Merge branch 'master' into drop-vestigial-tag-type

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -1,5 +1,4 @@
-As of the time of writing this you need this driver for best results:
-https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
+As of the time of writing this you need a recent driver. Updating to the latest driver is recommended.
 
 HOW TO RUN:
 
@@ -7,9 +6,9 @@ If you have a AMD gpu:
 
 run_amd_gpu.bat
 
-If you have memory issues you can try disabling the smart memory management by running comfyui with:
+If you have memory issues you can try enabling the new dynamic memory management by running comfyui with:
 
-run_amd_gpu_disable_smart_memory.bat
+run_amd_gpu_enable_dynamic_vram.bat
 
 IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
 

.github/workflows/check-line-endings.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Check for Windows line endings (CRLF)
         run: |
           # Get the list of changed files in the PR
-          CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }})
+          CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }} -- ':!.ci')
 
           # Flag to track if CRLF is found
           CRLF_FOUND=false

app/assets/scanner.py

@@ -33,6 +33,7 @@ from app.assets.services.file_utils import (
     verify_file_unchanged,
 )
 from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
+from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.metadata_extract import extract_file_metadata
 from app.assets.services.path_utils import (
     compute_relative_filename,
@@ -506,6 +507,10 @@ def enrich_asset(
 
     if extract_metadata and metadata:
         system_metadata = metadata.to_user_metadata()
+        if mime_type and mime_type.startswith("image/"):
+            dims = extract_image_dimensions(file_path, mime_type=mime_type)
+            if dims:
+                system_metadata.update(dims)
         set_reference_system_metadata(session, reference_id, system_metadata)
 
     if full_hash:

app/assets/services/image_dimensions.py

@@ -0,0 +1,63 @@
+"""Image dimension extraction for asset ingest.
+
+Reads only the image header via Pillow to capture width/height cheaply,
+without a full pixel decode. Returns a metadata dict suitable for merging
+into ``AssetReference.system_metadata``.
+"""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+def extract_image_dimensions(
+    file_path: str, mime_type: str | None = None
+) -> dict[str, Any] | None:
+    """Extract image dimensions for the file at ``file_path``.
+
+    Args:
+        file_path: Absolute path to a file on disk.
+        mime_type: Optional MIME type hint. When provided and not prefixed
+            with ``image/``, extraction is skipped without touching the file.
+
+    Returns:
+        ``{"kind": "image", "width": W, "height": H}`` when the file is a
+        recognizable image with positive dimensions, otherwise ``None``.
+
+    The dict shape is intended to be merged into ``system_metadata`` so the
+    asset response surfaces ``metadata.kind`` plus dimension fields for image
+    assets. Forward-compatible: future media kinds (e.g. ``"video"`` with
+    duration/fps) can extend this shape without schema changes.
+    """
+    if mime_type is not None and not mime_type.startswith("image/"):
+        return None
+
+    try:
+        from PIL import Image, UnidentifiedImageError
+    except ImportError:
+        logger.debug(
+            "Pillow not available; skipping image dimension extraction for %s",
+            file_path,
+        )
+        return None
+
+    try:
+        with Image.open(file_path) as img:
+            width, height = img.size
+    except (OSError, UnidentifiedImageError, ValueError) as exc:
+        logger.debug(
+            "Failed to read image dimensions from %s: %s", file_path, exc
+        )
+        return None
+
+    if (
+        not isinstance(width, int)
+        or not isinstance(height, int)
+        or width <= 0
+        or height <= 0
+    ):
+        return None
+
+    return {"kind": "image", "width": width, "height": height}

app/assets/services/ingest.py

@@ -17,9 +17,11 @@ from app.assets.database.queries import (
     get_reference_by_file_path,
     get_reference_tags,
     get_or_create_reference,
+    list_references_by_asset_id,
     reference_exists,
     remove_missing_tag_for_asset_id,
     set_reference_metadata,
+    set_reference_system_metadata,
     set_reference_tags,
     update_asset_hash_and_mime,
     upsert_asset,
@@ -29,6 +31,7 @@ from app.assets.database.queries import (
 from app.assets.helpers import get_utc_now, normalize_tags
 from app.assets.services.bulk_ingest import batch_insert_seed_assets
 from app.assets.services.file_utils import get_size_and_mtime_ns
+from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.path_utils import (
     compute_relative_filename,
     get_name_and_tags_from_asset_path,
@@ -118,6 +121,14 @@ def _ingest_file_from_path(
                 user_metadata=user_metadata,
             )
 
+            _maybe_store_image_dimensions(
+                session,
+                reference_id=reference_id,
+                file_path=locator,
+                mime_type=mime_type,
+                current_system_metadata=ref.system_metadata,
+            )
+
         try:
             remove_missing_tag_for_asset_id(session, asset_id=asset.id)
         except Exception:
@@ -288,6 +299,13 @@ def _register_existing_asset(
                 user_metadata=new_meta,
             )
 
+        _backfill_image_dimensions_from_siblings(
+            session,
+            asset_id=asset.id,
+            new_reference_id=ref.id,
+            current_system_metadata=ref.system_metadata,
+        )
+
         if tags is not None:
             set_reference_tags(
                 session,
@@ -334,6 +352,87 @@ def _update_metadata_with_filename(
         )
 
 
+_IMAGE_DIMENSION_KEYS = ("kind", "width", "height")
+
+
+def _maybe_store_image_dimensions(
+    session: Session,
+    reference_id: str,
+    file_path: str,
+    mime_type: str | None,
+    current_system_metadata: dict | None,
+) -> None:
+    """Populate ``kind``/``width``/``height`` on system_metadata for image refs.
+
+    Non-image MIME types are a no-op. Pre-existing keys (e.g. enricher-written
+    safetensors metadata, download provenance) are preserved by merge.
+    """
+    if not mime_type or not mime_type.startswith("image/"):
+        return
+
+    dims = extract_image_dimensions(file_path, mime_type=mime_type)
+    if not dims:
+        return
+
+    current = current_system_metadata or {}
+    merged = dict(current)
+    merged.update(dims)
+    if merged != current:
+        set_reference_system_metadata(
+            session,
+            reference_id=reference_id,
+            system_metadata=merged,
+        )
+
+
+def _backfill_image_dimensions_from_siblings(
+    session: Session,
+    asset_id: str,
+    new_reference_id: str,
+    current_system_metadata: dict | None,
+) -> None:
+    """Copy image dimension keys from any sibling reference of the same asset.
+
+    The from-hash path doesn't read the file bytes, so dimensions can't be
+    extracted there directly. When another reference of the same asset already
+    carries image dimensions, copy them onto the new reference so consumers
+    see consistent metadata regardless of how the asset was registered.
+
+    Best-effort: missing siblings, non-image siblings, or absent dimension
+    keys leave the target reference unchanged.
+    """
+    current = current_system_metadata or {}
+    if current.get("kind") == "image" and "width" in current and "height" in current:
+        return
+
+    for sibling in list_references_by_asset_id(session, asset_id):
+        if sibling.id == new_reference_id:
+            continue
+        meta = sibling.system_metadata or {}
+        if meta.get("kind") != "image":
+            continue
+        width = meta.get("width")
+        height = meta.get("height")
+        if (
+            type(width) is not int
+            or type(height) is not int
+            or width <= 0
+            or height <= 0
+        ):
+            continue
+        merged = dict(current)
+        merged["kind"] = "image"
+        merged["width"] = width
+        merged["height"] = height
+        if merged != current:
+            set_reference_system_metadata(
+                session,
+                reference_id=new_reference_id,
+                system_metadata=merged,
+            )
+        return
+
+
 def _sanitize_filename(name: str | None, fallback: str) -> str:
     n = os.path.basename((name or "").strip() or fallback)
     return n if n else fallback

comfy/cli_args.py

@@ -166,6 +166,8 @@ class PerformanceFeature(enum.Enum):
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
+parser.add_argument("--debug-hang", action="store_true", help="Enable stack trace dumps on Ctrl-C for debugging hangs.")
+
 parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
 
 parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")

comfy/image_encoders/dino2.py

@@ -1,7 +1,13 @@
 import torch
+import torch.nn.functional as F
+
 from comfy.text_encoders.bert import BertAttention
 import comfy.model_management
 from comfy.ldm.modules.attention import optimized_attention_for_device
+from comfy.ldm.depth_anything_3.reference_view_selector import (
+    select_reference_view, reorder_by_reference, restore_original_order,
+    THRESH_FOR_REF_SELECTION,
+)
 
 
 class Dino2AttentionOutput(torch.nn.Module):
@@ -14,13 +20,41 @@ class Dino2AttentionOutput(torch.nn.Module):
 
 
 class Dino2AttentionBlock(torch.nn.Module):
-    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations,
+                 qk_norm=False):
         super().__init__()
+        self.heads = heads
+        self.head_dim = embed_dim // heads
         self.attention = BertAttention(embed_dim, heads, dtype, device, operations)
         self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+        if qk_norm:
+            self.q_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
+            self.k_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
+        else:
+            self.q_norm = None
+            self.k_norm = None
 
-    def forward(self, x, mask, optimized_attention):
-        return self.output(self.attention(x, mask, optimized_attention))
+    def forward(self, x, mask, optimized_attention, pos=None, rope=None):
+        # Fast path used by the existing CLIP-vision DINOv2 (no DA3 extensions).
+        if self.q_norm is None and rope is None:
+            return self.output(self.attention(x, mask, optimized_attention))
+
+        # DA3 path: do QKV manually so we can apply per-head QK-norm and 2D RoPE.
+        attn = self.attention
+        B, N, C = x.shape
+        h = self.heads
+        d = self.head_dim
+        q = attn.query(x).view(B, N, h, d).transpose(1, 2)
+        k = attn.key(x).view(B, N, h, d).transpose(1, 2)
+        v = attn.value(x).view(B, N, h, d).transpose(1, 2)
+        if self.q_norm is not None:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+        if rope is not None and pos is not None:
+            q = rope(q, pos)
+            k = rope(k, pos)
+        out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
+        return self.output(out)
 
 
 class LayerScale(torch.nn.Module):
@@ -64,9 +98,11 @@ class SwiGLUFFN(torch.nn.Module):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn,
+                 qk_norm=False):
         super().__init__()
-        self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
+        self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations,
+                                             qk_norm=qk_norm)
         self.layer_scale1 = LayerScale(dim, dtype, device, operations)
         self.layer_scale2 = LayerScale(dim, dtype, device, operations)
         if use_swiglu_ffn:
@@ -76,19 +112,90 @@ class Dino2Block(torch.nn.Module):
         self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
         self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
-    def forward(self, x, optimized_attention):
-        x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))
+    def forward(self, x, optimized_attention, pos=None, rope=None, attn_mask=None):
+        x = x + self.layer_scale1(self.attention(self.norm1(x), attn_mask, optimized_attention,
+                                                 pos=pos, rope=rope))
         x = x + self.layer_scale2(self.mlp(self.norm2(x)))
         return x
 
 
-class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
+# -----------------------------------------------------------------------------
+# 2D Rotary position embedding (DA3 extension)
+# -----------------------------------------------------------------------------
+
+
+class _PositionGetter:
+    """Cache (h, w) -> flat (y, x) position grid used to feed ``rope``."""
+
+    def __init__(self):
+        self._cache: dict = {}
+
+    def __call__(self, batch_size: int, height: int, width: int, device) -> torch.Tensor:
+        key = (height, width, device)
+        if key not in self._cache:
+            y = torch.arange(height, device=device)
+            x = torch.arange(width, device=device)
+            self._cache[key] = torch.cartesian_prod(y, x)
+        cached = self._cache[key]
+        return cached.view(1, height * width, 2).expand(batch_size, -1, -1).clone()
+
+
+class RotaryPositionEmbedding2D(torch.nn.Module):
+    """2D RoPE used by DA3-Small/Base. No learnable parameters."""
+
+    def __init__(self, frequency: float = 100.0):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
-                                          for _ in range(num_layers)])
+        self.base_frequency = frequency
+        self._freq_cache: dict = {}
+
+    def _components(self, dim: int, seq_len: int, device, dtype):
+        key = (dim, seq_len, device, dtype)
+        if key not in self._freq_cache:
+            exp = torch.arange(0, dim, 2, device=device).float() / dim
+            inv_freq = 1.0 / (self.base_frequency ** exp)
+            pos = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            ang = torch.einsum("i,j->ij", pos, inv_freq)
+            ang = ang.to(dtype)
+            ang = torch.cat((ang, ang), dim=-1)
+            self._freq_cache[key] = (ang.cos().to(dtype), ang.sin().to(dtype))
+        return self._freq_cache[key]
+
+    @staticmethod
+    def _rotate(x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1]
+        x1, x2 = x[..., : d // 2], x[..., d // 2:]
+        return torch.cat((-x2, x1), dim=-1)
+
+    def _apply_1d(self, tokens, positions, cos_c, sin_c):
+        cos = F.embedding(positions, cos_c)[:, None, :, :]
+        sin = F.embedding(positions, sin_c)[:, None, :, :]
+        return (tokens * cos) + (self._rotate(tokens) * sin)
+
+    def forward(self, tokens: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
+        feature_dim = tokens.size(-1) // 2
+        max_pos = int(positions.max()) + 1
+        cos_c, sin_c = self._components(feature_dim, max_pos, tokens.device, tokens.dtype)
+        v, h = tokens.chunk(2, dim=-1)
+        v = self._apply_1d(v, positions[..., 0], cos_c, sin_c)
+        h = self._apply_1d(h, positions[..., 1], cos_c, sin_c)
+        return torch.cat((v, h), dim=-1)
+
+
+class Dino2Encoder(torch.nn.Module):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn,
+                 qknorm_start: int = -1):
+        super().__init__()
+        self.layer = torch.nn.ModuleList([
+            Dino2Block(
+                dim, num_heads, layer_norm_eps, dtype, device, operations,
+                use_swiglu_ffn=use_swiglu_ffn,
+                qk_norm=(qknorm_start != -1 and i >= qknorm_start),
+            )
+            for i in range(num_layers)
+        ])
 
     def forward(self, x, intermediate_output=None):
+        # Backward-compat path used by ``ClipVisionModel`` (no DA3 extensions).
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
 
         if intermediate_output is not None:
@@ -122,16 +229,27 @@ class Dino2PatchEmbeddings(torch.nn.Module):
 
 
 class Dino2Embeddings(torch.nn.Module):
-    def __init__(self, dim, dtype, device, operations):
+    def __init__(self, dim, dtype, device, operations,
+                 patch_size: int = 14, image_size: int = 518,
+                 use_mask_token: bool = True,
+                 num_camera_tokens: int = 0):
         super().__init__()
-        patch_size = 14
-        image_size = 518
         self.patch_size = patch_size
+        self.image_size = image_size
 
         self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
         self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
         self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
-        self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+        if use_mask_token:
+            self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+        else:
+            self.mask_token = None
+        if num_camera_tokens > 0:
+            # DA3 stores (ref_token, src_token) pairs that get injected at the
+            # alt-attn boundary; see ``Dinov2Model._inject_camera_token``.
+            self.camera_token = torch.nn.Parameter(torch.empty(1, num_camera_tokens, dim, dtype=dtype, device=device))
+        else:
+            self.camera_token = None
 
     def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
         pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
@@ -140,12 +258,22 @@ class Dino2Embeddings(torch.nn.Module):
         patch_pos = pos_embed[:, 1:]
         N = patch_pos.shape[1]
         M = int(N ** 0.5)
+        assert N == M * M, f"DINOv2 position grid must be square, got N={N} patches (sqrt={M})"
         h0 = h_pixels // self.patch_size
         w0 = w_pixels // self.patch_size
-        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)  # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+        # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+        # scale_factor is (height_scale, width_scale) -- height MUST come first;
+        # swapping these only happens to work for square inputs and breaks
+        # non-square paths like DA3-Small / DA3-Base multi-view.
+        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)
 
         patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
         patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
+        assert (h0, w0) == patch_pos.shape[-2:], (
+            f"Interpolated pos-embed grid {tuple(patch_pos.shape[-2:])} does not match "
+            f"target patch grid ({h0}, {w0}) for input {h_pixels}x{w_pixels} (patch_size={self.patch_size}); "
+            f"check scale_factor axis order and +0.1 rounding workaround"
+        )
         patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
         return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
 
@@ -168,12 +296,51 @@ class Dinov2Model(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         layer_norm_eps = config_dict["layer_norm_eps"]
         use_swiglu_ffn = config_dict["use_swiglu_ffn"]
+        patch_size = config_dict.get("patch_size", 14)
+        image_size = config_dict.get("image_size", 518)
+        use_mask_token = config_dict.get("use_mask_token", True)
 
-        self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
-        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
+        # DA3 extensions (all default to disabled).
+        self.alt_start = config_dict.get("alt_start", -1)
+        self.qknorm_start = config_dict.get("qknorm_start", -1)
+        self.rope_start = config_dict.get("rope_start", -1)
+        self.cat_token = config_dict.get("cat_token", False)
+        rope_freq = config_dict.get("rope_freq", 100.0)
+
+        self.embed_dim = dim
+        self.patch_size = patch_size
+        self.num_register_tokens = 0
+        self.patch_start_idx = 1
+
+        if self.rope_start != -1 and rope_freq > 0:
+            self.rope = RotaryPositionEmbedding2D(frequency=rope_freq)
+            self._position_getter = _PositionGetter()
+        else:
+            self.rope = None
+            self._position_getter = None
+
+        # camera_token shape: (1, 2, dim) -> (ref_token, src_token).
+        num_cam_tokens = 2 if self.alt_start != -1 else 0
+
+        self.embeddings = Dino2Embeddings(
+            dim, dtype, device, operations,
+            patch_size=patch_size, image_size=image_size,
+            use_mask_token=use_mask_token, num_camera_tokens=num_cam_tokens,
+        )
+        self.encoder = Dino2Encoder(
+            dim, heads, layer_norm_eps, num_layers, dtype, device, operations,
+            use_swiglu_ffn=use_swiglu_ffn,
+            qknorm_start=self.qknorm_start,
+        )
         self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
     def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
+        if self.alt_start != -1:
+            raise RuntimeError(
+                "Dinov2Model.forward() is the backward-compatible CLIP-vision path and does not "
+                "apply DA3 extensions (RoPE, alternating attention, camera-token injection). "
+                "Use get_intermediate_layers_da3() for Depth Anything 3 models."
+            )
         x = self.embeddings(pixel_values)
         x, i = self.encoder(x, intermediate_output=intermediate_output)
         x = self.layernorm(x)
@@ -181,6 +348,7 @@ class Dinov2Model(torch.nn.Module):
         return x, i, pooled_output, None
 
     def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
+        """Single-view multi-layer feature extraction."""
         x = self.embeddings(pixel_values)
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
         n_layers = len(self.encoder.layer)
@@ -197,3 +365,132 @@ class Dinov2Model(torch.nn.Module):
             if i >= max_idx:
                 break
         return [cache[i] for i in resolved]
+
+    # ------------------------------------------------------------------
+    # Depth Anything 3 forward
+    # ------------------------------------------------------------------
+    def _prepare_rope_positions(self, B, S, H, W, device):
+        if self.rope is None:
+            return None, None
+        ph, pw = H // self.patch_size, W // self.patch_size
+        pos = self._position_getter(B * S, ph, pw, device=device)
+        # Shift so the cls/cam token at position 0 is reserved for "no diff".
+        pos = pos + 1
+        cls_pos = torch.zeros(B * S, self.patch_start_idx, 2, device=device, dtype=pos.dtype)
+        # Per-view local: real grid positions for patches, 0 for cls token.
+        pos_local = torch.cat([cls_pos, pos], dim=1)
+        # Global (across views): same grid positions; cls token still at 0,
+        # but patches share the same positions in every view.
+        pos_global = torch.cat([cls_pos, torch.zeros_like(pos) + 1], dim=1)
+        return pos_local, pos_global
+
+    def _inject_camera_token(self, x: torch.Tensor, B: int, S: int, cam_token: "torch.Tensor | None") -> torch.Tensor:
+        # x: (B, S, N, C). Replace token at index 0 with the camera token.
+        if cam_token is not None:
+            inj = cam_token
+        else:
+            ct = comfy.model_management.cast_to_device(self.embeddings.camera_token, x.device, x.dtype)
+            ref_token = ct[:, :1].expand(B, -1, -1)
+            src_token = ct[:, 1:].expand(B, max(S - 1, 0), -1)
+            inj = torch.cat([ref_token, src_token], dim=1)
+        x = x.clone()
+        x[:, :, 0] = inj
+        return x
+
+    def get_intermediate_layers_da3(self, pixel_values, out_layers, cam_token=None, ref_view_strategy="saddle_balanced", export_feat_layers=None):
+        """Multi-view multi-layer feature extraction used by Depth Anything 3."""
+        if pixel_values.ndim == 4:
+            pixel_values = pixel_values.unsqueeze(1)
+        assert pixel_values.ndim == 5 and pixel_values.shape[2] == 3, \
+            f"expected (B,3,H,W) or (B,S,3,H,W); got {tuple(pixel_values.shape)}"
+        B, S, _, H, W = pixel_values.shape
+
+        # Patch + cls + (interpolated) pos embed for each view.
+        x = pixel_values.reshape(B * S, 3, H, W)
+        x = self.embeddings(x)                          # (B*S, 1+N, C)
+        x = x.reshape(B, S, x.shape[-2], x.shape[-1])    # (B, S, 1+N, C)
+
+        pos_local, pos_global = self._prepare_rope_positions(B, S, H, W, x.device)
+        # optimized_attention is only used by blocks without QK-norm/RoPE
+        # (vanilla DINOv2 path); enabling-aware blocks fall through to SDPA.
+        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+
+        out_set = set(out_layers)
+        export_set = set(export_feat_layers) if export_feat_layers else set()
+        outputs: list[torch.Tensor] = []
+        aux_outputs: list[torch.Tensor] = []
+        local_x = x
+        b_idx = None
+
+
+        for i, blk in enumerate(self.encoder.layer):
+            apply_rope = self.rope is not None and i >= self.rope_start
+            block_rope = self.rope if apply_rope else None
+            l_pos = pos_local if apply_rope else None
+            g_pos = pos_global if apply_rope else None
+
+            # Reference-view selection threshold: matches the upstream constant
+            # THRESH_FOR_REF_SELECTION = 3. Skipped when a user-supplied
+            # cam_token is provided (camera info already pins the geometry).
+            if (self.alt_start != -1 and i == self.alt_start - 1 and S >= THRESH_FOR_REF_SELECTION and cam_token is None):
+                b_idx = select_reference_view(x, strategy=ref_view_strategy)
+                x = reorder_by_reference(x, b_idx)
+                local_x = reorder_by_reference(local_x, b_idx)
+
+            if self.alt_start != -1 and i == self.alt_start:
+                x = self._inject_camera_token(x, B, S, cam_token)
+
+            if self.alt_start != -1 and i >= self.alt_start and (i % 2 == 1):
+                # Global attention across views: flatten S into the seq dim.
+                t = x.reshape(B, S * x.shape[-2], x.shape[-1])
+                p = g_pos.reshape(B, S * g_pos.shape[-2], g_pos.shape[-1]) if g_pos is not None else None
+                t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
+                x = t.reshape(B, S, x.shape[-2], x.shape[-1])
+            else:
+                # Per-view local attention.
+                t = x.reshape(B * S, x.shape[-2], x.shape[-1])
+                p = l_pos.reshape(B * S, l_pos.shape[-2], l_pos.shape[-1]) if l_pos is not None else None
+                t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
+                x = t.reshape(B, S, x.shape[-2], x.shape[-1])
+                local_x = x
+
+            if i in out_set:
+                if self.cat_token:
+                    out_x = torch.cat([local_x, x], dim=-1)
+                else:
+                    out_x = x
+                # Restore original view order on the way out so heads see views
+                # in the user's expected order.
+                if b_idx is not None and self.alt_start != -1:
+                    out_x = restore_original_order(out_x, b_idx)
+                outputs.append(out_x)
+
+            if i in export_set:
+                aux = x
+                if b_idx is not None and self.alt_start != -1:
+                    aux = restore_original_order(aux, b_idx)
+                aux_outputs.append(aux)
+
+        # Apply final norm. When cat_token is set, only the right half
+        # ("global" features) is normalised; the left half is left as-is to
+        # match the upstream DA3 head signature.
+        normed: list[torch.Tensor] = []
+        cls_tokens: list[torch.Tensor] = []
+        for out_x in outputs:
+            cls_tokens.append(out_x[:, :, 0])
+            if out_x.shape[-1] == self.embed_dim:
+                normed.append(self.layernorm(out_x))
+            elif out_x.shape[-1] == self.embed_dim * 2:
+                left = out_x[..., :self.embed_dim]
+                right = self.layernorm(out_x[..., self.embed_dim:])
+                normed.append(torch.cat([left, right], dim=-1))
+            else:
+                raise ValueError(f"Unexpected token width: {out_x.shape[-1]}")
+
+        # Drop cls/cam token from the patch sequence.
+        normed = [o[..., 1 + self.num_register_tokens:, :] for o in normed]
+
+        # Final layernorm + drop cls token from auxiliary features too.
+        aux_normed = [self.layernorm(o)[..., 1 + self.num_register_tokens:, :]
+                      for o in aux_outputs]
+        return list(zip(normed, cls_tokens)), aux_normed

comfy/ldm/colormap.py

@@ -0,0 +1,25 @@
+"""Colormap utilities for depth and geometry visualisation."""
+
+from __future__ import annotations
+
+import torch
+
+
+def turbo(x: torch.Tensor) -> torch.Tensor:
+    """Anton Mikhailov polynomial approximation of the Turbo colormap.
+
+    Args:
+        x: Float tensor with values in [0, 1].
+
+    Returns:
+        RGB tensor of the same shape as ``x`` with a trailing size-3 dimension.
+    """
+    x = x.clamp(0.0, 1.0)
+    x2 = x * x
+    x3 = x2 * x
+    x4 = x2 * x2
+    x5 = x4 * x
+    r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
+    g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 +   4.27729857*x4 +  2.82956604*x5
+    b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
+    return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)

