                                     new_cond_item[cond_key] = window.get_tensor(cond_value, device)
                             # Handle audio_embed (temporal dim is 1)
@@ -224,6 +275,7 @@ class IndexListContextHandler(ContextHandlerABC):
         return context_windows
 
     def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
+        self._model = model
         self.set_step(timestep, model_options)
         context_windows = self.get_context_windows(model, x_in, model_options)
         enumerated_context_windows = list(enumerate(context_windows))

comfy/ldm/flux/model.py

@@ -386,7 +386,7 @@ class Flux(nn.Module):
                     h = max(h, ref.shape[-2] + h_offset)
                     w = max(w, ref.shape[-1] + w_offset)
 
-                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
+                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset, transformer_options=transformer_options)
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
                 ref_num_tokens.append(kontext.shape[1])

comfy/ldm/lightricks/av_model.py

@@ -681,6 +681,33 @@ class LTXAVModel(LTXVModel):
         additional_args["has_spatial_mask"] = has_spatial_mask
 
         ax, a_latent_coords = self.a_patchifier.patchify(ax)
+
+        # Inject reference audio for ID-LoRA in-context conditioning
+        ref_audio = kwargs.get("ref_audio", None)
+        ref_audio_seq_len = 0
+        if ref_audio is not None:
+            ref_tokens = ref_audio["tokens"].to(dtype=ax.dtype, device=ax.device)
+            if ref_tokens.shape[0] < ax.shape[0]:
+                ref_tokens = ref_tokens.expand(ax.shape[0], -1, -1)
+            ref_audio_seq_len = ref_tokens.shape[1]
+            B = ax.shape[0]
+
+            # Compute negative temporal positions matching ID-LoRA convention:
+            # offset by -(end_of_last_token + time_per_latent) so reference ends just before t=0
+            p = self.a_patchifier
+            tpl = p.hop_length * p.audio_latent_downsample_factor / p.sample_rate
+            ref_start = p._get_audio_latent_time_in_sec(0, ref_audio_seq_len, torch.float32, ax.device)
+            ref_end = p._get_audio_latent_time_in_sec(1, ref_audio_seq_len + 1, torch.float32, ax.device)
+            time_offset = ref_end[-1].item() + tpl
+            ref_start = (ref_start - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1)
+            ref_end = (ref_end - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1)
+            ref_pos = torch.stack([ref_start, ref_end], dim=-1)
+
+            additional_args["ref_audio_seq_len"] = ref_audio_seq_len
+            additional_args["target_audio_seq_len"] = ax.shape[1]
+            ax = torch.cat([ref_tokens, ax], dim=1)
+            a_latent_coords = torch.cat([ref_pos.to(a_latent_coords), a_latent_coords], dim=2)
+
         ax = self.audio_patchify_proj(ax)
 
         # additional_args.update({"av_orig_shape": list(x.shape)})
@@ -721,6 +748,14 @@ class LTXAVModel(LTXVModel):
 
         # Prepare audio timestep
         a_timestep = kwargs.get("a_timestep")
+        ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0)
+        if ref_audio_seq_len > 0 and a_timestep is not None:
+            # Reference tokens must have timestep=0, expand scalar/1D timestep to per-token so ref=0 and target=sigma.
+            target_len = kwargs.get("target_audio_seq_len")
+            if a_timestep.dim() <= 1:
+                a_timestep = a_timestep.view(-1, 1).expand(batch_size, target_len)
+            ref_ts = torch.zeros(batch_size, ref_audio_seq_len, *a_timestep.shape[2:], device=a_timestep.device, dtype=a_timestep.dtype)
+            a_timestep = torch.cat([ref_ts, a_timestep], dim=1)
         if a_timestep is not None:
             a_timestep_scaled = a_timestep * self.timestep_scale_multiplier
             a_timestep_flat = a_timestep_scaled.flatten()
@@ -955,6 +990,13 @@ class LTXAVModel(LTXVModel):
         v_embedded_timestep = embedded_timestep[0]
         a_embedded_timestep = embedded_timestep[1]
 
+        # Trim reference audio tokens before unpatchification
+        ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0)
+        if ref_audio_seq_len > 0:
+            ax = ax[:, ref_audio_seq_len:]
+            if a_embedded_timestep.shape[1] > 1:
+                a_embedded_timestep = a_embedded_timestep[:, ref_audio_seq_len:]
+
         # Expand compressed video timestep if needed
         if isinstance(v_embedded_timestep, CompressedTimestep):
             v_embedded_timestep = v_embedded_timestep.expand()

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -23,6 +23,11 @@ class CausalConv3d(nn.Module):
         self.in_channels = in_channels
         self.out_channels = out_channels
 
+        if isinstance(stride, int):
+            self.time_stride = stride
+        else:
+            self.time_stride = stride[0]
+
         kernel_size = (kernel_size, kernel_size, kernel_size)
         self.time_kernel_size = kernel_size[0]
 
@@ -58,18 +63,23 @@ class CausalConv3d(nn.Module):
         pieces = [ cached, x ]
         if is_end and not causal:
             pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))
+        input_length = sum([piece.shape[2] for piece in pieces])
+        cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride)
 
         needs_caching = not is_end
-        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
+        if needs_caching and cache_length == 0:
+            self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False)
             needs_caching = False
-            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+        if needs_caching and x.shape[2] >= cache_length:
+            needs_caching = False
+            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
 
         x = torch.cat(pieces, dim=2)
         del pieces
         del cached
 
         if needs_caching:
-            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
         elif is_end:
             self.temporal_cache_state[tid] = (None, True)
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -233,10 +233,7 @@ class Encoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def forward_orig(self, sample: torch.FloatTensor) -> torch.FloatTensor:
-        r"""The forward method of the `Encoder` class."""
-
-        sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+    def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]:
         sample = self.conv_in(sample)
 
         checkpoint_fn = (
@@ -247,10 +244,14 @@ class Encoder(nn.Module):
 
         for down_block in self.down_blocks:
             sample = checkpoint_fn(down_block)(sample)
+            if sample is None or sample.shape[2] == 0:
+                return None
 
         sample = self.conv_norm_out(sample)
         sample = self.conv_act(sample)
         sample = self.conv_out(sample)
+        if sample is None or sample.shape[2] == 0:
+            return None
 
         if self.latent_log_var == "uniform":
             last_channel = sample[:, -1:, ...]
@@ -282,9 +283,35 @@ class Encoder(nn.Module):
 
         return sample
 
+    def forward_orig(self, sample: torch.FloatTensor, device=None) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+
+        max_chunk_size = get_max_chunk_size(sample.device if device is None else device) * 2  # encoder is more memory-efficient than decoder
+        frame_size = sample[:, :, :1, :, :].numel() * sample.element_size()
+        frame_size = int(frame_size * (self.conv_in.out_channels / self.conv_in.in_channels))
+
+        outputs = []
+        samples = [sample[:, :, :1, :, :]]
+        if sample.shape[2] > 1:
+            chunk_t = max(2, max_chunk_size // frame_size)
+            if chunk_t < 4:
+                chunk_t = 2
+            elif chunk_t < 8:
+                chunk_t = 4
+            else:
+                chunk_t = (chunk_t // 8) * 8
+            samples += list(torch.split(sample[:, :, 1:, :, :], chunk_t, dim=2))
+        for chunk_idx, chunk in enumerate(samples):
+            if chunk_idx == len(samples) - 1:
+                mark_conv3d_ended(self)
+            chunk = patchify(chunk, patch_size_hw=self.patch_size, patch_size_t=1).to(device=device)
+            output = self._forward_chunk(chunk)
+            if output is not None:
+                outputs.append(output)
+
+        return torch_cat_if_needed(outputs, dim=2)
+
     def forward(self, *args, **kwargs):
-        #No encoder support so just flag the end so it doesnt use the cache.
-        mark_conv3d_ended(self)
         try:
             return self.forward_orig(*args, **kwargs)
         finally:
@@ -509,6 +536,53 @@ class Decoder(nn.Module):
         c, (ts, hs, ws), to = self._output_scale
         return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws)
 
+    def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size):
+        sample = sample_ref[0]
+        sample_ref[0] = None
+        if idx >= len(self.up_blocks):
+            sample = self.conv_norm_out(sample)
+            if timestep_shift_scale is not None:
+                shift, scale = timestep_shift_scale
+                sample = sample * (1 + scale) + shift
+            sample = self.conv_act(sample)
+            if ended:
+                mark_conv3d_ended(self.conv_out)
+            sample = self.conv_out(sample, causal=self.causal)
+            if sample is not None and sample.shape[2] > 0:
+                sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+                t = sample.shape[2]
+                output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)
+                output_offset[0] += t
+            return
+
+        up_block = self.up_blocks[idx]
+        if ended:
+            mark_conv3d_ended(up_block)
+        if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
+            sample = checkpoint_fn(up_block)(
+                sample, causal=self.causal, timestep=scaled_timestep
+            )
+        else:
+            sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+        if sample is None or sample.shape[2] == 0:
+            return
+
+        total_bytes = sample.numel() * sample.element_size()
+        num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
+
+        if num_chunks == 1:
+            # when we are not chunking, detach our x so the callee can free it as soon as they are done
+            next_sample_ref = [sample]
+            del sample
+            self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
+            return
+        else:
+            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
+
+            for chunk_idx, sample1 in enumerate(samples):
+                self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
+
     def forward_orig(
         self,
         sample: torch.FloatTensor,
@@ -528,6 +602,7 @@ class Decoder(nn.Module):
         )
 
         timestep_shift_scale = None
+        scaled_timestep = None
         if self.timestep_conditioning:
             assert (
                 timestep is not None
@@ -564,54 +639,7 @@ class Decoder(nn.Module):
 
         max_chunk_size = get_max_chunk_size(sample.device)
 
