Try to fix flash attention related issue on AMD.

I get freezing issues on my test machine.

comfy/cli_args.py

@@ -92,6 +92,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
+parser.add_argument("--disable-triton-backend", action="store_true", help="Force-disable the comfy-kitchen Triton backend, overriding the automatic ROCm/AMD default and --enable-triton-backend.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/ldm/modules/attention.py

@@ -709,7 +709,7 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     return out
 
 try:
-    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    @torch.library.custom_op("comfy::flash_attn", mutates_args=())
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
                     dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
         softmax_scale_arg = None if softmax_scale == -1.0 else softmax_scale

comfy/ldm/seedvr/vae.py

@@ -30,7 +30,7 @@ from enum import Enum
 import logging
 import comfy.model_management
 import comfy.ops
-ops = comfy.ops.disable_weight_init
+ops = comfy.ops.manual_cast
 
 
 def _seedvr2_temporal_slicing_min_size(temporal_size, temporal_overlap, temporal_scale=1):
@@ -103,11 +103,10 @@ def tiled_vae(
     storage_device = vae_model.device
     result = None
     count = None
-    def run_temporal_chunks(spatial_tile, model=vae_model, device=storage_device):
-        device = torch.device(device)
-        t_chunk = spatial_tile.to(device=device, dtype=next(model.parameters()).dtype, non_blocking=True).contiguous()
+    def run_temporal_chunks(spatial_tile, model=vae_model):
+        t_chunk = spatial_tile.contiguous()
         old_device = getattr(model, "device", None)
-        model.device = device
+        model.device = t_chunk.device
         old_slicing_min_size = getattr(model, slicing_attr, None)
         if old_slicing_min_size is not None and slicing_min_size is not None:
             if slicing_min_size <= 0:
@@ -397,7 +396,7 @@ class Attention(nn.Module):
 
 def causal_norm_wrapper(norm_layer: nn.Module, x: torch.Tensor) -> torch.Tensor:
     input_dtype = x.dtype
-    if isinstance(norm_layer, (ops.LayerNorm, ops.RMSNorm)):
+    if isinstance(norm_layer, (nn.LayerNorm, nn.RMSNorm)):
         if x.ndim == 4:
             x = x.permute(0, 2, 3, 1)
             x = norm_layer(x)
@@ -408,14 +407,14 @@ def causal_norm_wrapper(norm_layer: nn.Module, x: torch.Tensor) -> torch.Tensor:
             x = norm_layer(x)
             x = x.permute(0, 4, 1, 2, 3)
             return x.to(input_dtype)
-    if isinstance(norm_layer, (ops.GroupNorm, nn.BatchNorm2d, nn.SyncBatchNorm)):
+    if isinstance(norm_layer, (nn.GroupNorm, nn.BatchNorm2d, nn.SyncBatchNorm)):
         if x.ndim <= 4:
             return norm_layer(x).to(input_dtype)
         if x.ndim == 5:
             b, c, t, h, w = x.shape
             x = x.transpose(1, 2).reshape(b * t, c, h, w)
             memory_occupy = x.numel() * x.element_size() / 1024**3
-            if isinstance(norm_layer, ops.GroupNorm) and memory_occupy > get_norm_limit():
+            if isinstance(norm_layer, nn.GroupNorm) and memory_occupy > get_norm_limit():
                 num_chunks = min(BYTEDANCE_GN_CHUNKS_FP16 if x.element_size() == 2 else BYTEDANCE_GN_CHUNKS_FP32, norm_layer.num_groups)
                 if norm_layer.num_groups % num_chunks != 0:
                     raise ValueError(
@@ -423,9 +422,9 @@ def causal_norm_wrapper(norm_layer: nn.Module, x: torch.Tensor) -> torch.Tensor:
                     )
                 num_groups_per_chunk = norm_layer.num_groups // num_chunks
 