comfy/ldm/depth_anything_3/camera.py

@@ -0,0 +1,177 @@
+"""Camera-token encoder and decoder for Depth Anything 3."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention_for_device
+from .transform import affine_inverse, extri_intri_to_pose_encoding
+
+
+# -----------------------------------------------------------------------
+# Building blocks (mirror depth_anything_3.model.utils.{attention,block})
+# -----------------------------------------------------------------------
+
+
+class _Mlp(nn.Module):
+    """Standard 2-layer MLP with GELU. Matches upstream ``utils.attention.Mlp``."""
+
+    def __init__(self, in_features, hidden_features=None, out_features=None, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, device=device, dtype=dtype)
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.fc2(F.gelu(self.fc1(x)))
+
+
+class _LayerScale(nn.Module):
+    """Per-channel learnable scaling. Matches upstream LayerScale."""
+
+    def __init__(self, dim, *, device=None, dtype=None):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+
+    def forward(self, x):
+        return x * self.gamma.to(dtype=x.dtype, device=x.device)
+
+
+class _Attention(nn.Module):
+    """ Self-attention with fused QKV projection. Mirrors upstream utils.attention.Attention;
+    Layout matches the HF safetensors (attn.qkv.{weight,bias} and attn.proj.{weight,bias})."""
+
+    def __init__(self, dim, num_heads, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        assert dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.qkv = operations.Linear(dim, dim * 3, bias=True, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, C)
+        q, k, v = qkv.unbind(2)                      # each (B, N, C)
+        attn_fn = optimized_attention_for_device(x.device, small_input=True)
+        out = attn_fn(q, k, v, heads=self.num_heads)
+        return self.proj(out)
+
+
+class _Block(nn.Module):
+    """Pre-norm transformer block with LayerScale. Used by :class:CameraEnc. Layout follows upstream utils.block.Block."""
+
+    def __init__(self, dim, num_heads, mlp_ratio=4, init_values=0.01, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.attn = _Attention(dim, num_heads, device=device, dtype=dtype, operations=operations)
+        self.ls1 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
+        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.mlp = _Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), device=device, dtype=dtype, operations=operations)
+        self.ls2 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
+
+    def forward(self, x):
+        x = x + self.ls1(self.attn(self.norm1(x)))
+        x = x + self.ls2(self.mlp(self.norm2(x)))
+        return x
+
+
+class CameraEnc(nn.Module):
+    """Encode per-view (extrinsics, intrinsics) into a camera token.
+
+    Maps a 9-D pose-encoding vector through a small MLP up to the backbone's
+    ``embed_dim``, then runs ``trunk_depth`` transformer blocks. The output
+    has shape ``(B, S, embed_dim)`` and is injected at block ``alt_start``
+    of the DINOv2 backbone in place of the cls token.
+
+    Parameters mirror the upstream ``cam_enc.py`` so HF weights load directly.
+    """
+
+    def __init__(
+        self,
+        dim_out: int = 1024,
+        dim_in: int = 9,
+        trunk_depth: int = 4,
+        target_dim: int = 9,
+        num_heads: int = 16,
+        mlp_ratio: int = 4,
+        init_values: float = 0.01,
+        *,
+        device=None, dtype=None, operations=None,
+        **_kwargs,
+    ):
+        super().__init__()
+        self.target_dim = target_dim
+        self.trunk_depth = trunk_depth
+        self.trunk = nn.Sequential(*[
+            _Block(dim_out, num_heads=num_heads, mlp_ratio=mlp_ratio,
+                   init_values=init_values,
+                   device=device, dtype=dtype, operations=operations)
+            for _ in range(trunk_depth)
+        ])
+        self.token_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
+        self.trunk_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
+        self.pose_branch = _Mlp(
+            in_features=dim_in,
+            hidden_features=dim_out // 2,
+            out_features=dim_out,
+            device=device, dtype=dtype, operations=operations,
+        )
+
+    def forward(self, extrinsics: torch.Tensor, intrinsics: torch.Tensor,
+                image_size_hw) -> torch.Tensor:
+        """Encode camera parameters into ``(B, S, dim_out)`` tokens."""
+        c2ws = affine_inverse(extrinsics)
+        pose_encoding = extri_intri_to_pose_encoding(c2ws, intrinsics, image_size_hw)
+        tokens = self.pose_branch(pose_encoding.to(self.pose_branch.fc1.weight.dtype))
+        tokens = self.token_norm(tokens)
+        tokens = self.trunk(tokens)
+        tokens = self.trunk_norm(tokens)
+        return tokens
+
+
+class CameraDec(nn.Module):
+    """Decode the final cam token into a 9-D pose encoding.
+
+    Output layout: ``[T(3), quat_xyzw(4), fov_h, fov_w]``. The translation is
+    always predicted by the network; the quaternion and FoV can either be
+    predicted or supplied via ``camera_encoding`` (used at training time
+    when GT cameras are available -- not exercised at inference here).
+
+    Parameters mirror the upstream ``cam_dec.py`` so HF weights load directly.
+    """
+
+    def __init__(self, dim_in: int = 1536,
+                 *, device=None, dtype=None, operations=None, **_kwargs):
+        super().__init__()
+        d = dim_in
+        self.backbone = nn.Sequential(
+            operations.Linear(d, d, device=device, dtype=dtype),
+            nn.ReLU(),
+            operations.Linear(d, d, device=device, dtype=dtype),
+            nn.ReLU(),
+        )
+        self.fc_t = operations.Linear(d, 3, device=device, dtype=dtype)
+        self.fc_qvec = operations.Linear(d, 4, device=device, dtype=dtype)
+        self.fc_fov = nn.Sequential(
+            operations.Linear(d, 2, device=device, dtype=dtype),
+            nn.ReLU(),
+        )
+
+    def forward(self, feat: torch.Tensor,
+                camera_encoding: "torch.Tensor | None" = None) -> torch.Tensor:
+        """Decode ``(B, N, dim_in)`` cam tokens into ``(B, N, 9)`` pose enc."""
+        B, N = feat.shape[:2]
+        feat = feat.reshape(B * N, -1)
+        feat = self.backbone(feat)
+        out_t = self.fc_t(feat.float()).reshape(B, N, 3)
+        if camera_encoding is None:
+            out_qvec = self.fc_qvec(feat.float()).reshape(B, N, 4)
+            out_fov = self.fc_fov(feat.float()).reshape(B, N, 2)
+        else:
+            out_qvec = camera_encoding[..., 3:7]
+            out_fov = camera_encoding[..., -2:]
+        return torch.cat([out_t, out_qvec, out_fov], dim=-1)

comfy/ldm/depth_anything_3/dpt.py

@@ -0,0 +1,489 @@
+"""DPT / DualDPT heads for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import List, Optional, Sequence, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Permute(nn.Module):
+    def __init__(self, dims: Tuple[int, ...]):
+        super().__init__()
+        self.dims = dims
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.permute(*self.dims)
+
+
+def _custom_interpolate(
+    x: torch.Tensor,
+    size: Optional[Tuple[int, int]] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "bilinear",
+    align_corners: bool = True,
+) -> torch.Tensor:
+    if size is None:
+        assert scale_factor is not None
+        size = (int(x.shape[-2] * scale_factor), int(x.shape[-1] * scale_factor))
+    INT_MAX = 1610612736
+    total = size[0] * size[1] * x.shape[0] * x.shape[1]
+    if total > INT_MAX:
+        chunks = torch.chunk(x, chunks=(total // INT_MAX) + 1, dim=0)
+        outs = [F.interpolate(c, size=size, mode=mode, align_corners=align_corners) for c in chunks]
+        return torch.cat(outs, dim=0).contiguous()
+    return F.interpolate(x, size=size, mode=mode, align_corners=align_corners)
+
+
+def _create_uv_grid(width: int, height: int, aspect_ratio: float, dtype, device) -> torch.Tensor:
+    """Normalised UV grid spanning (-x_span, -y_span)..(x_span, y_span)."""
+    diag_factor = (aspect_ratio ** 2 + 1.0) ** 0.5
+    span_x = aspect_ratio / diag_factor
+    span_y = 1.0 / diag_factor
+    left_x = -span_x * (width - 1) / width
+    right_x = span_x * (width - 1) / width
+    top_y = -span_y * (height - 1) / height
+    bottom_y = span_y * (height - 1) / height
+    x_coords = torch.linspace(left_x, right_x, steps=width, dtype=dtype, device=device)
+    y_coords = torch.linspace(top_y, bottom_y, steps=height, dtype=dtype, device=device)
+    uu, vv = torch.meshgrid(x_coords, y_coords, indexing="xy")
+    return torch.stack((uu, vv), dim=-1)  # (H, W, 2)
+
+
+def _make_sincos_pos_embed(embed_dim: int, pos: torch.Tensor, omega_0: float = 100.0) -> torch.Tensor:
+    omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)
+    omega = 1.0 / omega_0 ** (omega / (embed_dim / 2.0))
+    pos = pos.reshape(-1)
+    out = torch.einsum("m,d->md", pos, omega)
+    return torch.cat([out.sin(), out.cos()], dim=1).float()
+
+
+def _position_grid_to_embed(pos_grid: torch.Tensor, embed_dim: int, omega_0: float = 100.0) -> torch.Tensor:
+    H, W, _ = pos_grid.shape
+    pos_flat = pos_grid.reshape(-1, 2)
+    emb_x = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 0], omega_0=omega_0)
+    emb_y = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 1], omega_0=omega_0)
+    emb = torch.cat([emb_x, emb_y], dim=-1)
+    return emb.view(H, W, embed_dim)
+
+
+def _add_pos_embed(x: torch.Tensor, W: int, H: int, ratio: float = 0.1) -> torch.Tensor:
+    """Stateless UV positional embedding added to a feature map (B, C, h, w)."""
+    pw, ph = x.shape[-1], x.shape[-2]
+    pe = _create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device)
+    pe = _position_grid_to_embed(pe, x.shape[1]) * ratio
+    pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1).to(dtype=x.dtype)
+    return x + pe
+
+
+def _apply_activation(x: torch.Tensor, activation: str) -> torch.Tensor:
+    act = (activation or "linear").lower()
+    if act == "exp":
+        return torch.exp(x)
+    if act == "expp1":
+        return torch.exp(x) + 1
+    if act == "expm1":
+        return torch.expm1(x)
+    if act == "relu":
+        return torch.relu(x)
+    if act == "sigmoid":
+        return torch.sigmoid(x)
+    if act == "softplus":
+        return F.softplus(x)
+    if act == "tanh":
+        return torch.tanh(x)
+    return x
+
+
+# -----------------------------------------------------------------------------
+# Fusion building blocks
+# -----------------------------------------------------------------------------
+
+
+class ResidualConvUnit(nn.Module):
+    def __init__(self, features: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.conv1 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
+        self.conv2 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
+        self.activation = nn.ReLU(inplace=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.activation(x)
+        out = self.conv1(out)
+        out = self.activation(out)
+        out = self.conv2(out)
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    def __init__(self, features: int, has_residual: bool = True, align_corners: bool = True, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.align_corners = align_corners
+        self.has_residual = has_residual
+        if has_residual:
+            self.resConfUnit1 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
+        else:
+            self.resConfUnit1 = None
+        self.resConfUnit2 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
+        self.out_conv = operations.Conv2d(features, features, 1, 1, 0, bias=True, device=device, dtype=dtype)
+
+    def forward(self, *xs: torch.Tensor, size: Optional[Tuple[int, int]] = None) -> torch.Tensor:
+        y = xs[0]
+        if self.has_residual and len(xs) > 1 and self.resConfUnit1 is not None:
+            y = y + self.resConfUnit1(xs[1])
+        y = self.resConfUnit2(y)
+        if size is None:
+            up_kwargs = {"scale_factor": 2.0}
+        else:
+            up_kwargs = {"size": size}
+        y = _custom_interpolate(y, **up_kwargs, mode="bilinear", align_corners=self.align_corners)
+        y = self.out_conv(y)
+        return y
+
+
+class _Scratch(nn.Module):
+    """Container that mirrors upstream ``scratch`` attribute layout."""
+
+
+def _make_scratch(in_shape: List[int], out_shape: int, device=None, dtype=None, operations=None) -> _Scratch:
+    scratch = _Scratch()
+    scratch.layer1_rn = operations.Conv2d(in_shape[0], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer2_rn = operations.Conv2d(in_shape[1], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer3_rn = operations.Conv2d(in_shape[2], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer4_rn = operations.Conv2d(in_shape[3], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    return scratch
+
+
+def _make_fusion_block(features: int, has_residual: bool = True, device=None, dtype=None, operations=None) -> FeatureFusionBlock:
+    return FeatureFusionBlock(features, has_residual=has_residual, align_corners=True, device=device, dtype=dtype, operations=operations)
+
+
+# -----------------------------------------------------------------------------
+# DPT (single head + optional sky head) -- used by DA3Mono/Metric
+# -----------------------------------------------------------------------------
+
+
+class DPT(nn.Module):
+    """Single-head DPT used by DA3Mono-Large and DA3Metric-Large."""
+
+    def __init__(
+        self,
+        dim_in: int,
+        patch_size: int = 14,
+        output_dim: int = 1,
+        activation: str = "exp",
+        conf_activation: str = "expp1",
+        features: int = 256,
+        out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        pos_embed: bool = False,
+        down_ratio: int = 1,
+        head_name: str = "depth",
+        use_sky_head: bool = True,
+        sky_name: str = "sky",
+        sky_activation: str = "relu",
+        norm_type: str = "idt",
+        device=None, dtype=None, operations=None,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.activation = activation
+        self.conf_activation = conf_activation
+        self.pos_embed = pos_embed
+        self.down_ratio = down_ratio
+        self.head_main = head_name
+        self.sky_name = sky_name
+        self.out_dim = output_dim
+        self.has_conf = output_dim > 1
+        self.use_sky_head = use_sky_head
+        self.sky_activation = sky_activation
+        self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
+
+        if norm_type == "layer":
+            self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
+        else:
+            self.norm = nn.Identity()
+
+        out_channels = list(out_channels)
+        self.projects = nn.ModuleList([
+            operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
+            for oc in out_channels
+        ])
+        self.resize_layers = nn.ModuleList([
+            operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
+            operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
+            nn.Identity(),
+            operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
+        ])
+
+        self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+
+        head_features_1 = features
+        head_features_2 = 32
+        self.scratch.output_conv1 = operations.Conv2d(
+            head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
+            device=device, dtype=dtype,
+        )
+        self.scratch.output_conv2 = nn.Sequential(
+            operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+            nn.ReLU(inplace=False),
+            operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+        )
+
+        if self.use_sky_head:
+            self.scratch.sky_output_conv2 = nn.Sequential(
+                operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+                nn.ReLU(inplace=False),
+                operations.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+            )
+
+    def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
+        # feats[i][0] is the patch-token tensor with shape (B, S, N_patch, C)
+        B, S, N, C = feats[0][0].shape
+        feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
+
+        ph, pw = H // self.patch_size, W // self.patch_size
+        resized = []
+        for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
+            x = feats_flat[take_idx][:, patch_start_idx:]
+            x = self.norm(x)
+            x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
+            x = self.projects[stage_idx](x)
+            if self.pos_embed:
+                x = _add_pos_embed(x, W, H)
+            x = self.resize_layers[stage_idx](x)
+            resized.append(x)
+
+        l1_rn = self.scratch.layer1_rn(resized[0])
+        l2_rn = self.scratch.layer2_rn(resized[1])
+        l3_rn = self.scratch.layer3_rn(resized[2])
+        l4_rn = self.scratch.layer4_rn(resized[3])
+
+        out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
+        out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:])
+        out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:])
+        out = self.scratch.refinenet1(out, l1_rn)
+
+        h_out = int(ph * self.patch_size / self.down_ratio)
+        w_out = int(pw * self.patch_size / self.down_ratio)
+
+        fused = self.scratch.output_conv1(out)
+        fused = _custom_interpolate(fused, (h_out, w_out), mode="bilinear", align_corners=True)
+        if self.pos_embed:
+            fused = _add_pos_embed(fused, W, H)
+        feat = fused
+
+        main_logits = self.scratch.output_conv2(feat)
+        outs = {}
+        if self.has_conf:
+            fmap = main_logits.permute(0, 2, 3, 1)
+            pred = _apply_activation(fmap[..., :-1], self.activation)
+            conf = _apply_activation(fmap[..., -1], self.conf_activation)
+            outs[self.head_main] = pred.squeeze(-1).view(B, S, *pred.shape[1:-1])
+            outs[f"{self.head_main}_conf"] = conf.view(B, S, *conf.shape[1:])
+        else:
+            pred = _apply_activation(main_logits, self.activation)
+            outs[self.head_main] = pred.squeeze(1).view(B, S, *pred.shape[2:])
+
+        if self.use_sky_head:
+            sky_logits = self.scratch.sky_output_conv2(feat)
+            if self.sky_activation.lower() == "sigmoid":
+                sky = torch.sigmoid(sky_logits)
+            elif self.sky_activation.lower() == "relu":
+                sky = F.relu(sky_logits)
+            else:
+                sky = sky_logits
+            outs[self.sky_name] = sky.squeeze(1).view(B, S, *sky.shape[2:])
+
+        return outs
+
+
+# -----------------------------------------------------------------------------
+# DualDPT (depth + auxiliary "ray" head) -- used by DA3-Small / DA3-Base
+# -----------------------------------------------------------------------------
+
+
+class DualDPT(nn.Module):
+    """Two-head DPT used by DA3-Small / DA3-Base."""
+
+    def __init__(
+        self,
+        dim_in: int,
+        patch_size: int = 14,
+        output_dim: int = 2,
+        activation: str = "exp",
+        conf_activation: str = "expp1",
+        features: int = 256,
+        out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        pos_embed: bool = True,
+        down_ratio: int = 1,
+        aux_pyramid_levels: int = 4,
+        aux_out1_conv_num: int = 5,
+        head_names: Tuple[str, str] = ("depth", "ray"),
+        device=None, dtype=None, operations=None,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.activation = activation
+        self.conf_activation = conf_activation
+        self.pos_embed = pos_embed
+        self.down_ratio = down_ratio
+        self.aux_levels = aux_pyramid_levels
+        self.aux_out1_conv_num = aux_out1_conv_num
+        self.head_main, self.head_aux = head_names
+        self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
+        # Toggle the auxiliary ray branch at runtime. Default off (mono path).
+        # DepthAnything3Net flips this on when running multi-view + ray-pose.
+        self.enable_aux: bool = False
+
+        self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
+        out_channels = list(out_channels)
+        self.projects = nn.ModuleList([
+            operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
+            for oc in out_channels
+        ])
+        self.resize_layers = nn.ModuleList([
+            operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
+            operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
+            nn.Identity(),
+            operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
+        ])
+
+        self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
+        # Main fusion chain
+        self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+        # Auxiliary fusion chain (separate copies)
+        self.scratch.refinenet1_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+
+        head_features_1 = features
+        head_features_2 = 32
+
+        # Main head neck + final projection
+        self.scratch.output_conv1 = operations.Conv2d(
+            head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
+            device=device, dtype=dtype,
+        )
+        self.scratch.output_conv2 = nn.Sequential(
+            operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+            nn.ReLU(inplace=False),
+            operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+        )
+
+        # Aux pre-head per level (multi-level pyramid)
+        self.scratch.output_conv1_aux = nn.ModuleList([
+            self._make_aux_out1_block(head_features_1, device=device, dtype=dtype, operations=operations)
+            for _ in range(self.aux_levels)
+        ])
+
+        # Aux final projection per level (includes LayerNorm permute path).
+        ln_seq = [Permute((0, 2, 3, 1)),
+                  operations.LayerNorm(head_features_2, device=device, dtype=dtype),
+                  Permute((0, 3, 1, 2))]
+        self.scratch.output_conv2_aux = nn.ModuleList([
+            nn.Sequential(
+                operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+                *ln_seq,
+                nn.ReLU(inplace=False),
+                operations.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+            )
+            for _ in range(self.aux_levels)
+        ])
+
+    @staticmethod
+    def _make_aux_out1_block(in_ch: int, *, device=None, dtype=None, operations=None) -> nn.Sequential:
+        # aux_out1_conv_num=5 in all Apache-2.0 variants.
+        return nn.Sequential(
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+        )
+
+    def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
+        B, S, N, C = feats[0][0].shape
+        feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
+
+        ph, pw = H // self.patch_size, W // self.patch_size
+        resized = []
+        for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
+            x = feats_flat[take_idx][:, patch_start_idx:]
+            x = self.norm(x)
+            x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
+            x = self.projects[stage_idx](x)
+            if self.pos_embed:
+                x = _add_pos_embed(x, W, H)
+            x = self.resize_layers[stage_idx](x)
+            resized.append(x)
+
+        l1_rn = self.scratch.layer1_rn(resized[0])
+        l2_rn = self.scratch.layer2_rn(resized[1])
+        l3_rn = self.scratch.layer3_rn(resized[2])
+        l4_rn = self.scratch.layer4_rn(resized[3])
+
+        # Main pyramid (output_conv1 is applied inside the upstream `_fuse`,
+        # before interpolation -- replicate that order here).
+        m = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
+        if self.enable_aux:
+            a4 = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:])
+            aux_pyr = [a4]
+        m = self.scratch.refinenet3(m, l3_rn, size=l2_rn.shape[2:])
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet3_aux(aux_pyr[-1], l3_rn, size=l2_rn.shape[2:]))
+        m = self.scratch.refinenet2(m, l2_rn, size=l1_rn.shape[2:])
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet2_aux(aux_pyr[-1], l2_rn, size=l1_rn.shape[2:]))
+        m = self.scratch.refinenet1(m, l1_rn)
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet1_aux(aux_pyr[-1], l1_rn))
+        m = self.scratch.output_conv1(m)
+
+        h_out = int(ph * self.patch_size / self.down_ratio)
+        w_out = int(pw * self.patch_size / self.down_ratio)
+
+        m = _custom_interpolate(m, (h_out, w_out), mode="bilinear", align_corners=True)
+        if self.pos_embed:
+            m = _add_pos_embed(m, W, H)
+        main_logits = self.scratch.output_conv2(m)
+        fmap = main_logits.permute(0, 2, 3, 1)
+        depth_pred = _apply_activation(fmap[..., :-1], self.activation)
+        depth_conf = _apply_activation(fmap[..., -1], self.conf_activation)
+
+        outs = {
+            self.head_main: depth_pred.squeeze(-1).view(B, S, *depth_pred.shape[1:-1]),
+            f"{self.head_main}_conf": depth_conf.view(B, S, *depth_conf.shape[1:]),
+        }
+
+        if self.enable_aux:
+            # Auxiliary "ray" head (multi-level inside) -- only the last level
+            # is returned. Mirrors upstream ``DualDPT._fuse`` + ``_forward_impl``:
+            # each aux pyramid level goes through ``output_conv1_aux[i]``
+            # (5-layer conv stack that ends at ``features // 2`` channels),
+            # then the last level optionally gets a pos-embed and finally
+            # ``output_conv2_aux[-1]``.
+            aux_processed = [
+                self.scratch.output_conv1_aux[i](a) for i, a in enumerate(aux_pyr)
+            ]
+            last_aux = aux_processed[-1]
+            if self.pos_embed:
+                last_aux = _add_pos_embed(last_aux, W, H)
+            last_aux_logits = self.scratch.output_conv2_aux[-1](last_aux)
+            fmap_last = last_aux_logits.permute(0, 2, 3, 1)
+            # Channels: [ray(6), ray_conf(1)]; ray uses 'linear' activation.
+            aux_pred = fmap_last[..., :-1]
+            aux_conf = _apply_activation(fmap_last[..., -1], self.conf_activation)
+            outs[self.head_aux] = aux_pred.view(B, S, *aux_pred.shape[1:])
+            outs[f"{self.head_aux}_conf"] = aux_conf.view(B, S, *aux_conf.shape[1:])
+
+        return outs

comfy/ldm/depth_anything_3/model.py

@@ -0,0 +1,236 @@
+from __future__ import annotations
+
+from typing import Dict, Optional, Sequence
+
+import torch
+import torch.nn as nn
+
+from comfy.image_encoders.dino2 import Dinov2Model
+
+from .camera import CameraDec, CameraEnc
+from .dpt import DPT, DualDPT
+from .ray_pose import get_extrinsic_from_camray
+from .transform import affine_inverse, pose_encoding_to_extri_intri
+
+
+_HEAD_REGISTRY = {
+    "dpt": DPT,
+    "dualdpt": DualDPT,
+}
+
+
+# Backbone presets (mirror the upstream DINOv2 ViT variants).
+_BACKBONE_PRESETS = {
+    "vits": dict(hidden_size=384,  num_hidden_layers=12, num_attention_heads=6,  use_swiglu_ffn=False),
+    "vitb": dict(hidden_size=768,  num_hidden_layers=12, num_attention_heads=12, use_swiglu_ffn=False),
+    "vitl": dict(hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, use_swiglu_ffn=False),
+    "vitg": dict(hidden_size=1536, num_hidden_layers=40, num_attention_heads=24, use_swiglu_ffn=True),
+}
+
+
+def _build_backbone_config(
+    backbone_name: str,
+    *,
+    alt_start: int,
+    qknorm_start: int,
+    rope_start: int,
+    cat_token: bool,
+) -> dict:
+    if backbone_name not in _BACKBONE_PRESETS:
+        raise ValueError(f"Unknown DINOv2 backbone variant: {backbone_name!r}")
+    cfg = dict(_BACKBONE_PRESETS[backbone_name])
+    cfg.update(dict(
+        layer_norm_eps=1e-6,
+        patch_size=14,
+        image_size=518,
+        # No mask_token in DA3 weights; omit param to avoid load warnings.
+        use_mask_token=False,
+        alt_start=alt_start,
+        qknorm_start=qknorm_start,
+        rope_start=rope_start,
+        cat_token=cat_token,
+        rope_freq=100.0,
+    ))
+    return cfg
+
+
+class DepthAnything3Net(nn.Module):
+
+    PATCH_SIZE = 14
+
+    def __init__(
+        self,
+        # --- Backbone ---
+        backbone_name: str = "vitl",
+        out_layers: Sequence[int] = (4, 11, 17, 23),
+        alt_start: int = -1,
+        qknorm_start: int = -1,
+        rope_start: int = -1,
+        cat_token: bool = False,
+        # --- Head ---
+        head_type: str = "dpt",  # dpt or dualdpt
+        head_dim_in: int = 1024,
+        head_output_dim: int = 1,  # 1 = depth only, 2 = depth+conf
+        head_features: int = 256,
+        head_out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        head_use_sky_head: bool = True, # ignored by DualDPT
+        head_pos_embed: Optional[bool] = None,  # default: True for DualDPT, False for DPT
+        # --- Camera (multi-view) ---
+        has_cam_enc: bool = False,
+        has_cam_dec: bool = False,
+        cam_dim_out: Optional[int] = None,  # CameraEnc dim_out (defaults to embed_dim)
+        cam_dec_dim_in: Optional[int] = None,  # CameraDec dim_in  (defaults to 2*embed_dim with cat_token)
+        # ComfyUI plumbing
+        device=None, dtype=None, operations=None,
+        **_ignored,
+    ):
+        super().__init__()
+        head_cls = _HEAD_REGISTRY[head_type.lower()]
+        self.head_type = head_type.lower()
+        self.has_sky = (self.head_type == "dpt") and head_use_sky_head
+        self.has_conf = head_output_dim > 1
+        self.out_layers = list(out_layers)
+
+        backbone_cfg = _build_backbone_config(
+            backbone_name,
+            alt_start=alt_start,
+            qknorm_start=qknorm_start,
+            rope_start=rope_start,
+            cat_token=cat_token,
+        )
+        self.backbone = Dinov2Model(backbone_cfg, dtype, device, operations)
+
+        head_kwargs = dict(
+            dim_in=head_dim_in,
+            patch_size=self.PATCH_SIZE,
+            output_dim=head_output_dim,
+            features=head_features,
+            out_channels=tuple(head_out_channels),
+            device=device, dtype=dtype, operations=operations,
+        )
+        if self.head_type == "dpt":
+            head_kwargs.update(
+                use_sky_head=head_use_sky_head,
+                pos_embed=(False if head_pos_embed is None else head_pos_embed),
+            )
+        else:  # dualdpt
+            head_kwargs.update(
+                pos_embed=(True if head_pos_embed is None else head_pos_embed),
+            )
+        self.head = head_cls(**head_kwargs)
+
+        # Built only if checkpoint has weights; cam_enc output dim == embed_dim.
+        embed_dim = backbone_cfg["hidden_size"]
+        if has_cam_enc:
+            self.cam_enc = CameraEnc(
+                dim_out=cam_dim_out if cam_dim_out is not None else embed_dim,
+                num_heads=max(1, embed_dim // 64),
+                device=device, dtype=dtype, operations=operations,
+            )
+        else:
+            self.cam_enc = None
+        if has_cam_dec:
+            default_dim = embed_dim * (2 if cat_token else 1)
+            self.cam_dec = CameraDec(
+                dim_in=cam_dec_dim_in if cam_dec_dim_in is not None else default_dim,
+                device=device, dtype=dtype, operations=operations,
+            )
+        else:
+            self.cam_dec = None
+
+        self.dtype = dtype
+
+    def forward(
+        self,
+        image: torch.Tensor,
+        extrinsics: Optional[torch.Tensor] = None,
+        intrinsics: Optional[torch.Tensor] = None,
+        *,
+        use_ray_pose: bool = False,
+        ref_view_strategy: str = "saddle_balanced",
+        export_feat_layers: Optional[Sequence[int]] = None,
+        **_unused,
+    ) -> Dict[str, torch.Tensor]:
+        """Run depth and optionally pose prediction."""
+        if image.ndim == 4:
+            image = image.unsqueeze(1)  # (B, 1, 3, H, W)
+        assert image.ndim == 5 and image.shape[2] == 3, \
+            f"image must be (B,3,H,W) or (B,S,3,H,W); got {tuple(image.shape)}"
+
+        B, S, _, H, W = image.shape
+        assert H % self.PATCH_SIZE == 0 and W % self.PATCH_SIZE == 0, \
+            f"image H,W must be multiples of {self.PATCH_SIZE}; got {(H, W)}"
+
+        # Camera-token preparation (multi-view path).
+        cam_token = None
+        if extrinsics is not None and intrinsics is not None and self.cam_enc is not None:
+            cam_token = self.cam_enc(extrinsics, intrinsics, (H, W))
+
+        # Toggle aux ray output on/off depending on what the caller asked for.
+        if isinstance(self.head, DualDPT):
+            self.head.enable_aux = bool(use_ray_pose)
+
+        feats, aux_feats = self.backbone.get_intermediate_layers_da3(
+            image, self.out_layers, cam_token=cam_token,
+            ref_view_strategy=ref_view_strategy,
+            export_feat_layers=export_feat_layers,
+        )
+        head_out = self.head(feats, H=H, W=W, patch_start_idx=0)
+
+        # Pose prediction.
+        out: Dict[str, torch.Tensor] = {}
+        if use_ray_pose and "ray" in head_out and "ray_conf" in head_out:
+            ray = head_out["ray"]
+            ray_conf = head_out["ray_conf"]
+            extr_c2w, focal, pp = get_extrinsic_from_camray(
+                ray, ray_conf, ray.shape[-3], ray.shape[-2],
+            )
+            # Match the upstream output: w2c, drop the homogeneous row.
+            extr_w2c = affine_inverse(extr_c2w)[:, :, :3, :]
+            # Build pixel-space intrinsics from the normalised focal/pp output.
+            intr = torch.eye(3, device=ray.device, dtype=ray.dtype)
+            intr = intr[None, None].expand(extr_c2w.shape[0], extr_c2w.shape[1], 3, 3).clone()
+            intr[:, :, 0, 0] = focal[:, :, 0] / 2 * W
+            intr[:, :, 1, 1] = focal[:, :, 1] / 2 * H
+            intr[:, :, 0, 2] = pp[:, :, 0] * W * 0.5
+            intr[:, :, 1, 2] = pp[:, :, 1] * H * 0.5
+            out["extrinsics"] = extr_w2c
+            out["intrinsics"] = intr
+        elif self.cam_dec is not None and S > 1:
+            # Decode the cam-token of the final out_layer into a pose encoding.
+            cam_feat = feats[-1][1]  # (B, S, dim_in_to_cam_dec)
+            pose_enc = self.cam_dec(cam_feat)
+            c2w_3x4, intr = pose_encoding_to_extri_intri(pose_enc, (H, W))
+            # Match the upstream output convention: w2c (world->camera), 3x4.
+            c2w_4x4 = torch.cat([
+                c2w_3x4,
+                torch.tensor([0, 0, 0, 1], device=c2w_3x4.device, dtype=c2w_3x4.dtype)
+                    .view(1, 1, 1, 4).expand(B, S, 1, 4),
+            ], dim=-2)
+            out["extrinsics"] = affine_inverse(c2w_4x4)[:, :, :3, :]
+            out["intrinsics"] = intr
+
+        # Flatten the views axis for per-pixel outputs (depth/conf/sky) so the
+        # per-image consumer keeps its (B*S, H, W) interface.
+        for k, v in head_out.items():
+            if k in ("ray", "ray_conf"):
+                # Keep multi-view shape for downstream pose work.
+                out[k] = v
+            elif v.ndim >= 3 and v.shape[0] == B and v.shape[1] == S:
+                out[k] = v.reshape(B * S, *v.shape[2:])
+            else:
+                out[k] = v
+
+        if export_feat_layers:
+            out["aux_features"] = self._reshape_aux_features(aux_feats, H, W)
+        return out
+
+    def _reshape_aux_features(self, aux_feats, H: int, W: int):
+        """Reshape (B, S, N, C) aux features into (B, S, h_p, w_p, C)."""
+        ph, pw = H // self.PATCH_SIZE, W // self.PATCH_SIZE
+        out = []
+        for f in aux_feats:
+            B, S, N, C = f.shape
+            assert N == ph * pw, f"aux feature seq mismatch: {N} != {ph}*{pw}"
+            out.append(f.reshape(B, S, ph, pw, C))
+        return out

