Add support for small flux.2 decoder (#13314)

Co-authored-by: guill <jacob.e.segal@gmail.com>

.github/workflows/release-stable-all.yml

@@ -20,29 +20,12 @@ jobs:
       git_tag: ${{ inputs.git_tag }}
       cache_tag: "cu130"
       python_minor: "13"
-      python_patch: "11"
+      python_patch: "12"
       rel_name: "nvidia"
       rel_extra_name: ""
       test_release: true
     secrets: inherit
 
-  release_nvidia_cu128:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA cu128"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu128"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "nvidia"
-      rel_extra_name: "_cu128"
-      test_release: true
-    secrets: inherit
-
   release_nvidia_cu126:
     permissions:
       contents: "write"
@@ -76,3 +59,20 @@ jobs:
       rel_extra_name: ""
       test_release: false
     secrets: inherit
+
+  release_xpu:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release Intel XPU"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "xpu"
+      python_minor: "13"
+      python_patch: "12"
+      rel_name: "intel"
+      rel_extra_name: ""
+      test_release: true
+    secrets: inherit

README.md

@@ -61,6 +61,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
+- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI.
 - Image Models
    - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
    - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
@@ -136,7 +137,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
    - Builds a new release using the latest stable core version
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
-   - Weekly frontend updates are merged into the core repository
+   - Every 2+ weeks frontend updates are merged into the core repository
    - Features are frozen for the upcoming core release
    - Development continues for the next release cycle
 
@@ -232,7 +233,7 @@ Put your VAE in: models/vae
 
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2```
 
 This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
 
@@ -275,7 +276,7 @@ Nvidia users should install stable pytorch using this command:
 
 This is the command to install pytorch nightly instead which might have performance improvements.
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132```
 
 #### Troubleshooting
 

app/assets/database/queries/__init__.py

@@ -1,6 +1,7 @@
 from app.assets.database.queries.asset import (
     asset_exists_by_hash,
     bulk_insert_assets,
+    create_stub_asset,
     get_asset_by_hash,
     get_existing_asset_ids,
     reassign_asset_references,
@@ -12,6 +13,7 @@ from app.assets.database.queries.asset_reference import (
     UnenrichedReferenceRow,
     bulk_insert_references_ignore_conflicts,
     bulk_update_enrichment_level,
+    count_active_siblings,
     bulk_update_is_missing,
     bulk_update_needs_verify,
     convert_metadata_to_rows,
@@ -80,6 +82,8 @@ __all__ = [
     "bulk_insert_references_ignore_conflicts",
     "bulk_insert_tags_and_meta",
     "bulk_update_enrichment_level",
+    "count_active_siblings",
+    "create_stub_asset",
     "bulk_update_is_missing",
     "bulk_update_needs_verify",
     "convert_metadata_to_rows",

app/assets/database/queries/asset.py

@@ -78,6 +78,18 @@ def upsert_asset(
     return asset, created, updated
 
 
+def create_stub_asset(
+    session: Session,
+    size_bytes: int,
+    mime_type: str | None = None,
+) -> Asset:
+    """Create a new asset with no hash (stub for later enrichment)."""
+    asset = Asset(size_bytes=size_bytes, mime_type=mime_type, hash=None)
+    session.add(asset)
+    session.flush()
+    return asset
+
+
 def bulk_insert_assets(
     session: Session,
     rows: list[dict],

app/assets/database/queries/asset_reference.py

@@ -114,6 +114,23 @@ def get_reference_by_file_path(
     )
 
 
+def count_active_siblings(
+    session: Session,
+    asset_id: str,
+    exclude_reference_id: str,
+) -> int:
+    """Count active (non-deleted) references to an asset, excluding one reference."""
+    return (
+        session.query(AssetReference)
+        .filter(
+            AssetReference.asset_id == asset_id,
+            AssetReference.id != exclude_reference_id,
+            AssetReference.deleted_at.is_(None),
+        )
+        .count()
+    )
+
+
 def reference_exists_for_asset_id(
     session: Session,
     asset_id: str,

app/assets/scanner.py

@@ -13,6 +13,7 @@ from app.assets.database.queries import (
     delete_references_by_ids,
     ensure_tags_exist,
     get_asset_by_hash,
+    get_reference_by_id,
     get_references_for_prefixes,
     get_unenriched_references,
     mark_references_missing_outside_prefixes,
@@ -338,6 +339,7 @@ def build_asset_specs(
                 "metadata": metadata,
                 "hash": asset_hash,
                 "mime_type": mime_type,
+                "job_id": None,
             }
         )
         tag_pool.update(tags)
@@ -426,6 +428,7 @@ def enrich_asset(
     except OSError:
         return new_level
 
+    initial_mtime_ns = get_mtime_ns(stat_p)
     rel_fname = compute_relative_filename(file_path)
     mime_type: str | None = None
     metadata = None
@@ -489,6 +492,18 @@ def enrich_asset(
         except Exception as e:
             logging.warning("Failed to hash %s: %s", file_path, e)
 
+    # Optimistic guard: if the reference's mtime_ns changed since we
+    # started (e.g. ingest_existing_file updated it), our results are
+    # stale — discard them to avoid overwriting fresh registration data.
+    ref = get_reference_by_id(session, reference_id)
+    if ref is None or ref.mtime_ns != initial_mtime_ns:
+        session.rollback()
+        logging.info(
+            "Ref %s mtime changed during enrichment, discarding stale result",
+            reference_id,
+        )
+        return ENRICHMENT_STUB
+
     if extract_metadata and metadata:
         system_metadata = metadata.to_user_metadata()
         set_reference_system_metadata(session, reference_id, system_metadata)

app/assets/seeder.py

@@ -77,7 +77,9 @@ class _AssetSeeder:
     """
 
     def __init__(self) -> None:
-        self._lock = threading.Lock()
+        # RLock is required because _run_scan() drains pending work while
+        # holding _lock and re-enters start() which also acquires _lock.
+        self._lock = threading.RLock()
         self._state = State.IDLE
         self._progress: Progress | None = None
         self._last_progress: Progress | None = None
@@ -92,6 +94,7 @@ class _AssetSeeder:
         self._prune_first: bool = False
         self._progress_callback: ProgressCallback | None = None
         self._disabled: bool = False
+        self._pending_enrich: dict | None = None
 
     def disable(self) -> None:
         """Disable the asset seeder, preventing any scans from starting."""
@@ -196,6 +199,42 @@ class _AssetSeeder:
             compute_hashes=compute_hashes,
         )
 
+    def enqueue_enrich(
+        self,
+        roots: tuple[RootType, ...] = ("models", "input", "output"),
+        compute_hashes: bool = False,
+    ) -> bool:
+        """Start an enrichment scan now, or queue it for after the current scan.
+
+        If the seeder is idle, starts immediately. Otherwise, the enrich
+        request is stored and will run automatically when the current scan
+        finishes.
+
+        Args:
+            roots: Tuple of root types to scan
+            compute_hashes: If True, compute blake3 hashes
+
+        Returns:
+            True if started immediately, False if queued for later
+        """
+        with self._lock:
+            if self.start_enrich(roots=roots, compute_hashes=compute_hashes):
+                return True
+            if self._pending_enrich is not None:
+                existing_roots = set(self._pending_enrich["roots"])
+                existing_roots.update(roots)
+                self._pending_enrich["roots"] = tuple(existing_roots)
+                self._pending_enrich["compute_hashes"] = (
+                    self._pending_enrich["compute_hashes"] or compute_hashes
+                )
+            else:
+                self._pending_enrich = {
+                    "roots": roots,
+                    "compute_hashes": compute_hashes,
+                }
+            logging.info("Enrich scan queued (roots=%s)", self._pending_enrich["roots"])
+        return False
+
     def cancel(self) -> bool:
         """Request cancellation of the current scan.
 
@@ -381,9 +420,13 @@ class _AssetSeeder:
             return marked
         finally:
             with self._lock:
-                self._last_progress = self._progress
-                self._state = State.IDLE
-                self._progress = None
+                self._reset_to_idle()
+
+    def _reset_to_idle(self) -> None:
+        """Reset state to IDLE, preserving last progress. Caller must hold _lock."""
+        self._last_progress = self._progress
+        self._state = State.IDLE
+        self._progress = None
 
     def _is_cancelled(self) -> bool:
         """Check if cancellation has been requested."""
@@ -594,9 +637,18 @@ class _AssetSeeder:
                     },
                 )
             with self._lock:
-                self._last_progress = self._progress
-                self._state = State.IDLE
-                self._progress = None
+                self._reset_to_idle()
+                pending = self._pending_enrich
+                if pending is not None:
+                    self._pending_enrich = None
+                    if not self.start_enrich(
+                        roots=pending["roots"],
+                        compute_hashes=pending["compute_hashes"],
+                    ):
+                        logging.warning(
+                            "Pending enrich scan could not start (roots=%s)",
+                            pending["roots"],
+                        )
 
     def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]:
         """Run phase 1: fast scan to create stub records.

app/assets/services/__init__.py

@@ -23,6 +23,8 @@ from app.assets.services.ingest import (
     DependencyMissingError,
     HashMismatchError,
     create_from_hash,
+    ingest_existing_file,
+    register_output_files,
     upload_from_temp_path,
 )
 from app.assets.database.queries import (
@@ -72,6 +74,8 @@ __all__ = [
     "delete_asset_reference",
     "get_asset_by_hash",
     "get_asset_detail",
+    "ingest_existing_file",
+    "register_output_files",
     "get_mtime_ns",
     "get_size_and_mtime_ns",
     "list_assets_page",

app/assets/services/bulk_ingest.py

@@ -37,6 +37,7 @@ class SeedAssetSpec(TypedDict):
     metadata: ExtractedMetadata | None
     hash: str | None
     mime_type: str | None
+    job_id: str | None
 
 
 class AssetRow(TypedDict):
@@ -60,6 +61,7 @@ class ReferenceRow(TypedDict):
     name: str
     preview_id: str | None
     user_metadata: dict[str, Any] | None
+    job_id: str | None
     created_at: datetime
     updated_at: datetime
     last_access_time: datetime
@@ -167,6 +169,7 @@ def batch_insert_seed_assets(
                 "name": spec["info_name"],
                 "preview_id": None,
                 "user_metadata": user_metadata,
+                "job_id": spec.get("job_id"),
                 "created_at": current_time,
                 "updated_at": current_time,
                 "last_access_time": current_time,

app/assets/services/ingest.py

@@ -9,6 +9,9 @@ from sqlalchemy.orm import Session
 import app.assets.services.hashing as hashing
 from app.assets.database.queries import (
     add_tags_to_reference,
+    count_active_siblings,
+    create_stub_asset,
+    ensure_tags_exist,
     fetch_reference_and_asset,
     get_asset_by_hash,
     get_reference_by_file_path,
@@ -23,7 +26,8 @@ from app.assets.database.queries import (
     upsert_reference,
     validate_tags_exist,
 )
-from app.assets.helpers import normalize_tags
+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.path_utils import (
     compute_relative_filename,
@@ -130,6 +134,102 @@ def _ingest_file_from_path(
     )
 
 
+def register_output_files(
+    file_paths: Sequence[str],
+    user_metadata: UserMetadata = None,
+    job_id: str | None = None,
+) -> int:
+    """Register a batch of output file paths as assets.
+
+    Returns the number of files successfully registered.
+    """
+    registered = 0
+    for abs_path in file_paths:
+        if not os.path.isfile(abs_path):
+            continue
+        try:
+            if ingest_existing_file(
+                abs_path, user_metadata=user_metadata, job_id=job_id
+            ):
+                registered += 1
+        except Exception:
+            logging.exception("Failed to register output: %s", abs_path)
+    return registered
+
+
+def ingest_existing_file(
+    abs_path: str,
+    user_metadata: UserMetadata = None,
+    extra_tags: Sequence[str] = (),
+    owner_id: str = "",
+    job_id: str | None = None,
+) -> bool:
+    """Register an existing on-disk file as an asset stub.
+
+    If a reference already exists for this path, updates mtime_ns, job_id,
+    size_bytes, and resets enrichment so the enricher will re-hash it.
+
+    For brand-new paths, inserts a stub record (hash=NULL) for immediate
+    UX visibility.
+
+    Returns True if a row was inserted or updated, False otherwise.
+    """
+    locator = os.path.abspath(abs_path)
+    size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)
+    mime_type = mimetypes.guess_type(abs_path, strict=False)[0]
+    name, path_tags = get_name_and_tags_from_asset_path(abs_path)
+    tags = list(dict.fromkeys(path_tags + list(extra_tags)))
+
+    with create_session() as session:
+        existing_ref = get_reference_by_file_path(session, locator)
+        if existing_ref is not None:
+            now = get_utc_now()
+            existing_ref.mtime_ns = mtime_ns
+            existing_ref.job_id = job_id
+            existing_ref.is_missing = False
+            existing_ref.deleted_at = None
+            existing_ref.updated_at = now
+            existing_ref.enrichment_level = 0
+
+            asset = existing_ref.asset
+            if asset:
+                # If other refs share this asset, detach to a new stub
+                # instead of mutating the shared row.
+                siblings = count_active_siblings(session, asset.id, existing_ref.id)
+                if siblings > 0:
+                    new_asset = create_stub_asset(
+                        session,
+                        size_bytes=size_bytes,
+                        mime_type=mime_type or asset.mime_type,
+                    )
+                    existing_ref.asset_id = new_asset.id
+                else:
+                    asset.hash = None
+                    asset.size_bytes = size_bytes
+                    if mime_type:
+                        asset.mime_type = mime_type
+            session.commit()
+            return True
+
+        spec = {
+            "abs_path": abs_path,
+            "size_bytes": size_bytes,
+            "mtime_ns": mtime_ns,
+            "info_name": name,
+            "tags": tags,
+            "fname": os.path.basename(abs_path),
+            "metadata": None,
+            "hash": None,
+            "mime_type": mime_type,
+            "job_id": job_id,
+        }
+        if tags:
+            ensure_tags_exist(session, tags)
+        result = batch_insert_seed_assets(session, [spec], owner_id=owner_id)
+        session.commit()
+        return result.won_paths > 0
+
+
 def _register_existing_asset(
     asset_hash: str,
     name: str,

app/assets/services/path_utils.py

@@ -93,12 +93,13 @@ def compute_relative_filename(file_path: str) -> str | None:
 
 def get_asset_category_and_relative_path(
     file_path: str,
-) -> tuple[Literal["input", "output", "models"], str]:
+) -> tuple[Literal["input", "output", "temp", "models"], str]:
     """Determine which root category a file path belongs to.
 
     Categories:
       - 'input': under folder_paths.get_input_directory()
       - 'output': under folder_paths.get_output_directory()
+      - 'temp': under folder_paths.get_temp_directory()
       - 'models': under any base path from get_comfy_models_folders()
 
     Returns:
@@ -129,7 +130,12 @@ def get_asset_category_and_relative_path(
     if _check_is_within(fp_abs, output_base):
         return "output", _compute_relative(fp_abs, output_base)
 
-    # 3) models (check deepest matching base to avoid ambiguity)
+    # 3) temp
+    temp_base = os.path.abspath(folder_paths.get_temp_directory())
+    if _check_is_within(fp_abs, temp_base):
+        return "temp", _compute_relative(fp_abs, temp_base)
+
+    # 4) models (check deepest matching base to avoid ambiguity)
     best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
     for bucket, bases in get_comfy_models_folders():
         for b in bases:
@@ -146,7 +152,7 @@ def get_asset_category_and_relative_path(
         return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
 
     raise ValueError(
-        f"Path is not within input, output, or configured model bases: {file_path}"
+        f"Path is not within input, output, temp, or configured model bases: {file_path}"
     )
 
 

blueprints/.glsl/Color_Balance_15.frag

@@ -0,0 +1,90 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0;
+uniform float u_float1;
+uniform float u_float2;
+uniform float u_float3;
+uniform float u_float4;
+uniform float u_float5;
+uniform float u_float6;
+uniform float u_float7;
+uniform float u_float8;
+uniform bool u_bool0;
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+vec3 rgb2hsl(vec3 c) {
+    float maxC = max(c.r, max(c.g, c.b));
+    float minC = min(c.r, min(c.g, c.b));
+    float l = (maxC + minC) * 0.5;
+    if (maxC == minC) return vec3(0.0, 0.0, l);
+    float d = maxC - minC;
+    float s = l > 0.5 ? d / (2.0 - maxC - minC) : d / (maxC + minC);
+    float h;
+    if (maxC == c.r) {
+        h = (c.g - c.b) / d + (c.g < c.b ? 6.0 : 0.0);
+    } else if (maxC == c.g) {
+        h = (c.b - c.r) / d + 2.0;
+    } else {
+        h = (c.r - c.g) / d + 4.0;
+    }
+    h /= 6.0;
+    return vec3(h, s, l);
+}
+
+float hue2rgb(float p, float q, float t) {
+    if (t < 0.0) t += 1.0;
+    if (t > 1.0) t -= 1.0;
+    if (t < 1.0 / 6.0) return p + (q - p) * 6.0 * t;
+    if (t < 1.0 / 2.0) return q;
+    if (t < 2.0 / 3.0) return p + (q - p) * (2.0 / 3.0 - t) * 6.0;
+    return p;
+}
+
+vec3 hsl2rgb(vec3 hsl) {
+    float h = hsl.x, s = hsl.y, l = hsl.z;
+    if (s == 0.0) return vec3(l);
+    float q = l < 0.5 ? l * (1.0 + s) : l + s - l * s;
+    float p = 2.0 * l - q;
+    return vec3(
+        hue2rgb(p, q, h + 1.0 / 3.0),
+        hue2rgb(p, q, h),
+        hue2rgb(p, q, h - 1.0 / 3.0)
+    );
+}
+
+void main() {
+    vec4 tex = texture(u_image0, v_texCoord);
+    vec3 color = tex.rgb;
+
+    vec3 shadows = vec3(u_float0, u_float1, u_float2) * 0.01;
+    vec3 midtones = vec3(u_float3, u_float4, u_float5) * 0.01;
+    vec3 highlights = vec3(u_float6, u_float7, u_float8) * 0.01;
+
+    float maxC = max(color.r, max(color.g, color.b));
+    float minC = min(color.r, min(color.g, color.b));
+    float lightness = (maxC + minC) * 0.5;
+
+    // GIMP weight curves: linear ramps with constants a=0.25, b=0.333, scale=0.7
+    const float a = 0.25;
+    const float b = 0.333;
+    const float scale = 0.7;
+
+    float sw = clamp((lightness - b) / -a + 0.5, 0.0, 1.0) * scale;
+    float mw = clamp((lightness - b) / a + 0.5, 0.0, 1.0) *
+               clamp((lightness + b - 1.0) / -a + 0.5, 0.0, 1.0) * scale;
+    float hw = clamp((lightness + b - 1.0) / a + 0.5, 0.0, 1.0) * scale;
+
+    color += sw * shadows + mw * midtones + hw * highlights;
+
+    if (u_bool0) {
+        vec3 hsl = rgb2hsl(clamp(color, 0.0, 1.0));
+        hsl.z = lightness;
+        color = hsl2rgb(hsl);
+    }
+
+    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
+}

blueprints/.glsl/Color_Curves_8.frag

@@ -0,0 +1,49 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform sampler2D u_curve0;  // RGB master curve (256x1 LUT)
+uniform sampler2D u_curve1;  // Red channel curve
+uniform sampler2D u_curve2;  // Green channel curve
+uniform sampler2D u_curve3;  // Blue channel curve
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+// GIMP-compatible curve lookup with manual linear interpolation.
+// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:
+//   index = value * (n_samples - 1)
+//   f = fract(index)
+//   result = (1-f) * samples[floor] + f * samples[ceil]
+//
+// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues
+// that occur with texture() + GL_LINEAR on small 256x1 LUTs.
+float applyCurve(sampler2D curve, float value) {
+    value = clamp(value, 0.0, 1.0);
+
+    float pos = value * 255.0;
+    int lo = int(floor(pos));
+    int hi = min(lo + 1, 255);
+    float f = pos - float(lo);
+
+    float a = texelFetch(curve, ivec2(lo, 0), 0).r;
+    float b = texelFetch(curve, ivec2(hi, 0), 0).r;
+
+    return a + f * (b - a);
+}
+
+void main() {
+    vec4 color = texture(u_image0, v_texCoord);
+
+    // GIMP order: per-channel curves first, then RGB master curve.
+    // See gimp_curve_map_pixels() default case in gimpcurve-map.c:
+    //   dest = colors_curve( channel_curve( src ) )
+    float tmp_r = applyCurve(u_curve1, color.r);
+    float tmp_g = applyCurve(u_curve2, color.g);
+    float tmp_b = applyCurve(u_curve3, color.b);
+    color.r = applyCurve(u_curve0, tmp_r);
+    color.g = applyCurve(u_curve0, tmp_g);
+    color.b = applyCurve(u_curve0, tmp_b);
+
+    fragColor0 = vec4(color.rgb, color.a);
+}