-        def run_up(idx, sample_ref, ended):
-            sample = sample_ref[0]
-            sample_ref[0] = None
-            if idx >= len(self.up_blocks):
-                sample = self.conv_norm_out(sample)
-                if timestep_shift_scale is not None:
-                    shift, scale = timestep_shift_scale
-                    sample = sample * (1 + scale) + shift
-                sample = self.conv_act(sample)
-                if ended:
-                    mark_conv3d_ended(self.conv_out)
-                sample = self.conv_out(sample, causal=self.causal)
-                if sample is not None and sample.shape[2] > 0:
-                    sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
-                    t = sample.shape[2]
-                    output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)
-                    output_offset[0] += t
-                return
-
-            up_block = self.up_blocks[idx]
-            if (ended):
-                mark_conv3d_ended(up_block)
-            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
-                sample = checkpoint_fn(up_block)(
-                    sample, causal=self.causal, timestep=scaled_timestep
-                )
-            else:
-                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
-
-            if sample is None or sample.shape[2] == 0:
-                return
-
-            total_bytes = sample.numel() * sample.element_size()
-            num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
-
-            if num_chunks == 1:
-                # when we are not chunking, detach our x so the callee can free it as soon as they are done
-                next_sample_ref = [sample]
-                del sample
-                run_up(idx + 1, next_sample_ref, ended)
-                return
-            else:
-                samples = torch.chunk(sample, chunks=num_chunks, dim=2)
-
-                for chunk_idx, sample1 in enumerate(samples):
-                    run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1)
-
-        run_up(0, [sample], True)
+        self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
 
         return output_buffer
 
@@ -737,12 +765,25 @@ class SpaceToDepthDownsample(nn.Module):
             causal=True,
             spatial_padding_mode=spatial_padding_mode,
         )
+        self.temporal_cache_state = {}
 
     def forward(self, x, causal: bool = True):
-        if self.stride[0] == 2:
+        tid = threading.get_ident()
+        cached, pad_first, cached_x, cached_input = self.temporal_cache_state.get(tid, (None, True, None, None))
+        if cached_input is not None:
+            x = torch_cat_if_needed([cached_input, x], dim=2)
+            cached_input = None
+
+        if self.stride[0] == 2 and pad_first:
             x = torch.cat(
                 [x[:, :, :1, :, :], x], dim=2
             )  # duplicate first frames for padding
+            pad_first = False
+
+        if x.shape[2] < self.stride[0]:
+            cached_input = x
+            self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)
+            return None
 
         # skip connection
         x_in = rearrange(
@@ -757,15 +798,26 @@ class SpaceToDepthDownsample(nn.Module):
 
         # conv
         x = self.conv(x, causal=causal)
-        x = rearrange(
-            x,
-            "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
-            p1=self.stride[0],
-            p2=self.stride[1],
-            p3=self.stride[2],
-        )
+        if self.stride[0] == 2 and x.shape[2] == 1:
+            if cached_x is not None:
+                x = torch_cat_if_needed([cached_x, x], dim=2)
+                cached_x = None
+            else:
+                cached_x = x
+                x = None
 
-        x = x + x_in
+        if x is not None:
+            x = rearrange(
+                x,
+                "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
+                p1=self.stride[0],
+                p2=self.stride[1],
+                p3=self.stride[2],
+            )
+
+        cached = add_exchange_cache(x, cached, x_in, dim=2)
+
+        self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)
 
         return x
 
@@ -1098,6 +1150,8 @@ class processor(nn.Module):
         return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
 
 class VideoVAE(nn.Module):
+    comfy_has_chunked_io = True
+
     def __init__(self, version=0, config=None):
         super().__init__()
 
@@ -1240,11 +1294,9 @@ class VideoVAE(nn.Module):
             }
         return config
 
-    def encode(self, x):
-        frames_count = x.shape[2]
-        if ((frames_count - 1) % 8) != 0:
-            raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.")
-        means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
+    def encode(self, x, device=None):
+        x = x[:, :, :max(1, 1 + ((x.shape[2] - 1) // 8) * 8), :, :]
+        means, logvar = torch.chunk(self.encoder(x, device=device), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 
     def decode_output_shape(self, input_shape):

comfy/ldm/wan/vae.py

@@ -360,6 +360,43 @@ class Decoder3d(nn.Module):
             RMS_norm(out_dim, images=False), nn.SiLU(),
             CausalConv3d(out_dim, output_channels, 3, padding=1))
 
+    def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks):
+        x = x_ref[0]
+        x_ref[0] = None
+        if layer_idx >= len(self.upsamples):
+            for layer in self.head:
+                if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                    cache_x = x[:, :, -CACHE_T:, :, :]
+                    x = layer(x, feat_cache[feat_idx[0]])
+                    feat_cache[feat_idx[0]] = cache_x
+                    feat_idx[0] += 1
+                else:
+                    x = layer(x)
+            out_chunks.append(x)
+            return
+
+        layer = self.upsamples[layer_idx]
+        if feat_cache is not None:
+            x = layer(x, feat_cache, feat_idx)
+        else:
+            x = layer(x)
+
+        if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 2:
+            for frame_idx in range(0, x.shape[2], 2):
+                self.run_up(
+                    layer_idx + 1,
+                    [x[:, :, frame_idx:frame_idx + 2, :, :]],
+                    feat_cache,
+                    feat_idx.copy(),
+                    out_chunks,
+                )
+            del x
+            return
+
+        next_x_ref = [x]
+        del x
+        self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks)
+
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         ## conv1
         if feat_cache is not None:
@@ -380,42 +417,7 @@ class Decoder3d(nn.Module):
 
         out_chunks = []
 
-        def run_up(layer_idx, x_ref, feat_idx):
-            x = x_ref[0]
-            x_ref[0] = None
-            if layer_idx >= len(self.upsamples):
-                for layer in self.head:
-                    if isinstance(layer, CausalConv3d) and feat_cache is not None:
-                        cache_x = x[:, :, -CACHE_T:, :, :]
-                        x = layer(x, feat_cache[feat_idx[0]])
-                        feat_cache[feat_idx[0]] = cache_x
-                        feat_idx[0] += 1
-                    else:
-                        x = layer(x)
-                out_chunks.append(x)
-                return
-
-            layer = self.upsamples[layer_idx]
-            if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
-                for frame_idx in range(x.shape[2]):
-                    run_up(
-                        layer_idx,
-                        [x[:, :, frame_idx:frame_idx + 1, :, :]],
-                        feat_idx.copy(),
-                    )
-                del x
-                return
-
-            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
-            else:
-                x = layer(x)
-
-            next_x_ref = [x]
-            del x
-            run_up(layer_idx + 1, next_x_ref, feat_idx)
-
-        run_up(0, [x], feat_idx)
+        self.run_up(0, [x], feat_cache, feat_idx, out_chunks)
         return out_chunks
 
 

comfy/memory_management.py

@@ -39,7 +39,10 @@ def read_tensor_file_slice_into(tensor, destination):
     if (destination.device.type != "cpu"
             or file_obj is None
             or threading.get_ident() != info.thread_id
-            or destination.numel() * destination.element_size() < info.size):
+            or destination.numel() * destination.element_size() < info.size
+            or tensor.numel() * tensor.element_size() != info.size
+            or tensor.storage_offset() != 0
+            or not tensor.is_contiguous()):
         return False
 
     if info.size == 0:

comfy/model_base.py

@@ -21,6 +21,7 @@ import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
 import comfy.ldm.lightricks.av_model
+import comfy.context_windows
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
 from comfy.ldm.cascade.stage_b import StageB
@@ -285,6 +286,12 @@ class BaseModel(torch.nn.Module):
             return data
         return None
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        """Override in subclasses to handle model-specific cond slicing for context windows.
+        Return a sliced cond object, or None to fall through to default handling.
+        Use comfy.context_windows.slice_cond() for common cases."""
+        return None
+
     def extra_conds(self, **kwargs):
         out = {}
         concat_cond = self.concat_cond(**kwargs)
@@ -930,9 +937,10 @@ class LongCatImage(Flux):
         transformer_options = transformer_options.copy()
         rope_opts = transformer_options.get("rope_options", {})
         rope_opts = dict(rope_opts)
+        pe_len = float(c_crossattn.shape[1]) if c_crossattn is not None else 512.0
         rope_opts.setdefault("shift_t", 1.0)
-        rope_opts.setdefault("shift_y", 512.0)
-        rope_opts.setdefault("shift_x", 512.0)
+        rope_opts.setdefault("shift_y", pe_len)
+        rope_opts.setdefault("shift_x", pe_len)
         transformer_options["rope_options"] = rope_opts
         return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
 