comfy/ldm/depth_anything_3/preprocess.py

@@ -0,0 +1,128 @@
+"""Input/output preprocessing helpers for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Tuple
+
+import torch
+
+import comfy.utils
+
+PATCH_SIZE = 14
+
+# ImageNet normalization constants used during DA3 training.
+_IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406])
+_IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225])
+
+
+def _round_to_patch(x: int, patch: int = PATCH_SIZE) -> int:
+    down = (x // patch) * patch
+    up = down + patch
+    return up if abs(up - x) <= abs(x - down) else down
+
+
+def compute_target_size(orig_h: int, orig_w: int, process_res: int, method: str = "upper_bound_resize") -> Tuple[int, int]:
+    """Compute (target_h, target_w) for a single image.
+    upper_bound_resize: scale longest side to process_res, then round each dim to nearest multiple of 14 (default upstream method).
+    lower_bound_resize: scale shortest side to process_res, then round."""
+
+    if method == "upper_bound_resize":
+        longest = max(orig_h, orig_w)
+        scale = process_res / float(longest)
+    elif method == "lower_bound_resize":
+        shortest = min(orig_h, orig_w)
+        scale = process_res / float(shortest)
+    else:
+        raise ValueError(f"Unsupported process_res_method: {method}")
+
+    new_w = max(1, _round_to_patch(int(round(orig_w * scale))))
+    new_h = max(1, _round_to_patch(int(round(orig_h * scale))))
+    return new_h, new_w
+
+
+def preprocess_image(image: torch.Tensor, process_res: int = 504, method: str = "upper_bound_resize") -> torch.Tensor:
+    assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+    B, H, W, _ = image.shape
+    target_h, target_w = compute_target_size(H, W, process_res, method)
+
+    # (B, H, W, 3) -> (B, 3, H, W)
+    x = image.movedim(-1, 1).contiguous()
+    if (target_h, target_w) != (H, W):
+        # Upstream uses cv2 INTER_CUBIC (upscale) / INTER_AREA (downscale).
+        # Lanczos in ``common_upscale`` is anti-aliased and produces the
+        # closest pixel-wise match in a sweep across {bilinear, bicubic,
+        # area, lanczos, bislerp}. Used in both directions for simplicity.
+        x = comfy.utils.common_upscale(x.float(), target_w, target_h, "lanczos", "disabled",)
+    x = x.clamp(0.0, 1.0)
+
+    mean = _IMAGENET_MEAN.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    std = _IMAGENET_STD.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    x = (x - mean) / std
+    return x
+
+
+# -----------------------------------------------------------------------------
+# Output post-processing (sky-aware clipping for Mono/Metric variants)
+# -----------------------------------------------------------------------------
+
+
+def compute_non_sky_mask(sky_prediction: torch.Tensor, threshold: float = 0.3) -> torch.Tensor:
+    """Boolean mask: True for non-sky pixels (sky probability < threshold)."""
+    return sky_prediction < threshold
+
+
+def apply_sky_aware_clip(depth: torch.Tensor, sky: torch.Tensor, threshold: float = 0.3, quantile: float = 0.99) -> torch.Tensor:
+    """Clips sky regions to the 99th percentile of non-sky depth. Returns a new depth tensor."""
+    non_sky = compute_non_sky_mask(sky, threshold=threshold)
+    if non_sky.sum() <= 10 or (~non_sky).sum() <= 10:
+        return depth.clone()
+
+    non_sky_depth = depth[non_sky]
+    if non_sky_depth.numel() > 100_000:
+        idx = torch.randint(0, non_sky_depth.numel(), (100_000,), device=non_sky_depth.device)
+        sampled = non_sky_depth[idx]
+    else:
+        sampled = non_sky_depth
+
+    max_depth = torch.quantile(sampled, quantile)
+    out = depth.clone()
+    out[~non_sky] = max_depth
+    return out
+
+
+def normalize_depth_v2_style(depth: torch.Tensor, sky: torch.Tensor | None = None, low_quantile: float = 0.01, high_quantile: float = 0.99) -> torch.Tensor:
+    """V2-style normalization computes percentile bounds over non-sky pixels (when available), then maps depth into [0, 1] with near = white (1.0)."""
+    if sky is not None:
+        mask = compute_non_sky_mask(sky)
+        if mask.any():
+            valid = depth[mask]
+        else:
+            valid = depth.flatten()
+    else:
+        valid = depth.flatten()
+
+    if valid.numel() > 100_000:
+        idx = torch.randint(0, valid.numel(), (100_000,), device=valid.device)
+        sample = valid[idx]
+    else:
+        sample = valid
+
+    lo = torch.quantile(sample, low_quantile)
+    hi = torch.quantile(sample, high_quantile)
+    rng = (hi - lo).clamp(min=1e-6)
+    norm = ((depth - lo) / rng).clamp(0.0, 1.0)
+    # Nearer pixels are brighter (1.0)
+    norm = 1.0 - norm
+    if sky is not None:
+        # Sky pixels become black (far / unknown)
+        sky_mask = ~compute_non_sky_mask(sky)
+        norm = torch.where(sky_mask, torch.zeros_like(norm), norm)
+    return norm
+
+
+def normalize_depth_min_max(depth: torch.Tensor) -> torch.Tensor:
+    """Simple per-frame min/max normalization with near=1.0 convention."""
+    lo = depth.amin(dim=(-2, -1), keepdim=True)
+    hi = depth.amax(dim=(-2, -1), keepdim=True)
+    rng = (hi - lo).clamp(min=1e-6)
+    return 1.0 - ((depth - lo) / rng).clamp(0.0, 1.0)

comfy/ldm/depth_anything_3/ray_pose.py

@@ -0,0 +1,272 @@
+"""Ray-to-pose conversion for the multi-view path of Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import torch
+
+
+# qr/svd use fp32: CUDA often has no fp16/bf16 kernels for these ops.
+
+
+def _ql_decomposition(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Decompose A = Q @ L with Q orthogonal and L lower-triangular.
+    Implemented in terms of QR by reversing the columns/rows; the standard
+    trick from the upstream reference. Inputs A are (3, 3)."""
+    P = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]], device=A.device, dtype=A.dtype)
+    A_tilde = A @ P
+    # CUDA QR is not implemented for fp16/bf16; upcast just for this call.
+    Q_tilde, R_tilde = torch.linalg.qr(A_tilde.float())
+    Q_tilde = Q_tilde.to(A.dtype)
+    R_tilde = R_tilde.to(A.dtype)
+    Q = Q_tilde @ P
+    L = P @ R_tilde @ P
+    d = torch.diag(L)
+    sign = torch.sign(d)
+    Q = Q * sign[None, :]  # scale columns of Q
+    L = L * sign[:, None]  # scale rows of L
+    return Q, L
+
+
+def _homogenize_points(points: torch.Tensor) -> torch.Tensor:
+    return torch.cat([points, torch.ones_like(points[..., :1])], dim=-1)
+
+
+# -----------------------------------------------------------------------------
+# Weighted-LSQ + RANSAC homography (batched)
+# -----------------------------------------------------------------------------
+
+
+def _find_homography_weighted_lsq(src_pts: torch.Tensor, dst_pts: torch.Tensor, confident_weight: torch.Tensor,) -> torch.Tensor:
+    """Solve a single H with weighted least-squares (DLT)."""
+    N = src_pts.shape[0]
+    if N < 4:
+        raise ValueError("At least 4 points are required to compute a homography.")
+    w = confident_weight.sqrt().unsqueeze(1)  # (N, 1)
+    x = src_pts[:, 0:1]
+    y = src_pts[:, 1:2]
+    u = dst_pts[:, 0:1]
+    v = dst_pts[:, 1:2]
+    zeros = torch.zeros_like(x)
+    A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=1)
+    A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=1)
+    A = torch.cat([A1, A2], dim=0)        # (2N, 9)
+    # CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
+    _, _, Vh = torch.linalg.svd(A.float())
+    Vh = Vh.to(A.dtype)
+    H = Vh[-1].reshape(3, 3)
+    return H / H[-1, -1]
+
+
+def _find_homography_weighted_lsq_batched(src_pts_batch: torch.Tensor, dst_pts_batch: torch.Tensor, confident_weight_batch: torch.Tensor) -> torch.Tensor:
+    """Batched DLT solver. Inputs (B, K, 2) / (B, K); output (B, 3, 3)."""
+    B, K, _ = src_pts_batch.shape
+    w = confident_weight_batch.sqrt().unsqueeze(2)
+    x = src_pts_batch[:, :, 0:1]
+    y = src_pts_batch[:, :, 1:2]
+    u = dst_pts_batch[:, :, 0:1]
+    v = dst_pts_batch[:, :, 1:2]
+    zeros = torch.zeros_like(x)
+    A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=2)
+    A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=2)
+    A = torch.cat([A1, A2], dim=1)        # (B, 2K, 9)
+    # CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
+    _, _, Vh = torch.linalg.svd(A.float())
+    Vh = Vh.to(A.dtype)
+    H = Vh[:, -1].reshape(B, 3, 3)
+    return H / H[:, 2:3, 2:3]
+
+
+def _ransac_find_homography_weighted_batched(
+    src_pts: torch.Tensor,                # (B, N, 2)
+    dst_pts: torch.Tensor,                # (B, N, 2)
+    confident_weight: torch.Tensor,       # (B, N)
+    n_sample: int,
+    n_iter: int = 100,
+    reproj_threshold: float = 3.0,
+    num_sample_for_ransac: int = 8,
+    max_inlier_num: int = 10000,
+    rand_sample_iters_idx: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """Batched weighted-RANSAC homography estimator. Returns (B, 3, 3) homography matrices."""
+    B, N, _ = src_pts.shape
+    assert N >= 4
+    device = src_pts.device
+
+    sorted_idx = torch.argsort(confident_weight, descending=True, dim=1)
+    candidate_idx = sorted_idx[:, :n_sample]                  # (B, n_sample)
+
+    if rand_sample_iters_idx is None:
+        rand_sample_iters_idx = torch.stack(
+            [torch.randperm(n_sample, device=device)[:num_sample_for_ransac]
+             for _ in range(n_iter)],
+            dim=0,
+        )
+
+    rand_idx = candidate_idx[:, rand_sample_iters_idx]        # (B, n_iter, k)
+    b_idx = (
+        torch.arange(B, device=device)
+        .view(B, 1, 1)
+        .expand(B, n_iter, num_sample_for_ransac)
+    )
+    src_b = src_pts[b_idx, rand_idx]
+    dst_b = dst_pts[b_idx, rand_idx]
+    w_b = confident_weight[b_idx, rand_idx]
+
+    cB, cN = src_b.shape[:2]
+    H_batch = _find_homography_weighted_lsq_batched(
+        src_b.flatten(0, 1), dst_b.flatten(0, 1), w_b.flatten(0, 1),
+    ).unflatten(0, (cB, cN))                                  # (B, n_iter, 3, 3)
+
+    src_homo = torch.cat([src_pts, torch.ones(B, N, 1, device=device, dtype=src_pts.dtype)], dim=2)
+    proj = torch.bmm(
+        src_homo.unsqueeze(1).expand(B, n_iter, N, 3).reshape(-1, N, 3),
+        H_batch.reshape(-1, 3, 3).transpose(1, 2),
+    )                                                          # (B*n_iter, N, 3)
+    proj_xy = (proj[:, :, :2] / proj[:, :, 2:3]).reshape(B, n_iter, N, 2)
+    err = ((proj_xy - dst_pts.unsqueeze(1)) ** 2).sum(-1).sqrt()  # (B, n_iter, N)
+    inlier_mask = err < reproj_threshold
+    score = (inlier_mask * confident_weight.unsqueeze(1)).sum(dim=2)
+    best_idx = torch.argmax(score, dim=1)
+    best_inlier_mask = inlier_mask[torch.arange(B, device=device), best_idx]
+
+    # Refit with the inlier set (per-batch, since the inlier counts vary).
+    H_inlier_list = []
+    for b in range(B):
+        mask = best_inlier_mask[b]
+        in_src = src_pts[b][mask]
+        in_dst = dst_pts[b][mask]
+        in_w = confident_weight[b][mask]
+        if in_src.shape[0] < 4:
+            # Fall back to identity when RANSAC fails to find enough inliers.
+            H_inlier_list.append(torch.eye(3, device=device, dtype=src_pts.dtype))
+            continue
+        sorted_w = torch.argsort(in_w, descending=True)
+        if len(sorted_w) > max_inlier_num:
+            keep = max(int(len(sorted_w) * 0.95), max_inlier_num)
+            sorted_w = sorted_w[:keep][torch.randperm(keep, device=device)[:max_inlier_num]]
+        H_inlier_list.append(
+            _find_homography_weighted_lsq(in_src[sorted_w], in_dst[sorted_w], in_w[sorted_w])
+        )
+    return torch.stack(H_inlier_list, dim=0)
+
+
+# -----------------------------------------------------------------------------
+# Camera-ray utilities
+# -----------------------------------------------------------------------------
+
+
+def _unproject_identity(num_y: int, num_x: int, B: int, S: int, device, dtype) -> torch.Tensor:
+    """Camera-space unit rays for an identity intrinsic on a 2x2 image plane."""
+    dx = 1.0 / num_x
+    dy = 1.0 / num_y
+    # Centered camera-space coords directly (skip the K^-1 step since it's
+    # just a translation by -1 on x and y when K is identity-with-center=1).
+    y = torch.linspace(-(1 - dy), (1 - dy), num_y, device=device, dtype=dtype)
+    x = torch.linspace(-(1 - dx), (1 - dx), num_x, device=device, dtype=dtype)
+    yy, xx = torch.meshgrid(y, x, indexing="ij")
+    grid = torch.stack((xx, yy), dim=-1)            # (h, w, 2)
+    grid = grid.unsqueeze(0).unsqueeze(0).expand(B, S, num_y, num_x, 2)
+    return torch.cat([grid, torch.ones_like(grid[..., :1])], dim=-1)
+
+
+def _camray_to_caminfo(
+    camray: torch.Tensor,  # (B, S, h, w, 6)
+    confidence: Optional[torch.Tensor] = None,  # (B, S, h, w)
+    reproj_threshold: float = 0.2,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Convert per-pixel camera rays to per-view (R, T, focal, principal)."""
+    if confidence is None:
+        confidence = torch.ones_like(camray[..., 0])
+    B, S, h, w, _ = camray.shape
+    device = camray.device
+    dtype = camray.dtype
+
+    rays_target = camray[..., :3]                           # (B, S, h, w, 3)
+    rays_origin = _unproject_identity(h, w, B, S, device, dtype)
+
+    # Flatten (B*S, h*w, *) for the RANSAC routine.
+    rays_target = rays_target.flatten(0, 1).flatten(1, 2)
+    rays_origin = rays_origin.flatten(0, 1).flatten(1, 2)
+    weights = confidence.flatten(0, 1).flatten(1, 2).clone()
+
+    # Project to 2D in homogeneous form (the upstream calls this "perspective division").
+    z_thresh = 1e-4
+    mask = (rays_target[:, :, 2].abs() > z_thresh) & (rays_origin[:, :, 2].abs() > z_thresh)
+    weights = torch.where(mask, weights, torch.zeros_like(weights))
+    src = rays_origin.clone()
+    dst = rays_target.clone()
+    src[..., 0] = torch.where(mask, src[..., 0] / src[..., 2], src[..., 0])
+    src[..., 1] = torch.where(mask, src[..., 1] / src[..., 2], src[..., 1])
+    dst[..., 0] = torch.where(mask, dst[..., 0] / dst[..., 2], dst[..., 0])
+    dst[..., 1] = torch.where(mask, dst[..., 1] / dst[..., 2], dst[..., 1])
+    src = src[..., :2]
+    dst = dst[..., :2]
+
+    N = src.shape[1]
+    n_iter = 100
+    sample_ratio = 0.3
+    num_sample_for_ransac = 8
+    n_sample = max(num_sample_for_ransac, int(N * sample_ratio))
+    rand_idx = torch.stack(
+        [torch.randperm(n_sample, device=device)[:num_sample_for_ransac] for _ in range(n_iter)],
+        dim=0,
+    )
+
+    # Chunk along the view axis to keep peak memory predictable.
+    chunk = 2
+    A_list = []
+    for i in range(0, src.shape[0], chunk):
+        A = _ransac_find_homography_weighted_batched(
+            src[i:i + chunk], dst[i:i + chunk], weights[i:i + chunk],
+            n_sample=n_sample, n_iter=n_iter,
+            num_sample_for_ransac=num_sample_for_ransac,
+            reproj_threshold=reproj_threshold,
+            rand_sample_iters_idx=rand_idx,
+            max_inlier_num=8000,
+        )
+        # Flip sign on dets that come out < 0 (so that the QL produces a
+        # right-handed rotation). ``det`` lacks fp16/bf16 CUDA kernels, so
+        # do the comparison in fp32.
+        flip = torch.linalg.det(A.float()) < 0
+        A = torch.where(flip[:, None, None], -A, A)
+        A_list.append(A)
+    A = torch.cat(A_list, dim=0)                            # (B*S, 3, 3)
+
+    R_list, f_list, pp_list = [], [], []
+    for i in range(A.shape[0]):
+        R, L = _ql_decomposition(A[i])
+        L = L / L[2][2]
+        f_list.append(torch.stack((L[0][0], L[1][1])))
+        pp_list.append(torch.stack((L[2][0], L[2][1])))
+        R_list.append(R)
+    R = torch.stack(R_list).reshape(B, S, 3, 3)
+    focal = torch.stack(f_list).reshape(B, S, 2)
+    pp = torch.stack(pp_list).reshape(B, S, 2)
+
+    # Translation: confidence-weighted average of camray direction(s).
+    cf = confidence.flatten(0, 1).flatten(1, 2)
+    T = (camray.flatten(0, 1).flatten(1, 2)[..., 3:] * cf.unsqueeze(-1)).sum(dim=1)
+    T = T / cf.sum(dim=-1, keepdim=True)
+    T = T.reshape(B, S, 3)
+
+    # Match upstream output convention: focal -> 1/focal, pp + 1.
+    return R, T, 1.0 / focal, pp + 1.0
+
+
+def get_extrinsic_from_camray(
+    camray: torch.Tensor,  # (B, S, h, w, 6)
+    conf: torch.Tensor,  # (B, S, h, w, 1) or (B, S, h, w)
+    patch_size_y: int,
+    patch_size_x: int,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Wrap a 4x4 extrinsic + per-view focal + principal-point output."""
+    if conf.ndim == 5 and conf.shape[-1] == 1:
+        conf = conf.squeeze(-1)
+    R, T, focal, pp = _camray_to_caminfo(camray, confidence=conf)
+    extr = torch.cat([R, T.unsqueeze(-1)], dim=-1)           # (B, S, 3, 4)
+    homo_row = torch.tensor([0, 0, 0, 1], dtype=R.dtype, device=R.device)
+    homo_row = homo_row.view(1, 1, 1, 4).expand(R.shape[0], R.shape[1], 1, 4)
+    extr = torch.cat([extr, homo_row], dim=-2)               # (B, S, 4, 4)
+    return extr, focal, pp

comfy/ldm/depth_anything_3/reference_view_selector.py

@@ -0,0 +1,87 @@
+"""Reference-view selection for the multi-view path of Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Literal
+
+import torch
+
+
+RefViewStrategy = Literal["first", "middle", "saddle_balanced", "saddle_sim_range"]
+
+
+# Per the upstream constants module: ``THRESH_FOR_REF_SELECTION = 3``.
+# Reference selection only runs when there are at least this many views.
+THRESH_FOR_REF_SELECTION: int = 3
+
+
+def select_reference_view(x: torch.Tensor, strategy: RefViewStrategy = "saddle_balanced") -> torch.Tensor:
+    """Pick a reference view index per batch element."""
+    B, S, _, _ = x.shape
+    if S <= 1:
+        return torch.zeros(B, dtype=torch.long, device=x.device)
+    if strategy == "first":
+        return torch.zeros(B, dtype=torch.long, device=x.device)
+    if strategy == "middle":
+        return torch.full((B,), S // 2, dtype=torch.long, device=x.device)
+
+    # Feature-based strategies: normalised cls/cam token per view.
+    img_class_feat = x[:, :, 0] / x[:, :, 0].norm(dim=-1, keepdim=True)  # (B,S,C)
+
+    if strategy == "saddle_balanced":
+        sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2))  # (B,S,S)
+        sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
+        sim_score = sim_no_diag.sum(dim=-1) / (S - 1)               # (B,S)
+        feat_norm = x[:, :, 0].norm(dim=-1)                          # (B,S)
+        feat_var = img_class_feat.var(dim=-1)                        # (B,S)
+
+        def _normalize(metric):
+            mn = metric.min(dim=1, keepdim=True).values
+            mx = metric.max(dim=1, keepdim=True).values
+            return (metric - mn) / (mx - mn + 1e-8)
+
+        sim_n, norm_n, var_n = _normalize(sim_score), _normalize(feat_norm), _normalize(feat_var)
+        balance = (sim_n - 0.5).abs() + (norm_n - 0.5).abs() + (var_n - 0.5).abs()
+        return balance.argmin(dim=1)
+
+    if strategy == "saddle_sim_range":
+        sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2))
+        sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
+        sim_max = sim_no_diag.max(dim=-1).values
+        sim_min = sim_no_diag.min(dim=-1).values
+        return (sim_max - sim_min).argmax(dim=1)
+
+    raise ValueError(
+        f"Unknown reference view selection strategy: {strategy!r}. "
+        f"Must be one of: 'first', 'middle', 'saddle_balanced', 'saddle_sim_range'"
+    )
+
+
+def reorder_by_reference(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
+    """Reorder x so the reference view is at position 0 in axis S."""
+    B, S = x.shape[0], x.shape[1]
+    if S <= 1:
+        return x
+    positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
+    b_idx_exp = b_idx.unsqueeze(1)
+    reorder = torch.where(
+        (positions > 0) & (positions <= b_idx_exp),
+        positions - 1,
+        positions,
+    )
+    reorder[:, 0] = b_idx
+    batch = torch.arange(B, device=x.device).unsqueeze(1)
+    return x[batch, reorder]
+
+
+def restore_original_order(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
+    """Inverse of reorder_by_reference."""
+    B, S = x.shape[0], x.shape[1]
+    if S <= 1:
+        return x
+    target_positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
+    b_idx_exp = b_idx.unsqueeze(1)
+    restore = torch.where(target_positions < b_idx_exp, target_positions + 1, target_positions)
+    restore = torch.scatter(restore, dim=1, index=b_idx_exp, src=torch.zeros_like(b_idx_exp))
+    batch = torch.arange(B, device=x.device).unsqueeze(1)
+    return x[batch, restore]

