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

@@ -93,50 +93,6 @@ 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
@@ -221,17 +177,10 @@ 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.size(self.dim) == x_in.size(self.dim)) or \
+                                if (self.dim < cond_value.ndim and cond_value(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)
@@ -275,7 +224,6 @@ 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/lightricks/vae/causal_conv3d.py

@@ -23,11 +23,6 @@ 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]
 
@@ -63,23 +58,18 @@ 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 cache_length == 0:
-            self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False)
+        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
             needs_caching = False
-        if needs_caching and x.shape[2] >= cache_length:
-            needs_caching = False
-            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
 
         x = torch.cat(pieces, dim=2)
         del pieces
         del cached
 
         if needs_caching:
-            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
         elif is_end:
             self.temporal_cache_state[tid] = (None, True)
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -233,7 +233,10 @@ class Encoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]:
+    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)
         sample = self.conv_in(sample)
 
         checkpoint_fn = (
@@ -244,14 +247,10 @@ 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:, ...]
@@ -283,35 +282,9 @@ 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:
@@ -500,17 +473,6 @@ class Decoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-        # Precompute output scale factors: (channels, (t_scale, h_scale, w_scale), t_offset)
-        ts, hs, ws, to = 1, 1, 1, 0
-        for block in self.up_blocks:
-            if isinstance(block, DepthToSpaceUpsample):
-                ts *= block.stride[0]
-                hs *= block.stride[1]
-                ws *= block.stride[2]
-                if block.stride[0] > 1:
-                    to = to * block.stride[0] + 1
-        self._output_scale = (out_channels // (patch_size ** 2), (ts, hs * patch_size, ws * patch_size), to)
-
         self.timestep_conditioning = timestep_conditioning
 
         if timestep_conditioning:
@@ -532,62 +494,11 @@ class Decoder(nn.Module):
             )
 
 
-    def decode_output_shape(self, input_shape):
-        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(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
     def forward_orig(
         self,
         sample: torch.FloatTensor,
         timestep: Optional[torch.Tensor] = None,
-        output_buffer: Optional[torch.Tensor] = None,
     ) -> torch.FloatTensor:
         r"""The forward method of the `Decoder` class."""
         batch_size = sample.shape[0]
@@ -602,7 +513,6 @@ class Decoder(nn.Module):
         )
 
         timestep_shift_scale = None
-        scaled_timestep = None
         if self.timestep_conditioning:
             assert (
                 timestep is not None
@@ -630,18 +540,59 @@ class Decoder(nn.Module):
             )
             timestep_shift_scale = ada_values.unbind(dim=1)
 
-        if output_buffer is None:
-            output_buffer = torch.empty(
-                self.decode_output_shape(sample.shape),
-                dtype=sample.dtype, device=comfy.model_management.intermediate_device(),
-            )
-        output_offset = [0]
-
+        output = []
         max_chunk_size = get_max_chunk_size(sample.device)
 
-        self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
+        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:
+                    output.append(sample.to(comfy.model_management.intermediate_device()))
+                return
 
-        return output_buffer
+            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)
+        sample = torch.cat(output, dim=2)
+
+        sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+
+        return sample
 
     def forward(self, *args, **kwargs):
         try:
@@ -765,25 +716,12 @@ class SpaceToDepthDownsample(nn.Module):
             causal=True,
             spatial_padding_mode=spatial_padding_mode,
         )
-        self.temporal_cache_state = {}
 
     def forward(self, x, causal: bool = True):
-        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:
+        if self.stride[0] == 2:
             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(
@@ -798,26 +736,15 @@ class SpaceToDepthDownsample(nn.Module):
 
         # conv
         x = self.conv(x, causal=causal)
-        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 = 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 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)
+        x = x + x_in
 
         return x
 
@@ -1150,8 +1077,6 @@ 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__()
 
@@ -1294,15 +1219,14 @@ class VideoVAE(nn.Module):
             }
         return config
 
-    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)
+    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)
         return self.per_channel_statistics.normalize(means)
 