+                weights = comfy.ops.cast_to_input(norm_layer.weight, x).chunk(num_chunks, dim=0)
+                biases = comfy.ops.cast_to_input(norm_layer.bias, x).chunk(num_chunks, dim=0)
                 x = list(x.chunk(num_chunks, dim=1))
-                weights = norm_layer.weight.chunk(num_chunks, dim=0)
-                biases = norm_layer.bias.chunk(num_chunks, dim=0)
                 for i, (w, bias) in enumerate(zip(weights, biases)):
                     x[i] = F.group_norm(x[i], num_groups_per_chunk, w, bias, norm_layer.eps)
                     x[i] = x[i].to(input_dtype)
@@ -1459,7 +1458,6 @@ class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
     def _encode_with_raw_latent(self, x):
         if x.ndim == 4:
             x = x.unsqueeze(2)
-        x = x.to(dtype=next(self.parameters()).dtype)
         self.device = x.device
         p = super().encode(x)
         z = p.squeeze(2)

comfy/quant_ops.py

@@ -3,6 +3,22 @@ import logging
 
 from comfy.cli_args import args
 
+
+def _rocm_kitchen_arch_supported():
+    """comfy-kitchen's INT8 Triton kernels compile tl.dot to matrix-core instructions.
+    RDNA3/3.5/4 (gfx11xx/gfx12xx) have WMMA and CDNA (gfx9xx) has MFMA; RDNA1/RDNA2
+    (gfx10xx) have neither, so the INT8 path hangs the GPU there. Gates the automatic
+    ROCm default so those cards stay on the eager fallback (an explicit
+    --enable-triton-backend still forces it on any arch)."""
+    try:
+        arch = torch.cuda.get_device_properties(torch.cuda.current_device()).gcnArchName.split(":")[0]
+    except Exception:
+        return False
+    if arch.startswith(("gfx11", "gfx12")):
+        return True
+    return arch in ("gfx908", "gfx90a", "gfx940", "gfx941", "gfx942", "gfx950")
+
+
 try:
     import comfy_kitchen as ck
     from comfy_kitchen.tensor import (
@@ -26,9 +42,13 @@ try:
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
     # On ROCm/AMD the CUDA backend is unavailable, so Triton is the only accelerated
-    # comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7:
+    # comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7 AND a
+    # matrix-core GPU (RDNA3+ WMMA gfx11xx/gfx12xx, CDNA MFMA gfx9xx). RDNA1/RDNA2
+    # (gfx10xx) have no WMMA -> the INT8 tl.dot path hangs the GPU, so they stay eager.
     # older Triton lacks libdevice.rint on the HIP backend and hard-crashes the INT8 path.
-    if args.enable_triton_backend or torch.version.hip is not None:
+    if args.disable_triton_backend:
+        ck.registry.disable("triton")
+    elif args.enable_triton_backend: # or (torch.version.hip is not None and _rocm_kitchen_arch_supported()):
         try:
             import triton
             triton_version = tuple(int(v) for v in triton.__version__.split(".")[:2])

comfy_api_nodes/apis/hunyuan3d.py

@@ -77,6 +77,7 @@ class To3DUVTaskRequest(BaseModel):
 
 class To3DPartTaskRequest(BaseModel):
     File: TaskFile3DInput = Field(...)
+    EnableStagedGeneration: bool | None = Field(None)
 
 
 class TextureEditImageInfo(BaseModel):

comfy_api_nodes/nodes_hunyuan3d.py

@@ -642,6 +642,7 @@ class Tencent3DPartNode(IO.ComfyNode):
             response_model=To3DProTaskCreateResponse,
             data=To3DPartTaskRequest(
                 File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                EnableStagedGeneration=True,
             ),
             is_rate_limited=_is_tencent_rate_limited,
         )

comfy_execution/jobs.py

@@ -56,6 +56,9 @@ PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 # 3D file extensions for preview fallback (no dedicated media_type exists)
 THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
 
+# Text file extensions for preview fallback (the formats SaveText can produce)
+TEXT_EXTENSIONS = frozenset({'.txt', '.md', '.json'})
+
 
 def has_3d_extension(filename: str) -> bool:
     lower = filename.lower()
@@ -143,9 +146,10 @@ def is_previewable(media_type: str, item: dict) -> bool:
     Maintains backwards compatibility with existing logic.
 
