Fix single-GPU non-CUDA regressions on worksplit-multigpu

Two fixes for single-GPU users on non-NVIDIA backends; multi-GPU
non-CUDA support is intentionally out of scope here (tracked separately).

1. get_all_torch_devices: add AMD/ROCm, MLU, and a generic fallback arm.

   Previously the function only enumerated NVIDIA, Intel XPU, and Ascend
   NPU when cpu_state==GPU; on AMD/ROCm (which exposes its GPU through
   torch.cuda.*) and DirectML it fell through to an empty list. The
   biggest user-visible regression: unload_all_models() iterates this
   list, so it became a silent no-op on AMD/ROCm. /free, manager
   unloads, and shutdown stopped releasing VRAM.

   - is_amd() now shares the torch.cuda.* arm with is_nvidia(), since
     ROCm reuses the CUDA API surface.
   - is_mlu() gets its own arm using torch.mlu.device_count().
   - A final fallback appends get_torch_device() for any GPU backend
     the explicit arms miss (notably DirectML), so callers see at
     least the current device and unload_all_models works.

   MPS users are unaffected: cpu_state==MPS already routes to the
   else branch which appends get_torch_device() returning mps.

2. main.py DynamicVRAM init: guard the comfy_aimdo branch with an
   explicit is_nvidia() check.

   The outer condition allows entering the DynamicVRAM init block when
   the user passes --enable-dynamic-vram explicitly, bypassing the
   implicit is_nvidia() gate. On non-NVIDIA backends this then runs
   comfy_aimdo.control.init_devices(range(torch.cuda.device_count())),
   which is comfy-aimdo-only territory and may crash at startup. Add a
   leading is_nvidia() check that logs a clean warning and falls back
   to the legacy ModelPatcher path.

.ci/windows_amd_base_files/run_amd_gpu_enable_dynamic_vram.bat

@@ -1,2 +1,2 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --enable-dynamic-vram
 pause

.github/workflows/openapi-lint.yml

@@ -0,0 +1,31 @@
+name: OpenAPI Lint
+
+on:
+  pull_request:
+    paths:
+      - 'openapi.yaml'
+      - '.spectral.yaml'
+      - '.github/workflows/openapi-lint.yml'
+
+permissions:
+  contents: read
+
+jobs:
+  spectral:
+    name: Run Spectral
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Install Spectral
+        run: npm install -g @stoplight/spectral-cli@6
+
+      - name: Lint openapi.yaml
+        run: spectral lint openapi.yaml --ruleset .spectral.yaml --fail-severity=error

.github/workflows/stable-release.yml

@@ -145,6 +145,8 @@ jobs:
           cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
+          echo 'local-portable' > ComfyUI/.comfy_environment
+
           cd ..
 
           "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable

.gitignore

@@ -23,3 +23,4 @@ web_custom_versions/
 .DS_Store
 filtered-openapi.yaml
 uv.lock
+.comfy_environment

.spectral.yaml

@@ -0,0 +1,100 @@
+extends:
+  - spectral:oas
+
+# Severity levels: error, warn, info, hint, off
+# Rules from the built-in "spectral:oas" ruleset are active by default.
+# Below we tune severity and add custom rules for our conventions.
+#
+# This ruleset mirrors Comfy-Org/cloud/.spectral.yaml so specs across the
+# organization are linted against a single consistent standard.
+
+rules:
+  # -----------------------------------------------------------------------
+  # Built-in rule severity overrides
+  # -----------------------------------------------------------------------
+  operation-operationId: error
+  operation-description: warn
+  operation-tag-defined: error
+  info-contact: off
+  info-description: warn
+  no-eval-in-markdown: error
+  no-$ref-siblings: error
+
+  # -----------------------------------------------------------------------
+  # Custom rules: naming conventions
+  # -----------------------------------------------------------------------
+
+  # Property names should be snake_case
+  property-name-snake-case:
+    description: Property names must be snake_case
+    severity: warn
+    given: "$.components.schemas.*.properties[*]~"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-z0-9]*(_[a-z0-9]+)*$"
+
+  # Operation IDs should be camelCase
+  operation-id-camel-case:
+    description: Operation IDs must be camelCase
+    severity: warn
+    given: "$.paths.*.*.operationId"
+    then:
+      function: pattern
+      functionOptions:
+        match: "^[a-z][a-zA-Z0-9]*$"
+
+  # -----------------------------------------------------------------------
+  # Custom rules: response conventions
+  # -----------------------------------------------------------------------
+
+  # Error responses (4xx, 5xx) should use a consistent shape
+  error-response-schema:
+    description: Error responses should reference a standard error schema
+    severity: hint
+    given: "$.paths.*.*.responses[?(@property >= '400' && @property < '600')].content['application/json'].schema"
+    then:
+      field: "$ref"
+      function: truthy
+
+  # All 2xx responses with JSON body should have a schema
+  response-schema-defined:
+    description: Success responses with JSON content should define a schema
+    severity: warn
+    given: "$.paths.*.*.responses[?(@property >= '200' && @property < '300')].content['application/json']"
+    then:
+      field: schema
+      function: truthy
+
+  # -----------------------------------------------------------------------
+  # Custom rules: best practices
+  # -----------------------------------------------------------------------
+
+  # Path parameters must have a description
+  path-param-description:
+    description: Path parameters should have a description
+    severity: warn
+    given:
+      - "$.paths.*.parameters[?(@.in == 'path')]"
+      - "$.paths.*.*.parameters[?(@.in == 'path')]"
+    then:
+      field: description
+      function: truthy
+
+  # Schemas should have a description
+  schema-description:
+    description: Component schemas should have a description
+    severity: hint
+    given: "$.components.schemas.*"
+    then:
+      field: description
+      function: truthy
+
+overrides:
+  # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a
+  # WebSocket upgrade, but not a 2xx, so operation-success-response fires
+  # as a false positive. OpenAPI 3.x has no native WebSocket support.
+  - files:
+      - "openapi.yaml#/paths/~1ws"
+    rules:
+      operation-success-response: off

CODEOWNERS

@@ -1,2 +1,5 @@
-# Admins
-* @comfyanonymous @kosinkadink @guill
+* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai
+
+/CODEOWNERS @comfyanonymous
+/.ci/ @comfyanonymous
+/.github/ @comfyanonymous

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular visual AI engine and application.**
+**The most powerful and modular AI engine for content creation.**
 
 
 [![Website][website-shield]][website-url]
@@ -31,10 +31,16 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
+<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/36e065e0-bfae-4456-8c7f-8369d5ea48a2" />
+<br>
 </div>
 
-ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
+ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...
+- ComfyUI natively supports the latest open-source state of the art models.
+- API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc.
+- It is available on Windows, Linux, and macOS, locally with our [desktop application](https://www.comfy.org/download), our [portable install](#installing) or on our [cloud](https://www.comfy.org/cloud).
+- The most sophisticated workflows can be exposed through a simple UI thanks to App Mode.
+- It integrates seamlessly into production pipelines with our API endpoints.
 
 ## Get Started
 
@@ -77,6 +83,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
    - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
    - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
    - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
+   - Ernie Image
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -126,7 +133,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
-   - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - Releases a new major stable version (e.g., v0.7.0) roughly every 2 weeks.
    - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
    - Minor versions will be used for releases off the master branch.
    - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
@@ -193,13 +200,15 @@ If you have trouble extracting it, right click the file -> properties -> unblock
 
 The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
 
-#### Alternative Downloads:
+#### All Official Portable Downloads:
 
 [Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Experimental portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
+[Portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
 
-[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
+[Portable for Nvidia GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) (supports 20 series and above).
+
+[Portable for Nvidia GPUs with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?
 
@@ -420,6 +429,8 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 See also: [https://www.comfy.org/](https://www.comfy.org/)
 
+> _psst — we're hiring!_ Help build ComfyUI: [comfy.org/careers](https://www.comfy.org/careers)
+
 ## Frontend Development
 
 As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.

SECURITY.md

@@ -0,0 +1,44 @@
+# Security Policy
+
+## Scope
+
+ComfyUI is designed to run locally. By default, the server binds to `127.0.0.1`, meaning only the user's own machine can reach it. Our threat model assumes:
+
+- The user installed ComfyUI through a supported channel: the desktop application, the portable build, or a manual install following the README.
+- The user has not installed untrusted custom nodes. Custom nodes are arbitrary Python code and are trusted as much as any other software the user chooses to install.
+- Anyone with access to the ComfyUI URL is trusted (a direct consequence of the localhost-only default).
+- PyTorch and other dependencies are at the versions we ship or recommend in the README.
+
+A report is in scope only if it affects a user operating within this threat model.
+
+## What We Consider a Vulnerability
+
+We want to hear about issues where a **reasonable user** — someone who does not install random untrusted nodes and who reads UI prompts and warnings before clicking through them — can be harmed by ComfyUI itself.
+
+The clearest example: a workflow file that such a user might plausibly load and run, using only built-in nodes, that results in **untrusted code execution, arbitrary file read/write outside expected directories, or credential/data exfiltration**.
+
+When submitting a report, please include a clear description of *why this is a problem for a typical local ComfyUI user*. Reports without this context are difficult to act on.
+
+## What We Do Not Consider a Security Vulnerability
+
+Please report the following through our regular [GitHub issues](https://github.com/comfyanonymous/ComfyUI/issues) instead. Filing them as security reports will likely cause them to be deprioritized or closed.
+
+- **Issues requiring `--listen` or any non-default network exposure.** ComfyUI binds to localhost by default. If a remote attacker needs to reach the server for the attack to work, the user has chosen to expose it and is responsible for securing that deployment (firewall, reverse proxy, authentication, etc.). These are bugs, not vulnerabilities.
+- **`torch.load` and related deserialization issues in old PyTorch versions.** These are upstream PyTorch issues. Our distributions ship with — and our documentation recommends — recent PyTorch versions where these are addressed.
+- **Vulnerabilities that depend on outdated library versions** that we neither ship nor recommend (e.g., requiring PyTorch 2.6 or older).
+- **Issues that require a specific custom node to be installed.** Custom nodes are third-party code. Report these to the maintainer of that node.
+- **Crashes, hangs, or resource exhaustion from a loaded workflow.** Annoying, but not a security issue in our model. File a regular bug.
+- **Social-engineering scenarios** where the user is expected to ignore an explicit UI warning or prompt.
+
+## Reporting
+
+If you believe you have found an issue that falls within the scope above, please report it privately via GitHub's [Report a vulnerability](https://github.com/comfyanonymous/ComfyUI/security/advisories/new) feature rather than opening a public issue.
+
+Please include:
+
+1. A description of the vulnerability and the affected component.
+2. Reproduction steps, ideally with a minimal workflow file or proof-of-concept.
+3. The ComfyUI version, install method (desktop / portable / manual), and OS.
+4. An explanation of how this affects a typical local user as described in the threat model.
+
+We will acknowledge valid reports and coordinate a fix and disclosure timeline with you.

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:
@@ -38,40 +38,54 @@ def is_valid_version(version: str) -> bool:
     pattern = r"^(\d+)\.(\d+)\.(\d+)$"
     return bool(re.match(pattern, version))
 
-def get_installed_frontend_version():
-    """Get the currently installed frontend package version."""
-    frontend_version_str = version("comfyui-frontend-package")
-    return frontend_version_str
-
-
 def get_required_frontend_version():
     return get_required_packages_versions().get("comfyui-frontend-package", None)
 
 
-def check_frontend_version():
-    """Check if the frontend version is up to date."""
+COMFY_PACKAGE_VERSIONS = []
+def get_comfy_package_versions():
+    """List installed/required versions for every comfy* package in requirements.txt."""
+    if COMFY_PACKAGE_VERSIONS:
+        return COMFY_PACKAGE_VERSIONS.copy()
+    out = COMFY_PACKAGE_VERSIONS
+    for name, required in (get_required_packages_versions() or {}).items():
+        if not name.startswith("comfy"):
+            continue
+        try:
+            installed = version(name)
+        except Exception:
+            installed = None
+        out.append({"name": name, "installed": installed, "required": required})
+    return out.copy()
 
-    try:
-        frontend_version_str = get_installed_frontend_version()
-        frontend_version = parse_version(frontend_version_str)
-        required_frontend_str = get_required_frontend_version()
-        required_frontend = parse_version(required_frontend_str)
-        if frontend_version < required_frontend:
+
+def check_comfy_packages_versions():
+    """Warn for every comfy* package whose installed version is below requirements.txt."""
+    from packaging.version import InvalidVersion, parse as parse_pep440
+    for pkg in get_comfy_package_versions():
+        installed_str = pkg["installed"]
+        required_str = pkg["required"]
+        if not installed_str or not required_str:
+            continue
+        try:
+            outdated = parse_pep440(installed_str) < parse_pep440(required_str)
+        except InvalidVersion as e:
+            logging.error(f"Failed to check {pkg['name']} version: {e}")
+            continue
+        if outdated:
             app.logger.log_startup_warning(
                 f"""
 ________________________________________________________________________
 WARNING WARNING WARNING WARNING WARNING
 
-Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
+Installed {pkg["name"]} version {installed_str} is lower than the recommended version {required_str}.
 
-{frontend_install_warning_message()}
+{get_missing_requirements_message()}
 ________________________________________________________________________
 """.strip()
             )
         else:
-            logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
-    except Exception as e:
-        logging.error(f"Failed to check frontend version: {e}")
+            logging.info("{} version: {}".format(pkg["name"], installed_str))
 
 
 REQUEST_TIMEOUT = 10  # seconds
@@ -201,6 +215,11 @@ class FrontendManager:
     def get_required_templates_version(cls) -> str:
         return get_required_packages_versions().get("comfyui-workflow-templates", None)
 
+    @classmethod
+    def get_comfy_package_versions(cls):
+        """List installed/required versions for every comfy* package in requirements.txt."""
+        return get_comfy_package_versions()
+
     @classmethod
     def default_frontend_path(cls) -> str:
         try:
@@ -341,7 +360,7 @@ comfyui-workflow-templates is not installed.
             main error source might be request timeout or invalid URL.
         """
         if version_string == DEFAULT_VERSION_STRING:
-            check_frontend_version()
+            check_comfy_packages_versions()
             return cls.default_frontend_path()
 
         repo_owner, repo_name, version = cls.parse_version_string(version_string)
@@ -403,7 +422,7 @@ comfyui-workflow-templates is not installed.
         except Exception as e:
             logging.error("Failed to initialize frontend: %s", e)
             logging.info("Falling back to the default frontend.")
-            check_frontend_version()
+            check_comfy_packages_versions()
             return cls.default_frontend_path()
     @classmethod
     def template_asset_handler(cls):

app/node_replace_manager.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import logging
+
 from aiohttp import web
 
 from typing import TYPE_CHECKING, TypedDict
@@ -31,8 +33,22 @@ class NodeReplaceManager:
         self._replacements: dict[str, list[NodeReplace]] = {}
 
     def register(self, node_replace: NodeReplace):
-        """Register a node replacement mapping."""
-        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+        """Register a node replacement mapping.
+
+        Idempotent: if a replacement with the same (old_node_id, new_node_id)
+        is already registered, the duplicate is ignored. This prevents stale
+        entries from accumulating when custom nodes are reloaded in the same
+        process (e.g. via ComfyUI-Manager).
+        """
+        existing = self._replacements.setdefault(node_replace.old_node_id, [])
+        for entry in existing:
+            if entry.new_node_id == node_replace.new_node_id:
+                logging.debug(
+                    "Node replacement %s -> %s already registered, ignoring duplicate.",
+                    node_replace.old_node_id, node_replace.new_node_id,
+                )
+                return
+        existing.append(node_replace)
 
     def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
         """Get replacements for an old node ID."""

app/user_manager.py

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

blueprints/Brightness and Contrast.json

@@ -431,9 +431,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts image brightness and contrast using a real-time GPU fragment shader."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Canny to Image (Z-Image-Turbo).json

@@ -162,7 +162,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Canny to Image (Z-Image-Turbo)",
+        "name": "Canny to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1553,7 +1553,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Canny to image"
+        "category": "Image generation and editing/Canny to image",
+        "description": "Generates an image from a Canny edge map using Z-Image-Turbo, with text conditioning."
       }
     ]
   },
@@ -1574,4 +1575,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Canny to Video (LTX 2.0).json

@@ -192,7 +192,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Canny to Video (LTX 2.0)",
+        "name": "Canny to Video (LTX 2.0)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -3600,7 +3600,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Canny to video"
+        "category": "Video generation and editing/Canny to video",
+        "description": "Generates video from Canny edge maps using LTX-2, with optional synchronized audio."
       }
     ]
   },
@@ -3616,4 +3617,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Chromatic Aberration.json

@@ -377,8 +377,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds lens-style chromatic aberration (color fringing) using a real-time GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Color Adjustment.json

@@ -596,7 +596,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts saturation, temperature, tint, and vibrance using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Color Balance.json

@@ -1129,7 +1129,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Balances colors across shadows, midtones, and highlights using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Color Curves.json

@@ -608,7 +608,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Fine-tunes tone and color with per-channel curve adjustments using a real-time GPU fragment shader."
       }
     ]
   }

blueprints/Crop Images 2x2.json

@@ -1609,7 +1609,8 @@
           }
         ],
         "extra": {},
-        "category": "Image Tools/Crop"
+        "category": "Image Tools/Crop",
+        "description": "Splits an image into a 2×2 grid of four equal tiles."
       }
     ]
   },

blueprints/Crop Images 3x3.json

@@ -2946,7 +2946,8 @@
           }
         ],
         "extra": {},
-        "category": "Image Tools/Crop"
+        "category": "Image Tools/Crop",
+        "description": "Splits an image into a 3×3 grid of nine equal tiles."
       }
     ]
   },

blueprints/Depth to Image (Z-Image-Turbo).json

@@ -1579,7 +1579,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Depth to image"
+        "category": "Image generation and editing/Depth to image",
+        "description": "Generates an image from a depth map using Z-Image-Turbo with text conditioning."
       },
       {
         "id": "458bdf3c-4b58-421c-af50-c9c663a4d74c",
@@ -2461,7 +2462,8 @@
             ]
           },
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },

blueprints/Depth to Video (ltx 2.0).json

@@ -4233,7 +4233,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Depth to video"
+        "category": "Video generation and editing/Depth to video",
+        "description": "Generates depth-controlled video with LTX-2: motion and structure follow a depth-reference video alongside text prompting, optional first-frame image conditioning, with optional synchronized audio."
       },
       {
         "id": "38b60539-50a7-42f9-a5fe-bdeca26272e2",
@@ -5192,7 +5193,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },

blueprints/Edge-Preserving Blur.json

@@ -450,9 +450,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur"
+        "category": "Image Tools/Blur",
+        "description": "Applies bilateral (edge-preserving) blur to soften images while retaining detail."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Film Grain.json

@@ -580,8 +580,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds procedural film grain texture for a cinematic look via GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/First-Last-Frame to Video (LTX-2.3).json

@@ -3350,7 +3350,8 @@
           }
         ],
         "extra": {},
-        "category": "Video generation and editing/First-Last-Frame to Video"
+        "category": "Video generation and editing/First-Last-Frame to Video",
+        "description": "Generates a video interpolating between first and last keyframes using LTX-2.3."
       }
     ]
   },

blueprints/Frame Interpolation.json

@@ -0,0 +1,858 @@
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+          "type": "COMBO",
+          "widget": {
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+        }
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+          "name": "VIDEO_1",
+          "type": "VIDEO",
+          "links": []
+        },
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
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+        "cnr_id": "comfy-core",
+        "ver": "0.19.3"
+      },
+      "widgets_values": [],
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+  "links": [],
+  "version": 0.4,
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+                "name": "INTERP_MODEL",
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+            "origin_id": -10,
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+          {
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+            "origin_id": -10,
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+            "target_slot": 0,
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+          {
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+        ],
+        "extra": {},
+        "category": "Video Tools",
+        "description": "Increases video frame rate by synthesizing intermediate frames with a frame interpolation model."
+      }
+    ]
+  },
+  "extra": {}
+}

blueprints/Get Any Video Frame.json

@@ -0,0 +1,485 @@
+{
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+          "type": "INT",
+          "widget": {
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+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": []
+        }
+      ],
+      "title": "Get Any Video Frame",
+      "properties": {
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+            "outputs": [
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+                "name": "images",
+                "type": "IMAGE",
+                "links": [
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+                  2
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+              {
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+                "links": null
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+                "widget": {
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+              {
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+                "type": "INT",
+                "widget": {
+                  "name": "length"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
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+                "name": "IMAGE",
+                "type": "IMAGE",
+                "links": [
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+                ]
+              }
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+                "type": "FLOAT,INT",
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+                "name": "values.b",
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+              {
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+                "name": "values.c",
+                "shape": 7,
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+                "link": null
+              },
+              {
+                "localized_name": "expression",
+                "name": "expression",
+                "type": "STRING",
+                "widget": {
+                  "name": "expression"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
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+                "name": "FLOAT",
+                "type": "FLOAT",
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+                "name": "value",
+                "type": "INT",
+                "widget": {
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+            ],
+            "outputs": [
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+                "name": "INT",
+                "type": "INT",
+                "links": [
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+            "target_slot": 1,
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+          }
+        ],
+        "extra": {},
+        "category": "Video Tools",
+        "description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
+      }
+    ]
+  },
+  "extra": {
+    "ds": {
+      "scale": 1.197015527856339,
+      "offset": [
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+        540.6638955283997
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+    "frontendVersion": "1.42.8"
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+}

blueprints/Glow.json

@@ -575,8 +575,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adds a glow/bloom effect around bright image areas via GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Hue and Saturation.json