blueprints/Color Curves.json

@@ -0,0 +1,615 @@
+{
+  "revision": 0,
+  "last_node_id": 10,
+  "last_link_id": 0,
+  "nodes": [
+    {
+      "id": 10,
+      "type": "d5c462c8-1372-4af8-84f2-547c83470d04",
+      "pos": [
+        3610,
+        -2630
+      ],
+      "size": [
+        270,
+        420
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "image",
+          "localized_name": "images.image0",
+          "name": "images.image0",
+          "type": "IMAGE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "localized_name": "IMAGE0",
+          "name": "IMAGE0",
+          "type": "IMAGE",
+          "links": []
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
+          [
+            "4",
+            "curve"
+          ],
+          [
+            "5",
+            "curve"
+          ],
+          [
+            "6",
+            "curve"
+          ],
+          [
+            "7",
+            "curve"
+          ]
+        ]
+      },
+      "widgets_values": [],
+      "title": "Color Curves"
+    }
+  ],
+  "links": [],
+  "version": 0.4,
+  "definitions": {
+    "subgraphs": [
+      {
+        "id": "d5c462c8-1372-4af8-84f2-547c83470d04",
+        "version": 1,
+        "state": {
+          "lastGroupId": 0,
+          "lastNodeId": 9,
+          "lastLinkId": 38,
+          "lastRerouteId": 0
+        },
+        "revision": 0,
+        "config": {},
+        "name": "Color Curves",
+        "inputNode": {
+          "id": -10,
+          "bounding": [
+            2660,
+            -4500,
+            120,
+            60
+          ]
+        },
+        "outputNode": {
+          "id": -20,
+          "bounding": [
+            4270,
+            -4500,
+            120,
+            60
+          ]
+        },
+        "inputs": [
+          {
+            "id": "abc345b7-f55e-4f32-a11d-3aa4c2b0936b",
+            "name": "images.image0",
+            "type": "IMAGE",
+            "linkIds": [
+              29,
+              34
+            ],
+            "localized_name": "images.image0",
+            "label": "image",
+            "pos": [
+              2760,
+              -4480
+            ]
+          }
+        ],
+        "outputs": [
+          {
+            "id": "eb0ec079-46da-4408-8263-9ef85569d33d",
+            "name": "IMAGE0",
+            "type": "IMAGE",
+            "linkIds": [
+              28
+            ],
+            "localized_name": "IMAGE0",
+            "label": "IMAGE",
+            "pos": [
+              4290,
+              -4480
+            ]
+          }
+        ],
+        "widgets": [],
+        "nodes": [
+          {
+            "id": 4,
+            "type": "CurveEditor",
+            "pos": [
+              3060,
+              -4500
+            ],
+            "size": [
+              270,
+              200
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "curve",
+                "localized_name": "curve",
+                "name": "curve",
+                "type": "CURVE",
+                "widget": {
+                  "name": "curve"
+                },
+                "link": null
+              },
+              {
+                "label": "histogram",
+                "localized_name": "histogram",
+                "name": "histogram",
+                "type": "HISTOGRAM",
+                "shape": 7,
+                "link": 35
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "CURVE",
+                "name": "CURVE",
+                "type": "CURVE",
+                "links": [
+                  30
+                ]
+              }
+            ],
+            "title": "RGB Master",
+            "properties": {
+              "Node name for S&R": "CurveEditor"
+            },
+            "widgets_values": []
+          },
+          {
+            "id": 5,
+            "type": "CurveEditor",
+            "pos": [
+              3060,
+              -4250
+            ],
+            "size": [
+              270,
+              200
+            ],
+            "flags": {},
+            "order": 1,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "curve",
+                "localized_name": "curve",
+                "name": "curve",
+                "type": "CURVE",
+                "widget": {
+                  "name": "curve"
+                },
+                "link": null
+              },
+              {
+                "label": "histogram",
+                "localized_name": "histogram",
+                "name": "histogram",
+                "type": "HISTOGRAM",
+                "shape": 7,
+                "link": 36
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "CURVE",
+                "name": "CURVE",
+                "type": "CURVE",
+                "links": [
+                  31
+                ]
+              }
+            ],
+            "title": "Red",
+            "properties": {
+              "Node name for S&R": "CurveEditor"
+            },
+            "widgets_values": []
+          },
+          {
+            "id": 6,
+            "type": "CurveEditor",
+            "pos": [
+              3060,
+              -4000
+            ],
+            "size": [
+              270,
+              200
+            ],
+            "flags": {},
+            "order": 2,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "curve",
+                "localized_name": "curve",
+                "name": "curve",
+                "type": "CURVE",
+                "widget": {
+                  "name": "curve"
+                },
+                "link": null
+              },
+              {
+                "label": "histogram",
+                "localized_name": "histogram",
+                "name": "histogram",
+                "type": "HISTOGRAM",
+                "shape": 7,
+                "link": 37
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "CURVE",
+                "name": "CURVE",
+                "type": "CURVE",
+                "links": [
+                  32
+                ]
+              }
+            ],
+            "title": "Green",
+            "properties": {
+              "Node name for S&R": "CurveEditor"
+            },
+            "widgets_values": []
+          },
+          {
+            "id": 7,
+            "type": "CurveEditor",
+            "pos": [
+              3060,
+              -3750
+            ],
+            "size": [
+              270,
+              200
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "curve",
+                "localized_name": "curve",
+                "name": "curve",
+                "type": "CURVE",
+                "widget": {
+                  "name": "curve"
+                },
+                "link": null
+              },
+              {
+                "label": "histogram",
+                "localized_name": "histogram",
+                "name": "histogram",
+                "type": "HISTOGRAM",
+                "shape": 7,
+                "link": 38
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "CURVE",
+                "name": "CURVE",
+                "type": "CURVE",
+                "links": [
+                  33
+                ]
+              }
+            ],
+            "title": "Blue",
+            "properties": {
+              "Node name for S&R": "CurveEditor"
+            },
+            "widgets_values": []
+          },
+          {
+            "id": 8,
+            "type": "GLSLShader",
+            "pos": [
+              3590,
+              -4500
+            ],
+            "size": [
+              420,
+              500
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "image0",
+                "localized_name": "images.image0",
+                "name": "images.image0",
+                "type": "IMAGE",
+                "link": 29
+              },
+              {
+                "label": "image1",
+                "localized_name": "images.image1",
+                "name": "images.image1",
+                "shape": 7,
+                "type": "IMAGE",
+                "link": null
+              },
+              {
+                "label": "u_curve0",
+                "localized_name": "curves.u_curve0",
+                "name": "curves.u_curve0",
+                "shape": 7,
+                "type": "CURVE",
+                "link": 30
+              },
+              {
+                "label": "u_curve1",
+                "localized_name": "curves.u_curve1",
+                "name": "curves.u_curve1",
+                "shape": 7,
+                "type": "CURVE",
+                "link": 31
+              },
+              {
+                "label": "u_curve2",
+                "localized_name": "curves.u_curve2",
+                "name": "curves.u_curve2",
+                "shape": 7,
+                "type": "CURVE",
+                "link": 32
+              },
+              {
+                "label": "u_curve3",
+                "localized_name": "curves.u_curve3",
+                "name": "curves.u_curve3",
+                "shape": 7,
+                "type": "CURVE",
+                "link": 33
+              },
+              {
+                "localized_name": "fragment_shader",
+                "name": "fragment_shader",
+                "type": "STRING",
+                "widget": {
+                  "name": "fragment_shader"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "size_mode",
+                "name": "size_mode",
+                "type": "COMFY_DYNAMICCOMBO_V3",
+                "widget": {
+                  "name": "size_mode"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "IMAGE0",
+                "name": "IMAGE0",
+                "type": "IMAGE",
+                "links": [
+                  28
+                ]
+              },
+              {
+                "localized_name": "IMAGE1",
+                "name": "IMAGE1",
+                "type": "IMAGE",
+                "links": null
+              },
+              {
+                "localized_name": "IMAGE2",
+                "name": "IMAGE2",
+                "type": "IMAGE",
+                "links": null
+              },
+              {
+                "localized_name": "IMAGE3",
+                "name": "IMAGE3",
+                "type": "IMAGE",
+                "links": null
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GLSLShader"
+            },
+            "widgets_values": [
+              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform sampler2D u_curve0;  // RGB master curve (256x1 LUT)\nuniform sampler2D u_curve1;  // Red channel curve\nuniform sampler2D u_curve2;  // Green channel curve\nuniform sampler2D u_curve3;  // Blue channel curve\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\n// GIMP-compatible curve lookup with manual linear interpolation.\n// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:\n//   index = value * (n_samples - 1)\n//   f = fract(index)\n//   result = (1-f) * samples[floor] + f * samples[ceil]\n//\n// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues\n// that occur with texture() + GL_LINEAR on small 256x1 LUTs.\nfloat applyCurve(sampler2D curve, float value) {\n    value = clamp(value, 0.0, 1.0);\n\n    float pos = value * 255.0;\n    int lo = int(floor(pos));\n    int hi = min(lo + 1, 255);\n    float f = pos - float(lo);\n\n    float a = texelFetch(curve, ivec2(lo, 0), 0).r;\n    float b = texelFetch(curve, ivec2(hi, 0), 0).r;\n\n    return a + f * (b - a);\n}\n\nvoid main() {\n    vec4 color = texture(u_image0, v_texCoord);\n\n    // GIMP order: per-channel curves first, then RGB master curve.\n    // See gimp_curve_map_pixels() default case in gimpcurve-map.c:\n    //   dest = colors_curve( channel_curve( src ) )\n    float tmp_r = applyCurve(u_curve1, color.r);\n    float tmp_g = applyCurve(u_curve2, color.g);\n    float tmp_b = applyCurve(u_curve3, color.b);\n    color.r = applyCurve(u_curve0, tmp_r);\n    color.g = applyCurve(u_curve0, tmp_g);\n    color.b = applyCurve(u_curve0, tmp_b);\n\n    fragColor0 = vec4(color.rgb, color.a);\n}\n",
+              "from_input"
+            ]
+          },
+          {
+            "id": 9,
+            "type": "ImageHistogram",
+            "pos": [
+              2800,
+              -4300
+            ],
+            "size": [
+              210,
+              150
+            ],
+            "flags": {},
+            "order": 5,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "image",
+                "localized_name": "image",
+                "name": "image",
+                "type": "IMAGE",
+                "link": 34
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "HISTOGRAM",
+                "name": "rgb",
+                "type": "HISTOGRAM",
+                "links": [
+                  35
+                ]
+              },
+              {
+                "localized_name": "HISTOGRAM",
+                "name": "luminance",
+                "type": "HISTOGRAM",
+                "links": []
+              },
+              {
+                "localized_name": "HISTOGRAM",
+                "name": "red",
+                "type": "HISTOGRAM",
+                "links": [
+                  36
+                ]
+              },
+              {
+                "localized_name": "HISTOGRAM",
+                "name": "green",
+                "type": "HISTOGRAM",
+                "links": [
+                  37
+                ]
+              },
+              {
+                "localized_name": "HISTOGRAM",
+                "name": "blue",
+                "type": "HISTOGRAM",
+                "links": [
+                  38
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "ImageHistogram"
+            },
+            "widgets_values": []
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 29,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 8,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 28,
+            "origin_id": 8,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 30,
+            "origin_id": 4,
+            "origin_slot": 0,
+            "target_id": 8,
+            "target_slot": 2,
+            "type": "CURVE"
+          },
+          {
+            "id": 31,
+            "origin_id": 5,
+            "origin_slot": 0,
+            "target_id": 8,
+            "target_slot": 3,
+            "type": "CURVE"
+          },
+          {
+            "id": 32,
+            "origin_id": 6,
+            "origin_slot": 0,
+            "target_id": 8,
+            "target_slot": 4,
+            "type": "CURVE"
+          },
+          {
+            "id": 33,
+            "origin_id": 7,
+            "origin_slot": 0,
+            "target_id": 8,
+            "target_slot": 5,
+            "type": "CURVE"
+          },
+          {
+            "id": 34,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 9,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 35,
+            "origin_id": 9,
+            "origin_slot": 0,
+            "target_id": 4,
+            "target_slot": 1,
+            "type": "HISTOGRAM"
+          },
+          {
+            "id": 36,
+            "origin_id": 9,
+            "origin_slot": 2,
+            "target_id": 5,
+            "target_slot": 1,
+            "type": "HISTOGRAM"
+          },
+          {
+            "id": 37,
+            "origin_id": 9,
+            "origin_slot": 3,
+            "target_id": 6,
+            "target_slot": 1,
+            "type": "HISTOGRAM"
+          },
+          {
+            "id": 38,
+            "origin_id": 9,
+            "origin_slot": 4,
+            "target_id": 7,
+            "target_slot": 1,
+            "type": "HISTOGRAM"
+          }
+        ],
+        "extra": {
+          "workflowRendererVersion": "LG"
+        },
+        "category": "Image Tools/Color adjust"
+      }
+    ]
+  }
+}

blueprints/Image Channels.json

@@ -1 +1,322 @@
-{"revision": 0, "last_node_id": 29, "last_link_id": 0, "nodes": [{"id": 29, "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "pos": [1970, -230], "size": [180, 86], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "title": "Image Channels", "properties": {"proxyWidgets": []}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 28, "lastLinkId": 39, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Channels", "inputNode": {"id": -10, "bounding": [1820, -185, 120, 60]}, "outputNode": {"id": -20, "bounding": [2460, -215, 120, 120]}, "inputs": [{"id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image", "pos": [1920, -165]}], "outputs": [{"id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b", "name": "IMAGE0", "type": "IMAGE", "linkIds": [26], "localized_name": "IMAGE0", "label": "R", "pos": [2480, -195]}, {"id": "fb44a77e-0522-43e9-9527-82e7465b3596", "name": "IMAGE1", "type": "IMAGE", "linkIds": [27], "localized_name": "IMAGE1", "label": "G", "pos": [2480, -175]}, {"id": "81460ee6-0131-402a-874f-6bf3001fc4ff", "name": "IMAGE2", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE2", "label": "B", "pos": [2480, -155]}, {"id": "ae690246-80d4-4951-b1d9-9306d8a77417", "name": "IMAGE3", "type": "IMAGE", "linkIds": [29], "localized_name": "IMAGE3", "label": "A", "pos": [2480, -135]}], "widgets": [], "nodes": [{"id": 23, "type": "GLSLShader", "pos": [2000, -330], "size": [400, 172], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [26]}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": [27]}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": [28]}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": [29]}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n  vec4 color = texture(u_image0, v_texCoord);\n  // Output each channel as grayscale to separate render targets\n  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel\n  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel\n  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel\n  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel\n}\n", "from_input"]}], "groups": [], "links": [{"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 26, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}, {"id": 27, "origin_id": 23, "origin_slot": 1, "target_id": -20, "target_slot": 1, "type": "IMAGE"}, {"id": 28, "origin_id": 23, "origin_slot": 2, "target_id": -20, "target_slot": 2, "type": "IMAGE"}, {"id": 29, "origin_id": 23, "origin_slot": 3, "target_id": -20, "target_slot": 3, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}}
+{
+  "revision": 0,
+  "last_node_id": 29,
+  "last_link_id": 0,
+  "nodes": [
+    {
+      "id": 29,
+      "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
+      "pos": [
+        1970,
+        -230
+      ],
+      "size": [
+        180,
+        86
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "image",
+          "localized_name": "images.image0",
+          "name": "images.image0",
+          "type": "IMAGE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "label": "R",
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+              "from_input"
+            ]
+          }
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+      }
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+}

blueprints/Prompt Enhance.json

@@ -1 +1,278 @@
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+                "widget": {
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+                "type": "INT",
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+              },
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+                "shape": 7,
+                "type": "STRING",
+                "widget": {
+                  "name": "system_prompt"
+                },
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+              }
+            ],
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+            "color": "#432",
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+          "workflowRendererVersion": "LG"
+        },
+        "category": "Text generation/Prompt enhance"
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+    ]
+  },
+  "extra": {}
+}

blueprints/Sharpen.json

@@ -1 +1,309 @@
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+}

blueprints/Video Upscale(GAN x4).json

@@ -1 +1,420 @@
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+                "localized_name": "image",
+                "name": "image",
+                "type": "IMAGE",
+                "link": 14
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "IMAGE",
+                "name": "IMAGE",
+                "type": "IMAGE",
+                "links": [
+                  13
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.10.0",
+              "Node name for S&R": "ImageUpscaleWithModel"
+            }
+          },
+          {
+            "id": 11,
+            "type": "CreateVideo",
+            "pos": [
+              1110,
+              550
+            ],
+            "size": [
+              320,
+              78
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "link": 13
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "shape": 7,
+                "type": "AUDIO",
+                "link": 16
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "fps"
+                },
+                "link": 12
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "VIDEO",
+                "name": "VIDEO",
+                "type": "VIDEO",
+                "links": [
+                  15
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.10.0",
+              "Node name for S&R": "CreateVideo"
+            },
+            "widgets_values": [
+              30
+            ]
+          },
+          {
+            "id": 10,
+            "type": "GetVideoComponents",
+            "pos": [
+              1110,
+              330
+            ],
+            "size": [
+              320,
+              70
+            ],
+            "flags": {},
+            "order": 2,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "video",
+                "name": "video",
+                "type": "VIDEO",
+                "link": 10
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "links": [
+                  14
+                ]
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "type": "AUDIO",
+                "links": [
+                  16
+                ]
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "links": [
+                  12
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.10.0",
+              "Node name for S&R": "GetVideoComponents"
+            }
+          },
+          {
+            "id": 1,
+            "type": "UpscaleModelLoader",
+            "pos": [
+              750,
+              450
+            ],
+            "size": [
+              280,
+              60
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "model_name",
+                "name": "model_name",
+                "type": "COMBO",
+                "widget": {
+                  "name": "model_name"
+                },
+                "link": 19
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "UPSCALE_MODEL",
+                "name": "UPSCALE_MODEL",
+                "type": "UPSCALE_MODEL",
+                "links": [
+                  1
+                ]
+              }
+            ],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.10.0",
+              "Node name for S&R": "UpscaleModelLoader",
+              "models": [
+                {
+                  "name": "RealESRGAN_x4plus.safetensors",
+                  "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors",
+                  "directory": "upscale_models"
+                }
+              ]
+            },
+            "widgets_values": [
+              "RealESRGAN_x4plus.safetensors"
+            ]
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 1,
+            "origin_id": 1,
+            "origin_slot": 0,
+            "target_id": 2,
+            "target_slot": 0,
+            "type": "UPSCALE_MODEL"
+          },
+          {
+            "id": 14,
+            "origin_id": 10,
+            "origin_slot": 0,
+            "target_id": 2,
+            "target_slot": 1,
+            "type": "IMAGE"
+          },
+          {
+            "id": 13,
+            "origin_id": 2,
+            "origin_slot": 0,
+            "target_id": 11,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 16,
+            "origin_id": 10,
+            "origin_slot": 1,
+            "target_id": 11,
+            "target_slot": 1,
+            "type": "AUDIO"
+          },
+          {
+            "id": 12,
+            "origin_id": 10,
+            "origin_slot": 2,
+            "target_id": 11,
+            "target_slot": 2,
+            "type": "FLOAT"
+          },
+          {
+            "id": 10,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 10,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 15,
+            "origin_id": 11,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 19,
+            "origin_id": -10,
+            "origin_slot": 1,
+            "target_id": 1,
+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {
+          "workflowRendererVersion": "LG"
+        },
+        "category": "Video generation and editing/Enhance video"
+      }
+    ]
+  },
+  "extra": {}
+}

comfy/cli_args.py

@@ -110,11 +110,13 @@ parser.add_argument("--preview-method", type=LatentPreviewMethod, default=Latent
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
 