@@ -1053,6 +1061,10 @@ class LTXAV(BaseModel):
         if guide_attention_entries is not None:
             out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
+        ref_audio = kwargs.get("ref_audio", None)
+        if ref_audio is not None:
+            out['ref_audio'] = comfy.conds.CONDConstant(ref_audio)
+
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
@@ -1375,6 +1387,11 @@ class WAN21_Vace(WAN21):
         out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        if cond_key == "vace_context":
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 class WAN21_Camera(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel)
@@ -1427,6 +1444,11 @@ class WAN21_HuMo(WAN21):
 
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        if cond_key == "audio_embed":
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 class WAN22_Animate(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)
@@ -1444,6 +1466,13 @@ class WAN22_Animate(WAN21):
             out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        if cond_key == "face_pixel_values":
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1)
+        if cond_key == "pose_latents":
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 class WAN22_S2V(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
@@ -1480,6 +1509,11 @@ class WAN22_S2V(WAN21):
             out['reference_motion'] = reference_motion.shape
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        if cond_key == "audio_embed":
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 class WAN22(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)

comfy/model_management.py

@@ -1003,7 +1003,7 @@ def text_encoder_offload_device():
 def text_encoder_device():
     if args.gpu_only:
         return get_torch_device()
-    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled:
+    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else:

comfy/sample.py

@@ -8,12 +8,12 @@ import comfy.nested_tensor
 
 def prepare_noise_inner(latent_image, generator, noise_inds=None):
     if noise_inds is None:
-        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+        return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
 
     unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
     noises = []
     for i in range(unique_inds[-1]+1):
-        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
         if i in unique_inds:
             noises.append(noise)
     noises = [noises[i] for i in inverse]

comfy/samplers.py

@@ -985,8 +985,8 @@ class CFGGuider:
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
-        noise = noise.to(device)
-        latent_image = latent_image.to(device)
+        noise = noise.to(device=device, dtype=torch.float32)
+        latent_image = latent_image.to(device=device, dtype=torch.float32)
         sigmas = sigmas.to(device)
         cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
 
@@ -1028,6 +1028,7 @@ class CFGGuider:
                 denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)
             else:
                 denoise_mask = denoise_masks[0]
+            denoise_mask = denoise_mask.float()
 
         self.conds = {}
         for k in self.original_conds:

comfy/sd.py

@@ -953,7 +953,7 @@ class VAE:
 
             # Pre-allocate output for VAEs that support direct buffer writes
             preallocated = False
-            if hasattr(self.first_stage_model, 'decode_output_shape'):
+            if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):
                 pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype())
                 preallocated = True
 
@@ -978,6 +978,7 @@ class VAE:
             do_tile = True
 
         if do_tile:
+            comfy.model_management.soft_empty_cache()
             dims = samples_in.ndim - 2
             if dims == 1 or self.extra_1d_channel is not None:
                 pixel_samples = self.decode_tiled_1d(samples_in)
@@ -1038,8 +1039,13 @@ class VAE:
             batch_number = max(1, batch_number)
             samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                out = self.first_stage_model.encode(pixels_in).to(self.output_device).to(dtype=self.vae_output_dtype())
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype)
+                if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):
+                    out = self.first_stage_model.encode(pixels_in, device=self.device)
+                else:
+                    pixels_in = pixels_in.to(self.device)
+                    out = self.first_stage_model.encode(pixels_in)
+                out = out.to(self.output_device).to(dtype=self.vae_output_dtype())
                 if samples is None:
                     samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 samples[x:x + batch_number] = out
@@ -1054,6 +1060,7 @@ class VAE:
             do_tile = True
 
         if do_tile:
+            comfy.model_management.soft_empty_cache()
             if self.latent_dim == 3:
                 tile = 256
                 overlap = tile // 4

comfy/text_encoders/llama.py

@@ -1028,12 +1028,19 @@ class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
                 grid = e.get("extra", None)
                 start = e.get("index")
                 if position_ids is None:
-                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
+                    position_ids = torch.ones((3, embeds.shape[1]), device=embeds.device, dtype=torch.long)
                     position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
                 end = e.get("size") + start
                 len_max = int(grid.max()) // 2
                 start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                if attention_mask is not None:
+                    # Assign compact sequential positions to attended tokens only,
+                    # skipping over padding so post-padding tokens aren't inflated.
+                    after_mask = attention_mask[0, end:]
+                    text_positions = after_mask.cumsum(0) - 1 + start_next + offset
+                    position_ids[:, end:] = torch.where(after_mask.bool(), text_positions, position_ids[0, end:])
+                else:
+                    position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
                 position_ids[0, start:end] = start + offset
                 max_d = int(grid[0][1]) // 2
                 position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]

comfy/text_encoders/longcat_image.py

@@ -64,7 +64,13 @@ class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
         return [output]
 
 
+IMAGE_PAD_TOKEN_ID = 151655
+
 class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
+    T2I_PREFIX = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n"
+    EDIT_PREFIX = "<|im_start|>system\nAs an image editing expert, first analyze the content and attributes of the input image(s). Then, based on the user's editing instructions, clearly and precisely determine how to modify the given image(s), ensuring that only the specified parts are altered and all other aspects remain consistent with the original(s).<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+    SUFFIX = "<|im_end|>\n<|im_start|>assistant\n"
+
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(
             embedding_directory=embedding_directory,
@@ -72,10 +78,8 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
             name="qwen25_7b",
             tokenizer=LongCatImageBaseTokenizer,
         )
-        self.longcat_template_prefix = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n"
-        self.longcat_template_suffix = "<|im_end|>\n<|im_start|>assistant\n"
 
-    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
+    def tokenize_with_weights(self, text, return_word_ids=False, images=None, **kwargs):
         skip_template = False
         if text.startswith("<|im_start|>"):
             skip_template = True
@@ -90,11 +94,14 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
                 text, return_word_ids=return_word_ids, disable_weights=True, **kwargs
             )
         else:
+            has_images = images is not None and len(images) > 0
+            template_prefix = self.EDIT_PREFIX if has_images else self.T2I_PREFIX
+
             prefix_ids = base_tok.tokenizer(
-                self.longcat_template_prefix, add_special_tokens=False
+                template_prefix, add_special_tokens=False
             )["input_ids"]
             suffix_ids = base_tok.tokenizer(
-                self.longcat_template_suffix, add_special_tokens=False
+                self.SUFFIX, add_special_tokens=False
             )["input_ids"]
 
             prompt_tokens = base_tok.tokenize_with_weights(
@@ -106,6 +113,14 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
             suffix_pairs = [(t, 1.0) for t in suffix_ids]
 
             combined = prefix_pairs + prompt_pairs + suffix_pairs
+
+            if has_images:
+                embed_count = 0
+                for i in range(len(combined)):
+                    if combined[i][0] == IMAGE_PAD_TOKEN_ID and embed_count < len(images):
+                        combined[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"}, combined[i][1])
+                        embed_count += 1
+
             tokens = {"qwen25_7b": [combined]}
 
         return tokens

comfy/text_encoders/qwen_vl.py

@@ -425,4 +425,7 @@ class Qwen2VLVisionTransformer(nn.Module):
             hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention)
 
         hidden_states = self.merger(hidden_states)
+        # Potentially important for spatially precise edits. This is present in the HF implementation.
+        reverse_indices = torch.argsort(window_index)
+        hidden_states = hidden_states[reverse_indices, :]
         return hidden_states

comfy/utils.py

@@ -1135,8 +1135,8 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
                 pbar.update(1)
             continue
 
-        out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
-        out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+        out = output[b:b+1].zero_()
+        out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device)
 
         positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
 
@@ -1151,7 +1151,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
                 upscaled.append(round(get_pos(d, pos)))
 
             ps = function(s_in).to(output_device)
-            mask = torch.ones_like(ps)
+            mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device)
 
             for d in range(2, dims + 2):
                 feather = round(get_scale(d - 2, overlap[d - 2]))
@@ -1174,7 +1174,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
             if pbar is not None:
                 pbar.update(1)
 
-        output[b:b+1] = out/out_div
+        out.div_(out_div)
     return output
 
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):

comfy_api/input/__init__.py

@@ -5,6 +5,10 @@ from comfy_api.latest._input import (
     MaskInput,
     LatentInput,
     VideoInput,
+    CurvePoint,
+    CurveInput,
+    MonotoneCubicCurve,
+    LinearCurve,
 )
 
 __all__ = [
@@ -13,4 +17,8 @@ __all__ = [
     "MaskInput",
     "LatentInput",
     "VideoInput",
+    "CurvePoint",
+    "CurveInput",
+    "MonotoneCubicCurve",
+    "LinearCurve",
 ]

comfy_api/latest/_input/__init__.py

@@ -1,4 +1,5 @@
 from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
+from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
 from .video_types import VideoInput
 