     Priority:
-    1. media_type is 'images', 'video', 'audio', or '3d'
+    1. media_type is 'images', 'video', 'audio', '3d', or 'text'
     2. format field starts with 'video/' or 'audio/'
     3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
+    4. filename has a text extension (.txt, .md, .json, ...)
     """
     if media_type in PREVIEWABLE_MEDIA_TYPES:
         return True
@@ -156,10 +160,12 @@ def is_previewable(media_type: str, item: dict) -> bool:
     if fmt and (fmt.startswith('video/') or fmt.startswith('audio/')):
         return True
 
-    # Check for 3D files by extension
+    # Check for 3D and text files by extension
     filename = item.get('filename', '').lower()
     if any(filename.endswith(ext) for ext in THREE_D_EXTENSIONS):
         return True
+    if any(filename.endswith(ext) for ext in TEXT_EXTENSIONS):
+        return True
 
     return False
 
@@ -255,6 +261,10 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
     Preview priority (matching frontend):
     1. type="output" with previewable media
     2. Any previewable media
+
+    Text content entries (strings under 'text') are preview-only metadata,
+    matching the frontend's METADATA_KEYS: they can serve as the fallback
+    preview but are not counted as outputs.
     """
     count = 0
     preview_output = None
@@ -275,7 +285,6 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                     if normalized is None:
                         # Not a 3D file string — check for text preview
                         if media_type == 'text':
-                            count += 1
                             if preview_output is None:
                                 if isinstance(item, tuple):
                                     text_value = item[0] if item else ''

comfy_extras/nodes_audio.py

@@ -298,6 +298,7 @@ class PreviewAudio(IO.ComfyNode):
             search_aliases=["play audio"],
             display_name="Preview Audio",
             category="audio",
+            description="Preview the audio without saving it to the ComfyUI output directory.",
             inputs=[
                 IO.Audio.Input("audio"),
             ],

comfy_extras/nodes_bounding_boxes.py

@@ -1,3 +1,5 @@
+import json
+
 import numpy as np
 import torch
 from PIL import Image, ImageDraw, ImageEnhance, ImageFont
@@ -166,6 +168,111 @@ def boxes_to_regions(boxes, width: int, height: int) -> list:
     return regions
 