comfy/ldm/depth_anything_3/transform.py

@@ -0,0 +1,160 @@
+"""Geometry / camera transform helpers for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Tuple
+
+import torch
+import torch.nn.functional as F
+
+
+# -----------------------------------------------------------------------------
+# Affine 4x4 helpers
+# -----------------------------------------------------------------------------
+
+
+def as_homogeneous(ext: torch.Tensor) -> torch.Tensor:
+    """Promote (...,3,4) extrinsics to (...,4,4) homogeneous form. No-op when the input is already ``(...,4,4)``."""
+    if ext.shape[-2:] == (4, 4):
+        return ext
+    if ext.shape[-2:] == (3, 4):
+        ones = torch.zeros_like(ext[..., :1, :4])
+        ones[..., 0, 3] = 1.0
+        return torch.cat([ext, ones], dim=-2)
+    raise ValueError(f"Invalid affine shape: {ext.shape}")
+
+
+def affine_inverse(A: torch.Tensor) -> torch.Tensor:
+    """Inverse of an affine matrix ``[R|T; 0 0 0 1]``."""
+    R = A[..., :3, :3]
+    T = A[..., :3, 3:]
+    P = A[..., 3:, :]
+    return torch.cat([torch.cat([R.mT, -R.mT @ T], dim=-1), P], dim=-2)
+
+
+# -----------------------------------------------------------------------------
+# Quaternion <-> rotation matrix (xyzw / scalar-last)
+# -----------------------------------------------------------------------------
+
+
+def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
+    """sqrt(max(0, x)) with a zero subgradient where x == 0."""
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    if torch.is_grad_enabled():
+        ret[positive_mask] = torch.sqrt(x[positive_mask])
+    else:
+        ret = torch.where(positive_mask, torch.sqrt(x), ret)
+    return ret
+
+
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
+    """Force the real part of a unit quaternion (xyzw) to be non-negative."""
+    return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions)
+
+
+def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor:
+    """Convert quaternions (xyzw) to (...,3,3) rotation matrices."""
+    i, j, k, r = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor:
+    """Convert (...,3,3) rotation matrices to quaternions (xyzw)."""
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
+
+    batch_dim = matrix.shape[:-2]
+    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
+        matrix.reshape(batch_dim + (9,)), dim=-1
+    )
+
+    q_abs = _sqrt_positive_part(
+        torch.stack(
+            [
+                1.0 + m00 + m11 + m22,
+                1.0 + m00 - m11 - m22,
+                1.0 - m00 + m11 - m22,
+                1.0 - m00 - m11 + m22,
+            ],
+            dim=-1,
+        )
+    )
+
+    quat_by_rijk = torch.stack(
+        [
+            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
+            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
+            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
+            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+        ],
+        dim=-2,
+    )
+
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
+
+    out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape(
+        batch_dim + (4,)
+    )
+    # Reorder rijk -> xyzw (i.e. ijkr).
+    out = out[..., [1, 2, 3, 0]]
+    return standardize_quaternion(out)
+
+
+# -----------------------------------------------------------------------------
+# Pose-encoding <-> extrinsics + intrinsics
+# -----------------------------------------------------------------------------
+
+
+def extri_intri_to_pose_encoding(extrinsics: torch.Tensor, intrinsics: torch.Tensor, image_size_hw: Tuple[int, int]) -> torch.Tensor:
+    """Pack (extr, intr, image_size) into the 9-D pose-encoding vector.
+    extrinsics: camera-to-world (c2w) (B,S,4,4) matrices,
+    intrinsics: pixel-space (B,S,3,3) matrices,
+    image_size_hw: is a (H, W) pair.
+    """
+    R = extrinsics[..., :3, :3]
+    T = extrinsics[..., :3, 3]
+    quat = mat_to_quat(R)
+    H, W = image_size_hw
+    fov_h = 2 * torch.atan((H / 2) / intrinsics[..., 1, 1])
+    fov_w = 2 * torch.atan((W / 2) / intrinsics[..., 0, 0])
+    return torch.cat([T, quat, fov_h[..., None], fov_w[..., None]], dim=-1).float()
+
+
+def pose_encoding_to_extri_intri(pose_encoding: torch.Tensor, image_size_hw: Tuple[int, int]) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Inverse of extri_intri_to_pose_encoding."""
+    T = pose_encoding[..., :3]
+    quat = pose_encoding[..., 3:7]
+    fov_h = pose_encoding[..., 7]
+    fov_w = pose_encoding[..., 8]
+    # Normalize to unit quaternion. CameraDec outputs raw values; a near-zero
+    # quaternion causes two_s = 2/norm² → inf in quat_to_mat → NaN extrinsics.
+    quat = quat / quat.norm(dim=-1, keepdim=True).clamp(min=1e-6)
+    R = quat_to_mat(quat)
+    extrinsics = torch.cat([R, T[..., None]], dim=-1)
+    H, W = image_size_hw
+    fy = (H / 2.0) / torch.clamp(torch.tan(fov_h / 2.0), 1e-6)
+    fx = (W / 2.0) / torch.clamp(torch.tan(fov_w / 2.0), 1e-6)
+    intrinsics = torch.zeros(pose_encoding.shape[:2] + (3, 3), device=pose_encoding.device, dtype=pose_encoding.dtype)
+    intrinsics[..., 0, 0] = fx
+    intrinsics[..., 1, 1] = fy
+    intrinsics[..., 0, 2] = W / 2
+    intrinsics[..., 1, 2] = H / 2
+    intrinsics[..., 2, 2] = 1.0
+    return extrinsics, intrinsics

comfy/ldm/ideogram4/model.py

@@ -174,7 +174,7 @@ class Ideogram4Transformer(nn.Module):
             llm = self.llm_cond_proj(llm) * text_mask
             h[:, :L_text] = h[:, :L_text] + llm
 
-        h = h + self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long))
+        h = h + self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long), out_dtype=h.dtype)
 
         # Qwen3-VL interleaved MRoPE; position_ids (B, L, 3) -> (3, L) (same across batch).
         freqs_cis = precompute_freqs_cis(
@@ -235,7 +235,7 @@ class Ideogram4Transformer2DModel(Ideogram4Transformer):
     def _run_conditional(self, x_chunk, context_chunk, attn_mask_chunk, t_chunk, gh, gw, transformer_options):
         B = x_chunk.shape[0]
         device = x_chunk.device
-        img_tokens = self._img_to_tokens(x_chunk).to(self.dtype)
+        img_tokens = self._img_to_tokens(x_chunk)
         L_img = img_tokens.shape[1]
         L_text = context_chunk.shape[1]
         L = L_text + L_img
@@ -268,7 +268,7 @@ class Ideogram4Transformer2DModel(Ideogram4Transformer):
     def _run_image_only(self, x_chunk, t_chunk, gh, gw, transformer_options):
         B = x_chunk.shape[0]
         device = x_chunk.device
-        img_tokens = self._img_to_tokens(x_chunk).to(self.dtype)
+        img_tokens = self._img_to_tokens(x_chunk)
         L_img = img_tokens.shape[1]
 
         position_ids = self._image_position_ids(gh, gw, device).unsqueeze(0).expand(B, L_img, 3)

comfy/ldm/qwen_image/model.py

@@ -51,6 +51,18 @@ class FeedForward(nn.Module):
         return hidden_states
 
 
+# Addin this back because Nunchaku custom nodes rely on it, see comment here:
+# https://github.com/Comfy-Org/ComfyUI/pull/14178#issuecomment-4640475161
+# TODO: Eventually remove this once we natively support SVDQuants
+def apply_rotary_emb(x, freqs_cis):
+    if x.shape[1] == 0:
+        return x
+
+    t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
+    t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
+    return t_out.reshape(*x.shape)
+
+
 class QwenTimestepProjEmbeddings(nn.Module):
     def __init__(self, embedding_dim, pooled_projection_dim, use_additional_t_cond=False, dtype=None, device=None, operations=None):
         super().__init__()

comfy/ldm/wan/model.py

@@ -8,7 +8,7 @@ from einops import rearrange
 
 from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.flux.layers import EmbedND
-from comfy.ldm.flux.math import apply_rope1
+from comfy.ldm.flux.math import apply_rope1, rope
 import comfy.ldm.common_dit
 import comfy.model_management
 import comfy.patcher_extension
@@ -570,6 +570,14 @@ class WanModel(torch.nn.Module):
                 full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
                 x = torch.concat((full_ref, x), dim=1)
 
+        # In-context reference (Bernini)
+        context_latents = kwargs.get("context_latents", None)
+        main_len = x.shape[1]
+        if context_latents is not None:
+            for lat in context_latents:
+                cl = self.patch_embedding(lat.float().to(x.device)).to(x.dtype).flatten(2).transpose(1, 2)
+                x = torch.cat([x, cl], dim=1)
+
         # context
         context = self.text_embedding(context)
 
@@ -599,6 +607,9 @@ class WanModel(torch.nn.Module):
         # head
         x = self.head(x, e)
 
+        if context_latents is not None:
+            x = x[:, :main_len]
+
         if full_ref is not None:
             x = x[:, full_ref.shape[1]:]
 
@@ -606,7 +617,7 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}, source_id=0):
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
@@ -638,6 +649,13 @@ class WanModel(torch.nn.Module):
         img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
 
         freqs = self.rope_embedder(img_ids).movedim(1, 2)
+
+        # In-context reference: a non-zero source_id composes an extra rotation into the spatial rope
+        if source_id:
+            d = self.dim // self.num_heads
+            pos = torch.tensor([[float(source_id)]], device=freqs.device, dtype=torch.float32)
+            id_rot = rope(pos, d, self.rope_embedder.theta).reshape(1, 1, 1, d // 2, 2, 2).to(freqs.dtype)
+            freqs = torch.einsum('...ij,...jk->...ik', freqs, id_rot)
         return freqs
 
     def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
@@ -661,6 +679,15 @@ class WanModel(torch.nn.Module):
             t_len += 1
 
         freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+
+        # In-context reference: one rope block per stream, each with it's own source_id (1, 2, ...) to distinguish from the target (id 0).
+        context_latents = kwargs.get("context_latents", None)
+        if context_latents is not None:
+            context_latents = [comfy.ldm.common_dit.pad_to_patch_size(lat, self.patch_size) for lat in context_latents]
+            for i, lat in enumerate(context_latents):
+                freqs = torch.cat([freqs, self.rope_encode(lat.shape[-3], lat.shape[-2], lat.shape[-1], device=x.device, dtype=x.dtype, transformer_options=transformer_options, source_id=i + 1)], dim=1)
+            kwargs = {**kwargs, "context_latents": context_latents}
+
         return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
 
     def unpatchify(self, x, grid_sizes):
@@ -1631,13 +1658,15 @@ class SCAILWanModel(WanModel):
 
         self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
 
-    def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs):
+    def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, ref_mask_latents=None, sam_latents=None, **kwargs):
 
         if reference_latent is not None:
             x = torch.cat((reference_latent, x), dim=2)
 
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
+        if ref_mask_latents is not None:  # SCAIL-2 additive mask stream
+            x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
         transformer_options["grid_sizes"] = grid_sizes
         x = x.flatten(2).transpose(1, 2)
@@ -1645,6 +1674,8 @@ class SCAILWanModel(WanModel):
         scail_pose_seq_len = 0
         if pose_latents is not None:
             scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype)
+            if sam_latents is not None:  # SCAIL-2 additive mask stream
+                scail_x = scail_x + self.patch_embedding_mask(sam_latents.float()).to(x.dtype)
             scail_x = scail_x.flatten(2).transpose(1, 2)
             scail_pose_seq_len = scail_x.shape[1]
             x = torch.cat([x, scail_x], dim=1)
@@ -1695,7 +1726,36 @@ class SCAILWanModel(WanModel):
 
         return x
 
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}):
+    # ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode,
+    # which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset.
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, ref_mask_flag=None, transformer_options={}):
+        if ref_mask_flag is not None and not bool(ref_mask_flag):
+            REF_ROPE_H = 120.0
+            POSE_ROPE_W = 120.0
+
+            ref_t_patches = 0
+            if reference_latent is not None:
+                ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
+            main_t_patches = t - ref_t_patches
+
+            parts = []
+            if ref_t_patches > 0:
+                ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}}
+                parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf))
+            if main_t_patches > 0:
+                parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=transformer_options))
+
+            if pose_latents is not None:
+                F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
+                h_scale = h / H_pose
+                w_scale = w / W_pose
+                h_shift = (h_scale - 1) / 2
+                w_shift = (w_scale - 1) / 2
+                pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
+                parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf))
+
+            return torch.cat(parts, dim=1)
+
         main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options)
 
         if pose_latents is None:
@@ -1719,12 +1779,16 @@ class SCAILWanModel(WanModel):
 
         return torch.cat([main_freqs, pose_freqs], dim=1)
 
-    def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs):
+    def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, ref_mask_latents=None, sam_latents=None, **kwargs):
         bs, c, t, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
 
         if pose_latents is not None:
             pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size)
+        if ref_mask_latents is not None:  # SCAIL-2
+            ref_mask_latents = comfy.ldm.common_dit.pad_to_patch_size(ref_mask_latents, self.patch_size)
+        if sam_latents is not None:  # SCAIL-2
+            sam_latents = comfy.ldm.common_dit.pad_to_patch_size(sam_latents, self.patch_size)
 
         t_len = t
         if time_dim_concat is not None:
@@ -1737,5 +1801,15 @@ class SCAILWanModel(WanModel):
             reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size)
             t_len += reference_latent.shape[2]
 
-        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent)
-        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w]
+        ref_mask_flag = kwargs.pop("ref_mask_flag", None)  # SCAIL-2
+
+        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_flag=ref_mask_flag)
+        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_latents=ref_mask_latents, sam_latents=sam_latents, **kwargs)[:, :, :t, :h, :w]
+
+
+class SCAIL2WanModel(SCAILWanModel):
+    """SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream."""
+
+    def __init__(self, model_type="scail2", patch_size=(1, 2, 2), in_dim=20, mask_in_dim=28, dim=5120, operations=None, device=None, dtype=None, **kwargs):
+        super().__init__(model_type=model_type, patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs)
+        self.patch_embedding_mask = operations.Conv3d(mask_in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)

comfy/lora.py

@@ -357,6 +357,12 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = k[len("diffusion_model."):-len(".weight")]
                 key_map["transformer.{}".format(key_lora)] = k
 
+    if isinstance(model, (comfy.model_base.LTXV, comfy.model_base.LTXAV)):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+
     return key_map
 
 

comfy/model_base.py

@@ -65,6 +65,7 @@ import comfy.ldm.ernie.model
 import comfy.ldm.sam3.detector
 import comfy.ldm.hidream_o1.model
 from comfy.ldm.hidream_o1.conditioning import build_extra_conds
+import comfy.ldm.depth_anything_3.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -1518,8 +1519,26 @@ class WAN21(BaseModel):
         if reference_latents is not None:
             out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0])
 
+        # In-context reference conditioning (Bernini)
+        context_latents = kwargs.get("context_latents", None)
+        if context_latents is not None:
+            out['context_latents'] = comfy.conds.CONDList([self.process_latent_in(l) for l in context_latents])
+
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        # In-context cond slicing (Bernini)
+        if cond_key == "context_latents" and isinstance(getattr(cond_value, "cond", None), list):
+            dim = window.dim
+            out = []
+            for lat in cond_value.cond:
+                if lat.ndim > dim and lat.shape[dim] > 1 and lat.shape[dim] == x_in.shape[dim]:
+                    out.append(window.get_tensor(lat, device, dim=dim, retain_index_list=retain_index_list))
+                else:
+                    out.append(lat.to(device))
+            return cond_value._copy_with(out)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 
 class WAN21_CausalAR(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
@@ -1754,6 +1773,80 @@ class WAN21_SCAIL(WAN21):
 
         return out
 
+class WAN21_SCAIL2(WAN21_SCAIL):
+    """SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream."""
+
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAIL2WanModel)
+        self.memory_usage_factor_conds = ("reference_latent", "pose_latents", "ref_mask_latents", "sam_latents")
+        self.memory_usage_shape_process = {
+            "pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]],
+            "sam_latents":  lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]],
+        }
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        driving_mask_28ch = kwargs.get("driving_mask_28ch", None)
+        if driving_mask_28ch is not None:
+            out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous())
+
+        ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
+        if ref_mask_28ch is not None:
+            out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous())
+
+        ref_mask_flag = kwargs.get("ref_mask_flag", None)
+        if ref_mask_flag is not None:
+            out['ref_mask_flag'] = comfy.conds.CONDConstant(ref_mask_flag)
+
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = super().extra_conds_shapes(**kwargs)
+        driving_mask_28ch = kwargs.get("driving_mask_28ch", None)
+        if driving_mask_28ch is not None:
+            s = driving_mask_28ch.shape
+            out['sam_latents'] = [s[0], 28, s[1], s[3], s[4]]
+        ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
+        if ref_mask_28ch is not None:
+            s = ref_mask_28ch.shape
+            out['ref_mask_latents'] = [s[0], 28, s[1], s[3], s[4]]
+        return out
+
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        if cond_key in ("sam_latents", "pose_latents"):
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
+    def concat_cond(self, **kwargs):
+        # The 4 extra channels are the history_mask (1 at clean-anchor frames).
+        noise = kwargs.get("noise", None)
+        extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1]
+        if extra_channels != 4:
+            return super().concat_cond(**kwargs)
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            return torch.zeros_like(noise)[:, :4]
+
+        device = kwargs["device"]
+        if mask.shape[1] != 4:
+            mask = torch.mean(mask, dim=1, keepdim=True)
+        mask = 1.0 - mask
+        mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+        if mask.shape[-3] < noise.shape[-3]:
+            mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+        if mask.shape[1] == 1:
+            mask = mask.repeat(1, 4, 1, 1, 1)
+        mask = utils.resize_to_batch_size(mask, noise.shape[0])
+        return mask
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        # Hold anchor constant across all sigmas instead of base sigma*noise + (1-sigma)*latent_image.
+        return latent_image
+
+
 class WAN22_WanDancer(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=True, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_wandancer.WanDancerModel)
@@ -2227,6 +2320,12 @@ class RT_DETR_v4(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4)
 
+
+class DepthAnything3(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device,
+                         unet_model=comfy.ldm.depth_anything_3.model.DepthAnything3Net)
+
 class ErnieImage(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel)

comfy/model_detection.py

@@ -630,6 +630,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "humo"
         elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
             dit_config["model_type"] = "animate"
+        elif '{}patch_embedding_mask.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "scail2"
         elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
             dit_config["model_type"] = "scail"
         elif '{}patch_embedding_global.weight'.format(key_prefix) in state_dict_keys:
@@ -860,6 +862,95 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
         return dit_config
 
+    # Depth Anything 3 (repackaged to ComfyUI's native Dinov2Model layout via scripts/convert_da3.py)
+    if '{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix) in state_dict_keys:
+        dit_config = {}
+        dit_config["image_model"] = "DepthAnything3"
+
+        patch_w = state_dict['{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix)]
+        embed_dim = patch_w.shape[0]
+        depth = count_blocks(state_dict_keys, '{}backbone.encoder.layer.'.format(key_prefix) + '{}.')
+
+        # Backbone preset is determined by embed_dim (matches vits/vitb/vitl/vitg).
+        backbone_name = {384: "vits", 768: "vitb", 1024: "vitl", 1536: "vitg"}.get(embed_dim)
+        if backbone_name is None:
+            return None
+        dit_config["backbone_name"] = backbone_name
+
+        # Detect DA3 extensions on top of vanilla DINOv2.
+        has_camera_token = '{}backbone.embeddings.camera_token'.format(key_prefix) in state_dict_keys
+        # qk-norm shows up as `attention.q_norm.weight` on enabled blocks.
+        qknorm_indices = [
+            i for i in range(depth)
+            if '{}backbone.encoder.layer.{}.attention.q_norm.weight'.format(key_prefix, i) in state_dict_keys
+        ]
+        qknorm_start = qknorm_indices[0] if qknorm_indices else -1
+
+        # The DA3 main-series configs always set alt_start == qknorm_start == rope_start.
+        # cat_token=True is implied by the presence of camera_token.
+        if has_camera_token:
+            dit_config["alt_start"] = qknorm_start
+            dit_config["rope_start"] = qknorm_start
+            dit_config["qknorm_start"] = qknorm_start
+            dit_config["cat_token"] = True
+        else:
+            dit_config["alt_start"] = -1
+            dit_config["rope_start"] = -1
+            dit_config["qknorm_start"] = -1
+            dit_config["cat_token"] = False
+
+        # Detect head type and config.
+        has_aux = '{}head.scratch.refinenet1_aux.out_conv.weight'.format(key_prefix) in state_dict_keys
+        dit_config["head_dim_in"] = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1]
+        dit_config["head_features"] = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0]
+        dit_config["head_out_channels"] = [
+            state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0]
+            for i in range(4)
+        ]
+        if has_aux:
+            # DualDPT: dim_in = 2 * embed_dim (because cat_token doubles token width).
+            dit_config["head_type"] = "dualdpt"
+            dit_config["head_output_dim"] = 2
+            dit_config["head_use_sky_head"] = False
+        else:
+            dit_config["head_type"] = "dpt"
+            dit_config["head_output_dim"] = state_dict[
+                '{}head.scratch.output_conv2.2.weight'.format(key_prefix)
+            ].shape[0]
+            dit_config["head_use_sky_head"] = (
+                '{}head.scratch.sky_output_conv2.0.weight'.format(key_prefix) in state_dict_keys
+            )
+
+        # out_layers: hard-coded per upstream YAML config (depth-aware default).
+        if depth >= 24:
+            # vitl: depths used vary between DA3-Large (DualDPT) and Mono/Metric (DPT).
+            if has_aux:
+                dit_config["out_layers"] = [11, 15, 19, 23]
+            else:
+                dit_config["out_layers"] = [4, 11, 17, 23]
+        else:
+            # vits/vitb: 12 blocks
+            dit_config["out_layers"] = [5, 7, 9, 11]
+
+        # Camera encoder/decoder presence (multi-view + pose path).
+        has_cam_enc = '{}cam_enc.token_norm.weight'.format(key_prefix) in state_dict_keys
+        has_cam_dec = '{}cam_dec.fc_t.weight'.format(key_prefix) in state_dict_keys
+        dit_config["has_cam_enc"] = has_cam_enc
+        dit_config["has_cam_dec"] = has_cam_dec
+        if has_cam_enc:
+            cam_enc_w = state_dict.get(
+                '{}cam_enc.pose_branch.fc2.weight'.format(key_prefix)
+            )
+            if cam_enc_w is not None:
+                dit_config["cam_dim_out"] = cam_enc_w.shape[0]
+        if has_cam_dec:
+            cam_dec_w = state_dict.get(
+                '{}cam_dec.fc_t.weight'.format(key_prefix)
+            )
+            if cam_dec_w is not None:
+                dit_config["cam_dec_dim_in"] = cam_dec_w.shape[1]
+        return dit_config
+
     if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image
         dit_config = {}
         dit_config["image_model"] = "ernie"

comfy/model_management.py

@@ -651,8 +651,7 @@ def ensure_pin_budget(size, evict_active=False):
     to_free = shortfall + PIN_PRESSURE_HYSTERESIS
     return free_pins(to_free, evict_active=evict_active) >= shortfall
 
-def ensure_pin_registerable(size, evict_active=True):
-    shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
+def free_registrations(shortfall, evict_active=True):
     if MAX_PINNED_MEMORY <= 0:
         return False
     if shortfall <= 0:
@@ -674,6 +673,9 @@ def ensure_pin_registerable(size, evict_active=True):
                     return True
     return shortfall <= REGISTERABLE_PIN_HYSTERESIS
 
+def ensure_pin_registerable(size, evict_active=True):
+    return free_registrations(TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY, evict_active=evict_active)
+
 class LoadedModel:
     def __init__(self, model: ModelPatcher):
         self._set_model(model)
@@ -956,8 +958,6 @@ def loaded_models(only_currently_used=False):
 def cleanup_models_gc():
     do_gc = False
 
-    reset_cast_buffers()
-
     for i in range(len(current_loaded_models)):
         cur = current_loaded_models[i]
         if cur.is_dead():

comfy/model_patcher.py

@@ -379,10 +379,11 @@ class ModelPatcher:
     def get_clone_model_override(self):
         return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)
 
-    def clone(self, disable_dynamic=False, model_override=None):
+    def clone(self, disable_dynamic=False, model_override=None, force_deepcopy=False):
         class_ = self.__class__
-        if self.is_dynamic() and disable_dynamic:
-            class_ = ModelPatcher
+        if self.is_dynamic() and disable_dynamic or force_deepcopy:
+            if self.is_dynamic() and disable_dynamic:
+                class_ = ModelPatcher
             if model_override is None:
                 if self.cached_patcher_init is None:
                     raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")

comfy/pinned_memory.py

@@ -89,13 +89,26 @@ def pin_memory(module, subset="weights", size=None):
         not comfy.model_management.ensure_pin_registerable(registerable_size)):
         return _steal_pin(module, stack, buckets, size, priority)
 
+    extended = False
     try:
-        hostbuf.extend(size=size)
+        hostbuf.extend(size=size, register=False)
+        extended = True
+        pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
+        pin.untyped_storage()._comfy_hostbuf = hostbuf
+        if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
+            comfy.model_management.discard_cuda_async_error()
+            comfy.model_management.free_registrations(size)
+            if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
+                comfy.model_management.discard_cuda_async_error()
+                del pin
+                hostbuf.truncate(offset, do_unregister=False)
+                return _steal_pin(module, stack, buckets, size, priority)
     except RuntimeError:
+        if extended:
+            hostbuf.truncate(offset, do_unregister=False)
         return _steal_pin(module, stack, buckets, size, priority)
 
-    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
-    module._pin.untyped_storage()._comfy_hostbuf = hostbuf
+    module._pin = pin
     stack.append((module, offset))
     module._pin_registered = True
     module._pin_stack_index = len(stack) - 1

comfy/supported_models.py

@@ -1450,6 +1450,17 @@ class WAN21_SCAIL(WAN21_T2V):
         out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)
         return out
 
+
+class WAN21_SCAIL2(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "scail2",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_SCAIL2(self, image_to_video=False, device=device)
+        return out
+
 class WAN22_WanDancer(WAN21_T2V):
     unet_config = {
         "image_model": "wan2.1",
@@ -2045,6 +2056,23 @@ class RT_DETR_v4(supported_models_base.BASE):
         return None
 
 
+class DepthAnything3(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "DepthAnything3",
+    }
+
+    # Mono path: no num_heads / num_head_channels needed.
+    unet_extra_config = {}
+
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.DepthAnything3(self, device=device)
+
+    def clip_target(self, state_dict={}):
+        return None
+
+
 class ErnieImage(supported_models_base.BASE):
     unet_config = {
         "image_model": "ernie",
@@ -2259,6 +2287,7 @@ models = [
     WAN22_Animate,
     WAN21_FlowRVS,
     WAN21_SCAIL,
+    WAN21_SCAIL2,
     WAN22_WanDancer,
     Hunyuan3Dv2mini,
     Hunyuan3Dv2,
@@ -2286,4 +2315,5 @@ models = [
     CogVideoX_I2V,
     CogVideoX_T2V,
     SVD_img2vid,
+    DepthAnything3,
 ]

comfy/text_encoders/ideogram4.py

@@ -32,7 +32,9 @@ class Ideogram4Tokenizer(sd1_clip.SD1Tokenizer):
         self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
 
     def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
-        if llama_template is None:
+        if text.startswith('<|im_start|>'):
+            llama_text = text
+        elif llama_template is None:
             llama_text = self.llama_template.format(text)
         else:
             llama_text = llama_template.format(text)

comfy_api/latest/_io.py

@@ -755,6 +755,18 @@ class File3DKSPLAT(ComfyTypeIO):
     Type = File3D
 
 
+@comfytype(io_type="FILE_3D_SPLAT_ANY")
+class File3DSplatAny(ComfyTypeIO):
+    """General 3D Gaussian splat file type - accepts any supported splat container (.ply / .spz / .splat / .ksplat)."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_POINT_CLOUD_ANY")
+class File3DPointCloudAny(ComfyTypeIO):
+    """General point cloud file type - accepts any supported point cloud container (currently .ply)."""
+    Type = File3D
+
+
 @comfytype(io_type="HOOKS")
 class Hooks(ComfyTypeIO):
     if TYPE_CHECKING:
@@ -2336,6 +2348,8 @@ __all__ = [
     "File3DSPLAT",
     "File3DSPZ",
     "File3DKSPLAT",
+    "File3DSplatAny",
+    "File3DPointCloudAny",
     "Hooks",
     "HookKeyframes",
     "TimestepsRange",

comfy_api/latest/_ui.py

@@ -285,7 +285,7 @@ class AudioSaveHelper:
         results = []
         for batch_number, waveform in enumerate(audio["waveform"].cpu()):
             filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
-            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+            file = f"{filename_with_batch_num}_{counter:05}.{format}"
             output_path = os.path.join(full_output_folder, file)
 