 __all__ = [
@@ -7,4 +8,8 @@ __all__ = [
     "VideoInput",
     "MaskInput",
     "LatentInput",
+    "CurvePoint",
+    "CurveInput",
+    "MonotoneCubicCurve",
+    "LinearCurve",
 ]

comfy_api/latest/_input/curve_types.py

@@ -0,0 +1,219 @@
+from __future__ import annotations
+
+import logging
+import math
+from abc import ABC, abstractmethod
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+CurvePoint = tuple[float, float]
+
+
+class CurveInput(ABC):
+    """Abstract base class for curve inputs.
+
+    Subclasses represent different curve representations (control-point
+    interpolation, analytical functions, LUT-based, etc.) while exposing a
+    uniform evaluation interface to downstream nodes.
+    """
+
+    @property
+    @abstractmethod
+    def points(self) -> list[CurvePoint]:
+        """The control points that define this curve."""
+
+    @abstractmethod
+    def interp(self, x: float) -> float:
+        """Evaluate the curve at a single *x* value in [0, 1]."""
+
+    def interp_array(self, xs: np.ndarray) -> np.ndarray:
+        """Vectorised evaluation over a numpy array of x values.
+
+        Subclasses should override this for better performance. The default
+        falls back to scalar ``interp`` calls.
+        """
+        return np.fromiter((self.interp(float(x)) for x in xs), dtype=np.float64, count=len(xs))
+
+    def to_lut(self, size: int = 256) -> np.ndarray:
+        """Generate a float64 lookup table of *size* evenly-spaced samples in [0, 1]."""
+        return self.interp_array(np.linspace(0.0, 1.0, size))
+
+    @staticmethod
+    def from_raw(data) -> CurveInput:
+        """Convert raw curve data (dict or point list) to a CurveInput instance.
+
+        Accepts:
+        - A ``CurveInput`` instance (returned as-is).
+        - A dict with ``"points"`` and optional ``"interpolation"`` keys.
+        - A bare list/sequence of ``(x, y)`` pairs (defaults to monotone cubic).
+        """
+        if isinstance(data, CurveInput):
+            return data
+        if isinstance(data, dict):
+            raw_points = data["points"]
+            interpolation = data.get("interpolation", "monotone_cubic")
+        else:
+            raw_points = data
+            interpolation = "monotone_cubic"
+        points = [(float(x), float(y)) for x, y in raw_points]
+        if interpolation == "linear":
+            return LinearCurve(points)
+        if interpolation != "monotone_cubic":
+            logger.warning("Unknown curve interpolation %r, falling back to monotone_cubic", interpolation)
+        return MonotoneCubicCurve(points)
+
+
+class MonotoneCubicCurve(CurveInput):
+    """Monotone cubic Hermite interpolation over control points.
+
+    Mirrors the frontend ``createMonotoneInterpolator`` in
+    ``ComfyUI_frontend/src/components/curve/curveUtils.ts`` so that
+    backend evaluation matches the editor preview exactly.
+
+    All heavy work (sorting, slope computation) happens once at construction.
+    ``interp_array`` is fully vectorised with numpy.
+    """
+
+    def __init__(self, control_points: list[CurvePoint]):
+        sorted_pts = sorted(control_points, key=lambda p: p[0])
+        self._points = [(float(x), float(y)) for x, y in sorted_pts]
+        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
+        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
+        self._slopes = self._compute_slopes()
+
+    @property
+    def points(self) -> list[CurvePoint]:
+        return list(self._points)
+
+    def _compute_slopes(self) -> np.ndarray:
+        xs, ys = self._xs, self._ys
+        n = len(xs)
+        if n < 2:
+            return np.zeros(n, dtype=np.float64)
+
+        dx = np.diff(xs)
+        dy = np.diff(ys)
+        dx_safe = np.where(dx == 0, 1.0, dx)
+        deltas = np.where(dx == 0, 0.0, dy / dx_safe)
+
+        slopes = np.empty(n, dtype=np.float64)
+        slopes[0] = deltas[0]
+        slopes[-1] = deltas[-1]
+        for i in range(1, n - 1):
+            if deltas[i - 1] * deltas[i] <= 0:
+                slopes[i] = 0.0
+            else:
+                slopes[i] = (deltas[i - 1] + deltas[i]) / 2
+
+        for i in range(n - 1):
+            if deltas[i] == 0:
+                slopes[i] = 0.0
+                slopes[i + 1] = 0.0
+            else:
+                alpha = slopes[i] / deltas[i]
+                beta = slopes[i + 1] / deltas[i]
+                s = alpha * alpha + beta * beta
+                if s > 9:
+                    t = 3 / math.sqrt(s)
+                    slopes[i] = t * alpha * deltas[i]
+                    slopes[i + 1] = t * beta * deltas[i]
+        return slopes
+
+    def interp(self, x: float) -> float:
+        xs, ys, slopes = self._xs, self._ys, self._slopes
+        n = len(xs)
+        if n == 0:
+            return 0.0
+        if n == 1:
+            return float(ys[0])
+        if x <= xs[0]:
+            return float(ys[0])
+        if x >= xs[-1]:
+            return float(ys[-1])
+
+        hi = int(np.searchsorted(xs, x, side='right'))
+        hi = min(hi, n - 1)
+        lo = hi - 1
+
+        dx = xs[hi] - xs[lo]
+        if dx == 0:
+            return float(ys[lo])
+
+        t = (x - xs[lo]) / dx
+        t2 = t * t
+        t3 = t2 * t
+        h00 = 2 * t3 - 3 * t2 + 1
+        h10 = t3 - 2 * t2 + t
+        h01 = -2 * t3 + 3 * t2
+        h11 = t3 - t2
+        return float(h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi])
+
+    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
+        """Fully vectorised evaluation using numpy."""
+        xs, ys, slopes = self._xs, self._ys, self._slopes
+        n = len(xs)
+        if n == 0:
+            return np.zeros_like(xs_in, dtype=np.float64)
+        if n == 1:
+            return np.full_like(xs_in, ys[0], dtype=np.float64)
+
+        hi = np.searchsorted(xs, xs_in, side='right').clip(1, n - 1)
+        lo = hi - 1
+
+        dx = xs[hi] - xs[lo]
+        dx_safe = np.where(dx == 0, 1.0, dx)
+        t = np.where(dx == 0, 0.0, (xs_in - xs[lo]) / dx_safe)
+        t2 = t * t
+        t3 = t2 * t
+
+        h00 = 2 * t3 - 3 * t2 + 1
+        h10 = t3 - 2 * t2 + t
+        h01 = -2 * t3 + 3 * t2
+        h11 = t3 - t2
+
+        result = h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi]
+        result = np.where(xs_in <= xs[0], ys[0], result)
+        result = np.where(xs_in >= xs[-1], ys[-1], result)
+        return result
+
+    def __repr__(self) -> str:
+        return f"MonotoneCubicCurve(points={self._points})"
+
+
+class LinearCurve(CurveInput):
+    """Piecewise linear interpolation over control points.
+
+    Mirrors the frontend ``createLinearInterpolator`` in
+    ``ComfyUI_frontend/src/components/curve/curveUtils.ts``.
+    """
+
+    def __init__(self, control_points: list[CurvePoint]):
+        sorted_pts = sorted(control_points, key=lambda p: p[0])
+        self._points = [(float(x), float(y)) for x, y in sorted_pts]
+        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
+        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
+
+    @property
+    def points(self) -> list[CurvePoint]:
+        return list(self._points)
+
+    def interp(self, x: float) -> float:
+        xs, ys = self._xs, self._ys
+        n = len(xs)
+        if n == 0:
+            return 0.0
+        if n == 1:
+            return float(ys[0])
+        return float(np.interp(x, xs, ys))
+
+    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
+        if len(self._xs) == 0:
+            return np.zeros_like(xs_in, dtype=np.float64)
+        if len(self._xs) == 1:
+            return np.full_like(xs_in, self._ys[0], dtype=np.float64)
+        return np.interp(xs_in, self._xs, self._ys)
+
+    def __repr__(self) -> str:
+        return f"LinearCurve(points={self._points})"

comfy_api/latest/_io.py

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
     from comfy.samplers import CFGGuider, Sampler
     from comfy.sd import CLIP, VAE
     from comfy.sd import StyleModel as StyleModel_
-    from comfy_api.input import VideoInput
+    from comfy_api.input import VideoInput, CurveInput as CurveInput_
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
 from comfy_execution.graph_utils import ExecutionBlocker
@@ -1242,8 +1242,9 @@ class BoundingBox(ComfyTypeIO):
 
 @comfytype(io_type="CURVE")
 class Curve(ComfyTypeIO):
-    CurvePoint = tuple[float, float]
-    Type = list[CurvePoint]
+    from comfy_api.input import CurvePoint
+    if TYPE_CHECKING:
+        Type = CurveInput_
 
     class Input(WidgetInput):
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
@@ -1252,6 +1253,18 @@ class Curve(ComfyTypeIO):
             if default is None:
                 self.default = [(0.0, 0.0), (1.0, 1.0)]
 
+        def as_dict(self):
+            d = super().as_dict()
+            if self.default is not None:
+                d["default"] = {"points": [list(p) for p in self.default], "interpolation": "monotone_cubic"}
+            return d
+
+
+@comfytype(io_type="HISTOGRAM")
+class Histogram(ComfyTypeIO):
+    """A histogram represented as a list of bin counts."""
+    Type = list[int]
+
 
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
@@ -2240,5 +2253,6 @@ __all__ = [
     "PriceBadge",
     "BoundingBox",
     "Curve",
+    "Histogram",
     "NodeReplace",
 ]

comfy_api_nodes/apis/grok.py

@@ -29,13 +29,21 @@ class ImageEditRequest(BaseModel):
 class VideoGenerationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
-    image: InputUrlObject | None = Field(...)
+    image: InputUrlObject | None = Field(None)
+    reference_images: list[InputUrlObject] | None = Field(None)
     duration: int = Field(...)
     aspect_ratio: str | None = Field(...)
     resolution: str = Field(...)
     seed: int = Field(...)
 