+CACHE_RAM_AUTO_GB = -1.0
+
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
+cache_group.add_argument("--cache-ram", nargs='?', const=CACHE_RAM_AUTO_GB, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threshold the cache removes large items to free RAM. Default (when no value is provided): 25%% of system RAM (min 4GB, max 32GB).")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")

comfy/ldm/ace/ace_step15.py

@@ -611,6 +611,7 @@ class AceStepDiTModel(nn.Module):
         intermediate_size,
         patch_size,
         audio_acoustic_hidden_dim,
+        condition_dim=None,
         layer_types=None,
         sliding_window=128,
         rms_norm_eps=1e-6,
@@ -640,7 +641,7 @@ class AceStepDiTModel(nn.Module):
 
         self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
         self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
-        self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+        self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
 
         if layer_types is None:
             layer_types = ["full_attention"] * num_layers
@@ -1035,6 +1036,9 @@ class AceStepConditionGenerationModel(nn.Module):
         fsq_dim=2048,
         fsq_levels=[8, 8, 8, 5, 5, 5],
         fsq_input_num_quantizers=1,
+        encoder_hidden_size=2048,
+        encoder_intermediate_size=6144,
+        encoder_num_heads=16,
         audio_model=None,
         dtype=None,
         device=None,
@@ -1054,24 +1058,24 @@ class AceStepConditionGenerationModel(nn.Module):
 
         self.decoder = AceStepDiTModel(
             in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
-            intermediate_size, patch_size, audio_acoustic_hidden_dim,
+            intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
             layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.encoder = AceStepConditionEncoder(
-            text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
-            num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
+            text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
+            encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.tokenizer = AceStepAudioTokenizer(
-            audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
+            audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
             dtype=dtype, device=device, operations=operations
         )
         self.detokenizer = AudioTokenDetokenizer(
-            hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
+            encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
             dtype=dtype, device=device, operations=operations
         )
-        self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
+        self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
 
     def prepare_condition(
         self,

comfy/ldm/flux/model.py

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

comfy/ldm/lightricks/av_model.py

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

comfy/ldm/models/autoencoder.py

@@ -155,6 +155,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
     def __init__(self, embed_dim: int, **kwargs):
         self.max_batch_size = kwargs.pop("max_batch_size", None)
         ddconfig = kwargs.pop("ddconfig")
+        decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig)
         super().__init__(
             encoder_config={
                 "target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
@@ -162,7 +163,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             },
             decoder_config={
                 "target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
-                "params": ddconfig,
+                "params": decoder_ddconfig,
             },
             **kwargs,
         )

comfy/ldm/modules/encoders/noise_aug_modules.py

@@ -3,12 +3,9 @@ from ..diffusionmodules.openaimodel import Timestep
 import torch
 
 class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
-    def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
+    def __init__(self, *args, timestep_dim=256, **kwargs):
         super().__init__(*args, **kwargs)
-        if clip_stats_path is None:
-            clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
-        else:
-            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
+        clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
         self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
         self.register_buffer("data_std", clip_std[None, :], persistent=False)
         self.time_embed = Timestep(timestep_dim)

comfy/ldm/rt_detr/rtdetr_v4.py

@@ -0,0 +1,725 @@
+from collections import OrderedDict
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+COCO_CLASSES = [
+    'person','bicycle','car','motorcycle','airplane','bus','train','truck','boat',
+    'traffic light','fire hydrant','stop sign','parking meter','bench','bird','cat',
+    'dog','horse','sheep','cow','elephant','bear','zebra','giraffe','backpack',
+    'umbrella','handbag','tie','suitcase','frisbee','skis','snowboard','sports ball',
+    'kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket',
+    'bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple',
+    'sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair',
+    'couch','potted plant','bed','dining table','toilet','tv','laptop','mouse',
+    'remote','keyboard','cell phone','microwave','oven','toaster','sink',
+    'refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush',
+]
+
+# ---------------------------------------------------------------------------
+# HGNetv2 backbone
+# ---------------------------------------------------------------------------
+
+class ConvBNAct(nn.Module):
+    """Conv→BN→ReLU.  padding='same' adds asymmetric zero-pad (stem)."""
+    def __init__(self, ic, oc, k=3, s=1, groups=1, use_act=True, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        self.conv = operations.Conv2d(ic, oc, k, s, (k - 1) // 2, groups=groups, bias=False, device=device, dtype=dtype)
+        self.bn   = nn.BatchNorm2d(oc, device=device, dtype=dtype)
+        self.act  = nn.ReLU() if use_act else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+class LightConvBNAct(nn.Module):
+    def __init__(self, ic, oc, k, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.conv1 = ConvBNAct(ic, oc, 1, use_act=False, device=device, dtype=dtype, operations=operations)
+        self.conv2 = ConvBNAct(oc, oc, k, groups=oc, use_act=True, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x):
+        return self.conv2(self.conv1(x))
+
+class _StemBlock(nn.Module):
+    def __init__(self, ic, mc, oc, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.stem1  = ConvBNAct(ic,    mc,    3, 2, device=device, dtype=dtype, operations=operations)
+        # stem2a/stem2b use kernel=2, stride=1, no internal padding;
+        # padding is applied manually in forward (matching PaddlePaddle original)
+        self.stem2a = ConvBNAct(mc,    mc//2, 2, 1, device=device, dtype=dtype, operations=operations)
+        self.stem2b = ConvBNAct(mc//2, mc,    2, 1, device=device, dtype=dtype, operations=operations)
+        self.stem3  = ConvBNAct(mc*2,  mc,    3, 2, device=device, dtype=dtype, operations=operations)
+        self.stem4  = ConvBNAct(mc,    oc,    1, device=device, dtype=dtype, operations=operations)
+        self.pool   = nn.MaxPool2d(2, 1, ceil_mode=True)
+
+    def forward(self, x):
+        x  = self.stem1(x)
+        x  = F.pad(x, (0, 1, 0, 1))   # pad before pool and stem2a
+        x2 = self.stem2a(x)
+        x2 = F.pad(x2, (0, 1, 0, 1))  # pad before stem2b
+        x2 = self.stem2b(x2)
+        x1 = self.pool(x)
+        return self.stem4(self.stem3(torch.cat([x1, x2], 1)))
+
+
+class _HG_Block(nn.Module):
+    def __init__(self, ic, mc, oc, layer_num, k=3, residual=False, light=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.residual = residual
+        if light:
+            self.layers = nn.ModuleList(
+                [LightConvBNAct(ic if i == 0 else mc, mc, k, device=device, dtype=dtype, operations=operations) for i in range(layer_num)])
+        else:
+            self.layers = nn.ModuleList(
+                [ConvBNAct(ic if i == 0 else mc, mc, k, device=device, dtype=dtype, operations=operations) for i in range(layer_num)])
+        total = ic + layer_num * mc
+
+        self.aggregation = nn.Sequential(
+            ConvBNAct(total,   oc // 2, 1, device=device, dtype=dtype, operations=operations),
+            ConvBNAct(oc // 2, oc,      1, device=device, dtype=dtype, operations=operations))
+
+    def forward(self, x):
+        identity = x
+        outs = [x]
+        for layer in self.layers:
+            x = layer(x)
+            outs.append(x)
+        x = self.aggregation(torch.cat(outs, 1))
+        return x + identity if self.residual else x
+
+
+class _HG_Stage(nn.Module):
+    # config order: ic, mc, oc, num_blocks, downsample, light, k, layer_num
+    def __init__(self, ic, mc, oc, num_blocks, downsample=True, light=False, k=3, layer_num=6, device=None, dtype=None, operations=None):
+        super().__init__()
+        if downsample:
+            self.downsample = ConvBNAct(ic, ic, 3, 2, groups=ic, use_act=False, device=device, dtype=dtype, operations=operations)
+        else:
+            self.downsample = nn.Identity()
+        self.blocks = nn.Sequential(*[
+            _HG_Block(ic if i == 0 else oc, mc, oc, layer_num,
+                      k=k, residual=(i != 0), light=light, device=device, dtype=dtype, operations=operations)
+            for i in range(num_blocks)
+        ])
+
+    def forward(self, x):
+        return self.blocks(self.downsample(x))
+
+
+class HGNetv2(nn.Module):
+    # B5 config: stem=[3,32,64], stages=[ic, mc, oc, blocks, down, light, k, layers]
+    _STAGE_CFGS = [[64,  64,  128,  1, False, False, 3, 6],
+                   [128, 128, 512,  2, True,  False, 3, 6],
+                   [512, 256, 1024, 5, True,  True,  5, 6],
+                   [1024,512, 2048, 2, True,  True,  5, 6]]
+
+    def __init__(self, return_idx=(1, 2, 3), device=None, dtype=None, operations=None):
+        super().__init__()
+        self.stem   = _StemBlock(3, 32, 64, device=device, dtype=dtype, operations=operations)
+        self.stages = nn.ModuleList([_HG_Stage(*cfg, device=device, dtype=dtype, operations=operations) for cfg in self._STAGE_CFGS])
+        self.return_idx  = list(return_idx)
+        self.out_channels = [self._STAGE_CFGS[i][2] for i in return_idx]
+
+    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+        x = self.stem(x)
+        outs = []
+        for i, stage in enumerate(self.stages):
+            x = stage(x)
+            if i in self.return_idx:
+                outs.append(x)
+        return outs
+
+
+# ---------------------------------------------------------------------------
+# Encoder — HybridEncoder  (dfine version: RepNCSPELAN4 + SCDown PAN)
+# ---------------------------------------------------------------------------
+
+class ConvNormLayer(nn.Module):
+    """Conv→act (expects pre-fused BN weights)."""
+    def __init__(self, ic, oc, k, s, g=1, padding=None, act=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        p = (k - 1) // 2 if padding is None else padding
+        self.conv = operations.Conv2d(ic, oc, k, s, p, groups=g, bias=True, device=device, dtype=dtype)
+        self.act  = nn.SiLU() if act == 'silu' else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.conv(x))
+
+
+class VGGBlock(nn.Module):
+    """Rep-VGG block (expects pre-fused weights)."""
+    def __init__(self, ic, oc, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv2d(ic, oc, 3, 1, padding=1, bias=True, device=device, dtype=dtype)
+        self.act  = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.conv(x))
+
+
+class CSPLayer(nn.Module):
+    def __init__(self, ic, oc, num_blocks=3, expansion=1.0, act='silu', device=None, dtype=None, operations=None):
+        super().__init__()
+        h = int(oc * expansion)
+        self.conv1 = ConvNormLayer(ic, h, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
+        self.conv2 = ConvNormLayer(ic, h, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
+        self.bottlenecks = nn.Sequential(*[VGGBlock(h, h, device=device, dtype=dtype, operations=operations) for _ in range(num_blocks)])
+        self.conv3 = ConvNormLayer(h, oc, 1, 1, act=act, device=device, dtype=dtype, operations=operations) if h != oc else nn.Identity()
+
+    def forward(self, x):
+        return self.conv3(self.bottlenecks(self.conv1(x)) + self.conv2(x))
+
+
+class RepNCSPELAN4(nn.Module):
+    """CSP-ELAN block — the FPN/PAN block in RTv4's HybridEncoder."""
+    def __init__(self, c1, c2, c3, c4, n=3, act='silu', device=None, dtype=None, operations=None):
+        super().__init__()
+        self.c = c3 // 2
+        self.cv1 = ConvNormLayer(c1, c3, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
+        self.cv2 = nn.Sequential(CSPLayer(c3 // 2, c4, n, 1.0, act=act, device=device, dtype=dtype, operations=operations), ConvNormLayer(c4, c4, 3, 1, act=act, device=device, dtype=dtype, operations=operations))
+        self.cv3 = nn.Sequential(CSPLayer(c4, c4, n, 1.0, act=act, device=device, dtype=dtype, operations=operations), ConvNormLayer(c4, c4, 3, 1, act=act, device=device, dtype=dtype, operations=operations))
+        self.cv4 = ConvNormLayer(c3 + 2 * c4, c2, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x):
+        y = list(self.cv1(x).split((self.c, self.c), 1))
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+
+class SCDown(nn.Module):
+    """Separable conv downsampling used in HybridEncoder PAN bottom-up path."""
+    def __init__(self, ic, oc, k, s, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.cv1 = ConvNormLayer(ic, oc, 1, 1, device=device, dtype=dtype, operations=operations)
+        self.cv2 = ConvNormLayer(oc, oc, k, s, g=oc, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x):
+        return self.cv2(self.cv1(x))
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim  = embed_dim // num_heads
+        self.q_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+        self.k_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+        self.v_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+        self.out_proj = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, query, key, value, attn_mask=None):
+        optimized_attention = optimized_attention_for_device(query.device, False, small_input=True)
+        q, k, v = self.q_proj(query), self.k_proj(key), self.v_proj(value)
+        out = optimized_attention(q, k, v, heads=self.num_heads, mask=attn_mask)
+        return self.out_proj(out)
+
+
+class _TransformerEncoderLayer(nn.Module):
+    """Single AIFI encoder layer (pre- or post-norm, GELU by default)."""
+    def __init__(self, d_model, nhead, dim_feedforward, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.self_attn  = SelfAttention(d_model, nhead, device=device, dtype=dtype, operations=operations)
+        self.linear1    = operations.Linear(d_model, dim_feedforward, device=device, dtype=dtype)
+        self.linear2    = operations.Linear(dim_feedforward, d_model, device=device, dtype=dtype)
+        self.norm1      = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.norm2      = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.activation = nn.GELU()
+
+    def forward(self, src, src_mask=None, pos_embed=None):
+        q = k = src if pos_embed is None else src + pos_embed
+        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)
+        src = self.norm1(src + src2)
+        src2 = self.linear2(self.activation(self.linear1(src)))
+        return self.norm2(src + src2)
+
+
+class _TransformerEncoder(nn.Module):
+    """Thin wrapper so state-dict keys are  encoder.0.layers.N.*"""
+    def __init__(self, num_layers, d_model, nhead, dim_feedforward, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.layers = nn.ModuleList([
+            _TransformerEncoderLayer(d_model, nhead, dim_feedforward, device=device, dtype=dtype, operations=operations)
+            for _ in range(num_layers)
+        ])
+
+    def forward(self, src, src_mask=None, pos_embed=None):
+        for layer in self.layers:
+            src = layer(src, src_mask=src_mask, pos_embed=pos_embed)
+        return src
+
+
+class HybridEncoder(nn.Module):
+    def __init__(self, in_channels=(512, 1024, 2048), feat_strides=(8, 16, 32), hidden_dim=256, nhead=8, dim_feedforward=2048, use_encoder_idx=(2,), num_encoder_layers=1,
+                 pe_temperature=10000, expansion=1.0, depth_mult=1.0, act='silu', eval_spatial_size=(640, 640), device=None, dtype=None, operations=None):
+        super().__init__()
+        self.in_channels       = list(in_channels)
+        self.feat_strides      = list(feat_strides)
+        self.hidden_dim        = hidden_dim
+        self.use_encoder_idx   = list(use_encoder_idx)
+        self.pe_temperature    = pe_temperature
+        self.eval_spatial_size = eval_spatial_size
+        self.out_channels      = [hidden_dim] * len(in_channels)
+        self.out_strides       = list(feat_strides)
+
+        # channel projection (expects pre-fused weights)
+        self.input_proj = nn.ModuleList([
+            nn.Sequential(OrderedDict([('conv', operations.Conv2d(ch, hidden_dim, 1, bias=True, device=device, dtype=dtype))]))
+            for ch in in_channels
+        ])
+
+        # AIFI transformer — use _TransformerEncoder so keys are  encoder.0.layers.N.*
+        self.encoder = nn.ModuleList([
+            _TransformerEncoder(num_encoder_layers, hidden_dim, nhead, dim_feedforward, device=device, dtype=dtype, operations=operations)
+            for _ in range(len(use_encoder_idx))
+        ])
+
+        nb  = round(3 * depth_mult)
+        exp = expansion
+
+        # top-down FPN  (dfine: lateral conv has no act)
+        self.lateral_convs = nn.ModuleList(
+            [ConvNormLayer(hidden_dim, hidden_dim, 1, 1, device=device, dtype=dtype, operations=operations)
+             for _ in range(len(in_channels) - 1)])
+        self.fpn_blocks = nn.ModuleList(
+            [RepNCSPELAN4(hidden_dim * 2, hidden_dim, hidden_dim * 2, round(exp * hidden_dim // 2), nb, act=act, device=device, dtype=dtype, operations=operations)
+             for _ in range(len(in_channels) - 1)])
+
+        # bottom-up PAN  (dfine: nn.Sequential(SCDown) — keeps checkpoint key  .0.cv1/.0.cv2)
+        self.downsample_convs = nn.ModuleList(
+            [nn.Sequential(SCDown(hidden_dim, hidden_dim, 3, 2, device=device, dtype=dtype, operations=operations))
+             for _ in range(len(in_channels) - 1)])
+        self.pan_blocks = nn.ModuleList(
+            [RepNCSPELAN4(hidden_dim * 2, hidden_dim, hidden_dim * 2, round(exp * hidden_dim // 2), nb, act=act, device=device, dtype=dtype, operations=operations)
+             for _ in range(len(in_channels) - 1)])
+
+        # cache positional embeddings for fixed spatial size
+        if eval_spatial_size:
+            for idx in self.use_encoder_idx:
+                stride = self.feat_strides[idx]
+                pe = self._build_pe(eval_spatial_size[1] // stride,
+                                    eval_spatial_size[0] // stride,
+                                    hidden_dim, pe_temperature)
+                setattr(self, f'pos_embed{idx}', pe)
+
+    @staticmethod
+    def _build_pe(w, h, dim=256, temp=10000.):
+        assert dim % 4 == 0
+        gw = torch.arange(w, dtype=torch.float32)
+        gh = torch.arange(h, dtype=torch.float32)
+        gw, gh = torch.meshgrid(gw, gh, indexing='ij')
+        pdim  = dim // 4
+        omega = 1. / (temp ** (torch.arange(pdim, dtype=torch.float32) / pdim))
+        ow = gw.flatten()[:, None] @ omega[None]
+        oh = gh.flatten()[:, None] @ omega[None]
+        return torch.cat([ow.sin(), ow.cos(), oh.sin(), oh.cos()], 1)[None]
+
+    def forward(self, feats: List[torch.Tensor]) -> List[torch.Tensor]:
+        proj = [self.input_proj[i](f) for i, f in enumerate(feats)]
+
+        for i, enc_idx in enumerate(self.use_encoder_idx):
+            h, w = proj[enc_idx].shape[2:]
+            src  = proj[enc_idx].flatten(2).permute(0, 2, 1)
+            pe = getattr(self, f'pos_embed{enc_idx}').to(device=src.device, dtype=src.dtype)
+            for layer in self.encoder[i].layers:
+                src = layer(src, pos_embed=pe)
+            proj[enc_idx] = src.permute(0, 2, 1).reshape(-1, self.hidden_dim, h, w).contiguous()
+
+        n = len(self.in_channels)
+        inner = [proj[-1]]
+        for k in range(n - 1, 0, -1):
+            j = n - 1 - k
+            top = self.lateral_convs[j](inner[0])
+            inner[0] = top
+            up = F.interpolate(top, scale_factor=2., mode='nearest')
+            inner.insert(0, self.fpn_blocks[j](torch.cat([up, proj[k - 1]], 1)))
+
+        outs = [inner[0]]
+        for k in range(n - 1):
+            outs.append(self.pan_blocks[k](
+                torch.cat([self.downsample_convs[k](outs[-1]), inner[k + 1]], 1)))
+        return outs
+
+
+# ---------------------------------------------------------------------------
+# Decoder — DFINETransformer
+# ---------------------------------------------------------------------------
+
+def _deformable_attn_v2(value: list, spatial_shapes, sampling_locations: torch.Tensor, attention_weights: torch.Tensor, num_points_list: List[int]) -> torch.Tensor:
+    """
+    value            : list of per-level tensors  [bs*n_head, c, h_l, w_l]
+    sampling_locations: [bs, Lq, n_head, sum(pts), 2]  in [0,1]
+    attention_weights : [bs, Lq, n_head, sum(pts)]
+    """
+    _, c = value[0].shape[:2]      # bs*n_head, c
+    _, Lq, n_head, _, _ = sampling_locations.shape
+    bs = sampling_locations.shape[0]
+    n_h = n_head
+
+    grids = (2 * sampling_locations - 1)          # [bs, Lq, n_head, sum_pts, 2]
+    grids = grids.permute(0, 2, 1, 3, 4).flatten(0, 1)  # [bs*n_head, Lq, sum_pts, 2]
+    grids_per_lvl = grids.split(num_points_list, dim=2)  # list of [bs*n_head, Lq, pts_l, 2]
+
+    sampled = []
+    for lvl, (h, w) in enumerate(spatial_shapes):
+        val_l = value[lvl].reshape(bs * n_h, c, h, w)
+        sv = F.grid_sample(val_l, grids_per_lvl[lvl], mode='bilinear', padding_mode='zeros', align_corners=False)
+        sampled.append(sv) # sv: [bs*n_head, c, Lq, pts_l]
+
+    attn = attention_weights.permute(0, 2, 1, 3)  # [bs, n_head, Lq, sum_pts]
+    attn = attn.flatten(0, 1).unsqueeze(1)         # [bs*n_head, 1, Lq, sum_pts]
+    out  = (torch.cat(sampled, -1) * attn).sum(-1) # [bs*n_head, c, Lq]
+    out  = out.reshape(bs, n_h * c, Lq)
+    return out.permute(0, 2, 1)                    # [bs, Lq, hidden]
+
+
+class MSDeformableAttention(nn.Module):
+    def __init__(self, embed_dim=256, num_heads=8, num_levels=3, num_points=4, offset_scale=0.5, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.embed_dim, self.num_heads = embed_dim, num_heads
+        self.head_dim  = embed_dim // num_heads
+        pts = num_points if isinstance(num_points, list) else [num_points] * num_levels
+        self.num_points_list = pts
+        self.offset_scale    = offset_scale
+        total = num_heads * sum(pts)
+        self.register_buffer('num_points_scale', torch.tensor([1. / n for n in pts for _ in range(n)], dtype=torch.float32))
+        self.sampling_offsets  = operations.Linear(embed_dim, total * 2, device=device, dtype=dtype)
+        self.attention_weights = operations.Linear(embed_dim, total, device=device, dtype=dtype)
+
+    def forward(self, query, ref_pts, value, spatial_shapes):
+        bs, Lq = query.shape[:2]
+        offsets = self.sampling_offsets(query).reshape(
+            bs, Lq, self.num_heads, sum(self.num_points_list), 2)
+        attn_w  = F.softmax(
+            self.attention_weights(query).reshape(
+                bs, Lq, self.num_heads, sum(self.num_points_list)), -1)
+        scale   = self.num_points_scale.to(query).unsqueeze(-1)
+        offset  = offsets * scale * ref_pts[:, :, None, :, 2:] * self.offset_scale
+        locs    = ref_pts[:, :, None, :, :2] + offset  # [bs, Lq, n_head, sum_pts, 2]
+        return _deformable_attn_v2(value, spatial_shapes, locs, attn_w, self.num_points_list)
+
+
+class Gate(nn.Module):
+    def __init__(self, d_model, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.gate = operations.Linear(2 * d_model, 2 * d_model, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+
+    def forward(self, x1, x2):
+        g1, g2 = torch.sigmoid(self.gate(torch.cat([x1, x2], -1))).chunk(2, -1)
+        return self.norm(g1 * x1 + g2 * x2)
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim, out_dim, num_layers, device=None, dtype=None, operations=None):
+        super().__init__()
+        dims = [in_dim] + [hidden_dim] * (num_layers - 1) + [out_dim]
+        self.layers = nn.ModuleList(operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) for i in range(num_layers))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = nn.SiLU()(layer(x)) if i < len(self.layers) - 1 else layer(x)
+        return x
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, d_model=256, nhead=8, dim_feedforward=1024, num_levels=3, num_points=4, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.self_attn  = SelfAttention(d_model, nhead, device=device, dtype=dtype, operations=operations)
+        self.norm1      = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.cross_attn = MSDeformableAttention(d_model, nhead, num_levels, num_points, device=device, dtype=dtype, operations=operations)
+        self.gateway    = Gate(d_model, device=device, dtype=dtype, operations=operations)
+        self.linear1    = operations.Linear(d_model, dim_feedforward, device=device, dtype=dtype)
+        self.activation = nn.ReLU()
+        self.linear2    = operations.Linear(dim_feedforward, d_model, device=device, dtype=dtype)
+        self.norm3      = operations.LayerNorm(d_model, device=device, dtype=dtype)
+
+    def forward(self, target, ref_pts, value, spatial_shapes, attn_mask=None, query_pos=None):
+        q = k = target if query_pos is None else target + query_pos
+        t2 = self.self_attn(q, k, value=target, attn_mask=attn_mask)
+        target = self.norm1(target + t2)
+        t2 = self.cross_attn(
+            target if query_pos is None else target + query_pos,
+            ref_pts, value, spatial_shapes)
+        target = self.gateway(target, t2)
+        t2 = self.linear2(self.activation(self.linear1(target)))
+        target = self.norm3((target + t2).clamp(-65504, 65504))
+        return target
+
+
+# ---------------------------------------------------------------------------
+# FDR utilities
+# ---------------------------------------------------------------------------
+
+def weighting_function(reg_max, up, reg_scale):
+    """Non-uniform weighting function W(n) for FDR box regression."""
+    ub1 = (abs(up[0]) * abs(reg_scale)).item()
+    ub2 = ub1 * 2
+    step = (ub1 + 1) ** (2 / (reg_max - 2))
+    left  = [-(step ** i) + 1 for i in range(reg_max // 2 - 1, 0, -1)]
+    right = [ (step ** i) - 1 for i in range(1, reg_max // 2)]
+    vals  = [-ub2] + left + [0] + right + [ub2]
+    return torch.tensor(vals, dtype=up.dtype, device=up.device)
+
+
+def distance2bbox(points, distance, reg_scale):
+    """Decode edge-distances → cxcywh boxes."""
+    rs = abs(reg_scale).to(dtype=points.dtype)
+    x1 = points[..., 0] - (0.5 * rs + distance[..., 0]) * (points[..., 2] / rs)
+    y1 = points[..., 1] - (0.5 * rs + distance[..., 1]) * (points[..., 3] / rs)
+    x2 = points[..., 0] + (0.5 * rs + distance[..., 2]) * (points[..., 2] / rs)
+    y2 = points[..., 1] + (0.5 * rs + distance[..., 3]) * (points[..., 3] / rs)
+    x0, y0, x1_, y1_ = (x1 + x2) / 2, (y1 + y2) / 2, x2 - x1, y2 - y1
+    return torch.stack([x0, y0, x1_, y1_], -1)
+
+
+class Integral(nn.Module):
+    """Sum Pr(n)·W(n) over the distribution bins."""
+    def __init__(self, reg_max=32):
+        super().__init__()
+        self.reg_max = reg_max
+
+    def forward(self, x, project):
+        shape = x.shape
+        x = F.softmax(x.reshape(-1, self.reg_max + 1), 1)
+        x = F.linear(x, project.to(device=x.device, dtype=x.dtype)).reshape(-1, 4)
+        return x.reshape(list(shape[:-1]) + [-1])
+
+
+class LQE(nn.Module):
+    """Location Quality Estimator — refines class scores using corner distribution."""
+    def __init__(self, k=4, hidden_dim=64, num_layers=2, reg_max=32, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.k, self.reg_max = k, reg_max
+        self.reg_conf = MLP(4 * (k + 1), hidden_dim, 1, num_layers, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, scores, pred_corners):
+        B, L, _ = pred_corners.shape
+        prob     = F.softmax(pred_corners.reshape(B, L, 4, self.reg_max + 1), -1)
+        topk, _  = prob.topk(self.k, -1)
+        stat     = torch.cat([topk, topk.mean(-1, keepdim=True)], -1)
+        return scores + self.reg_conf(stat.reshape(B, L, -1))
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, hidden_dim, nhead, dim_feedforward, num_levels, num_points, num_layers, reg_max, reg_scale, up, eval_idx=-1, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.nhead      = nhead
+        self.eval_idx   = eval_idx if eval_idx >= 0 else num_layers + eval_idx
+        self.up, self.reg_scale, self.reg_max = up, reg_scale, reg_max
+        self.layers = nn.ModuleList([
+            TransformerDecoderLayer(hidden_dim, nhead, dim_feedforward, num_levels, num_points, device=device, dtype=dtype, operations=operations)
+            for _ in range(self.eval_idx + 1)
+        ])
+        self.lqe_layers = nn.ModuleList([LQE(4, 64, 2, reg_max, device=device, dtype=dtype, operations=operations) for _ in range(self.eval_idx + 1)])
+        self.register_buffer('project', weighting_function(reg_max, up, reg_scale))
+
+    def _value_op(self, memory, spatial_shapes):
+        """Reshape memory to per-level value tensors for deformable attention."""
+        c = self.hidden_dim // self.nhead
+        split = [h * w for h, w in spatial_shapes]
+        val = memory.reshape(memory.shape[0], memory.shape[1], self.nhead, c) # memory: [bs, sum(h*w), hidden_dim]
+        # → [bs, n_head, c, sum_hw]
+        val = val.permute(0, 2, 3, 1).flatten(0, 1)  # [bs*n_head, c, sum_hw]
+        return val.split(split, dim=-1)  # list of [bs*n_head, c, h_l*w_l]
+
+    def forward(self, target, ref_pts_unact, memory, spatial_shapes, bbox_head, score_head, query_pos_head, pre_bbox_head, integral):
+        val_split_flat = self._value_op(memory, spatial_shapes) # pre-split value for deformable attention
+
+        # reshape to [bs*n_head, c, h_l, w_l]
+        value = []
+        for lvl, (h, w) in enumerate(spatial_shapes):
+            v = val_split_flat[lvl]   # [bs*n_head, c, h*w]
+            value.append(v.reshape(v.shape[0], v.shape[1], h, w))
+
+        ref_pts  = F.sigmoid(ref_pts_unact)
+        output   = target
+        output_detach = pred_corners_undetach = 0
+
+        dec_bboxes, dec_logits = [], []
+
+        for i, layer in enumerate(self.layers):
+            ref_input    = ref_pts.unsqueeze(2)           # [bs, Lq, 1, 4]
+            query_pos    = query_pos_head(ref_pts).clamp(-10, 10)
+            output       = layer(output, ref_input, value, spatial_shapes, query_pos=query_pos)
+
+            if i == 0:
+                ref_unact = ref_pts.clamp(1e-5, 1 - 1e-5)
+                ref_unact = torch.log(ref_unact / (1 - ref_unact))
+                pre_bboxes = F.sigmoid(pre_bbox_head(output) + ref_unact)
+                ref_pts_initial = pre_bboxes.detach()
+
+            pred_corners = bbox_head[i](output + output_detach) + pred_corners_undetach
+            inter_ref_bbox = distance2bbox(ref_pts_initial, integral(pred_corners, self.project), self.reg_scale)
+
+            if i == self.eval_idx:
+                scores = score_head[i](output)
+                scores = self.lqe_layers[i](scores, pred_corners)
+                dec_bboxes.append(inter_ref_bbox)
+                dec_logits.append(scores)
+                break
+
+            pred_corners_undetach = pred_corners
+            ref_pts        = inter_ref_bbox.detach()
+            output_detach  = output.detach()
+
+        return torch.stack(dec_bboxes), torch.stack(dec_logits)
+
+
+class DFINETransformer(nn.Module):
+    def __init__(self, num_classes=80, hidden_dim=256, num_queries=300, feat_channels=[256, 256, 256], feat_strides=[8, 16, 32],
+                 num_levels=3, num_points=[3, 6, 3], nhead=8, num_layers=6, dim_feedforward=1024, eval_idx=-1, eps=1e-2, reg_max=32,
+                 reg_scale=8.0, eval_spatial_size=(640, 640), device=None, dtype=None, operations=None):
+        super().__init__()
+        assert len(feat_strides) == len(feat_channels)
+        self.hidden_dim  = hidden_dim
+        self.num_queries = num_queries
+        self.num_levels  = num_levels
+        self.eps         = eps
+        self.eval_spatial_size = eval_spatial_size
+
+        self.feat_strides = list(feat_strides)
+        for i in range(num_levels - len(feat_strides)):
+            self.feat_strides.append(feat_strides[-1] * 2 ** (i + 1))
+
+        # input projection (expects pre-fused weights)
+        self.input_proj = nn.ModuleList()
+        for ch in feat_channels:
+            if ch == hidden_dim:
+                self.input_proj.append(nn.Identity())
+            else:
+                self.input_proj.append(nn.Sequential(OrderedDict([
+                    ('conv', operations.Conv2d(ch, hidden_dim, 1, bias=True, device=device, dtype=dtype))])))
+        in_ch = feat_channels[-1]
+        for i in range(num_levels - len(feat_channels)):
+            self.input_proj.append(nn.Sequential(OrderedDict([
+                ('conv', operations.Conv2d(in_ch if i == 0 else hidden_dim,
+                                           hidden_dim, 3, 2, 1, bias=True, device=device, dtype=dtype))])))
+            in_ch = hidden_dim
+
+        # FDR parameters (non-trainable placeholders, set from config)
+        self.up        = nn.Parameter(torch.tensor([0.5]),      requires_grad=False)
+        self.reg_scale = nn.Parameter(torch.tensor([reg_scale]), requires_grad=False)
+
+        pts = num_points if isinstance(num_points, (list, tuple)) else [num_points] * num_levels
+        self.decoder = TransformerDecoder(hidden_dim, nhead, dim_feedforward, num_levels, pts,
+                                          num_layers, reg_max, self.reg_scale, self.up, eval_idx, device=device, dtype=dtype, operations=operations)
+
+        self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, 2, device=device, dtype=dtype, operations=operations)
+        self.enc_output     = nn.Sequential(OrderedDict([
+            ('proj', operations.Linear(hidden_dim, hidden_dim, device=device, dtype=dtype)),
+            ('norm', operations.LayerNorm(hidden_dim, device=device, dtype=dtype))]))
+        self.enc_score_head = operations.Linear(hidden_dim, num_classes, device=device, dtype=dtype)
+        self.enc_bbox_head  = MLP(hidden_dim, hidden_dim, 4, 3, device=device, dtype=dtype, operations=operations)
+
+        self.eval_idx_ = eval_idx if eval_idx >= 0 else num_layers + eval_idx
+        self.dec_score_head = nn.ModuleList(
+            [operations.Linear(hidden_dim, num_classes, device=device, dtype=dtype) for _ in range(self.eval_idx_ + 1)])
+        self.pre_bbox_head  = MLP(hidden_dim, hidden_dim, 4, 3, device=device, dtype=dtype, operations=operations)
+        self.dec_bbox_head  = nn.ModuleList(
+            [MLP(hidden_dim, hidden_dim, 4 * (reg_max + 1), 3, device=device, dtype=dtype, operations=operations)
+             for _ in range(self.eval_idx_ + 1)])
+        self.integral = Integral(reg_max)
+
+        if eval_spatial_size:
+            # Register as buffers so checkpoint values override the freshly-computed defaults
+            anchors, valid_mask = self._gen_anchors()
+            self.register_buffer('anchors', anchors)
+            self.register_buffer('valid_mask', valid_mask)
+
+    def _gen_anchors(self, spatial_shapes=None, grid_size=0.05, dtype=torch.float32, device='cpu'):
+        if spatial_shapes is None:
+            h0, w0 = self.eval_spatial_size
+            spatial_shapes = [[int(h0 / s), int(w0 / s)] for s in self.feat_strides]
+        anchors = []
+        for lvl, (h, w) in enumerate(spatial_shapes):
+            gy, gx = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
+            gxy = (torch.stack([gx, gy], -1).float() + 0.5) / torch.tensor([w, h], dtype=dtype)
+            wh  = torch.ones_like(gxy) * grid_size * (2. ** lvl)
+            anchors.append(torch.cat([gxy, wh], -1).reshape(-1, h * w, 4))
+        anchors    = torch.cat(anchors, 1).to(device)
+        valid_mask = ((anchors > self.eps) & (anchors < 1 - self.eps)).all(-1, keepdim=True)
+        anchors    = torch.log(anchors / (1 - anchors))
+        anchors    = torch.where(valid_mask, anchors, torch.full_like(anchors, float('inf')))
+        return anchors, valid_mask
+
+    def _encoder_input(self, feats: List[torch.Tensor]):
+        proj = [self.input_proj[i](f) for i, f in enumerate(feats)]
+        for i in range(len(feats), self.num_levels):
+            proj.append(self.input_proj[i](feats[-1] if i == len(feats) else proj[-1]))
+        flat, shapes = [], []
+        for f in proj:
+            _, _, h, w = f.shape
+            flat.append(f.flatten(2).permute(0, 2, 1))
+            shapes.append([h, w])
+        return torch.cat(flat, 1), shapes
+
+    def _decoder_input(self, memory: torch.Tensor):
+        anchors, valid_mask = self.anchors.to(memory), self.valid_mask
+        if memory.shape[0] > 1:
+            anchors = anchors.repeat(memory.shape[0], 1, 1)
+
+        mem      = valid_mask.to(memory) * memory
+        out_mem  = self.enc_output(mem)
+        logits   = self.enc_score_head(out_mem)
+        _, idx   = torch.topk(logits.max(-1).values, self.num_queries, dim=-1)
+        idx_e    = idx.unsqueeze(-1)
+        topk_mem = out_mem.gather(1, idx_e.expand(-1, -1, out_mem.shape[-1]))
+        topk_anc = anchors.gather(1, idx_e.expand(-1, -1, anchors.shape[-1]))
+        topk_ref = self.enc_bbox_head(topk_mem) + topk_anc
+        return topk_mem.detach(), topk_ref.detach()
+
+    def forward(self, feats: List[torch.Tensor]):
+        memory, shapes = self._encoder_input(feats)
+        content, ref   = self._decoder_input(memory)
+        out_bboxes, out_logits = self.decoder(
+            content, ref, memory, shapes,
+            self.dec_bbox_head, self.dec_score_head,
+            self.query_pos_head, self.pre_bbox_head, self.integral)
+        return {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1]}
+
+
+# ---------------------------------------------------------------------------
+# Main model
+# ---------------------------------------------------------------------------
+
+class RTv4(nn.Module):
+    def __init__(self, num_classes=80, num_queries=300, enc_h=256, dec_h=256, enc_ff=2048, dec_ff=1024, feat_strides=[8, 16, 32], device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        self.device = device
+        self.dtype = dtype
+        self.operations = operations
+
+        self.backbone = HGNetv2(device=device, dtype=dtype, operations=operations)
+        self.encoder  = HybridEncoder(hidden_dim=enc_h, dim_feedforward=enc_ff, device=device, dtype=dtype, operations=operations)
+        self.decoder  = DFINETransformer(num_classes=num_classes, hidden_dim=dec_h, num_queries=num_queries,
+            feat_channels=[enc_h] * len(feat_strides), feat_strides=feat_strides, dim_feedforward=dec_ff, device=device, dtype=dtype, operations=operations)
+
+        self.num_classes = num_classes
+        self.num_queries = num_queries
+        self.load_device = comfy.model_management.get_torch_device()
+
+    def _forward(self, x: torch.Tensor):
+        return self.decoder(self.encoder(self.backbone(x)))
+
+    def postprocess(self, outputs, orig_size: tuple = (640, 640)) -> List[dict]:
+        logits = outputs['pred_logits']
+        boxes  = torchvision.ops.box_convert(outputs['pred_boxes'], 'cxcywh', 'xyxy')
+        boxes  = boxes * torch.tensor(orig_size, device=boxes.device, dtype=boxes.dtype).repeat(1, 2).unsqueeze(1)
+        scores = F.sigmoid(logits)
+        scores, idx = torch.topk(scores.flatten(1), self.num_queries, dim=-1)
+        labels = idx % self.num_classes
+        boxes  = boxes.gather(1, (idx // self.num_classes).unsqueeze(-1).expand(-1, -1, 4))
+        return [{'labels': lbl, 'boxes': b, 'scores': s} for lbl, b, s in zip(labels, boxes, scores)]
+
+    def forward(self, x: torch.Tensor, orig_size: tuple = (640, 640), **kwargs):
+        outputs = self._forward(x.to(device=self.load_device, dtype=self.dtype))
+        return self.postprocess(outputs, orig_size)