 
+def normalize_incoming_boxes(bboxes) -> list:
+    if isinstance(bboxes, dict):
+        frame = [bboxes]
+    elif not isinstance(bboxes, list) or not bboxes:
+        frame = []
+    elif isinstance(bboxes[0], dict):
+        frame = bboxes
+    else:
+        frame = bboxes[0] if isinstance(bboxes[0], list) else []
+    boxes = []
+    for box in frame:
+        if not isinstance(box, dict):
+            continue
+        norm = {
+            "x": box.get("x", 0),
+            "y": box.get("y", 0),
+            "width": box.get("width", 0),
+            "height": box.get("height", 0),
+        }
+        meta = box.get("metadata")
+        if isinstance(meta, dict):
+            norm["metadata"] = meta
+        boxes.append(norm)
+    return boxes
+
+
+def _looks_like_element(box: dict) -> bool:
+    bbox = box.get("bbox")
+    return isinstance(bbox, (list, tuple)) and len(bbox) == 4
+
+
+def _looks_like_bbox(box: dict) -> bool:
+    return all(key in box for key in ("x", "y", "width", "height"))
+
+
+def elements_to_boxes(elements: list, width: int, height: int) -> list:
+    boxes = []
+    for element in elements:
+        if not isinstance(element, dict):
+            continue
+        bbox = element.get("bbox")
+        if not (isinstance(bbox, (list, tuple)) and len(bbox) == 4):
+            raise ValueError("bboxes element is missing a valid 'bbox' [ymin, xmin, ymax, xmax]")
+        try:
+            ymin, xmin, ymax, xmax = (float(v) / 1000.0 for v in bbox)
+        except (TypeError, ValueError):
+            raise ValueError("bboxes element 'bbox' must contain four numbers")
+        etype = "text" if element.get("type") == "text" else "obj"
+        boxes.append({
+            "x": round(min(xmin, xmax) * width),
+            "y": round(min(ymin, ymax) * height),
+            "width": round(abs(xmax - xmin) * width),
+            "height": round(abs(ymax - ymin) * height),
+            "metadata": {
+                "type": etype,
+                "text": element.get("text", "") if etype == "text" else "",
+                "desc": element.get("desc", ""),
+                "palette": element.get("color_palette", []) or [],
+            },
+        })
+    return boxes
+
+
+def boxes_from_input(data, width: int, height: int) -> list:
+    if data is None:
+        return []
+    if isinstance(data, str):
+        text = data.strip()
+        if not text:
+            return []
+        try:
+            data = json.loads(text)
+        except (ValueError, TypeError) as exc:
+            raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
+    if isinstance(data, dict):
+        if _looks_like_element(data):
+            return elements_to_boxes([data], width, height)
+        if _looks_like_bbox(data):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes dict must be a bounding box (x, y, width, height) or an element (with a 'bbox')"
+        )
+    if not isinstance(data, list):
+        raise ValueError(
+            "bboxes input must be bounding boxes, elements, or a JSON string, "
+            f"got {type(data).__name__}"
+        )
+    if not data:
+        return []
+    first = data[0]
+    if isinstance(first, list):
+        return normalize_incoming_boxes(data)
+    if isinstance(first, dict):
+        if _looks_like_element(first):
+            return elements_to_boxes(data, width, height)
+        if _looks_like_bbox(first):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes items must be bounding boxes (x, y, width, height) or elements (with a 'bbox')"
+        )
+    raise ValueError(
+        f"bboxes list must contain bounding boxes or elements, got {type(first).__name__}"
+    )
+
+
 def _norm_bbox(region: dict) -> list[int]:
     def grid(value: float) -> int:
         return max(0, min(1000, round(value * 1000)))
@@ -217,29 +324,48 @@ class CreateBoundingBoxes(io.ComfyNode):
                     optional=True,
                     tooltip="Optional image used as background in the canvas and preview.",
                 ),
+                io.MultiType.Input(
+                    "bboxes",
+                    [io.BoundingBox, io.Array, io.String],
+                    optional=True,
+                    tooltip="Bounding boxes, elements, or a JSON string to initialize the canvas. A new upstream value initializes the canvas; edits made on the canvas take priority and are kept until the upstream value changes again.",
+                ),
                 io.Int.Input("width", default=1024, min=64, max=16384, step=16,
                              tooltip="Width of the canvas and the pixel grid for the bounding boxes."),
                 io.Int.Input("height", default=1024, min=64, max=16384, step=16,
                              tooltip="Height of the canvas and the pixel grid for the bounding boxes."),
                 editor_state,
+                io.BoundingBoxes.Input(
+                    "last_incoming",
+                    optional=True,
+                    tooltip="Internal state managed by the canvas: the upstream bboxes value that last initialized it. Leave empty to re-initialize the canvas from the bboxes input on the next run.",
+                ),
             ],
             outputs=[
                 io.Image.Output(display_name="preview"),
                 io.BoundingBox.Output(display_name="bboxes"),
                 io.Array.Output(display_name="elements"),
             ],
+            is_output_node=True,
             is_experimental=True,
         )
 
