Revert "Fix Content-Disposition header missing 'attachment;' prefix (#13093)" (#13733)

This reverts commit ea6880b04b.

app/user_manager.py

@@ -28,8 +28,8 @@ def get_file_info(path: str, relative_to: str) -> FileInfo:
     return {
         "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
         "size": os.path.getsize(path),
-        "modified": os.path.getmtime(path),
-        "created": os.path.getctime(path)
+        "modified": int(os.path.getmtime(path) * 1000),
+        "created": int(os.path.getctime(path) * 1000),
     }
 
 

comfy/context_windows.py

@@ -63,7 +63,11 @@ class IndexListContextWindow(ContextWindowABC):
             dim = self.dim
         if dim == 0 and full.shape[dim] == 1:
             return full
-        idx = tuple([slice(None)] * dim + [self.index_list])
+        indices = self.index_list
+        anchor_idx = getattr(self, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            indices = [anchor_idx] + list(indices)
+        idx = tuple([slice(None)] * dim + [indices])
         window = full[idx]
         if retain_index_list:
             idx = tuple([slice(None)] * dim + [retain_index_list])
@@ -113,7 +117,14 @@ def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, d
 
     # 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:]]
+        anchor_idx = getattr(window, 'causal_anchor_index', None)
+        if anchor_idx is not None and anchor_idx >= 0:
+            # anchor occupies one of the no-cond positions, so skip one fewer from window.index_list
+            skip_count = temporal_offset - 1
+        else:
+            skip_count = temporal_offset
+
+        indices = [i - temporal_offset for i in window.index_list[skip_count:]]
         indices = [i for i in indices if 0 <= i]
     else:
         indices = list(window.index_list)
@@ -150,7 +161,8 @@ class ContextFuseMethod:
 ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
 class IndexListContextHandler(ContextHandlerABC):
     def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
-                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
+                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False,
+                 causal_window_fix: bool=True):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -162,6 +174,7 @@ class IndexListContextHandler(ContextHandlerABC):
         self.freenoise = freenoise
         self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
         self.split_conds_to_windows = split_conds_to_windows
+        self.causal_window_fix = causal_window_fix
 
         self.callbacks = {}
 
@@ -318,6 +331,14 @@ class IndexListContextHandler(ContextHandlerABC):
             # allow processing to end between context window executions for faster Cancel
             comfy.model_management.throw_exception_if_processing_interrupted()
 
+            # causal_window_fix: prepend a pre-window frame that will be stripped post-forward
+            anchor_applied = False
+            if self.causal_window_fix:
+                anchor_idx = window.index_list[0] - 1
+                if 0 <= anchor_idx < x_in.size(self.dim):
+                    window.causal_anchor_index = anchor_idx
+                    anchor_applied = True
+
             for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
                 callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
 
@@ -332,6 +353,12 @@ class IndexListContextHandler(ContextHandlerABC):
             if device is not None:
                 for i in range(len(sub_conds_out)):
                     sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
+
+            # strip causal_window_fix anchor if applied
+            if anchor_applied:
+                for i in range(len(sub_conds_out)):
+                    sub_conds_out[i] = sub_conds_out[i].narrow(self.dim, 1, sub_conds_out[i].shape[self.dim] - 1)
+
             results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
         return results
 

comfy/latent_formats.py

@@ -9,6 +9,7 @@ class LatentFormat:
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
     spacial_downscale_ratio = 8
+    temporal_downscale_ratio = 1
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -235,6 +236,7 @@ class Flux2(LatentFormat):
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
+    temporal_downscale_ratio = 6
 
     def __init__(self):
         self.scale_factor = 1.0
@@ -278,6 +280,7 @@ class LTXV(LatentFormat):
     latent_channels = 128
     latent_dimensions = 3
     spacial_downscale_ratio = 32
+    temporal_downscale_ratio = 8
 
     def __init__(self):
         self.latent_rgb_factors = [
@@ -421,6 +424,7 @@ class LTXAV(LTXV):
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
     scale_factor = 0.476986
     latent_rgb_factors = [
         [-0.0395, -0.0331,  0.0445],
@@ -447,6 +451,7 @@ class HunyuanVideo(LatentFormat):
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 8
 
     latent_rgb_factors = [
         [ 0.1817,  0.2284,  0.2423],
@@ -472,6 +477,7 @@ class Cosmos1CV8x8x8(LatentFormat):
 class Wan21(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     latent_rgb_factors = [
             [-0.1299, -0.1692,  0.2932],
@@ -734,6 +740,7 @@ class HunyuanVideo15(LatentFormat):
     latent_channels = 32
     latent_dimensions = 3
     spacial_downscale_ratio = 16
+    temporal_downscale_ratio = 4
     scale_factor = 1.03682
     taesd_decoder_name = "lighttaehy1_5"
 
@@ -786,8 +793,27 @@ class ZImagePixelSpace(ChromaRadiance):
     pass
 
 class CogVideoX(LatentFormat):
+    """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
+
+    scale_factor matches the vae/config.json scaling_factor for the 2b variant.
+    The 5b-class checkpoints (CogVideoX-5b, CogVideoX-1.5-5B, CogVideoX-Fun-V1.5-*)
+    use a different value; see CogVideoX1_5 below.
+    """
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     def __init__(self):
         self.scale_factor = 1.15258426
+
+
+class CogVideoX1_5(CogVideoX):
+    """Latent format for 5b-class CogVideoX checkpoints.
+
+    Covers THUDM/CogVideoX-5b, THUDM/CogVideoX-1.5-5B, and the CogVideoX-Fun
+    V1.5-5b family (including VOID inpainting). All of these have
+    scaling_factor=0.7 in their vae/config.json. Auto-selected in
+    supported_models.CogVideoX_T2V based on transformer hidden dim.
+    """
+    def __init__(self):
+        self.scale_factor = 0.7

comfy/sd.py

@@ -66,6 +66,7 @@ import comfy.text_encoders.longcat_image
 import comfy.text_encoders.qwen35
 import comfy.text_encoders.ernie
 import comfy.text_encoders.gemma4
+import comfy.text_encoders.cogvideo
 
 import comfy.model_patcher
 import comfy.lora
@@ -1224,6 +1225,7 @@ class CLIPType(Enum):
     NEWBIE = 24
     FLUX2 = 25
     LONGCAT_IMAGE = 26
+    COGVIDEOX = 27
 
 
 
@@ -1428,6 +1430,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
+            elif clip_type == CLIPType.COGVIDEOX:
+                clip_target.clip = comfy.text_encoders.cogvideo.cogvideo_te(**t5xxl_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.cogvideo.CogVideoXTokenizer
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer

comfy/supported_models.py

@@ -1872,6 +1872,14 @@ class CogVideoX_T2V(supported_models_base.BASE):
     vae_key_prefix = ["vae."]
     text_encoder_key_prefix = ["text_encoders."]
 