             # Use original sample rate initially

comfy_api_nodes/apis/bfl.py

@@ -43,6 +43,7 @@ class BFLFluxEraseRequest(BaseModel):
         "white (255) marks areas to remove, black (0) marks areas to preserve.",
     )
     dilate_pixels: int = Field(10)
+    seed: int | None = Field(None)
     output_format: str = Field("png")
 
 

comfy_api_nodes/apis/bria.py

@@ -97,3 +97,28 @@ class BriaRemoveVideoBackgroundResult(BaseModel):
 class BriaRemoveVideoBackgroundResponse(BaseModel):
     status: str = Field(...)
     result: BriaRemoveVideoBackgroundResult | None = Field(None)
+
+
+class BriaVideoGreenScreenRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input video.")
+    green_shade: str = Field(
+        default="broadcast_green",
+        description="Solid chroma-key shade applied behind the foreground "
+        "(broadcast_green, chroma_green, or blue_screen).",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
+
+
+class BriaVideoReplaceBackgroundRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input (foreground) video.")
+    background_url: str = Field(
+        ...,
+        description="Publicly accessible URL of the background image or video to composite behind "
+        "the foreground. Stretched to the foreground frame; match its aspect ratio for "
+        "undistorted results.",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)

comfy_api_nodes/apis/gemini.py

@@ -108,13 +108,19 @@ class GeminiVideoMetadata(BaseModel):
     startOffset: GeminiOffset | None = Field(None)
 
 
+class GeminiThinkingConfig(BaseModel):
+    includeThoughts: bool | None = Field(None)
+    thinkingLevel: str = Field(...)
+
+
 class GeminiGenerationConfig(BaseModel):
-    maxOutputTokens: int | None = Field(None, ge=16, le=8192)
+    maxOutputTokens: int | None = Field(None, ge=16, le=65536)
     seed: int | None = Field(None)
     stopSequences: list[str] | None = Field(None)
     temperature: float | None = Field(None, ge=0.0, le=2.0)
     topK: int | None = Field(None, ge=1)
     topP: float | None = Field(None, ge=0.0, le=1.0)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
 
 
 class GeminiImageOutputOptions(BaseModel):
@@ -128,11 +134,6 @@ class GeminiImageConfig(BaseModel):
     imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
-class GeminiThinkingConfig(BaseModel):
-    includeThoughts: bool | None = Field(None)
-    thinkingLevel: str = Field(...)
-
-
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
     responseModalities: list[str] | None = Field(None)
     imageConfig: GeminiImageConfig | None = Field(None)

comfy_api_nodes/nodes_bfl.py

@@ -534,6 +534,15 @@ class FluxEraseNode(IO.ComfyNode):
                     max=25,
                     tooltip="Expands the mask boundaries to ensure clean coverage of the object's edges.",
                 ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
             ],
             outputs=[IO.Image.Output()],
             hidden=[
@@ -553,6 +562,7 @@ class FluxEraseNode(IO.ComfyNode):
         image: Input.Image,
         mask: Input.Image,
         dilate_pixels: int = 10,
+        seed: int = 0,
     ) -> IO.NodeOutput:
         validate_image_dimensions(image, min_width=256, min_height=256)
         mask = resize_mask_to_image(mask, image)
@@ -565,6 +575,7 @@ class FluxEraseNode(IO.ComfyNode):
                 image=tensor_to_base64_string(image[:, :, :, :3]),  # make sure image will have alpha channel removed
                 mask=mask,
                 dilate_pixels=dilate_pixels,
+                seed=seed,
             ),
         )
 

comfy_api_nodes/nodes_bria.py

@@ -1,14 +1,19 @@
+import av
+import torch
+from av.codec import CodecContext
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.bria import (
     BriaEditImageRequest,
+    BriaImageEditResponse,
     BriaRemoveBackgroundRequest,
     BriaRemoveBackgroundResponse,
     BriaRemoveVideoBackgroundRequest,
     BriaRemoveVideoBackgroundResponse,
-    BriaImageEditResponse,
     BriaStatusResponse,
+    BriaVideoGreenScreenRequest,
+    BriaVideoReplaceBackgroundRequest,
     InputModerationSettings,
 )
 from comfy_api_nodes.util import (
@@ -316,6 +321,248 @@ class BriaRemoveVideoBackground(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
 
 
+class BriaVideoGreenScreen(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoGreenScreen",
+            display_name="Bria Video Green Screen",
+            category="partner/video/Bria",
+            description="Replace a video's background with a solid chroma-key screen using Bria.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input(
+                    "green_shade",
+                    options=["broadcast_green", "chroma_green", "blue_screen"],
+                    tooltip="Solid chroma-key shade applied behind the foreground: "
+                    "broadcast_green (#00B140), chroma_green (#00FF00), or blue_screen (#0000FF).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        green_shade: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/green_screen", method="POST"),
+            data=BriaVideoGreenScreenRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                green_shade=green_shade,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+class BriaVideoReplaceBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoReplaceBackground",
+            display_name="Bria Video Replace Background",
+            category="partner/video/Bria",
+            description="Replace a video's background with a supplied image or video using Bria. "
+            "The output keeps the foreground's resolution and frame rate; a background with a "
+            "different aspect ratio is stretched to fit, so match it for undistorted results.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Foreground video whose background is replaced."),
+                IO.Image.Input(
+                    "background_image",
+                    optional=True,
+                    tooltip="Background image to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Video.Input(
+                    "background_video",
+                    optional=True,
+                    tooltip="Background video to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+        background_image: Input.Image | None = None,
+        background_video: Input.Video | None = None,
+    ) -> IO.NodeOutput:
+        if (background_image is None) == (background_video is None):
+            raise ValueError("Provide either a background image or a background video, not both.")
+        validate_video_duration(video, max_duration=60.0)
+        if background_video is not None:
+            validate_video_duration(background_video, max_duration=60.0)
+            background_url = await upload_video_to_comfyapi(cls, background_video, wait_label="Uploading background")
+        else:
+            background_url = await upload_image_to_comfyapi(cls, background_image, wait_label="Uploading background")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/replace_background", method="POST"),
+            data=BriaVideoReplaceBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_url=background_url,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+def _video_to_images_and_mask(video: Input.Video) -> tuple[Input.Image, Input.Mask]:
+    """Decode a transparent webm (VP9 + alpha) into image frames and an alpha mask.
+
+    VP9 keeps its alpha in a side layer that PyAV's default vp9 decoder drops, so the frames
+    are decoded with libvpx-vp9. Returns RGB images [B,H,W,3] in 0..1 and a mask [B,H,W]
+    following the Load Image convention (1 = transparent) for compositing or Save WEBM.
+    """
+    rgb_frames: list[torch.Tensor] = []
+    alpha_frames: list[torch.Tensor] = []
+    with av.open(video.get_stream_source(), mode="r") as container:
+        stream = container.streams.video[0]
+        decoder = CodecContext.create("libvpx-vp9", "r") if stream.codec_context.name == "vp9" else None
+        for packet in container.demux(stream):
+            for frame in (decoder.decode(packet) if decoder is not None else packet.decode()):
+                rgba = torch.from_numpy(frame.to_ndarray(format="rgba")).float() / 255.0
+                rgb_frames.append(rgba[..., :3])
+                alpha_frames.append(rgba[..., 3])
+    images = torch.stack(rgb_frames) if rgb_frames else torch.zeros(0, 0, 0, 3)
+    mask = (1.0 - torch.stack(alpha_frames)) if alpha_frames else torch.zeros((images.shape[0], 64, 64))
+    return images, mask
+
+
+class BriaTransparentVideoBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaTransparentVideoBackground",
+            display_name="Bria Remove Video Background (Transparent)",
+            category="partner/video/Bria",
+            description="Remove the background from a video using Bria and return the cut-out frames "
+            "plus an alpha mask. Connect both to a compositing node, or feed them to Save WEBM to "
+            "write a transparent video.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(display_name="images"),
+                IO.Mask.Output(display_name="mask"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
+            data=BriaRemoveVideoBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_color="Transparent",
+                output_container_and_codec="webm_vp9",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        video_out = await download_url_to_video_output(response.result.video_url)
+        images, mask = _video_to_images_and_mask(video_out)
+        return IO.NodeOutput(images, mask)
+
+
 class BriaExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -323,6 +570,9 @@ class BriaExtension(ComfyExtension):
             BriaImageEditNode,
             BriaRemoveImageBackground,
             BriaRemoveVideoBackground,
+            BriaVideoGreenScreen,
+            # BriaVideoReplaceBackground,  # server returns Status 500 when we pass background video
+            BriaTransparentVideoBackground,
         ]
 
 

comfy_api_nodes/nodes_bytedance.py

@@ -7,6 +7,7 @@ from io import BytesIO
 import torch
 from typing_extensions import override
 
+from comfy.utils import common_upscale
 from comfy_api.latest import IO, ComfyExtension, Input, Types
 from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS,
@@ -131,6 +132,44 @@ def _prepare_seedance_image(image: Input.Image) -> Input.Image:
     return image
 
 
+# Supported output aspect ratios, used to pre-size FLF frames to matching pixel pair to avoid the 1080p stretch jump.
+SEEDANCE2_RATIO_WH = {
+    "16:9": (16, 9),
+    "4:3": (4, 3),
+    "1:1": (1, 1),
+    "3:4": (3, 4),
+    "9:16": (9, 16),
+    "21:9": (21, 9),
+}
+SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080}
+
+
+def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]:
+    """Exact supported output (width, height) for (resolution, ratio).
+
+    The shorter side equals the resolution number (e.g. 1080p 16:9 -> 1920x1080). For ratio
+    "adaptive" (or any unexpected value) the ratio is derived from the image's own aspect, snapped
+    to the nearest supported ratio, so the output keeps the frame's orientation.
+    """
+    short = SEEDANCE2_RES_SHORT_SIDE[resolution]
+    if ratio not in SEEDANCE2_RATIO_WH:
+        aspect = image.shape[-2] / image.shape[-3]  # W / H; tensor is (B, H, W, C)
+        ratio = min(SEEDANCE2_RATIO_WH, key=lambda k: abs(SEEDANCE2_RATIO_WH[k][0] / SEEDANCE2_RATIO_WH[k][1] - aspect))
+    rw, rh = SEEDANCE2_RATIO_WH[ratio]
+    if rw >= rh:  # landscape or square: shorter side is the height
+        out_w, out_h = round(short * rw / rh), short
+    else:  # portrait: shorter side is the width
+        out_w, out_h = short, round(short * rh / rw)
+    return out_w - out_w % 2, out_h - out_h % 2
+
+
+def _resize_to_exact(image: torch.Tensor, width: int, height: int) -> torch.Tensor:
+    """Center-crop to the target aspect and resize to exactly width x height (lanczos)."""
+    samples = image.movedim(-1, 1)  # (B, H, W, C) -> (B, C, H, W)
+    resized = common_upscale(samples, width, height, "lanczos", "center")
+    return resized.movedim(1, -1)
+
+
 async def _resolve_reference_assets(
     cls: type[IO.ComfyNode],
     asset_ids: list[str],
@@ -1790,10 +1829,28 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         if last_frame is not None and last_frame_asset_id:
             raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.")
 
-        if first_frame is not None:
-            first_frame = _prepare_seedance_image(first_frame)
-        if last_frame is not None:
-            last_frame = _prepare_seedance_image(last_frame)
+        request_ratio = model["ratio"]
+        if first_frame_asset_id or last_frame_asset_id:
+            if first_frame is not None:
+                first_frame = _prepare_seedance_image(first_frame)
+            if last_frame is not None:
+                last_frame = _prepare_seedance_image(last_frame)
+        else:
+            # The 1080p FLF stretch fix (pre-size frames to a supported pixel pair + submit ratio="adaptive")
+            # only applies to local image inputs we can resize.
+            request_ratio = "adaptive"
+            target_dims: tuple[int, int] | None = None
+            if first_frame is not None:
+                validate_image_aspect_ratio(first_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(first_frame, min_width=300, min_height=300)
+                target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], first_frame)
+                first_frame = _resize_to_exact(first_frame, *target_dims)
+            if last_frame is not None:
+                validate_image_aspect_ratio(last_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(last_frame, min_width=300, min_height=300)
+                if target_dims is None:
+                    target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], last_frame)
+                last_frame = _resize_to_exact(last_frame, *target_dims)
 
         asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a]
         image_assets: dict[str, str] = {}
@@ -1844,7 +1901,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                 content=content,
                 generate_audio=model["generate_audio"],
                 resolution=model["resolution"],
-                ratio=model["ratio"],
+                ratio=request_ratio,
                 duration=model["duration"],
                 seed=seed,
                 watermark=watermark,

comfy_api_nodes/nodes_gemini.py

@@ -8,7 +8,7 @@ import os
 from enum import Enum
 from fnmatch import fnmatch
 from io import BytesIO
-from typing import Literal
+from typing import Any, Literal
 
 import torch
 from typing_extensions import override
@@ -19,6 +19,7 @@ from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
     GeminiGenerateContentRequest,
+    GeminiGenerationConfig,
     GeminiGenerateContentResponse,
     GeminiImageConfig,
     GeminiImageGenerateContentRequest,
@@ -40,13 +41,18 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
     upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
     validate_string,
     video_to_base64_string,
 )
 
 GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
 GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+GEMINI_URL_INPUT_BUDGET = 10
+GEMINI_MAX_INLINE_BYTES = 18 * 1024 * 1024
 GEMINI_IMAGE_SYS_PROMPT = (
     "You are an expert image-generation engine. You must ALWAYS produce an image.\n"
     "Interpret all user input—regardless of "
@@ -285,6 +291,140 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
     return final_price / 1_000_000.0
 
 
+def create_video_parts(video_input: Input.Video) -> list[GeminiPart]:
+    """Convert a single video input to Gemini API compatible parts (inline MP4/H.264)."""
+    base_64_string = video_to_base64_string(
+        video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+    )
+    return [
+        GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=GeminiMimeType.video_mp4,
+                data=base_64_string,
+            )
+        )
+    ]
+
+
+def create_audio_parts(audio_input: Input.Audio) -> list[GeminiPart]:
+    """Convert an audio input to Gemini API compatible parts (one inline MP3 part per batch item)."""
+    audio_parts: list[GeminiPart] = []
+    for batch_index in range(audio_input["waveform"].shape[0]):
+        # Recreate an IO.AUDIO object for the given batch dimension index
+        audio_at_index = Input.Audio(
+            waveform=audio_input["waveform"][batch_index].unsqueeze(0),
+            sample_rate=audio_input["sample_rate"],
+        )
+        # Convert to MP3 format for compatibility with Gemini API
+        audio_bytes = audio_to_base64_string(
+            audio_at_index,
+            container_format="mp3",
+            codec_name="libmp3lame",
+        )
+        audio_parts.append(
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.audio_mp3,
+                    data=audio_bytes,
+                )
+            )
+        )
+    return audio_parts
+
+
+def _flatten_images(images: list[Input.Image]) -> list[torch.Tensor]:
+    """Expand any batched image tensors into individual (H, W, C) frames, preserving order."""
+    frames: list[torch.Tensor] = []
+    for img in images:
+        if len(img.shape) == 4:
+            frames.extend(img[i] for i in range(img.shape[0]))
+        else:
+            frames.append(img)
+    return frames
+
+
+def _flatten_audio(audios: list[Input.Audio]) -> list[Input.Audio]:
+    """Expand any batched audio inputs into individual single-clip audio inputs, preserving order."""
+    clips: list[Input.Audio] = []
+    for audio in audios:
+        waveform = audio["waveform"]
+        for i in range(waveform.shape[0]):
+            clips.append(Input.Audio(waveform=waveform[i].unsqueeze(0), sample_rate=audio["sample_rate"]))
+    return clips
+
+
+async def _media_url_part(cls: type[IO.ComfyNode], kind: str, payload: Any) -> GeminiPart:
+    """Upload a single media unit to ComfyAPI storage and return a fileData (URL) part."""
+    if kind == "image":
+        url = await upload_image_to_comfyapi(cls, payload, mime_type="image/png", wait_label="Uploading image")
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.image_png, fileUri=url))
+    if kind == "audio":
+        url = await upload_audio_to_comfyapi(
+            cls, payload, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mp3"
+        )
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.audio_mp3, fileUri=url))
+    url = await upload_video_to_comfyapi(cls, payload, wait_label="Uploading video")
+    return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.video_mp4, fileUri=url))
+
+
+def _media_inline_part(kind: str, payload: Any) -> tuple[GeminiPart, int]:
+    """Encode a single media unit as an inline base64 part; returns (part, base64_length)."""
+    if kind == "image":
+        data = tensor_to_base64_string(payload, mime_type="image/webp")
+        mime = GeminiMimeType.image_webp
+    elif kind == "audio":
+        data = audio_to_base64_string(payload, container_format="mp3", codec_name="libmp3lame")
+        mime = GeminiMimeType.audio_mp3
+    else:
+        data = video_to_base64_string(
+            payload, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+        )
+        mime = GeminiMimeType.video_mp4
+    return GeminiPart(inlineData=GeminiInlineData(mimeType=mime, data=data)), len(data)
+
+
+async def build_gemini_media_parts(
+    cls: type[IO.ComfyNode],
+    images: list[Input.Image],
+    audios: list[Input.Audio],
+    videos: list[Input.Video],
+    *,
+    url_budget: int = GEMINI_URL_INPUT_BUDGET,
+    max_inline_bytes: int = GEMINI_MAX_INLINE_BYTES,
+) -> list[GeminiPart]:
+    """Build Gemini parts for multimodal inputs (images, audio, video).
+
+    fileData URLs are preferred for every media type: the upload is fetched directly by the
+    model, keeping the request body tiny regardless of media size. The URL budget is shared
+    across all media and assigned largest-first (video, then audio, then images), so that if it
+    is ever exhausted the inline-base64 overflow is limited to the smallest items. Total inline
+    payload is capped by `max_inline_bytes`.
+    """
+    units: list[tuple[str, Any]] = (
+        [("video", v) for v in videos]
+        + [("audio", a) for a in _flatten_audio(audios)]
+        + [("image", f) for f in _flatten_images(images)]
+    )
+
+    parts: list[GeminiPart] = []
+    url_used = 0
+    inline_bytes = 0
+    for kind, payload in units:
+        if url_used < url_budget:
+            parts.append(await _media_url_part(cls, kind, payload))
+            url_used += 1
+            continue
+        part, nbytes = _media_inline_part(kind, payload)
+        inline_bytes += nbytes
+        if inline_bytes > max_inline_bytes:
+            raise ValueError(
+                f"Too much media to send inline (over {max_inline_bytes // (1024 * 1024)}MB after the first "
+                f"{url_budget} inputs are uploaded as URLs). Reduce the number or size of attached media."
+            )
+        parts.append(part)
+    return parts
+
+
 class GeminiNode(IO.ComfyNode):
     """
     Node to generate text responses from a Gemini model.
@@ -407,58 +547,9 @@ class GeminiNode(IO.ComfyNode):
                 )
                 """,
             ),
+            is_deprecated=True,
         )
 
-    @classmethod
-    def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]:
-        """Convert video input to Gemini API compatible parts."""
-
-        base_64_string = video_to_base64_string(
-            video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
-        )
-        return [
-            GeminiPart(
-                inlineData=GeminiInlineData(
-                    mimeType=GeminiMimeType.video_mp4,
-                    data=base_64_string,
-                )
-            )
-        ]
-
-    @classmethod
-    def create_audio_parts(cls, audio_input: Input.Audio) -> list[GeminiPart]:
-        """
-        Convert audio input to Gemini API compatible parts.
-
-        Args:
-            audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
-
-        Returns:
-            List of GeminiPart objects containing the encoded audio.
-        """
-        audio_parts: list[GeminiPart] = []
-        for batch_index in range(audio_input["waveform"].shape[0]):
-            # Recreate an IO.AUDIO object for the given batch dimension index
-            audio_at_index = Input.Audio(
-                waveform=audio_input["waveform"][batch_index].unsqueeze(0),
-                sample_rate=audio_input["sample_rate"],
-            )
-            # Convert to MP3 format for compatibility with Gemini API
-            audio_bytes = audio_to_base64_string(
-                audio_at_index,
-                container_format="mp3",
-                codec_name="libmp3lame",
-            )
-            audio_parts.append(
-                GeminiPart(
-                    inlineData=GeminiInlineData(
-                        mimeType=GeminiMimeType.audio_mp3,
-                        data=audio_bytes,
-                    )
-                )
-            )
-        return audio_parts
-
     @classmethod
     async def execute(
         cls,
@@ -482,9 +573,9 @@ class GeminiNode(IO.ComfyNode):
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
         if audio is not None:
-            parts.extend(cls.create_audio_parts(audio))
+            parts.extend(create_audio_parts(audio))
         if video is not None:
-            parts.extend(cls.create_video_parts(video))
+            parts.extend(create_video_parts(video))
         if files is not None:
             parts.extend(files)
 
@@ -512,6 +603,210 @@ class GeminiNode(IO.ComfyNode):
         return IO.NodeOutput(output_text or "Empty response from Gemini model...")
 
 
+GEMINI_V2_MODELS: dict[str, str] = {
+    "Gemini 3.1 Pro": "gemini-3.1-pro-preview",
+    "Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview",
+}
+
+
+def _gemini_text_model_inputs(thinking_default: str) -> list[Input]:
+    """Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls)."""
+    return [
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 17)],
+                min=0,
+            ),
+            tooltip="Optional image(s) to use as context for the model. Up to 16 images.",
+        ),
+        IO.Autogrow.Input(
+            "audio",
+            template=IO.Autogrow.TemplateNames(
+                IO.Audio.Input("audio"),
+                names=["audio_1"],
+                min=0,
+            ),
+            tooltip="Optional audio clip to use as context for the model.",
+        ),
+        IO.Autogrow.Input(
+            "video",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=["video_1"],
+                min=0,
+            ),
+            tooltip="Optional video clip to use as context for the model.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+            "Accepts inputs from the Gemini Input Files node.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=["LOW", "HIGH"],
+            default=thinking_default,
+            tooltip="How hard the model reasons internally before answering. "
+            "HIGH improves quality on difficult tasks but costs more (thinking) tokens and is slower.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more creative.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+        IO.Int.Input(
+            "max_output_tokens",
+            default=32768,
+            min=16,
+            max=65536,
+            tooltip="Maximum tokens to generate, including the model's internal thinking. "
+            "With thinking_level HIGH, a low value can leave no room for the answer; raise this if "
+            "responses come back empty or truncated. The model stops early when finished, so a higher "
+            "cap costs nothing extra for short replies.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNodeV2",
+            display_name="Google Gemini",
+            category="partner/text/Gemini",
+            essentials_category="Text Generation",
+            description="Generate text responses with Google's Gemini models. Provide a text prompt and, "
+            "optionally, one or more images, audio clips, videos, or files as multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model. Include detailed instructions, questions, or context.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Gemini 3.1 Pro", _gemini_text_model_inputs("HIGH")),
+                        IO.DynamicCombo.Option("Gemini 3.1 Flash-Lite", _gemini_text_model_inputs("LOW")),
+                    ],
+                    tooltip="The Gemini model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 for a random seed. Deterministic output isn't guaranteed.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  } : {
+                    "type": "list_usd",
+                    "usd": [0.002, 0.012],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = GEMINI_V2_MODELS[model["model"]]
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        audios = [a for a in (model.get("audio") or {}).values() if a is not None]
+        videos = [v for v in (model.get("video") or {}).values() if v is not None]
+        if images or audios or videos:
+            parts.extend(await build_gemini_media_parts(cls, images, audios, videos))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role=GeminiRole.user,
+                        parts=parts,
+                    )
+                ],
+                generationConfig=GeminiGenerationConfig(
+                    temperature=model["temperature"],
+                    topP=model["top_p"],
+                    maxOutputTokens=model["max_output_tokens"],
+                    seed=seed if seed > 0 else None,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+
+        output_text = get_text_from_response(response)
+        return IO.NodeOutput(output_text or "Empty response from Gemini model...")
+
+
 class GeminiInputFiles(IO.ComfyNode):
     """
     Loads and formats input files for use with the Gemini API.
@@ -1129,6 +1424,26 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     tooltip="Foundational instructions that dictate an AI's behavior.",
                     advanced=True,
                 ),
+                IO.Float.Input(
+                    "temperature",
+                    default=1.0,
+                    min=0.0,
+                    max=2.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Controls randomness in generation. Lower is more focused/deterministic.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "top_p",
+                    default=0.95,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Nucleus sampling threshold. Lower is more focused, higher more diverse.",
+                    advanced=True,
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -1165,6 +1480,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         seed: int,
         response_modalities: str,
         system_prompt: str = "",
+        temperature: float = 1.0,
+        top_p: float = 0.95,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_choice = model["model"]
@@ -1204,6 +1521,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
                     imageConfig=image_config,
                     thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                    temperature=temperature,
+                    topP=top_p,
                 ),
                 systemInstruction=gemini_system_prompt,
             ),
@@ -1222,6 +1541,7 @@ class GeminiExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             GeminiNode,
+            GeminiNodeV2,
             GeminiImage,
             GeminiImage2,
             GeminiNanoBanana2,

comfy_api_nodes/nodes_krea.py

@@ -42,9 +42,11 @@ async def _upload_image_to_krea_assets(cls: type[IO.ComfyNode], image: Input.Ima
 
 
 _MODEL_MEDIUM = "Krea 2 Medium"
+_MODEL_MEDIUM_TURBO = "Krea 2 Medium Turbo"
 _MODEL_LARGE = "Krea 2 Large"
 _MODEL_ENDPOINTS: dict[str, str] = {
     _MODEL_MEDIUM: "/proxy/krea/generate/image/krea/krea-2/medium",
+    _MODEL_MEDIUM_TURBO: "/proxy/krea/generate/image/krea/krea-2/medium-turbo",
     _MODEL_LARGE: "/proxy/krea/generate/image/krea/krea-2/large",
 }
 