 
+class VideoExtensionRequest(BaseModel):
+    prompt: str = Field(...)
+    video: InputUrlObject = Field(...)
+    duration: int = Field(default=6)
+    model: str | None = Field(default=None)
+
+
 class VideoEditRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)

comfy_api_nodes/apis/quiver.py

@@ -0,0 +1,43 @@
+from pydantic import BaseModel, Field
+
+
+class QuiverImageObject(BaseModel):
+    url: str = Field(...)
+
+
+class QuiverTextToSVGRequest(BaseModel):
+    model: str = Field(default="arrow-preview")
+    prompt: str = Field(...)
+    instructions: str | None = Field(default=None)
+    references: list[QuiverImageObject] | None = Field(default=None, max_length=4)
+    temperature: float | None = Field(default=None, ge=0, le=2)
+    top_p: float | None = Field(default=None, ge=0, le=1)
+    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
+
+
+class QuiverImageToSVGRequest(BaseModel):
+    model: str = Field(default="arrow-preview")
+    image: QuiverImageObject = Field(...)
+    auto_crop: bool | None = Field(default=None)
+    target_size: int | None = Field(default=None, ge=128, le=4096)
+    temperature: float | None = Field(default=None, ge=0, le=2)
+    top_p: float | None = Field(default=None, ge=0, le=1)
+    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
+
+
+class QuiverSVGResponseItem(BaseModel):
+    svg: str = Field(...)
+    mime_type: str | None = Field(default="image/svg+xml")
+
+
+class QuiverSVGUsage(BaseModel):
+    total_tokens: int | None = Field(default=None)
+    input_tokens: int | None = Field(default=None)
+    output_tokens: int | None = Field(default=None)
+
+
+class QuiverSVGResponse(BaseModel):
+    id: str | None = Field(default=None)
+    created: int | None = Field(default=None)
+    data: list[QuiverSVGResponseItem] = Field(...)
+    usage: QuiverSVGUsage | None = Field(default=None)

comfy_api_nodes/nodes_bytedance.py

@@ -47,6 +47,10 @@ SEEDREAM_MODELS = {
 BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
 BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
 
+DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
+
+logger = logging.getLogger(__name__)
+
 
 def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
     if response.error:
@@ -135,6 +139,7 @@ class ByteDanceImageNode(IO.ComfyNode):
             price_badge=IO.PriceBadge(
                 expr="""{"type":"usd","usd":0.03}""",
             ),
+            is_deprecated=True,
         )
 
     @classmethod
@@ -942,7 +947,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         ]
         return await process_video_task(
             cls,
-            payload=Image2VideoTaskCreationRequest(model=model, content=x),
+            payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
 
@@ -952,6 +957,12 @@ async def process_video_task(
     payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest,
     estimated_duration: int | None,
 ) -> IO.NodeOutput:
+    if payload.model in DEPRECATED_MODELS:
+        logger.warning(
+            "Model '%s' is deprecated and will be deactivated on May 13, 2026. "
+            "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.",
+            payload.model,
+        )
     initial_response = await sync_op(
         cls,
         ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),

comfy_api_nodes/nodes_grok.py

@@ -8,6 +8,7 @@ from comfy_api_nodes.apis.grok import (
     ImageGenerationResponse,
     InputUrlObject,
     VideoEditRequest,
+    VideoExtensionRequest,
     VideoGenerationRequest,
     VideoGenerationResponse,
     VideoStatusResponse,
@@ -21,6 +22,7 @@ from comfy_api_nodes.util import (
     poll_op,
     sync_op,
     tensor_to_base64_string,
+    upload_images_to_comfyapi,
     upload_video_to_comfyapi,
     validate_string,
     validate_video_duration,
@@ -33,6 +35,13 @@ def _extract_grok_price(response) -> float | None:
     return None
 
 
+def _extract_grok_video_price(response) -> float | None:
+    price = _extract_grok_price(response)
+    if price is not None:
+        return price * 1.43
+    return None
+
+
 class GrokImageNode(IO.ComfyNode):
 
     @classmethod
@@ -354,6 +363,8 @@ class GrokVideoNode(IO.ComfyNode):
         seed: int,
         image: Input.Image | None = None,
     ) -> IO.NodeOutput:
+        if model == "grok-imagine-video-beta":
+            model = "grok-imagine-video"
         image_url = None
         if image is not None:
             if get_number_of_images(image) != 1:
@@ -462,6 +473,244 @@ class GrokVideoEditNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 
 
+class GrokVideoReferenceNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoReferenceNode",
+            display_name="Grok Reference-to-Video",
+            category="api node/video/Grok",
+            description="Generate video guided by reference images as style and content references.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of the desired video.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-video",
+                            [
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplatePrefix(
+                                        IO.Image.Input("image"),
+                                        prefix="reference_",
+                                        min=1,
+                                        max=7,
+                                    ),
+                                    tooltip="Up to 7 reference images to guide the video generation.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["480p", "720p"],
+                                    tooltip="The resolution of the output video.",
+                                ),
+                                IO.Combo.Input(
+                                    "aspect_ratio",
+                                    options=["16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
+                                    tooltip="The aspect ratio of the output video.",
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=6,
+                                    min=2,
+                                    max=10,
+                                    step=1,
+                                    tooltip="The duration of the output video in seconds.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for video generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model.duration", "model.resolution"],
+                    input_groups=["model.reference_images"],
+                ),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $refs := inputGroups["model.reference_images"];
+                  $rate := $res = "720p" ? 0.07 : 0.05;
+                  $price := ($rate * $dur + 0.002 * $refs) * 1.43;
+                  {"type":"usd","usd": $price}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        ref_image_urls = await upload_images_to_comfyapi(
+            cls,
+            list(model["reference_images"].values()),
+            mime_type="image/png",
+            wait_label="Uploading base images",
+            max_images=7,
+        )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
+            data=VideoGenerationRequest(
+                model=model["model"],
+                reference_images=[InputUrlObject(url=i) for i in ref_image_urls],
+                prompt=prompt,
+                resolution=model["resolution"],
+                duration=model["duration"],
+                aspect_ratio=model["aspect_ratio"],
+                seed=seed,
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+            price_extractor=_extract_grok_video_price,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
+class GrokVideoExtendNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrokVideoExtendNode",
+            display_name="Grok Video Extend",
+            category="api node/video/Grok",
+            description="Extend an existing video with a seamless continuation based on a text prompt.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text description of what should happen next in the video.",
+                ),
+                IO.Video.Input("video", tooltip="Source video to extend. MP4 format, 2-15 seconds."),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "grok-imagine-video",
+                            [
+                                IO.Int.Input(
+                                    "duration",
+                                    default=8,
+                                    min=2,
+                                    max=10,
+                                    step=1,
+                                    tooltip="Length of the extension in seconds.",
+                                    display_mode=IO.NumberDisplay.slider,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="The model to use for video extension.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model.duration"]),
+                expr="""
+                (
+                  $dur := $lookup(widgets, "model.duration");
+                  {
+                    "type": "range_usd",
+                    "min_usd": (0.02 + 0.05 * $dur) * 1.43,
+                    "max_usd": (0.15 + 0.05 * $dur) * 1.43
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        video: Input.Video,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        validate_video_duration(video, min_duration=2, max_duration=15)
+        video_size = get_fs_object_size(video.get_stream_source())
+        if video_size > 50 * 1024 * 1024:
+            raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.")
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/xai/v1/videos/extensions", method="POST"),
+            data=VideoExtensionRequest(
+                prompt=prompt,
+                video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)),
+                duration=model["duration"],
+            ),
+            response_model=VideoGenerationResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
+            status_extractor=lambda r: r.status if r.status is not None else "complete",
+            response_model=VideoStatusResponse,
+            price_extractor=_extract_grok_video_price,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
+
+
 class GrokExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -469,7 +718,9 @@ class GrokExtension(ComfyExtension):
             GrokImageNode,
             GrokImageEditNode,
             GrokVideoNode,
+            GrokVideoReferenceNode,
             GrokVideoEditNode,
+            GrokVideoExtendNode,
         ]
 