+    def __init__(self, unet_config):
+        # 2b-class (dim=1920, heads=30) uses scale_factor=1.15258426.
+        # 5b-class (dim=3072, heads=48) — incl. CogVideoX-5b, 1.5-5B, and
+        # Fun-V1.5 inpainting — uses scale_factor=0.7 per vae/config.json.
+        if unet_config.get("num_attention_heads", 0) >= 48:
+            self.latent_format = latent_formats.CogVideoX1_5
+        super().__init__(unet_config)
+
     def get_model(self, state_dict, prefix="", device=None):
         # CogVideoX 1.5 (patch_size_t=2) has different training base dimensions for RoPE
         if self.unet_config.get("patch_size_t") is not None:
@@ -1898,6 +1906,20 @@ class CogVideoX_I2V(CogVideoX_T2V):
         out = model_base.CogVideoX(self, image_to_video=True, device=device)
         return out
 
+class CogVideoX_Inpaint(CogVideoX_T2V):
+    unet_config = {
+        "image_model": "cogvideox",
+        "in_channels": 48,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        if self.unet_config.get("patch_size_t") is not None:
+            self.unet_config.setdefault("sample_height", 96)
+            self.unet_config.setdefault("sample_width", 170)
+            self.unet_config.setdefault("sample_frames", 81)
+        out = model_base.CogVideoX(self, image_to_video=True, device=device)
+        return out
+
 
 models = [
     LotusD,
@@ -1978,6 +2000,7 @@ models = [
     ErnieImage,
     SAM3,
     SAM31,
+    CogVideoX_Inpaint,
     CogVideoX_I2V,
     CogVideoX_T2V,
     SVD_img2vid,

comfy/text_encoders/cogvideo.py

@@ -1,6 +1,48 @@
 import comfy.text_encoders.sd3_clip
+from comfy import sd1_clip
 
 
 class CogVideoXT5Tokenizer(comfy.text_encoders.sd3_clip.T5XXLTokenizer):
+    """Inner T5 tokenizer for CogVideoX.
+
+    CogVideoX was trained with T5 embeddings padded to 226 tokens (not 77 like SD3).
+    Used both directly by supported_models.CogVideoX_T2V.clip_target (paired with
+    the raw T5XXLModel) and by the CogVideoXTokenizer outer wrapper below.
+    """
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, min_length=226)
+
+
+class CogVideoXTokenizer(sd1_clip.SD1Tokenizer):
+    """Outer tokenizer wrapper for CLIPLoader (type="cogvideox")."""
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
+                         clip_name="t5xxl", tokenizer=CogVideoXT5Tokenizer)
+
+
+class CogVideoXT5XXL(sd1_clip.SD1ClipModel):
+    """Outer T5XXL model wrapper for CLIPLoader (type="cogvideox").
+
+    Wraps the raw T5XXL model in the SD1ClipModel interface so that CLIP.__init__
+    (which reads self.dtypes) works correctly. The inner model is the standard
+    sd3_clip.T5XXLModel (no attention_mask change needed for CogVideoX).
+    """
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="t5xxl",
+                         clip_model=comfy.text_encoders.sd3_clip.T5XXLModel,
+                         model_options=model_options)
+
+
+def cogvideo_te(dtype_t5=None, t5_quantization_metadata=None):
+    """Factory that returns a CogVideoXT5XXL class configured with the detected
+    T5 dtype and optional quantization metadata, for use in load_text_encoder_state_dicts.
+    """
+    class CogVideoXTEModel_(CogVideoXT5XXL):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
+            if dtype_t5 is not None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return CogVideoXTEModel_

comfy_extras/nodes_ace.py

@@ -42,7 +42,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode):
                 IO.Int.Input("bpm", default=120, min=10, max=300),
                 IO.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1),
                 IO.Combo.Input("timesignature", options=['2', '3', '4', '6']),
-                IO.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
+                IO.Combo.Input("language", options=['ar', 'az', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'es', 'fa', 'fi', 'fr', 'he', 'hi', 'hr', 'ht', 'hu', 'id', 'is', 'it', 'ja', 'ko', 'la', 'lt', 'ms', 'ne', 'nl', 'no', 'pa', 'pl', 'pt', 'ro', 'ru', 'sa', 'sk', 'sr', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'uk', 'ur', 'vi', 'yue', 'zh', 'unknown'], default='en'),
                 IO.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
                 IO.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
                 IO.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),

comfy_extras/nodes_context_windows.py

@@ -29,6 +29,7 @@ class ContextWindowsManualNode(io.ComfyNode):
                 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.Boolean.Input("causal_window_fix", default=True, tooltip="Whether to add a causal fix frame to non-0-indexed context windows."),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with context windows applied during sampling."),
@@ -38,7 +39,7 @@ class ContextWindowsManualNode(io.ComfyNode):
 
     @classmethod
     def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
-                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
+                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, causal_window_fix: bool=True) -> io.Model:
         model = model.clone()
         model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler(
             context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
@@ -50,7 +51,8 @@ class ContextWindowsManualNode(io.ComfyNode):
             dim=dim,
             freenoise=freenoise,
             cond_retain_index_list=cond_retain_index_list,
-            split_conds_to_windows=split_conds_to_windows
+            split_conds_to_windows=split_conds_to_windows,
+            causal_window_fix=causal_window_fix,
         )
         # make memory usage calculation only take into account the context window latents
         comfy.context_windows.create_prepare_sampling_wrapper(model)