@@ -752,8 +752,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts hue, saturation, and lightness of an image using a real-time GPU fragment shader."
       }
     ]
   }
-}
+}

blueprints/Image Blur.json

@@ -374,7 +374,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Blur"
+        "category": "Image Tools/Blur",
+        "description": "Applies Gaussian, Box, or Radial blur to soften images and create stylized depth or motion effects."
       }
     ]
   }

blueprints/Image Captioning (gemini).json

@@ -310,7 +310,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Image Captioning"
+        "category": "Text generation/Image Captioning",
+        "description": "Generates descriptive captions for images using Google's Gemini multimodal LLM."
       }
     ]
   }

blueprints/Image Channels.json

@@ -315,8 +315,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Manipulates individual RGBA channels for masking, compositing, and channel effects."
       }
     ]
   }
-}
+}

blueprints/Image Edit (Flux.2 Klein 4B).json

@@ -1472,7 +1472,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits an input image via text instructions using FLUX.2 [klein] 4B."
       },
       {
         "id": "6007e698-2ebd-4917-84d8-299b35d7b7ab",
@@ -1821,7 +1822,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Applies reference image conditioning for style/identity transfer (Flux.2 Klein 4B)."
       }
     ]
   },
@@ -1837,4 +1839,4 @@
     }
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   "version": 0.4
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+}

blueprints/Image Edit (LongCat Image Edit).json

@@ -1417,7 +1417,8 @@
           }
         ],
         "extra": {},
-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits images via text instructions using LongCat Image Edit, an instruction-following image editing diffusion model."
       }
     ]
   },

blueprints/Image Edit (Qwen 2511).json

@@ -132,7 +132,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Edit (Qwen 2511)",
+        "name": "Image Edit (Qwen 2511)",
         "inputNode": {
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@@ -1468,7 +1468,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Edit image"
+        "category": "Image generation and editing/Edit image",
+        "description": "Edits images via text instructions using Qwen-Image-Edit-2511 with improved character consistency and integrated LoRA."
       }
     ]
   },
@@ -1489,4 +1490,4 @@
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+}

blueprints/Image Inpainting (Flux.1 Fill Dev).json

@@ -1188,7 +1188,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Inpaint image"
+        "category": "Image generation and editing/Inpaint image",
+        "description": "Inpaints masked image regions using Flux.1 fill [dev], Black Forest Labs' inpainting/outpainting model."
       }
     ]
   },
@@ -1202,4 +1203,4 @@
     },
     "ue_links": []
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-}
+}

blueprints/Image Inpainting (Qwen-image).json

@@ -1548,7 +1548,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Inpaint image"
+        "category": "Image generation and editing/Inpaint image",
+        "description": "Inpaints masked regions using Qwen-Image, extending its multilingual text rendering to inpainting tasks."
       },
       {
         "id": "56a1f603-fbd2-40ed-94ef-c9ecbd96aca8",
@@ -1907,7 +1908,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Expands and softens mask edges to reduce visible seams after image processing."
       }
     ]
   },

blueprints/Image Levels.json

@@ -742,9 +742,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Color adjust"
+        "category": "Image Tools/Color adjust",
+        "description": "Adjusts black point, white point, and gamma for tonal range control via GPU shader."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Image Outpainting (Qwen-Image).json

@@ -1919,7 +1919,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Outpaint image"
+        "category": "Image generation and editing/Outpaint image",
+        "description": "Outpaints beyond image boundaries using Qwen-Image's outpainting capabilities."
       },
       {
         "id": "f93c215e-c393-460e-9534-ed2c3d8a652e",
@@ -2278,7 +2279,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Expands and softens mask edges to reduce visible seams after image processing."
       },
       {
         "id": "2a4b2cc0-db37-4302-a067-da392f38f06b",
@@ -2733,7 +2735,8 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Scales both image and mask together while preserving alignment for editing workflows."
       }
     ]
   },

blueprints/Image Segmentation (SAM3).json

@@ -0,0 +1,714 @@
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+          "type": "IMAGE",
+          "link": null
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+        {
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+          "name": "text",
+          "type": "STRING",
+          "widget": {
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+          "link": null
+        },
+        {
+          "name": "bboxes",
+          "type": "BOUNDING_BOX",
+          "link": null
+        },
+        {
+          "name": "positive_coords",
+          "type": "STRING",
+          "link": null
+        },
+        {
+          "name": "negative_coords",
+          "type": "STRING",
+          "link": null
+        },
+        {
+          "name": "threshold",
+          "type": "FLOAT",
+          "widget": {
+            "name": "threshold"
+          },
+          "link": null
+        },
+        {
+          "name": "refine_iterations",
+          "type": "INT",
+          "widget": {
+            "name": "refine_iterations"
+          },
+          "link": null
+        },
+        {
+          "name": "individual_masks",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "individual_masks"
+          },
+          "link": null
+        },
+        {
+          "name": "ckpt_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "ckpt_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "masks",
+          "name": "masks",
+          "type": "MASK",
+          "links": []
+        },
+        {
+          "localized_name": "bboxes",
+          "name": "bboxes",
+          "type": "BOUNDING_BOX",
+          "links": []
+        }
+      ],
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+            "78",
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+        "tabWidth": 65,
+        "tabXOffset": 10,
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+        "secondTabText": "Send Back",
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+        "secondTabWidth": 65
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+      "widgets_values": [],
+      "title": "Image Segmentation (SAM3)"
+    }
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+}

blueprints/Image Upscale(Z-image-Turbo).json

@@ -141,7 +141,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Upscale(Z-image-Turbo)",
+        "name": "Image Upscale (Z-image-Turbo)",
         "inputNode": {
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           "bounding": [
@@ -1302,7 +1302,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Enhance"
+        "category": "Image generation and editing/Enhance",
+        "description": "Upscales images to higher resolution using Z-Image-Turbo."
       }
     ]
   },

blueprints/Image to Depth Map (Lotus).json

@@ -99,7 +99,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Depth Map (Lotus)",
+        "name": "Image to Depth Map (Lotus)",
         "inputNode": {
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           "bounding": [
@@ -948,7 +948,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Depth to image"
+        "category": "Image generation and editing/Depth to image",
+        "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model."
       }
     ]
   },
@@ -964,4 +965,4 @@
     "workflowRendererVersion": "LG"
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   "version": 0.4
-}
+}

blueprints/Image to Layers(Qwen-Image-Layered).json

@@ -1586,7 +1586,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Image to layers"
+        "category": "Image generation and editing/Image to layers",
+        "description": "Decomposes an image into variable-resolution RGBA layers for independent editing using Qwen-Image-Layered."
       }
     ]
   },

blueprints/Image to Model (Hunyuan3d 2.1).json

@@ -72,7 +72,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Model (Hunyuan3d 2.1)",
+        "name": "Image to 3D Model (Hunyuan3d 2.1)",
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           "bounding": [
@@ -765,7 +765,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "3D/Image to 3D Model"
+        "category": "3D/Image to 3D Model",
+        "description": "Generates 3D mesh models from a single input image using Hunyuan3D 2.0/2.1."
       }
     ]
   },

blueprints/Image to Video (LTX-2.3).json

@@ -4223,7 +4223,8 @@
         "extra": {
           "workflowRendererVersion": "Vue-corrected"
         },
-        "category": "Video generation and editing/Image to video"
+        "category": "Video generation and editing/Image to video",
+        "description": "Generates video from a single input image using LTX-2.3."
       }
     ]
   },

blueprints/Image to Video (Wan 2.2).json

@@ -206,7 +206,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image to Video (Wan 2.2)",
+        "name": "Image to Video (Wan 2.2)",
         "inputNode": {
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           "bounding": [
@@ -2027,7 +2027,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Image to video"
+        "category": "Video generation and editing/Image to video",
+        "description": "Image-to-video with Wan 2.2 using a start image plus text prompt to extend motion from the still frame."
       }
     ]
   },

blueprints/Pose to Image (Z-Image-Turbo).json

@@ -134,7 +134,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Pose to Image (Z-Image-Turbo)",
+        "name": "Pose to Image (Z-Image-Turbo)",
         "inputNode": {
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@@ -1298,7 +1298,8 @@
           "VHS_MetadataImage": true,
           "VHS_KeepIntermediate": true
         },
-        "category": "Image generation and editing/Pose to image"
+        "category": "Image generation and editing/Pose to image",
+        "description": "Generates an image from pose keypoints using Z-Image-Turbo with text conditioning."
       }
     ]
   },
@@ -1319,4 +1320,4 @@
     }
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   "version": 0.4
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+}

blueprints/Pose to Video (LTX 2.0).json

@@ -3870,7 +3870,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Pose to video"
+        "category": "Video generation and editing/Pose to video",
+        "description": "Generates video from pose reference frames using LTX-2, with optional synchronized audio."
       }
     ]
   },

blueprints/Prompt Enhance.json

@@ -270,9 +270,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Text generation/Prompt enhance"
+        "category": "Text generation/Prompt enhance",
+        "description": "Expands short text prompts into detailed descriptions using a text generation model for better generation quality."
       }
     ]
   },
   "extra": {}
-}
+}

blueprints/Remove Background (BiRefNet).json

@@ -0,0 +1,397 @@
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+                "name": "bg_model",
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+        "extra": {},
+        "category": "Image generation and editing/Background Removal"
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+}

blueprints/Sharpen.json

@@ -302,8 +302,9 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image Tools/Sharpen"
+        "category": "Image Tools/Sharpen",
+        "description": "Sharpens image details using a GPU fragment shader for enhanced clarity."
       }
     ]
   }
-}
+}

blueprints/Text to Audio (ACE-Step 1.5).json

@@ -222,7 +222,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Text to Audio (ACE-Step 1.5)",
+        "name": "Text to Audio (ACE-Step 1.5)",
         "inputNode": {
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           "bounding": [
@@ -1502,7 +1502,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Audio/Music generation"
+        "category": "Audio/Music generation",
+        "description": "Generates audio/music from text prompts using ACE-Step 1.5, a diffusion-based audio generation model."
       }
     ]
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@@ -1518,4 +1519,4 @@
     }
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   "version": 0.4
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+}

blueprints/Text to Image (Flux.1 Dev).json

@@ -1029,7 +1029,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from prompts using FLUX.1 [dev]: a 12B rectified-flow MMDiT with dual CLIP plus T5-XXL text encoders and guidance-distilled sampling for sharp prompt following versus classic DDPM diffusion."
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@@ -1043,4 +1044,4 @@
     },
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+}

blueprints/Text to Image (Flux.1 Krea Dev).json

@@ -1023,7 +1023,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "FLUX.1 Krea [dev] (Black Forest Labs × Krea): open-weight 12B rectified-flow text-to-image drop-in alongside FLUX.1 [dev], tuned away from overcooked saturation toward more natural diversity in people, realism, and style while keeping ecosystem compatibility."
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blueprints/Text to Image (NetaYume Lumina).json

@@ -1104,7 +1104,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using NetaYume Lumina, fine-tuned from Neta Lumina for anime-style and illustration generation."
       },
       {
         "id": "a07fdf06-1bda-4dac-bdbd-63ee8ebca1c9",
@@ -1458,11 +1459,12 @@
         ],
         "extra": {
           "workflowRendererVersion": "LG"
-        }
+        },
+        "description": "Encodes a negative text prompt via CLIP for classifier-free guidance in anime-style generation (NetaYume Lumina)."
       }
     ]
   },
   "extra": {
     "ue_links": []
   }
-}
+}

blueprints/Text to Image (Qwen-Image 2512).json

@@ -1941,7 +1941,8 @@
         "extra": {
           "workflowRendererVersion": "Vue-corrected"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using Qwen-Image-2512, with enhanced human realism and finer natural detail over the base version."
       }
     ]
   },

blueprints/Text to Image (Qwen-Image).json

@@ -1873,7 +1873,8 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Image generation and editing/Text to image"
+        "category": "Image generation and editing/Text to image",
+        "description": "Generates images from text prompts using Qwen-Image, Alibaba's 20B MMDiT model with excellent multilingual text rendering."
       }
     ]
   },

blueprints/Text to Image (Z-Image-Turbo).json

@@ -1,22 +1,21 @@
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   "revision": 0,
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@@ -44,6 +43,22 @@
           },
           "link": null
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+        {
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+          "widget": {
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@@ -80,15 +95,15 @@
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@@ -97,19 +112,23 @@
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+          [
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@@ -122,48 +141,40 @@
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-        "ae.safetensors"
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         "revision": 0,
         "config": {},
-        "name": "local-Text to Image (Z-Image-Turbo)",
+        "name": "Text to Image (Z-Image-Turbo)",
         "inputNode": {
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@@ -178,8 +189,8 @@
             ],
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@@ -202,44 +213,68 @@
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@@ -253,8 +288,8 @@
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@@ -264,15 +299,15 @@
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@@ -282,7 +317,7 @@
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-                "link": 39
+                "link": 74
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                 "localized_name": "type",
@@ -315,9 +350,9 @@
               }
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@@ -343,15 +378,15 @@
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@@ -361,7 +396,7 @@
                 "widget": {
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@@ -375,9 +410,9 @@
               }
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@@ -401,12 +436,12 @@
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@@ -430,9 +465,9 @@
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@@ -440,22 +475,21 @@
               "secondTabText": "Send Back",
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               "secondTabWidth": 65
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@@ -483,9 +517,9 @@
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@@ -493,22 +527,21 @@
               "secondTabText": "Send Back",
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@@ -518,7 +551,7 @@
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+                "link": 73
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               {
                 "localized_name": "weight_dtype",
@@ -541,9 +574,9 @@
               }
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@@ -568,15 +601,15 @@
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@@ -607,9 +640,9 @@
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@@ -626,15 +659,15 @@
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@@ -677,9 +710,9 @@
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@@ -694,19 +727,77 @@
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@@ -749,7 +840,7 @@
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@@ -800,9 +891,9 @@
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@@ -896,12 +929,12 @@
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blueprints/Text to Video (LTX-2.3).json

@@ -4286,7 +4286,8 @@
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blueprints/Text to Video (Wan 2.2).json

@@ -1572,7 +1572,8 @@
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blueprints/Unsharp Mask.json

@@ -434,8 +434,9 @@
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blueprints/Video Captioning (Gemini).json

@@ -307,7 +307,8 @@
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blueprints/Video Inpaint(Wan2.1 VACE).json

@@ -165,7 +165,7 @@
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+            "inputs": [
+              {
+                "localized_name": "ckpt_name",
+                "name": "ckpt_name",
+                "type": "COMBO",
+                "widget": {
+                  "name": "ckpt_name"
+                },
+                "link": 273
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "MODEL",
+                "name": "MODEL",
+                "type": "MODEL",
+                "links": [
+                  237
+                ]
+              },
+              {
+                "localized_name": "CLIP",
+                "name": "CLIP",
+                "type": "CLIP",
+                "links": [
+                  240
+                ]
+              },
+              {
+                "localized_name": "VAE",
+                "name": "VAE",
+                "type": "VAE",
+                "links": null
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "CheckpointLoaderSimple",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.3",
+              "enableTabs": false,
+              "tabWidth": 65,
+              "tabXOffset": 10,
+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65,
+              "models": [
+                {
+                  "name": "sam3.1_multiplex_fp16.safetensors",
+                  "url": "https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors",
+                  "directory": "checkpoints"
+                }
+              ]
+            },
+            "widgets_values": [
+              "sam3.1_multiplex_fp16.safetensors"
+            ]
+          },
+          {
+            "id": 128,
+            "type": "GetVideoComponents",
+            "pos": [
+              -1910,
+              -3540
+            ],
+            "size": [
+              230,
+              120
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "video",
+                "name": "video",
+                "type": "VIDEO",
+                "link": 252
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "links": [
+                  253
+                ]
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "type": "AUDIO",
+                "links": [
+                  259
+                ]
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "links": [
+                  260
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GetVideoComponents",
+              "cnr_id": "comfy-core",
+              "ver": "0.19.3",
+              "enableTabs": false,
+              "tabWidth": 65,
+              "tabXOffset": 10,
+              "hasSecondTab": false,
+              "secondTabText": "Send Back",
+              "secondTabOffset": 80,
+              "secondTabWidth": 65
+            }
+          },
+          {
+            "id": 129,
+            "type": "Note",
+            "pos": [
+              -1980,
+              -2790
+            ],
+            "size": [
+              370,
+              250
+            ],
+            "flags": {},
+            "order": 0,
+            "mode": 0,
+            "inputs": [],
+            "outputs": [],
+            "title": "Note: Prompt format",
+            "properties": {},
+            "widgets_values": [
+              "Max tokens for this model is only 32, to separately prompt multiple subjects you can separate prompts with comma, and set the max amount of objects detected for each prompt with :N\n\nFor example above test prompt finds 2 cakes, one apron, 4 window panels"
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
+          }
+        ],
+        "groups": [],
+        "links": [
+          {
+            "id": 237,
+            "origin_id": 127,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 0,
+            "type": "MODEL"
+          },
+          {
+            "id": 200,
+            "origin_id": 125,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 2,
+            "type": "CONDITIONING"
+          },
+          {
+            "id": 240,
+            "origin_id": 127,
+            "origin_slot": 1,
+            "target_id": 125,
+            "target_slot": 0,
+            "type": "CLIP"
+          },
+          {
+            "id": 231,
+            "origin_id": 126,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "MASK"
+          },
+          {
+            "id": 232,
+            "origin_id": 126,
+            "origin_slot": 1,
+            "target_id": -20,
+            "target_slot": 1,
+            "type": "BOUNDING_BOX"
+          },
+          {
+            "id": 252,
+            "origin_id": -10,
+            "origin_slot": 0,
+            "target_id": 128,
+            "target_slot": 0,
+            "type": "VIDEO"
+          },
+          {
+            "id": 253,
+            "origin_id": 128,
+            "origin_slot": 0,
+            "target_id": 126,
+            "target_slot": 1,
+            "type": "IMAGE"
+          },
+          {
+            "id": 254,
+            "origin_id": -10,
+            "origin_slot": 1,
+            "target_id": 125,
+            "target_slot": 1,
+            "type": "STRING"
+          },
+          {
+            "id": 255,
+            "origin_id": -10,
+            "origin_slot": 2,
+            "target_id": 126,
+            "target_slot": 3,
+            "type": "BOUNDING_BOX"
+          },
+          {
+            "id": 256,
+            "origin_id": -10,
+            "origin_slot": 3,
+            "target_id": 126,
+            "target_slot": 4,
+            "type": "STRING"
+          },
+          {
+            "id": 257,
+            "origin_id": -10,
+            "origin_slot": 4,
+            "target_id": 126,
+            "target_slot": 5,
+            "type": "STRING"
+          },
+          {
+            "id": 259,
+            "origin_id": 128,
+            "origin_slot": 1,
+            "target_id": -20,
+            "target_slot": 2,
+            "type": "AUDIO"
+          },
+          {
+            "id": 260,
+            "origin_id": 128,
+            "origin_slot": 2,
+            "target_id": -20,
+            "target_slot": 3,
+            "type": "FLOAT"
+          },
+          {
+            "id": 261,
+            "origin_id": -10,
+            "origin_slot": 5,
+            "target_id": 126,
+            "target_slot": 6,
+            "type": "FLOAT"
+          },
+          {
+            "id": 262,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 126,
+            "target_slot": 7,
+            "type": "INT"
+          },
+          {
+            "id": 263,
+            "origin_id": -10,
+            "origin_slot": 7,
+            "target_id": 126,
+            "target_slot": 8,
+            "type": "BOOLEAN"
+          },
+          {
+            "id": 273,
+            "origin_id": -10,
+            "origin_slot": 8,
+            "target_id": 127,
+            "target_slot": 0,
+            "type": "COMBO"
+          }
+        ],
+        "extra": {},
+        "category": "Video Tools",
+        "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts."
+      }
+    ]
+  },
+  "extra": {}
+}