 

comfy_api_nodes/nodes_quiver.py

@@ -0,0 +1,291 @@
+from io import BytesIO
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis.quiver import (
+    QuiverImageObject,
+    QuiverImageToSVGRequest,
+    QuiverSVGResponse,
+    QuiverTextToSVGRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    sync_op,
+    upload_image_to_comfyapi,
+    validate_string,
+)
+from comfy_extras.nodes_images import SVG
+
+
+class QuiverTextToSVGNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QuiverTextToSVGNode",
+            display_name="Quiver Text to SVG",
+            category="api node/image/Quiver",
+            description="Generate an SVG from a text prompt using Quiver AI.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the desired SVG output.",
+                ),
+                IO.String.Input(
+                    "instructions",
+                    multiline=True,
+                    default="",
+                    tooltip="Additional style or formatting guidance.",
+                    optional=True,
+                ),
+                IO.Autogrow.Input(
+                    "reference_images",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Image.Input("image"),
+                        prefix="ref_",
+                        min=0,
+                        max=4,
+                    ),
+                    tooltip="Up to 4 reference images to guide the generation.",
+                    optional=True,
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "arrow-preview",
+                            [
+                                IO.Float.Input(
+                                    "temperature",
+                                    default=1.0,
+                                    min=0.0,
+                                    max=2.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Randomness control. Higher values increase randomness.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "top_p",
+                                    default=1.0,
+                                    min=0.05,
+                                    max=1.0,
+                                    step=0.05,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Nucleus sampling parameter.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "presence_penalty",
+                                    default=0.0,
+                                    min=-2.0,
+                                    max=2.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Token presence penalty.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for SVG generation.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.429}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        instructions: str = None,
+        reference_images: IO.Autogrow.Type = None,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=False, min_length=1)
+
+        references = None
+        if reference_images:
+            references = []
+            for key in reference_images:
+                url = await upload_image_to_comfyapi(cls, reference_images[key])
+                references.append(QuiverImageObject(url=url))
+            if len(references) > 4:
+                raise ValueError("Maximum 4 reference images are allowed.")
+
+        instructions_val = instructions.strip() if instructions else None
+        if instructions_val == "":
+            instructions_val = None
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/quiver/v1/svgs/generations", method="POST"),
+            response_model=QuiverSVGResponse,
+            data=QuiverTextToSVGRequest(
+                model=model["model"],
+                prompt=prompt,
+                instructions=instructions_val,
+                references=references,
+                temperature=model.get("temperature"),
+                top_p=model.get("top_p"),
+                presence_penalty=model.get("presence_penalty"),
+            ),
+        )
+
+        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
+        return IO.NodeOutput(SVG(svg_data))
+
+
+class QuiverImageToSVGNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="QuiverImageToSVGNode",
+            display_name="Quiver Image to SVG",
+            category="api node/image/Quiver",
+            description="Vectorize a raster image into SVG using Quiver AI.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Input image to vectorize.",
+                ),
+                IO.Boolean.Input(
+                    "auto_crop",
+                    default=False,
+                    tooltip="Automatically crop to the dominant subject.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "arrow-preview",
+                            [
+                                IO.Int.Input(
+                                    "target_size",
+                                    default=1024,
+                                    min=128,
+                                    max=4096,
+                                    tooltip="Square resize target in pixels.",
+                                ),
+                                IO.Float.Input(
+                                    "temperature",
+                                    default=1.0,
+                                    min=0.0,
+                                    max=2.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Randomness control. Higher values increase randomness.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "top_p",
+                                    default=1.0,
+                                    min=0.05,
+                                    max=1.0,
+                                    step=0.05,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Nucleus sampling parameter.",
+                                    advanced=True,
+                                ),
+                                IO.Float.Input(
+                                    "presence_penalty",
+                                    default=0.0,
+                                    min=-2.0,
+                                    max=2.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Token presence penalty.",
+                                    advanced=True,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for SVG vectorization.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; "
+                    "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.SVG.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.429}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image,
+        auto_crop: bool,
+        model: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        image_url = await upload_image_to_comfyapi(cls, image)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/quiver/v1/svgs/vectorizations", method="POST"),
+            response_model=QuiverSVGResponse,
+            data=QuiverImageToSVGRequest(
+                model=model["model"],
+                image=QuiverImageObject(url=image_url),
+                auto_crop=auto_crop if auto_crop else None,
+                target_size=model.get("target_size"),
+                temperature=model.get("temperature"),
+                top_p=model.get("top_p"),
+                presence_penalty=model.get("presence_penalty"),
+            ),
+        )
+
+        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
+        return IO.NodeOutput(SVG(svg_data))
+
+
+class QuiverExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            QuiverTextToSVGNode,
+            QuiverImageToSVGNode,
+        ]
+
+
+async def comfy_entrypoint() -> QuiverExtension:
+    return QuiverExtension()

comfy_extras/nodes_canny.py

@@ -3,6 +3,7 @@ from typing_extensions import override
 
 import comfy.model_management
 from comfy_api.latest import ComfyExtension, io
+import torch
 
 
 class Canny(io.ComfyNode):
@@ -29,8 +30,8 @@ class Canny(io.ComfyNode):
 
     @classmethod
     def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput:
-        output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
-        img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
+        output = canny(image.to(device=comfy.model_management.get_torch_device(), dtype=torch.float32).movedim(-1, 1), low_threshold, high_threshold)
+        img_out = output[1].to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()).repeat(1, 3, 1, 1).movedim(1, -1)
         return io.NodeOutput(img_out)
 
 

comfy_extras/nodes_context_windows.py

@@ -27,8 +27,8 @@ class ContextWindowsManualNode(io.ComfyNode):
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
                 io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
-                #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
-                #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
+                io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
+                io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with context windows applied during sampling."),

comfy_extras/nodes_curve.py

@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.input import CurveInput
+from typing_extensions import override
+
+
+class CurveEditor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CurveEditor",
+            display_name="Curve Editor",
+            category="utils",
+            inputs=[
+                io.Curve.Input("curve"),
+                io.Histogram.Input("histogram", optional=True),
+            ],
+            outputs=[
+                io.Curve.Output("curve"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, curve, histogram=None) -> io.NodeOutput:
+        result = CurveInput.from_raw(curve)
+
+        ui = {}
+        if histogram is not None:
+            ui["histogram"] = histogram if isinstance(histogram, list) else list(histogram)
+
+        return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result)
+
+
+class CurveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self):
+        return [CurveEditor]
+
+
+async def comfy_entrypoint():
+    return CurveExtension()

comfy_extras/nodes_lt.py

@@ -3,6 +3,7 @@ import node_helpers
 import torch
 import comfy.model_management
 import comfy.model_sampling
+import comfy.samplers
 import comfy.utils
 import math
 import numpy as np
@@ -682,6 +683,84 @@ class LTXVSeparateAVLatent(io.ComfyNode):
         return io.NodeOutput(video_latent, audio_latent)
 
 
+class LTXVReferenceAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVReferenceAudio",
+            display_name="LTXV Reference Audio (ID-LoRA)",
+            category="conditioning/audio",
+            description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Audio.Input("reference_audio", tooltip="Reference audio clip whose speaker identity to transfer. ~5 seconds recommended (training duration). Shorter or longer clips may degrade voice identity transfer."),
+                io.Vae.Input(id="audio_vae", display_name="Audio VAE", tooltip="LTXV Audio VAE for encoding."),
+                io.Float.Input("identity_guidance_scale", default=3.0, min=0.0, max=100.0, step=0.01, round=0.01, tooltip="Strength of identity guidance. Runs an extra forward pass without reference each step to amplify speaker identity. Set to 0 to disable (no extra pass)."),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="Start of the sigma range where identity guidance is active."),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="End of the sigma range where identity guidance is active."),
+            ],
+            outputs=[
+                io.Model.Output(),
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput:
+        # Encode reference audio to latents and patchify
+        audio_latents = audio_vae.encode(reference_audio)
+        b, c, t, f = audio_latents.shape
+        ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f)
+        ref_audio = {"tokens": ref_tokens}
+
+        positive = node_helpers.conditioning_set_values(positive, {"ref_audio": ref_audio})
+        negative = node_helpers.conditioning_set_values(negative, {"ref_audio": ref_audio})
+
+        # Patch model with identity guidance
+        m = model.clone()
+        scale = identity_guidance_scale
+        model_sampling = m.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            if scale == 0:
+                return args["denoised"]
+
+            sigma = args["sigma"]
+            sigma_ = sigma[0].item()
+            if sigma_ > sigma_start or sigma_ < sigma_end:
+                return args["denoised"]
+
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            model_options = args["model_options"].copy()
+            x = args["input"]
+
+            # Strip ref_audio from conditioning for the no-reference pass
+            noref_cond = []
+            for entry in cond:
+                new_entry = entry.copy()
+                mc = new_entry.get("model_conds", {}).copy()
+                mc.pop("ref_audio", None)
+                new_entry["model_conds"] = mc
+                noref_cond.append(new_entry)
+
+            (pred_noref,) = comfy.samplers.calc_cond_batch(
+                args["model"], [noref_cond], x, sigma, model_options
+            )
+
+            return cfg_result + (cond_pred - pred_noref) * scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return io.NodeOutput(m, positive, negative)
+
+
 class LtxvExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -697,6 +776,7 @@ class LtxvExtension(ComfyExtension):
             LTXVCropGuides,
             LTXVConcatAVLatent,
             LTXVSeparateAVLatent,
+            LTXVReferenceAudio,
         ]
 