comfy_extras/nodes_void.py

@@ -0,0 +1,483 @@
+import logging
+
+import torch
+
+import comfy
+import comfy.model_management
+import comfy.model_patcher
+import comfy.samplers
+import comfy.utils
+import folder_paths
+import node_helpers
+import nodes
+from comfy.utils import model_trange as trange
+from comfy_api.latest import ComfyExtension, io
+from torchvision.models.optical_flow import raft_large
+from typing_extensions import override
+
+
+from comfy_extras.void_noise_warp import RaftOpticalFlow, get_noise_from_video
+
+OpticalFlow = io.Custom("OPTICAL_FLOW")
+
+TEMPORAL_COMPRESSION = 4
+PATCH_SIZE_T = 2
+
+
+def _valid_void_length(length: int) -> int:
+    """Round ``length`` down to a value that produces an even latent_t.
+
+    VOID / CogVideoX-Fun-V1.5 uses patch_size_t=2, so the VAE-encoded latent
+    must have an even temporal dimension. If latent_t is odd, the transformer
+    pad_to_patch_size circular-wraps an extra latent frame onto the end; after
+    the post-transformer crop the last real latent frame has been influenced
+    by the wrapped phantom frame, producing visible jitter and "disappearing"
+    subjects near the end of the decoded video. Rounding down fixes this.
+    """
+    latent_t = ((length - 1) // TEMPORAL_COMPRESSION) + 1
+    if latent_t % PATCH_SIZE_T == 0:
+        return length
+    # Round latent_t down to the nearest multiple of PATCH_SIZE_T, then invert
+    # the ((length - 1) // TEMPORAL_COMPRESSION) + 1 formula. Floor at 1 frame
+    # so we never return a non-positive length.
+    target_latent_t = max(PATCH_SIZE_T, (latent_t // PATCH_SIZE_T) * PATCH_SIZE_T)
+    return (target_latent_t - 1) * TEMPORAL_COMPRESSION + 1
+
+
+class OpticalFlowLoader(io.ComfyNode):
+    """Load an optical flow model from ``models/optical_flow/``.
+
+    Only torchvision's RAFT-large format is recognized today (the model used
+    by VOIDWarpedNoise).  The checkpoint must be placed under
+    ``models/optical_flow/`` — ComfyUI never downloads optical-flow weights
+    at runtime.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="OpticalFlowLoader",
+            display_name="Load Optical Flow Model",
+            category="loaders",
+            inputs=[
+                io.Combo.Input(
+                    "model_name",
+                    options=folder_paths.get_filename_list("optical_flow"),
+                    tooltip=(
+                        "Optical flow model to load.  Files must be placed in the "
+                        "'optical_flow' folder.  Today only torchvision's "
+                        "raft_large.pth is supported."
+                    ),
+                ),
+            ],
+            outputs=[
+                OpticalFlow.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+
+        model_path = folder_paths.get_full_path_or_raise("optical_flow", model_name)
+        sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+
+        has_raft_keys = (
+            any(k.startswith("feature_encoder.") for k in sd)
+            and any(k.startswith("context_encoder.") for k in sd)
+            and any(k.startswith("update_block.") for k in sd)
+        )
+        if not has_raft_keys:
+            raise ValueError(
+                "Unrecognized optical flow model format: expected a torchvision "
+                "RAFT-large state dict with 'feature_encoder.', 'context_encoder.' "
+                "and 'update_block.' prefixes."
+            )
+
+        model = raft_large(weights=None, progress=False)
+        model.load_state_dict(sd)
+        model.eval().to(torch.float32)
+
+        patcher = comfy.model_patcher.ModelPatcher(
+            model,
+            load_device=comfy.model_management.get_torch_device(),
+            offload_device=comfy.model_management.unet_offload_device(),
+        )
+        return io.NodeOutput(patcher)
+
+
+class VOIDQuadmaskPreprocess(io.ComfyNode):
+    """Preprocess a quadmask video for VOID inpainting.
+
+    Quantizes mask values to four semantic levels, inverts, and normalizes:
+      0   -> primary object to remove
+      63  -> overlap of primary + affected
+      127 -> affected region (interactions)
+      255 -> background (keep)
+
+    After inversion and normalization, the output mask has values in [0, 1]
+    with four discrete levels: 1.0 (remove), ~0.75, ~0.50, 0.0 (keep).
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VOIDQuadmaskPreprocess",
+            category="mask/video",
+            inputs=[
+                io.Mask.Input("mask"),
+                io.Int.Input("dilate_width", default=0, min=0, max=50, step=1,
+                             tooltip="Dilation radius for the primary mask region (0 = no dilation)"),
+            ],
+            outputs=[
+                io.Mask.Output(display_name="quadmask"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, mask, dilate_width=0) -> io.NodeOutput:
+        m = mask.clone()
+
+        if m.max() <= 1.0:
+            m = m * 255.0
+
+        if dilate_width > 0 and m.ndim >= 3:
+            binary = (m < 128).float()
+            kernel_size = dilate_width * 2 + 1
+            if binary.ndim == 3:
+                binary = binary.unsqueeze(1)
+            dilated = torch.nn.functional.max_pool2d(
+                binary, kernel_size=kernel_size, stride=1, padding=dilate_width
+            )
+            if dilated.ndim == 4:
+                dilated = dilated.squeeze(1)
+            m = torch.where(dilated > 0.5, torch.zeros_like(m), m)
+
+        m = torch.where(m <= 31, torch.zeros_like(m), m)
+        m = torch.where((m > 31) & (m <= 95), torch.full_like(m, 63), m)
+        m = torch.where((m > 95) & (m <= 191), torch.full_like(m, 127), m)
+        m = torch.where(m > 191, torch.full_like(m, 255), m)
+
+        m = (255.0 - m) / 255.0
+
+        return io.NodeOutput(m)
+
+
+class VOIDInpaintConditioning(io.ComfyNode):
+    """Build VOID inpainting conditioning for CogVideoX.
+
+    Encodes the processed quadmask and masked source video through the VAE,
+    producing a 32-channel concat conditioning (16ch mask + 16ch masked video)
+    that gets concatenated with the 16ch noise latent by the model.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VOIDInpaintConditioning",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Image.Input("video", tooltip="Source video frames [T, H, W, 3]"),
+                io.Mask.Input("quadmask", tooltip="Preprocessed quadmask from VOIDQuadmaskPreprocess [T, H, W]"),
+                io.Int.Input("width", default=672, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=384, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("length", default=45, min=1, max=nodes.MAX_RESOLUTION, step=1,
+                             tooltip="Number of pixel frames to process. For CogVideoX-Fun-V1.5 "
+                                     "(patch_size_t=2), latent_t must be even — lengths that "
+                                     "produce odd latent_t are rounded down (e.g. 49 → 45)."),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, video, quadmask,
+                width, height, length, batch_size) -> io.NodeOutput:
+
+        adjusted_length = _valid_void_length(length)
+        if adjusted_length != length:
+            logging.warning(
+                "VOIDInpaintConditioning: rounding length %d down to %d so that "
+                "latent_t is even (required by CogVideoX-Fun-V1.5 patch_size_t=2). "
+                "Using odd latent_t causes the last frame to be corrupted by "
+                "circular padding.", length, adjusted_length,
+            )
+            length = adjusted_length
+
+        latent_t = ((length - 1) // TEMPORAL_COMPRESSION) + 1
+        latent_h = height // 8
+        latent_w = width // 8
+
+        vid = video[:length]
+        vid = comfy.utils.common_upscale(
+            vid.movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+
+        qm = quadmask[:length]
+        if qm.ndim == 3:
+            qm = qm.unsqueeze(-1)
+        qm = comfy.utils.common_upscale(
+            qm.movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+        if qm.ndim == 4 and qm.shape[-1] == 1:
+            qm = qm.squeeze(-1)
+
+        mask_condition = qm
+        if mask_condition.ndim == 3:
+            mask_condition_3ch = mask_condition.unsqueeze(-1).expand(-1, -1, -1, 3)
+        else:
+            mask_condition_3ch = mask_condition
+
+        inverted_mask_3ch = 1.0 - mask_condition_3ch
+        masked_video = vid[:, :, :, :3] * (1.0 - mask_condition_3ch)
+
+        mask_latents = vae.encode(inverted_mask_3ch)
+        masked_video_latents = vae.encode(masked_video)
+
+        def _match_temporal(lat, target_t):
+            if lat.shape[2] > target_t:
+                return lat[:, :, :target_t]
+            elif lat.shape[2] < target_t:
+                pad = target_t - lat.shape[2]
+                return torch.cat([lat, lat[:, :, -1:].repeat(1, 1, pad, 1, 1)], dim=2)
+            return lat
+
+        mask_latents = _match_temporal(mask_latents, latent_t)
+        masked_video_latents = _match_temporal(masked_video_latents, latent_t)
+
+        inpaint_latents = torch.cat([mask_latents, masked_video_latents], dim=1)
+
+        # No explicit scaling needed here: the model's CogVideoX.concat_cond()
+        # applies process_latent_in (×latent_format.scale_factor) to each 16-ch
+        # block of the stored conditioning. For 5b-class checkpoints (incl. the
+        # VOID/CogVideoX-Fun-V1.5 inpainting model) that scale_factor is auto-
+        # selected as 0.7 in supported_models.CogVideoX_T2V, which matches the
+        # diffusers vae/config.json scaling_factor VOID was trained with.
+
+        positive = node_helpers.conditioning_set_values(
+            positive, {"concat_latent_image": inpaint_latents}
+        )
+        negative = node_helpers.conditioning_set_values(
+            negative, {"concat_latent_image": inpaint_latents}
+        )
+
+        noise_latent = torch.zeros(
+            [batch_size, 16, latent_t, latent_h, latent_w],
+            device=comfy.model_management.intermediate_device()
+        )
+
+        return io.NodeOutput(positive, negative, {"samples": noise_latent})
+
+
+class VOIDWarpedNoise(io.ComfyNode):
+    """Generate optical-flow warped noise for VOID Pass 2 refinement.
+
+    Takes the Pass 1 output video and produces temporally-correlated noise
+    by warping Gaussian noise along optical flow vectors. This noise is used
+    as the initial latent for Pass 2, resulting in better temporal consistency.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VOIDWarpedNoise",
+            category="latent/video",
+            inputs=[
+                OpticalFlow.Input(
+                    "optical_flow",
+                    tooltip="Optical flow model from OpticalFlowLoader (RAFT-large).",
+                ),
+                io.Image.Input("video", tooltip="Pass 1 output video frames [T, H, W, 3]"),
+                io.Int.Input("width", default=672, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=384, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("length", default=45, min=1, max=nodes.MAX_RESOLUTION, step=1,
+                             tooltip="Number of pixel frames. Rounded down to make latent_t "
+                                     "even (patch_size_t=2 requirement), e.g. 49 → 45."),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="warped_noise"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, optical_flow, video, width, height, length, batch_size) -> io.NodeOutput:
+
+        adjusted_length = _valid_void_length(length)
+        if adjusted_length != length:
+            logging.warning(
+                "VOIDWarpedNoise: rounding length %d down to %d so that "
+                "latent_t is even (required by CogVideoX-Fun-V1.5 patch_size_t=2).",
+                length, adjusted_length,
+            )
+            length = adjusted_length
+
+        latent_t = ((length - 1) // TEMPORAL_COMPRESSION) + 1
+        latent_h = height // 8
+        latent_w = width // 8
+
+        # RAFT + noise warp is real compute, not an "intermediate" buffer, so
+        # we want the actual torch device (CUDA/MPS).  The final latent is
+        # moved back to intermediate_device() before returning to match the
+        # rest of the ComfyUI pipeline.
+        device = comfy.model_management.get_torch_device()
+
+        comfy.model_management.load_model_gpu(optical_flow)
+        raft = RaftOpticalFlow(optical_flow.model, device=device)
+
+        vid = video[:length].to(device)
+        vid = comfy.utils.common_upscale(
+            vid.movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+        vid_uint8 = (vid.clamp(0, 1) * 255).to(torch.uint8)
+
+        FRAME = 2**-1
+        FLOW = 2**3
+        LATENT_SCALE = 8
+
+        warped = get_noise_from_video(
+            vid_uint8,
+            raft,
+            noise_channels=16,
+            resize_frames=FRAME,
+            resize_flow=FLOW,
+            downscale_factor=round(FRAME * FLOW) * LATENT_SCALE,
+            device=device,
+        )
+
+        if warped.shape[0] != latent_t:
+            indices = torch.linspace(0, warped.shape[0] - 1, latent_t,
+                                     device=device).long()
+            warped = warped[indices]
+
+        if warped.shape[1] != latent_h or warped.shape[2] != latent_w:
+            # (T, H, W, C) → (T, C, H, W) → bilinear resize → back
+            warped = warped.permute(0, 3, 1, 2)
+            warped = torch.nn.functional.interpolate(
+                warped, size=(latent_h, latent_w),
+                mode="bilinear", align_corners=False,
+            )
+            warped = warped.permute(0, 2, 3, 1)
+
+        # (T, H, W, C) → (B, C, T, H, W)
+        warped_tensor = warped.permute(3, 0, 1, 2).unsqueeze(0)
+        if batch_size > 1:
+            warped_tensor = warped_tensor.repeat(batch_size, 1, 1, 1, 1)
+
+        warped_tensor = warped_tensor.to(comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": warped_tensor})
+
+
+class Noise_FromLatent:
+    """Wraps a pre-computed LATENT tensor as a NOISE source."""
+    def __init__(self, latent_dict):
+        self.seed = 0
+        self._samples = latent_dict["samples"]
+
+    def generate_noise(self, input_latent):
+        return self._samples.clone().cpu()
+
+
+class VOIDWarpedNoiseSource(io.ComfyNode):
+    """Convert a LATENT (e.g. from VOIDWarpedNoise) into a NOISE source
+    for use with SamplerCustomAdvanced."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VOIDWarpedNoiseSource",
+            category="sampling/custom_sampling/noise",
+            inputs=[
+                io.Latent.Input("warped_noise",
+                    tooltip="Warped noise latent from VOIDWarpedNoise"),
+            ],
+            outputs=[io.Noise.Output()],
+        )
+
+    @classmethod
+    def execute(cls, warped_noise) -> io.NodeOutput:
+        return io.NodeOutput(Noise_FromLatent(warped_noise))
+
+
+class VOID_DDIM(comfy.samplers.Sampler):
+    """DDIM sampler for VOID inpainting models.
+
+    VOID was trained with the diffusers CogVideoXDDIMScheduler which operates in
+    alpha-space (input std ≈ 1). The standard KSampler applies noise_scaling that
+    multiplies by sqrt(1+sigma^2) ≈ 4500x, which is incompatible with VOID's
+    training. This sampler skips noise_scaling and implements the DDIM update rule
+    directly using sigma-to-alpha conversion.
+    """
+
+    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+        x = noise.to(torch.float32)
+        model_options = extra_args.get("model_options", {})
+        seed = extra_args.get("seed", None)
+        s_in = x.new_ones([x.shape[0]])
+
+        for i in trange(len(sigmas) - 1, disable=disable_pbar):
+            sigma = sigmas[i]
+            sigma_next = sigmas[i + 1]
+
+            denoised = model_wrap(x, sigma * s_in, model_options=model_options, seed=seed)
+
+            if callback is not None:
+                callback(i, denoised, x, len(sigmas) - 1)
+
+            if sigma_next == 0:
+                x = denoised
+            else:
+                alpha_t = 1.0 / (1.0 + sigma ** 2)
+                alpha_prev = 1.0 / (1.0 + sigma_next ** 2)
+
+                pred_eps = (x - (alpha_t ** 0.5) * denoised) / (1.0 - alpha_t) ** 0.5
+                x = (alpha_prev ** 0.5) * denoised + (1.0 - alpha_prev) ** 0.5 * pred_eps
+
+        return x
+
+
+class VOIDSampler(io.ComfyNode):
+    """VOID DDIM sampler for use with SamplerCustom / SamplerCustomAdvanced.
+
+    Required for VOID inpainting models. Implements the same DDIM loop that VOID
+    was trained with (diffusers CogVideoXDDIMScheduler), without the noise_scaling
+    that the standard KSampler applies. Use with RandomNoise or VOIDWarpedNoiseSource.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VOIDSampler",
+            category="sampling/custom_sampling/samplers",
+            inputs=[],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls) -> io.NodeOutput:
+        return io.NodeOutput(VOID_DDIM())
+
+    get_sampler = execute
+
+
+class VOIDExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            OpticalFlowLoader,
+            VOIDQuadmaskPreprocess,
+            VOIDInpaintConditioning,
+            VOIDWarpedNoise,
+            VOIDWarpedNoiseSource,
+            VOIDSampler,
+        ]
+
+
+async def comfy_entrypoint() -> VOIDExtension:
+    return VOIDExtension()