@@ -57,7 +59,7 @@ _UUID_RE = re.compile(r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F
 
 
 def _krea_model_inputs() -> list:
-    """Nested inputs shared by both Krea 2 Medium and Large under the DynamicCombo."""
+    """Nested inputs shared by Krea 2 Medium, Medium Turbo and Large under the DynamicCombo."""
     return [
         IO.Combo.Input(
             "aspect_ratio",
@@ -123,6 +125,7 @@ class Krea2ImageNode(IO.ComfyNode):
                     "model",
                     options=[
                         IO.DynamicCombo.Option(_MODEL_MEDIUM, _krea_model_inputs()),
+                        IO.DynamicCombo.Option(_MODEL_MEDIUM_TURBO, _krea_model_inputs()),
                         IO.DynamicCombo.Option(_MODEL_LARGE, _krea_model_inputs()),
                     ],
                     tooltip="Krea 2 Medium is best for expressive illustrations; "
@@ -151,14 +154,15 @@ class Krea2ImageNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $isLarge := widgets.model = "krea 2 large";
+                  $rates := {
+                    "krea 2 medium turbo": {"text": 0.015, "style": 0.0175, "moodboard": 0.02},
+                    "krea 2 medium": {"text": 0.03, "style": 0.035, "moodboard": 0.04},
+                    "krea 2 large": {"text": 0.06, "style": 0.065, "moodboard": 0.07}
+                  };
+                  $r := $lookup($rates, widgets.model);
                   $hasMoodboard := $length($lookup(widgets, "model.moodboard_id")) > 0;
                   $hasStyle := $lookup(inputs, "model.style_reference").connected;
-                  $usd := $hasMoodboard
-                    ? ($isLarge ? 0.07 : 0.04)
-                    : ($hasStyle
-                        ? ($isLarge ? 0.065 : 0.035)
-                        : ($isLarge ? 0.06 : 0.03));
+                  $usd := $hasMoodboard ? $r.moodboard : ($hasStyle ? $r.style : $r.text);
                   {"type":"usd","usd": $usd}
                 )
                 """,

comfy_extras/nodes_audio.py

@@ -158,7 +158,7 @@ class SaveAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudio",
             search_aliases=["export flac"],
-            display_name="Save Audio (FLAC)",
+            display_name="Save Audio (FLAC) (Deprecated)",
             category="audio",
             essentials_category="Audio",
             inputs=[
@@ -167,6 +167,7 @@ class SaveAudio(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -186,7 +187,7 @@ class SaveAudioMP3(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudioMP3",
             search_aliases=["export mp3"],
-            display_name="Save Audio (MP3)",
+            display_name="Save Audio (MP3) (Deprecated)",
             category="audio",
             essentials_category="Audio",
             inputs=[
@@ -196,6 +197,7 @@ class SaveAudioMP3(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -217,7 +219,7 @@ class SaveAudioOpus(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudioOpus",
             search_aliases=["export opus"],
-            display_name="Save Audio (Opus)",
+            display_name="Save Audio (Opus) (Deprecated)",
             category="audio",
             inputs=[
                 IO.Audio.Input("audio"),
@@ -226,6 +228,7 @@ class SaveAudioOpus(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -241,6 +244,54 @@ class SaveAudioOpus(IO.ComfyNode):
     save_opus = execute  # TODO: remove
 
 
+class SaveAudioAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioAdvanced",
+            search_aliases=["save audio", "export audio", "output audio", "write audio", "flac", "mp3", "opus"],
+            display_name="Save Audio (Advanced)",
+            description="Saves the input audio to your ComfyUI output directory.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio", tooltip="The audio to save."),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="audio/ComfyUI",
+                    tooltip=(
+                        "The prefix for the file to save. May include formatting tokens "
+                        "such as %date:yyyy-MM-dd%."
+                    ),
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option("flac", []),
+                        IO.DynamicCombo.Option("mp3", [
+                            IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+                        ]),
+                        IO.DynamicCombo.Option("opus", [
+                            IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+                        ]),
+                    ],
+                    tooltip="The file format in which to save the audio.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix: str, format: dict) -> IO.NodeOutput:
+        file_format = format.get("format", None)
+        quality = format.get("quality", None)
+        if quality:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format, quality=quality)
+        else:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format)
+        return IO.NodeOutput(ui=ui)
+
+
 class PreviewAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -822,6 +873,7 @@ class AudioExtension(ComfyExtension):
             SaveAudio,
             SaveAudioMP3,
             SaveAudioOpus,
+            SaveAudioAdvanced,
             LoadAudio,
             PreviewAudio,
             ConditioningStableAudio,

comfy_extras/nodes_bernini.py

@@ -0,0 +1,115 @@
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.utils
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+def _resize_long_edge(image, max_size, stride=16):
+    """Resize (preserve aspect) so the long edge <= max_size, then snap each side to `stride`"""
+    h, w = image.shape[1], image.shape[2]
+    scale = min(max_size / max(h, w), 1.0)
+    nh = max(stride, round(h * scale / stride) * stride)
+    nw = max(stride, round(w * scale / stride) * stride)
+    return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, "area", "disabled").movedim(1, -1)
+
+
+class BerniniConditioning(io.ComfyNode):
+    """Bernini in-context conditioning for a Wan2.2-A14B model.
+
+    Attaches the VAE-encoded source video / reference images to the conditioning
+    source video first, then each reference image
+
+    The task is inferred from which inputs are connected:
+      (nothing)                  -> t2v (text-to-video)
+      source_video               -> v2v (video-to-video)
+      source_video + ref_images  -> rv2v (reference-guided video editing)
+      ref_images only            -> r2v (reference-to-video)
+      source_video + ref_video   -> ads2v (insert image/video into video)
+
+    source_video is the edit base / canvas (resized to width x height).
+    reference_video is moving content to composite in.
+    Streams are ordered source_video, reference_video, then reference_images -> source_id (1, 2, 3, ...).
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BerniniConditioning",
+            display_name="Bernini Conditioning",
+            category="conditioning/video_models",
+            description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
+                         "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=8192, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("source_video", optional=True, tooltip=(
+                    "Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
+                io.Image.Input("reference_video", optional=True, tooltip=(
+                    "Video to insert into the source video (ads2v).")),
+                io.Autogrow.Input("reference_images", optional=True,
+                    template=io.Autogrow.TemplatePrefix(
+                        input=io.Image.Input("reference_image", tooltip=(
+                            "Reference image injected as an in-context token (r2v, rv2v).")),
+                        prefix="reference_image_", min=0, max=8)),
+                io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
+                    "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
+            ],
+            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, width, height, length, batch_size,
+                source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
+                             device=comfy.model_management.intermediate_device())
+
+        # source_video (1), reference_video (2), reference_images (3, 4, ...).
+        context = []
+        if source_video is not None:
+            vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
+            context.append(vae.encode(vid[:, :, :, :3]))
+
+        if reference_video is not None:
+            ref_vid = _resize_long_edge(reference_video[:length], ref_max_size)  # moving content, native aspect
+            context.append(vae.encode(ref_vid[:, :, :, :3]))
+
+        # reference_images is an autogrow dict {reference_image_0: IMAGE, ...}; each slot is a
+        # separate stream at its own native aspect (a multi-image batch in one slot -> one stream per frame).
+        if reference_images:
+            for name in sorted(reference_images):
+                imgs = reference_images[name]
+                if imgs is None:
+                    continue
+                for i in range(imgs.shape[0]):
+                    img = _resize_long_edge(imgs[i:i + 1], ref_max_size)  # native aspect per ref
+                    context.append(vae.encode(img[:, :, :, :3]))
+
+        if context:
+            positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
+            negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
+
+        return io.NodeOutput(positive, negative, {"samples": latent})
+
+
+class BerniniExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            BerniniConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> BerniniExtension:
+    return BerniniExtension()

comfy_extras/nodes_bg_removal.py

@@ -36,15 +36,15 @@ class RemoveBackground(IO.ComfyNode):
             category="image/background removal",
             description="Generates a foreground mask to remove the background from an image using a background removal model.",
             inputs=[
-                IO.Image.Input("image", tooltip="Input image to remove the background from"),
-                IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask")
+                IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask"),
+                IO.Image.Input("image", tooltip="Input image to remove the background from")
             ],
             outputs=[
                 IO.Mask.Output("mask", tooltip="Generated foreground mask")
             ]
         )
     @classmethod
-    def execute(cls, image, bg_removal_model):
+    def execute(cls, bg_removal_model, image):
         mask = bg_removal_model.encode_image(image)
         return IO.NodeOutput(mask)
 

comfy_extras/nodes_color.py

@@ -7,29 +7,29 @@ class ColorToRGBInt(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="ColorToRGBInt",
-            display_name="Color to RGB Int",
+            display_name="Color Picker",
             category="utilities",
-            description="Convert a color to a RGB integer value.",
+            description="Return a color RGB integer value and hexadecimal representation.",
             inputs=[
                 io.Color.Input("color"),
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
+                io.Color.Output(display_name="hex")
             ],
         )
 
     @classmethod
-    def execute(
-        cls,
-        color: str,
-    ) -> io.NodeOutput:
+    def execute(cls, color: str) -> io.NodeOutput:
         # expect format #RRGGBB
         if len(color) != 7 or color[0] != "#":
             raise ValueError("Color must be in format #RRGGBB")
         r = int(color[1:3], 16)
         g = int(color[3:5], 16)
         b = int(color[5:7], 16)
-        return io.NodeOutput(r * 256 * 256 + g * 256 + b)
+
+        rgb_int = r * 256 * 256 + g * 256 + b
+        return io.NodeOutput(rgb_int, color)
 
 
 class ColorExtension(ComfyExtension):

comfy_extras/nodes_custom_sampler.py

@@ -933,9 +933,10 @@ class Guider_DualModel(comfy.samplers.CFGGuider):
 
     def predict_noise(self, x, timestep, model_options={}, seed=None):
         positive = self.conds.get("positive", None)
-        if self.uncond_inner is None:  # cfg == 1 or no negative -> single model, cond only
-            return comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
         cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
+        # uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only
+        if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)):
+            return cond
 
         uncond_model_options = model_options
         if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
@@ -1140,7 +1141,7 @@ class CFGOverride(io.ComfyNode):
         return io.Schema(
             node_id="CFGOverride",
             display_name="CFG Override",
-            description="Override cfg to a fixed value over a [start, end] percent slice of the steps. "
+            description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
                         "With multiple overrides, the one nearest the sampler wins on overlap.",
             category="sampling/custom_sampling",
             inputs=[

comfy_extras/nodes_dataset.py

@@ -411,6 +411,21 @@ class ImageProcessingNode(io.ComfyNode):
 
         return has_group
 
+    @classmethod
+    def _ensure_image_list(cls, images):
+        """Normalize to a flat list of [1, H, W, C] tensors."""
+        if isinstance(images, torch.Tensor):
+            if images.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got shape {tuple(images.shape)}")
+            return [images[i:i+1] for i in range(images.shape[0])]
+
+        flat = []
+        for item in images:
+            if not isinstance(item, torch.Tensor) or item.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got {type(item).__name__} shape {getattr(item, 'shape', None)}")
+            flat.extend([item[i:i+1] for i in range(item.shape[0])])
+        return flat
+
     @classmethod
     def define_schema(cls):
         if cls.node_id is None:
@@ -458,6 +473,9 @@ class ImageProcessingNode(io.ComfyNode):
         """Execute the node. Routes to _process or _group_process based on mode."""
         is_group = cls._detect_processing_mode()
 
+        if is_group:
+            images = cls._ensure_image_list(images)
+
         # Extract scalar values from lists for parameters
         params = {}
         for k, v in kwargs.items():

comfy_extras/nodes_depth_anything_3.py

@@ -0,0 +1,681 @@
+"""ComfyUI nodes for Depth Anything 3.
+Model capability matrix:
+
+Variant               head_type  has_sky  has_conf  cam_dec
+DA3-Small             dualdpt    False    True      yes
+DA3-Base              dualdpt    False    True      yes
+DA3-Mono-Large        dpt        True     False     no
+DA3-Metric-Large      dpt        True     False     no  (raw output is metres)
+"""
+
+from __future__ import annotations
+
+import logging
+from typing_extensions import override
+
+import torch
+
+import comfy.model_management as mm
+import comfy.sd
+import folder_paths
+from comfy.ldm.colormap import turbo as _turbo
+from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess
+from comfy_api.latest import ComfyExtension, Types, io
+from comfy.ldm.moge.geometry import triangulate_grid_mesh
+
+DA3ModelType = io.Custom("DA3_MODEL")
+DA3Geometry = io.Custom("DA3_GEOMETRY")
+DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
+
+# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
+#
+# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
+#   "depth":       torch.Tensor (B, H, W)         -- raw model depth (always present; matches MoGe convention)
+#   "image":       torch.Tensor (B, H, W, 3)      -- source image in [0, 1], CPU (always present)
+#   "mode":        str                            -- "mono" or "multiview" (always present)
+#   "sky":         torch.Tensor (B, H, W)         -- sky probability in [0, 1] (Mono/Metric variants only)
+#   "confidence":  torch.Tensor (B, H, W)         -- raw model confidence output (Small/Base variants only)
+#
+# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
+#   "extrinsics":  torch.Tensor (1, S, 3, 4)      -- world-to-camera [R|t] matrices
+#   "intrinsics":  torch.Tensor (1, S, 3, 3)      -- pixel-space intrinsics
+#
+# DA3_POINT_CLOUD is a dict:
+#   "points":     torch.Tensor (N, 3)  -- 3-D coords in glTF convention (Y-up, Z-back)
+#   "colors":     torch.Tensor (N, 3)  -- RGB in [0, 1], or None
+#   "confidence": torch.Tensor (N,)    -- raw confidence per point, or None
+
+
+def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
+    """Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space."""
+    H, W = depth.shape
+    u = torch.arange(W, dtype=torch.float32, device=depth.device)
+    v = torch.arange(H, dtype=torch.float32, device=depth.device)
+    u, v = torch.meshgrid(u, v, indexing='xy')             # both (H, W)
+    pix = torch.stack([u, v, torch.ones_like(u)], dim=-1)  # (H, W, 3)
+    rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix)
+    return rays * depth.unsqueeze(-1)                       # (H, W, 3)
+
+
+def _da3_default_K(H: int, W: int) -> torch.Tensor:
+    """Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry)."""
+    fx = fy = float(W) * 0.7
+    return torch.tensor([[fx, 0.0, (W - 1) / 2.0],
+                         [0.0, fy, (H - 1) / 2.0],
+                         [0.0, 0.0, 1.0]], dtype=torch.float32)
+
+
+def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
+    """Return pixel-space K for batch element b, falling back to a default estimate."""
+    if "intrinsics" in geometry:
+        # shape (1, S, 3, 3) - leading scene dimension from the multiview head
+        return geometry["intrinsics"][0, b].float()
+    logging.getLogger("comfy").warning(
+        "DA3_GEOMETRY has no intrinsics (mono-mode model). "
+        "Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate."
+    )
+    return _da3_default_K(H, W)
+
+
+def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None:
+    """Return the world-to-camera extrinsic for batch element b, or None in mono mode.
+
+    The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4).
+    _da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing.
+    """
+    if "extrinsics" not in geometry:
+        return None
+    return geometry["extrinsics"][0, b].float()
+
+
+def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor:
+    """Transform (H,W,3) OpenCV camera-space points to world space."""
+    E = E.to(points_cam.device).float()
+    if not torch.isfinite(E).all():
+        logging.getLogger("comfy").warning(
+            "DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). "
+            "Falling back to camera-space coordinates."
+        )
+        return points_cam
+    H, W, _ = points_cam.shape
+    R = E[:3, :3]           # (3, 3) rotation
+    t = E[:3, 3]            # (3,)   translation
+    R_inv = R.T             # rotation inverse = transpose for orthogonal R
+    t_inv = -(R_inv @ t)    # (3,)
+    pts = points_cam.reshape(-1, 3)                 # (N, 3)
+    pts_world = pts @ R_inv.T + t_inv               # (N, 3)
+    return pts_world.reshape(H, W, 3)
+
+
+def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor:
+    """Map raw confidence to [0, 1] per image."""
+    B = conf.shape[0]
+    out = []
+    for i in range(B):
+        c = conf[i]
+        c_min, c_max = c.min(), c.max()
+        out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c))
+    return torch.stack(out, dim=0)
+
+
+def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor:
+    """Build (H,W) bool keep-mask from sky probability and confidence."""
+    mask = torch.ones(H, W, dtype=torch.bool)
+    if use_sky_mask and "sky" in geometry:
+        mask = mask & (geometry["sky"][b] < 0.5)
+    if "confidence" in geometry and confidence_threshold > 0.0:
+        conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0]
+        mask = mask & (conf_norm >= confidence_threshold)
+    return mask
+
+
+class LoadDA3Model(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadDA3Model",
+            display_name="Load Depth Anything 3",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input(
+                    "model_name",
+                    options=folder_paths.get_filename_list("geometry_estimation"),
+                ),
+                io.Combo.Input(
+                    "weight_dtype",
+                    options=["default", "fp16", "bf16", "fp32"],
+                    default="default",
+                ),
+            ],
+            outputs=[DA3ModelType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_name, weight_dtype) -> io.NodeOutput:
+        model_options = {}
+        if weight_dtype == "fp16":
+            model_options["dtype"] = torch.float16
+        elif weight_dtype == "bf16":
+            model_options["dtype"] = torch.bfloat16
+        elif weight_dtype == "fp32":
+            model_options["dtype"] = torch.float32
+
+        path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name)
+        model = comfy.sd.load_diffusion_model(path, model_options=model_options)
+        return io.NodeOutput(model)
+
+
+def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"):
+    """Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None)."""
+    assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+
+    B, H, W, _ = image.shape
+    mm.load_model_gpu(model_patcher)
+    diffusion = model_patcher.model.diffusion_model
+    device = mm.get_torch_device()
+    dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+    depths, confs, skies = [], [], []
+    for i in range(B):
+        single = image[i:i + 1].to(device)
+        x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method)
+        x = x.to(dtype=dtype)
+        with torch.no_grad():
+            out = diffusion(x)
+
+        depth_lr = out["depth"]
+        depth_full = torch.nn.functional.interpolate(
+            depth_lr.unsqueeze(1).float(), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+        depths.append(depth_full)
+
+        if "depth_conf" in out:
+            conf_full = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            confs.append(conf_full)
+        if "sky" in out:
+            sky_full = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            skies.append(sky_full)
+
+    depth = torch.cat(depths, dim=0)
+    confidence = torch.cat(confs, dim=0) if confs else None
+    sky = torch.cat(skies, dim=0) if skies else None
+    return depth, confidence, sky
+
+
+class DA3Inference(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Inference",
+            search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
+            display_name="Run Depth Anything 3",
+            category="image/geometry estimation",
+            description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
+            inputs=[
+                DA3ModelType.Input("da3_model"),
+                io.Image.Input("image"),
+                io.Int.Input("resolution", default=504, min=140, max=2520, step=14,
+                    tooltip="Resolution the model runs at (longest side, multiple of 14).\n"
+                        "Lower = faster / less VRAM.\n"
+                        "Higher = more detail.\n"
+                        "Output is upsampled back to the original size."),
+                io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize",
+                    tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n"
+                        "lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."),
+                io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n"
+                    "multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).",
+                    options=[
+                        io.DynamicCombo.Option("mono", []),
+                        io.DynamicCombo.Option("multiview", [
+                        io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced",
+                            tooltip="Which view acts as the geometric anchor.\n"
+                                "- saddle_balanced: the view most 'average' across all others (best general choice).\n"
+                                "- saddle_sim_range: the view most visually distinct from the others.\n"
+                                "- first / middle: fixed positional picks."),
+                        io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec",
+                            tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n"
+                                "- cam_dec: learned from image features.\n"
+                                "- ray_pose: derived geometrically from the model's 3D ray output.\n"
+                                "Affects perspective correctness of the 3D output. Try both if results look distorted."),
+                    ]),
+                ]),
+            ],
+            outputs=[
+                DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n"
+                    "Always has the keys: depth, image, mode.\n"
+                    "Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput:
+        mode_val = mode["mode"]  # "mono" or "multiview"
+
+        if mode_val == "mono":
+            return cls._execute_mono(da3_model, image, resolution, resize_method)
+
+        # Capability checks for multi-view mode.
+        diffusion = da3_model.model.diffusion_model
+        pose_method = mode["pose_method"]
+        ref_view_strategy = mode["ref_view_strategy"]
+
+        has_cam_dec = diffusion.cam_dec is not None
+        has_dualdpt = diffusion.head_type == "dualdpt"
+
+        if not has_cam_dec and not has_dualdpt:
+            raise ValueError(
+                "multi-view mode requires Small or Base model. The loaded model "
+                f"(head_type='{diffusion.head_type}') does not support cross-view "
+                "attention or camera pose estimation. Switch mode to 'mono', or "
+                "load Small or Base model for mult-view."
+            )
+
+        if pose_method == "cam_dec" and not has_cam_dec:
+            raise ValueError(
+                "pose_method='cam_dec' requires a camera decoder, but the loaded "
+                f"model (head_type='{diffusion.head_type}') does not have one. "
+                "Use pose_method='ray_pose' instead."
+            )
+        if pose_method == "ray_pose" and not has_dualdpt:
+            raise ValueError(
+                "pose_method='ray_pose' requires a DualDPT head, but the loaded "
+                f"model has a '{diffusion.head_type}' head. "
+                "Use pose_method='cam_dec' instead."
+            )
+
+        return cls._execute_multiview(
+            da3_model, image, resolution, resize_method,
+            ref_view_strategy, pose_method,
+        )
+
+    @classmethod
+    def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput:
+        depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method)
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "mono",
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if confidence is not None:
+            geometry["confidence"] = confidence.contiguous()
+        return io.NodeOutput(geometry)
+
+    @classmethod
+    def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput:
+        assert image.ndim == 4 and image.shape[-1] == 3, \
+            f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+        S, H, W, _ = image.shape
+
+        mm.load_model_gpu(model)
+        diffusion = model.model.diffusion_model
+        device = mm.get_torch_device()
+        dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+        # All views in a single forward pass: (1, S, 3, H', W').
+        x = image.to(device)
+        x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method)
+        x = x.to(dtype=dtype).unsqueeze(0)
+
+        use_ray_pose = (pose_method == "ray_pose")
+        with torch.no_grad():
+            out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy)
+
+        depth = torch.nn.functional.interpolate(
+            out["depth"].float().unsqueeze(1), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+
+        sky = None
+        if "sky" in out:
+            sky = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+
+        if "extrinsics" in out and "intrinsics" in out:
+            extrinsics = out["extrinsics"].float().cpu()
+            intrinsics = out["intrinsics"].float().cpu()
+        else:
+            extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone()
+            intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "multiview",
+            "extrinsics": extrinsics.contiguous(),
+            "intrinsics": intrinsics.contiguous(),
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if "depth_conf" in out:
+            conf = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            geometry["confidence"] = conf.contiguous()
+        return io.NodeOutput(geometry)
+
+
+class DA3Render(io.ComfyNode):
+    """Render a visualization from a DA3_GEOMETRY packet."""
+
+    _DEPTH_RENDER_INPUTS = [
+        io.Combo.Input("normalization",
+            options=["v2_style", "min_max", "raw"],
+            default="v2_style",
+            tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
+                "- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
+                "- raw: no scaling,preserves metric units for Metric model."),
+        io.Boolean.Input("apply_sky_clip", default=False,
+            tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. "
+                "Requires a sky key in the da3_geometry input (for Mono/Metric models only)."),
+    ]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Render",
+            display_name="Render Depth Anything 3",
+            category="image/geometry estimation",
+            description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.DynamicCombo.Input("output",
+                    tooltip="- depth: normalised greyscale depth image.\n"
+                        "- depth_colored: depth mapped through the Turbo colormap.\n"
+                        "- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
+                        "- confidence: normalised depth confidence (for Small/Base models only).",
+                options=[
+                    io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("sky_mask", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."),
+                    ]),
+                    io.DynamicCombo.Option("confidence", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."),
+                    ]),
+                ]),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, output) -> io.NodeOutput:
+        output_val = output["output"]
+
+        if output_val in ("depth", "depth_colored"):
+            normalization = output["normalization"]
+            apply_sky_clip = output["apply_sky_clip"]
+            if apply_sky_clip and "sky" not in da3_geometry:
+                raise ValueError(
+                    "apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
+                    "Run with Mono/Metric models or set apply_sky_clip=False."
+                )
+            depth = da3_geometry["depth"]
+            sky = da3_geometry.get("sky")
+            if apply_sky_clip and sky is not None:
+                depth = torch.stack([
+                    da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
+                    for i in range(depth.shape[0])
+                ], dim=0)
+            grey = cls._depth_to_image(depth, sky, normalization)  # (B,H,W,3) greyscale
+            result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey
+
+        elif output_val == "sky_mask":
+            if "sky" not in da3_geometry:
+                raise ValueError("geometry has no sky output; run with Mono/Metric models.")
+            sky = da3_geometry["sky"]
+            if output["colored"]:
+                result = _turbo(sky)
+            else:
+                result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
+
+        elif output_val == "confidence":
+            if "confidence" not in da3_geometry:
+                raise ValueError("da3_geometry has no confidence output; run with Small/Base models.")
+            conf = _normalize_confidence(da3_geometry["confidence"])
+            if output["colored"]:
+                result = _turbo(conf)
+            else:
+                result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous()
+
+        else:
+            raise ValueError(f"Unknown output mode: {output_val}")
+
+        return io.NodeOutput(result.float())
+
+    @staticmethod
+    def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor:
+        """Normalise depth and pack as an (B,H,W,3) image tensor."""
+
+        N = depth.shape[0]
+        if normalization == "v2_style":
+            norm = torch.stack([
+                da3_preprocess.normalize_depth_v2_style(
+                    depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
+                for i in range(N)
+            ], dim=0)
+        elif normalization == "min_max":
+            norm = da3_preprocess.normalize_depth_min_max(depth)
+        else:
+            norm = depth
+
+        out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
+        if normalization != "raw":
+            out = out.clamp(0.0, 1.0)
+        return out.contiguous()
+
+
+class DA3GeometryToMesh(io.ComfyNode):
+    """Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToMesh",
+            search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
+            display_name="Convert DA3 Geometry to Mesh",
+            category="image/geometry estimation",
+            description="Convert a depth map into a triangulated 3D mesh.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."),
+                io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."),
+                io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). "
+                        "Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."),
+            ],
+            outputs=[io.Mesh.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index]      # (H, W)
+        H, W = depth.shape
+
+        # NaN/inf depth would propagate silently through unproject and produce an
+        # empty mesh; replace them with 0 here so those pixels are later excluded
+        # by the isfinite check inside triangulate_grid_mesh.
+        depth = depth.clone()
+        n_bad = (~torch.isfinite(depth)).sum().item()
+        if n_bad:
+            logging.getLogger("comfy").warning(
+                f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
+                f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
+            )
+        depth[~torch.isfinite(depth)] = 0.0
+        logging.getLogger("comfy").debug(
+            f"DA3GeometryToMesh: depth[{batch_index}] range "
+            f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}"
+        )
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+        points = _da3_unproject(depth, K)   # (H, W, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        # Also exclude pixels where depth was invalid.
+        mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index])
+        points = points.clone()
+        points[~mask] = float('inf')
+
+        verts, faces, uvs = triangulate_grid_mesh(
+            points,
+            decimation=decimation,
+            discontinuity_threshold=discontinuity_threshold,
+            depth=depth,
+        )
+        if verts.shape[0] == 0 or faces.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToMesh produced an empty mesh. "
+                "Try raising discontinuity_threshold, lowering confidence_threshold, "
+                "or disabling use_sky_mask."
+            )
+
+        # OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back).
+        # Same transform as MoGePointMapToMesh perspective branch.
+        verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype)
+        faces = faces[:, [0, 2, 1]].contiguous()
+
+        tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None
+        mesh = Types.MESH(
+            vertices=verts.unsqueeze(0),
+            faces=faces.unsqueeze(0),
+            uvs=uvs.unsqueeze(0),
+            texture=tex,
+        )
+        return io.NodeOutput(mesh)
+
+
+class DA3GeometryToPointCloud(io.ComfyNode):
+    """Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToPointCloud",
+            search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
+            display_name="Convert DA3 Geometry to Point Cloud",
+            category="image/geometry estimation",
+            description="Convert a depth map into a 3D point cloud.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True,
+                    tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Int.Input("downsample", default=1, min=1, max=16,
+                    tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."),
+            ],
+            # TODO: add a proper PointCloud output type
+            outputs=[DA3PointCloud.Output(display_name="point_cloud")],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index].clone()  # (H, W)
+        depth[~torch.isfinite(depth)] = 0.0
+        H, W = depth.shape
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+
+        if downsample > 1:
+            depth = depth[::downsample, ::downsample].contiguous()
+            # Scale intrinsics to the downsampled grid.
+            K = K.clone()
+            K[0, :] /= downsample
+            K[1, :] /= downsample
+
+        H_ds, W_ds = depth.shape
+        points = _da3_unproject(depth, K)   # (H_ds, W_ds, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Rebuild mask at downsampled resolution.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        if downsample > 1:
+            mask = mask[::downsample, ::downsample]
+
+        mask = mask & torch.isfinite(depth)
+
+        # OpenCV → glTF: flip Y and Z.
+        points_gltf = points.clone()
+        points_gltf[..., 1] *= -1.0
+        points_gltf[..., 2] *= -1.0
+
+        pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)]
+
+        colors_flat = None
+        if "image" in da3_geometry:
+            img = da3_geometry["image"][batch_index]     # (H, W, 3)
+            if downsample > 1:
+                img = img[::downsample, ::downsample]
+            colors_flat = img.reshape(-1, 3)[mask.reshape(-1)]
+
+        conf_flat = None
+        if "confidence" in da3_geometry:
+            conf = da3_geometry["confidence"][batch_index]   # (H, W)
+            if downsample > 1:
+                conf = conf[::downsample, ::downsample]
+            conf_flat = conf.reshape(-1)[mask.reshape(-1)]
+
+        if pts_flat.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToPointCloud produced zero points after filtering. "
+                "Try lowering confidence_threshold or disabling use_sky_mask."
+            )
+
+        return io.NodeOutput({
+            "points": pts_flat,
+            "colors": colors_flat,
+            "confidence": conf_flat,
+        })
+
+
+class DA3Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoadDA3Model,
+            DA3Inference,
+            DA3Render,
+            DA3GeometryToMesh,
+            # DA3GeometryToPointCloud,  # Keep this commented out for now until we have a proper PointCloud output type
+        ]
+
+
+async def comfy_entrypoint() -> DA3Extension:
+    return DA3Extension()

comfy_extras/nodes_gaussian_splat.py

@@ -488,7 +488,7 @@ class SplatToFile3D(IO.ComfyNode):
                                        "spz: Niantic gzip-compressed (~10x smaller), base color only "
                                        ),
             ],
-            outputs=[IO.File3DAny.Output(display_name="model_3d")],
+            outputs=[IO.File3DSplatAny.Output(display_name="model_3d")],
         )
 