 

comfy_extras/nodes_number_convert.py

@@ -0,0 +1,79 @@
+"""Number Convert node for unified numeric type conversion.
+
+Provides a single node that converts INT, FLOAT, STRING, and BOOL
+inputs into FLOAT and INT outputs.
+"""
+
+from __future__ import annotations
+
+import math
+
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class NumberConvertNode(io.ComfyNode):
+    """Converts various types to numeric FLOAT and INT outputs."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ComfyNumberConvert",
+            display_name="Number Convert",
+            category="math",
+            search_aliases=[
+                "int to float", "float to int", "number convert",
+                "int2float", "float2int", "cast", "parse number",
+                "string to number", "bool to int",
+            ],
+            inputs=[
+                io.MultiType.Input(
+                    "value",
+                    [io.Int, io.Float, io.String, io.Boolean],
+                    display_name="value",
+                ),
+            ],
+            outputs=[
+                io.Float.Output(display_name="FLOAT"),
+                io.Int.Output(display_name="INT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, value) -> io.NodeOutput:
+        if isinstance(value, bool):
+            float_val = 1.0 if value else 0.0
+        elif isinstance(value, (int, float)):
+            float_val = float(value)
+        elif isinstance(value, str):
+            text = value.strip()
+            if not text:
+                raise ValueError("Cannot convert empty string to number.")
+            try:
+                float_val = float(text)
+            except ValueError:
+                raise ValueError(
+                    f"Cannot convert string to number: {value!r}"
+                ) from None
+        else:
+            raise TypeError(
+                f"Unsupported input type: {type(value).__name__}"
+            )
+
+        if not math.isfinite(float_val):
+            raise ValueError(
+                f"Cannot convert non-finite value to number: {float_val}"
+            )
+
+        return io.NodeOutput(float_val, int(float_val))
+
+
+class NumberConvertExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [NumberConvertNode]
+
+
+async def comfy_entrypoint() -> NumberConvertExtension:
+    return NumberConvertExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.17.0"
+__version__ = "0.18.1"

main.py

@@ -471,6 +471,9 @@ if __name__ == "__main__":
     if sys.version_info.major == 3 and sys.version_info.minor < 10:
         logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.")
 
+    if args.disable_dynamic_vram:
+        logging.warning("Dynamic vram disabled with argument. If you have any issues with dynamic vram enabled please give us a detailed reports as this argument will be removed soon.")
+
     event_loop, _, start_all_func = start_comfyui()
     try:
         x = start_all_func()

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1b6
+comfyui_manager==4.1b8

nodes.py

@@ -1966,9 +1966,11 @@ class EmptyImage:
     CATEGORY = "image"
 
     def generate(self, width, height, batch_size=1, color=0):
-        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF)
-        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF)
-        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)
+        dtype = comfy.model_management.intermediate_dtype()
+        device = comfy.model_management.intermediate_device()
+        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF, device=device, dtype=dtype)
+        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF, device=device, dtype=dtype)
+        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF, device=device, dtype=dtype)
         return (torch.cat((r, g, b), dim=-1), )
 
 class ImagePadForOutpaint:
@@ -2202,58 +2204,6 @@ def get_module_name(module_path: str) -> str:
     return base_path
 
 
-def load_node_replacements_json(module_dir: str, module_name: str):
-    """Load node_replacements.json from a custom node directory and register replacements.
-
-    Custom node authors can ship a node_replacements.json file in their repo root
-    to define node replacements declaratively, without writing Python registration code.
-    The file format matches the output of NodeReplace.as_dict(), keyed by old_node_id.
-    """
-    replacements_path = os.path.join(module_dir, "node_replacements.json")
-    if not os.path.isfile(replacements_path):
-        return
-
-    try:
-        with open(replacements_path, "r", encoding="utf-8") as f:
-            data = json.load(f)
-
-        if not isinstance(data, dict):
-            logging.warning(f"node_replacements.json in {module_name} must be a JSON object, skipping.")
-            return
-
-        from server import PromptServer
-        from comfy_api.latest._io import NodeReplace
-
-        manager = PromptServer.instance.node_replace_manager
-        count = 0
-        for old_node_id, replacements in data.items():
-            if not isinstance(replacements, list):
-                logging.warning(f"node_replacements.json in {module_name}: value for '{old_node_id}' must be a list, skipping.")
-                continue
-            for entry in replacements:
-                if not isinstance(entry, dict):
-                    continue
-                new_node_id = entry.get("new_node_id", "")
-                if not new_node_id:
-                    logging.warning(f"node_replacements.json in {module_name}: entry for '{old_node_id}' missing 'new_node_id', skipping.")
-                    continue
-                manager.register(NodeReplace(
-                    new_node_id=new_node_id,
-                    old_node_id=entry.get("old_node_id", old_node_id),
-                    old_widget_ids=entry.get("old_widget_ids"),
-                    input_mapping=entry.get("input_mapping"),
-                    output_mapping=entry.get("output_mapping"),
-                ))
-                count += 1
-
-        if count > 0:
-            logging.info(f"Loaded {count} node replacement(s) from {module_name}/node_replacements.json")
-    except json.JSONDecodeError as e:
-        logging.warning(f"Failed to parse node_replacements.json in {module_name}: {e}")
-    except Exception as e:
-        logging.warning(f"Failed to load node_replacements.json from {module_name}: {e}")
-
-
 async def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool:
     module_name = get_module_name(module_path)
     if os.path.isfile(module_path):
@@ -2278,11 +2228,6 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
 
         LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir)
 
-        # Only load node_replacements.json from directory-based custom nodes (proper packs).
-        # Single-file .py nodes share a parent dir, so checking there would be incorrect.
-        if os.path.isdir(module_path):
-            load_node_replacements_json(module_dir, module_name)
-
         try:
             from comfy_config import config_parser
 
@@ -2509,7 +2454,9 @@ async def init_builtin_extra_nodes():
         "nodes_nag.py",
         "nodes_sdpose.py",
         "nodes_math.py",
+        "nodes_number_convert.py",
         "nodes_painter.py",
+        "nodes_curve.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.17.0"
+version = "0.18.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.41.21
-comfyui-workflow-templates==0.9.26
+comfyui-frontend-package==1.42.8
+comfyui-workflow-templates==0.9.36
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

tests-unit/comfy_extras_test/nodes_number_convert_test.py

@@ -0,0 +1,123 @@
+import pytest
+from unittest.mock import patch, MagicMock
+
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+mock_server = MagicMock()
+
+with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
+    from comfy_extras.nodes_number_convert import NumberConvertNode
+
+
+class TestNumberConvertExecute:
+    @staticmethod
+    def _exec(value) -> object:
+        return NumberConvertNode.execute(value)
+
+    # --- INT input ---
+
+    def test_int_input(self):
+        result = self._exec(42)
+        assert result[0] == 42.0
+        assert result[1] == 42
+
+    def test_int_zero(self):
+        result = self._exec(0)
+        assert result[0] == 0.0
+        assert result[1] == 0
+
+    def test_int_negative(self):
+        result = self._exec(-7)
+        assert result[0] == -7.0
+        assert result[1] == -7
+
+    # --- FLOAT input ---
+
+    def test_float_input(self):
+        result = self._exec(3.14)
+        assert result[0] == 3.14
+        assert result[1] == 3
+
+    def test_float_truncation_toward_zero(self):
+        result = self._exec(-2.9)
+        assert result[0] == -2.9
+        assert result[1] == -2  # int() truncates toward zero, not floor
+
+    def test_float_output_type(self):
+        result = self._exec(5)
+        assert isinstance(result[0], float)
+
+    def test_int_output_type(self):
+        result = self._exec(5.7)
+        assert isinstance(result[1], int)
+
+    # --- BOOL input ---
+
+    def test_bool_true(self):
+        result = self._exec(True)
+        assert result[0] == 1.0
+        assert result[1] == 1
+
+    def test_bool_false(self):
+        result = self._exec(False)
+        assert result[0] == 0.0
+        assert result[1] == 0
+
+    # --- STRING input ---
+
+    def test_string_integer(self):
+        result = self._exec("42")
+        assert result[0] == 42.0
+        assert result[1] == 42
+
+    def test_string_float(self):
+        result = self._exec("3.14")
+        assert result[0] == 3.14
+        assert result[1] == 3
+
+    def test_string_negative(self):
+        result = self._exec("-5.5")
+        assert result[0] == -5.5
+        assert result[1] == -5
+
+    def test_string_with_whitespace(self):
+        result = self._exec("  7.0  ")
+        assert result[0] == 7.0
+        assert result[1] == 7
+
+    def test_string_scientific_notation(self):
+        result = self._exec("1e3")
+        assert result[0] == 1000.0
+        assert result[1] == 1000
+
+    # --- STRING error paths ---
+
+    def test_empty_string_raises(self):
+        with pytest.raises(ValueError, match="Cannot convert empty string"):
+            self._exec("")
+
+    def test_whitespace_only_string_raises(self):
+        with pytest.raises(ValueError, match="Cannot convert empty string"):
+            self._exec("   ")
+
+    def test_non_numeric_string_raises(self):
+        with pytest.raises(ValueError, match="Cannot convert string to number"):
+            self._exec("abc")
+
+    def test_string_inf_raises(self):
+        with pytest.raises(ValueError, match="non-finite"):
+            self._exec("inf")
+
+    def test_string_nan_raises(self):
+        with pytest.raises(ValueError, match="non-finite"):
+            self._exec("nan")
+
+    def test_string_negative_inf_raises(self):
+        with pytest.raises(ValueError, match="non-finite"):
+            self._exec("-inf")
+
+    # --- Unsupported type ---
+
+    def test_unsupported_type_raises(self):
+        with pytest.raises(TypeError, match="Unsupported input type"):
+            self._exec([1, 2, 3])