comfy/memory_management.py

@@ -141,3 +141,17 @@ def interpret_gathered_like(tensors, gathered):
     return dest_views
 
 aimdo_enabled = False
+
+extra_ram_release_callback = None
+RAM_CACHE_HEADROOM = 0
+
+def set_ram_cache_release_state(callback, headroom):
+    global extra_ram_release_callback
+    global RAM_CACHE_HEADROOM
+    extra_ram_release_callback = callback
+    RAM_CACHE_HEADROOM = max(0, int(headroom))
+
+def extra_ram_release(target):
+    if extra_ram_release_callback is None:
+        return 0
+    return extra_ram_release_callback(target)

comfy/model_base.py

@@ -52,6 +52,7 @@ import comfy.ldm.qwen_image.model
 import comfy.ldm.kandinsky5.model
 import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
+import comfy.ldm.rt_detr.rtdetr_v4
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -890,7 +891,7 @@ class Flux(BaseModel):
         return torch.cat((image, mask), dim=1)
 
     def encode_adm(self, **kwargs):
-        return kwargs["pooled_output"]
+        return kwargs.get("pooled_output", None)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -937,9 +938,10 @@ class LongCatImage(Flux):
         transformer_options = transformer_options.copy()
         rope_opts = transformer_options.get("rope_options", {})
         rope_opts = dict(rope_opts)
+        pe_len = float(c_crossattn.shape[1]) if c_crossattn is not None else 512.0
         rope_opts.setdefault("shift_t", 1.0)
-        rope_opts.setdefault("shift_y", 512.0)
-        rope_opts.setdefault("shift_x", 512.0)
+        rope_opts.setdefault("shift_y", pe_len)
+        rope_opts.setdefault("shift_x", pe_len)
         transformer_options["rope_options"] = rope_opts
         return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
 
@@ -1060,6 +1062,10 @@ class LTXAV(BaseModel):
         if guide_attention_entries is not None:
             out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
+        ref_audio = kwargs.get("ref_audio", None)
+        if ref_audio is not None:
+            out['ref_audio'] = comfy.conds.CONDConstant(ref_audio)
+
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
@@ -1952,3 +1958,7 @@ class Kandinsky5Image(Kandinsky5):
 
     def concat_cond(self, **kwargs):
         return None
+
+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)

comfy/model_detection.py

@@ -696,6 +696,21 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
     if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
         dit_config = {}
         dit_config["audio_model"] = "ace1.5"
+        head_dim = 128
+        dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
+        dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
+        dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
+
+        dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
+        dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
+        dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
+        dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
+        return dit_config
+
+    if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
+        dit_config = {}
+        dit_config["image_model"] = "RT_DETR_v4"
+        dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
         return dit_config
 
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:

comfy/model_management.py

@@ -55,6 +55,7 @@ total_vram = 0
 
 # Training Related State
 in_training = False
+training_fp8_bwd = False
 
 
 def get_supported_float8_types():
@@ -668,7 +669,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
-        if shift_model.device == device:
+        if device is None or shift_model.device == device:
             if shift_model not in keep_loaded and not shift_model.is_dead():
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
@@ -678,8 +679,8 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
         i = x[-1]
         memory_to_free = 1e32
         pins_to_free = 1e32
-        if not DISABLE_SMART_MEMORY:
-            memory_to_free = memory_required - get_free_memory(device)
+        if not DISABLE_SMART_MEMORY or device is None:
+            memory_to_free = 0 if device is None else memory_required - get_free_memory(device)
             pins_to_free = pins_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
@@ -707,7 +708,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
-    else:
+    elif device is not None:
         if vram_state != VRAMState.HIGH_VRAM:
             mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
             if mem_free_torch > mem_free_total * 0.25:
@@ -1325,9 +1326,9 @@ MAX_PINNED_MEMORY = -1
 if not args.disable_pinned_memory:
     if is_nvidia() or is_amd():
         if WINDOWS:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.40  # Windows limit is apparently 50%
         else:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.90
         logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
 
 PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"])
@@ -1402,8 +1403,6 @@ def unpin_memory(tensor):
 
     if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
         TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
-        if len(PINNED_MEMORY) == 0:
-            TOTAL_PINNED_MEMORY = 0
         return True
     else:
         logging.warning("Unpin error.")

comfy/model_patcher.py

@@ -300,9 +300,6 @@ class ModelPatcher:
     def model_mmap_residency(self, free=False):
         return comfy.model_management.module_mmap_residency(self.model, free=free)
 
-    def get_ram_usage(self):
-        return self.model_size()
-
     def loaded_size(self):
         return self.model.model_loaded_weight_memory
 

comfy/ops.py

@@ -777,8 +777,16 @@ from .quant_ops import (
 
 
 class QuantLinearFunc(torch.autograd.Function):
-    """Custom autograd function for quantized linear: quantized forward, compute_dtype backward.
-    Handles any input rank by flattening to 2D for matmul and restoring shape after.
+    """Custom autograd function for quantized linear: quantized forward, optionally FP8 backward.
+
+    When training_fp8_bwd is enabled:
+      - Forward: quantize input per layout (FP8/NVFP4), use quantized matmul
+      - Backward: all matmuls use FP8 tensor cores via torch.mm dispatch
+      - Cached input is FP8 (half the memory of bf16)
+
+    When training_fp8_bwd is disabled:
+      - Forward: quantize input per layout, use quantized matmul
+      - Backward: dequantize weight to compute_dtype, use standard matmul
     """
 
     @staticmethod
@@ -786,7 +794,7 @@ class QuantLinearFunc(torch.autograd.Function):
         input_shape = input_float.shape
         inp = input_float.detach().flatten(0, -2)  # zero-cost view to 2D
 
-        # Quantize input (same as inference path)
+        # Quantize input for forward (same layout as weight)
         if layout_type is not None:
             q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale)
         else:
@@ -797,43 +805,68 @@ class QuantLinearFunc(torch.autograd.Function):
 
         output = torch.nn.functional.linear(q_input, w, b)
 
-        # Restore original input shape
+        # Unflatten output to match original input shape
         if len(input_shape) > 2:
             output = output.unflatten(0, input_shape[:-1])
 
-        ctx.save_for_backward(input_float, weight)
+        # Save for backward
         ctx.input_shape = input_shape
         ctx.has_bias = bias is not None
         ctx.compute_dtype = compute_dtype
         ctx.weight_requires_grad = weight.requires_grad
+        ctx.fp8_bwd = comfy.model_management.training_fp8_bwd
+
+        if ctx.fp8_bwd:
+            # Cache FP8 quantized input — half the memory of bf16
+            if isinstance(q_input, QuantizedTensor) and layout_type.startswith('TensorCoreFP8'):
+                ctx.q_input = q_input  # already FP8, reuse
+            else:
+                # NVFP4 or other layout — quantize input to FP8 for backward
+                ctx.q_input = QuantizedTensor.from_float(inp, "TensorCoreFP8E4M3Layout")
+            ctx.save_for_backward(weight)
+        else:
+            ctx.q_input = None
+            ctx.save_for_backward(input_float, weight)
 
         return output
 
     @staticmethod
     @torch.autograd.function.once_differentiable
     def backward(ctx, grad_output):
-        input_float, weight = ctx.saved_tensors
         compute_dtype = ctx.compute_dtype
         grad_2d = grad_output.flatten(0, -2).to(compute_dtype)
 
-        # Dequantize weight to compute dtype for backward matmul
-        if isinstance(weight, QuantizedTensor):
-            weight_f = weight.dequantize().to(compute_dtype)
+        # Value casting — only difference between fp8 and non-fp8 paths
+        if ctx.fp8_bwd:
+            weight, = ctx.saved_tensors
+            # Wrap as FP8 QuantizedTensors → torch.mm dispatches to _scaled_mm
+            grad_mm = QuantizedTensor.from_float(grad_2d, "TensorCoreFP8E5M2Layout")
+            if isinstance(weight, QuantizedTensor) and weight._layout_cls.startswith("TensorCoreFP8"):
+                weight_mm = weight
+            elif isinstance(weight, QuantizedTensor):
+                weight_mm = QuantizedTensor.from_float(weight.dequantize().to(compute_dtype), "TensorCoreFP8E4M3Layout")
+            else:
+                weight_mm = QuantizedTensor.from_float(weight.to(compute_dtype), "TensorCoreFP8E4M3Layout")
+            input_mm = ctx.q_input
         else:
-            weight_f = weight.to(compute_dtype)
+            input_float, weight = ctx.saved_tensors
+            # Standard tensors → torch.mm does regular matmul
+            grad_mm = grad_2d
+            if isinstance(weight, QuantizedTensor):
+                weight_mm = weight.dequantize().to(compute_dtype)
+            else:
+                weight_mm = weight.to(compute_dtype)
+            input_mm = input_float.flatten(0, -2).to(compute_dtype) if ctx.weight_requires_grad else None
 
-        # grad_input = grad_output @ weight
-        grad_input = torch.mm(grad_2d, weight_f)
+        # Computation — same for both paths, dispatch handles the rest
+        grad_input = torch.mm(grad_mm, weight_mm)
         if len(ctx.input_shape) > 2:
             grad_input = grad_input.unflatten(0, ctx.input_shape[:-1])
 
-        # grad_weight (only if weight requires grad, typically frozen for quantized training)
         grad_weight = None
         if ctx.weight_requires_grad:
-            input_f = input_float.flatten(0, -2).to(compute_dtype)
-            grad_weight = torch.mm(grad_2d.t(), input_f)
+            grad_weight = torch.mm(grad_mm.t(), input_mm)
 
-        # grad_bias
         grad_bias = None
         if ctx.has_bias:
             grad_bias = grad_2d.sum(dim=0)
@@ -895,6 +928,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 weight = state_dict.pop(weight_key, None)
                 if weight is None:
                     logging.warning(f"Missing weight for layer {layer_name}")
+                    self.weight = None
                     return
 
                 manually_loaded_keys = [weight_key]
@@ -1001,6 +1035,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 if self.bias is not None:
                     sd["{}bias".format(prefix)] = self.bias
 
+                if self.weight is None:
+                    return sd
+
                 if isinstance(self.weight, QuantizedTensor):
                     sd_out = self.weight.state_dict("{}weight".format(prefix))
                     for k in sd_out:

comfy/pinned_memory.py

@@ -2,6 +2,7 @@ import comfy.model_management
 import comfy.memory_management
 import comfy_aimdo.host_buffer
 import comfy_aimdo.torch
+import psutil
 
 from comfy.cli_args import args
 
@@ -12,6 +13,11 @@ def pin_memory(module):
     if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None:
         return
     #FIXME: This is a RAM cache trigger event
+    ram_headroom = comfy.memory_management.RAM_CACHE_HEADROOM
+    #we split the difference and assume half the RAM cache headroom is for us
+    if ram_headroom > 0 and psutil.virtual_memory().available < (ram_headroom * 0.5):
+        comfy.memory_management.extra_ram_release(ram_headroom)
+
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
 
     if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:

comfy/sd.py

@@ -61,6 +61,7 @@ import comfy.text_encoders.newbie
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
+import comfy.text_encoders.qwen35
 
 import comfy.model_patcher
 import comfy.lora
@@ -279,9 +280,6 @@ class CLIP:
         n.apply_hooks_to_conds = self.apply_hooks_to_conds
         return n
 
-    def get_ram_usage(self):
-        return self.patcher.get_ram_usage()
-
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         return self.patcher.add_patches(patches, strength_patch, strength_model)
 
@@ -425,13 +423,13 @@ class CLIP:
     def get_key_patches(self):
         return self.patcher.get_key_patches()
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
         self.cond_stage_model.reset_clip_options()
 
         self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
-        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)
 
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
@@ -558,12 +556,19 @@ class VAE:
                         old_memory_used_decode = self.memory_used_decode
                         self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) *  4.0
 
+                    decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1]
+                    if decoder_ch != ddconfig['ch']:
+                        decoder_ddconfig = ddconfig.copy()
+                        decoder_ddconfig['ch'] = decoder_ch
+                    else:
+                        decoder_ddconfig = None
+
                     if 'post_quant_conv.weight' in sd:
-                        self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
+                        self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {}))
                     else:
                         self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                                     encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
-                                                                    decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+                                                                    decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig})
             elif "decoder.layers.1.layers.0.beta" in sd:
                 config = {}
                 param_key = None
@@ -839,9 +844,6 @@ class VAE:
         self.size = comfy.model_management.module_size(self.first_stage_model)
         return self.size
 
-    def get_ram_usage(self):
-        return self.model_size()
-
     def throw_exception_if_invalid(self):
         if self.first_stage_model is None:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
@@ -1228,6 +1230,11 @@ class TEModel(Enum):
     QWEN3_8B = 20
     QWEN3_06B = 21
     GEMMA_3_4B_VISION = 22
+    QWEN35_08B = 23
+    QWEN35_2B = 24
+    QWEN35_4B = 25
+    QWEN35_9B = 26
+    QWEN35_27B = 27
 
 
 def detect_te_model(sd):
@@ -1267,6 +1274,17 @@ def detect_te_model(sd):
             return TEModel.QWEN25_3B
         if weight.shape[0] == 512:
             return TEModel.QWEN25_7B
+    if "model.language_model.layers.0.linear_attn.A_log" in sd and "model.language_model.layers.0.input_layernorm.weight" in sd:
+        weight = sd['model.language_model.layers.0.input_layernorm.weight']
+        if weight.shape[0] == 1024:
+            return TEModel.QWEN35_08B
+        if weight.shape[0] == 2560:
+            return TEModel.QWEN35_4B
+        if weight.shape[0] == 4096:
+            return TEModel.QWEN35_9B
+        if weight.shape[0] == 5120:
+            return TEModel.QWEN35_27B
+        return TEModel.QWEN35_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         weight = sd['model.layers.0.post_attention_layernorm.weight']
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1299,11 +1317,12 @@ def t5xxl_detect(clip_data):
     return {}
 
 def llama_detect(clip_data):
-    weight_name = "model.layers.0.self_attn.k_proj.weight"
+    weight_names = ["model.layers.0.self_attn.k_proj.weight", "model.layers.0.linear_attn.in_proj_a.weight"]
 
     for sd in clip_data:
-        if weight_name in sd:
-            return comfy.text_encoders.hunyuan_video.llama_detect(sd)
+        for weight_name in weight_names:
+            if weight_name in sd:
+                return comfy.text_encoders.hunyuan_video.llama_detect(sd)
 
     return {}
 
@@ -1431,6 +1450,11 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.JINA_CLIP_2:
             clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
             clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
+        elif te_model in (TEModel.QWEN35_08B, TEModel.QWEN35_2B, TEModel.QWEN35_4B, TEModel.QWEN35_9B, TEModel.QWEN35_27B):
+            clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+            qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
+            clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
+            clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
         elif te_model == TEModel.QWEN3_06B:
             clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
@@ -1719,15 +1743,18 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
     """
     dtype = model_options.get("dtype", None)
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
+
     #Allow loading unets from checkpoint files
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
     if len(temp_sd) > 0:
         sd = temp_sd
+        if custom_operations is None:
+            sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
 
-    custom_operations = model_options.get("custom_operations", None)
-    if custom_operations is None:
-        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
     parameters = comfy.utils.calculate_parameters(sd)
     weight_dtype = comfy.utils.weight_dtype(sd)
 

comfy/sd1_clip.py

@@ -308,14 +308,14 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
         if isinstance(tokens, dict):
             tokens_only = next(iter(tokens.values())) # todo: get this better?
         else:
             tokens_only = tokens
         tokens_only = [[t[0] for t in b] for b in tokens_only]
         embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=presence_penalty)
 
 def parse_parentheses(string):
     result = []
@@ -740,5 +740,5 @@ class SD1ClipModel(torch.nn.Module):
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
-        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
+        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)

comfy/supported_models.py

@@ -1734,6 +1734,21 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+
+class RT_DETR_v4(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "RT_DETR_v4",
+    }
+
+    supported_inference_dtypes = [torch.float16, torch.float32]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.RT_DETR_v4(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return None
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4]
 
 models += [SVD_img2vid]

comfy/text_encoders/llama.py

@@ -224,7 +224,7 @@ class Qwen3_8BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
+    lm_head: bool = True
     stop_tokens = [151643, 151645]
 
 @dataclass
@@ -655,6 +655,17 @@ class Llama2_(nn.Module):
         if config.lm_head:
             self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
+    def get_past_len(self, past_key_values):
+        return past_key_values[0][2]
+
+    def compute_freqs_cis(self, position_ids, device):
+        return precompute_freqs_cis(self.config.head_dim,
+                                    position_ids,
+                                    self.config.rope_theta,
+                                    self.config.rope_scale,
+                                    self.config.rope_dims,
+                                    device=device)
+
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None):
         if embeds is not None:
             x = embeds
@@ -667,17 +678,12 @@ class Llama2_(nn.Module):
         seq_len = x.shape[1]
         past_len = 0
         if past_key_values is not None and len(past_key_values) > 0:
-            past_len = past_key_values[0][2]
+            past_len = self.get_past_len(past_key_values)
 
         if position_ids is None:
             position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)
 
-        freqs_cis = precompute_freqs_cis(self.config.head_dim,
-                                         position_ids,
-                                         self.config.rope_theta,
-                                         self.config.rope_scale,
-                                         self.config.rope_dims,
-                                         device=x.device)
+        freqs_cis = self.compute_freqs_cis(position_ids, x.device)
 
         mask = None
         if attention_mask is not None:
@@ -812,9 +818,16 @@ class BaseGenerate:
         comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
         return x
 
-    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):
-        device = embeds.device
+    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
         model_config = self.model.config
+        past_key_values = []
+        for x in range(model_config.num_hidden_layers):
+            past_key_values.append((torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+        return past_key_values
+
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0):
+        device = embeds.device
 
         if stop_tokens is None:
             stop_tokens = self.model.config.stop_tokens
@@ -829,11 +842,8 @@ class BaseGenerate:
         if embeds.ndim == 2:
             embeds = embeds.unsqueeze(0)
 
-        past_key_values = [] #kv_cache init
         max_cache_len = embeds.shape[1] + max_length
-        for x in range(model_config.num_hidden_layers):
-            past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
-                                    torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+        past_key_values = self.init_kv_cache(embeds.shape[0], max_cache_len, device, execution_dtype)
 
         generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None
 
@@ -844,7 +854,7 @@ class BaseGenerate:
         for step in tqdm(range(max_length), desc="Generating tokens"):
             x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)
             logits = self.logits(x)[:, -1]
-            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)
+            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty)
             token_id = next_token[0].item()
             generated_token_ids.append(token_id)
 
@@ -856,7 +866,7 @@ class BaseGenerate:
 
         return generated_token_ids
 
-    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):
+    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True, presence_penalty=0.0):
 
         if not do_sample or temperature == 0.0:
             return torch.argmax(logits, dim=-1, keepdim=True)
@@ -867,6 +877,11 @@ class BaseGenerate:
                 for token_id in set(token_history):
                     logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
 
+        if presence_penalty is not None and presence_penalty != 0.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] -= presence_penalty
+
         if temperature != 1.0:
             logits = logits / temperature
 
@@ -897,6 +912,9 @@ class BaseGenerate:
 class BaseQwen3:
     def logits(self, x):
         input = x[:, -1:]
+        if self.model.config.lm_head:
+            return self.model.lm_head(input)
+
         module = self.model.embed_tokens
 
         offload_stream = None
@@ -1028,12 +1046,19 @@ class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
                 grid = e.get("extra", None)
                 start = e.get("index")
                 if position_ids is None:
-                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
+                    position_ids = torch.ones((3, embeds.shape[1]), device=embeds.device, dtype=torch.long)
                     position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
                 end = e.get("size") + start
                 len_max = int(grid.max()) // 2
                 start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                if attention_mask is not None:
+                    # Assign compact sequential positions to attended tokens only,
+                    # skipping over padding so post-padding tokens aren't inflated.
+                    after_mask = attention_mask[0, end:]
+                    text_positions = after_mask.cumsum(0) - 1 + start_next + offset
+                    position_ids[:, end:] = torch.where(after_mask.bool(), text_positions, position_ids[0, end:])
+                else:
+                    position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
                 position_ids[0, start:end] = start + offset
                 max_d = int(grid[0][1]) // 2
                 position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]

comfy/text_encoders/longcat_image.py

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

comfy/text_encoders/lt.py

@@ -91,11 +91,11 @@ class Gemma3_12BModel(sd1_clip.SDClipModel):
         self.dtypes.add(dtype)
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
         tokens_only = [[t[0] for t in b] for b in tokens]
         embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
         comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106], presence_penalty=presence_penalty)  # 106 is <end_of_turn>
 
 class DualLinearProjection(torch.nn.Module):
     def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):
@@ -189,8 +189,8 @@ class LTXAVTEModel(torch.nn.Module):
 
         return out.to(device=out_device, dtype=torch.float), pooled, extra
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
-        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
+        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty)
 
     def load_sd(self, sd):
         if "model.layers.47.self_attn.q_norm.weight" in sd:

comfy/text_encoders/qwen35.py

@@ -0,0 +1,833 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass, field
+import os
+import math
+
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention_for_device
+from comfy import sd1_clip
+import comfy.text_encoders.qwen_vl
+
+from .llama import BaseLlama, BaseGenerate, Llama2_, MLP, RMSNorm, apply_rope
+
+
+def _qwen35_layer_types(n):
+    return [("full_attention" if (i + 1) % 4 == 0 else "linear_attention") for i in range(n)]
+
+@dataclass
+class Qwen35Config:
+    vocab_size: int = 248320
+    hidden_size: int = 2048
+    intermediate_size: int = 6144
+    num_hidden_layers: int = 24
+    # Full attention params
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 2
+    head_dim: int = 256
+    partial_rotary_factor: float = 0.25
+    # Linear attention (DeltaNet) params
+    linear_num_key_heads: int = 16
+    linear_num_value_heads: int = 16
+    linear_key_head_dim: int = 128
+    linear_value_head_dim: int = 128
+    conv_kernel_size: int = 4
+    # Shared params
+    max_position_embeddings: int = 32768
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000000.0
+    mrope_section: list = field(default_factory=lambda: [11, 11, 10])
+    layer_types: list = field(default_factory=lambda: _qwen35_layer_types(24))
+    rms_norm_add: bool = True
+    mlp_activation: str = "silu"
+    qkv_bias: bool = False
+    final_norm: bool = True
+    lm_head: bool = False
+    stop_tokens: list = field(default_factory=lambda: [248044, 248046])
+    # These are needed for BaseLlama/BaseGenerate compatibility but unused directly
+    transformer_type: str = "qwen35_2b"
+    rope_dims: list = None
+    rope_scale: float = None
+
+QWEN35_VISION_DEFAULTS = dict(hidden_size=1024, num_heads=16, intermediate_size=4096, depth=24, patch_size=16, temporal_patch_size=2, in_channels=3, spatial_merge_size=2, num_position_embeddings=2304)
+
+QWEN35_MODELS = {
+    "qwen35_08b": dict(hidden_size=1024, intermediate_size=3584, vision=dict(hidden_size=768, num_heads=12, intermediate_size=3072, depth=12)),
+    "qwen35_2b": dict(hidden_size=2048, intermediate_size=6144, num_hidden_layers=24, num_attention_heads=8, num_key_value_heads=2, linear_num_value_heads=16),
+    "qwen35_4b": dict(hidden_size=2560, intermediate_size=9216, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32),
+    "qwen35_9b": dict(hidden_size=4096, intermediate_size=12288, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
+    "qwen35_27b": dict(hidden_size=5120, intermediate_size=17408, num_hidden_layers=64, num_attention_heads=24, num_key_value_heads=4, linear_num_value_heads=48, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
+}
+
+
+def _make_config(model_type, config_dict={}):
+    overrides = QWEN35_MODELS.get(model_type, {}).copy()
+    overrides.pop("vision", None)
+    if "num_hidden_layers" in overrides:
+        overrides["layer_types"] = _qwen35_layer_types(overrides["num_hidden_layers"])
+    overrides.update(config_dict)
+    return Qwen35Config(**overrides)
+
+
+class RMSNormGated(RMSNorm):
+    def forward(self, x, gate):
+        return super().forward(x) * F.silu(gate.to(x.dtype))
+
+def torch_chunk_gated_delta_rule(query, key, value, g, beta, chunk_size=64, initial_state=None, output_final_state=False):
+    initial_dtype = query.dtype
+    query = F.normalize(query, dim=-1)
+    key = F.normalize(key, dim=-1)
+    query, key, value, beta, g = [x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)]
+
+    batch_size, num_heads, sequence_length, k_head_dim = key.shape
+    v_head_dim = value.shape[-1]
+    pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size
+    query = F.pad(query, (0, 0, 0, pad_size))
+    key = F.pad(key, (0, 0, 0, pad_size))
+    value = F.pad(value, (0, 0, 0, pad_size))
+    beta = F.pad(beta, (0, pad_size))
+    g = F.pad(g, (0, pad_size))
+    total_sequence_length = sequence_length + pad_size
+    scale = 1 / (query.shape[-1] ** 0.5)
+    query = query * scale
+
+    v_beta = value * beta.unsqueeze(-1)
+    k_beta = key * beta.unsqueeze(-1)
+    query, key, value, k_beta, v_beta = [x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta)]
+    g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
+    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
+
+    g = g.cumsum(dim=-1)
+    decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
+    attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
+    for i in range(1, chunk_size):
+        row = attn[..., i, :i].clone()
+        sub = attn[..., :i, :i].clone()
+        attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
+    attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
+    value = attn @ v_beta
+    k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
+    last_recurrent_state = (
+        torch.zeros(batch_size, num_heads, k_head_dim, v_head_dim).to(value)
+        if initial_state is None
+        else initial_state.to(value)
+    )
+    core_attn_out = torch.zeros_like(value)
+    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
+
+    for i in range(0, total_sequence_length // chunk_size):
+        q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
+        attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        v_new = v_i - v_prime
+        attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        last_recurrent_state = (
+            last_recurrent_state * g[:, :, i, -1, None, None].exp()
+            + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
+        )
+
+    if not output_final_state:
+        last_recurrent_state = None
+    core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
+    core_attn_out = core_attn_out[:, :, :sequence_length]
+    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
+    return core_attn_out, last_recurrent_state
+
+
+def torch_causal_conv1d_update(x, conv_state, weight, bias=None):
+    # conv_state: [B, channels, kernel_size-1], x: [B, channels, 1]
+    # weight: [channels, kernel_size]
+    state_len = conv_state.shape[-1]
+    combined = torch.cat([conv_state, x], dim=-1).to(weight.dtype)  # [B, channels, kernel_size]
+    conv_state.copy_(combined[:, :, -state_len:])
+    out = (combined * weight).sum(dim=-1, keepdim=True)  # [B, channels, 1]
+    if bias is not None:
+        out = out + bias.unsqueeze(0).unsqueeze(-1)
+    return F.silu(out).to(x.dtype)
+
+
+# GatedDeltaNet - Linear Attention Layer
+
+class GatedDeltaNet(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        hidden = config.hidden_size
+        self.num_key_heads = config.linear_num_key_heads
+        self.num_value_heads = config.linear_num_value_heads
+        self.key_head_dim = config.linear_key_head_dim
+        self.value_head_dim = config.linear_value_head_dim
+        self.conv_kernel_size = config.conv_kernel_size
+
+        key_dim = self.num_key_heads * self.key_head_dim
+        value_dim = self.num_value_heads * self.value_head_dim
+        self.key_dim = key_dim
+        self.value_dim = value_dim
+        conv_dim = key_dim * 2 + value_dim
+
+        self.in_proj_qkv = ops.Linear(hidden, conv_dim, bias=False, device=device, dtype=dtype)
+        self.in_proj_z = ops.Linear(hidden, value_dim, bias=False, device=device, dtype=dtype)
+        self.in_proj_b = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
+        self.in_proj_a = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
+        self.out_proj = ops.Linear(value_dim, hidden, bias=False, device=device, dtype=dtype)
+
+        self.dt_bias = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
+        self.A_log = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
+
+        self.conv1d = ops.Conv1d(in_channels=conv_dim, out_channels=conv_dim, bias=False, kernel_size=self.conv_kernel_size,
+            groups=conv_dim, padding=self.conv_kernel_size - 1, device=device, dtype=dtype)
+
+        self.norm = RMSNormGated(self.value_head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype)
+
+    def forward(self, x, past_key_value=None, **kwargs):
+        batch_size, seq_len, _ = x.shape
+
+        use_recurrent = (
+            past_key_value is not None
+            and past_key_value[2] > 0
+            and seq_len == 1
+        )
+
+        # Projections (shared)
+        mixed_qkv = self.in_proj_qkv(x).transpose(1, 2)  # [B, conv_dim, seq_len]
+        z = self.in_proj_z(x)
+        b = self.in_proj_b(x)
+        a = self.in_proj_a(x)
+
+        # Conv1d
+        if use_recurrent:
+            recurrent_state, conv_state, step_index = past_key_value
+            conv_weight = comfy.model_management.cast_to_device(self.conv1d.weight, mixed_qkv.device, mixed_qkv.dtype).squeeze(1)
+            conv_bias = comfy.model_management.cast_to_device(self.conv1d.bias, mixed_qkv.device, mixed_qkv.dtype) if self.conv1d.bias is not None else None
+            mixed_qkv = torch_causal_conv1d_update(mixed_qkv, conv_state, conv_weight, conv_bias)
+        else:
+            if past_key_value is not None:
+                recurrent_state, conv_state, step_index = past_key_value
+                conv_state_init = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
+                conv_state.copy_(conv_state_init[:, :, -conv_state.shape[-1]:])
+            mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
+
+        # Split QKV and compute beta/g
+        mixed_qkv = mixed_qkv.transpose(1, 2)  # [B, seq_len, conv_dim]
+        query, key, value = mixed_qkv.split([self.key_dim, self.key_dim, self.value_dim], dim=-1)
+        beta = b.sigmoid()
+        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias.float())
+
+        # Delta rule
+        if use_recurrent:
+            # single-token path: work in [B, heads, dim] without seq dim
+            query = query.reshape(batch_size, self.num_key_heads, self.key_head_dim)
+            key = key.reshape(batch_size, self.num_key_heads, self.key_head_dim)
+            value = value.reshape(batch_size, self.num_value_heads, self.value_head_dim)
+
+            if self.num_value_heads != self.num_key_heads:
+                rep = self.num_value_heads // self.num_key_heads
+                query = query.repeat_interleave(rep, dim=1)
+                key = key.repeat_interleave(rep, dim=1)
+
+            scale = self.key_head_dim ** -0.5
+            q = F.normalize(query.float(), dim=-1) * scale
+            k = F.normalize(key.float(), dim=-1)
+            v = value.float()
+            beta_t = beta.reshape(batch_size, -1)
+            g_t = g.reshape(batch_size, -1).exp()
+
+            # In-place state update: [B, heads, k_dim, v_dim]
+            recurrent_state.mul_(g_t[:, :, None, None])
+            kv_mem = torch.einsum('bhk,bhkv->bhv', k, recurrent_state)
+            delta = (v - kv_mem) * beta_t[:, :, None]
+            recurrent_state.add_(k.unsqueeze(-1) * delta.unsqueeze(-2))
+            core_attn_out = torch.einsum('bhk,bhkv->bhv', q, recurrent_state)
+
+            core_attn_out = core_attn_out.to(x.dtype).unsqueeze(1)
+            present_key_value = (recurrent_state, conv_state, step_index + 1)
+        else:
+            query = query.reshape(batch_size, seq_len, -1, self.key_head_dim)
+            key = key.reshape(batch_size, seq_len, -1, self.key_head_dim)
+            value = value.reshape(batch_size, seq_len, -1, self.value_head_dim)
+
+            if self.num_value_heads != self.num_key_heads:
+                rep = self.num_value_heads // self.num_key_heads
+                query = query.repeat_interleave(rep, dim=2)
+                key = key.repeat_interleave(rep, dim=2)
+
+            core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule(
+                query, key, value, g=g, beta=beta,
+                initial_state=None,
+                output_final_state=past_key_value is not None,
+            )
+
+            present_key_value = None
+            if past_key_value is not None:
+                if last_recurrent_state is not None:
+                    recurrent_state.copy_(last_recurrent_state.to(recurrent_state.dtype))
+                present_key_value = (recurrent_state, conv_state, step_index + seq_len)
+
+        # Gated norm + output projection (shared)
+        core_attn_out = self.norm(core_attn_out.reshape(-1, self.value_head_dim), z.reshape(-1, self.value_head_dim))
+        output = self.out_proj(core_attn_out.reshape(batch_size, seq_len, -1))
+        return output, present_key_value
+
+
+# GatedAttention - Full Attention with output gating
+def precompute_partial_rope(head_dim, rotary_dim, position_ids, theta, device=None, mrope_section=None):
+    """Compute RoPE frequencies for partial rotary embeddings."""
+    theta_numerator = torch.arange(0, rotary_dim, 2, device=device).float()
+    inv_freq = 1.0 / (theta ** (theta_numerator / rotary_dim))
+
+    inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+    position_ids_expanded = position_ids[:, None, :].float()
+    freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+    emb = torch.cat((freqs, freqs), dim=-1)
+    cos = emb.cos()
+    sin = emb.sin()
+
+    if mrope_section is not None and position_ids.shape[0] == 3:
+        mrope_section_2 = [s * 2 for s in mrope_section]
+        cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
+        sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
+
+    cos = cos.unsqueeze(1)
+    sin = sin.unsqueeze(1)
+    sin_split = sin.shape[-1] // 2
+    return (cos, sin[..., :sin_split], -sin[..., sin_split:])
+
+
+def apply_partial_rope(xq, xk, freqs_cis, rotary_dim):
+    """Apply RoPE to only the first rotary_dim dimensions."""
+    xq_rot = xq[..., :rotary_dim]
+    xq_pass = xq[..., rotary_dim:]
+    xk_rot = xk[..., :rotary_dim]
+    xk_pass = xk[..., rotary_dim:]
+
+    xq_rot, xk_rot = apply_rope(xq_rot, xk_rot, freqs_cis)
+
+    xq = torch.cat([xq_rot, xq_pass], dim=-1)
+    xk = torch.cat([xk_rot, xk_pass], dim=-1)
+    return xq, xk
+
+
+class GatedAttention(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        self.inner_size = self.num_heads * self.head_dim
+        self.rotary_dim = int(self.head_dim * config.partial_rotary_factor)
+
+        # q_proj outputs 2x: query + gate
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_size * 2, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+
+        # QK norms with (1+weight) scaling
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
+        batch_size, seq_length, _ = x.shape
+
+        # Project Q (with gate), K, V
+        qg = self.q_proj(x)
+        # Split into query and gate: each is [B, seq, inner_size]
+        qg = qg.view(batch_size, seq_length, self.num_heads, self.head_dim * 2)
+        xq, gate = qg[..., :self.head_dim], qg[..., self.head_dim:]
+        gate = gate.reshape(batch_size, seq_length, -1)  # [B, seq, inner_size]
+
+        xk = self.k_proj(x)
+        xv = self.v_proj(x)
+
+        xq = self.q_norm(xq).transpose(1, 2)  # [B, heads, seq, head_dim]
+        xk = self.k_norm(xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim)).transpose(1, 2)
+        xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+        # Apply partial RoPE
+        xq, xk = apply_partial_rope(xq, xk, freqs_cis, self.rotary_dim)
+
+        # KV cache
+        present_key_value = None
+        if past_key_value is not None:
+            past_key, past_value, index = past_key_value
+            num_tokens = xk.shape[2]
+            if past_key.shape[2] >= (index + num_tokens):
+                past_key[:, :, index:index + num_tokens] = xk
+                past_value[:, :, index:index + num_tokens] = xv
+                xk = past_key[:, :, :index + num_tokens]
+                xv = past_value[:, :, :index + num_tokens]
+                present_key_value = (past_key, past_value, index + num_tokens)
+            else:
+                if index > 0:
+                    xk = torch.cat((past_key[:, :, :index], xk), dim=2)
+                    xv = torch.cat((past_value[:, :, :index], xv), dim=2)
+                present_key_value = (xk, xv, index + num_tokens)
+
+        # Expand KV heads for GQA
+        if self.num_heads != self.num_kv_heads:
+            xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+            xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        output = output * gate.sigmoid()
+
+        return self.o_proj(output), present_key_value
+
+
+# Hybrid Transformer Block
+class Qwen35TransformerBlock(nn.Module):
+    def __init__(self, config, index, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.layer_type = config.layer_types[index]
+        if self.layer_type == "linear_attention":
+            self.linear_attn = GatedDeltaNet(config, device=device, dtype=dtype, ops=ops)
+        else:
+            self.self_attn = GatedAttention(config, device=device, dtype=dtype, ops=ops)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
+        if self.layer_type == "linear_attention":
+            h, present_key_value = self.linear_attn(self.input_layernorm(x), attention_mask=attention_mask, past_key_value=past_key_value)
+        else:
+            h, present_key_value = self.self_attn(self.input_layernorm(x), attention_mask=attention_mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_key_value)
+
+        x = x + h
+        x = x + self.mlp(self.post_attention_layernorm(x))
+        return x, present_key_value
+
+
+# Qwen35 Transformer Backbone
+class Qwen35Transformer(Llama2_):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        nn.Module.__init__(self)
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.normalize_in = False
+
+        self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype)
+        self.layers = nn.ModuleList([
+            Qwen35TransformerBlock(config, index=i, device=device, dtype=dtype, ops=ops)
+            for i in range(config.num_hidden_layers)
+        ])
+
+        if config.final_norm:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
+        if config.lm_head:
+            self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+
+    def get_past_len(self, past_key_values):
+        for i, layer in enumerate(self.layers):
+            if layer.layer_type == "full_attention":
+                if len(past_key_values) > i:
+                    return past_key_values[i][2]
+                break
+        return 0
+
+    def compute_freqs_cis(self, position_ids, device):
+        rotary_dim = int(self.config.head_dim * self.config.partial_rotary_factor)
+        return precompute_partial_rope(
+            self.config.head_dim, rotary_dim, position_ids,
+            self.config.rope_theta, device=device,
+            mrope_section=self.config.mrope_section,
+        )
+
+
+# Vision Encoder
+class Qwen35VisionPatchEmbed(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.patch_size = config["patch_size"]
+        self.temporal_patch_size = config["temporal_patch_size"]
+        self.in_channels = config["in_channels"]
+        self.embed_dim = config["hidden_size"]
+        kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
+        self.proj = ops.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        target_dtype = self.proj.weight.dtype
+        x = x.view(-1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size)
+        return self.proj(x.to(target_dtype)).view(-1, self.embed_dim)
+
+
+class Qwen35VisionMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.linear_fc1 = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype)
+
+    def forward(self, hidden_state):
+        return self.linear_fc2(F.gelu(self.linear_fc1(hidden_state), approximate="tanh"))
+
+
+class Qwen35VisionRotaryEmbedding(nn.Module):
+    def __init__(self, dim, theta=10000.0):
+        super().__init__()
+        self.dim = dim
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen):
+        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class Qwen35VisionAttention(nn.Module):
+    def __init__(self, hidden_size, num_heads, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.dim = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = self.dim // self.num_heads
+        self.qkv = ops.Linear(self.dim, self.dim * 3, bias=True, device=device, dtype=dtype)
+        self.proj = ops.Linear(self.dim, self.dim, device=device, dtype=dtype)
+
+    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
+        seq_length = x.shape[0]
+        query_states, key_states, value_states = (
+            self.qkv(x).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+        )
+        query_states, key_states = apply_rope(query_states, key_states, position_embeddings)
+
+        # Process per-sequence attention
+        lengths = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        q_splits = torch.split(query_states, lengths, dim=0)
+        k_splits = torch.split(key_states, lengths, dim=0)
+        v_splits = torch.split(value_states, lengths, dim=0)
+
+        attn_outputs = []
+        for q, k, v in zip(q_splits, k_splits, v_splits):
+            q = q.transpose(0, 1).unsqueeze(0)
+            k = k.transpose(0, 1).unsqueeze(0)
+            v = v.transpose(0, 1).unsqueeze(0)
+            attn_outputs.append(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
+
+        attn_output = torch.cat(attn_outputs, dim=1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        return self.proj(attn_output)
+
+
+class Qwen35VisionBlock(nn.Module):
+    def __init__(self, hidden_size, num_heads, intermediate_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.norm1 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.norm2 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.attn = Qwen35VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops)
+        self.mlp = Qwen35VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops)
+
+    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
+        x = x + self.attn(self.norm1(x), cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+        return x + self.mlp(self.norm2(x))
+
+
+class Qwen35VisionPatchMerger(nn.Module):
+    def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        merge_dim = hidden_size * (spatial_merge_size ** 2)
+        self.norm = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.linear_fc1 = ops.Linear(merge_dim, merge_dim, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(merge_dim, out_hidden_size, device=device, dtype=dtype)
+        self.merge_dim = merge_dim
+
+    def forward(self, x):
+        x = self.norm(x).view(-1, self.merge_dim)
+        return self.linear_fc2(F.gelu(self.linear_fc1(x)))
+
+
+class Qwen35VisionModel(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.spatial_merge_size = config["spatial_merge_size"]
+        self.patch_size = config["patch_size"]
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+        self.hidden_size = config["hidden_size"]
+        self.num_heads = config["num_heads"]
+        self.num_position_embeddings = config["num_position_embeddings"]
+
+        self.patch_embed = Qwen35VisionPatchEmbed(config, device=device, dtype=dtype, ops=ops)
+        self.pos_embed = ops.Embedding(self.num_position_embeddings, self.hidden_size, device=device, dtype=dtype)
+        self.num_grid_per_side = int(self.num_position_embeddings ** 0.5)
+        self.rotary_pos_emb = Qwen35VisionRotaryEmbedding(self.hidden_size // self.num_heads // 2)
+        self.blocks = nn.ModuleList([
+            Qwen35VisionBlock(self.hidden_size, self.num_heads, config["intermediate_size"], device=device, dtype=dtype, ops=ops)
+            for _ in range(config["depth"])
+        ])
+        self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+
+    def rot_pos_emb(self, grid_thw):
+        merge_size = self.spatial_merge_size
+        grid_thw_list = grid_thw.tolist()
+        max_hw = max(max(h, w) for _, h, w in grid_thw_list)
+        freq_table = self.rotary_pos_emb(max_hw)
+        device = freq_table.device
+        total_tokens = sum(int(t * h * w) for t, h, w in grid_thw_list)
+        pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
+        offset = 0
+        for num_frames, height, width in grid_thw_list:
+            num_frames, height, width = int(num_frames), int(height), int(width)
+            merged_h, merged_w = height // merge_size, width // merge_size
+            block_rows = torch.arange(merged_h, device=device)
+            block_cols = torch.arange(merged_w, device=device)
+            intra_row = torch.arange(merge_size, device=device)
+            intra_col = torch.arange(merge_size, device=device)
+            row_idx = block_rows[:, None, None, None] * merge_size + intra_row[None, None, :, None]
+            col_idx = block_cols[None, :, None, None] * merge_size + intra_col[None, None, None, :]
+            row_idx = row_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+            col_idx = col_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+            coords = torch.stack((row_idx, col_idx), dim=-1)
+            if num_frames > 1:
+                coords = coords.repeat(num_frames, 1)
+            num_tokens = coords.shape[0]
+            pos_ids[offset:offset + num_tokens] = coords
+            offset += num_tokens
+        embeddings = freq_table[pos_ids]
+        embeddings = embeddings.flatten(1)
+        return embeddings
+
+    def fast_pos_embed_interpolate(self, grid_thw):
+        grid_thw_list = grid_thw.tolist()
+        grid_ts = [int(row[0]) for row in grid_thw_list]
+        grid_hs = [int(row[1]) for row in grid_thw_list]
+        grid_ws = [int(row[2]) for row in grid_thw_list]
+        device = self.pos_embed.weight.device
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+        for t, h, w in grid_thw_list:
+            h, w = int(h), int(w)
+            h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h, device=device)
+            w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w, device=device)
+            h_idxs_floor = h_idxs.int()
+            w_idxs_floor = w_idxs.int()
+            h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+            base_h = h_idxs_floor * self.num_grid_per_side
+            base_h_ceil = h_idxs_ceil * self.num_grid_per_side
+            indices = [
+                (base_h[None].T + w_idxs_floor[None]).flatten(),
+                (base_h[None].T + w_idxs_ceil[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
+            ]
+            weights = [
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
+                ((1 - dh)[None].T * dw[None]).flatten(),
+                (dh[None].T * (1 - dw)[None]).flatten(),
+                (dh[None].T * dw[None]).flatten(),
+            ]
+            for j in range(4):
+                idx_list[j].extend(indices[j].tolist())
+                weight_list[j].extend(weights[j].tolist())
+        idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=device)
+        weight_tensor = torch.tensor(weight_list, dtype=self.pos_embed.weight.dtype, device=device)
+        pos_embeds = self.pos_embed(idx_tensor).to(device) * weight_tensor[:, :, None]
+        patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
+        patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
+        patch_pos_embeds_permute = []
+        merge_size = self.spatial_merge_size
+        for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
+            pos_embed = pos_embed.repeat(t, 1)
+            pos_embed = (
+                pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
+                .permute(0, 1, 3, 2, 4, 5)
+                .flatten(0, 4)
+            )
+            patch_pos_embeds_permute.append(pos_embed)
+        return torch.cat(patch_pos_embeds_permute)
+
+    def forward(self, x, grid_thw):
+        x = self.patch_embed(x)
+        pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device)
+        x = x + pos_embeds
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        seq_len = x.shape[0]
+        x = x.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        cos = emb.cos().unsqueeze(-2)
+        sin = emb.sin().unsqueeze(-2)
+        sin_half = sin.shape[-1] // 2
+        position_embeddings = (cos, sin[..., :sin_half], -sin[..., sin_half:])
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
+        for blk in self.blocks:
+            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+        merged = self.merger(x)
+        return merged
+
+# Model Wrapper
+class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
+    model_type = "qwen35_2b"
+
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = _make_config(self.model_type, config_dict)
+        self.num_layers = config.num_hidden_layers
+        self.model = Qwen35Transformer(config, device=device, dtype=dtype, ops=operations)
+        vision_overrides = QWEN35_MODELS.get(self.model_type, {}).get("vision", {})
+        vision_config = {**QWEN35_VISION_DEFAULTS, **vision_overrides, "out_hidden_size": config.hidden_size}
+        self.visual = Qwen35VisionModel(vision_config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16)
+            return self.visual(image.to(device, dtype=torch.float32), grid), grid
+        return None, None
+
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
+        grid = None
+        position_ids = None
+        offset = 0
+        for e in embeds_info:
+            if e.get("type") == "image":
+                grid = e.get("extra", None)
+                start = e.get("index")
+                if position_ids is None:
+                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
+                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
+                end = e.get("size") + start
+                len_max = int(grid.max()) // 2
+                start_next = len_max + start
+                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                position_ids[0, start:end] = start + offset
+                max_d = int(grid[0][1]) // 2
+                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
+                max_d = int(grid[0][2]) // 2
+                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+                offset += len_max - (end - start)
+
+        if grid is None:
+            position_ids = None
+
+        return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values)
+
+    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
+        model_config = self.model.config
+        past_key_values = []
+        for i in range(model_config.num_hidden_layers):
+            if model_config.layer_types[i] == "linear_attention":
+                recurrent_state = torch.zeros(
+                    [batch, model_config.linear_num_value_heads, model_config.linear_key_head_dim, model_config.linear_value_head_dim],
+                    device=device, dtype=torch.float32
+                )
+                conv_dim = model_config.linear_num_key_heads * model_config.linear_key_head_dim * 2 + model_config.linear_num_value_heads * model_config.linear_value_head_dim
+                conv_state = torch.zeros(
+                    [batch, conv_dim, model_config.conv_kernel_size - 1],
+                    device=device, dtype=execution_dtype
+                )
+                past_key_values.append((recurrent_state, conv_state, 0))
+            else:
+                past_key_values.append((
+                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                    0
+                ))
+        return past_key_values
+
+# Tokenizer and Text Encoder Wrappers
+
+class Qwen35Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=2048, embedding_key="qwen35_2b"):
+        from transformers import Qwen2Tokenizer
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen35_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer,
+            has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=248044, tokenizer_data=tokenizer_data)
+
+
+class Qwen35ImageTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen35_2b"):
+        embedding_size = QWEN35_MODELS.get(model_type, {}).get("hidden_size", 2048)
+        tokenizer = lambda *a, **kw: Qwen35Tokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
+        image = kwargs.get("image", None)
+        if image is not None and len(images) == 0:
+            images = [image]
+
+        skip_template = False
+        if text.startswith('<|im_start|>'):
+            skip_template = True
+        if prevent_empty_text and text == '':
+            text = ' '
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is None:
+                if len(images) > 0:
+                    llama_text = self.llama_template_images.format(text)
+                else:
+                    llama_text = self.llama_template.format(text)
+            else:
+                llama_text = llama_template.format(text)
+            if not thinking:
+                llama_text += "<think>\n</think>\n"
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        key_name = next(iter(tokens))
+        embed_count = 0
+        qwen_tokens = tokens[key_name]
+        for r in qwen_tokens:
+            for i in range(len(r)):
+                if r[i][0] == 248056:  # <|image_pad|>
+                    if len(images) > embed_count:
+                        r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
+                        embed_count += 1
+        return tokens
+
+
+class Qwen35ClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}, model_type="qwen35_2b"):
+        class Qwen35_(Qwen35):
+            pass
+        Qwen35_.model_type = model_type
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
+            dtype=dtype, special_tokens={"pad": 248044}, layer_norm_hidden_state=False,
+            model_class=Qwen35_, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Qwen35TEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen35_2b"):
+        clip_model = lambda **kw: Qwen35ClipModel(**kw, model_type=model_type)
+        super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
+
+
+def tokenizer(model_type="qwen35_2b"):
+    class Qwen35ImageTokenizer_(Qwen35ImageTokenizer):
+        def __init__(self, embedding_directory=None, tokenizer_data={}):
+            super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
+    return Qwen35ImageTokenizer_
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen35_2b"):
+    class Qwen35TEModel_(Qwen35TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
+    return Qwen35TEModel_