comfy_extras/void_noise_warp.py

@@ -0,0 +1,494 @@
+"""
+Optical-flow-warped noise for VOID Pass 2 refinement.
+
+Adapted from RyannDaGreat/CommonSource (MIT License, Ryan Burgert):
+  https://github.com/RyannDaGreat/CommonSource
+  - noise_warp.py  (NoiseWarper / warp_xyωc / regaussianize / get_noise_from_video)
+  - raft.py        (RaftOpticalFlow)
+
+Only the code paths that ``comfy_extras/nodes_void.py::VOIDWarpedNoise`` actually
+uses (torch THWC uint8 input, no background removal, no visualization, no disk
+I/O, default warp/noise params) have been inlined.  External ``rp`` utilities
+have been replaced with equivalents from torch.nn.functional / einops.  The
+RAFT optical-flow model itself is loaded offline via ``OpticalFlowLoader`` in
+``nodes_void.py`` and passed into ``get_noise_from_video`` by the caller; this
+module never downloads weights at runtime.
+"""
+
+import logging
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+
+import comfy.model_management
+
+
+# ---------------------------------------------------------------------------
+# Low-level torch image helpers (drop-in replacements for rp.torch_* primitives)
+# ---------------------------------------------------------------------------
+
+def _torch_resize_chw(image, size, interp, copy=True):
+    """Resize a CHW tensor.
+
+    ``size`` is either a scalar factor or a (h, w) tuple.  ``interp`` is one
+    of ``"bilinear"``, ``"nearest"``, ``"area"``.  When ``copy`` is False and
+    the requested size matches the input, returns the input tensor as is
+    (faster but callers must not mutate the result).
+    """
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_resize_chw expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
+    _, in_h, in_w = image.shape
+    if isinstance(size, (int, float)) and not isinstance(size, bool):
+        new_h = max(1, int(in_h * size))
+        new_w = max(1, int(in_w * size))
+    else:
+        new_h, new_w = size
+
+    if (new_h, new_w) == (in_h, in_w):
+        return image.clone() if copy else image
+
+    kwargs = {}
+    if interp in ("bilinear", "bicubic"):
+        kwargs["align_corners"] = False
+    out = F.interpolate(image[None], size=(new_h, new_w), mode=interp, **kwargs)[0]
+    return out
+
+
+def _torch_remap_relative(image, dx, dy, interp="bilinear"):
+    """Relative remap of a CHW image via ``F.grid_sample``.
+
+    Equivalent to ``rp.torch_remap_image(image, dx, dy, relative=True, interp=interp)``
+    for ``interp`` in {"bilinear", "nearest"}.  Out-of-bounds samples are 0.
+    """
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_remap_relative expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
+    if dx.shape != dy.shape:
+        raise ValueError(
+            f"_torch_remap_relative: dx and dy must match, got {tuple(dx.shape)} vs {tuple(dy.shape)}"
+        )
+    _, h, w = image.shape
+
+    x_abs = dx + torch.arange(w, device=dx.device, dtype=dx.dtype)
+    y_abs = dy + torch.arange(h, device=dy.device, dtype=dy.dtype)[:, None]
+
+    x_norm = (x_abs / (w - 1)) * 2 - 1
+    y_norm = (y_abs / (h - 1)) * 2 - 1
+
+    grid = torch.stack([x_norm, y_norm], dim=-1)[None].to(image.dtype)
+    out = F.grid_sample(
+        image[None], grid, mode=interp, align_corners=True, padding_mode="zeros"
+    )[0]
+    return out
+
+
+def _torch_scatter_add_relative(image, dx, dy):
+    """Scatter-add a CHW image using relative floor-rounded (dx, dy) offsets.
+
+    Equivalent to ``rp.torch_scatter_add_image(image, dx, dy, relative=True,
+    interp='floor')``.  Out-of-bounds targets are dropped.
+    """
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_scatter_add_relative expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
+    in_c, in_h, in_w = image.shape
+    if dx.shape != (in_h, in_w) or dy.shape != (in_h, in_w):
+        raise ValueError(
+            f"_torch_scatter_add_relative: dx/dy must be ({in_h}, {in_w}), "
+            f"got dx={tuple(dx.shape)} dy={tuple(dy.shape)}"
+        )
+
+    x = dx.long() + torch.arange(in_w, device=dx.device, dtype=torch.long)
+    y = dy.long() + torch.arange(in_h, device=dy.device, dtype=torch.long)[:, None]
+
+    valid = ((y >= 0) & (y < in_h) & (x >= 0) & (x < in_w)).reshape(-1)
+    indices = (y * in_w + x).reshape(-1)[valid]
+
+    flat_image = rearrange(image, "c h w -> (h w) c")[valid]
+    out = torch.zeros((in_h * in_w, in_c), dtype=image.dtype, device=image.device)
+    out.index_add_(0, indices, flat_image)
+    return rearrange(out, "(h w) c -> c h w", h=in_h, w=in_w)
+
+
+# ---------------------------------------------------------------------------
+# Noise warping primitives (ported from noise_warp.py)
+# ---------------------------------------------------------------------------
+
+def unique_pixels(image):
+    """Find unique pixel values in a CHW tensor.
+
+    Returns ``(unique_colors [U, C], counts [U], index_matrix [H, W])`` where
+    ``index_matrix[i, j]`` is the index of the unique color at that pixel.
+    """
+    _, h, w = image.shape
+    flat = rearrange(image, "c h w -> (h w) c")
+    unique_colors, inverse_indices, counts = torch.unique(
+        flat, dim=0, return_inverse=True, return_counts=True, sorted=False,
+    )
+    index_matrix = rearrange(inverse_indices, "(h w) -> h w", h=h, w=w)
+    return unique_colors, counts, index_matrix
+
+
+def sum_indexed_values(image, index_matrix):
+    """For each unique index, sum the CHW image values at its pixels."""
+    _, h, w = image.shape
+    u = int(index_matrix.max().item()) + 1
+    flat = rearrange(image, "c h w -> (h w) c")
+    out = torch.zeros((u, flat.shape[1]), dtype=flat.dtype, device=flat.device)
+    out.index_add_(0, index_matrix.view(-1), flat)
+    return out
+
+
+def indexed_to_image(index_matrix, unique_colors):
+    """Build a CHW image from an index matrix and a (U, C) color table."""
+    h, w = index_matrix.shape
+    flat = unique_colors[index_matrix.view(-1)]
+    return rearrange(flat, "(h w) c -> c h w", h=h, w=w)
+
+
+def regaussianize(noise):
+    """Variance-preserving re-sampling of a CHW noise tensor.
+
+    Wherever the noise contains groups of identical pixel values (e.g. after
+    a nearest-neighbor warp that duplicated source pixels), adds zero-mean
+    foreign noise within each group and scales by ``1/sqrt(count)`` so the
+    output is unit-variance gaussian again.
+    """
+    _, hs, ws = noise.shape
+    _, counts, index_matrix = unique_pixels(noise[:1])
+
+    foreign_noise = torch.randn_like(noise)