tests/test_node_replacements_json.py

@@ -1,237 +0,0 @@
-"""Tests for auto-registration of node_replacements.json from custom node directories."""
-import json
-import os
-import tempfile
-import unittest
-from unittest.mock import MagicMock
-
-# We can't import nodes.py directly (torch dependency), so we test the
-# load_node_replacements_json logic by re-creating it from the same source.
-# This validates the JSON parsing and NodeReplace construction logic.
-
-
-class MockNodeReplace:
-    """Mirrors comfy_api.latest._io.NodeReplace for testing."""
-    def __init__(self, new_node_id, old_node_id, old_widget_ids=None,
-                 input_mapping=None, output_mapping=None):
-        self.new_node_id = new_node_id
-        self.old_node_id = old_node_id
-        self.old_widget_ids = old_widget_ids
-        self.input_mapping = input_mapping
-        self.output_mapping = output_mapping
-
-
-def load_node_replacements_json(module_dir, module_name, manager, NodeReplace=MockNodeReplace):
-    """Standalone version of the function from nodes.py for testing."""
-    import logging
-    replacements_path = os.path.join(module_dir, "node_replacements.json")
-    if not os.path.isfile(replacements_path):
-        return
-
-    try:
-        with open(replacements_path, "r", encoding="utf-8") as f:
-            data = json.load(f)
-
-        if not isinstance(data, dict):
-            logging.warning(f"node_replacements.json in {module_name} must be a JSON object, skipping.")
-            return
-
-        count = 0
-        for old_node_id, replacements in data.items():
-            if not isinstance(replacements, list):
-                logging.warning(f"node_replacements.json in {module_name}: value for '{old_node_id}' must be a list, skipping.")
-                continue
-            for entry in replacements:
-                if not isinstance(entry, dict):
-                    continue
-                new_node_id = entry.get("new_node_id", "")
-                if not new_node_id:
-                    logging.warning(f"node_replacements.json in {module_name}: entry for '{old_node_id}' missing 'new_node_id', skipping.")
-                    continue
-                manager.register(NodeReplace(
-                    new_node_id=new_node_id,
-                    old_node_id=entry.get("old_node_id", old_node_id),
-                    old_widget_ids=entry.get("old_widget_ids"),
-                    input_mapping=entry.get("input_mapping"),
-                    output_mapping=entry.get("output_mapping"),
-                ))
-                count += 1
-
-        if count > 0:
-            logging.info(f"Loaded {count} node replacement(s) from {module_name}/node_replacements.json")
-    except json.JSONDecodeError as e:
-        logging.warning(f"Failed to parse node_replacements.json in {module_name}: {e}")
-    except Exception as e:
-        logging.warning(f"Failed to load node_replacements.json from {module_name}: {e}")
-
-
-class TestLoadNodeReplacementsJson(unittest.TestCase):
-    """Test auto-registration of node_replacements.json from custom node directories."""
-
-    def setUp(self):
-        self.tmpdir = tempfile.mkdtemp()
-        self.mock_manager = MagicMock()
-
-    def _write_json(self, data):
-        path = os.path.join(self.tmpdir, "node_replacements.json")
-        with open(path, "w") as f:
-            json.dump(data, f)
-
-    def _load(self):
-        load_node_replacements_json(self.tmpdir, "test-node-pack", self.mock_manager)
-
-    def test_no_file_does_nothing(self):
-        """No node_replacements.json — should silently do nothing."""
-        self._load()
-        self.mock_manager.register.assert_not_called()
-
-    def test_empty_object(self):
-        """Empty {} — should do nothing."""
-        self._write_json({})
-        self._load()
-        self.mock_manager.register.assert_not_called()
-
-    def test_single_replacement(self):
-        """Single replacement entry registers correctly."""
-        self._write_json({
-            "OldNode": [{
-                "new_node_id": "NewNode",
-                "old_node_id": "OldNode",
-                "input_mapping": [{"new_id": "model", "old_id": "ckpt_name"}],
-                "output_mapping": [{"new_idx": 0, "old_idx": 0}],
-            }]
-        })
-        self._load()
-        self.mock_manager.register.assert_called_once()
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertEqual(registered.new_node_id, "NewNode")
-        self.assertEqual(registered.old_node_id, "OldNode")
-        self.assertEqual(registered.input_mapping, [{"new_id": "model", "old_id": "ckpt_name"}])
-        self.assertEqual(registered.output_mapping, [{"new_idx": 0, "old_idx": 0}])
-
-    def test_multiple_replacements(self):
-        """Multiple old_node_ids each with entries."""
-        self._write_json({
-            "NodeA": [{"new_node_id": "NodeB", "old_node_id": "NodeA"}],
-            "NodeC": [{"new_node_id": "NodeD", "old_node_id": "NodeC"}],
-        })
-        self._load()
-        self.assertEqual(self.mock_manager.register.call_count, 2)
-
-    def test_multiple_alternatives_for_same_node(self):
-        """Multiple replacement options for the same old node."""
-        self._write_json({
-            "OldNode": [
-                {"new_node_id": "AltA", "old_node_id": "OldNode"},
-                {"new_node_id": "AltB", "old_node_id": "OldNode"},
-            ]
-        })
-        self._load()
-        self.assertEqual(self.mock_manager.register.call_count, 2)
-
-    def test_null_mappings(self):
-        """Null input/output mappings (trivial replacement)."""
-        self._write_json({
-            "OldNode": [{
-                "new_node_id": "NewNode",
-                "old_node_id": "OldNode",
-                "input_mapping": None,
-                "output_mapping": None,
-            }]
-        })
-        self._load()
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertIsNone(registered.input_mapping)
-        self.assertIsNone(registered.output_mapping)
-
-    def test_old_node_id_defaults_to_key(self):
-        """If old_node_id is missing from entry, uses the dict key."""
-        self._write_json({
-            "OldNode": [{"new_node_id": "NewNode"}]
-        })
-        self._load()
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertEqual(registered.old_node_id, "OldNode")
-
-    def test_invalid_json_skips(self):
-        """Invalid JSON file — should warn and skip, not crash."""
-        path = os.path.join(self.tmpdir, "node_replacements.json")
-        with open(path, "w") as f:
-            f.write("{invalid json")
-        self._load()
-        self.mock_manager.register.assert_not_called()
-
-    def test_non_object_json_skips(self):
-        """JSON array instead of object — should warn and skip."""
-        self._write_json([1, 2, 3])
-        self._load()
-        self.mock_manager.register.assert_not_called()
-
-    def test_non_list_value_skips(self):
-        """Value is not a list — should warn and skip that key."""
-        self._write_json({
-            "OldNode": "not a list",
-            "GoodNode": [{"new_node_id": "NewNode", "old_node_id": "GoodNode"}],
-        })
-        self._load()
-        self.assertEqual(self.mock_manager.register.call_count, 1)
-
-    def test_with_old_widget_ids(self):
-        """old_widget_ids are passed through."""
-        self._write_json({
-            "OldNode": [{
-                "new_node_id": "NewNode",
-                "old_node_id": "OldNode",
-                "old_widget_ids": ["width", "height"],
-            }]
-        })
-        self._load()
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertEqual(registered.old_widget_ids, ["width", "height"])
-
-    def test_set_value_in_input_mapping(self):
-        """input_mapping with set_value entries."""
-        self._write_json({
-            "OldNode": [{
-                "new_node_id": "NewNode",
-                "old_node_id": "OldNode",
-                "input_mapping": [
-                    {"new_id": "method", "set_value": "lanczos"},
-                    {"new_id": "size", "old_id": "dimension"},
-                ],
-            }]
-        })
-        self._load()
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertEqual(len(registered.input_mapping), 2)
-        self.assertEqual(registered.input_mapping[0]["set_value"], "lanczos")
-        self.assertEqual(registered.input_mapping[1]["old_id"], "dimension")
-
-    def test_missing_new_node_id_skipped(self):
-        """Entry without new_node_id is skipped."""
-        self._write_json({
-            "OldNode": [
-                {"old_node_id": "OldNode"},
-                {"new_node_id": "", "old_node_id": "OldNode"},
-                {"new_node_id": "ValidNew", "old_node_id": "OldNode"},
-            ]
-        })
-        self._load()
-        self.assertEqual(self.mock_manager.register.call_count, 1)
-        registered = self.mock_manager.register.call_args[0][0]
-        self.assertEqual(registered.new_node_id, "ValidNew")
-
-    def test_non_dict_entry_skipped(self):
-        """Non-dict entries in the list are silently skipped."""
-        self._write_json({
-            "OldNode": [
-                "not a dict",
-                {"new_node_id": "NewNode", "old_node_id": "OldNode"},
-            ]
-        })
-        self._load()
-        self.assertEqual(self.mock_manager.register.call_count, 1)
-
-
-if __name__ == "__main__":
-    unittest.main()