blueprints/Video Stitch.json

@@ -1,21 +1,21 @@
 {
   "revision": 0,
-  "last_node_id": 84,
+  "last_node_id": 85,
   "last_link_id": 0,
   "nodes": [
     {
-      "id": 84,
-      "type": "8e8aa94a-647e-436d-8440-8ee4691864de",
+      "id": 85,
+      "type": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
       "pos": [
-        -6100,
-        2620
+        -880,
+        -2260
       ],
       "size": [
         290,
         160
       ],
       "flags": {},
-      "order": 0,
+      "order": 2,
       "mode": 0,
       "inputs": [
         {
@@ -76,31 +76,26 @@
       "properties": {
         "proxyWidgets": [
           [
-            "-1",
+            "79",
             "direction"
           ],
           [
-            "-1",
+            "79",
             "match_image_size"
           ],
           [
-            "-1",
+            "79",
             "spacing_width"
           ],
           [
-            "-1",
+            "79",
             "spacing_color"
           ]
         ],
         "cnr_id": "comfy-core",
         "ver": "0.13.0"
       },
-      "widgets_values": [
-        "right",
-        true,
-        0,
-        "white"
-      ],
+      "widgets_values": [],
       "title": "Video Stitch"
     }
   ],
@@ -109,12 +104,12 @@
   "definitions": {
     "subgraphs": [
       {
-        "id": "8e8aa94a-647e-436d-8440-8ee4691864de",
+        "id": "637913e7-0206-46ba-8ded-70ae3a7c2e19",
         "version": 1,
         "state": {
           "lastGroupId": 1,
-          "lastNodeId": 84,
-          "lastLinkId": 262,
+          "lastNodeId": 97,
+          "lastLinkId": 282,
           "lastRerouteId": 0
         },
         "revision": 0,
@@ -123,8 +118,8 @@
         "inputNode": {
           "id": -10,
           "bounding": [
-            -6580,
-            2649,
+            -6810,
+            2580,
             143.55859375,
             160
           ]
@@ -132,8 +127,8 @@
         "outputNode": {
           "id": -20,
           "bounding": [
-            -5720,
-            2659,
+            -4770,
+            2600,
             120,
             60
           ]
@@ -149,8 +144,8 @@
             "localized_name": "video",
             "label": "Before Video",
             "pos": [
-              -6456.44140625,
-              2669
+              -6686.44140625,
+              2600
             ]
           },
           {
@@ -163,8 +158,8 @@
             "localized_name": "video_1",
             "label": "After Video",
             "pos": [
-              -6456.44140625,
-              2689
+              -6686.44140625,
+              2620
             ]
           },
           {
@@ -175,8 +170,8 @@
               259
             ],
             "pos": [
-              -6456.44140625,
-              2709
+              -6686.44140625,
+              2640
             ]
           },
           {
@@ -187,8 +182,8 @@
               260
             ],
             "pos": [
-              -6456.44140625,
-              2729
+              -6686.44140625,
+              2660
             ]
           },
           {
@@ -199,8 +194,8 @@
               261
             ],
             "pos": [
-              -6456.44140625,
-              2749
+              -6686.44140625,
+              2680
             ]
           },
           {
@@ -211,8 +206,8 @@
               262
             ],
             "pos": [
-              -6456.44140625,
-              2769
+              -6686.44140625,
+              2700
             ]
           }
         ],
@@ -226,8 +221,8 @@
             ],
             "localized_name": "VIDEO",
             "pos": [
-              -5700,
-              2679
+              -4750,
+              2620
             ]
           }
         ],
@@ -238,11 +233,11 @@
             "type": "GetVideoComponents",
             "pos": [
               -6390,
-              2560
+              2600
             ],
             "size": [
-              193.530859375,
-              66
+              230,
+              120
             ],
             "flags": {},
             "order": 1,
@@ -278,9 +273,9 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "GetVideoComponents"
+              "ver": "0.13.0"
             }
           },
           {
@@ -291,8 +286,8 @@
               2420
             ],
             "size": [
-              193.530859375,
-              66
+              230,
+              120
             ],
             "flags": {},
             "order": 0,
@@ -332,21 +327,254 @@
               }
             ],
             "properties": {
+              "Node name for S&R": "GetVideoComponents",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "GetVideoComponents"
+              "ver": "0.13.0"
             }
           },
+          {
+            "id": 90,
+            "type": "GetImageSize",
+            "pos": [
+              -6390,
+              3030
+            ],
+            "size": [
+              230,
+              120
+            ],
+            "flags": {},
+            "order": 4,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "image",
+                "name": "image",
+                "type": "IMAGE",
+                "link": 266
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "width",
+                "name": "width",
+                "type": "INT",
+                "links": [
+                  274
+                ]
+              },
+              {
+                "localized_name": "height",
+                "name": "height",
+                "type": "INT",
+                "links": [
+                  276
+                ]
+              },
+              {
+                "localized_name": "batch_size",
+                "name": "batch_size",
+                "type": "INT",
+                "links": null
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "GetImageSize"
+            }
+          },
+          {
+            "id": 80,
+            "type": "CreateVideo",
+            "pos": [
+              -5190,
+              2420
+            ],
+            "size": [
+              270,
+              130
+            ],
+            "flags": {},
+            "order": 3,
+            "mode": 0,
+            "inputs": [
+              {
+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "link": 282
+              },
+              {
+                "localized_name": "audio",
+                "name": "audio",
+                "shape": 7,
+                "type": "AUDIO",
+                "link": 251
+              },
+              {
+                "localized_name": "fps",
+                "name": "fps",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "fps"
+                },
+                "link": 252
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "VIDEO",
+                "name": "VIDEO",
+                "type": "VIDEO",
+                "links": [
+                  255
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "CreateVideo",
+              "cnr_id": "comfy-core",
+              "ver": "0.13.0"
+            },
+            "widgets_values": [
+              30
+            ]
+          },
+          {
+            "id": 95,
+            "type": "ComfyMathExpression",
+            "pos": [
+              -6040,
+              3020
+            ],
+            "size": [
+              400,
+              200
+            ],
+            "flags": {},
+            "order": 5,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "a",
+                "localized_name": "values.a",
+                "name": "values.a",
+                "type": "FLOAT,INT",
+                "link": 274
+              },
+              {
+                "label": "b",
+                "localized_name": "values.b",
+                "name": "values.b",
+                "shape": 7,
+                "type": "FLOAT,INT",
+                "link": null
+              },
+              {
+                "localized_name": "expression",
+                "name": "expression",
+                "type": "STRING",
+                "widget": {
+                  "name": "expression"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "FLOAT",
+                "name": "FLOAT",
+                "type": "FLOAT",
+                "links": null
+              },
+              {
+                "localized_name": "INT",
+                "name": "INT",
+                "type": "INT",
+                "links": [
+                  279
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "ComfyMathExpression"
+            },
+            "widgets_values": [
+              "a & ~1"
+            ]
+          },
+          {
+            "id": 96,
+            "type": "ComfyMathExpression",
+            "pos": [
+              -6040,
+              3290
+            ],
+            "size": [
+              400,
+              200
+            ],
+            "flags": {},
+            "order": 6,
+            "mode": 0,
+            "inputs": [
+              {
+                "label": "a",
+                "localized_name": "values.a",
+                "name": "values.a",
+                "type": "FLOAT,INT",
+                "link": 276
+              },
+              {
+                "label": "b",
+                "localized_name": "values.b",
+                "name": "values.b",
+                "shape": 7,
+                "type": "FLOAT,INT",
+                "link": null
+              },
+              {
+                "localized_name": "expression",
+                "name": "expression",
+                "type": "STRING",
+                "widget": {
+                  "name": "expression"
+                },
+                "link": null
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "FLOAT",
+                "name": "FLOAT",
+                "type": "FLOAT",
+                "links": null
+              },
+              {
+                "localized_name": "INT",
+                "name": "INT",
+                "type": "INT",
+                "links": [
+                  280
+                ]
+              }
+            ],
+            "properties": {
+              "Node name for S&R": "ComfyMathExpression"
+            },
+            "widgets_values": [
+              "a & ~1"
+            ]
+          },
           {
             "id": 79,
             "type": "ImageStitch",
             "pos": [
               -6390,
-              2700
+              2780
             ],
             "size": [
               270,
-              150
+              160
             ],
             "flags": {},
             "order": 2,
@@ -408,14 +636,15 @@
                 "name": "IMAGE",
                 "type": "IMAGE",
                 "links": [
-                  250
+                  266,
+                  281
                 ]
               }
             ],
             "properties": {
+              "Node name for S&R": "ImageStitch",
               "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "ImageStitch"
+              "ver": "0.13.0"
             },
             "widgets_values": [
               "right",
@@ -425,60 +654,91 @@
             ]
           },
           {
-            "id": 80,
-            "type": "CreateVideo",
+            "id": 97,
+            "type": "ResizeImageMaskNode",
             "pos": [
-              -6040,
-              2610
+              -5560,
+              2790
             ],
             "size": [
               270,
-              78
+              160
             ],
             "flags": {},
-            "order": 3,
+            "order": 7,
             "mode": 0,
             "inputs": [
               {
-                "localized_name": "images",
-                "name": "images",
-                "type": "IMAGE",
-                "link": 250
+                "localized_name": "input",
+                "name": "input",
+                "type": "IMAGE,MASK",
+                "link": 281
               },
               {
-                "localized_name": "audio",
-                "name": "audio",
-                "shape": 7,
-                "type": "AUDIO",
-                "link": 251
-              },
-              {
-                "localized_name": "fps",
-                "name": "fps",
-                "type": "FLOAT",
+                "localized_name": "resize_type",
+                "name": "resize_type",
+                "type": "COMFY_DYNAMICCOMBO_V3",
                 "widget": {
-                  "name": "fps"
+                  "name": "resize_type"
                 },
-                "link": 252
+                "link": null
+              },
+              {
+                "localized_name": "width",
+                "name": "resize_type.width",
+                "type": "INT",
+                "widget": {
+                  "name": "resize_type.width"
+                },
+                "link": 279
+              },
+              {
+                "localized_name": "height",
+                "name": "resize_type.height",
+                "type": "INT",
+                "widget": {
+                  "name": "resize_type.height"
+                },
+                "link": 280
+              },
+              {
+                "localized_name": "crop",
+                "name": "resize_type.crop",
+                "type": "COMBO",
+                "widget": {
+                  "name": "resize_type.crop"
+                },
+                "link": null
+              },
+              {
+                "localized_name": "scale_method",
+                "name": "scale_method",
+                "type": "COMBO",
+                "widget": {
+                  "name": "scale_method"
+                },
+                "link": null
               }
             ],
             "outputs": [
               {
-                "localized_name": "VIDEO",
-                "name": "VIDEO",
-                "type": "VIDEO",
+                "localized_name": "resized",
+                "name": "resized",
+                "type": "*",
                 "links": [
-                  255
+                  282
                 ]
               }
             ],
             "properties": {
-              "cnr_id": "comfy-core",
-              "ver": "0.13.0",
-              "Node name for S&R": "CreateVideo"
+              "Node name for S&R": "ResizeImageMaskNode"
             },
             "widgets_values": [
-              30
+              "scale dimensions",
+              512,
+              512,
+              "center",
+              "area"
             ]
           }
         ],
@@ -500,14 +760,6 @@
             "target_slot": 1,
             "type": "IMAGE"
           },
-          {
-            "id": 250,
-            "origin_id": 79,
-            "origin_slot": 0,
-            "target_id": 80,
-            "target_slot": 0,
-            "type": "IMAGE"
-          },
           {
             "id": 251,
             "origin_id": 77,
@@ -579,13 +831,71 @@
             "target_id": 79,
             "target_slot": 5,
             "type": "COMBO"
+          },
+          {
+            "id": 266,
+            "origin_id": 79,
+            "origin_slot": 0,
+            "target_id": 90,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 274,
+            "origin_id": 90,
+            "origin_slot": 0,
+            "target_id": 95,
+            "target_slot": 0,
+            "type": "INT"
+          },
+          {
+            "id": 276,
+            "origin_id": 90,
+            "origin_slot": 1,
+            "target_id": 96,
+            "target_slot": 0,
+            "type": "INT"
+          },
+          {
+            "id": 279,
+            "origin_id": 95,
+            "origin_slot": 1,
+            "target_id": 97,
+            "target_slot": 2,
+            "type": "INT"
+          },
+          {
+            "id": 280,
+            "origin_id": 96,
+            "origin_slot": 1,
+            "target_id": 97,
+            "target_slot": 3,
+            "type": "INT"
+          },
+          {
+            "id": 281,
+            "origin_id": 79,
+            "origin_slot": 0,
+            "target_id": 97,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
+            "id": 282,
+            "origin_id": 97,
+            "origin_slot": 0,
+            "target_id": 80,
+            "target_slot": 0,
+            "type": "IMAGE"
           }
         ],
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video Tools/Stitch videos"
+        "category": "Video Tools/Stitch videos",
+        "description": "Stitches multiple video clips into a single sequential video file."
       }
     ]
-  }
-}
+  },
+  "extra": {}
+}

blueprints/Video Upscale(GAN x4).json

@@ -412,9 +412,10 @@
         "extra": {
           "workflowRendererVersion": "LG"
         },
-        "category": "Video generation and editing/Enhance video"
+        "category": "Video generation and editing/Enhance video",
+        "description": "Upscales video to 4× resolution using a GAN-based upscaling model."
       }
     ]
   },
   "extra": {}
-}
+}

comfy/background_removal/birefnet.json

@@ -0,0 +1,7 @@
+{
+    "model_type": "birefnet",
+    "image_std": [1.0, 1.0, 1.0],
+    "image_mean": [0.0, 0.0, 0.0],
+    "image_size": 1024,
+    "resize_to_original": true
+}

comfy/background_removal/birefnet.py

@@ -0,0 +1,689 @@
+import torch
+import comfy.ops
+import numpy as np
+import torch.nn as nn
+from functools import partial
+import torch.nn.functional as F
+from torchvision.ops import deform_conv2d
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+CXT = [3072, 1536, 768, 384][1:][::-1][-3:]
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)
+        self.kv = operations.Linear(dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+
+        x = optimized_attention(
+            q, k, v, heads=self.num_heads, skip_output_reshape=True, skip_reshape=True
+        ).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+
+        return x
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = operations.Linear(in_features, hidden_features, device=device, dtype=dtype)
+        self.act = nn.GELU()
+        self.fc2 = operations.Linear(hidden_features, out_features, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+def window_partition(x, window_size):
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, device=None, dtype=None, operations=None):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads, device=device, dtype=dtype))
+
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 norm_layer=nn.LayerNorm, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+
+        self.norm1 = norm_layer(dim, device=device, dtype=dtype)
+        self.attn = WindowAttention(
+            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, device=device, dtype=dtype, operations=operations)
+
+        self.norm2 = norm_layer(dim, device=device, dtype=dtype)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, device=device, dtype=dtype, operations=operations)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        B, L, C = x.shape
+        H, W = self.H, self.W
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        x_windows = window_partition(shifted_x, self.window_size)
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
+
+        attn_windows = self.attn(x_windows, mask=attn_mask)
+
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    def __init__(self, dim, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.reduction = operations.Linear(4 * dim, 2 * dim, bias=False, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(4 * dim, device=device, dtype=dtype)
+
+    def forward(self, x, H, W):
+        B, L, C = x.shape
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(
+                dim=dim,
+                num_heads=num_heads,
+                window_size=window_size,
+                shift_size=0 if (i % 2 == 0) else window_size // 2,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                norm_layer=norm_layer,
+                device=device, dtype=dtype, operations=operations)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, device=device, dtype=dtype, operations=operations)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        patch_size = (patch_size, patch_size)
+        self.patch_size = patch_size
+
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_channels=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+
+        norm_layer = partial(operations.LayerNorm, device=device, dtype=dtype)
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
+            device=device, dtype=dtype, operations=operations,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2 ** i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                norm_layer=norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                device=device, dtype=dtype, operations=operations)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+
+        outs = []
+        x = x.flatten(2).transpose(1, 2)
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+
+        return tuple(outs)
+
+class DeformableConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1,
+                 bias=False, device=None, dtype=None, operations=None):
+
+        super(DeformableConv2d, self).__init__()
+
+        kernel_size = kernel_size if type(kernel_size) is tuple else (kernel_size, kernel_size)
+        self.stride = stride if type(stride) is tuple else (stride, stride)
+        self.padding = padding
+
+        self.offset_conv = operations.Conv2d(in_channels,
+                                     2 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.modulator_conv = operations.Conv2d(in_channels,
+                                     1 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.regular_conv = operations.Conv2d(in_channels,
+                                      out_channels=out_channels,
+                                      kernel_size=kernel_size,
+                                      stride=stride,
+                                      padding=self.padding,
+                                      bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        offset = self.offset_conv(x)
+        modulator = 2. * torch.sigmoid(self.modulator_conv(x))
+        weight, bias, offload_info = comfy.ops.cast_bias_weight(self.regular_conv, x, offloadable=True)
+
+        x = deform_conv2d(
+            input=x,
+            offset=offset,
+            weight=weight,
+            bias=None,
+            padding=self.padding,
+            mask=modulator,
+            stride=self.stride,
+        )
+        comfy.ops.uncast_bias_weight(self.regular_conv, weight, bias, offload_info)
+        return x
+
+class BasicDecBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, inter_channels=64, device=None, dtype=None, operations=None):
+        super(BasicDecBlk, self).__init__()
+        inter_channels = 64
+        self.conv_in = operations.Conv2d(in_channels, inter_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.relu_in = nn.ReLU(inplace=True)
+        self.dec_att = ASPPDeformable(in_channels=inter_channels, device=device, dtype=dtype, operations=operations)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.bn_in = operations.BatchNorm2d(inter_channels, device=device, dtype=dtype)
+        self.bn_out = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        x = self.bn_in(x)
+        x = self.relu_in(x)
+        x = self.dec_att(x)
+        x = self.conv_out(x)
+        x = self.bn_out(x)
+        return x
+
+
+class BasicLatBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, device=None, dtype=None, operations=None):
+        super(BasicLatBlk, self).__init__()
+        self.conv = operations.Conv2d(in_channels, out_channels, 1, 1, 0, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class _ASPPModuleDeformable(nn.Module):
+    def __init__(self, in_channels, planes, kernel_size, padding, device, dtype, operations):
+        super(_ASPPModuleDeformable, self).__init__()
+        self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
+                                            stride=1, padding=padding, bias=False, device=device, dtype=dtype, operations=operations)
+        self.bn = operations.BatchNorm2d(planes, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.atrous_conv(x)
+        x = self.bn(x)
+
+        return self.relu(x)
+
+
+class ASPPDeformable(nn.Module):
+    def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7], device=None, dtype=None, operations=None):
+        super(ASPPDeformable, self).__init__()
+        self.down_scale = 1
+        if out_channels is None:
+            out_channels = in_channels
+        self.in_channelster = 256 // self.down_scale
+
+        self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0, device=device, dtype=dtype, operations=operations)
+        self.aspp_deforms = nn.ModuleList([
+            _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2), device=device, dtype=dtype, operations=operations)
+              for conv_size in parallel_block_sizes
+        ])
+
+        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
+                                             operations.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False, device=device, dtype=dtype),
+                                             operations.BatchNorm2d(self.in_channelster, device=device, dtype=dtype),
+                                             nn.ReLU(inplace=True))
+        self.conv1 = operations.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False, device=device, dtype=dtype)
+        self.bn1 = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x1 = self.aspp1(x)
+        x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
+        x5 = self.global_avg_pool(x)
+        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        return x
+
+class BiRefNet(nn.Module):
+    def __init__(self, config=None, dtype=None, device=None, operations=None):
+        super(BiRefNet, self).__init__()
+        self.bb = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12, device=device, dtype=dtype, operations=operations)
+
+        channels = [1536, 768, 384, 192]
+        channels = [c * 2 for c in channels]
+        self.cxt = channels[1:][::-1][-3:]
+        self.squeeze_module = nn.Sequential(*[
+            BasicDecBlk(channels[0]+sum(self.cxt), channels[0], device=device, dtype=dtype, operations=operations)
+            for _ in range(1)
+        ])
+
+        self.decoder = Decoder(channels, device=device, dtype=dtype, operations=operations)
+
+    def forward_enc(self, x):
+        x1, x2, x3, x4 = self.bb(x)
+        B, C, H, W = x.shape
+        x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
+        x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat(
+            (
+                *[
+                    F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                ][-len(CXT):],
+                x4
+            ),
+            dim=1
+        )
+        return (x1, x2, x3, x4)
+
+    def forward_ori(self, x):
+        (x1, x2, x3, x4) = self.forward_enc(x)
+        x4 = self.squeeze_module(x4)
+        features = [x, x1, x2, x3, x4]
+        scaled_preds = self.decoder(features)
+        return scaled_preds
+
+    def forward(self, pixel_values, intermediate_output=None):
+        scaled_preds = self.forward_ori(pixel_values)
+        return scaled_preds
+
+
+class Decoder(nn.Module):
+    def __init__(self, channels, device, dtype, operations):
+        super(Decoder, self).__init__()
+        # factory kwargs
+        fk = {"device":device, "dtype":dtype, "operations":operations}
+        DecoderBlock = partial(BasicDecBlk, **fk)
+        LateralBlock = partial(BasicLatBlk, **fk)
+        DBlock = partial(SimpleConvs, **fk)
+
+        self.split = True
+        N_dec_ipt = 64
+        ic = 64
+        ipt_cha_opt = 1
+        self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
+
+        self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[1])
+        self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[2])
+        self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt]), channels[3])
+        self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt]), channels[3]//2)
+
+        fk = {"device":device, "dtype":dtype}
+
+        self.conv_out1 = nn.Sequential(operations.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt]), 1, 1, 1, 0, **fk))
+
+        self.lateral_block4 = LateralBlock(channels[1], channels[1])
+        self.lateral_block3 = LateralBlock(channels[2], channels[2])
+        self.lateral_block2 = LateralBlock(channels[3], channels[3])
+
+        self.conv_ms_spvn_4 = operations.Conv2d(channels[1], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_3 = operations.Conv2d(channels[2], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_2 = operations.Conv2d(channels[3], 1, 1, 1, 0, **fk)
+
+        _N = 16
+
+        self.gdt_convs_4 = nn.Sequential(operations.Conv2d(channels[0] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_3 = nn.Sequential(operations.Conv2d(channels[1] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_2 = nn.Sequential(operations.Conv2d(channels[2] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+
+        [setattr(self, f"gdt_convs_pred_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+        [setattr(self, f"gdt_convs_attn_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+
+    def get_patches_batch(self, x, p):
+        _size_h, _size_w = p.shape[2:]
+        patches_batch = []
+        for idx in range(x.shape[0]):
+            columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
+            patches_x = []
+            for column_x in columns_x:
+                patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
+            patch_sample = torch.cat(patches_x, dim=1)
+            patches_batch.append(patch_sample)
+        return torch.cat(patches_batch, dim=0)
+
+    def forward(self, features):
+        x, x1, x2, x3, x4 = features
+
+        patches_batch = self.get_patches_batch(x, x4) if self.split else x
+        x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p4 = self.decoder_block4(x4)
+        p4_gdt = self.gdt_convs_4(p4)
+        gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
+        p4 = p4 * gdt_attn_4
+        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
+        _p3 = _p4 + self.lateral_block4(x3)
+
+        patches_batch = self.get_patches_batch(x, _p3) if self.split else x
+        _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p3 = self.decoder_block3(_p3)
+
+        p3_gdt = self.gdt_convs_3(p3)
+        gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
+        p3 = p3 * gdt_attn_3
+        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
+        _p2 = _p3 + self.lateral_block3(x2)
+
+        patches_batch = self.get_patches_batch(x, _p2) if self.split else x
+        _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p2 = self.decoder_block2(_p2)
+
+        p2_gdt = self.gdt_convs_2(p2)
+        gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
+        p2 = p2 * gdt_attn_2
+
+        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
+        _p1 = _p2 + self.lateral_block2(x1)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
+        _p1 = self.decoder_block1(_p1)
+        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p1_out = self.conv_out1(_p1)
+        return p1_out
+
+
+class SimpleConvs(nn.Module):
+    def __init__(
+        self, in_channels: int, out_channels: int, inter_channels=64, device=None, dtype=None, operations=None
+    ) -> None:
+        super().__init__()
+        self.conv1 = operations.Conv2d(in_channels, inter_channels, 3, 1, 1, device=device, dtype=dtype)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, 1, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.conv_out(self.conv1(x))

comfy/bg_removal_model.py

@@ -0,0 +1,85 @@
+from .utils import load_torch_file
+import os
+import json
+import torch
+import logging
+
+import comfy.ops
+import comfy.model_patcher
+import comfy.model_management
+import comfy.clip_model
+import comfy.background_removal.birefnet
+
+BG_REMOVAL_MODELS = {
+    "birefnet": comfy.background_removal.birefnet.BiRefNet
+}
+
+class BackgroundRemovalModel():
+    def __init__(self, json_config):
+        with open(json_config) as f:
+            config = json.load(f)
+
+        self.image_size = config.get("image_size", 1024)
+        self.image_mean = config.get("image_mean", [0.0, 0.0, 0.0])
+        self.image_std = config.get("image_std", [1.0, 1.0, 1.0])
+        self.model_type = config.get("model_type", "birefnet")
+        self.config = config.copy()
+        model_class = BG_REMOVAL_MODELS.get(self.model_type)
+
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model.eval()
+
+        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def encode_image(self, image):
+        comfy.model_management.load_model_gpu(self.patcher)
+        H, W = image.shape[1], image.shape[2]
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=False)
+
+        if pixel_values.shape[0] > 1:
+            out = torch.cat([
+                self.model(pixel_values=pixel_values[i:i+1])
+                for i in range(pixel_values.shape[0])
+            ], dim=0)
+        else:
+            out = self.model(pixel_values=pixel_values)
+        out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
+
+        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(0)
+        if mask.shape[1] != 1:
+            mask = mask.movedim(-1, 1)
+
+        return mask
+
+
+def load_background_removal_model(sd):
+    if "bb.layers.1.blocks.0.attn.relative_position_index" in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "background_removal"), "birefnet.json")
+    else:
+        return None
+
+    bg_model = BackgroundRemovalModel(json_config)
+    m, u = bg_model.load_sd(sd)
+    if len(m) > 0:
+        logging.warning("missing background removal: {}".format(m))
+    u = set(u)
+    keys = list(sd.keys())
+    for k in keys:
+        if k not in u:
+            sd.pop(k)
+    return bg_model
+
+def load(ckpt_path):
+    sd = load_torch_file(ckpt_path)
+    return load_background_removal_model(sd)

comfy/cli_args.py

@@ -49,7 +49,7 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
 parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.")
+parser.add_argument("--cuda-device", type=str, default=None, metavar="DEVICE_ID", help="Set the ids of cuda devices this instance will use, as a comma-separated list (e.g. '0' or '0,1'). All other devices will not be visible.")
 parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
 cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
@@ -90,8 +90,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
+parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -110,13 +110,11 @@ 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-ram", nargs='*', type=float, default=[], metavar="GB", help="Use RAM pressure caching with the specified headroom thresholds. This is the default caching mode. The first value sets the active-cache threshold; the optional second value sets the inactive-cache/pin threshold. Defaults when no values are provided: active 25%% of system RAM (min 4GB, max 32GB), inactive 75%% of system RAM (min 12GB, max 96GB).")
 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=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.")
@@ -141,8 +139,7 @@ manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", he
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
-vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
+vram_group.add_argument("--lowvram", action="store_true", help="Doesn't do anything if dynamic vram is enabled. If dynamic vram isn't being used this option makes the text encoders run on the CPU.")
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
@@ -238,12 +235,17 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
+parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
+parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:
     args = parser.parse_args([])
 