+    summed = sum_indexed_values(foreign_noise, index_matrix)
+    meaned = indexed_to_image(index_matrix, summed / rearrange(counts, "u -> u 1"))
+    zeroed_foreign = foreign_noise - meaned
+
+    counts_image = indexed_to_image(index_matrix, rearrange(counts, "u -> u 1"))
+
+    output = noise / counts_image ** 0.5 + zeroed_foreign
+    return output, counts_image
+
+
+def xy_meshgrid_like_image(image):
+    """Return a (2, H, W) tensor of (x, y) pixel coordinates matching ``image``."""
+    _, h, w = image.shape
+    y, x = torch.meshgrid(
+        torch.arange(h, device=image.device, dtype=image.dtype),
+        torch.arange(w, device=image.device, dtype=image.dtype),
+        indexing="ij",
+    )
+    return torch.stack([x, y])
+
+
+def noise_to_state(noise):
+    """Pack a (C, H, W) noise tensor into a state tensor (3+C, H, W) = [dx, dy, ω, noise]."""
+    zeros = torch.zeros_like(noise[:1])
+    ones = torch.ones_like(noise[:1])
+    return torch.cat([zeros, zeros, ones, noise])
+
+
+def state_to_noise(state):
+    """Unpack the noise channels from a state tensor."""
+    return state[3:]
+
+
+def warp_state(state, flow):
+    """Warp a noise-warper state tensor along the given optical flow.
+
+    ``state`` has shape ``(3+c, h, w)`` (= dx, dy, ω, c noise channels).
+    ``flow`` has shape ``(2, h, w)`` (= dx, dy).
+    """
+    if flow.device != state.device:
+        raise ValueError(
+            f"warp_state: flow and state must be on the same device, "
+            f"got flow={flow.device} state={state.device}"
+        )
+    if state.ndim != 3:
+        raise ValueError(
+            f"warp_state: state must be 3D (3+C, H, W), got shape {tuple(state.shape)}"
+        )
+    xyoc, h, w = state.shape
+    if flow.shape != (2, h, w):
+        raise ValueError(
+            f"warp_state: flow must have shape (2, {h}, {w}), got {tuple(flow.shape)}"
+        )
+    device = state.device
+
+    x_ch, y_ch = 0, 1
+    xy = 2         # state[:xy]  = [dx, dy]
+    xyw = 3        # state[:xyw] = [dx, dy, ω]
+    w_ch = 2       # state[w_ch] = ω
+    c = xyoc - xyw
+    oc = xyoc - xy
+    if c <= 0:
+        raise ValueError(
+            f"warp_state: state has no noise channels (expected 3+C with C>0, got {xyoc} channels)"
+        )
+    if not (state[w_ch] > 0).all():
+        raise ValueError("warp_state: all weights in state[2] must be > 0")
+
+    grid = xy_meshgrid_like_image(state)
+
+    init = torch.empty_like(state)
+    init[:xy] = 0
+    init[w_ch] = 1
+    init[-c:] = 0
+
+    # --- Expansion branch: nearest-neighbor remap with negated flow ---
+    pre_expand = torch.empty_like(state)
+    pre_expand[:xy] = _torch_remap_relative(state[:xy], -flow[0], -flow[1], "nearest")
+    pre_expand[-oc:] = _torch_remap_relative(state[-oc:], -flow[0], -flow[1], "nearest")
+    pre_expand[w_ch][pre_expand[w_ch] == 0] = 1
+
+    # --- Shrink branch: scatter-add state into new positions ---
+    pre_shrink = state.clone()
+    pre_shrink[:xy] += flow
+
+    pos = (grid + pre_shrink[:xy]).round()
+    in_bounds = (pos[x_ch] >= 0) & (pos[x_ch] < w) & (pos[y_ch] >= 0) & (pos[y_ch] < h)
+    pre_shrink = torch.where(~in_bounds[None], init, pre_shrink)
+
+    scat_xy = pre_shrink[:xy].round()
+    pre_shrink[:xy] -= scat_xy
+    pre_shrink[:xy] = 0  # xy_mode='none' in upstream
+
+    def scat(tensor):
+        return _torch_scatter_add_relative(tensor, scat_xy[0], scat_xy[1])
+
+    # rp.torch_scatter_add_image on a bool tensor errors on modern torch;
+    # scatter-sum a float ones tensor and threshold to get the mask instead.
+    shrink_mask = scat(torch.ones(1, h, w, dtype=state.dtype, device=device)) > 0
+
+    # Drop expansion samples at positions that will be filled by shrink.
+    pre_expand = torch.where(shrink_mask, init, pre_expand)
+
+    # Regaussianize both branches together so duplicated-source groups are
+    # counted globally, then split back apart.
+    concat = torch.cat([pre_shrink, pre_expand], dim=2)  # along width
+    concat[-c:], counts_image = regaussianize(concat[-c:])
+    concat[w_ch] = concat[w_ch] / counts_image[0]
+    concat[w_ch] = concat[w_ch].nan_to_num()
+    pre_shrink, expand = torch.chunk(concat, chunks=2, dim=2)
+
+    shrink = torch.empty_like(pre_shrink)
+    shrink[w_ch] = scat(pre_shrink[w_ch][None])[0]
+    shrink[:xy] = scat(pre_shrink[:xy] * pre_shrink[w_ch][None]) / shrink[w_ch][None]
+    shrink[-c:] = scat(pre_shrink[-c:] * pre_shrink[w_ch][None]) / scat(
+        pre_shrink[w_ch][None] ** 2
+    ).sqrt()
+
+    output = torch.where(shrink_mask, shrink, expand)
+    output[w_ch] = output[w_ch] / output[w_ch].mean()
+    output[w_ch] += 1e-5
+    output[w_ch] **= 0.9999
+    return output
+
+
+class NoiseWarper:
+    """Maintain a warpable noise state and emit gaussian noise per frame.
+
+    Simplified from RyannDaGreat/CommonSource/noise_warp.py::NoiseWarper:
+    ``scale_factor``, ``post_noise_alpha``, ``progressive_noise_alpha``, and
+    ``warp_kwargs`` are all dropped since VOIDWarpedNoise always uses defaults.
+    """
+
+    def __init__(self, c, h, w, device, dtype=torch.float32):
+        if c <= 0 or h <= 0 or w <= 0:
+            raise ValueError(
+                f"NoiseWarper: c/h/w must all be positive, got c={c} h={h} w={w}"
+            )
+        self.c = c
+        self.h = h
+        self.w = w
+        self.device = device
+        self.dtype = dtype
+
+        noise = torch.randn(c, h, w, dtype=dtype, device=device)
+        self._state = noise_to_state(noise)
+
+    @property
+    def noise(self):
+        # With scale_factor=1 the "downsample to respect weights" step is a
+        # size-preserving no-op; the weight-variance correction math still
+        # runs to stay faithful to upstream.
+        n = state_to_noise(self._state)
+        weights = self._state[2:3]
+        return n * weights / (weights ** 2).sqrt()
+
+    def __call__(self, dx, dy):
+        if dx.shape != dy.shape:
+            raise ValueError(
+                f"NoiseWarper: dx and dy must match, got {tuple(dx.shape)} vs {tuple(dy.shape)}"
+            )
+        flow = torch.stack([dx, dy]).to(self.device, self.dtype)
+        _, oflowh, ofloww = flow.shape
+
+        flow = _torch_resize_chw(flow, (self.h, self.w), "bilinear", copy=True)
+        flowh, floww = flow.shape[-2:]
+
+        # Upstream scales flow[0] by flowh/oflowh and flow[1] by floww/ofloww
+        # (channel-order appears swapped but harmless when H and W are scaled