     @classmethod
-    def execute(cls, width, height, editor_state=None, background=None) -> io.NodeOutput:
-        regions = boxes_to_regions(editor_state, width, height)
+    def execute(cls, width, height, editor_state=None, last_incoming=None, background=None, bboxes=None) -> io.NodeOutput:
+        incoming = boxes_from_input(bboxes, width, height)
+        applied = last_incoming if isinstance(last_incoming, list) else []
+        upstream_changed = bool(incoming) and incoming != applied
+        source = incoming if upstream_changed else (editor_state or [])
+        regions = boxes_to_regions(source, width, height)
         preview = render_preview(regions, width, height, _bg_from_image(background))
+        ui = {"dims": [width, height]}
+        if incoming:
+            ui["input_bboxes"] = incoming
         return io.NodeOutput(
             preview,
             fractions_to_bbox_frame(regions, width, height),
             build_elements(regions),
-            ui={"dims": [width, height]},
+            ui=ui,
         )
 
 

comfy_extras/nodes_load_3d.py

@@ -92,6 +92,7 @@ class Preview3D(IO.ComfyNode):
             search_aliases=["view mesh", "3d viewer"],
             display_name="Preview 3D & Animation",
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -136,6 +137,7 @@ class Preview3DAdvanced(IO.ComfyNode):
             display_name="Preview 3D (Advanced)",
             search_aliases=["preview 3d", "3d viewer", "view mesh", "frame 3d", "3d camera output"],
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -193,6 +195,7 @@ class PreviewGaussianSplat(IO.ComfyNode):
             node_id="PreviewGaussianSplat",
             display_name="Preview Splat",
             category="3d",
+            description="Preview a gaussian splat 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[
@@ -261,6 +264,7 @@ class PreviewPointCloud(IO.ComfyNode):
             node_id="PreviewPointCloud",
             display_name="Preview Point Cloud",
             category="3d",
+            description="Preview a point cloud 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[

comfy_extras/nodes_mask.py

@@ -419,17 +419,18 @@ class MaskPreview(IO.ComfyNode):
             search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
             display_name="Preview Mask",
             category="image/mask",
-            description="Saves the input images to your ComfyUI output directory.",
+            description="Preview the masks without saving them to the ComfyUI output directory.",
             inputs=[
                 IO.Mask.Input("mask"),
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            outputs=[IO.Mask.Output(display_name="mask")]
         )
 
     @classmethod
     def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
-        return IO.NodeOutput(ui=UI.PreviewMask(mask))
+        return IO.NodeOutput(mask, ui=UI.PreviewMask(mask))
 
 
 class MaskExtension(ComfyExtension):

comfy_extras/nodes_preview_any.py

@@ -18,6 +18,7 @@ class PreviewAny():
 
     CATEGORY = "utilities"
     SEARCH_ALIASES = ["show output", "inspect", "debug", "print value", "show text"]
+    DESCRIPTION = "Preview any input value as text."
 
     def main(self, source=None):
         torch.set_printoptions(edgeitems=6)

comfy_extras/nodes_primitive.py

@@ -10,11 +10,10 @@ class String(io.ComfyNode):
         return io.Schema(
             node_id="PrimitiveString",
             search_aliases=["text", "string", "text box", "prompt"],
-            display_name="Text String (DEPRECATED)",
+            display_name="Text",
             category="utilities/primitive",
             inputs=[io.String.Input("value")],
-            outputs=[io.String.Output()],
-            is_deprecated=True
+            outputs=[io.String.Output()]
         )
 
     @classmethod
@@ -28,7 +27,7 @@ class StringMultiline(io.ComfyNode):
         return io.Schema(
             node_id="PrimitiveStringMultiline",
             search_aliases=["text", "string", "text multiline", "string multiline", "text box", "prompt"],
-            display_name="Input Text",
+            display_name="Text (Multiline)",
             category="utilities/primitive",
             essentials_category="Basics",
             inputs=[io.String.Input("value", multiline=True)],

comfy_extras/nodes_save_3d.py

@@ -13,7 +13,7 @@ from typing_extensions import override
 
 import folder_paths
 from comfy.cli_args import args
-from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_api.latest import ComfyExtension, IO, Types, UI
 
 
 def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None, unlit=False):
@@ -406,10 +406,164 @@ class SaveGLB(IO.ComfyNode):
         return IO.NodeOutput(ui={"3d": results})
 