+if args.cache_ram is not None and len(args.cache_ram) > 2:
+    parser.error("--cache-ram accepts at most two values: active GB and inactive GB")
+
 if args.windows_standalone_build:
     args.auto_launch = True
 

comfy/context_windows.py

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

comfy/controlnet.py

@@ -15,13 +15,14 @@
     You should have received a copy of the GNU General Public License
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
-
+from __future__ import annotations
 
 import torch
 from enum import Enum
 import math
 import os
 import logging
+import copy
 import comfy.utils
 import comfy.model_management
 import comfy.model_detection
@@ -38,7 +39,7 @@ import comfy.ldm.hydit.controlnet
 import comfy.ldm.flux.controlnet
 import comfy.ldm.qwen_image.controlnet
 import comfy.cldm.dit_embedder
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 if TYPE_CHECKING:
     from comfy.hooks import HookGroup
 
@@ -64,6 +65,18 @@ class StrengthType(Enum):
     CONSTANT = 1
     LINEAR_UP = 2
 
+class ControlIsolation:
+    '''Temporarily set a ControlBase object's previous_controlnet to None to prevent cascading calls.'''
+    def __init__(self, control: ControlBase):
+        self.control = control
+        self.orig_previous_controlnet = control.previous_controlnet
+
+    def __enter__(self):
+        self.control.previous_controlnet = None
+
+    def __exit__(self, *args):
+        self.control.previous_controlnet = self.orig_previous_controlnet
+
 class ControlBase:
     def __init__(self):
         self.cond_hint_original = None
@@ -77,7 +90,7 @@ class ControlBase:
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
         self.extra_args = {}
-        self.previous_controlnet = None
+        self.previous_controlnet: Union[ControlBase, None] = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
         self.concat_mask = False
@@ -85,6 +98,7 @@ class ControlBase:
         self.extra_concat = None
         self.extra_hooks: HookGroup = None
         self.preprocess_image = lambda a: a
+        self.multigpu_clones: dict[torch.device, ControlBase] = {}
 
     def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
@@ -111,17 +125,38 @@ class ControlBase:
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-
+        for device_cnet in self.multigpu_clones.values():
+            with ControlIsolation(device_cnet):
+                device_cnet.cleanup()
         self.cond_hint = None
         self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
         out = []
+        for device_cnet in self.multigpu_clones.values():
+            out += device_cnet.get_models_only_self()
         if self.previous_controlnet is not None:
             out += self.previous_controlnet.get_models()
         return out
 
+    def get_models_only_self(self):
+        'Calls get_models, but temporarily sets previous_controlnet to None.'
+        with ControlIsolation(self):
+            return self.get_models()
+
+    def get_instance_for_device(self, device):
+        'Returns instance of this Control object intended for selected device.'
+        return self.multigpu_clones.get(device, self)
+
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        '''
+        Create deep clone of Control object where model(s) is set to other devices.
+
+        When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
+        '''
+        raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
+
     def get_extra_hooks(self):
         out = []
         if self.extra_hooks is not None:
@@ -130,7 +165,7 @@ class ControlBase:
             out += self.previous_controlnet.get_extra_hooks()
         return out
 
-    def copy_to(self, c):
+    def copy_to(self, c: ControlBase):
         c.cond_hint_original = self.cond_hint_original
         c.strength = self.strength
         c.timestep_percent_range = self.timestep_percent_range
@@ -284,6 +319,14 @@ class ControlNet(ControlBase):
         self.copy_to(c)
         return c
 
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        c = self.copy()
+        c.control_model = copy.deepcopy(c.control_model)
+        c.control_model_wrapped = comfy.model_patcher.ModelPatcher(c.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+        if autoregister:
+            self.multigpu_clones[load_device] = c
+        return c
+
     def get_models(self):
         out = super().get_models()
         out.append(self.control_model_wrapped)
@@ -314,6 +357,10 @@ class QwenFunControlNet(ControlNet):
         super().pre_run(model, percent_to_timestep_function)
         self.set_extra_arg("base_model", model.diffusion_model)
 
+    def cleanup(self):
+        self.extra_args.pop("base_model", None)
+        super().cleanup()
+
     def copy(self):
         c = QwenFunControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
         c.control_model = self.control_model
@@ -906,6 +953,14 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        c = self.copy()
+        c.t2i_model = copy.deepcopy(c.t2i_model)
+        c.device = load_device
+        if autoregister:
+            self.multigpu_clones[load_device] = c
+        return c
+
 def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'

comfy/deploy_environment.py

@@ -0,0 +1,34 @@
+import functools
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_DEPLOY_ENV = "local-git"
+_ENV_FILENAME = ".comfy_environment"
+
+# Resolve the ComfyUI install directory (the parent of this `comfy/` package).
+# We deliberately avoid `folder_paths.base_path` here because that is overridden
+# by the `--base-directory` CLI arg to a user-supplied path, whereas the
+# `.comfy_environment` marker is written by launchers/installers next to the
+# ComfyUI install itself.
+_COMFY_INSTALL_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+
+@functools.cache
+def get_deploy_environment() -> str:
+    env_file = os.path.join(_COMFY_INSTALL_DIR, _ENV_FILENAME)
+    try:
+        with open(env_file, encoding="utf-8") as f:
+            # Cap the read so a malformed or maliciously crafted file (e.g.
+            # a single huge line with no newline) can't blow up memory.
+            first_line = f.readline(128).strip()
+            value = "".join(c for c in first_line if 32 <= ord(c) < 127)
+            if value:
+                return value
+    except FileNotFoundError:
+        pass
+    except Exception as e:
+        logger.error("Failed to read %s: %s", env_file, e)
+
+    return _DEFAULT_DEPLOY_ENV

comfy/hooks.py

@@ -93,7 +93,7 @@ class Hook:
         self.hook_scope = hook_scope
         '''Scope of where this hook should apply in terms of the conds used in sampling run.'''
         self.custom_should_register = default_should_register
-        '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
+        '''Can be overridden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
 
     @property
     def strength(self):

comfy/image_encoders/dino2.py

@@ -106,6 +106,7 @@ class Dino2Encoder(torch.nn.Module):
 class Dino2PatchEmbeddings(torch.nn.Module):
     def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):
         super().__init__()
+        self.patch_size = patch_size
         self.projection = operations.Conv2d(
             in_channels=num_channels,
             out_channels=dim,
@@ -125,17 +126,37 @@ class Dino2Embeddings(torch.nn.Module):
         super().__init__()
         patch_size = 14
         image_size = 518
+        self.patch_size = patch_size
 
         self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
         self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
-        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))
+        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
         self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
 
+    def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
+        pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
+
+        class_pos = pos_embed[:, 0:1]
+        patch_pos = pos_embed[:, 1:]
+        N = patch_pos.shape[1]
+        M = int(N ** 0.5)
+        h0 = h_pixels // self.patch_size
+        w0 = w_pixels // self.patch_size
+        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)  # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+
+        patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
+        patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
+        patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
+        return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
+
     def forward(self, pixel_values):
         x = self.patch_embeddings(pixel_values)
-        # TODO: mask_token?
         x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1)
-        x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+        if x.shape[1] - 1 == self.position_embeddings.shape[1] - 1:
+            x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+        else:
+            h, w = pixel_values.shape[-2:]
+            x = x + self.interpolate_pos_encoding(x, h, w)
         return x
 
 
@@ -158,3 +179,21 @@ class Dinov2Model(torch.nn.Module):
         x = self.layernorm(x)
         pooled_output = x[:, 0, :]
         return x, i, pooled_output, None
+
+    def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
+        x = self.embeddings(pixel_values)
+        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+        n_layers = len(self.encoder.layer)
+        resolved = [(i if i >= 0 else n_layers + i) for i in indices]
+        target = set(resolved)
+        max_idx = max(resolved)
+        n_skip = 1  # skip cls token
+        cache = {}
+        for i, layer in enumerate(self.encoder.layer):
+            x = layer(x, optimized_attention)
+            if i in target:
+                normed = self.layernorm(x) if apply_norm else x
+                cache[i] = (normed[:, n_skip:], normed[:, 0])
+            if i >= max_idx:
+                break
+        return [cache[i] for i in resolved]

comfy/k_diffusion/sampling.py

@@ -242,6 +242,7 @@ def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -373,6 +374,7 @@ def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None,
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -686,6 +688,7 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
     lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
@@ -747,6 +750,7 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -832,6 +836,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     old_denoised = None
     h, h_last = None, None
@@ -889,6 +894,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
@@ -1006,23 +1012,39 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
 
 @torch.no_grad()
-def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, s_noise=1.0, s_noise_end=None, noise_clip_std=0.0):
+
+    # s_noise / s_noise_end: per-step noise multiplier, linearly interpolated across steps
+    # noise_clip_std: clamp injected noise to +/- N stddevs (0 disables).
+
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
+    n_steps = max(1, len(sigmas) - 1)
+    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+
+    s_start = float(s_noise)
+    s_end = s_start if s_noise_end is None else float(s_noise_end)
+    for i in trange(n_steps, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
 
         x = denoised
         if sigmas[i + 1] > 0:
-            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
+            noise = noise_sampler(sigmas[i], sigmas[i + 1])
+            if noise_clip_std > 0:
+                clip_val = noise_clip_std * noise.std()
+                noise = noise.clamp(min=-clip_val, max=clip_val)
+            t = (i / (n_steps - 1)) if n_steps > 1 else 0.0
+            s_noise_i = s_start + (s_end - s_start) * t
+            if s_noise_i != 1.0:
+                noise = noise * s_noise_i
+            x = model_sampling.noise_scaling(sigmas[i + 1], noise, x)
     return x
 
 
-
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -1249,6 +1271,7 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
 
     uncond_denoised = None
 
@@ -1296,6 +1319,7 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
 
     temp = [0]
     def post_cfg_function(args):
@@ -1371,6 +1395,7 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
     t_fn = lambda sigma: sigma.log().neg()
@@ -1504,6 +1529,7 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0)
     s_in = x.new_ones([x.shape[0]])
 
     def default_er_sde_noise_scaler(x):
@@ -1574,9 +1600,10 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
+    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1645,9 +1672,10 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    inject_noise = eta > 0 and s_noise > 0
 
     model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
+    inject_noise = eta > 0 and s_noise > 0
     sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
@@ -1713,6 +1741,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     s_in = x.new_ones([x.shape[0]])
 
     model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+    s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
     lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
 
@@ -1810,3 +1839,119 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
     """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
     return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)
+
+
+@torch.no_grad()
+def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=None,
+                    num_frame_per_block=1):
+    """
+    Autoregressive video sampler: block-by-block denoising with KV cache
+    and flow-match re-noising for Causal Forcing / Self-Forcing models.
+
+    Requires a Causal-WAN compatible model (diffusion_model must expose
+    init_kv_caches / init_crossattn_caches) and 5-D latents [B,C,T,H,W].
+
+    All AR-loop parameters are passed via the SamplerARVideo node, not read
+    from the checkpoint or transformer_options.
+    """
+    extra_args = {} if extra_args is None else extra_args
+    model_options = extra_args.get("model_options", {})
+    transformer_options = model_options.get("transformer_options", {})
+
+    if x.ndim != 5:
+        raise ValueError(
+            f"ar_video sampler requires 5-D video latents [B,C,T,H,W], got {x.ndim}-D tensor with shape {x.shape}. "
+            "This sampler is only compatible with autoregressive video models (e.g. Causal-WAN)."
+        )
+
+    inner_model = model.inner_model.inner_model
+    causal_model = inner_model.diffusion_model
+
+    if not (hasattr(causal_model, "init_kv_caches") and hasattr(causal_model, "init_crossattn_caches")):
+        raise TypeError(
+            "ar_video sampler requires a Causal-WAN compatible model whose diffusion_model "
+            "exposes init_kv_caches() and init_crossattn_caches(). The loaded checkpoint "
+            "does not support this interface — choose a different sampler."
+        )
+
+    seed = extra_args.get("seed", 0)
+
+    bs, c, lat_t, lat_h, lat_w = x.shape
+    frame_seq_len = -(-lat_h // 2) * -(-lat_w // 2) # ceiling division
+    num_blocks = -(-lat_t // num_frame_per_block)   # ceiling division
+    device = x.device
+    model_dtype = inner_model.get_dtype()
+
+    kv_caches = causal_model.init_kv_caches(bs, lat_t * frame_seq_len, device, model_dtype)
+    crossattn_caches = causal_model.init_crossattn_caches(bs, device, model_dtype)
+
+    output = torch.zeros_like(x)
+    s_in = x.new_ones([x.shape[0]])
+    current_start_frame = 0
+
+    # I2V: seed KV cache with the initial image latent before the denoising loop
+    initial_latent = transformer_options.get("ar_config", {}).get("initial_latent", None)
+    if initial_latent is not None:
+        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
+        n_init = initial_latent.shape[2]
+        output[:, :, :n_init] = initial_latent
+
+        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
+        transformer_options["ar_state"] = ar_state
+        zero_sigma = sigmas.new_zeros([1])
+        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
+
+        current_start_frame = n_init
+        remaining = lat_t - n_init
+        num_blocks = -(-remaining // num_frame_per_block)
+
+    num_sigma_steps = len(sigmas) - 1
+    total_real_steps = num_blocks * num_sigma_steps
+    step_count = 0
+
+    try:
+        for block_idx in trange(num_blocks, disable=disable):
+            bf = min(num_frame_per_block, lat_t - current_start_frame)
+            fs, fe = current_start_frame, current_start_frame + bf
+            noisy_input = x[:, :, fs:fe]
+
+            ar_state = {
+                "start_frame": current_start_frame,
+                "kv_caches": kv_caches,
+                "crossattn_caches": crossattn_caches,
+            }
+            transformer_options["ar_state"] = ar_state
+
+            for i in range(num_sigma_steps):
+                denoised = model(noisy_input, sigmas[i] * s_in, **extra_args)
+
+                if callback is not None:
+                    scaled_i = step_count * num_sigma_steps // total_real_steps
+                    callback({"x": noisy_input, "i": scaled_i, "sigma": sigmas[i],
+                              "sigma_hat": sigmas[i], "denoised": denoised})
+
+                if sigmas[i + 1] == 0:
+                    noisy_input = denoised
+                else:
+                    sigma_next = sigmas[i + 1]
+                    torch.manual_seed(seed + block_idx * 1000 + i)
+                    fresh_noise = torch.randn_like(denoised)
+                    noisy_input = (1.0 - sigma_next) * denoised + sigma_next * fresh_noise
+
+                    for cache in kv_caches:
+                        cache["end"] -= bf * frame_seq_len
+
+                step_count += 1
+
+            output[:, :, fs:fe] = noisy_input
+
+            for cache in kv_caches:
+                cache["end"] -= bf * frame_seq_len
+            zero_sigma = sigmas.new_zeros([1])
+            _ = model(noisy_input, zero_sigma * s_in, **extra_args)
+
+            current_start_frame += bf
+    finally:
+        transformer_options.pop("ar_state", None)
+
+    return output

comfy/latent_formats.py

@@ -9,6 +9,7 @@ class LatentFormat:
     latent_rgb_factors_reshape = None
     taesd_decoder_name = None
     spacial_downscale_ratio = 8
+    temporal_downscale_ratio = 1
 
     def process_in(self, latent):
         return latent * self.scale_factor
@@ -149,6 +150,12 @@ class SD3(LatentFormat):
 class StableAudio1(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
+    temporal_downscale_ratio = 2048
+
+class StableAudio3(LatentFormat):
+    latent_channels = 256
+    latent_dimensions = 1
+    temporal_downscale_ratio = 4096
 
 class Flux(SD3):
     latent_channels = 16
@@ -235,6 +242,7 @@ class Flux2(LatentFormat):
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
+    temporal_downscale_ratio = 6
 
     def __init__(self):
         self.scale_factor = 1.0
@@ -278,6 +286,7 @@ class LTXV(LatentFormat):
     latent_channels = 128
     latent_dimensions = 3
     spacial_downscale_ratio = 32
+    temporal_downscale_ratio = 8
 
     def __init__(self):
         self.latent_rgb_factors = [
@@ -421,6 +430,7 @@ class LTXAV(LTXV):
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
     scale_factor = 0.476986
     latent_rgb_factors = [
         [-0.0395, -0.0331,  0.0445],
@@ -447,6 +457,7 @@ class HunyuanVideo(LatentFormat):
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 8
 
     latent_rgb_factors = [
         [ 0.1817,  0.2284,  0.2423],
@@ -472,6 +483,7 @@ class Cosmos1CV8x8x8(LatentFormat):
 class Wan21(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     latent_rgb_factors = [
             [-0.1299, -0.1692,  0.2932],
@@ -734,6 +746,7 @@ class HunyuanVideo15(LatentFormat):
     latent_channels = 32
     latent_dimensions = 3
     spacial_downscale_ratio = 16
+    temporal_downscale_ratio = 4
     scale_factor = 1.03682
     taesd_decoder_name = "lighttaehy1_5"
 
@@ -759,6 +772,7 @@ class ACEAudio(LatentFormat):
 class ACEAudio15(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
+    temporal_downscale_ratio = 1764
 
 class ChromaRadiance(LatentFormat):
     latent_channels = 3
@@ -785,9 +799,35 @@ class ZImagePixelSpace(ChromaRadiance):
     """
     pass
 
+
+class HiDreamO1Pixel(ChromaRadiance):
+    """Pixel-space latent format for HiDream-O1.
+    No VAE — model patches/unpatches raw RGB internally with patch_size=32.
+    """
+    pass
+
 class CogVideoX(LatentFormat):
+    """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b).
+
+    scale_factor matches the vae/config.json scaling_factor for the 2b variant.
+    The 5b-class checkpoints (CogVideoX-5b, CogVideoX-1.5-5B, CogVideoX-Fun-V1.5-*)
+    use a different value; see CogVideoX1_5 below.
+    """
     latent_channels = 16
     latent_dimensions = 3
+    temporal_downscale_ratio = 4
 
     def __init__(self):
         self.scale_factor = 1.15258426
+
+
+class CogVideoX1_5(CogVideoX):
+    """Latent format for 5b-class CogVideoX checkpoints.
+
+    Covers THUDM/CogVideoX-5b, THUDM/CogVideoX-1.5-5B, and the CogVideoX-Fun
+    V1.5-5b family (including VOID inpainting). All of these have
+    scaling_factor=0.7 in their vae/config.json. Auto-selected in
+    supported_models.CogVideoX_T2V based on transformer hidden dim.
+    """
+    def __init__(self):
+        self.scale_factor = 0.7

comfy/ldm/audio/dit.py

@@ -10,6 +10,17 @@ from torch import nn
 from torch.nn import functional as F
 import math
 import comfy.ops
+from .embedders import ExpoFourierFeatures
+
+
+def _left_pad_to_match(emb, target_len):
+    emb_len = emb.shape[-2]
+    if emb_len < target_len:
+        return F.pad(emb, (0, 0, target_len - emb_len, 0), value=0.)
+    elif emb_len > target_len:
+        return emb[:, -target_len:, :]
+    return emb
+
 
 class FourierFeatures(nn.Module):
     def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
@@ -22,6 +33,7 @@ class FourierFeatures(nn.Module):
         f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
         return torch.cat([f.cos(), f.sin()], dim=-1)
 
+
 # norms
 class LayerNorm(nn.Module):
     def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
@@ -43,6 +55,16 @@ class LayerNorm(nn.Module):
             beta = comfy.ops.cast_to_input(beta, x)
         return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
 
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, dtype=None, device=None):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+    def forward(self, x):
+        return F.rms_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x))
+
+
 class GLU(nn.Module):
     def __init__(
         self,
@@ -236,13 +258,6 @@ class FeedForward(nn.Module):
 
         linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
 
-        # # init last linear layer to 0
-        # if zero_init_output:
-        #     nn.init.zeros_(linear_out.weight)
-        #     if not no_bias:
-        #         nn.init.zeros_(linear_out.bias)
-
-
         self.ff = nn.Sequential(
             linear_in,
             rearrange('b d n -> b n d') if use_conv else nn.Identity(),
@@ -261,8 +276,10 @@ class Attention(nn.Module):
         dim_context = None,
         causal = False,
         zero_init_output=True,
-        qk_norm = False,
+        qk_norm = "none",
+        differential = False,
         natten_kernel_size = None,
+        feat_scale = False,
         dtype=None,
         device=None,
         operations=None,
@@ -271,6 +288,7 @@ class Attention(nn.Module):
         self.dim = dim
         self.dim_heads = dim_heads
         self.causal = causal
+        self.differential = differential
 
         dim_kv = dim_context if dim_context is not None else dim
 
@@ -278,18 +296,37 @@ class Attention(nn.Module):
         self.kv_heads = dim_kv // dim_heads
 
         if dim_context is not None:
-            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
-            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+            if differential:
+                self.to_q = operations.Linear(dim, dim * 2, bias=False, dtype=dtype, device=device)
+                self.to_kv = operations.Linear(dim_kv, dim_kv * 3, bias=False, dtype=dtype, device=device)
+            else:
+                self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+                self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
         else:
-            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+            if differential:
+                self.to_qkv = operations.Linear(dim, dim * 5, bias=False, dtype=dtype, device=device)
+            else:
+                self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
 
         self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
 
-        # if zero_init_output:
-        #     nn.init.zeros_(self.to_out.weight)
-
+        # Accept bool for backward compat
+        if isinstance(qk_norm, bool):
+            qk_norm = "l2" if qk_norm else "none"
         self.qk_norm = qk_norm
 
+        if self.qk_norm == "ln":
+            self.q_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.k_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif self.qk_norm == "rms":
+            self.q_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
+            self.k_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
+
+        self.feat_scale = feat_scale
+
+        if self.feat_scale:
+            self.lambda_dc = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+            self.lambda_hf = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
 
     def forward(
         self,
@@ -306,22 +343,51 @@ class Attention(nn.Module):
         kv_input = context if has_context else x
 
         if hasattr(self, 'to_q'):
-            # Use separate linear projections for q and k/v
-            q = self.to_q(x)
-            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+            if self.differential:
+                # cross-attention differential: to_q → (q, q_diff), to_kv → (k, k_diff, v)
+                q, q_diff = self.to_q(x).chunk(2, dim=-1)
+                q      = rearrange(q,      'b n (h d) -> b h n d', h=h)
+                q_diff = rearrange(q_diff, 'b n (h d) -> b h n d', h=h)
+                q = torch.stack([q, q_diff], dim=1)  # (B, 2, H, N, D)
+                k, k_diff, v = self.to_kv(kv_input).chunk(3, dim=-1)
+                k      = rearrange(k,      'b n (h d) -> b h n d', h=kv_h)
+                k_diff = rearrange(k_diff, 'b n (h d) -> b h n d', h=kv_h)
+                v      = rearrange(v,      'b n (h d) -> b h n d', h=kv_h)
+                k = torch.stack([k, k_diff], dim=1)  # (B, 2, H, M, D)
+            else:
+                # Use separate linear projections for q and k/v
+                q = self.to_q(x)
+                q = rearrange(q, 'b n (h d) -> b h n d', h = h)
 