+        # by the same factor, which is always the case for our callers).
+        flow[0] *= flowh / oflowh
+        flow[1] *= floww / ofloww
+
+        self._state = warp_state(self._state, flow)
+        return self
+
+
+# ---------------------------------------------------------------------------
+# RAFT optical flow wrapper (ported from raft.py)
+# ---------------------------------------------------------------------------
+
+class RaftOpticalFlow:
+    """RAFT-large wrapper around a pre-loaded torchvision model.
+
+    ``model`` must be the ``torchvision.models.optical_flow.raft_large`` module
+    with its weights already populated; this class is load-agnostic so the
+    caller owns downloading/offload concerns (see ``OpticalFlowLoader`` in
+    ``nodes_void.py``).  ``__call__`` returns a ``(2, H, W)`` flow.
+    """
+
+    def __init__(self, model, device=None):
+        if device is None:
+            device = comfy.model_management.get_torch_device()
+        device = torch.device(device) if not isinstance(device, torch.device) else device
+
+        model = model.to(device)
+        model.eval()
+        self.device = device
+        self.model = model
+
+    def _preprocess(self, image_chw):
+        image = image_chw.to(self.device, torch.float32)
+        _, h, w = image.shape
+        new_h = (h // 8) * 8
+        new_w = (w // 8) * 8
+        image = _torch_resize_chw(image, (new_h, new_w), "bilinear", copy=False)
+        image = image * 2 - 1
+        return image[None]
+
+    def __call__(self, from_image, to_image):
+        """``from_image``, ``to_image``: CHW float tensors in [0, 1]."""
+        if from_image.shape != to_image.shape:
+            raise ValueError(
+                f"RaftOpticalFlow: from_image and to_image must match, "
+                f"got {tuple(from_image.shape)} vs {tuple(to_image.shape)}"
+            )
+        _, h, w = from_image.shape
+        with torch.no_grad():
+            img1 = self._preprocess(from_image)
+            img2 = self._preprocess(to_image)
+            list_of_flows = self.model(img1, img2)
+            flow = list_of_flows[-1][0]  # (2, new_h, new_w)
+            if flow.shape[-2:] != (h, w):
+                flow = _torch_resize_chw(flow, (h, w), "bilinear", copy=False)
+        return flow
+
+
+# ---------------------------------------------------------------------------
+# Narrow entry point used by VOIDWarpedNoise
+# ---------------------------------------------------------------------------
+
+def get_noise_from_video(
+    video_frames: torch.Tensor,
+    raft: RaftOpticalFlow,
+    *,
+    noise_channels: int = 16,
+    resize_frames: float = 0.5,
+    resize_flow: int = 8,
+    downscale_factor: int = 32,
+    device: Optional[torch.device] = None,
+) -> torch.Tensor:
+    """Produce optical-flow-warped gaussian noise from a video.
+
+    Args:
+        video_frames: ``(T, H, W, 3)`` uint8 torch tensor.
+        raft: Pre-loaded RAFT optical-flow wrapper (see ``RaftOpticalFlow``).
+        noise_channels: Channels in the output noise.
+        resize_frames: Pre-RAFT frame scale factor.
+        resize_flow: Post-flow up-scale factor applied to the optical flow;
+            the internal noise state is allocated at
+            ``(resize_flow * resize_frames * H, resize_flow * resize_frames * W)``.
+        downscale_factor: Area-pool factor applied to the noise before return;
+            should evenly divide the internal noise resolution.
+        device: Target device.  Defaults to ``comfy.model_management.get_torch_device()``.
+
+    Returns:
+        ``(T, H', W', noise_channels)`` float32 noise tensor on ``device``.
+    """
+    if not isinstance(resize_flow, int) or resize_flow < 1:
+        raise ValueError(
+            f"get_noise_from_video: resize_flow must be a positive int, got {resize_flow!r}"
+        )
+    if video_frames.ndim != 4 or video_frames.shape[-1] != 3:
+        raise ValueError(
+            "get_noise_from_video: video_frames must have shape (T, H, W, 3), "
+            f"got {tuple(video_frames.shape)}"
+        )
+    if video_frames.dtype != torch.uint8:
+        raise TypeError(
+            "get_noise_from_video: video_frames must be uint8 in [0, 255], "
+            f"got dtype {video_frames.dtype}"
+        )
+
+    if device is None:
+        device = comfy.model_management.get_torch_device()
+    device = torch.device(device) if not isinstance(device, torch.device) else device
+
+    if device.type == "cpu":
+        logging.warning(
+            "VOIDWarpedNoise: running get_noise_from_video on CPU; this will be "
+            "slow (minutes for ~45 frames).  Use CUDA for interactive use."
+        )
+
+    T = video_frames.shape[0]
+    frames = video_frames.to(device).permute(0, 3, 1, 2).to(torch.float32) / 255.0
+    if resize_frames != 1.0:
+        new_h = max(1, int(frames.shape[2] * resize_frames))
+        new_w = max(1, int(frames.shape[3] * resize_frames))
+        frames = F.interpolate(frames, size=(new_h, new_w), mode="area")
+
+    _, _, H, W = frames.shape
+    internal_h = resize_flow * H
+    internal_w = resize_flow * W
+    if internal_h % downscale_factor or internal_w % downscale_factor:
+        logging.warning(
+            "VOIDWarpedNoise: internal noise size %dx%d is not divisible by "
+            "downscale_factor %d; output noise may have artifacts.",
+            internal_h, internal_w, downscale_factor,
+        )
+
+    with torch.no_grad():
+        warper = NoiseWarper(
+            c=noise_channels, h=internal_h, w=internal_w, device=device,
+        )
+        down_h = warper.h // downscale_factor
+        down_w = warper.w // downscale_factor
+        output = torch.empty(
+            (T, down_h, down_w, noise_channels), dtype=torch.float32, device=device,
+        )
+
+        def downscale(noise_chw):
+            # Area-pool to 1/downscale_factor then multiply by downscale_factor
+            # to adjust std (sqrt of pool area == downscale_factor for a
+            # square pool).
+            down = _torch_resize_chw(noise_chw, 1.0 / downscale_factor, "area", copy=False)
+            return down * downscale_factor
+
+        output[0] = downscale(warper.noise).permute(1, 2, 0)
+
+        prev = frames[0]
+        for i in range(1, T):
+            curr = frames[i]
+            flow = raft(prev, curr).to(device)
+            warper(flow[0], flow[1])
+            output[i] = downscale(warper.noise).permute(1, 2, 0)
+            prev = curr
+
+    return output