     @classmethod
@@ -516,7 +516,7 @@ class File3DToSplat(IO.ComfyNode):
             inputs=[
                 IO.MultiType.Input(
                     IO.File3DAny.Input("model_3d"),
-                    types=[IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
+                    types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
                     tooltip="A gaussian splat 3D file",
                 ),
             ],

comfy_extras/nodes_load_3d.py

@@ -51,6 +51,14 @@ class Load3D(IO.ComfyNode):
             ],
         )
 
+    @classmethod
+    def validate_inputs(cls, model_file, **kwargs) -> bool | str:
+        if not model_file or model_file == "none":
+            return True
+        if not folder_paths.exists_annotated_filepath(model_file):
+            return f"Invalid 3D model file: {model_file}"
+        return True
+
     @classmethod
     def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
         image_path = folder_paths.get_annotated_filepath(image['image'])
@@ -136,7 +144,7 @@ class Preview3DAdvanced(IO.ComfyNode):
             is_output_node=True,
             inputs=[
                 IO.MultiType.Input(
-                    "model_file",
+                    "model_3d",
                     types=[
                         IO.File3DGLB,
                         IO.File3DGLTF,
@@ -148,34 +156,161 @@ class Preview3DAdvanced(IO.ComfyNode):
                     ],
                     tooltip="3D model file from an upstream 3D node.",
                 ),
-                IO.Load3D.Input("image"),
-                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
                 IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                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_file"),
-                IO.Load3DCamera.Output(display_name="camera_info"),
+                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_file: Types.File3D, image, width: int, height: int, **kwargs) -> IO.NodeOutput:
-        filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_file.format}"
-        model_file.save_to(os.path.join(folder_paths.get_output_directory(), filename))
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
 
         camera_info_input = kwargs.get("camera_info", None)
-        camera_info = camera_info_input if camera_info_input is not None else image['camera_info']
+        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 image.get('model_3d_info', [])
+        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_file,
-            camera_info,
+            model_3d,
             model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewGaussianSplat",
+            display_name="Preview Splat",
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view splat",
+                "view gaussian",
+                "view gaussian splat",
+                "preview gaussian",
+                "preview gaussian splat",
+                "view 3dgs",
+                "preview 3dgs",
+                "preview ply",
+                "preview spz",
+                "preview splat",
+                "preview ksplat",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                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, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        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(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewPointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewPointCloud",
+            display_name="Preview Point Cloud",
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view point cloud",
+                "view pointcloud",
+                "preview point cloud",
+                "preview pointcloud",
+                "preview ply",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                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, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        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(filename, camera_info, model_3d_info),
@@ -189,6 +324,8 @@ class Load3DExtension(ComfyExtension):
             Load3D,
             Preview3D,
             Preview3DAdvanced,
+            PreviewGaussianSplat,
+            PreviewPointCloud,
         ]
 
 

comfy_extras/nodes_moge.py

@@ -8,6 +8,7 @@ import folder_paths
 from comfy_api.latest import ComfyExtension, Types, io
 from typing_extensions import override
 
+from comfy.ldm.colormap import turbo as _turbo
 from comfy.ldm.moge.model import MoGeModel
 from comfy.ldm.moge.geometry import triangulate_grid_mesh
 from comfy.ldm.moge.panorama import get_panorama_cameras, split_panorama_image, merge_panorama_depth, spherical_uv_to_directions, _uv_grid
@@ -27,19 +28,6 @@ MoGeGeometry = io.Custom("MOGE_GEOMETRY")
 #   "image":      torch.Tensor (B, H, W, 3) in [0, 1], CPU (always present)
 
 
-def _turbo(x: torch.Tensor) -> torch.Tensor:
-    """Anton Mikhailov polynomial approximation of the turbo colormap."""
-    x = x.clamp(0.0, 1.0)
-    x2 = x * x
-    x3 = x2 * x
-    x4 = x2 * x2
-    x5 = x4 * x
-    r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
-    g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5
-    b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
-    return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)
-
-
 def _normals_from_points(points: torch.Tensor) -> torch.Tensor:
     """Camera-space surface normals from a (B, H, W, 3) point map (v1 fallback)."""
     finite = torch.isfinite(points).all(dim=-1)

comfy_extras/nodes_resolution.py

@@ -6,24 +6,24 @@ from comfy_api.latest import ComfyExtension, io
 
 class AspectRatio(str, Enum):
     SQUARE = "1:1 (Square)"
+    PHOTO_V = "2:3 (Portrait Photo)"
     PHOTO_H = "3:2 (Photo)"
+    STANDARD_V = "3:4 (Portrait Standard)"
     STANDARD_H = "4:3 (Standard)"
+    WIDESCREEN_V = "9:16 (Portrait Widescreen)"
     WIDESCREEN_H = "16:9 (Widescreen)"
     ULTRAWIDE_H = "21:9 (Ultrawide)"
-    PHOTO_V = "2:3 (Portrait Photo)"
-    STANDARD_V = "3:4 (Portrait Standard)"
-    WIDESCREEN_V = "9:16 (Portrait Widescreen)"
 
 
 ASPECT_RATIOS: dict[AspectRatio, tuple[int, int]] = {
     AspectRatio.SQUARE: (1, 1),
+    AspectRatio.PHOTO_V: (2, 3),
     AspectRatio.PHOTO_H: (3, 2),
+    AspectRatio.STANDARD_V: (3, 4),
     AspectRatio.STANDARD_H: (4, 3),
+    AspectRatio.WIDESCREEN_V: (9, 16),
     AspectRatio.WIDESCREEN_H: (16, 9),
     AspectRatio.ULTRAWIDE_H: (21, 9),
-    AspectRatio.PHOTO_V: (2, 3),
-    AspectRatio.STANDARD_V: (3, 4),
-    AspectRatio.WIDESCREEN_V: (9, 16),
 }
 
 
@@ -50,26 +50,35 @@ class ResolutionSelector(io.ComfyNode):
                     min=0.1,
                     max=16.0,
                     step=0.1,
-                    tooltip="Target total megapixels. 1.0 MP ≈ 1024×1024 for square.",
+                    tooltip="Target total megapixels. 1.0 MP ≈ 1024x1024 for square.",
+                ),
+                io.Int.Input(
+                    id="multiple",
+                    default=8,
+                    min=8,
+                    max=128,
+                    step=4,
+                    tooltip="Nearest multiple of the result to set the selected resolution to.",
+                    advanced=True,
                 ),
             ],
             outputs=[
                 io.Int.Output(
-                    "width", tooltip="Calculated width in pixels (multiple of 8)."
+                    "width", tooltip="Calculated width in pixels multiplied by the selected multiple."
                 ),
                 io.Int.Output(
-                    "height", tooltip="Calculated height in pixels (multiple of 8)."
+                    "height", tooltip="Calculated height in pixels multiplied by the selected multiple."
                 ),
             ],
         )
 
     @classmethod
-    def execute(cls, aspect_ratio: str, megapixels: float) -> io.NodeOutput:
+    def execute(cls, aspect_ratio: str, megapixels: float, multiple: int) -> io.NodeOutput:
         w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio]
         total_pixels = megapixels * 1024 * 1024
         scale = math.sqrt(total_pixels / (w_ratio * h_ratio))
-        width = round(w_ratio * scale / 8) * 8
-        height = round(h_ratio * scale / 8) * 8
+        width = round(w_ratio * scale / multiple) * multiple
+        height = round(h_ratio * scale / multiple) * multiple
         return io.NodeOutput(width, height)
 
 

comfy_extras/nodes_save_3d.py

@@ -337,6 +337,12 @@ class SaveGLB(IO.ComfyNode):
                         IO.File3DFBX,
                         IO.File3DSTL,
                         IO.File3DUSDZ,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                        IO.File3DSplatAny,
+                        IO.File3DPointCloudAny,
                         IO.File3DAny,
                     ],
                     tooltip="Mesh or 3D file to save",

comfy_extras/nodes_scail.py

@@ -0,0 +1,321 @@
+"""SCAIL / SCAIL-2 nodes: the WanSCAILToVideo conditioning node and the SAM3
+preprocessing that turns video tracks into the bundle the SCAIL-2 model consumes."""
+
+from typing_extensions import override
+
+import torch
+import torch.nn.functional as F
+
+import nodes
+import node_helpers
+import comfy.model_management
+import comfy.utils
+from comfy_api.latest import ComfyExtension, io
+from comfy.ldm.sam3.tracker import unpack_masks
+
+SAM3TrackData = io.Custom("SAM3_TRACK_DATA")
+
+
+# Model was trained on these exact colors; deviating degrades multi-identity quality.
+DEFAULT_PALETTE = [
+    (0.0, 0.0, 1.0),  # Blue
+    (1.0, 0.0, 0.0),  # Red
+    (0.0, 1.0, 0.0),  # Green
+    (1.0, 0.0, 1.0),  # Magenta
+    (0.0, 1.0, 1.0),  # Cyan
+    (1.0, 1.0, 0.0),  # Yellow
+]
+
+
+def _unpack(track_data):
+    packed = track_data["packed_masks"]
+    if packed is None or packed.shape[1] == 0:
+        return None
+    return unpack_masks(packed)
+
+
+def _first_frame_cx_area(masks_bool):
+    first = masks_bool[0].float()
+    H, W = first.shape[-2], first.shape[-1]
+    n_pixels = H * W
+    grid_x = torch.arange(W, device=first.device, dtype=first.dtype).view(1, W)
+    area = first.sum(dim=(-1, -2)).clamp_(min=1)
+    cx = (first * grid_x).sum(dim=(-1, -2)) / area
+    return (cx / W).tolist(), (area / n_pixels).tolist()
+
+
+def _subset_track_data(track_data, obj_indices):
+    out = dict(track_data)
+    packed = track_data["packed_masks"]
+    if packed is None or not obj_indices:
+        out["packed_masks"] = None
+        if "scores" in out:
+            out["scores"] = []
+        return out
+    out["packed_masks"] = packed[:, obj_indices].contiguous()
+    scores = track_data.get("scores")
+    if scores is not None:
+        out["scores"] = [scores[i] for i in obj_indices if i < len(scores)]
+    return out
+
+
+def _render_colored_masks(track_data, background="black"):
+    packed = track_data["packed_masks"]
+    H, W = track_data["orig_size"]
+    device = comfy.model_management.intermediate_device()
+    dtype = comfy.model_management.intermediate_dtype()
+    bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0)
+    if packed is None or packed.shape[1] == 0:
+        T = track_data.get("n_frames", 1) if packed is None else packed.shape[0]
+        out = torch.empty(T, H, W, 3, device=device, dtype=dtype)
+        out[..., 0], out[..., 1], out[..., 2] = bg_rgb[0], bg_rgb[1], bg_rgb[2]
+        return out
+    T, N_obj = packed.shape[0], packed.shape[1]
+    colors = torch.tensor(
+        [DEFAULT_PALETTE[i % len(DEFAULT_PALETTE)] for i in range(N_obj)],
+        device=device, dtype=dtype,
+    )
+    masks_full = unpack_masks(packed.to(device)).float()
+    Hm, Wm = masks_full.shape[-2], masks_full.shape[-1]
+    masks_full = F.interpolate(
+        masks_full.view(T * N_obj, 1, Hm, Wm), size=(H, W), mode="nearest"
+    ).view(T, N_obj, H, W) > 0.5
+    any_mask = masks_full.any(dim=1)
+    obj_idx_map = masks_full.to(torch.uint8).argmax(dim=1)
+    color_overlay = colors[obj_idx_map]
+    bg_tensor = torch.tensor(bg_rgb, device=device, dtype=color_overlay.dtype).view(1, 1, 1, 3)
+    return torch.where(any_mask.unsqueeze(-1), color_overlay, bg_tensor.expand_as(color_overlay))
+
+
+def _extract_mask_to_28ch(rgb_video):
+    """Colored RGB mask (T, H, W, 3) in [0, 1] -> SCAIL-2 28-channel binary latent
+    (1, T_lat, 28, H_lat, W_lat). 7 per-color binary channels (white/r/g/b/y/m/c)
+    threshold-extracted at 225/255, 8x spatial downsample, 4-frame temporal stacking."""
+    T, H, W, _ = rgb_video.shape
+    _ON_THRESH = 225.0 / 255.0
+    mask = rgb_video.movedim(-1, 1).float()
+    R = (mask[:, 0:1] > _ON_THRESH).float()
+    G = (mask[:, 1:2] > _ON_THRESH).float()
+    B = (mask[:, 2:3] > _ON_THRESH).float()
+    nR, nG, nB = 1 - R, 1 - G, 1 - B
+    binary_7ch = torch.cat([
+        R * G * B,    # white
+        R * nG * nB,  # red
+        nR * G * nB,  # green
+        nR * nG * B,  # blue
+        R * G * nB,   # yellow
+        R * nG * B,   # magenta
+        nR * G * B,   # cyan
+    ], dim=1)
+    H_lat, W_lat = H, W
+    for _ in range(3):
+        H_lat = (H_lat + 1) // 2
+        W_lat = (W_lat + 1) // 2
+    binary_7ch = torch.nn.functional.interpolate(binary_7ch, size=(H_lat, W_lat), mode='area')
+    T_latent = (T - 1) // 4 + 1
+    padded = torch.cat([binary_7ch[:1].repeat(4, 1, 1, 1), binary_7ch[1:]], dim=0)
+    out = padded.view(T_latent, 28, H_lat, W_lat)
+    return out.unsqueeze(0)
+
+
+class WanSCAILToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanSCAILToVideo",
+            category="model/conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
+                io.Image.Input("pose_video_mask", optional=True, tooltip="SCAIL-2 only. Colored per-identity SAM3 mask video at the same resolution as pose_video."),
+                io.Boolean.Input("replacement_mode", default=False, optional=True, tooltip="SCAIL-2 only. False = Animation Mode (pose_video_mask should have black background). True = Replacement Mode (pose_video_mask should have white background)."),
+                io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
+                io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step of the pose conditioning."),
+                io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step of the pose conditioning."),
+                io.Image.Input("reference_image", optional=True, tooltip="Reference image, for multiple references composite all on single image."),
+                io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask at the same resolution as reference_image."),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="CLIP vision features for conditioning. Model is trained with stretch resize to aspect ratio."),
+                io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="Cumulative output frame this chunk begins at. Wire from the previous chunk's video_frame_offset output."),
+                io.Int.Input("previous_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="Tail frames of previous_frames to anchor. SCAIL-2 trained at 5 (81-frame chunks, 76-frame step)."),
+                io.Image.Input("previous_frames", optional=True, tooltip="SCAIL-2 only. Full decoded output of the previous chunk. Only the last previous_frame_count are used as the extension anchor."),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
+                io.Int.Output(display_name="video_frame_offset", tooltip="Adjusted offset + length. Wire into the next chunk."),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end,
+                video_frame_offset, previous_frame_count, replacement_mode=False, reference_image=None, clip_vision_output=None, pose_video=None,
+                pose_video_mask=None, reference_image_mask=None, previous_frames=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        noise_mask = None
+
+        ref_mask_flag = not replacement_mode
+        positive = node_helpers.conditioning_set_values(positive, {"ref_mask_flag": ref_mask_flag})
+        negative = node_helpers.conditioning_set_values(negative, {"ref_mask_flag": ref_mask_flag})
+
+        prev_trimmed = None
+        if previous_frames is not None and previous_frames.shape[0] > 0:
+            prev_trimmed = previous_frames[-previous_frame_count:]
+            video_frame_offset -= prev_trimmed.shape[0]
+            video_frame_offset = max(0, video_frame_offset)
+
+        ref_latent = None
+        if reference_image is not None:
+            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
+            # Replacement Mode: composite ref on black bg using reference_image_mask as alpha matte
+            if replacement_mode and reference_image_mask is not None:
+                rm = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1)
+                is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(reference_image.dtype)
+                reference_image = reference_image * is_char
+            ref_latent = vae.encode(reference_image[:, :, :, :3])
+
+        if ref_latent is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True)
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        if pose_video is not None:
+            if pose_video.shape[0] <= video_frame_offset:
+                pose_video = None
+            else:
+                pose_video = pose_video[video_frame_offset:]
+        if pose_video_mask is not None:
+            if pose_video_mask.shape[0] <= video_frame_offset:
+                pose_video_mask = None
+            else:
+                pose_video_mask = pose_video_mask[video_frame_offset:]
+
+        # Truncate pose+mask jointly to the shorter of the two, capped at length.
+        ts = [v.shape[0] for v in (pose_video, pose_video_mask) if v is not None]
+        if ts:
+            T_kept = ((min(min(ts), length) - 1) // 4) * 4 + 1
+            if pose_video is not None:
+                pose_video = pose_video[:T_kept]
+            if pose_video_mask is not None:
+                pose_video_mask = pose_video_mask[:T_kept]
+
+        if pose_video is not None:
+            pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
+            pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
+            positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+            negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+
+        if pose_video_mask is not None:
+            mask_video_hw = comfy.utils.common_upscale(pose_video_mask[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
+            driving_mask_28ch = _extract_mask_to_28ch(mask_video_hw)
+            positive = node_helpers.conditioning_set_values(positive, {"driving_mask_28ch": driving_mask_28ch})
+            negative = node_helpers.conditioning_set_values(negative, {"driving_mask_28ch": driving_mask_28ch})
+
+        if reference_image_mask is not None:
+            ref_mask_hw = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
+            ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw)
+            zeros = torch.zeros((1, latent.shape[2], 28, ref_mask_1f.shape[-2], ref_mask_1f.shape[-1]), device=ref_mask_1f.device, dtype=ref_mask_1f.dtype)
+            ref_mask_28ch = torch.cat([ref_mask_1f, zeros], dim=1)
+            positive = node_helpers.conditioning_set_values(positive, {"ref_mask_28ch": ref_mask_28ch})
+            negative = node_helpers.conditioning_set_values(negative, {"ref_mask_28ch": ref_mask_28ch})
+
+        if prev_trimmed is not None:
+            pf = comfy.utils.common_upscale(prev_trimmed.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
+            prev_latent = vae.encode(pf[:, :, :, :3])
+            prev_latent_frames  = min(prev_latent.shape[2], latent.shape[2])
+            latent[:, :, :prev_latent_frames] = prev_latent[:, :, :prev_latent_frames].to(latent.dtype)
+            noise_mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=latent.device, dtype=latent.dtype)
+            noise_mask[:, :, :prev_latent_frames] = 0.0
+
+        out_latent = {"samples": latent}
+        if noise_mask is not None:
+            out_latent["noise_mask"] = noise_mask
+        return io.NodeOutput(positive, negative, out_latent, video_frame_offset + length)
+
+
+class SCAIL2ColoredMask(io.ComfyNode):
+    """Render SAM3 tracks for the driving pose video and (optionally) the reference
+    image into the two colored masks WanSCAILToVideo consumes. Shared `sort_by`
+    across both outputs guarantees identity K maps to the same color on both
+    sides, for multi-person workflow consistency.
+    reference_image_mask is always rendered black-bg (model convention)
+    pose_video_mask bg follows replacement_mode: black = Animation Mode, white = Replacement Mode
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SCAIL2ColoredMask",
+            display_name="Create SCAIL-2 Colored Mask",
+            category="conditioning/video_models/scail",
+            inputs=[
+                SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
+                SAM3TrackData.Input("ref_track_data", optional=True,
+                                    tooltip="SAM3 track of the reference image."),
+                io.String.Input("object_indices", default="",
+                                tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
+                io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
+                               tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
+                io.Boolean.Input("replacement_mode", default=False,
+                                 tooltip="False = mask_video has black bg (Animation Mode). True = white bg (Replacement Mode). Set the matching replacement_mode on WanSCAILToVideo. reference_image_mask is always black-bg regardless."),
+            ],
+            outputs=[
+                io.Image.Output("pose_video_mask"),
+                io.Image.Output("reference_image_mask"),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, driving_track_data, object_indices, sort_by, replacement_mode, ref_track_data=None):
+        def _prep(td):
+            masks_bool = _unpack(td)
+            if sort_by != "none" and masks_bool is not None:
+                cx, area = _first_frame_cx_area(masks_bool)
+                if sort_by == "left_to_right":
+                    order = sorted(range(len(cx)), key=lambda i: cx[i])
+                else:  # "area"
+                    order = sorted(range(len(area)), key=lambda i: -area[i])
+                td = _subset_track_data(td, order)
+            if object_indices.strip():
+                indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()]
+                packed = td.get("packed_masks")
+                n_obj = packed.shape[1] if packed is not None else 0
+                indices = [i for i in indices if 0 <= i < n_obj]
+                td = _subset_track_data(td, indices)
+            return td
+
+        drv = _prep(driving_track_data)
+        mask_video = _render_colored_masks(drv, "white" if replacement_mode else "black")
+
+        if ref_track_data is not None:
+            ref = _prep(ref_track_data)
+            reference_image_mask = _render_colored_masks(ref, "black")
+        else:
+            H, W = drv["orig_size"]
+            reference_image_mask = torch.zeros(1, H, W, 3, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+
+        return io.NodeOutput(mask_video, reference_image_mask)
+
+
+class SCAILExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            WanSCAILToVideo,
+            SCAIL2ColoredMask,
+        ]
+
+
+async def comfy_entrypoint() -> SCAILExtension:
+    return SCAILExtension()

comfy_extras/nodes_train.py

@@ -15,6 +15,7 @@ import comfy.sampler_helpers
 import comfy.sd
 import comfy.utils
 import comfy.model_management
+from comfy.conds import CONDRegular, CONDList
 from comfy.cli_args import args, PerformanceFeature
 import comfy_extras.nodes_custom_sampler
 import folder_paths
@@ -120,6 +121,11 @@ def process_cond_list(d, prefix=""):
                 process_cond_list(v, f"{prefix}.{k}")
             elif isinstance(v, torch.Tensor):
                 d[k] = v.clone()
+            elif isinstance(v, CONDList):
+                v.cond = [t.detach() if isinstance(t, torch.Tensor) else t for t in v.cond]
+            elif isinstance(v, CONDRegular):
+                if isinstance(v.cond, torch.Tensor):
+                    v.cond = v.cond.detach()
             elif isinstance(v, (list, tuple)):
                 for index, item in enumerate(v):
                     process_cond_list(item, f"{prefix}.{k}.{index}")
@@ -1143,45 +1149,45 @@ class TrainLoraNode(io.ComfyNode):
         # Process conditioning
         positive = _process_conditioning(positive)
 
-        # Setup model and dtype
-        mp = model.clone()
-        use_grad_scaler = False
-        lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
-        if training_dtype != "none":
-            dtype = node_helpers.string_to_torch_dtype(training_dtype)
-            mp.set_model_compute_dtype(dtype)
-        else:
-            # Detect model's native dtype for autocast
-            model_dtype = mp.model.get_dtype()
-            if model_dtype == torch.float16:
-                dtype = torch.float16
-                # GradScaler only supports float16 gradients, not bfloat16.
-                # Only enable it when lora params will also be in float16.
-                if lora_dtype != torch.bfloat16:
-                    use_grad_scaler = True
-                # Warn about fp16 accumulation instability during training
-                if PerformanceFeature.Fp16Accumulation in args.fast:
-                    logging.warning(
-                        "WARNING: FP16 model detected with fp16_accumulation enabled. "
-                        "This combination can be numerically unstable during training and may cause NaN values. "
-                        "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)."
-                    )
-            else:
-                # For fp8, bf16, or other dtypes, use bf16 autocast
-                dtype = torch.bfloat16
-
-        # Prepare latents and compute counts
-        latents_dtype = dtype if dtype not in (None,) else torch.bfloat16
-        latents, num_images, multi_res = _prepare_latents_and_count(
-            latents, latents_dtype, bucket_mode
-        )
-
-        # Validate and expand conditioning
-        positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode)
-
         with torch.inference_mode(False):
+            # Setup model and dtype
+            mp = model.clone(force_deepcopy=True)
+            use_grad_scaler = False
+            lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
+            if training_dtype != "none":
+                dtype = node_helpers.string_to_torch_dtype(training_dtype)
+                mp.set_model_compute_dtype(dtype)
+            else:
+                # Detect model's native dtype for autocast
+                model_dtype = mp.model.get_dtype()
+                if model_dtype == torch.float16:
+                    dtype = torch.float16
+                    # GradScaler only supports float16 gradients, not bfloat16.
+                    # Only enable it when lora params will also be in float16.
+                    if lora_dtype != torch.bfloat16:
+                        use_grad_scaler = True
+                    # Warn about fp16 accumulation instability during training
+                    if PerformanceFeature.Fp16Accumulation in args.fast:
+                        logging.warning(
+                            "WARNING: FP16 model detected with fp16_accumulation enabled. "
+                            "This combination can be numerically unstable during training and may cause NaN values. "
+                            "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)."
+                        )
+                else:
+                    # For fp8, bf16, or other dtypes, use bf16 autocast
+                    dtype = torch.bfloat16
+
+            # Prepare latents and compute counts
+            latents_dtype = dtype if dtype not in (None,) else torch.bfloat16
+            latents, num_images, multi_res = _prepare_latents_and_count(
+                latents, latents_dtype, bucket_mode
+            )
+
+            # Validate and expand conditioning
+            positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode)
+
             # Setup models for training
-            mp.model.requires_grad_(False)
+            mp.model.requires_grad_(False).train()
 