-            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+                k, v = self.to_kv(kv_input).chunk(2, dim=-1)
 
-            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+                k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
         else:
-            # Use fused linear projection
-            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+            if self.differential:
+                # self-attention differential: to_qkv → (q, k, v, q_diff, k_diff)
+                q, k, v, q_diff, k_diff = self.to_qkv(x).chunk(5, dim=-1)
+                q, k, v, q_diff, k_diff = map(
+                    lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h),
+                    (q, k, v, q_diff, k_diff)
+                )
+                q = torch.stack([q, q_diff], dim=1)  # (B, 2, H, N, D)
+                k = torch.stack([k, k_diff], dim=1)
+            else:
+                # Use fused linear projection
+                q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+                q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
 
         # Normalize q and k for cosine sim attention
-        if self.qk_norm:
+        if self.qk_norm == "l2":
             q = F.normalize(q, dim=-1)
             k = F.normalize(k, dim=-1)
+        elif self.qk_norm == "rms":
+            q_type, k_type = q.dtype, k.dtype
+            q = self.q_norm(q).to(q_type)
+            k = self.k_norm(k).to(k_type)
+        elif self.qk_norm != 'none':
+            q = self.q_norm(q)
+            k = self.k_norm(k)
 
         if rotary_pos_emb is not None and not has_context:
             freqs, _ = rotary_pos_emb
@@ -364,9 +430,24 @@ class Attention(nn.Module):
             heads_per_kv_head = h // kv_h
             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
 
-        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
+        if self.differential:
+            q, q_diff = q.unbind(dim=1)
+            k, k_diff = k.unbind(dim=1)
+            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, transformer_options=transformer_options)
+            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, transformer_options=transformer_options)
+            out = out - out_diff
+        else:
+            out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
+
         out = self.to_out(out)
 
+        if self.feat_scale:
+            out_dc = out.mean(dim=-2, keepdim=True)
+            out_hf = out - out_dc
+
+            # Selectively modulate DC and high frequency components
+            out = out + comfy.ops.cast_to_input(self.lambda_dc, out) * out_dc + comfy.ops.cast_to_input(self.lambda_hf, out) * out_hf
+
         if mask is not None:
             mask = rearrange(mask, 'b n -> b n 1')
             out = out.masked_fill(~mask, 0.)
@@ -417,11 +498,14 @@ class TransformerBlock(nn.Module):
             cross_attend = False,
             dim_context = None,
             global_cond_dim = None,
+            global_cond_shared_embed = False,
+            local_add_cond_dim = None,
             causal = False,
             zero_init_branch_outputs = True,
             conformer = False,
             layer_ix = -1,
             remove_norms = False,
+            norm_type = "layer_norm",
             attn_kwargs = {},
             ff_kwargs = {},
             norm_kwargs = {},
@@ -436,8 +520,20 @@ class TransformerBlock(nn.Module):
         self.cross_attend = cross_attend
         self.dim_context = dim_context
         self.causal = causal
+        self.global_cond_shared_embed = global_cond_shared_embed
 
-        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+        norm_layer_map = {
+            "layer_norm": LayerNorm,
+            "rms_norm": RMSNorm,
+        }
+        norm_cls = norm_layer_map.get(norm_type, LayerNorm)
+
+        def make_norm():
+            if remove_norms:
+                return nn.Identity()
+            return norm_cls(dim, dtype=dtype, device=device, **norm_kwargs)
+
+        self.pre_norm = make_norm()
 
         self.self_attn = Attention(
             dim,
@@ -451,7 +547,7 @@ class TransformerBlock(nn.Module):
         )
 
         if cross_attend:
-            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+            self.cross_attend_norm = make_norm()
             self.cross_attn = Attention(
                 dim,
                 dim_heads = dim_heads,
@@ -464,37 +560,56 @@ class TransformerBlock(nn.Module):
                 **attn_kwargs
             )
 
-        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
-        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
+        self.ff_norm = make_norm()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **ff_kwargs)
 
         self.layer_ix = layer_ix
 
         self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
 
-        self.global_cond_dim = global_cond_dim
+        # Global conditioning
+        self.has_global_cond = (global_cond_dim is not None) or global_cond_shared_embed
 
-        if global_cond_dim is not None:
+        if global_cond_shared_embed:
+            # SA3 style: learnable per-block additive bias; global_cond is pre-projected to (B, dim*6)
+            self.to_scale_shift_gate = nn.Parameter(torch.empty(dim * 6, device=device, dtype=dtype))
+        elif global_cond_dim is not None:
+            # SA1 style: per-block MLP projects global_cond → (B, dim*6)
             self.to_scale_shift_gate = nn.Sequential(
                 nn.SiLU(),
-                nn.Linear(global_cond_dim, dim * 6, bias=False)
+                operations.Linear(global_cond_dim, dim * 6, bias=False, device=device, dtype=dtype)
             )
 
-            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
-            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+        # Local additive conditioning (e.g. inpaint mask + masked latent)
+        self.local_add_cond_dim = local_add_cond_dim
+        if local_add_cond_dim is not None:
+            self.to_local_embed = nn.Sequential(
+                operations.Linear(local_add_cond_dim, dim, bias=True, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(dim, dim, bias=True, dtype=dtype, device=device),
+            )
+        else:
+            self.to_local_embed = None
 
     def forward(
         self,
         x,
         context = None,
         global_cond=None,
+        local_add_cond=None,
         mask = None,
         context_mask = None,
         rotary_pos_emb = None,
         transformer_options={}
     ):
-        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+        if self.has_global_cond and global_cond is not None:
+            if self.global_cond_shared_embed:
+                # global_cond already has shape (B, dim*6)
+                ssg = (comfy.ops.cast_to_input(self.to_scale_shift_gate, global_cond) + global_cond).unsqueeze(1)
+            else:
+                ssg = self.to_scale_shift_gate(global_cond).unsqueeze(1)
 
-            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = ssg.chunk(6, dim = -1)
 
             # self-attention with adaLN
             residual = x
@@ -510,6 +625,9 @@ class TransformerBlock(nn.Module):
             if self.conformer is not None:
                 x = x + self.conformer(x)
 
+            if local_add_cond is not None and self.to_local_embed is not None:
+                x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
+
             # feedforward with adaLN
             residual = x
             x = self.ff_norm(x)
@@ -527,6 +645,9 @@ class TransformerBlock(nn.Module):
             if self.conformer is not None:
                 x = x + self.conformer(x)
 
+            if local_add_cond is not None and self.to_local_embed is not None:
+                x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
+
             x = x + self.ff(self.ff_norm(x))
 
         return x
@@ -543,6 +664,8 @@ class ContinuousTransformer(nn.Module):
         cross_attend=False,
         cond_token_dim=None,
         global_cond_dim=None,
+        global_cond_shared_embed=False,
+        local_add_cond_dim=None,
         causal=False,
         rotary_pos_emb=True,
         zero_init_branch_outputs=True,
@@ -550,6 +673,7 @@ class ContinuousTransformer(nn.Module):
         use_sinusoidal_emb=False,
         use_abs_pos_emb=False,
         abs_pos_emb_max_length=10000,
+        num_memory_tokens=0,
         dtype=None,
         device=None,
         operations=None,
@@ -562,6 +686,8 @@ class ContinuousTransformer(nn.Module):
         self.depth = depth
         self.causal = causal
         self.layers = nn.ModuleList([])
+        self.num_memory_tokens = num_memory_tokens
+        self.global_cond_shared_embed = global_cond_shared_embed
 
         self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
         self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
@@ -577,7 +703,22 @@ class ContinuousTransformer(nn.Module):
 
         self.use_abs_pos_emb = use_abs_pos_emb
         if use_abs_pos_emb:
-            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length + num_memory_tokens)
+
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.empty(num_memory_tokens, dim, device=device, dtype=dtype))
+
+        # Shared global-cond embedder (SA3 style): projects (B, global_cond_dim) → (B, dim*6)
+        self.global_cond_embedder = None
+        if global_cond_shared_embed and global_cond_dim is not None:
+            self.global_cond_embedder = nn.Sequential(
+                operations.Linear(global_cond_dim, dim, bias=True, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(dim, dim * 6, bias=True, dtype=dtype, device=device),
+            )
+
+        # When using shared embed, TransformerBlocks use per-block Parameter (not per-block MLP)
+        block_global_cond_dim = None if global_cond_shared_embed else global_cond_dim
 
         for i in range(depth):
             self.layers.append(
@@ -586,7 +727,9 @@ class ContinuousTransformer(nn.Module):
                     dim_heads = dim_heads,
                     cross_attend = cross_attend,
                     dim_context = cond_token_dim,
-                    global_cond_dim = global_cond_dim,
+                    global_cond_dim = block_global_cond_dim,
+                    global_cond_shared_embed = global_cond_shared_embed,
+                    local_add_cond_dim = local_add_cond_dim,
                     causal = causal,
                     zero_init_branch_outputs = zero_init_branch_outputs,
                     conformer=conformer,
@@ -605,6 +748,7 @@ class ContinuousTransformer(nn.Module):
         prepend_embeds = None,
         prepend_mask = None,
         global_cond = None,
+        local_add_cond = None,
         return_info = False,
         **kwargs
     ):
@@ -632,7 +776,9 @@ class ContinuousTransformer(nn.Module):
 
                 mask = torch.cat((prepend_mask, mask), dim = -1)
 
-        # Attention layers
+        if self.num_memory_tokens > 0:
+            memory_tokens = comfy.ops.cast_to_input(self.memory_tokens, x).expand(batch, -1, -1)
+            x = torch.cat((memory_tokens, x), dim=1)
 
         if self.rotary_pos_emb is not None:
             rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
@@ -642,6 +788,10 @@ class ContinuousTransformer(nn.Module):
         if self.use_sinusoidal_emb or self.use_abs_pos_emb:
             x = x + self.pos_emb(x)
 
+        # Project global_cond once (SA3 shared-embed path)
+        if global_cond is not None and self.global_cond_embedder is not None:
+            global_cond = self.global_cond_embedder(global_cond)
+
         blocks_replace = patches_replace.get("dit", {})
         # Iterate over the transformer layers
         for i, layer in enumerate(self.layers):
@@ -654,12 +804,17 @@ class ContinuousTransformer(nn.Module):
                 out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
-            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+                x = layer(x, rotary_pos_emb=rotary_pos_emb, global_cond=global_cond,
+                          local_add_cond=local_add_cond, context=context,
+                          transformer_options=transformer_options)
 
             if return_info:
                 info["hidden_states"].append(x)
 
+        # Strip memory tokens before projecting out
+        if self.num_memory_tokens > 0:
+            x = x[:, self.num_memory_tokens:, :]
+
         x = self.project_out(x)
 
         if return_info:
@@ -682,6 +837,7 @@ class AudioDiffusionTransformer(nn.Module):
         num_heads=24,
         transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
         global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        timestep_features_type: str = "learned",
         audio_model="",
         dtype=None,
         device=None,
@@ -696,7 +852,10 @@ class AudioDiffusionTransformer(nn.Module):
         # Timestep embeddings
         timestep_features_dim = 256
 
-        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+        if timestep_features_type == "expo":
+            self.timestep_features = ExpoFourierFeatures(timestep_features_dim, 0.5, 10000.0)
+        else:
+            self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
 
         self.to_timestep_embed = nn.Sequential(
             operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
@@ -781,6 +940,7 @@ class AudioDiffusionTransformer(nn.Module):
         cross_attn_cond=None,
         cross_attn_cond_mask=None,
         input_concat_cond=None,
+        local_add_cond=None,
         global_embed=None,
         prepend_cond=None,
         prepend_cond_mask=None,
@@ -802,9 +962,13 @@ class AudioDiffusionTransformer(nn.Module):
             prepend_cond = self.to_prepend_embed(prepend_cond)
 
             prepend_inputs = prepend_cond
+            prepend_length = prepend_cond.shape[1]
             if prepend_cond_mask is not None:
                 prepend_mask = prepend_cond_mask
 
+        if local_add_cond is not None and local_add_cond.dim() == 3:
+            local_add_cond = local_add_cond.permute(0, 2, 1)
+
         if input_concat_cond is not None:
 
             # Interpolate input_concat_cond to the same length as x
@@ -850,7 +1014,7 @@ class AudioDiffusionTransformer(nn.Module):
         if self.transformer_type == "x-transformers":
             output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
         elif self.transformer_type == "continuous_transformer":
-            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, local_add_cond=local_add_cond, **extra_args, **kwargs)
 
             if return_info:
                 output, info = output
@@ -876,6 +1040,7 @@ class AudioDiffusionTransformer(nn.Module):
         context=None,
         context_mask=None,
         input_concat_cond=None,
+        local_add_cond=None,
         global_embed=None,
         negative_global_embed=None,
         prepend_cond=None,
@@ -890,6 +1055,7 @@ class AudioDiffusionTransformer(nn.Module):
                 cross_attn_cond=context,
                 cross_attn_cond_mask=context_mask,
                 input_concat_cond=input_concat_cond,
+                local_add_cond=local_add_cond,
                 global_embed=global_embed,
                 prepend_cond=prepend_cond,
                 prepend_cond_mask=prepend_cond_mask,

comfy/ldm/audio/embedders.py

@@ -31,15 +31,39 @@ def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
     )
 
 
+class ExpoFourierFeatures(nn.Module):
+    """Exponentially-spaced Fourier features (no learnable parameters)."""
+    def __init__(self, dim, min_freq=0.5, max_freq=10000.0):
+        super().__init__()
+        self.dim = dim
+        self.min_freq = min_freq
+        self.max_freq = max_freq
+
+    def forward(self, t):
+        in_dtype = t.dtype
+        t = t.float()
+        if t.dim() == 1:
+            t = t.unsqueeze(-1)
+        half_dim = self.dim // 2
+        ramp = torch.linspace(0, 1, half_dim, device=t.device, dtype=torch.float32)
+        freqs = torch.exp(ramp * (math.log(self.max_freq) - math.log(self.min_freq)) + math.log(self.min_freq))
+        args = t * freqs * 2 * math.pi
+        return torch.cat([args.cos(), args.sin()], dim=-1).to(in_dtype)
+
+
 class NumberEmbedder(nn.Module):
     def __init__(
         self,
         features: int,
         dim: int = 256,
+        fourier_features_type="learned",
     ):
         super().__init__()
         self.features = features
-        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+        if fourier_features_type == "expo":
+            self.embedding = nn.Sequential(ExpoFourierFeatures(dim=dim), comfy.ops.manual_cast.Linear(in_features=dim, out_features=features))
+        else:
+            self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
 
     def forward(self, x: Union[List[float], Tensor]) -> Tensor:
         if not torch.is_tensor(x):
@@ -77,14 +101,15 @@ class NumberConditioner(Conditioner):
     def __init__(self,
                 output_dim: int,
                 min_val: float=0,
-                max_val: float=1
+                max_val: float=1,
+                fourier_features_type: str = "learned",
                 ):
         super().__init__(output_dim, output_dim)
 
         self.min_val = min_val
         self.max_val = max_val
 
-        self.embedder = NumberEmbedder(features=output_dim)
+        self.embedder = NumberEmbedder(features=output_dim, fourier_features_type=fourier_features_type)
 