folder_paths.py

@@ -54,6 +54,8 @@ folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_enc
 
 folder_names_and_paths["frame_interpolation"] = ([os.path.join(models_dir, "frame_interpolation")], supported_pt_extensions)
 
+folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)
+
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
 input_directory = os.path.join(base_path, "input")
@@ -432,7 +434,9 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
         prefix_len = len(os.path.basename(filename_prefix))
         prefix = filename[:prefix_len + 1]
         try:
-            digits = int(filename[prefix_len + 1:].split('_')[0])
+            remainder = filename[prefix_len + 1:]
+            base_remainder = remainder.split('.')[0]
+            digits = int(base_remainder.split('_')[0])
         except:
             digits = 0
         return digits, prefix

nodes.py

@@ -958,7 +958,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -968,7 +968,7 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B"
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -2430,6 +2430,7 @@ async def init_builtin_extra_nodes():
         "nodes_rtdetr.py",
         "nodes_frame_interpolation.py",
         "nodes_sam3.py",
+        "nodes_void.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -631,7 +631,7 @@ paths:
       operationId: getFeatures
       tags: [system]
       summary: Get enabled feature flags
-      description: Returns a dictionary of feature flag names to their enabled state.
+      description: Returns a dictionary of feature flag names to their enabled state. Cloud deployments may include additional typed fields alongside the boolean flags.
       responses:
         "200":
           description: Feature flags
@@ -641,6 +641,43 @@ paths:
                 type: object
                 additionalProperties:
                   type: boolean
+                properties:
+                  max_upload_size:
+                    type: integer
+                    format: int64
+                    minimum: 0
+                    description: "Maximum file upload size in bytes."
+                  free_tier_credits:
+                    type: integer
+                    format: int32
+                    minimum: 0
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Credits available to free-tier users. Local ComfyUI returns null."
+                  posthog_api_host:
+                    type: string
+                    format: uri
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] PostHog analytics proxy URL for frontend telemetry. Local ComfyUI returns null."
+                  max_concurrent_jobs:
+                    type: integer
+                    format: int32
+                    minimum: 0
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Maximum concurrent jobs the authenticated user can run. Local ComfyUI returns null."
+                  workflow_templates_version:
+                    type: string
+                    nullable: true
+                    x-runtime: [cloud]
+                    description: "[cloud-only] Version identifier for the workflow templates bundle. Local ComfyUI returns null."
+                  workflow_templates_source:
+                    type: string
+                    nullable: true
+                    enum: [dynamic_config_override, workflow_templates_version_json]
+                    x-runtime: [cloud]
+                    description: "[cloud-only] How the templates version was resolved. Local ComfyUI returns null."
 
   # ---------------------------------------------------------------------------
   # Node / Object Info
@@ -1497,6 +1534,24 @@ paths:
             type: string
             enum: [asc, desc]
           description: Sort direction
+        - name: job_ids
+          in: query
+          schema:
+            type: string
+          x-runtime: [cloud]
+          description: "[cloud-only] Comma-separated UUIDs to filter assets by associated job."
+        - name: include_public
+          in: query
+          schema:
+            type: boolean
+          x-runtime: [cloud]
+          description: "[cloud-only] Include workspace-public assets in addition to the caller's own."
+        - name: asset_hash
+          in: query
+          schema:
+            type: string
+          x-runtime: [cloud]
+          description: "[cloud-only] Filter by exact content hash."
       responses:
         "200":
           description: Asset list
@@ -1542,6 +1597,49 @@ paths:
                   type: string
                   format: uuid
                   description: ID of an existing asset to use as the preview image
+                id:
+                  type: string
+                  format: uuid
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned."
+          application/json:
+            schema:
+              type: object
+              x-runtime: [cloud]
+              description: "[cloud-only] URL-based asset upload. Caller supplies a URL instead of a file body; the server fetches the content."
+              required:
+                - url
+              properties:
+                url:
+                  type: string
+                  format: uri
+                  description: "[cloud-only] URL of the file to import as an asset"
+                name:
+                  type: string
+                  description: Display name for the asset
+                tags:
+                  type: string
+                  description: Comma-separated tags
+                user_metadata:
+                  type: string
+                  description: JSON-encoded user metadata
+                hash:
+                  type: string
+                  description: "Blake3 hash of the file content (e.g. blake3:abc123...)"
+                mime_type:
+                  type: string
+                  description: MIME type of the file (overrides auto-detected type)
+                preview_id:
+                  type: string
+                  format: uuid
+                  description: ID of an existing asset to use as the preview image
+                id:
+                  type: string
+                  format: uuid
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned."
       responses:
         "201":
           description: Asset created
@@ -1580,6 +1678,11 @@ paths:
                 user_metadata:
                   type: object
                   additionalProperties: true
+                mime_type:
+                  type: string
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] MIME type of the content, so the type is preserved without re-inspecting content. Ignored by local ComfyUI."
       responses:
         "201":
           description: Asset created from hash
@@ -1644,6 +1747,11 @@ paths:
                   type: string
                   format: uuid
                   description: ID of the asset to use as the preview
+                mime_type:
+                  type: string
+                  nullable: true
+                  x-runtime: [cloud]
+                  description: "[cloud-only] MIME type override when auto-detection was wrong. Ignored by local ComfyUI."
       responses:
         "200":
           description: Asset updated
@@ -2004,21 +2112,13 @@ components:
           format: uuid
           nullable: true
           x-runtime: [cloud]
-          description: |
-            UUID identifying a hosted-cloud workflow entity to associate with this
-            job. Local ComfyUI doesn't track workflow entities and returns `null`
-            (or omits the field). The `x-runtime: [cloud]` extension marks this
-            as populated only by the hosted-cloud runtime; absence of the tag
-            means a field is populated by all runtimes.
+          description: "[cloud-only] Cloud workflow entity ID for tracking and gallery association. Ignored by local ComfyUI."
         workflow_version_id:
           type: string
           format: uuid
           nullable: true
           x-runtime: [cloud]
-          description: |
-            UUID identifying a hosted-cloud workflow version to associate with
-            this job. Local ComfyUI returns `null` (or omits the field). See
-            `workflow_id` above for `x-runtime` semantics.
+          description: "[cloud-only] Cloud workflow version ID for pinning execution to a specific version. Ignored by local ComfyUI."
 
     PromptResponse:
       type: object

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.42.15
+comfyui-frontend-package==1.43.17
 comfyui-workflow-templates==0.9.69
 comfyui-embedded-docs==0.4.4
 torch

server.py

@@ -560,7 +560,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type=f'image/{image_format}',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
 
                     if 'channel' not in request.rel_url.query:
                         channel = 'rgba'
@@ -580,7 +580,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
 
                     elif channel == 'a':
                         with Image.open(file) as img:
@@ -597,7 +597,7 @@ class PromptServer():
                             alpha_buffer.seek(0)
 
                             return web.Response(body=alpha_buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
                     else:
                         # Use the content type from asset resolution if available,
                         # otherwise guess from the filename.
@@ -614,7 +614,7 @@ class PromptServer():
                         return web.FileResponse(
                             file,
                             headers={
-                                "Content-Disposition": f"attachment; filename=\"{filename}\"",
+                                "Content-Disposition": f"filename=\"{filename}\"",
                                 "Content-Type": content_type
                             }
                         )

tests-unit/prompt_server_test/user_manager_test.py

@@ -69,7 +69,11 @@ async def test_listuserdata_full_info(aiohttp_client, app, tmp_path):
     assert len(result) == 1
     assert result[0]["path"] == "file1.txt"
     assert "size" in result[0]
-    assert "modified" in result[0]
+    assert isinstance(result[0]["modified"], int)
+    assert isinstance(result[0]["created"], int)
+    # Verify millisecond magnitude (timestamps after year 2000 in ms are > 946684800000)
+    assert result[0]["modified"] > 946684800000
+    assert result[0]["created"] > 946684800000
 
 
 async def test_listuserdata_split_path(aiohttp_client, app, tmp_path):