-    def decode_output_shape(self, input_shape):
-        return self.decoder.decode_output_shape(input_shape)
-
-    def decode(self, x, output_buffer=None):
+    def decode(self, x):
         if self.timestep_conditioning: #TODO: seed
             x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x
-        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep, output_buffer=output_buffer)
+        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep)

comfy/ldm/wan/vae.py

@@ -360,43 +360,6 @@ 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 isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
-            for frame_idx in range(x.shape[2]):
-                self.run_up(
-                    layer_idx,
-                    [x[:, :, frame_idx:frame_idx + 1, :, :]],
-                    feat_cache,
-                    feat_idx.copy(),
-                    out_chunks,
-                )
-            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
-        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:
@@ -417,7 +380,42 @@ class Decoder3d(nn.Module):
 
         out_chunks = []
 
-        self.run_up(0, [x], feat_cache, feat_idx, 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)
         return out_chunks
 
 

comfy/memory_management.py

@@ -39,10 +39,7 @@ 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 tensor.numel() * tensor.element_size() != info.size
-            or tensor.storage_offset() != 0
-            or not tensor.is_contiguous()):
+            or destination.numel() * destination.element_size() < info.size):
         return False
 
     if info.size == 0:

comfy/model_base.py

@@ -285,12 +285,6 @@ 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)
@@ -1381,12 +1375,6 @@ 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":
-            import comfy.context_windows
-            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)
@@ -1439,12 +1427,6 @@ 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":
-            import comfy.context_windows
-            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)
@@ -1462,14 +1444,6 @@ 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=[]):
-        import comfy.context_windows
-        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)
@@ -1506,12 +1480,6 @@ 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":
-            import comfy.context_windows
-            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) or comfy.memory_management.aimdo_enabled:
+    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled:
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else:

comfy/sample.py

@@ -64,10 +64,10 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
     sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
     samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+    samples = samples.to(comfy.model_management.intermediate_device())
     return samples
 
 def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
     samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+    samples = samples.to(comfy.model_management.intermediate_device())
     return samples

comfy/sd.py

@@ -951,23 +951,12 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            # Pre-allocate output for VAEs that support direct buffer writes
-            preallocated = False
-            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
-
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype)
-                if preallocated:
-                    self.first_stage_model.decode(samples, output_buffer=pixel_samples[x:x+batch_number], **vae_options)
-                else:
-                    out = self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True)
-                    if pixel_samples is None:
-                        pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
-                    pixel_samples[x:x+batch_number].copy_(out)
-                    del out
-                self.process_output(pixel_samples[x:x+batch_number])
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
+                if pixel_samples is None:
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
+                pixel_samples[x:x+batch_number] = out
         except Exception as e:
             model_management.raise_non_oom(e)
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
@@ -978,7 +967,6 @@ 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)
@@ -1039,13 +1027,8 @@ 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)
-                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())
+                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())
                 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
@@ -1060,7 +1043,6 @@ 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/sd1_clip.py

@@ -46,7 +46,7 @@ class ClipTokenWeightEncoder:
         out, pooled = o[:2]
 
         if pooled is not None:
-            first_pooled = pooled[0:1].to(device=model_management.intermediate_device())
+            first_pooled = pooled[0:1].to(model_management.intermediate_device())
         else:
             first_pooled = pooled
 
@@ -63,16 +63,16 @@ class ClipTokenWeightEncoder:
             output.append(z)
 
         if (len(output) == 0):
-            r = (out[-1:].to(device=model_management.intermediate_device()), first_pooled)
+            r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
         else:
-            r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device()), first_pooled)
+            r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
 
         if len(o) > 2:
             extra = {}
             for k in o[2]:
                 v = o[2][k]
                 if k == "attention_mask":
-                    v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device())
+                    v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
                 extra[k] = v
 
             r = r + (extra,)

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 = output[b:b+1].zero_()
-        out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device)
+        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)
 
         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([1, 1] + list(ps.shape[2:]), device=output_device)
+            mask = torch.ones_like(ps)
 
             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)
 
-        out.div_(out_div)
+        output[b:b+1] = out/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/latest/__init__.py