     def forward(self, floats, device=None):
             # Cast the inputs to floats

comfy/ldm/audio/vae_sa3.py

@@ -0,0 +1,533 @@
+import torch
+import torch.nn as nn
+
+import comfy.ops
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.audio.autoencoder import WNConv1d
+
+ops = comfy.ops.disable_weight_init
+
+class Transpose(nn.Module):
+    def forward(self, x, **kwargs):
+        return x.transpose(-2, -1)
+
+
+def _zero_pad_modulo_sequence(x, size, dim=-2):
+    input_len = x.shape[dim]
+    pad_len = (size - input_len % size) % size
+    if pad_len > 0:
+        pad_shape = list(x.shape)
+        pad_shape[dim] = pad_len
+        x = torch.cat([x, torch.zeros(pad_shape, device=x.device, dtype=x.dtype)], dim=dim)
+    return x
+
+
+def _sliding_window_mask(seq_len, window, device, dtype):
+    """Additive attention mask enforcing a ±window local window (matches flash_attn window_size)."""
+    i = torch.arange(seq_len, device=device).unsqueeze(1)
+    j = torch.arange(seq_len, device=device).unsqueeze(0)
+    out_of_window = (j - i).abs() > window
+    return torch.where(
+        out_of_window,
+        torch.full((1,), torch.finfo(dtype).min / 4, device=device, dtype=dtype),
+        torch.zeros(1, device=device, dtype=dtype),
+    )
+
+
+class DynamicTanh(nn.Module):
+    def __init__(self, dim, init_alpha=4.0, dtype=None, device=None, **kwargs):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.empty(1, dtype=dtype, device=device))
+        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+        self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+    def forward(self, x):
+        alpha = comfy.ops.cast_to_input(self.alpha, x)
+        gamma = comfy.ops.cast_to_input(self.gamma, x)
+        beta = comfy.ops.cast_to_input(self.beta, x)
+        return gamma * torch.tanh(alpha * x) + beta
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, base=10000, base_rescale_factor=1., dtype=None, device=None):
+        super().__init__()
+        base = base * base_rescale_factor ** (dim / (dim - 2))
+        self.register_buffer("inv_freq", torch.empty(dim // 2, dtype=dtype, device=device))
+
+    def forward_from_seq_len(self, seq_len, device, dtype=None):
+        t = torch.arange(seq_len, device=device, dtype=torch.float32)
+        return self.forward(t)
+
+    def forward(self, t):
+        freqs = torch.outer(t.float(), comfy.model_management.cast_to(self.inv_freq, dtype=torch.float32, device=t.device))
+        freqs = torch.cat((freqs, freqs), dim=-1)
+        return freqs, 1.
+
+
+def _rotate_half(x):
+    d = x.shape[-1] // 2
+    return torch.cat((-x[..., d:], x[..., :d]), dim=-1)
+
+
+def _apply_rotary_pos_emb(t, freqs):
+    out_dtype = t.dtype
+    rot_dim = freqs.shape[-1]
+    seq_len = t.shape[-2]
+    freqs = freqs[-seq_len:]
+    t_rot, t_pass = t[..., :rot_dim], t[..., rot_dim:]
+    t_rot = t_rot * freqs.cos() + _rotate_half(t_rot) * freqs.sin()
+    return torch.cat((t_rot.to(out_dtype), t_pass.to(out_dtype)), dim=-1)
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, dim_heads=64, qk_norm="none", qk_norm_eps=1e-6,
+                 differential=False, zero_init_output=True,
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.num_heads = dim // dim_heads
+        self.differential = differential
+        self.qk_norm = qk_norm
+
+        self.to_qkv = operations.Linear(
+            dim, dim * (5 if differential else 3), bias=False, dtype=dtype, device=device)
+        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        if qk_norm == "dyt":
+            self.q_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
+            self.k_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
+        elif qk_norm == "rms":
+            self.q_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
+            self.k_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
+
+    def forward(self, x, rotary_pos_emb=None, mask=None, **kwargs):
+        B, N, _ = x.shape
+        h = self.num_heads
+
+        qkv = self.to_qkv(x)
+        if self.differential:
+            q, k, v, q_diff, k_diff = qkv.chunk(5, dim=-1)
+            del qkv
+            q = q.view(B, N, h, -1).transpose(1, 2)
+            k = k.view(B, N, h, -1).transpose(1, 2)
+            v = v.view(B, N, h, -1).transpose(1, 2)
+            q_diff = q_diff.view(B, N, h, -1).transpose(1, 2)
+            k_diff = k_diff.view(B, N, h, -1).transpose(1, 2)
+        else:
+            q, k, v = qkv.chunk(3, dim=-1)
+            del qkv
+            q = q.view(B, N, h, -1).transpose(1, 2)
+            k = k.view(B, N, h, -1).transpose(1, 2)
+            v = v.view(B, N, h, -1).transpose(1, 2)
+
+        if self.qk_norm != "none":
+            q_dtype, k_dtype = q.dtype, k.dtype
+            q = self.q_norm(q).to(q_dtype)
+            k = self.k_norm(k).to(k_dtype)
+            if self.differential:
+                q_diff = self.q_norm(q_diff).to(q_dtype)
+                k_diff = self.k_norm(k_diff).to(k_dtype)
+
+        if rotary_pos_emb is not None:
+            freqs, _ = rotary_pos_emb
+            q_dtype, k_dtype = q.dtype, k.dtype
+            q = _apply_rotary_pos_emb(q.float(), freqs).to(q_dtype)
+            k = _apply_rotary_pos_emb(k.float(), freqs).to(k_dtype)
+            if self.differential:
+                q_diff = _apply_rotary_pos_emb(q_diff.float(), freqs).to(q_dtype)
+                k_diff = _apply_rotary_pos_emb(k_diff.float(), freqs).to(k_dtype)
+
+        if self.differential:
+            out = (optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
+                   - optimized_attention(q_diff, k_diff, v, h, mask=mask, skip_reshape=True))
+            del q, k, v, q_diff, k_diff
+        else:
+            out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
+            del q, k, v
+
+        return self.to_out(out)
+
+
+class _Sin(nn.Module):
+    def forward(self, x):
+        return torch.sin(3.14159265359 * x)
+
+
+class _GLU(nn.Module):
+    def __init__(self, dim_in, dim_out, activation, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.act = activation
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x, gate = x.chunk(2, dim=-1)
+        return x * self.act(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, mult=4, no_bias=False, zero_init_output=True,
+                 sinusoidal=False, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        act = _Sin() if sinusoidal else nn.SiLU()
+        self.ff = nn.Sequential(
+            _GLU(dim, inner_dim, act, dtype=dtype, device=device, operations=operations),
+            nn.Identity(),
+            operations.Linear(inner_dim, dim, bias=not no_bias, dtype=dtype, device=device),
+            nn.Identity(),
+        )
+
+    def forward(self, x, **kwargs):
+        return self.ff(x)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, dim, dim_heads=64, causal=False, zero_init_branch_outputs=True,
+                 norm_type="dyt", add_rope=False, attn_kwargs=None, ff_kwargs=None,
+                 norm_kwargs=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        if attn_kwargs is None:
+            attn_kwargs = {}
+        if ff_kwargs is None:
+            ff_kwargs = {}
+        if norm_kwargs is None:
+            norm_kwargs = {}
+        dim_heads = min(dim_heads, dim)
+
+        Norm = DynamicTanh if norm_type == "dyt" else operations.RMSNorm
+        norm_kw = {**norm_kwargs, "dtype": dtype, "device": device}
+
+        self.pre_norm = Norm(dim, **norm_kw)
+        self.self_attn = Attention(dim, dim_heads=dim_heads,
+                                   zero_init_output=zero_init_branch_outputs,
+                                   dtype=dtype, device=device, operations=operations,
+                                   **attn_kwargs)
+        self.ff_norm = Norm(dim, **norm_kw)
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs,
+                              dtype=dtype, device=device, operations=operations, **ff_kwargs)
+        self.rope = RotaryEmbedding(dim_heads // 2, dtype=dtype, device=device) if add_rope else None
+
+    def forward(self, x, mask=None, **kwargs):
+        rope = self.rope.forward_from_seq_len(x.shape[-2], device=x.device) \
+               if self.rope is not None else None
+        x = x + self.self_attn(self.pre_norm(x), rotary_pos_emb=rope, mask=mask)
+        x = x + self.ff(self.ff_norm(x))
+        return x
+
+
+class TransformerResamplingBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, type="encoder",
+                 transformer_depth=3, dim_heads=128, differential=True,
+                 sliding_window=None, chunk_size=128, chunk_midpoint_shift=False,
+                 dyt=True, ff_mult=3, mapping_bias=True, variable_stride=False,
+                 sinusoidal_blocks=0, conv_mapping=False, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        if type not in ("encoder", "decoder"):
+            raise ValueError(f"type must be 'encoder' or 'decoder', got {type!r}")
+
+        self.type = type
+        self.stride = stride
+        self.chunk_size = chunk_size
+        self.chunk_midpoint_shift = chunk_midpoint_shift
+        self.variable_stride = variable_stride
+        self.transformer_depth = transformer_depth
+
+        transformer_dim = out_channels if type == "encoder" else in_channels
+
+        self.mapping = (WNConv1d(in_channels, out_channels, 3 if conv_mapping else 1, padding="same", bias=mapping_bias)
+                        if in_channels != out_channels else nn.Identity())
+
+        self.sliding_window_latents = sliding_window
+        self.sliding_window_seq = self._get_sliding_window_size(sliding_window, stride)
+        self.input_seg_size, self.output_seg_size, self.sub_chunk_size = self._get_seg_sizes(stride)
+
+        token_seq = 1 if variable_stride else self.output_seg_size
+        self.new_tokens = nn.Parameter(torch.empty(1, token_seq, transformer_dim, dtype=dtype, device=device))
+
+        norm_type = "dyt" if dyt else "rms_norm"
+        attn_kwargs = {"qk_norm": "dyt" if dyt else "rms", "qk_norm_eps": 1e-3,
+                       "differential": differential}
+        norm_kwargs = {"eps": 1e-3}
+        transformers = []
+        for i in range(transformer_depth):
+            sinusoidal = (transformer_depth - i) < sinusoidal_blocks
+            transformers.append(TransformerBlock(
+                transformer_dim,
+                dim_heads=dim_heads,
+                causal=False,
+                zero_init_branch_outputs=True,
+                norm_type=norm_type,
+                add_rope=True,
+                attn_kwargs=attn_kwargs,
+                ff_kwargs={"mult": ff_mult, "no_bias": False, "sinusoidal": sinusoidal},
+                norm_kwargs=norm_kwargs,
+                dtype=dtype, device=device, operations=operations,
+            ))
+        self.transformers = nn.ModuleList(transformers)
+
+    def _get_sliding_window_size(self, window, stride, prepend_cond_length=0):
+        if window is None:
+            return None
+        return [w * (stride + 1 + prepend_cond_length) for w in window]
+
+    def _get_seg_sizes(self, stride, prepend_cond_length=0):
+        sub_chunk_size = stride + 1 + prepend_cond_length
+        input_seg_size = stride if self.type == "encoder" else 1
+        output_seg_size = 1 if self.type == "encoder" else stride
+        return input_seg_size, output_seg_size, sub_chunk_size
+
+    def forward(self, x, stride=None, **kwargs):
+        B = x.shape[0]
+
+        if stride is None:
+            input_seg = self.input_seg_size
+            output_seg = self.output_seg_size
+            sub_chunk = self.sub_chunk_size
+            sliding_window = self.sliding_window_seq
+        else:
+            input_seg, output_seg, sub_chunk = self._get_seg_sizes(stride)
+            sliding_window = self._get_sliding_window_size(self.sliding_window_latents, stride)
+
+        if self.type == "encoder":
+            if self.transformer_depth > 0:
+                pad_mod = self.chunk_size if sliding_window is None else input_seg
+                x = _zero_pad_modulo_sequence(x, pad_mod, dim=-1)
+            x = self.mapping(x)
+
+        if self.transformer_depth > 0:
+            x = x.permute(0, 2, 1)
+
+            if self.type != "encoder":
+                pad_mod = 1 if sliding_window is not None else (
+                    self.chunk_size // (stride if stride is not None else self.stride))
+                x = _zero_pad_modulo_sequence(x, pad_mod)
+
+            C = x.shape[2]
+            x = x.reshape(-1, input_seg, C)
+
+            new_tokens = self.new_tokens.expand(x.shape[0], output_seg, -1)
+            x = torch.cat([x, comfy.ops.cast_to_input(new_tokens, x)], dim=-2)
+            del new_tokens
+
+            x = x.reshape(B, -1, C)
+
+            if sliding_window is None:
+                eff_chunk = self.chunk_size + self.chunk_size // (stride if stride is not None else self.stride)
+
+            if sliding_window is None and self.chunk_midpoint_shift:
+                split = self.transformer_depth // 2
+                shift = eff_chunk // 2
+
+                x = x.reshape(-1, eff_chunk, C)
+                for layer in self.transformers[:split]:
+                    x = layer(x)
+                x = x.reshape(B, -1, C)
+
+                shifted = torch.cat([x[:, :shift, :], x, x[:, -shift:, :]], dim=1)
+                del x
+                x = shifted.reshape(-1, eff_chunk, C)
+                del shifted
+                for layer in self.transformers[split:]:
+                    x = layer(x)
+                x = x.reshape(B, -1, C)
+                x = x[:, shift:-shift, :]
+            elif sliding_window is None:
+                x = x.reshape(-1, eff_chunk, C)
+                for layer in self.transformers:
+                    x = layer(x)
+                x = x.reshape(B, -1, C)
+            else:
+                attn_mask = _sliding_window_mask(x.shape[1], sliding_window[0], x.device, x.dtype)
+                for layer in self.transformers:
+                    x = layer(x, mask=attn_mask)
+
+            x = x.reshape(-1, sub_chunk, C)
+            x = x[:, -output_seg:, :]
+            x = x.reshape(B, -1, C).transpose(1, 2)
+
+        if self.type == "decoder":
+            x = self.mapping(x)
+
+        return x
+
+
+class SAMEEncoder(nn.Module):
+    def __init__(self, in_channels=2, channels=128, latent_dim=32,
+                 c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
+                 transformer_depths=(3, 3, 3, 3),
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        channel_dims = [in_channels] + [channels * c for c in c_mults]
+        layers = []
+        for i in range(len(c_mults)):
+            layers.append(TransformerResamplingBlock(
+                in_channels=channel_dims[i], out_channels=channel_dims[i + 1],
+                stride=strides[i], type="encoder",
+                transformer_depth=transformer_depths[i],
+                dtype=dtype, device=device, operations=operations, **kwargs))
+        layers += [
+            Transpose(),
+            operations.Linear(channel_dims[-1], latent_dim, dtype=dtype, device=device),
+            Transpose(),
+        ]
+        self.layers = nn.ModuleList(layers)
+
+    def forward(self, x, **kwargs):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class SAMEDecoder(nn.Module):
+    def __init__(self, out_channels=2, channels=128, latent_dim=32,
+                 c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
+                 transformer_depths=(3, 3, 3, 3), sinusoidal_blocks=None,
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        if sinusoidal_blocks is None:
+            sinusoidal_blocks = [0] * len(c_mults)
+        channel_dims = [out_channels] + [channels * c for c in c_mults]
+        layers = [
+            Transpose(),
+            operations.Linear(latent_dim, channel_dims[-1], dtype=dtype, device=device),
+            Transpose(),
+        ]
+        for i in range(len(c_mults) - 1, -1, -1):
+            layers.append(TransformerResamplingBlock(
+                in_channels=channel_dims[i + 1], out_channels=channel_dims[i],
+                stride=strides[i], type="decoder",
+                transformer_depth=transformer_depths[i],
+                sinusoidal_blocks=sinusoidal_blocks[i],
+                dtype=dtype, device=device, operations=operations, **kwargs))
+        self.layers = nn.ModuleList(layers)
+
+    def forward(self, x, **kwargs):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class SoftNormBottleneck(nn.Module):
+    def __init__(self, dim=32, noise_augment_dim=0, noise_regularize=False,
+                 auto_scale=False, freeze=False, dtype=None, device=None, **kwargs):
+        super().__init__()
+        self.noise_augment_dim = noise_augment_dim
+        self.noise_regularize = noise_regularize
+        self.scaling_factor = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
+        self.bias = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
+        self.noise_scaling_factor = nn.Parameter(torch.empty(1, noise_augment_dim, 1, dtype=dtype, device=device))
+        if auto_scale:
+            self.register_parameter("running_std", nn.Parameter(
+                torch.empty(1, dtype=dtype, device=device), requires_grad=False))
+        if freeze:
+            for p in self.parameters():
+                p.requires_grad = False
+
+    def encode(self, x, return_info=False, **kwargs):
+        x = x * comfy.ops.cast_to_input(self.scaling_factor, x) \
+              + comfy.ops.cast_to_input(self.bias, x)
+        if hasattr(self, "running_std"):
+            x = x / comfy.ops.cast_to_input(self.running_std, x)
+        if return_info:
+            return x, {}
+        return x
+
+    def decode(self, x, **kwargs):
+        if hasattr(self, "running_std"):
+            x = x * comfy.ops.cast_to_input(self.running_std, x)
+        if self.noise_regularize:
+            scaling = self.running_std if hasattr(self, "running_std") \
+                      else x.std(dim=-1, keepdim=True)
+            noise = torch.randn_like(x) * comfy.ops.cast_to_input(scaling, x) * 1e-3
+            x = x + noise
+        if self.noise_augment_dim > 0:
+            noise = comfy.ops.cast_to_input(self.noise_scaling_factor, x) * torch.randn(
+                x.shape[0], self.noise_augment_dim, x.shape[-1], device=x.device, dtype=x.dtype)
+            x = torch.cat([x, noise], dim=1)
+        return x
+
+
+class PatchedPretransform(nn.Module):
+    def __init__(self, channels, patch_size, **kwargs):
+        super().__init__()
+        self.channels = channels
+        self.patch_size = patch_size
+        self.enable_grad = False
+
+    def _pad(self, x):
+        pad_len = (self.patch_size - x.shape[-1] % self.patch_size) % self.patch_size
+        if pad_len > 0:
+            x = torch.cat([x, torch.zeros_like(x[:, :, :pad_len])], dim=-1)
+        return x
+
+    def encode(self, x):
+        x = self._pad(x)
+        B, C, T = x.shape
+        h = self.patch_size
+        L = T // h
+        # b c (l h) -> b (c h) l
+        return x.reshape(B, C, L, h).permute(0, 1, 3, 2).reshape(B, C * h, L)
+
+    def decode(self, x):
+        B, Ch, L = x.shape
+        h = self.patch_size
+        C = Ch // h
+        # b (c h) l -> b c (l h)
+        return x.reshape(B, C, h, L).permute(0, 1, 3, 2).reshape(B, C, L * h)
+
+
+class SA3AudioVAE(nn.Module):
+    """SA3 VAE. State dict keys match checkpoint after stripping 'pretransform.model.'"""
+
+    def __init__(self, channels=256, transformer_depths=12, sinusoidal_blocks=8,
+                 sliding_window=None, decoder_conv_mapping=False,
+                 chunk_size=128, chunk_midpoint_shift=False,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        if operations is None:
+            operations = ops
+
+        self.pretransform = PatchedPretransform(channels=2, patch_size=256)
+
+        common_kwargs = dict(
+            differential=True, dyt=True, dim_heads=64,
+            sliding_window=sliding_window, variable_stride=True,
+            chunk_size=chunk_size, chunk_midpoint_shift=chunk_midpoint_shift,
+            dtype=dtype, device=device, operations=operations,
+        )
+        self.encoder = SAMEEncoder(
+            in_channels=512, channels=channels, c_mults=[6], strides=[16],
+            latent_dim=256, transformer_depths=[transformer_depths],
+            conv_mapping=False, **common_kwargs,
+        )
+        self.decoder = SAMEDecoder(
+            out_channels=512, channels=channels, c_mults=[6], strides=[16],
+            latent_dim=256, transformer_depths=[transformer_depths], sinusoidal_blocks=[sinusoidal_blocks],
+            conv_mapping=decoder_conv_mapping, **common_kwargs,
+        )
+        self.bottleneck = SoftNormBottleneck(
+            dim=256, noise_augment_dim=0, noise_regularize=True,
+            auto_scale=True, freeze=True,
+            dtype=dtype, device=device,
+        )
+
+    @torch.no_grad()
+    def _pretransform_encode(self, x):
+        return self.pretransform.encode(x)
+
+    @torch.no_grad()
+    def _pretransform_decode(self, x):
+        return self.pretransform.decode(x)
+
+    def encode(self, x):
+        x = self._pretransform_encode(x)
+        x = self.encoder(x)
+        x = self.bottleneck.encode(x)
+        return x
+
+    def decode(self, x):
+        x = self.bottleneck.decode(x)
+        x = self.decoder(x)
+        x = self._pretransform_decode(x)
+        return x

comfy/ldm/hidream_o1/attention.py

@@ -0,0 +1,41 @@
+"""HiDream-O1 two-pass attention: tokens [0, ar_len) are causal, [ar_len, T)
+attend full K/V. Splitting Q at the boundary avoids the (B, 1, T, T) additive
+mask the general-purpose path would build (~500 MB at T~16K) and lets the
+gen half hit the user's preferred backend via optimized_attention.
+"""
+
+import torch
+
+import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def make_two_pass_attention(ar_len: int, transformer_options=None):
+    """Build a two-pass attention callable. AR pass uses SDPA-causal directly, gen pass routes through optimized_attention.
+    The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
+    """
+
+    def two_pass_attention(q, k, v, heads, **kwargs):
+        B, H, T, D = q.shape
+
+        if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+        elif ar_len >= T:
+            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+        elif ar_len <= 0:
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+        else:
+            out_ar = comfy.ops.scaled_dot_product_attention(
+                q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
+                attn_mask=None, dropout_p=0.0, is_causal=True,
+            )
+            out_gen = optimized_attention(
+                q[:, :, ar_len:], k, v, heads,
+                mask=None, skip_reshape=True, skip_output_reshape=True,
+                transformer_options=transformer_options,
+            )
+            out = torch.cat([out_ar, out_gen], dim=2)
+
+        return out.transpose(1, 2).reshape(B, T, H * D)
+
+    return two_pass_attention

comfy/ldm/hidream_o1/conditioning.py

@@ -0,0 +1,230 @@
+"""HiDream-O1 conditioning prep — ref-image dual path + extra_conds assembly.
+
+Each ref image goes through two paths: a 32x32 patchified stream concatenated
+to the noised target, and a Qwen3-VL ViT path producing tokens that scatter
+into input_ids at <|image_pad|> positions.
+"""
+
+from typing import List
+
+import torch
+
+import comfy.utils
+from comfy.text_encoders.qwen_vl import process_qwen2vl_images
+
+from .utils import (PATCH_SIZE, calculate_dimensions, cond_image_size, ref_max_size, resize_tensor)
+
+# Qwen3-VL ViT preprocessing constants (preprocessor_config.json).
+VIT_PATCH = 16
+VIT_MERGE = 2
+VIT_IMAGE_MEAN = [0.5, 0.5, 0.5]
+VIT_IMAGE_STD = [0.5, 0.5, 0.5]
+
+
+def prepare_ref_images(
+    ref_images: List[torch.Tensor],
+    target_h: int,
+    target_w: int,
+    device: torch.device,
+    dtype: torch.dtype,
+):
+    """Build the dual-path tensors for K reference images at (target_h, target_w).
+
+    Returns None for K=0, else a dict with ref_patches, ref_pixel_values,
+    ref_image_grid_thw, per_ref_vit_tokens, per_ref_patch_grids.
+    """
+    K = len(ref_images)
+    if K == 0:
+        return None
+    max_size = ref_max_size(max(target_h, target_w), K)
+    cis = cond_image_size(K)
+
+    refs_t = [img[0].clamp(0, 1).permute(2, 0, 1).unsqueeze(0).contiguous().float() for img in ref_images]
+    refs_t = [resize_tensor(t, max_size, PATCH_SIZE) for t in refs_t]
+
+    # 32-patch path.
+    ref_patches_per = []
+    per_ref_patch_grids = []
+    for t in refs_t:
+        t_norm = (t.squeeze(0) - 0.5) / 0.5  # (3, H, W) in [-1, 1]
+        h_p, w_p = t_norm.shape[-2] // PATCH_SIZE, t_norm.shape[-1] // PATCH_SIZE
+        per_ref_patch_grids.append((h_p, w_p))
+        patches = (
+            t_norm.reshape(3, h_p, PATCH_SIZE, w_p, PATCH_SIZE)
+            .permute(1, 3, 0, 2, 4)
+            .reshape(h_p * w_p, 3 * PATCH_SIZE * PATCH_SIZE)
+        )
+        ref_patches_per.append(patches)
+    ref_patches = torch.cat(ref_patches_per, dim=0).unsqueeze(0).to(device=device, dtype=dtype)
+
+    # ViT path.
+    refs_vlm_t = []
+    for t in refs_t:
+        _, _, h, w = t.shape
+        cond_w, cond_h = calculate_dimensions(cis, w / h)
+        cond_w = max(cond_w, VIT_PATCH * VIT_MERGE)
+        cond_h = max(cond_h, VIT_PATCH * VIT_MERGE)
+        refs_vlm_t.append(comfy.utils.common_upscale(t, cond_w, cond_h, "lanczos", "disabled"))
+
+    pv_list, grid_list, per_ref_vit_tokens = [], [], []
+    for t_v in refs_vlm_t:
+        pv, grid_thw = process_qwen2vl_images(
+            t_v.permute(0, 2, 3, 1),
+            min_pixels=0, max_pixels=10**12,
+            patch_size=VIT_PATCH, merge_size=VIT_MERGE,
+            image_mean=VIT_IMAGE_MEAN, image_std=VIT_IMAGE_STD,
+        )
+        grid_thw = grid_thw[0]
+        pv_list.append(pv.to(device=device, dtype=dtype))
+        grid_list.append(grid_thw.to(device=device))
+        # Post-merge token count = number of <|image_pad|> tokens this image expands to in input_ids.
+        gh, gw = int(grid_thw[1].item()), int(grid_thw[2].item())
+        per_ref_vit_tokens.append((gh // VIT_MERGE) * (gw // VIT_MERGE))
+
+    return {
+        "ref_patches": ref_patches,
+        "ref_pixel_values": torch.cat(pv_list, dim=0),
+        "ref_image_grid_thw": torch.stack(grid_list, dim=0),
+        "per_ref_vit_tokens": per_ref_vit_tokens,
+        "per_ref_patch_grids": per_ref_patch_grids,
+    }
+
+
+def build_ref_input_ids(
+    text_input_ids: torch.Tensor,
+    per_ref_vit_tokens: List[int],
+    image_token_id: int,
+    vision_start_id: int,
+    vision_end_id: int,
+):
+    """Splice [vision_start, image_pad*N, vision_end] blocks into input_ids
+    after the [im_start, user, \\n] prefix (matches original chat template).
+    """
+    ids = text_input_ids[0].tolist()
+    inserted = []
+    for n_pad in per_ref_vit_tokens:
+        inserted.extend([vision_start_id] + [image_token_id] * n_pad + [vision_end_id])
+    new_ids = ids[:3] + inserted + ids[3:]  # 3 = len([im_start, user, \n])
+    return torch.tensor([new_ids], dtype=text_input_ids.dtype, device=text_input_ids.device)
+
+
+def build_extra_conds(
+    text_input_ids: torch.Tensor,
+    noise: torch.Tensor,
+    ref_images: List[torch.Tensor] = None,
+    target_patch_size: int = 32,
+):
+    """Assemble all conditioning tensors for HiDreamO1Transformer.forward:
+    input_ids (with ref-vision tokens spliced in for the edit/IP path),
+    position_ids (MRoPE), token_types, vinput_mask, plus the ref
+    dual-path tensors when refs are provided.
+    """
+    from .utils import get_rope_index_fix_point
+    from comfy.text_encoders.hidream_o1 import (
+        IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
+    )
+
+    if text_input_ids.dim() == 1:
+        text_input_ids = text_input_ids.unsqueeze(0)
+    text_input_ids = text_input_ids.long().to(noise.device)
+    B = noise.shape[0]
+    if text_input_ids.shape[0] == 1 and B > 1:
+        text_input_ids = text_input_ids.expand(B, -1)
+
+    H, W = noise.shape[-2], noise.shape[-1]
+    h_p, w_p = H // target_patch_size, W // target_patch_size
+    image_len = h_p * w_p
+    image_grid_thw_tgt = torch.tensor(
+        [[1, h_p, w_p]], dtype=torch.long, device=text_input_ids.device,
+    )
+
+    out = {}
+    if ref_images:
+        ref = prepare_ref_images(ref_images, H, W, device=noise.device, dtype=noise.dtype)
+        text_input_ids = build_ref_input_ids(
+            text_input_ids, ref["per_ref_vit_tokens"],
+            IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID,
+        )
+        new_txt_len = text_input_ids.shape[1]
+
+        # Each ref's patchified stream gets a [vision_start, image_pad*N-1]
+        # block in the position-id stream after the noised target.
+        ref_grid_lengths = [hp * wp for (hp, wp) in ref["per_ref_patch_grids"]]
+        tgt_vision = torch.full((1, image_len), IMAGE_TOKEN_ID,
+                                dtype=text_input_ids.dtype, device=text_input_ids.device)
+        tgt_vision[:, 0] = VISION_START_ID
+        ref_vision_blocks = []
+        for rl in ref_grid_lengths:
+            blk = torch.full((1, rl), IMAGE_TOKEN_ID,
+                             dtype=text_input_ids.dtype, device=text_input_ids.device)
+            blk[:, 0] = VISION_START_ID
+            ref_vision_blocks.append(blk)
+        ref_vision_cat = torch.cat([tgt_vision] + ref_vision_blocks, dim=1)
+        input_ids_pad = torch.cat([text_input_ids, ref_vision_cat], dim=-1)
+        total_ref_patches_len = sum(ref_grid_lengths)
+        total_len = new_txt_len + image_len + total_ref_patches_len
+
+        # K (ViT, post-merge) + 1 (target) + K (ref-patches) image grids.
+        K = len(ref_images)
+        igthw_cond = ref["ref_image_grid_thw"].clone()
+        igthw_cond[:, 1] //= 2
+        igthw_cond[:, 2] //= 2
+        image_grid_thw_ref = torch.tensor(
+            [[1, hp, wp] for (hp, wp) in ref["per_ref_patch_grids"]],
+            dtype=torch.long, device=text_input_ids.device,
+        )
+        igthw_all = torch.cat([
+            igthw_cond.to(text_input_ids.device),
+            image_grid_thw_tgt,
+            image_grid_thw_ref,
+        ], dim=0)
+        position_ids, _ = get_rope_index_fix_point(
+            spatial_merge_size=1,
+            image_token_id=IMAGE_TOKEN_ID,
+            vision_start_token_id=VISION_START_ID,
+            input_ids=input_ids_pad, image_grid_thw=igthw_all,
+            attention_mask=None,
+            skip_vision_start_token=[0] * K + [1] + [1] * K,
+            fix_point=4096,
+        )
+
+        # tms + target_image + ref_patches are all gen.
+        tms_pos = new_txt_len - 1
+        ar_len = tms_pos
+        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
+        token_types[:, tms_pos:] = 1
+        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
+        vinput_mask[:, new_txt_len:] = True
+
+        # Leading batch dim sidesteps CONDRegular.process_cond's repeat_to_batch_size truncation
+        out["ref_pixel_values"] = ref["ref_pixel_values"].unsqueeze(0)
+        out["ref_image_grid_thw"] = ref["ref_image_grid_thw"].unsqueeze(0)
+        out["ref_patches"] = ref["ref_patches"]
+    else:
+        # T2I: text + noised target only, vision_start replaces the first image token
+        txt_len = text_input_ids.shape[1]
+        total_len = txt_len + image_len
+        vision_tokens = torch.full((B, image_len), IMAGE_TOKEN_ID,
+                                   dtype=text_input_ids.dtype, device=text_input_ids.device)
+        vision_tokens[:, 0] = VISION_START_ID
+        input_ids_pad = torch.cat([text_input_ids, vision_tokens], dim=-1)
+        position_ids, _ = get_rope_index_fix_point(
+            spatial_merge_size=1,
+            image_token_id=IMAGE_TOKEN_ID,
+            vision_start_token_id=VISION_START_ID,
+            input_ids=input_ids_pad, image_grid_thw=image_grid_thw_tgt,
+            attention_mask=None,
+            skip_vision_start_token=[1],
+        )
+        ar_len = txt_len - 1
+        token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device)
+        token_types[:, ar_len:] = 1
+        vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device)
+        vinput_mask[:, txt_len:] = True
+
+    out["input_ids"] = text_input_ids
+    out["position_ids"] = position_ids[:, 0].unsqueeze(0) # Collapse position_ids batch and add a leading dim so CONDRegular's batch-resize doesn't truncate the 3-axis MRoPE dim
+    out["token_types"] = token_types
+    out["vinput_mask"] = vinput_mask
+    out["ar_len"] = ar_len
+    return out