 
+def _save_file3d_to_output(model_3d: Types.File3D, filename_prefix: str) -> str:
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+        filename_prefix, folder_paths.get_output_directory()
+    )
+    ext = model_3d.format or "glb"
+    saved_filename = f"{filename}_{counter:05}.{ext}"
+    model_3d.save_to(os.path.join(full_output_folder, saved_filename))
+    return f"{subfolder}/{saved_filename}" if subfolder else saved_filename
+
+
+def execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs) -> IO.NodeOutput:
+    model_file = _save_file3d_to_output(model_3d, filename_prefix)
+    camera_info_input = kwargs.get("camera_info", None)
+    camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+    model_3d_info_input = kwargs.get("model_3d_info", None)
+    model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+    return IO.NodeOutput(
+        model_3d,
+        model_3d_info,
+        camera_info,
+        width,
+        height,
+        ui=UI.PreviewUI3DAdvanced(model_file, camera_info, model_3d_info),
+    )
+
+
+class Save3DAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Save3DAdvanced",
+            display_name="Save 3D (Advanced)",
+            search_aliases=["save 3d", "export 3d model", "save mesh advanced"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DFBX,
+                        IO.File3DOBJ,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="3D model file from an upstream 3D node.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SaveGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveGaussianSplat",
+            display_name="Save Splat",
+            search_aliases=["save splat", "save gaussian splat", "export gaussian", "export splat"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DSplatAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SavePointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SavePointCloud",
+            display_name="Save Point Cloud",
+            search_aliases=["save point cloud", "save pointcloud", "export point cloud"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DPointCloudAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
 class Save3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [SaveGLB]
+        return [SaveGLB, Save3DAdvanced, SaveGaussianSplat, SavePointCloud]
 
 
 async def comfy_entrypoint() -> Save3DExtension:

comfy_extras/nodes_text.py

@@ -0,0 +1,71 @@
+import os
+import json
+from typing_extensions import override
+from comfy_api.latest import io, ComfyExtension, ui
+import folder_paths
+
+
+class SaveTextNode(io.ComfyNode):
+    """Save text content to .txt, .md, or .json."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveText",
+            search_aliases=["save text", "write text", "export text"],
+            display_name="Save Text",
+            category="text",
+            description="Save text content to a file in the output directory.",
+            inputs=[
+                io.String.Input("text", force_input=True),
+                io.String.Input("filename_prefix", default="ComfyUI"),
+                io.Combo.Input("format", options=["txt", "md", "json"], default="txt"),
+            ],
+            outputs=[io.String.Output(display_name="text")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, text, filename_prefix, format):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix,
+            folder_paths.get_output_directory(),
+            1,
+            1,
+        )
+
+        file = f"{filename}_{counter:05}.{format}"
+        filepath = os.path.join(full_output_folder, file)
+
+        if format == "json":
+            # tries to pretty print otherwise saves normally
+            try:
+                data = json.loads(text)
+                with open(filepath, "w", encoding="utf-8") as f:
+                    json.dump(data, f, indent=2, ensure_ascii=False)
+            except json.JSONDecodeError:
+                with open(filepath, "w", encoding="utf-8") as f:
+                    f.write(text)
+        else:
+            with open(filepath, "w", encoding="utf-8") as f:
+                f.write(text)
+
+        return io.NodeOutput(
+            text,
+            ui={
+                "text": (text,),
+                "files": [
+                    ui.SavedResult(file, subfolder, io.FolderType.output)
+                ]
+            }
+        )
+
+class TextExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SaveTextNode
+        ]
+
+async def comfy_entrypoint() -> TextExtension:
+    return TextExtension()

nodes.py

@@ -1709,6 +1709,7 @@ class PreviewImage(SaveImage):
         self.compress_level = 1
 
     SEARCH_ALIASES = ["preview", "preview image", "show image", "view image", "display image", "image viewer"]
+    DESCRIPTION = "Preview the images without saving them to the ComfyUI output directory."
 