comfy/text_encoders/qwen_vl.py

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

comfy_api/input/__init__.py

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

comfy_api/latest/_input/__init__.py

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

comfy_api/latest/_input/curve_types.py

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

comfy_api/latest/_io.py

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
     from comfy.samplers import CFGGuider, Sampler
     from comfy.sd import CLIP, VAE
     from comfy.sd import StyleModel as StyleModel_
-    from comfy_api.input import VideoInput
+    from comfy_api.input import VideoInput, CurveInput as CurveInput_
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
 from comfy_execution.graph_utils import ExecutionBlocker
@@ -1242,8 +1242,9 @@ class BoundingBox(ComfyTypeIO):
 
 @comfytype(io_type="CURVE")
 class Curve(ComfyTypeIO):
-    CurvePoint = tuple[float, float]
-    Type = list[CurvePoint]
+    from comfy_api.input import CurvePoint
+    if TYPE_CHECKING:
+        Type = CurveInput_
 
     class Input(WidgetInput):
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
@@ -1252,6 +1253,18 @@ class Curve(ComfyTypeIO):
             if default is None:
                 self.default = [(0.0, 0.0), (1.0, 1.0)]
 
+        def as_dict(self):
+            d = super().as_dict()
+            if self.default is not None:
+                d["default"] = {"points": [list(p) for p in self.default], "interpolation": "monotone_cubic"}
+            return d
+
+
+@comfytype(io_type="HISTOGRAM")
+class Histogram(ComfyTypeIO):
+    """A histogram represented as a list of bin counts."""
+    Type = list[int]
+
 
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
@@ -1360,6 +1373,7 @@ class NodeInfoV1:
     price_badge: dict | None = None
     search_aliases: list[str]=None
     essentials_category: str=None
+    has_intermediate_output: bool=None
 
 
 @dataclass
@@ -1483,6 +1497,16 @@ class Schema:
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
     essentials_category: str | None = None
     """Optional category for the Essentials tab. Path-based like category field (e.g., 'Basic', 'Image Tools/Editing')."""
+    has_intermediate_output: bool=False
+    """Flags this node as having intermediate output that should persist across page refreshes.
+
+    Nodes with this flag behave like output nodes (their UI results are cached and resent
+    to the frontend) but do NOT automatically get added to the execution list. This means
+    they will only execute if they are on the dependency path of a real output node.
+
+    Use this for nodes with interactive/operable UI regions that produce intermediate outputs
+    (e.g., Image Crop, Painter) rather than final outputs (e.g., Save Image).
+    """
 
     def validate(self):
         '''Validate the schema:
@@ -1582,6 +1606,7 @@ class Schema:
             category=self.category,
             description=self.description,
             output_node=self.is_output_node,
+            has_intermediate_output=self.has_intermediate_output,
             deprecated=self.is_deprecated,
             experimental=self.is_experimental,
             dev_only=self.is_dev_only,
@@ -1873,6 +1898,14 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._OUTPUT_NODE
 
+    _HAS_INTERMEDIATE_OUTPUT = None
+    @final
+    @classproperty
+    def HAS_INTERMEDIATE_OUTPUT(cls):  # noqa
+        if cls._HAS_INTERMEDIATE_OUTPUT is None:
+            cls.GET_SCHEMA()
+        return cls._HAS_INTERMEDIATE_OUTPUT
+
     _INPUT_IS_LIST = None
     @final
     @classproperty
@@ -1965,6 +1998,8 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls._API_NODE = schema.is_api_node
         if cls._OUTPUT_NODE is None:
             cls._OUTPUT_NODE = schema.is_output_node
+        if cls._HAS_INTERMEDIATE_OUTPUT is None:
+            cls._HAS_INTERMEDIATE_OUTPUT = schema.has_intermediate_output
         if cls._INPUT_IS_LIST is None:
             cls._INPUT_IS_LIST = schema.is_input_list
         if cls._NOT_IDEMPOTENT is None:
@@ -2240,5 +2275,6 @@ __all__ = [
     "PriceBadge",
     "BoundingBox",
     "Curve",
+    "Histogram",
     "NodeReplace",
 ]

comfy_api_nodes/apis/grok.py

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

comfy_api_nodes/apis/wan.py

@@ -0,0 +1,226 @@
+from pydantic import BaseModel, Field
+
+
+class Text2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+
+
+class Image2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    images: list[str] = Field(..., min_length=1, max_length=2)
+
+
+class Text2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    audio_url: str | None = Field(None)
+
+
+class Image2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    img_url: str = Field(...)
+    audio_url: str | None = Field(None)
+
+
+class Reference2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    reference_video_urls: list[str] = Field(...)
+
+
+class Txt2ImageParametersField(BaseModel):
+    size: str = Field(...)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+
+
+class Image2ImageParametersField(BaseModel):
+    size: str | None = Field(None)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(False)
+
+
+class Text2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    audio: bool = Field(False, description="Whether to generate audio automatically.")
+    shot_type: str = Field("single")
+
+
+class Image2VideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    audio: bool = Field(False, description="Whether to generate audio automatically.")
+    shot_type: str = Field("single")
+
+
+class Reference2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    duration: int = Field(5, ge=5, le=15)
+    shot_type: str = Field("single")
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(False)
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2ImageInputField = Field(...)
+    parameters: Txt2ImageParametersField = Field(...)
+
+
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2ImageInputField = Field(...)
+    parameters: Image2ImageParametersField = Field(...)
+
+
+class Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2VideoInputField = Field(...)
+    parameters: Text2VideoParametersField = Field(...)
+
+
+class Image2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2VideoInputField = Field(...)
+    parameters: Image2VideoParametersField = Field(...)
+
+
+class Reference2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Reference2VideoInputField = Field(...)
+    parameters: Reference2VideoParametersField = Field(...)
+
+
+class Wan27MediaItem(BaseModel):
+    type: str = Field(...)
+    url: str = Field(...)
+
+
+class Wan27ReferenceVideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27ReferenceVideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int = Field(5, ge=2, le=10)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27ReferenceVideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27ReferenceVideoInputField = Field(...)
+    parameters: Wan27ReferenceVideoParametersField = Field(...)
+
+
+class Wan27ImageToVideoInputField(BaseModel):
+    prompt: str | None = Field(None)
+    negative_prompt: str | None = Field(None)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27ImageToVideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    duration: int = Field(5, ge=2, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27ImageToVideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27ImageToVideoInputField = Field(...)
+    parameters: Wan27ImageToVideoParametersField = Field(...)
+
+
+class Wan27VideoEditInputField(BaseModel):
+    prompt: str = Field(...)
+    media: list[Wan27MediaItem] = Field(...)
+
+
+class Wan27VideoEditParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int = Field(0)
+    audio_setting: str = Field("auto")
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27VideoEditTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Wan27VideoEditInputField = Field(...)
+    parameters: Wan27VideoEditParametersField = Field(...)
+
+
+class Wan27Text2VideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    ratio: str | None = Field(None)
+    duration: int = Field(5, ge=2, le=15)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(False)
+    seed: int = Field(..., ge=0, le=2147483647)
+
+
+class Wan27Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2VideoInputField = Field(...)
+    parameters: Wan27Text2VideoParametersField = Field(...)
+
+
+class TaskCreationOutputField(BaseModel):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+
+
+class TaskCreationResponse(BaseModel):
+    output: TaskCreationOutputField | None = Field(None)
+    request_id: str = Field(...)
+    code: str | None = Field(None, description="Error code for the failed request.")
+    message: str | None = Field(None, description="Details about the failed request.")
+
+
+class TaskResult(BaseModel):
+    url: str | None = Field(None)
+    code: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class ImageTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    results: list[TaskResult] | None = Field(None)
+
+
+class VideoTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    video_url: str | None = Field(None)
+    code: str | None = Field(None)
+    message: str | None = Field(None)
+
+
+class ImageTaskStatusResponse(BaseModel):
+    output: ImageTaskStatusOutputField | None = Field(None)
+    request_id: str = Field(...)
+
+
+class VideoTaskStatusResponse(BaseModel):
+    output: VideoTaskStatusOutputField | None = Field(None)
+    request_id: str = Field(...)

comfy_api_nodes/nodes_gemini.py

@@ -201,6 +201,16 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
             returned_image = await download_url_to_image_tensor(part.fileData.fileUri)
         image_tensors.append(returned_image)
     if len(image_tensors) == 0:
+        if not thought:
+            # No images generated --> extract text response for a meaningful error
+            model_message = get_text_from_response(response).strip()
+            if model_message:
+                raise ValueError(f"Gemini did not generate an image. Model response: {model_message}")
+            raise ValueError(
+                "Gemini did not generate an image. "
+                "Try rephrasing your prompt or changing the response modality to 'IMAGE+TEXT' "
+                "to see the model's reasoning."
+            )
         return torch.zeros((1, 1024, 1024, 4))
     return torch.cat(image_tensors, dim=0)
 

comfy_api_nodes/nodes_grok.py

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

comfy_api_nodes/nodes_hunyuan3d.py

@@ -132,7 +132,7 @@ class TencentTextToModelNode(IO.ComfyNode):
                     tooltip="The LowPoly option is unavailable for the `3.1` model.",
                 ),
                 IO.String.Input("prompt", multiline=True, default="", tooltip="Supports up to 1024 characters."),
-                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
                 IO.DynamicCombo.Input(
                     "generate_type",
                     options=[
@@ -251,7 +251,7 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.Image.Input("image_left", optional=True),
                 IO.Image.Input("image_right", optional=True),
                 IO.Image.Input("image_back", optional=True),
-                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
                 IO.DynamicCombo.Input(
                     "generate_type",
                     options=[
@@ -422,6 +422,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             outputs=[
                 IO.File3DOBJ.Output(display_name="OBJ"),
                 IO.File3DFBX.Output(display_name="FBX"),
+                IO.Image.Output(display_name="uv_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -468,9 +469,16 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
+        uv_image_file = get_file_from_response(result.ResultFile3Ds, "uv_image", raise_if_not_found=False)
+        uv_image = (
+            await download_url_to_image_tensor(uv_image_file.Url)
+            if uv_image_file is not None
+            else torch.zeros(1, 1, 1, 3)
+        )
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            uv_image,
         )
 
 

comfy_api_nodes/nodes_reve.py

@@ -145,7 +145,20 @@ class ReveImageCreateNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.03432,"format":{"approximate":true,"note":"(base)"}}""",
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["upscale", "upscale.upscale_factor"],
+                ),
+                expr="""
+                (
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $fmt := {"approximate": true, "note": "(base)"};
+                    widgets.upscale = "enabled" ? (
+                        $factor = 4 ? {"type": "usd", "usd": 0.0762, "format": $fmt}
+                        : $factor = 3 ? {"type": "usd", "usd": 0.0591, "format": $fmt}
+                        : {"type": "usd", "usd": 0.0457, "format": $fmt}
+                    ) : {"type": "usd", "usd": 0.03432, "format": $fmt}
+                )
+                """,
             ),
         )
 
@@ -225,13 +238,21 @@ class ReveImageEditNode(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
+                    widgets=["model", "upscale", "upscale.upscale_factor"],
                 ),
                 expr="""
                 (
+                    $fmt := {"approximate": true, "note": "(base)"};
                     $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
+                    $enabled := widgets.upscale = "enabled";
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $isFast
+                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
+                        : $enabled ? (
+                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
+                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
+                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
+                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
                 )
                 """,
             ),
@@ -327,13 +348,21 @@ class ReveImageRemixNode(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
+                    widgets=["model", "upscale", "upscale.upscale_factor"],
                 ),
                 expr="""
                 (
+                    $fmt := {"approximate": true, "note": "(base)"};
                     $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
+                    $enabled := widgets.upscale = "enabled";
+                    $factor := $lookup(widgets, "upscale.upscale_factor");
+                    $isFast
+                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
+                        : $enabled ? (
+                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
+                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
+                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
+                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
                 )
                 """,
             ),

comfy_api_nodes/nodes_topaz.py

@@ -38,6 +38,7 @@ from comfy_api_nodes.util import (
 UPSCALER_MODELS_MAP = {
     "Starlight (Astra) Fast": "slf-1",
     "Starlight (Astra) Creative": "slc-1",
+    "Starlight Precise 2.5": "slp-2.5",
 }
 
 

comfy_execution/caching.py

@@ -1,6 +1,5 @@
 import asyncio
 import bisect
-import gc
 import itertools
 import psutil
 import time
@@ -475,6 +474,10 @@ class LRUCache(BasicCache):
         self._mark_used(node_id)
         return await self._set_immediate(node_id, value)
 
+    def set_local(self, node_id, value):
+        self._mark_used(node_id)
+        BasicCache.set_local(self, node_id, value)
+
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
         await super()._ensure_subcache(node_id, children_ids)
@@ -489,15 +492,10 @@ class LRUCache(BasicCache):
         return self
 
 
-#Iterating the cache for usage analysis might be expensive, so if we trigger make sure
-#to take a chunk out to give breathing space on high-node / low-ram-per-node flows.
+#Small baseline weight used when a cache entry has no measurable CPU tensors.
+#Keeps unknown-sized entries in eviction scoring without dominating tensor-backed entries.
 
-RAM_CACHE_HYSTERESIS = 1.1
-
-#This is kinda in GB but not really. It needs to be non-zero for the below heuristic
-#and as long as Multi GB models dwarf this it will approximate OOM scoring OK
-
-RAM_CACHE_DEFAULT_RAM_USAGE = 0.1
+RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 #Exponential bias towards evicting older workflows so garbage will be taken out
 #in constantly changing setups.
@@ -521,19 +519,17 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         return await super().get(node_id)
 
-    def poll(self, ram_headroom):
-        def _ram_gb():
-            return psutil.virtual_memory().available / (1024**3)
+    def set_local(self, node_id, value):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        super().set_local(node_id, value)
 
-        if _ram_gb() > ram_headroom:
-            return
-        gc.collect()
-        if _ram_gb() > ram_headroom:
+    def ram_release(self, target):
+        if psutil.virtual_memory().available >= target:
             return
 
         clean_list = []
 
-        for key, (outputs, _), in self.cache.items():
+        for key, cache_entry in self.cache.items():
             oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
@@ -542,22 +538,20 @@ class RAMPressureCache(LRUCache):
                 if outputs is None:
                     return
                 for output in outputs:
-                    if isinstance(output, list):
+                    if isinstance(output, (list, tuple)):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
-                        #score Tensors at a 50% discount for RAM usage as they are likely to
-                        #be high value intermediates
-                        ram_usage += (output.numel() * output.element_size()) * 0.5
-                    elif hasattr(output, "get_ram_usage"):
-                        ram_usage += output.get_ram_usage()
-            scan_list_for_ram_usage(outputs)
+                        ram_usage += output.numel() * output.element_size()
+            scan_list_for_ram_usage(cache_entry.outputs)
 
             oom_score *= ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
             bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
 
-        while _ram_gb() < ram_headroom * RAM_CACHE_HYSTERESIS and clean_list:
+        while psutil.virtual_memory().available < target and clean_list:
             _, _, key = clean_list.pop()
             del self.cache[key]
-            gc.collect()
+            self.used_generation.pop(key, None)
+            self.timestamps.pop(key, None)
+            self.children.pop(key, None)

comfy_extras/nodes_ace.py

@@ -80,7 +80,7 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
     @classmethod
     def execute(cls, seconds, batch_size) -> io.NodeOutput:
         length = int(seconds * 44100 / 512 / 8)
-        latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
+        latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return io.NodeOutput({"samples": latent, "type": "audio"})
 
 
@@ -103,7 +103,7 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
     @classmethod
     def execute(cls, seconds, batch_size) -> io.NodeOutput:
         length = round((seconds * 48000 / 1920))
-        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return io.NodeOutput({"samples": latent, "type": "audio"})
 
 class ReferenceAudio(io.ComfyNode):

comfy_extras/nodes_curve.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+import numpy as np
+
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.input import CurveInput
+from typing_extensions import override
+
+
+class CurveEditor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CurveEditor",
+            display_name="Curve Editor",
+            category="utils",
+            inputs=[
+                io.Curve.Input("curve"),
+                io.Histogram.Input("histogram", optional=True),
+            ],
+            outputs=[
+                io.Curve.Output("curve"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, curve, histogram=None) -> io.NodeOutput:
+        result = CurveInput.from_raw(curve)
+
+        ui = {}
+        if histogram is not None:
+            ui["histogram"] = histogram if isinstance(histogram, list) else list(histogram)
+
+        return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result)
+
+
+class ImageHistogram(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageHistogram",
+            display_name="Image Histogram",
+            category="utils",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Histogram.Output("rgb"),
+                io.Histogram.Output("luminance"),
+                io.Histogram.Output("red"),
+                io.Histogram.Output("green"),
+                io.Histogram.Output("blue"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
+        img = image[0].cpu().numpy()
+        img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8)
+
+        def bincount(data):
+            return np.bincount(data.ravel(), minlength=256)[:256]
+
+        hist_r = bincount(img_uint8[:, :, 0])
+        hist_g = bincount(img_uint8[:, :, 1])
+        hist_b = bincount(img_uint8[:, :, 2])
+
+        # Average of R, G, B histograms (same as Photoshop's RGB composite)
+        rgb = ((hist_r + hist_g + hist_b) // 3).tolist()
+
+        # ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal
+        # https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en
+        lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2]
+        luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist()
+
+        return io.NodeOutput(
+            rgb,
+            luminance,
+            hist_r.tolist(),
+            hist_g.tolist(),
+            hist_b.tolist(),
+        )
+
+
+class CurveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self):
+        return [CurveEditor, ImageHistogram]
+
+
+async def comfy_entrypoint():
+    return CurveExtension()

comfy_extras/nodes_glsl.py

@@ -87,7 +87,9 @@ class SizeModeInput(TypedDict):
 