comfy/ldm/hidream_o1/model.py

@@ -0,0 +1,306 @@
+"""HiDream-O1-Image transformer.
+
+Pixel-space DiT built on Qwen3-VL: the vision tower (Qwen35VisionModel)
+encodes ref images, the Qwen3-VL-8B decoder (Llama2_ with interleaved MRoPE)
+processes a unified text+image sequence, and 32x32 patch embed/unembed
+shims map raw RGB in and out of LLM hidden space. The Qwen3-VL deepstack
+mergers go unused — their weights are dropped at load.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import einops
+import torch
+import torch.nn as nn
+
+import comfy.patcher_extension
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
+from comfy.text_encoders.llama import Llama2_
+from comfy.text_encoders.qwen35 import Qwen35VisionModel
+
+from .attention import make_two_pass_attention
+
+
+IMAGE_TOKEN_ID = 151655   # Qwen3-VL <|image_pad|>
+TMS_TOKEN_ID = 151673     # HiDream-O1 <|tms_token|>
+PATCH_SIZE = 32
+
+
+@dataclass
+class HiDreamO1TextConfig:
+    """Qwen3-VL-8B text-decoder dims (matches public Qwen3-VL-8B-Instruct)."""
+    vocab_size: int = 151936
+    hidden_size: int = 4096
+    intermediate_size: int = 12288
+    num_hidden_layers: int = 36
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 8
+    head_dim: int = 128
+    max_position_embeddings: int = 128000
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 5000000.0
+    rope_scale: Optional[float] = None
+    rope_dims: List[int] = field(default_factory=lambda: [24, 20, 20])
+    interleaved_mrope: bool = True
+    transformer_type: str = "llama"
+    rms_norm_add: bool = False
+    mlp_activation: str = "silu"
+    qkv_bias: bool = False
+    q_norm: str = "gemma3"
+    k_norm: str = "gemma3"
+    final_norm: bool = True
+    lm_head: bool = False
+    stop_tokens: List[int] = field(default_factory=lambda: [151643, 151645])
+
+
+QWEN3VL_VISION_DEFAULTS = dict(
+    hidden_size=1152,
+    num_heads=16,
+    intermediate_size=4304,
+    depth=27,
+    patch_size=16,
+    temporal_patch_size=2,
+    in_channels=3,
+    spatial_merge_size=2,
+    num_position_embeddings=2304,
+    deepstack_visual_indexes=(8, 16, 24),
+    out_hidden_size=4096,  # final merger projects directly into LLM hidden
+)
+
+
+class BottleneckPatchEmbed(nn.Module):
+    # 3072 -> 1024 -> 4096 (raw 32x32 RGB patch -> bottleneck -> LLM hidden).
+    def __init__(self, patch_size=32, in_chans=3, pca_dim=1024, embed_dim=4096, bias=True, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.proj1 = ops.Linear(patch_size * patch_size * in_chans, pca_dim, bias=False, device=device, dtype=dtype)
+        self.proj2 = ops.Linear(pca_dim, embed_dim, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.proj2(self.proj1(x))
+
+
+class FinalLayer(nn.Module):
+    # 4096 -> 3072 (LLM hidden -> flat pixel patch).
+    def __init__(self, hidden_size, patch_size=32, out_channels=3, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.linear = ops.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class HiDreamO1Transformer(nn.Module):
+    """HiDream-O1 unified pixel-level transformer."""
+
+    def __init__(self, image_model=None, dtype=None, device=None, operations=None,
+                 text_config_overrides=None, vision_config_overrides=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+
+        text_cfg = HiDreamO1TextConfig(**(text_config_overrides or {}))
+        vision_cfg = dict(QWEN3VL_VISION_DEFAULTS)
+        if vision_config_overrides:
+            vision_cfg.update(vision_config_overrides)
+        vision_cfg["out_hidden_size"] = text_cfg.hidden_size
+
+        self.text_config = text_cfg
+        self.vision_config = vision_cfg
+        self.hidden_size = text_cfg.hidden_size
+        self.patch_size = PATCH_SIZE
+        self.in_channels = 3
+        self.tms_token_id = TMS_TOKEN_ID
+
+        self.visual = Qwen35VisionModel(vision_cfg, device=device, dtype=dtype, ops=operations)
+        self.language_model = Llama2_(text_cfg, device=device, dtype=dtype, ops=operations)
+        self.t_embedder1 = TimestepEmbedder(
+            text_cfg.hidden_size, device=device, dtype=dtype, operations=operations,
+        )
+        self.x_embedder = BottleneckPatchEmbed(
+            patch_size=self.patch_size, in_chans=self.in_channels,
+            pca_dim=text_cfg.hidden_size // 4, embed_dim=text_cfg.hidden_size,
+            bias=True, device=device, dtype=dtype, ops=operations,
+        )
+        self.final_layer2 = FinalLayer(
+            text_cfg.hidden_size, patch_size=self.patch_size,
+            out_channels=self.in_channels, device=device, dtype=dtype, ops=operations,
+        )
+
+        self._visual_cache = None
+        self._kv_cache_entries = []
+
+    def clear_kv_cache(self):
+        self._kv_cache_entries = []
+        self._visual_cache = None
+
+    def forward(self, x, timesteps, context=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(x, timesteps, context, transformer_options, **kwargs)
+
+    def _forward(self, x, timesteps, context=None, transformer_options={}, input_ids=None, attention_mask=None, position_ids=None,
+                 vinput_mask=None, ar_len=None, ref_pixel_values=None, ref_image_grid_thw=None, ref_patches=None, **kwargs):
+        """Returns flow-match velocity (x - x_pred) / sigma"""
+
+        if input_ids is None or position_ids is None:
+            raise ValueError("HiDreamO1Transformer requires input_ids and position_ids in conditioning")
+
+        B, _, H, W = x.shape
+        h_p, w_p = H // self.patch_size, W // self.patch_size
+        tgt_image_len = h_p * w_p
+
+        z = einops.rearrange(
+            x, 'B C (H p1) (W p2) -> B (H W) (C p1 p2)',
+            p1=self.patch_size, p2=self.patch_size,
+        )
+        vinputs = torch.cat([z, ref_patches.to(z.dtype)], dim=1) if ref_patches is not None else z
+
+        inputs_embeds = self.language_model.embed_tokens(input_ids).to(x.dtype)
+
+        if ref_pixel_values is not None and ref_image_grid_thw is not None:
+            # ViT output is constant across sampling steps within a generation
+            # identity-key by the input tensor so refs don't recompute every step.
+            cached = self._visual_cache
+            if cached is not None and cached[0] is ref_pixel_values:
+                image_embeds = cached[1]
+            else:
+                ref_pv = ref_pixel_values.to(inputs_embeds.device)
+                ref_grid = ref_image_grid_thw.to(inputs_embeds.device).long()
+                # extra_conds wraps with a leading batch dim; refs are model-level so [0] always recovers them.
+                if ref_pv.dim() == 3:
+                    ref_pv = ref_pv[0]
+                if ref_grid.dim() == 3:
+                    ref_grid = ref_grid[0]
+                image_embeds = self.visual(ref_pv, ref_grid).to(inputs_embeds.dtype)
+                self._visual_cache = (ref_pixel_values, image_embeds)
+            # image_pad positions identical across batch (input_ids shared cond/uncond).
+            image_idx = (input_ids[0] == IMAGE_TOKEN_ID).nonzero(as_tuple=True)[0]
+            if image_idx.shape[0] != image_embeds.shape[0]:
+                raise ValueError(
+                    f"Image-token count {image_idx.shape[0]} != ViT output count "
+                    f"{image_embeds.shape[0]}; check tokenizer/processor alignment."
+                )
+            inputs_embeds[:, image_idx] = image_embeds.unsqueeze(0).expand(B, -1, -1)
+
+        sigma = timesteps.float() / 1000.0
+        t_pixeldit = 1.0 - sigma
+        t_emb = self.t_embedder1(t_pixeldit * 1000, inputs_embeds.dtype)
+        tms_mask_3d = (input_ids == self.tms_token_id).unsqueeze(-1).expand_as(inputs_embeds)
+        inputs_embeds = torch.where(tms_mask_3d, t_emb.unsqueeze(1).expand_as(inputs_embeds), inputs_embeds)
+
+        vinputs_embedded = self.x_embedder(vinputs.to(inputs_embeds.dtype))
+        inputs_embeds = torch.cat([inputs_embeds, vinputs_embedded], dim=1)
+
+        # extra_conds stores position_ids as (1, 3, T); process_cond repeats dim 0 to B. Take row 0.
+        freqs_cis = self.language_model.compute_freqs_cis(position_ids[0].to(x.device), x.device)
+        freqs_cis = tuple(t.to(x.dtype) for t in freqs_cis)
+
+        two_pass_attn = make_two_pass_attention(ar_len, transformer_options=transformer_options)
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.language_model.layers)
+        transformer_options["block_type"] = "double"
+
+        # Cache prefix K/V across steps. Key includes input_ids (prompt), ref_id
+        # (refs scatter into inputs_embeds), and position_ids (RoPE baked into cached K).
+        can_cache = not blocks_replace and ar_len > 0
+        cache_len = ar_len if can_cache else 0
+        ref_id = id(ref_pixel_values) if ref_pixel_values is not None else None
+        pos_ids_key = position_ids[..., :cache_len] if can_cache else position_ids
+        cache_entries = self._kv_cache_entries
+        # Drop stale entries from a previous device (model was unloaded and reloaded).
+        if cache_entries and cache_entries[0]["input_ids"].device != input_ids.device:
+            cache_entries = []
+            self._kv_cache_entries = []
+        kv_cache = None
+        if can_cache:
+            for entry in cache_entries:
+                ck = entry["input_ids"]
+                ep = entry["position_ids"]
+                if (entry["cache_len"] == cache_len
+                        and ck.shape == input_ids.shape and torch.equal(ck, input_ids)
+                        and entry["ref_id"] == ref_id
+                        and ep.shape == pos_ids_key.shape and torch.equal(ep, pos_ids_key)):
+                    kv_cache = entry
+                    break
+
+        if kv_cache is not None:
+            # Hot path: project Q/K/V only for fresh positions; past_key_value prepends cached AR K/V.
+            hidden_states = inputs_embeds[:, cache_len:]
+            sliced_freqs = tuple(t[..., cache_len:, :] for t in freqs_cis)
+            for i, layer in enumerate(self.language_model.layers):
+                transformer_options["block_index"] = i
+                K_i, V_i = kv_cache["kv"][i]
+                hidden_states, _ = layer(
+                    x=hidden_states, attention_mask=None, freqs_cis=sliced_freqs, optimized_attention=two_pass_attn,
+                    past_key_value=(K_i, V_i, cache_len),
+                )
+        else:
+            # Cold path: run full sequence; if cacheable, snapshot K/V at AR positions.
+            snapshots = [] if can_cache else None
+            past_kv_cold = () if can_cache else None
+            hidden_states = inputs_embeds
+            for i, layer in enumerate(self.language_model.layers):
+                transformer_options["block_index"] = i
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args, _layer=layer):
+                        out = {}
+                        out["x"], _ = _layer(
+                            x=args["x"], attention_mask=args.get("attention_mask"),
+                            freqs_cis=args["freqs_cis"], optimized_attention=args["optimized_attention"],
+                            past_key_value=None,
+                        )
+                        return out
+                    out = blocks_replace[("double_block", i)](
+                        {"x": hidden_states, "attention_mask": None,
+                         "freqs_cis": freqs_cis, "optimized_attention": two_pass_attn,
+                         "transformer_options": transformer_options},
+                        {"original_block": block_wrap},
+                    )
+                    hidden_states = out["x"]
+                else:
+                    hidden_states, present_kv = layer(
+                        x=hidden_states, attention_mask=None,
+                        freqs_cis=freqs_cis, optimized_attention=two_pass_attn,
+                        past_key_value=past_kv_cold,
+                    )
+                    if snapshots is not None:
+                        K, V, _ = present_kv
+                        snapshots.append((K[:, :, :cache_len].contiguous(),
+                                          V[:, :, :cache_len].contiguous()))
+            if snapshots is not None:
+                # Cap at 2 entries (cond + uncond). Multi-cond workflows LRU-evict.
+                new_entry = {
+                    "input_ids": input_ids.clone(),
+                    "cache_len": cache_len,
+                    "kv": snapshots,
+                    "ref_id": ref_id,
+                    "position_ids": pos_ids_key.clone(),
+                }
+                self._kv_cache_entries = (cache_entries + [new_entry])[-2:]
+
+        if self.language_model.norm is not None:
+            hidden_states = self.language_model.norm(hidden_states)
+
+        # Slice target-image positions before the final projection so the Linear only runs on tgt_image_len tokens.
+        # In the hot path hidden_states starts at original position cache_len, so masks/indices shift by cache_len.
+        sliced_offset = cache_len if kv_cache is not None else 0
+        if vinput_mask is not None:
+            vmask = vinput_mask.to(x.device).bool()
+            if sliced_offset > 0:
+                vmask = vmask[:, sliced_offset:]
+            target_hidden = hidden_states[vmask].view(B, -1, hidden_states.shape[-1])[:, :tgt_image_len]
+        else:
+            txt_seq_len = input_ids.shape[1]
+            start = txt_seq_len - sliced_offset
+            target_hidden = hidden_states[:, start:start + tgt_image_len]
+        x_pred_tgt = self.final_layer2(target_hidden)
+
+        # fp32 final subtraction, bf16 here noticeably degrades samples.
+        x_pred_img = einops.rearrange(
+            x_pred_tgt, 'B (H W) (C p1 p2) -> B C (H p1) (W p2)',
+            H=h_p, W=w_p, p1=self.patch_size, p2=self.patch_size,
+        )
+        return (x.float() - x_pred_img.float()) / sigma.view(B, 1, 1, 1).clamp_min(1e-3)

comfy/ldm/hidream_o1/utils.py

@@ -0,0 +1,173 @@
+"""HiDream-O1 input-prep helpers: image/resolution math and unified-sequence
+RoPE position-id assembly. The fix_point offset in get_rope_index_fix_point
+lets the target image and patchified ref images share spatial RoPE positions
+despite living at different sequence indices — same 2D image plane.
+"""
+
+import math
+from typing import Optional
+
+import torch
+
+
+PATCH_SIZE = 32
+CONDITION_IMAGE_SIZE = 384  # ViT-side base size for ref images
+
+
+def resize_tensor(img_t, image_size, patch_size=16):
+    """img_t: (1, 3, H, W) float [0, 1]. Fit to image_size**2 area, patch-aligned, center-cropped."""
+
+    while min(img_t.shape[-2], img_t.shape[-1]) >= 2 * image_size: # Pre-halves with 2x2 box averaging while the image is still very large
+        img_t = torch.nn.functional.avg_pool2d(img_t, kernel_size=2, stride=2)
+
+    _, _, height, width = img_t.shape
+    m = patch_size
+    s_max = image_size * image_size
+    scale = math.sqrt(s_max / (width * height))
+
+    candidates = [
+        (round(width * scale) // m * m, round(height * scale) // m * m),
+        (round(width * scale) // m * m, math.floor(height * scale) // m * m),
+        (math.floor(width * scale) // m * m, round(height * scale) // m * m),
+        (math.floor(width * scale) // m * m, math.floor(height * scale) // m * m),
+    ]
+    candidates = sorted(candidates, key=lambda x: x[0] * x[1], reverse=True)
+    new_size = candidates[-1]
+    for c in candidates:
+        if c[0] * c[1] <= s_max:
+            new_size = c
+            break
+
+    new_w, new_h = new_size
+    s1 = width / new_w
+    s2 = height / new_h
+    if s1 < s2:
+        resize_w, resize_h = new_w, round(height / s1)
+    else:
+        resize_w, resize_h = round(width / s2), new_h
+    img_t = torch.nn.functional.interpolate(img_t, size=(resize_h, resize_w), mode="bicubic")
+    top = (resize_h - new_h) // 2
+    left = (resize_w - new_w) // 2
+    return img_t[..., top:top + new_h, left:left + new_w]
+
+
+def calculate_dimensions(max_size, ratio):
+    """(W, H) for an aspect ratio fitting in max_size**2 area, 32-aligned."""
+    width = math.sqrt(max_size * max_size * ratio)
+    height = width / ratio
+    width = int(width / 32) * 32
+    height = int(height / 32) * 32
+    return width, height
+
+
+def ref_max_size(target_max_dim, k):
+    """K-dependent ref-image max dim before patchifying."""
+    if k == 1:
+        return target_max_dim
+    if k == 2:
+        return target_max_dim * 48 // 64
+    if k <= 4:
+        return target_max_dim // 2
+    if k <= 8:
+        return target_max_dim * 24 // 64
+    return target_max_dim // 4
+
+
+def cond_image_size(k):
+    """K-dependent ViT-side image size."""
+    if k <= 4:
+        return CONDITION_IMAGE_SIZE
+    if k <= 8:
+        return CONDITION_IMAGE_SIZE * 48 // 64
+    return CONDITION_IMAGE_SIZE // 2
+
+
+def get_rope_index_fix_point(
+    spatial_merge_size: int,
+    image_token_id: int,
+    vision_start_token_id: int,
+    input_ids: Optional[torch.LongTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    skip_vision_start_token=None,
+    fix_point: int = 4096,
+):
+    mrope_position_deltas = []
+    if input_ids is not None and image_grid_thw is not None:
+        total_input_ids = input_ids
+        if attention_mask is None:
+            attention_mask = torch.ones_like(total_input_ids)
+        position_ids = torch.ones(
+            3, input_ids.shape[0], input_ids.shape[1],
+            dtype=input_ids.dtype, device=input_ids.device,
+        )
+        attention_mask = attention_mask.to(total_input_ids.device)
+        for i, input_ids_b in enumerate(total_input_ids):
+            fp = fix_point
+            image_index = 0
+            input_ids_b = input_ids_b[attention_mask[i] == 1]
+            vision_start_indices = torch.argwhere(input_ids_b == vision_start_token_id).squeeze(1)
+            vision_tokens = input_ids_b[vision_start_indices + 1]
+            image_nums = (vision_tokens == image_token_id).sum()
+            input_tokens = input_ids_b.tolist()
+            llm_pos_ids_list = []
+            st = 0
+            remain_images = image_nums
+            for _ in range(image_nums):
+                if image_token_id in input_tokens and remain_images > 0:
+                    ed = input_tokens.index(image_token_id, st)
+                else:
+                    ed = len(input_tokens) + 1
+                t = image_grid_thw[image_index][0]
+                h = image_grid_thw[image_index][1]
+                w = image_grid_thw[image_index][2]
+                image_index += 1
+                remain_images -= 1
+                llm_grid_t = t.item()
+                llm_grid_h = h.item() // spatial_merge_size
+                llm_grid_w = w.item() // spatial_merge_size
+                text_len = ed - st
+                text_len -= skip_vision_start_token[image_index - 1]
+                text_len = max(0, text_len)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten()
+                h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
+                w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
+
+                if skip_vision_start_token[image_index - 1]:
+                    if fp > 0:
+                        fp = fp - st_idx
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + fp + st_idx)
+                    fp = 0
+                else:
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+                st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+            if st < len(input_tokens):
+                st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                text_len = len(input_tokens) - st
+                llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+            position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
+            mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
+        mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
+        return position_ids, mrope_position_deltas
+
+    if attention_mask is not None:
+        position_ids = attention_mask.long().cumsum(-1) - 1
+        position_ids.masked_fill_(attention_mask == 0, 1)
+        position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
+        max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+        mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+    else:
+        position_ids = (
+            torch.arange(input_ids.shape[1], device=input_ids.device)
+            .view(1, 1, -1).expand(3, input_ids.shape[0], -1)
+        )
+        mrope_position_deltas = torch.zeros(
+            [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype,
+        )
+    return position_ids, mrope_position_deltas

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -328,7 +328,7 @@ class CrossAttention(nn.Module):
         kv = torch.cat((k, v), dim=-1)
         split_size = kv.shape[-1] // self.num_heads // 2
 