@@ -15,7 +15,6 @@ from comfy_execution.progress import get_progress_state, PreviewImageTuple
 from PIL import Image
 from comfy.cli_args import args
 import numpy as np
-import os
 
 
 class ComfyAPI_latest(ComfyAPIBase):
@@ -26,7 +25,6 @@ class ComfyAPI_latest(ComfyAPIBase):
         super().__init__()
         self.node_replacement = self.NodeReplacement()
         self.execution = self.Execution()
-        self.environment = self.Environment()
         self.caching = self.Caching()
 
     class NodeReplacement(ProxiedSingleton):
@@ -87,27 +85,6 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
-    class Environment(ProxiedSingleton):
-        """
-        Query the current execution environment.
-
-        Managed deployments set the ``COMFY_EXECUTION_ENVIRONMENT`` env var
-        so custom nodes can adapt their behaviour at runtime.
-
-        Example::
-
-            from comfy_api.latest import api
-
-            env = api.environment.get()  # "local" | "cloud" | "remote"
-        """
-
-        _VALID = {"local", "cloud", "remote"}
-
-        async def get(self) -> str:
-            """Return the execution environment: ``"local"``, ``"cloud"``, or ``"remote"``."""
-            value = os.environ.get("COMFY_EXECUTION_ENVIRONMENT", "local").lower().strip()
-            return value if value in self._VALID else "local"
-
     class Caching(ProxiedSingleton):
         """
         External cache provider API for sharing cached node outputs

comfy_api_nodes/apis/gemini.py

@@ -67,7 +67,6 @@ class GeminiPart(BaseModel):
     inlineData: GeminiInlineData | None = Field(None)
     fileData: GeminiFileData | None = Field(None)
     text: str | None = Field(None)
-    thought: bool | None = Field(None)
 
 
 class GeminiTextPart(BaseModel):

comfy_api_nodes/nodes_bytedance.py

@@ -47,10 +47,6 @@ 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:
@@ -139,7 +135,6 @@ class ByteDanceImageNode(IO.ComfyNode):
             price_badge=IO.PriceBadge(
                 expr="""{"type":"usd","usd":0.03}""",
             ),
-            is_deprecated=True,
         )
 
     @classmethod
@@ -947,7 +942,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         ]
         return await process_video_task(
             cls,
-            payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None),
+            payload=Image2VideoTaskCreationRequest(model=model, content=x),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
 
@@ -957,12 +952,6 @@ 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_gemini.py

@@ -63,7 +63,7 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
       $m := widgets.model;
       $r := widgets.resolution;
       $isFlash := $contains($m, "nano banana 2");
-      $flashPrices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+      $flashPrices := {"1k": 0.0696, "2k": 0.0696, "4k": 0.123};
       $proPrices := {"1k": 0.134, "2k": 0.134, "4k": 0.24};
       $prices := $isFlash ? $flashPrices : $proPrices;
       {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
@@ -188,12 +188,10 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
     return "\n".join([part.text for part in parts])
 
 
-async def get_image_from_response(response: GeminiGenerateContentResponse, thought: bool = False) -> Input.Image:
+async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
     image_tensors: list[Input.Image] = []
     parts = get_parts_by_type(response, "image/*")
     for part in parts:
-        if (part.thought is True) != thought:
-            continue
         if part.inlineData:
             image_data = base64.b64decode(part.inlineData.data)
             returned_image = bytesio_to_image_tensor(BytesIO(image_data))
@@ -933,11 +931,6 @@ class GeminiNanoBanana2(IO.ComfyNode):
             outputs=[
                 IO.Image.Output(),
                 IO.String.Output(),
-                IO.Image.Output(
-                    display_name="thought_image",
-                    tooltip="First image from the model's thinking process. "
-                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
-                ),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -999,11 +992,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-        return IO.NodeOutput(
-            await get_image_from_response(response),
-            get_text_from_response(response),
-            await get_image_from_response(response, thought=True),
-        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiExtension(ComfyExtension):

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_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()

nodes.py

@@ -2452,6 +2452,7 @@ async def init_builtin_extra_nodes():
         "nodes_nag.py",
         "nodes_sdpose.py",
         "nodes_math.py",
+        "nodes_number_convert.py",
         "nodes_painter.py",
     ]
 

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.21
+comfyui-frontend-package==1.41.20
 comfyui-workflow-templates==0.9.26
 comfyui-embedded-docs==0.4.3
 torch

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])