 
 MAX_IMAGES = 5      # u_image0-4
-MAX_UNIFORMS = 5    # u_float0-4, u_int0-4
+MAX_UNIFORMS = 20   # u_float0-19, u_int0-19
+MAX_BOOLS = 10      # u_bool0-9
+MAX_CURVES = 4      # u_curve0-3 (1D LUT textures)
 MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
 
 # Vertex shader using gl_VertexID trick - no VBO needed.
@@ -497,6 +499,8 @@ def _render_shader_batch(
     image_batches: list[list[np.ndarray]],
     floats: list[float],
     ints: list[int],
+    bools: list[bool] | None = None,
+    curves: list[np.ndarray] | None = None,
 ) -> list[list[np.ndarray]]:
     """
     Render a fragment shader for multiple batches efficiently.
@@ -511,6 +515,8 @@ def _render_shader_batch(
         image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
         floats: List of float uniforms
         ints: List of int uniforms
+        bools: List of bool uniforms (passed as int 0/1 to GLSL bool uniforms)
+        curves: List of 1D LUT arrays (float32) of arbitrary size for u_curve0-N
 
     Returns:
         List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
@@ -533,11 +539,17 @@ def _render_shader_batch(
     # Detect multi-pass rendering
     num_passes = _detect_pass_count(fragment_code)
 
+    if bools is None:
+        bools = []
+    if curves is None:
+        curves = []
+
     # Track resources for cleanup
     program = None
     fbo = None
     output_textures = []
     input_textures = []
+    curve_textures = []
     ping_pong_textures = []
     ping_pong_fbos = []
 
@@ -624,6 +636,28 @@ def _render_shader_batch(
             if loc >= 0:
                 gl.glUniform1i(loc, v)
 
+        for i, v in enumerate(bools):
+            loc = gl.glGetUniformLocation(program, f"u_bool{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, 1 if v else 0)
+
+        # Create 1D LUT textures for curves (bound after image texture units)
+        for i, lut in enumerate(curves):
+            tex = gl.glGenTextures(1)
+            curve_textures.append(tex)
+            unit = MAX_IMAGES + i
+            gl.glActiveTexture(gl.GL_TEXTURE0 + unit)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_R32F, len(lut), 1, 0, gl.GL_RED, gl.GL_FLOAT, lut)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+            loc = gl.glGetUniformLocation(program, f"u_curve{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, unit)
+
         # Get u_pass uniform location for multi-pass
         pass_loc = gl.glGetUniformLocation(program, "u_pass")
 
@@ -718,6 +752,8 @@ def _render_shader_batch(
 
         for tex in input_textures:
             gl.glDeleteTextures(int(tex))
+        for tex in curve_textures:
+            gl.glDeleteTextures(int(tex))
         for tex in output_textures:
             gl.glDeleteTextures(int(tex))
         for tex in ping_pong_textures:
@@ -754,6 +790,20 @@ class GLSLShader(io.ComfyNode):
             max=MAX_UNIFORMS,
         )
 
+        bool_template = io.Autogrow.TemplatePrefix(
+            io.Boolean.Input("bool", default=False),
+            prefix="u_bool",
+            min=0,
+            max=MAX_BOOLS,
+        )
+
+        curve_template = io.Autogrow.TemplatePrefix(
+            io.Curve.Input("curve"),
+            prefix="u_curve",
+            min=0,
+            max=MAX_CURVES,
+        )
+
         return io.Schema(
             node_id="GLSLShader",
             display_name="GLSL Shader",
@@ -762,6 +812,8 @@ class GLSLShader(io.ComfyNode):
                 "Apply GLSL ES fragment shaders to images. "
                 "u_resolution (vec2) is always available."
             ),
+            is_experimental=True,
+            has_intermediate_output=True,
             inputs=[
                 io.String.Input(
                     "fragment_shader",
@@ -796,6 +848,8 @@ class GLSLShader(io.ComfyNode):
                 io.Autogrow.Input("images", template=image_template, tooltip=f"Images are available as u_image0-{MAX_IMAGES-1} (sampler2D) in the shader code"),
                 io.Autogrow.Input("floats", template=float_template, tooltip=f"Floats are available as u_float0-{MAX_UNIFORMS-1} in the shader code"),
                 io.Autogrow.Input("ints", template=int_template, tooltip=f"Ints are available as u_int0-{MAX_UNIFORMS-1} in the shader code"),
+                io.Autogrow.Input("bools", template=bool_template, tooltip=f"Booleans are available as u_bool0-{MAX_BOOLS-1} (bool) in the shader code"),
+                io.Autogrow.Input("curves", template=curve_template, tooltip=f"Curves are available as u_curve0-{MAX_CURVES-1} (sampler2D, 1D LUT) in the shader code. Sample with texture(u_curve0, vec2(x, 0.5)).r"),
             ],
             outputs=[
                 io.Image.Output(display_name="IMAGE0", tooltip="Available via layout(location = 0) out vec4 fragColor0 in the shader code"),
@@ -813,13 +867,19 @@ class GLSLShader(io.ComfyNode):
         images: io.Autogrow.Type,
         floats: io.Autogrow.Type = None,
         ints: io.Autogrow.Type = None,
+        bools: io.Autogrow.Type = None,
+        curves: io.Autogrow.Type = None,
         **kwargs,
     ) -> io.NodeOutput:
+
         image_list = [v for v in images.values() if v is not None]
         float_list = (
             [v if v is not None else 0.0 for v in floats.values()] if floats else []
         )
         int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
+        bool_list = [v if v is not None else False for v in bools.values()] if bools else []
+
+        curve_luts = [v.to_lut().astype(np.float32) for v in curves.values() if v is not None] if curves else []
 
         if not image_list:
             raise ValueError("At least one input image is required")
@@ -846,6 +906,8 @@ class GLSLShader(io.ComfyNode):
             image_batches,
             float_list,
             int_list,
+            bool_list,
+            curve_luts,
         )
 
         # Collect outputs into tensors

comfy_extras/nodes_images.py

@@ -59,6 +59,7 @@ class ImageCropV2(IO.ComfyNode):
             display_name="Image Crop",
             category="image/transform",
             essentials_category="Image Tools",
+            has_intermediate_output=True,
             inputs=[
                 IO.Image.Input("image"),
                 IO.BoundingBox.Input("crop_region", component="ImageCrop"),

comfy_extras/nodes_lt.py

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

comfy_extras/nodes_number_convert.py

@@ -0,0 +1,92 @@
+"""Number Convert node for unified numeric type conversion.
+
+Provides a single node that converts INT, FLOAT, STRING, and BOOL
+inputs into FLOAT and INT outputs.
+"""
+
+from __future__ import annotations
+
+import math
+
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class NumberConvertNode(io.ComfyNode):
+    """Converts various types to numeric FLOAT and INT outputs."""
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ComfyNumberConvert",
+            display_name="Number Convert",
+            category="math",
+            search_aliases=[
+                "int to float", "float to int", "number convert",
+                "int2float", "float2int", "cast", "parse number",
+                "string to number", "bool to int",
+            ],
+            inputs=[
+                io.MultiType.Input(
+                    "value",
+                    [io.Int, io.Float, io.String, io.Boolean],
+                    display_name="value",
+                ),
+            ],
+            outputs=[
+                io.Float.Output(display_name="FLOAT"),
+                io.Int.Output(display_name="INT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, value) -> io.NodeOutput:
+        if isinstance(value, bool):
+            float_val = 1.0 if value else 0.0
+            int_val = 1 if value else 0
+        elif isinstance(value, int):
+            float_val = float(value)
+            int_val = value
+        elif isinstance(value, float):
+            float_val = value
+            int_val = int(value)
+        elif isinstance(value, str):
+            text = value.strip()
+            if not text:
+                raise ValueError("Cannot convert empty string to number.")
+            try:
+                float_val = float(text)
+            except ValueError:
+                raise ValueError(
+                    f"Cannot convert string to number: {value!r}"
+                ) from None
+            if not math.isfinite(float_val):
+                raise ValueError(
+                    f"Cannot convert non-finite value to number: {float_val}"
+                )
+            try:
+                int_val = int(text)
+            except ValueError:
+                int_val = int(float_val)
+        else:
+            raise TypeError(
+                f"Unsupported input type: {type(value).__name__}"
+            )
+
+        if not math.isfinite(float_val):
+            raise ValueError(
+                f"Cannot convert non-finite value to number: {float_val}"
+            )
+
+        return io.NodeOutput(float_val, int_val)
+
+
+class NumberConvertExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [NumberConvertNode]
+
+
+async def comfy_entrypoint() -> NumberConvertExtension:
+    return NumberConvertExtension()

comfy_extras/nodes_painter.py

@@ -30,6 +30,7 @@ class PainterNode(io.ComfyNode):
             node_id="Painter",
             display_name="Painter",
             category="image",
+            has_intermediate_output=True,
             inputs=[
                 io.Image.Input(
                     "image",

comfy_extras/nodes_post_processing.py

@@ -67,11 +67,11 @@ class Blend(io.ComfyNode):
     def g(cls, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
-def gaussian_kernel(kernel_size: int, sigma: float, device=None):
+def gaussian_kernel(kernel_size: int, sigma: float, device=None, dtype=torch.float32):
     x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
     d = torch.sqrt(x * x + y * y)
     g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
-    return g / g.sum()
+    return (g / g.sum()).to(dtype)
 
 class Blur(io.ComfyNode):
     @classmethod
@@ -99,7 +99,7 @@ class Blur(io.ComfyNode):
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype).repeat(channels, 1, 1).unsqueeze(1)
 
         image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
         padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
@@ -200,7 +200,7 @@ class Sharpen(io.ComfyNode):
         image = image.to(comfy.model_management.get_torch_device())
 
         kernel_size = sharpen_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype) * -(alpha*10)
         kernel = kernel.to(dtype=image.dtype)
         center = kernel_size // 2
         kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0

comfy_extras/nodes_rtdetr.py

@@ -0,0 +1,154 @@
+from typing_extensions import override
+
+import torch
+from comfy.ldm.rt_detr.rtdetr_v4 import COCO_CLASSES
+import comfy.model_management
+import comfy.utils
+from comfy_api.latest import ComfyExtension, io
+from torchvision.transforms import ToPILImage, ToTensor
+from PIL import ImageDraw, ImageFont
+
+
+class RTDETR_detect(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RTDETR_detect",
+            display_name="RT-DETR Detect",
+            category="detection/",
+            search_aliases=["bbox", "bounding box", "object detection", "coco"],
+            inputs=[
+                io.Model.Input("model", display_name="model"),
+                io.Image.Input("image", display_name="image"),
+                io.Float.Input("threshold", display_name="threshold", default=0.5),
+                io.Combo.Input("class_name", options=["all"] + COCO_CLASSES, default="all", tooltip="Filter detections by class. Set to 'all' to disable filtering."),
+                io.Int.Input("max_detections", display_name="max_detections", default=100, tooltip="Maximum number of detections to return per image. In order of descending confidence score."),
+            ],
+            outputs=[
+                io.BoundingBox.Output("bboxes")],
+        )
+
+    @classmethod
+    def execute(cls, model, image, threshold, class_name, max_detections) -> io.NodeOutput:
+        B, H, W, C = image.shape
+
+        image_in = comfy.utils.common_upscale(image.movedim(-1, 1), 640, 640, "bilinear", crop="disabled")
+
+        comfy.model_management.load_model_gpu(model)
+        results = model.model.diffusion_model(image_in, (W, H))  # list of B dicts
+
+        all_bbox_dicts = []
+
+        for det in results:
+            keep   = det['scores'] > threshold
+            boxes  = det['boxes'][keep].cpu()
+            labels = det['labels'][keep].cpu()
+            scores = det['scores'][keep].cpu()
+
+            bbox_dicts = [
+                {
+                    "x": float(box[0]),
+                    "y": float(box[1]),
+                    "width": float(box[2] - box[0]),
+                    "height": float(box[3] - box[1]),
+                    "label": COCO_CLASSES[int(label)],
+                    "score": float(score)
+                }
+                for box, label, score in zip(boxes, labels, scores)
+                if class_name == "all" or COCO_CLASSES[int(label)] == class_name
+            ]
+            bbox_dicts.sort(key=lambda d: d["score"], reverse=True)
+            all_bbox_dicts.append(bbox_dicts[:max_detections])
+
+        return io.NodeOutput(all_bbox_dicts)
+
+
+class DrawBBoxes(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DrawBBoxes",
+            display_name="Draw BBoxes",
+            category="detection/",
+            search_aliases=["bbox", "bounding box", "object detection", "rt_detr", "visualize detections", "coco"],
+            inputs=[
+                io.Image.Input("image", optional=True),
+                io.BoundingBox.Input("bboxes", force_input=True),
+            ],
+            outputs=[
+                io.Image.Output("out_image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, bboxes, image=None) -> io.NodeOutput:
+        # Normalise to list[list[dict]], then fit to batch size B.
+        B = image.shape[0] if image is not None else 1
+        if isinstance(bboxes, dict):
+            bboxes = [[bboxes]]
+        elif not isinstance(bboxes, list) or not bboxes:
+            bboxes = [[]]
+        elif isinstance(bboxes[0], dict):
+            bboxes = [bboxes]  # flat list → same detections for every image
+
+        if len(bboxes) == 1:
+            bboxes = bboxes * B
+        bboxes = (bboxes + [[]] * B)[:B]
+
+        if image is None:
+            B = len(bboxes)
+            max_w = max((int(d["x"] + d["width"])  for frame in bboxes for d in frame), default=640)
+            max_h = max((int(d["y"] + d["height"]) for frame in bboxes for d in frame), default=640)
+            image = torch.zeros((B, max_h, max_w, 3), dtype=torch.float32)
+
+        all_out_images = []
+        for i in range(B):
+            detections = bboxes[i]
+            if detections:
+                boxes  = torch.tensor([[d["x"], d["y"], d["x"] + d["width"], d["y"] + d["height"]] for d in detections])
+                labels = [d.get("label") if d.get("label") in COCO_CLASSES else None for d in detections]
+                scores = torch.tensor([d.get("score", 1.0) for d in detections])
+            else:
+                boxes  = torch.zeros((0, 4))
+                labels = []
+                scores = torch.zeros((0,))
+
+            pil_image = image[i].movedim(-1, 0)
+            img = ToPILImage()(pil_image)
+            if detections:
+                img = cls.draw_detections(img, boxes, labels, scores)
+            all_out_images.append(ToTensor()(img).unsqueeze(0).movedim(1, -1))
+
+        out_images = torch.cat(all_out_images, dim=0).to(comfy.model_management.intermediate_device())
+        return io.NodeOutput(out_images)
+
+    @classmethod
+    def draw_detections(cls, img, boxes, labels, scores):
+        draw = ImageDraw.Draw(img)
+        try:
+            font = ImageFont.truetype('arial.ttf', 16)
+        except Exception:
+            font = ImageFont.load_default()
+        colors = [(255,0,0),(0,200,0),(0,0,255),(255,165,0),(128,0,128),
+                (0,255,255),(255,20,147),(100,149,237)]
+        for box, label, score in sorted(zip(boxes, labels, scores), key=lambda x: x[2].item()):
+            x1, y1, x2, y2 = box.tolist()
+            color_idx = COCO_CLASSES.index(label) if label is not None else 0
+            c = colors[color_idx % len(colors)]
+            draw.rectangle([x1, y1, x2, y2], outline=c, width=3)
+            if label is not None:
+                draw.text((x1 + 2, y1 + 2), f'{label} {score:.2f}', fill=c, font=font)
+        return img
+
+
+class RTDETRExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            RTDETR_detect,
+            DrawBBoxes,
+        ]
+
+
+async def comfy_entrypoint() -> RTDETRExtension:
+    return RTDETRExtension()

comfy_extras/nodes_sdpose.py

@@ -661,6 +661,7 @@ class CropByBBoxes(io.ComfyNode):
                 io.Int.Input("output_width",  default=512, min=64, max=4096, step=8, tooltip="Width each crop is resized to."),
                 io.Int.Input("output_height", default=512, min=64, max=4096, step=8, tooltip="Height each crop is resized to."),
                 io.Int.Input("padding", default=0, min=0, max=1024, step=1, tooltip="Extra padding in pixels added on each side of the bbox before cropping."),
+                io.Combo.Input("keep_aspect", options=["stretch", "pad"], default="stretch", tooltip="Whether to stretch the crop to fit the output size, or pad with black pixels to preserve aspect ratio."),
             ],
             outputs=[
                 io.Image.Output(tooltip="All crops stacked into a single image batch."),
@@ -668,7 +669,7 @@ class CropByBBoxes(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, image, bboxes, output_width, output_height, padding) -> io.NodeOutput:
+    def execute(cls, image, bboxes, output_width, output_height, padding, keep_aspect="stretch") -> io.NodeOutput:
         total_frames = image.shape[0]
         img_h = image.shape[1]
         img_w = image.shape[2]
@@ -716,7 +717,19 @@ class CropByBBoxes(io.ComfyNode):
                 x1, y1, x2, y2 = fb_x1, fb_y1, fb_x2, fb_y2
 
             crop_chw = frame_chw[:, :, y1:y2, x1:x2]  # (1, C, crop_h, crop_w)
-            resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
+
+            if keep_aspect == "pad":
+                crop_h, crop_w = y2 - y1, x2 - x1
+                scale = min(output_width / crop_w, output_height / crop_h)
+                scaled_w = int(round(crop_w * scale))
+                scaled_h = int(round(crop_h * scale))
+                scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
+                pad_left = (output_width  - scaled_w) // 2
+                pad_top  = (output_height - scaled_h) // 2
+                resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device)
+                resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
+            else:  # "stretch"
+                resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
             crops.append(resized)
 
         if not crops:

comfy_extras/nodes_string.py

@@ -9,9 +9,9 @@ class StringConcatenate(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringConcatenate",
-            display_name="Concatenate",
+            display_name="Text Concatenate",
             category="utils/string",
-            search_aliases=["text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
+            search_aliases=["Concatenate", "text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
             inputs=[
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
@@ -32,8 +32,8 @@ class StringSubstring(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringSubstring",
-            search_aliases=["extract text", "text portion"],
-            display_name="Substring",
+            search_aliases=["Substring", "extract text", "text portion"],
+            display_name="Text Substring",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -55,8 +55,8 @@ class StringLength(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringLength",
-            search_aliases=["character count", "text size"],
-            display_name="Length",
+            search_aliases=["character count", "text size", "string length"],
+            display_name="Text Length",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -76,8 +76,8 @@ class CaseConverter(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CaseConverter",
-            search_aliases=["text case", "uppercase", "lowercase", "capitalize"],
-            display_name="Case Converter",
+            search_aliases=["Case Converter", "text case", "uppercase", "lowercase", "capitalize"],
+            display_name="Text Case Converter",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -109,8 +109,8 @@ class StringTrim(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringTrim",
-            search_aliases=["clean whitespace", "remove whitespace"],
-            display_name="Trim",
+            search_aliases=["Trim", "clean whitespace", "remove whitespace", "strip"],
+            display_name="Text Trim",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -140,8 +140,8 @@ class StringReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringReplace",
-            search_aliases=["find and replace", "substitute", "swap text"],
-            display_name="Replace",
+            search_aliases=["Replace", "find and replace", "substitute", "swap text"],
+            display_name="Text Replace",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -163,8 +163,8 @@ class StringContains(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringContains",
-            search_aliases=["text includes", "string includes"],
-            display_name="Contains",
+            search_aliases=["Contains", "text includes", "string includes"],
+            display_name="Text Contains",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -191,8 +191,8 @@ class StringCompare(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringCompare",
-            search_aliases=["text match", "string equals", "starts with", "ends with"],
-            display_name="Compare",
+            search_aliases=["Compare", "text match", "string equals", "starts with", "ends with"],
+            display_name="Text Compare",
             category="utils/string",
             inputs=[
                 io.String.Input("string_a", multiline=True),
@@ -227,8 +227,8 @@ class RegexMatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexMatch",
-            search_aliases=["pattern match", "text contains", "string match"],
-            display_name="Regex Match",
+            search_aliases=["Regex Match", "regex", "pattern match", "text contains", "string match"],
+            display_name="Text Match",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -268,8 +268,8 @@ class RegexExtract(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexExtract",
-            search_aliases=["pattern extract", "text parser", "parse text"],
-            display_name="Regex Extract",
+            search_aliases=["Regex Extract", "regex", "pattern extract", "text parser", "parse text"],
+            display_name="Text Extract Substring",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -343,8 +343,8 @@ class RegexReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexReplace",
-            search_aliases=["pattern replace", "find and replace", "substitution"],
-            display_name="Regex Replace",
+            search_aliases=["Regex Replace", "regex", "pattern replace", "regex replace", "substitution"],
+            display_name="Text Replace (Regex)",
             category="utils/string",
             description="Find and replace text using regex patterns.",
             inputs=[