             # Load existing LoRA weights if provided
             existing_weights, existing_steps = _load_existing_lora(existing_lora)

comfy_extras/nodes_video.py

@@ -19,7 +19,7 @@ class SaveWEBM(io.ComfyNode):
             category="video",
             is_experimental=True,
             inputs=[
-                io.Image.Input("images"),
+                io.Image.Input("images", tooltip="RGBA images are saved with their alpha channel as transparency (vp9 codec only)."),
                 io.String.Input("filename_prefix", default="ComfyUI"),
                 io.Combo.Input("codec", options=["vp9", "av1"]),
                 io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01),
@@ -45,18 +45,25 @@ class SaveWEBM(io.ComfyNode):
             for x in cls.hidden.extra_pnginfo:
                 container.metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
+        # Save transparency when the images carry an alpha channel (RGBA) and the codec supports it.
+        # vp9 -> yuva420p; other codecs have no usable alpha path, so the alpha is ignored.
+        save_alpha = images.shape[-1] == 4 and codec == "vp9"
+
         codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"}
         stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000))
         stream.width = images.shape[-2]
         stream.height = images.shape[-3]
-        stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p"
+        stream.pix_fmt = "yuva420p" if save_alpha else ("yuv420p10le" if codec == "av1" else "yuv420p")
         stream.bit_rate = 0
         stream.options = {'crf': str(crf)}
         if codec == "av1":
             stream.options["preset"] = "6"
 
         for frame in images:
-            frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24")
+            if save_alpha:
+                frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :4] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgba")
+            else:
+                frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24")
             for packet in stream.encode(frame):
                 container.mux(packet)
         container.mux(stream.encode())

comfy_extras/nodes_wan.py

@@ -1456,63 +1456,6 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
         return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
 
 
-class WanSCAILToVideo(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="WanSCAILToVideo",
-            category="model/conditioning/video_models",
-            inputs=[
-                io.Conditioning.Input("positive"),
-                io.Conditioning.Input("negative"),
-                io.Vae.Input("vae"),
-                io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
-                io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
-                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
-                io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
-                io.Image.Input("reference_image", optional=True),
-                io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
-                io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
-                io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."),
-                io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."),
-            ],
-            outputs=[
-                io.Conditioning.Output(display_name="positive"),
-                io.Conditioning.Output(display_name="negative"),
-                io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
-            ],
-            is_experimental=True,
-        )
-
-    @classmethod
-    def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput:
-        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-
-        ref_latent = None
-        if reference_image is not None:
-            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
-            ref_latent = vae.encode(reference_image[:, :, :, :3])
-
-        if ref_latent is not None:
-            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
-            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True)
-
-        if clip_vision_output is not None:
-            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
-            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
-
-        if pose_video is not None:
-            pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
-            pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
-            positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
-            negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
-
-        out_latent = {}
-        out_latent["samples"] = latent
-        return io.NodeOutput(positive, negative, out_latent)
-
-
 class WanExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -1533,7 +1476,6 @@ class WanExtension(ComfyExtension):
             WanAnimateToVideo,
             Wan22ImageToVideoLatent,
             WanInfiniteTalkToVideo,
-            WanSCAILToVideo,
         ]
 
 async def comfy_entrypoint() -> WanExtension:

cuda_malloc.py

@@ -2,6 +2,7 @@ import os
 import importlib.util
 from comfy.cli_args import args, PerformanceFeature
 import subprocess
+import re
 
 #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
 def get_gpu_names():
@@ -77,11 +78,24 @@ try:
 except:
     pass
 
+def get_raw_cuda_version(version_str):
+    match = re.search(r'\+cu(\d+)', version_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except:
+            pass
+    return None
+
 if not args.cuda_malloc:
     try:
         if int(version[0]) >= 2 and "+cu" in version:  # enable by default for torch version 2.0 and up only on cuda torch
             if PerformanceFeature.AutoTune not in args.fast:  # Autotune has issues with cuda malloc
-                args.cuda_malloc = cuda_malloc_supported()
+                cuda_version = get_raw_cuda_version(version)
+                if cuda_version is not None and cuda_version >= 130:
+                    args.cuda_malloc = True
+                else:
+                    args.cuda_malloc = cuda_malloc_supported()
     except:
         pass
 

main.py

@@ -26,6 +26,7 @@ import utils.extra_config
 from utils.mime_types import init_mime_types
 import faulthandler
 import logging
+import signal
 import sys
 from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
@@ -37,7 +38,19 @@ if __name__ == "__main__":
     os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
     os.environ['DO_NOT_TRACK'] = '1'
 
-faulthandler.enable(file=sys.stderr, all_threads=False)
+faulthandler.enable(file=sys.stderr, all_threads=args.debug_hang)
+if __name__ == "__main__" and args.debug_hang:
+    dumping_traceback = False
+
+    def dump_traceback_on_sigint(signum, frame):
+        global dumping_traceback
+        if dumping_traceback:
+            raise KeyboardInterrupt
+        dumping_traceback = True
+        faulthandler.dump_traceback(file=sys.stderr, all_threads=True)
+        raise KeyboardInterrupt
+
+    signal.signal(signal.SIGINT, dump_traceback_on_sigint)
 
 import comfy_aimdo.control
 

nodes.py

@@ -2404,6 +2404,7 @@ async def init_builtin_extra_nodes():
         "nodes_video.py",
         "nodes_lumina2.py",
         "nodes_wan.py",
+        "nodes_bernini.py",
         "nodes_lotus.py",
         "nodes_hunyuan3d.py",
         "nodes_primitive.py",
@@ -2450,6 +2451,7 @@ async def init_builtin_extra_nodes():
         "nodes_rtdetr.py",
         "nodes_frame_interpolation.py",
         "nodes_sam3.py",
+        "nodes_scail.py",
         "nodes_void.py",
         "nodes_wandancer.py",
         "nodes_hidream_o1.py",
@@ -2457,7 +2459,8 @@ async def init_builtin_extra_nodes():
         "nodes_moge.py",
         "nodes_mediapipe.py",
         "nodes_gaussian_splat.py",
-        "nodes_triposplat.py"
+        "nodes_triposplat.py",
+        "nodes_depth_anything_3.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -3,11 +3,6 @@ components:
         Asset:
             description: Represents a user-owned asset (image, video, or other generated output).
             properties:
-                asset_hash:
-                    deprecated: true
-                    description: 'Deprecated: use hash instead. Blake3 hash of the asset content.'
-                    pattern: ^blake3:[a-f0-9]{64}$
-                    type: string
                 created_at:
                     description: Timestamp when the asset was created
                     format: date-time
@@ -16,8 +11,12 @@ components:
                     description: Display name of the asset. Mirrors name for backwards compatibility.
                     nullable: true
                     type: string
+                file_path:
+                    description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors")
+                    nullable: true
+                    type: string
                 hash:
-                    description: Blake3 hash of the asset content. Preferred over asset_hash.
+                    description: Blake3 hash of the asset content.
                     pattern: ^blake3:[a-f0-9]{64}$
                     type: string
                 id:
@@ -139,17 +138,16 @@ components:
         AssetUpdated:
             description: Response returned when an existing asset is successfully updated.
             properties:
-                asset_hash:
-                    deprecated: true
-                    description: 'Deprecated: use hash instead. Blake3 hash of the asset content.'
-                    pattern: ^blake3:[a-f0-9]{64}$
-                    type: string
                 display_name:
                     description: Display name of the asset. Mirrors name for backwards compatibility.
                     nullable: true
                     type: string
+                file_path:
+                    description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors")
+                    nullable: true
+                    type: string
                 hash:
-                    description: Blake3 hash of the asset content. Preferred over asset_hash.
+                    description: Blake3 hash of the asset content.
                     pattern: ^blake3:[a-f0-9]{64}$
                     type: string
                 id:
@@ -828,7 +826,11 @@ components:
                     type: string
             type: object
         PaginationInfo:
-            description: Offset/limit-based pagination metadata included in list responses.
+            description: |
+                Pagination metadata included in list responses. Supports both legacy
+                offset/limit pagination and cursor-based pagination. When cursor-based
+                pagination is used, `next_cursor` is the primary pagination token and
+                `offset`/`total` may be zero.
             properties:
                 has_more:
                     description: Whether more items are available beyond this page
@@ -837,12 +839,19 @@ components:
                     description: Items per page
                     minimum: 1
                     type: integer
+                next_cursor:
+                    description: |
+                        Opaque cursor for the next page. Pass this value as the `after`
+                        query parameter on the next request. Empty or absent when there
+                        are no more results.
+                    type: string
                 offset:
-                    description: Current offset (0-based)
+                    deprecated: true
+                    description: 'Current offset (0-based). Deprecated: use cursor-based pagination.'
                     minimum: 0
                     type: integer
                 total:
-                    description: Total number of items matching filters
+                    description: Total number of items matching filters (may be 0 when using cursor pagination)
                     minimum: 0
                     type: integer
             required:
@@ -1518,17 +1527,11 @@ paths:
                   schema:
                     default: true
                     type: boolean
-                - description: Filter assets by exact content hash. Preferred over asset_hash.
+                - description: Filter assets by exact content hash.
                   in: query
                   name: hash
                   schema:
                     type: string
-                - deprecated: true
-                  description: 'Deprecated: use hash instead. Filter assets by exact content hash.'
-                  in: query
-                  name: asset_hash
-                  schema:
-                    type: string
                 - description: |
                     Opaque cursor for keyset pagination. Pass the `next_cursor` value
                     from the previous response to fetch the next page. When provided,
@@ -1571,42 +1574,12 @@ paths:
                 - file
         post:
             description: |
-                Uploads a new asset to the system with associated metadata.
-                Supports two upload methods:
-                1. Direct file upload (multipart/form-data)
-                2. URL-based upload (application/json with source: "url")
+                Creates a new asset from a direct file upload (multipart/form-data) with associated metadata.
 
                 If an asset with the same hash already exists, returns the existing asset.
-            operationId: uploadAsset
+            operationId: createAsset
             requestBody:
                 content:
-                    application/json:
-                        schema:
-                            properties:
-                                name:
-                                    description: Display name for the asset (used to determine file extension)
-                                    type: string
-                                preview_id:
-                                    description: Optional preview asset ID
-                                    format: uuid
-                                    type: string
-                                tags:
-                                    description: Freeform tags for the asset. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
-                                    items:
-                                        type: string
-                                    type: array
-                                url:
-                                    description: HTTP/HTTPS URL to download the asset from
-                                    format: uri
-                                    type: string
-                                user_metadata:
-                                    additionalProperties: true
-                                    description: Custom metadata to store with the asset
-                                    type: object
-                            required:
-                                - url
-                                - name
-                            type: object
                     multipart/form-data:
                         schema:
                             properties:
@@ -1614,6 +1587,10 @@ paths:
                                     description: The asset file to upload
                                     format: binary
                                     type: string
+                                hash:
+                                    description: Content hash of the file.
+                                    pattern: ^(blake3|sha256):[a-f0-9]{64}$
+                                    type: string
                                 id:
                                     description: Optional asset ID for idempotent creation. If provided and asset exists, returns existing asset.
                                     format: uuid
@@ -1629,10 +1606,8 @@ paths:
                                     format: uuid
                                     type: string
                                 tags:
-                                    description: Freeform tags for the asset. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
-                                    items:
-                                        type: string
-                                    type: array
+                                    description: JSON-encoded array of freeform tag strings, e.g. '["models","checkpoint"]'. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
+                                    type: string
                                 user_metadata:
                                     description: Custom JSON metadata as a string
                                     type: string
@@ -1641,36 +1616,32 @@ paths:
                             type: object
                 required: true
             responses:
+                "200":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/AssetCreated'
+                    description: |
+                        Asset already existed for this user (deduplicated by content hash); the
+                        existing asset is returned with created_new=false.
                 "201":
                     content:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/AssetCreated'
-                    description: Asset created successfully
+                    description: Asset created successfully (created_new=true)
                 "400":
                     content:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
-                    description: Invalid request (bad file, invalid URL, invalid content type, etc.)
+                    description: Invalid request (bad file, invalid content type, etc.)
                 "401":
                     content:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Unauthorized
-                "403":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Source URL requires authentication or access denied
-                "404":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Source URL not found
                 "413":
                     content:
                         application/json:
@@ -1683,19 +1654,13 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Unsupported media type
-                "422":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Download failed due to network error or timeout
                 "500":
                     content:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Internal server error
-            summary: Upload a new asset
+            summary: Create a new asset
             tags:
                 - file
     /api/assets/{id}:
@@ -1730,7 +1695,7 @@ paths:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
-                    description: Asset cannot be deleted because it is referenced by another resource (e.g., workflow version)
+                    description: 'Asset cannot be deleted because it is referenced by another resource, e.g. a workflow version (error code: ASSET_IN_USE)'
                 "500":
                     content:
                         application/json:
@@ -1783,7 +1748,7 @@ paths:
             description: |
                 Updates an asset's metadata. At least one field must be provided.
                 Only name, mime_type, preview_id, and user_metadata can be updated.
-                For tag management, use the dedicated PUT /api/assets/{id}/tags endpoint.
+                For tag management, use POST (add) and DELETE (remove) /api/assets/{id}/tags.
             operationId: updateAsset
             parameters:
                 - description: Asset ID
@@ -1982,76 +1947,6 @@ paths:
             summary: Add tags to asset
             tags:
                 - file
-        put:
-            description: Adds and removes tags from an asset in a single operation
-            operationId: updateAssetTags
-            parameters:
-                - description: Asset ID
-                  in: path
-                  name: id
-                  required: true
-                  schema:
-                    format: uuid
-                    type: string
-            requestBody:
-                content:
-                    application/json:
-                        schema:
-                            description: At least one of add or remove must contain items. Empty arrays are allowed when the other array has items.
-                            minProperties: 1
-                            properties:
-                                add:
-                                    description: Tags to add to the asset. Can be empty if remove has items.
-                                    items:
-                                        type: string
-                                    type: array
-                                remove:
-                                    description: Tags to remove from the asset. Can be empty if add has items.
-                                    items:
-                                        type: string
-                                    type: array
-                            type: object
-                required: true
-            responses:
-                "200":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/TagsModificationResponse'
-                    description: Tags updated successfully
-                "400":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Invalid request
-                "401":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Unauthorized
-                "404":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Asset not found
-                "422":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Reserved tag validation error
-                "500":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Internal server error
-            summary: Update asset tags
-            tags:
-                - file
     /api/assets/from-hash:
         post:
             description: |
@@ -2065,8 +1960,8 @@ paths:
                         schema:
                             properties:
                                 hash:
-                                    description: Hash of the existing asset. Supports Blake3 (blake3:) or SHA256 (sha256:) formats
-                                    pattern: ^(blake3|sha256):[a-f0-9]{64}$
+                                    description: 'Blake3 content hash of the existing asset (blake3: prefix)'
+                                    pattern: ^blake3:[a-f0-9]{64}$
                                     type: string
                                 mime_type:
                                     description: MIME type of the asset (e.g., "image/png", "video/mp4")
@@ -2090,12 +1985,20 @@ paths:
                             type: object
                 required: true
             responses:
+                "200":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/AssetCreated'
+                    description: |
+                        Asset reference already existed for this user (deduplicated by content
+                        hash); the existing asset is returned with created_new=false.
                 "201":
                     content:
                         application/json:
                             schema:
                                 $ref: '#/components/schemas/AssetCreated'
-                    description: Asset reference created successfully
+                    description: Asset reference created successfully (created_new=true)
                 "400":
                     content:
                         application/json:
@@ -2887,7 +2790,21 @@ paths:
                         - asc
                         - desc
                     type: string
-                - description: Pagination offset (0-based)
+                - description: |
+                    Opaque cursor for keyset pagination. Pass the `next_cursor` value
+                    from a previous response to fetch the next page.
+                    Cursor pagination is supported only when `sort_by=create_time`
+                    (default). If `sort_by=execution_time`, `after` is ignored and
+                    offset/limit pagination is used.
+                    Cursors are opaque base64url payloads — clients should treat them
+                    as strings and not parse the contents.
+                  example: eyJzIjoiY3JlYXRlX3RpbWUiLCJ2IjoiMTcxNjIwMDAwMDAwMDAwMCIsImlkIjoiYTFiMmMzZDQtZTVmNi03YTg5LWIwYzEtZDJlM2Y0YTViNmM3In0
+                  in: query
+                  name: after
+                  schema:
+                    type: string
+                - deprecated: true
+                  description: 'Pagination offset (0-based). Deprecated: prefer cursor-based pagination via `after`.'
                   in: query
                   name: offset
                   schema:
@@ -2909,6 +2826,12 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/JobsListResponse'
                     description: Success - Jobs retrieved
+                "400":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Bad request (e.g. malformed pagination cursor).
                 "401":
                     content:
                         application/json:

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.44.19
-comfyui-workflow-templates==0.9.94
-comfyui-embedded-docs==0.5.2
+comfyui-frontend-package==1.45.15
+comfyui-workflow-templates==0.9.98
+comfyui-embedded-docs==0.5.3
 torch
 torchsde
 torchvision
@@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
 comfy-kitchen==0.2.10
-comfy-aimdo==0.4.8
+comfy-aimdo==0.4.9
 requests
 simpleeval>=1.0.0
 blake3

server.py

@@ -1253,6 +1253,15 @@ class PromptServer():
 
         if verbose:
             logging.info("Starting server\n")
+            if args.debug_hang:
+                logging.info(
+                    f"{'-' * 80}\n"
+                    "ComfyUI has been started in debug-hang mode. Run your workflow as normal up to\n"
+                    "the point of the hang or freeze, then use ctrl-C in the cmd or controlling\n"
+                    "terminal to dump the python backtraces for debugging. Please attach the extra\n"
+                    "debug info to your bug report.\n"
+                    f"{'-' * 80}"
+                )
         for addr in addresses:
             address = addr[0]
             port = addr[1]

tests-unit/assets_test/services/test_image_dimensions.py

@@ -0,0 +1,86 @@
+"""Tests for the image_dimensions service."""
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+from PIL import Image
+
+from app.assets.services.image_dimensions import extract_image_dimensions
+
+
+def _make_png(path: Path, size: tuple[int, int]) -> Path:
+    img = Image.new("RGB", size, color=(123, 45, 67))
+    img.save(path, format="PNG")
+    return path
+
+
+def _make_jpeg(path: Path, size: tuple[int, int]) -> Path:
+    img = Image.new("RGB", size, color=(10, 20, 30))
+    img.save(path, format="JPEG", quality=80)
+    return path
+
+
+class TestExtractImageDimensions:
+    def test_extracts_png_dimensions(self, tmp_path: Path):
+        f = _make_png(tmp_path / "rect.png", (320, 240))
+
+        result = extract_image_dimensions(str(f), mime_type="image/png")
+
+        assert result == {"kind": "image", "width": 320, "height": 240}
+
+    def test_extracts_jpeg_dimensions(self, tmp_path: Path):
+        f = _make_jpeg(tmp_path / "shot.jpg", (1920, 1080))
+
+        result = extract_image_dimensions(str(f), mime_type="image/jpeg")
+
+        assert result == {"kind": "image", "width": 1920, "height": 1080}
+
+    def test_works_when_mime_type_is_none(self, tmp_path: Path):
+        f = _make_png(tmp_path / "no_mime.png", (50, 100))
+
+        result = extract_image_dimensions(str(f), mime_type=None)
+
+        assert result == {"kind": "image", "width": 50, "height": 100}
+
+    def test_skips_non_image_mime_without_touching_file(self, tmp_path: Path):
+        # Path doesn't need to exist — non-image MIME short-circuits.
+        result = extract_image_dimensions(
+            str(tmp_path / "model.safetensors"),
+            mime_type="application/octet-stream",
+        )
+
+        assert result is None
+
+    @pytest.mark.parametrize(
+        "mime",
+        ["application/json", "text/plain", "video/mp4", "audio/mpeg"],
+    )
+    def test_skips_all_non_image_mime_types(self, tmp_path: Path, mime: str):
+        f = tmp_path / "file.bin"
+        f.write_bytes(b"\x00\x01\x02")
+
+        assert extract_image_dimensions(str(f), mime_type=mime) is None
+
+    def test_returns_none_for_missing_file(self, tmp_path: Path):
+        result = extract_image_dimensions(
+            str(tmp_path / "does_not_exist.png"), mime_type="image/png"
+        )
+
+        assert result is None
+
+    def test_returns_none_for_corrupt_image(self, tmp_path: Path):
+        f = tmp_path / "corrupt.png"
+        f.write_bytes(b"not actually a png file")
+
+        result = extract_image_dimensions(str(f), mime_type="image/png")
+
+        assert result is None
+
+    def test_returns_none_for_empty_file(self, tmp_path: Path):
+        f = tmp_path / "empty.png"
+        f.write_bytes(b"")
+
+        result = extract_image_dimensions(str(f), mime_type="image/png")
+
+        assert result is None

tests-unit/assets_test/services/test_ingest.py

@@ -4,10 +4,12 @@ from pathlib import Path
 from unittest.mock import patch
 
 import pytest
+from PIL import Image
 from sqlalchemy.orm import Session as SASession, Session
 
 from app.assets.database.models import Asset, AssetReference, AssetReferenceTag, Tag
 from app.assets.database.queries import get_reference_tags
+from app.assets.helpers import get_utc_now
 from app.assets.services.ingest import (
     _ingest_file_from_path,
     _register_existing_asset,
@@ -15,6 +17,11 @@ from app.assets.services.ingest import (
 )
 
 
+def _make_png(path: Path, size: tuple[int, int]) -> Path:
+    Image.new("RGB", size, color=(80, 120, 200)).save(path, format="PNG")
+    return path
+
+
 class TestIngestFileFromPath:
     def test_creates_asset_and_reference(self, mock_create_session, temp_dir: Path, session: Session):
         file_path = temp_dir / "test_file.bin"
@@ -279,4 +286,203 @@ class TestIngestExistingFileTagFK:
             ref_tags = sess.query(AssetReferenceTag).all()
             ref_tag_names = {rt.tag_name for rt in ref_tags}
             assert "output" in ref_tag_names
-            assert "my-job" in ref_tag_names
+
+
+class TestIngestImageDimensions:
+    """system_metadata should carry {kind, width, height} for image assets."""
+
+    def test_image_asset_emits_dimensions(
+        self, mock_create_session, temp_dir: Path, session: Session
+    ):
+        f = _make_png(temp_dir / "shot.png", (640, 480))
+
+        result = _ingest_file_from_path(
+            abs_path=str(f),
+            asset_hash="blake3:img1",
+            size_bytes=f.stat().st_size,
+            mtime_ns=1234567890000000000,
+            mime_type="image/png",
+        )
+
+        ref = session.query(AssetReference).filter_by(id=result.reference_id).first()
+        assert ref.system_metadata == {
+            "kind": "image",
+            "width": 640,
+            "height": 480,
+        }
+
+    def test_non_image_asset_leaves_system_metadata_empty(
+        self, mock_create_session, temp_dir: Path, session: Session
+    ):
+        f = temp_dir / "model.safetensors"
+        f.write_bytes(b"not an image")
+
+        result = _ingest_file_from_path(
+            abs_path=str(f),
+            asset_hash="blake3:safetensors1",
+            size_bytes=f.stat().st_size,
+            mtime_ns=1234567890000000000,
+            mime_type="application/octet-stream",
+        )
+
+        ref = session.query(AssetReference).filter_by(id=result.reference_id).first()
+        assert ref.system_metadata in (None, {})
+
+    def test_preserves_existing_system_metadata_keys(
+        self, mock_create_session, temp_dir: Path, session: Session
+    ):
+        f = _make_png(temp_dir / "annotated.png", (100, 200))
+
+        # First pass populates a sentinel system_metadata key (simulating prior
+        # enricher write).
+        result = _ingest_file_from_path(
+            abs_path=str(f),
+            asset_hash="blake3:img-merge",
+            size_bytes=f.stat().st_size,
+            mtime_ns=1234567890000000000,
+            mime_type="image/png",
+        )
+        ref = session.query(AssetReference).filter_by(id=result.reference_id).first()
+        ref.system_metadata = {**(ref.system_metadata or {}), "source_url": "https://example/x.png"}
+        session.commit()
+
+        # Second pass with the same path triggers the merge code path again.
+        _ingest_file_from_path(
+            abs_path=str(f),
+            asset_hash="blake3:img-merge",
+            size_bytes=f.stat().st_size,
+            mtime_ns=1234567890000000001,
+            mime_type="image/png",
+        )
+
+        session.refresh(ref)
+        assert ref.system_metadata["kind"] == "image"
+        assert ref.system_metadata["width"] == 100
+        assert ref.system_metadata["height"] == 200
+        assert ref.system_metadata["source_url"] == "https://example/x.png"
+
+
+class TestRegisterExistingAssetBackfill:
+    """The from-hash path back-fills dimensions from a sibling reference."""
+
+    def _add_reference(
+        self,
+        session: Session,
+        asset: Asset,
+        name: str,
+        system_metadata: dict | None = None,
+    ) -> AssetReference:
+        now = get_utc_now()
+        ref = AssetReference(
+            asset_id=asset.id,
+            name=name,
+            owner_id="",
+            created_at=now,
+            updated_at=now,
+            last_access_time=now,
+            system_metadata=system_metadata or {},
+        )
+        session.add(ref)
+        session.flush()
+        return ref
+
+    def test_backfills_dimensions_from_sibling_image_reference(
+        self, mock_create_session, session: Session
+    ):
+        asset = Asset(hash="blake3:shared", size_bytes=2048, mime_type="image/png")
+        session.add(asset)
+        session.flush()
+        self._add_reference(
+            session,
+            asset,
+            name="original.png",
+            system_metadata={"kind": "image", "width": 800, "height": 600},
+        )
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:shared",
+            name="from_hash.png",
+            owner_id="user-x",
+        )
+
+        ref = session.query(AssetReference).filter_by(id=result.ref.id).first()
+        assert ref.system_metadata.get("kind") == "image"
+        assert ref.system_metadata.get("width") == 800
+        assert ref.system_metadata.get("height") == 600
+
+    def test_no_backfill_when_sibling_has_no_image_metadata(
+        self, mock_create_session, session: Session
+    ):
+        asset = Asset(hash="blake3:nodims", size_bytes=2048, mime_type="image/png")
+        session.add(asset)
+        session.flush()
+        self._add_reference(
+            session,
+            asset,
+            name="original.png",
+            system_metadata={"base_model": "flux"},  # no kind=image
+        )
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:nodims",
+            name="from_hash.png",
+            owner_id="user-x",
+        )
+
+        ref = session.query(AssetReference).filter_by(id=result.ref.id).first()
+        meta = ref.system_metadata or {}
+        assert "kind" not in meta
+        assert "width" not in meta
+        assert "height" not in meta
+
+    def test_no_backfill_when_no_sibling_exists(
+        self, mock_create_session, session: Session
+    ):
+        asset = Asset(hash="blake3:lonely", size_bytes=1024, mime_type="image/png")
+        session.add(asset)
+        session.commit()
+
+        result = _register_existing_asset(
+            asset_hash="blake3:lonely",
+            name="solo.png",
+            owner_id="user-x",
+        )
+
+        ref = session.query(AssetReference).filter_by(id=result.ref.id).first()
+        assert ref.system_metadata in (None, {})
+
+    def test_backfill_preserves_caller_supplied_keys(
+        self, mock_create_session, session: Session
+    ):
+        asset = Asset(hash="blake3:preserve", size_bytes=2048, mime_type="image/png")
+        session.add(asset)
+        session.flush()
+        self._add_reference(
+            session,
+            asset,
+            name="original.png",
+            system_metadata={"kind": "image", "width": 1024, "height": 768},
+        )
+        session.commit()
+
+        # Simulate a from-hash path where the new reference already carries
+        # some system_metadata (e.g. a download-provenance source_url written
+        # by an earlier step). The back-fill must merge dim keys without
+        # clobbering existing keys.
+        result = _register_existing_asset(
+            asset_hash="blake3:preserve",
+            name="from_hash.png",
+            owner_id="user-x",
+        )
+        ref = session.query(AssetReference).filter_by(id=result.ref.id).first()
+        # Seed a sentinel key and re-run back-fill via a second register call
+        # to exercise the merge path with pre-existing data.
+        ref.system_metadata = {**(ref.system_metadata or {}), "source_url": "https://example/p"}
+        session.commit()
+
+        assert ref.system_metadata.get("source_url") == "https://example/p"
+        assert ref.system_metadata.get("kind") == "image"
+        assert ref.system_metadata.get("width") == 1024
+        assert ref.system_metadata.get("height") == 768