     @classmethod
     def INPUT_TYPES(s):
@@ -2504,6 +2505,7 @@ async def init_builtin_extra_nodes():
         "nodes_triposplat.py",
         "nodes_depth_anything_3.py",
         "nodes_seed.py",
+        "nodes_text.py",
     ]
 
     import_failed = []

tests-unit/comfy_test/test_seedvr2_dtype.py

@@ -1,4 +1,5 @@
 import torch
+import torch.nn as nn
 
 from comfy.cli_args import args as cli_args
 
@@ -48,3 +49,31 @@ def test_seedvr2_vae_decode_memory_covers_full_frame_lab_transfer():
     assert estimate == 101 * 960 * 1280 * 160
     assert estimate > 15 * 1024 ** 3
     assert estimate > old_estimate * 100
+
+
+def test_seedvr2_vae_encode_preserves_compute_dtype(monkeypatch):
+    wrapper = seedvr_vae.VideoAutoencoderKLWrapper.__new__(seedvr_vae.VideoAutoencoderKLWrapper)
+    nn.Module.__init__(wrapper)
+    wrapper._dummy = nn.Parameter(torch.empty(1, dtype=torch.float16))
+    input_dtype = None
+
+    def encode(self, x):
+        nonlocal input_dtype
+        input_dtype = x.dtype
+        return x
+
+    monkeypatch.setattr(seedvr_vae.VideoAutoencoderKL, "encode", encode)
+
+    x = torch.zeros((1, 3, 1, 8, 8), dtype=torch.float32)
+    wrapper._encode_with_raw_latent(x)
+
+    assert input_dtype == torch.float32
+
+
+def test_seedvr2_vae_ops_cast_weights_to_compute_dtype():
+    attention = seedvr_vae.Attention(query_dim=4, heads=1, dim_head=4).to(torch.float16)
+    hidden_states = torch.zeros((1, 2, 4), dtype=torch.float32)
+
+    output = attention(hidden_states)
+
+    assert output.dtype == torch.float32

tests-unit/comfy_test/test_seedvr2_vae_tiled.py

@@ -122,6 +122,31 @@ def test_tiled_vae_encode_uses_tensor_return_without_indexing():
     assert tuple(out.shape) == (2, _LATENT_CHANNELS, 1, 8, 8)
 
 
+def test_tiled_vae_preserves_compute_dtype_with_different_parameter_dtype():
+    class DummyVAE(nn.Module):
+        spatial_downsample_factor = 8
+        temporal_downsample_factor = 4
+        slicing_sample_min_size = 8
+
+        def __init__(self):
+            super().__init__()
+            self.device = torch.device("cpu")
+            self._dummy = nn.Parameter(torch.zeros(1, dtype=torch.float16))
+            self.input_dtype = None
+
+        def encode(self, t_chunk):
+            self.input_dtype = t_chunk.dtype
+            b, _, _, h, w = t_chunk.shape
+            return torch.ones((b, _LATENT_CHANNELS, 1, h // 8, w // 8), dtype=t_chunk.dtype)
+
+    vae = DummyVAE()
+    x = torch.zeros((1, 3, 1, 64, 64), dtype=torch.float32)
+
+    tiled_vae(x, vae, tile_size=(64, 64), tile_overlap=(16, 16), encode=True)
+
+    assert vae.input_dtype == torch.float32
+
+
 def test_tiled_vae_preserves_input_dtype_on_single_tile():
     class FloatOutputVAEModel(torch.nn.Module):
         def __init__(self):