-        kv = kv.view(1, -1, self.num_heads, split_size * 2)
+        kv = kv.view(b, -1, self.num_heads, split_size * 2)
         k, v = torch.split(kv, split_size, dim=-1)
 
         q = q.view(b, s1, self.num_heads, self.head_dim)
@@ -398,7 +398,7 @@ class Attention(nn.Module):
         qkv_combined = torch.cat((query, key, value), dim=-1)
         split_size = qkv_combined.shape[-1] // self.num_heads // 3
 
-        qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)
+        qkv = qkv_combined.view(B, -1, self.num_heads, split_size * 3)
         query, key, value = torch.split(qkv, split_size, dim=-1)
 
         query = query.reshape(B, N, self.num_heads, self.head_dim)
@@ -607,9 +607,13 @@ class HunYuanDiTPlain(nn.Module):
     def forward(self, x, t, context, transformer_options = {}, **kwargs):
 
         x = x.movedim(-1, -2)
-        uncond_emb, cond_emb = context.chunk(2, dim = 0)
 
-        context = torch.cat([cond_emb, uncond_emb], dim = 0)
+        swap_cfg_halves = context.shape[0] >= 2
+
+        if swap_cfg_halves:
+            first_half, second_half = context.chunk(2, dim = 0)
+            context = torch.cat([second_half, first_half], dim = 0)
+
         main_condition = context
 
         t = 1.0 - t
@@ -657,5 +661,8 @@ class HunYuanDiTPlain(nn.Module):
         output = self.final_layer(combined)
         output =  output.movedim(-2, -1) * (-1.0)
 
-        cond_emb, uncond_emb = output.chunk(2, dim = 0)
-        return torch.cat([uncond_emb, cond_emb])
+        if swap_cfg_halves:
+            first_half, second_half = output.chunk(2, dim = 0)
+            output = torch.cat([second_half, first_half], dim = 0)
+
+        return output

comfy/ldm/lightricks/av_model.py

@@ -16,31 +16,31 @@ from comfy.ldm.lightricks.model import (
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import comfy.ldm.common_dit
+import comfy.model_prefetch
 
 class CompressedTimestep:
     """Store video timestep embeddings in compressed form using per-frame indexing."""
     __slots__ = ('data', 'batch_size', 'num_frames', 'patches_per_frame', 'feature_dim')
 
-    def __init__(self, tensor: torch.Tensor, patches_per_frame: int):
+    def __init__(self, tensor: torch.Tensor, patches_per_frame: int, per_frame: bool = False):
         """
-        tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame
-        patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression
+        tensor: [batch, num_tokens, feature_dim] (per-token, default) or
+                [batch, num_frames, feature_dim] (per_frame=True, already compressed).
+        patches_per_frame: spatial patches per frame; pass None to disable compression.
         """
-        self.batch_size, num_tokens, self.feature_dim = tensor.shape
-
-        # Check if compression is valid (num_tokens must be divisible by patches_per_frame)
-        if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame:
+        self.batch_size, n, self.feature_dim = tensor.shape
+        if per_frame:
             self.patches_per_frame = patches_per_frame
-            self.num_frames = num_tokens // patches_per_frame
-
-            # Reshape to [batch, frames, patches_per_frame, feature_dim] and store one value per frame
-            # All patches in a frame are identical, so we only keep the first one
-            reshaped = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim)
-            self.data = reshaped[:, :, 0, :].contiguous()  # [batch, frames, feature_dim]
+            self.num_frames = n
+            self.data = tensor
+        elif patches_per_frame is not None and n >= patches_per_frame and n % patches_per_frame == 0:
+            self.patches_per_frame = patches_per_frame
+            self.num_frames = n // patches_per_frame
+            # All patches in a frame are identical — keep only the first.
+            self.data = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim)[:, :, 0, :].contiguous()
         else:
-            # Not divisible or too small - store directly without compression
             self.patches_per_frame = 1
-            self.num_frames = num_tokens
+            self.num_frames = n
             self.data = tensor
 
     def expand(self):
@@ -715,32 +715,35 @@ class LTXAVModel(LTXVModel):
 
     def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
         """Prepare timestep embeddings."""
-        # TODO: some code reuse is needed here.
         grid_mask = kwargs.get("grid_mask", None)
-        if grid_mask is not None:
-            timestep = timestep[:, grid_mask]
-
-        timestep_scaled = timestep * self.timestep_scale_multiplier
-
-        v_timestep, v_embedded_timestep = self.adaln_single(
-            timestep_scaled.flatten(),
-            {"resolution": None, "aspect_ratio": None},
-            batch_size=batch_size,
-            hidden_dtype=hidden_dtype,
-        )
-
-        # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width]
-        # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width
         orig_shape = kwargs.get("orig_shape")
         has_spatial_mask = kwargs.get("has_spatial_mask", None)
         v_patches_per_frame = None
         if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5:
-            # orig_shape[3] = height, orig_shape[4] = width (in latent space)
             v_patches_per_frame = orig_shape[3] * orig_shape[4]
 
-        # Reshape to [batch_size, num_tokens, dim] and compress for storage
-        v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame)
-        v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame)
+        # Used by compute_prompt_timestep and the audio cross-attention paths.
+        timestep_scaled = (timestep[:, grid_mask] if grid_mask is not None else timestep) * self.timestep_scale_multiplier
+
+        # When patches in a frame share a timestep (no spatial mask), project one row per frame instead of one per token
+        per_frame_path = v_patches_per_frame is not None and (timestep.numel() // batch_size) % v_patches_per_frame == 0
+        if per_frame_path:
+            per_frame = timestep.reshape(batch_size, -1, v_patches_per_frame)[:, :, 0]
+            if grid_mask is not None:
+                # All-or-nothing per frame when has_spatial_mask=False.
+                per_frame = per_frame[:, grid_mask[::v_patches_per_frame]]
+            ts_input = per_frame * self.timestep_scale_multiplier
+        else:
+            ts_input = timestep_scaled
+
+        v_timestep, v_embedded_timestep = self.adaln_single(
+            ts_input.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+        v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame, per_frame=per_frame_path)
+        v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame, per_frame=per_frame_path)
 
         v_prompt_timestep = compute_prompt_timestep(
             self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
@@ -907,9 +910,11 @@ class LTXAVModel(LTXVModel):
         """Process transformer blocks for LTXAV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        prefetch_queue = comfy.model_prefetch.make_prefetch_queue(list(self.transformer_blocks), vx.device, transformer_options)
 
         # Process transformer blocks
         for i, block in enumerate(self.transformer_blocks):
+            comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, block)
             if ("double_block", i) in blocks_replace:
 
                 def block_wrap(args):
@@ -982,6 +987,8 @@ class LTXAVModel(LTXVModel):
                     a_prompt_timestep=a_prompt_timestep,
                 )
 
+        comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, None)
+
         return [vx, ax]
 
     def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):

comfy/ldm/lightricks/model.py

@@ -358,6 +358,61 @@ def apply_split_rotary_emb(input_tensor, cos, sin):
     return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output
 
 
+class GuideAttentionMask:
+    """Holds the two per-group masks for LTXV guide self-attention.
+    _attention_with_guide_mask splits queries into noisy and tracked-guide
+    groups, so the largest mask is (1, 1, tracked_count, T).
+    """
+    __slots__ = ("guide_start", "tracked_count", "noisy_mask", "tracked_mask")
+
+    def __init__(self, total_tokens, guide_start, tracked_count, tracked_weights):
+        device = tracked_weights.device
+        dtype = tracked_weights.dtype
+        finfo = torch.finfo(dtype)
+
+        pos = tracked_weights > 0
+        log_w = torch.full_like(tracked_weights, finfo.min)
+        log_w[pos] = torch.log(tracked_weights[pos].clamp(min=finfo.tiny))
+
+        self.guide_start = guide_start
+        self.tracked_count = tracked_count
+
+        self.noisy_mask = torch.zeros((1, 1, 1, total_tokens), device=device, dtype=dtype)
+        self.noisy_mask[:, :, :, guide_start:guide_start + tracked_count] = log_w.view(1, 1, 1, -1)
+
+        self.tracked_mask = torch.zeros((1, 1, tracked_count, total_tokens), device=device, dtype=dtype)
+        self.tracked_mask[:, :, :, :guide_start] = log_w.view(1, 1, -1, 1)
+
+
+def _attention_with_guide_mask(q, k, v, heads, guide_mask, attn_precision, transformer_options):
+    """Apply the guide mask by partitioning Q into noisy and tracked-guide
+    groups, so each group needs only its own sub-mask. Avoids materializing
+    the (1,1,T,T) dense mask.
+    """
+    guide_start = guide_mask.guide_start
+    tracked_end = guide_start + guide_mask.tracked_count
+
+    out = torch.empty_like(q)
+
+    if guide_start > 0: # In practice currently guides are always after noise, guard for safety if this changes.
+        out[:, :guide_start, :] = comfy.ldm.modules.attention.optimized_attention(
+            q[:, :guide_start, :], k, v, heads, mask=guide_mask.noisy_mask,
+            attn_precision=attn_precision, transformer_options=transformer_options,
+            low_precision_attention=False, # sageattn mask support is unreliable
+        )
+    out[:, guide_start:tracked_end, :] = comfy.ldm.modules.attention.optimized_attention(
+        q[:, guide_start:tracked_end, :], k, v, heads, mask=guide_mask.tracked_mask,
+        attn_precision=attn_precision, transformer_options=transformer_options,
+        low_precision_attention=False,
+    )
+    if tracked_end < q.shape[1]: # Every guide token is tracked, and nothing comes after them, guard for safety if this changes.
+        out[:, tracked_end:, :] = comfy.ldm.modules.attention.optimized_attention(
+            q[:, tracked_end:, :], k, v, heads,
+            attn_precision=attn_precision, transformer_options=transformer_options,
+        )
+    return out
+
+
 class CrossAttention(nn.Module):
     def __init__(
         self,
@@ -412,8 +467,10 @@ class CrossAttention(nn.Module):
 
         if mask is None:
             out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+        elif isinstance(mask, GuideAttentionMask):
+            out = _attention_with_guide_mask(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
         else:
-            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, mask=mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
 
         # Apply per-head gating if enabled
         if self.to_gate_logits is not None:
@@ -1063,7 +1120,9 @@ class LTXVModel(LTXBaseModel):
                 additional_args["resolved_guide_entries"] = resolved_entries
 
             keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]
-            pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
+
+            if keyframe_idxs.shape[2] > 0: # Guard for the case of no keyframes surviving
+                pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
 
             # Total surviving guide tokens (all guides)
             additional_args["num_guide_tokens"] = keyframe_idxs.shape[2]
@@ -1099,12 +1158,12 @@ class LTXVModel(LTXBaseModel):
         if not resolved_entries:
             return None
 
-        # Check if any attenuation is actually needed
-        needs_attenuation = any(
-            e["strength"] < 1.0 or e.get("pixel_mask") is not None
+        # strength != 1.0 means we want to either attenuate (< 1) or amplify (> 1) guide attention.
+        needs_mask = any(
+            e["strength"] != 1.0 or e.get("pixel_mask") is not None
             for e in resolved_entries
         )
-        if not needs_attenuation:
+        if not needs_mask:
             return None
 
         # Build per-guide-token weights for all tracked guide tokens.
@@ -1159,16 +1218,11 @@ class LTXVModel(LTXBaseModel):
         # Concatenate per-token weights for all tracked guides
         tracked_weights = torch.cat(all_weights, dim=1)  # (1, total_tracked)
 
-        # Check if any weight is actually < 1.0 (otherwise no attenuation needed)
-        if (tracked_weights >= 1.0).all():
+        # Skip when every weight is exactly 1.0 (additive bias would be 0).
+        if (tracked_weights == 1.0).all():
             return None
 
-        # Build the mask: guide tokens are at the end of the sequence.
-        # Tracked guides come first (in order), untracked follow.
-        return self._build_self_attention_mask(
-            total_tokens, num_guide_tokens, total_tracked,
-            tracked_weights, guide_start, device, dtype,
-        )
+        return GuideAttentionMask(total_tokens, guide_start, total_tracked, tracked_weights)
 
     @staticmethod
     def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):
@@ -1234,45 +1288,6 @@ class LTXVModel(LTXBaseModel):
 
         return rearrange(latent_mask, "b 1 f h w -> b (f h w)")
 
-    @staticmethod
-    def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked_count,
-                                    tracked_weights, guide_start, device, dtype):
-        """Build a log-space additive self-attention bias mask.
-
-        Attenuates attention between noisy tokens and tracked guide tokens.
-        Untracked guide tokens (at the end of the guide portion) keep full attention.
-
-        Args:
-            total_tokens: Total sequence length.
-            num_guide_tokens: Total guide tokens (all guides) at end of sequence.
-            tracked_count: Number of tracked guide tokens (first in the guide portion).
-            tracked_weights: (1, tracked_count) tensor, values in [0, 1].
-            guide_start: Index where guide tokens begin in the sequence.
-            device: Target device.
-            dtype: Target dtype.
-
-        Returns:
-            (1, 1, total_tokens, total_tokens) additive bias mask.
-            0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked.
-        """
-        finfo = torch.finfo(dtype)
-        mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype)
-        tracked_end = guide_start + tracked_count
-
-        # Convert weights to log-space bias
-        w = tracked_weights.to(device=device, dtype=dtype)  # (1, tracked_count)
-        log_w = torch.full_like(w, finfo.min)
-        positive_mask = w > 0
-        if positive_mask.any():
-            log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny))
-
-        # noisy → tracked guides: each noisy row gets the same per-guide weight
-        mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1)
-        # tracked guides → noisy: each guide row broadcasts its weight across noisy cols
-        mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1)
-
-        return mask
-
     def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs):
         """Process transformer blocks for LTXV."""
         patches_replace = transformer_options.get("patches_replace", {})

comfy/ldm/modules/attention.py

@@ -14,6 +14,8 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
 
+TORCH_HAS_GQA = model_management.torch_version_numeric >= (2, 5)
+
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
@@ -150,7 +152,12 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    scale = dim_head ** -0.5
+    if kwargs.get("enable_gqa", False) and q.shape[-3] != k.shape[-3]:
+        n_rep = q.shape[-3] // k.shape[-3]
+        k = k.repeat_interleave(n_rep, dim=-3)
+        v = v.repeat_interleave(n_rep, dim=-3)
+
+    scale = kwargs.get("scale", dim_head ** -0.5)
 
     h = heads
     if skip_reshape:
@@ -219,6 +226,10 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         b, _, dim_head = query.shape
         dim_head //= heads
 
+    if "scale" in kwargs:
+        # Pre-scale query to match requested scale (cancels internal 1/sqrt(dim_head))
+        query = query * (kwargs["scale"] * dim_head ** 0.5)
+
     if skip_reshape:
         query = query.reshape(b * heads, -1, dim_head)
         value = value.reshape(b * heads, -1, dim_head)
@@ -290,7 +301,7 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    scale = dim_head ** -0.5
+    scale = kwargs.get("scale", dim_head ** -0.5)
 
     if skip_reshape:
          q, k, v = map(
@@ -500,8 +511,13 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
+    # Pass through extra SDPA kwargs (scale, enable_gqa) if provided
+    # enable_gqa requires PyTorch 2.5+; older versions use manual KV expansion above
+    sdpa_keys = ("scale", "enable_gqa") if TORCH_HAS_GQA else ("scale",)
+    sdpa_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys}
+
     if SDP_BATCH_LIMIT >= b:
-        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False, **sdpa_extra)
         if not skip_output_reshape:
             out = (
                 out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -519,7 +535,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
                 k[i : i + SDP_BATCH_LIMIT],
                 v[i : i + SDP_BATCH_LIMIT],
                 attn_mask=m,
-                dropout_p=0.0, is_causal=False
+                dropout_p=0.0, is_causal=False, **sdpa_extra
             ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 

comfy/ldm/modules/diffusionmodules/util.py

@@ -140,7 +140,7 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     alphas = alphacums[ddim_timesteps]
     alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
 
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    # according to the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
     if verbose:
         logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')

comfy/ldm/moge/geometry.py

@@ -0,0 +1,189 @@
+"""Pure-torch + scipy geometry helpers for MoGe inference and mesh export."""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy.optimize import least_squares
+
+def normalized_view_plane_uv(width: int, height: int, aspect_ratio: Optional[float] = None,
+                             dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> torch.Tensor:
+    """Normalized view-plane UV coordinates with corners at +/-(W, H)/diagonal."""
+    if aspect_ratio is None:
+        aspect_ratio = width / height
+    span_x = aspect_ratio / (1 + aspect_ratio ** 2) ** 0.5
+    span_y = 1.0 / (1 + aspect_ratio ** 2) ** 0.5
+    u = torch.linspace(-span_x * (width - 1) / width, span_x * (width - 1) / width, width, dtype=dtype, device=device)
+    v = torch.linspace(-span_y * (height - 1) / height, span_y * (height - 1) / height, height, dtype=dtype, device=device)
+    u, v = torch.meshgrid(u, v, indexing="xy")
+    return torch.stack([u, v], dim=-1)
+
+
+def intrinsics_from_focal_center(fx: torch.Tensor, fy: torch.Tensor, cx: torch.Tensor, cy: torch.Tensor) -> torch.Tensor:
+    """Assemble (..., 3, 3) intrinsics from broadcastable fx, fy, cx, cy."""
+    fx, fy, cx, cy = [torch.as_tensor(v) for v in (fx, fy, cx, cy)]
+    fx, fy, cx, cy = torch.broadcast_tensors(fx, fy, cx, cy)
+    zero = torch.zeros_like(fx)
+    one = torch.ones_like(fx)
+    return torch.stack([
+        torch.stack([fx,   zero, cx], dim=-1),
+        torch.stack([zero, fy,   cy], dim=-1),
+        torch.stack([zero, zero, one], dim=-1),
+    ], dim=-2)
+
+
+def depth_map_to_point_map(depth: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor:
+    """Back-project a (..., H, W) depth map through K^-1 to (..., H, W, 3) camera-space points.
+
+    Intrinsics use normalized image coords (x in [0, 1] left->right, y in [0, 1] top->bottom).
+    """
+    H, W = depth.shape[-2:]
+    device, dtype = depth.device, depth.dtype
+    u = (torch.arange(W, dtype=dtype, device=device) + 0.5) / W
+    v = (torch.arange(H, dtype=dtype, device=device) + 0.5) / H
+    grid_v, grid_u = torch.meshgrid(v, u, indexing="ij")
+    pix = torch.stack([grid_u, grid_v, torch.ones_like(grid_u)], dim=-1)
+    K_inv = torch.linalg.inv(intrinsics)
+    rays = torch.einsum("...ij,hwj->...hwi", K_inv, pix)
+    return rays * depth.unsqueeze(-1)
+
+
+def _solve_optimal_shift(uv: np.ndarray, xyz: np.ndarray,
+                         focal: Optional[float] = None) -> Tuple[float, float]:
+    """LM-solve for z-shift; when focal is None, also recovers the optimal focal."""
+    uv = uv.reshape(-1, 2)
+    xy = xyz[..., :2].reshape(-1, 2)
+    z = xyz[..., 2].reshape(-1)
+
+    def fn(shift):
+        xy_proj = xy / (z + shift)[:, None]
+        f = focal if focal is not None else (xy_proj * uv).sum() / np.square(xy_proj).sum()
+        return (f * xy_proj - uv).ravel()
+
+    sol = least_squares(fn, x0=0.0, ftol=1e-3, method="lm")
+    shift = float(np.asarray(sol["x"]).squeeze())
+    if focal is None:
+        xy_proj = xy / (z + shift)[:, None]
+        focal = float((xy_proj * uv).sum() / np.square(xy_proj).sum())
+    return shift, focal
+
+
+def recover_focal_shift(points: torch.Tensor, mask: Optional[torch.Tensor] = None,
+                        focal: Optional[torch.Tensor] = None, downsample_size: Tuple[int, int] = (64, 64)
+                        ) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Recover the focal length and z-shift that turn points into a metric point map.
+
+    Optical center is at the image center; returned focal is relative to half the image diagonal.
+    Returns (focal, shift) on the same device/dtype as points.
+    """
+    shape = points.shape
+    H, W = shape[-3], shape[-2]
+    points_b = points.reshape(-1, H, W, 3)
+    mask_b = None if mask is None else mask.reshape(-1, H, W)
+    focal_b = None if focal is None else focal.reshape(-1)
+
+    uv = normalized_view_plane_uv(W, H, dtype=points.dtype, device=points.device)
+
+    points_lr = F.interpolate(points_b.permute(0, 3, 1, 2), downsample_size, mode="nearest").permute(0, 2, 3, 1)
+    uv_lr = F.interpolate(uv.unsqueeze(0).permute(0, 3, 1, 2), downsample_size, mode="nearest").squeeze(0).permute(1, 2, 0)
+    mask_lr = None
+    if mask_b is not None:
+        mask_lr = F.interpolate(mask_b.to(torch.float32).unsqueeze(1), downsample_size, mode="nearest").squeeze(1) > 0
+
+    uv_np = uv_lr.detach().cpu().numpy()
+    points_np = points_lr.detach().cpu().numpy()
+    mask_np = None if mask_lr is None else mask_lr.detach().cpu().numpy()
+    focal_np = None if focal_b is None else focal_b.detach().cpu().numpy()
+
+    out_focal: list = []
+    out_shift: list = []
+    for i in range(points_b.shape[0]):
+        if mask_np is None:
+            xyz_i = points_np[i].reshape(-1, 3)
+            uv_i = uv_np.reshape(-1, 2)
+        else:
+            sel = mask_np[i]
+            if sel.sum() < 2:
+                out_focal.append(1.0)
+                out_shift.append(0.0)
+                continue
+            xyz_i = points_np[i][sel]
+            uv_i = uv_np[sel]
+        if focal_np is None:
+            shift_i, focal_i = _solve_optimal_shift(uv_i, xyz_i)
+            out_focal.append(focal_i)
+        else:
+            shift_i, _ = _solve_optimal_shift(uv_i, xyz_i, focal=float(focal_np[i]))
+        out_shift.append(shift_i)
+
+    shift_t = torch.tensor(out_shift, device=points.device, dtype=points.dtype).reshape(shape[:-3])
+    if focal is None:
+        focal_t = torch.tensor(out_focal, device=points.device, dtype=points.dtype).reshape(shape[:-3])
+    else:
+        focal_t = focal.reshape(shape[:-3])
+    return focal_t, shift_t
+
+
+def depth_map_edge(depth: torch.Tensor, atol: Optional[float] = None, rtol: Optional[float] = None, kernel_size: int = 3) -> torch.Tensor:
+    """Per-pixel boolean: True where the local depth window's max-min span exceeds atol or rtol*depth."""
+    shape = depth.shape
+    d = depth.reshape(-1, 1, *shape[-2:])
+    pad = kernel_size // 2
+    diff = F.max_pool2d(d, kernel_size, stride=1, padding=pad) + F.max_pool2d(-d, kernel_size, stride=1, padding=pad)
+    edge = torch.zeros_like(d, dtype=torch.bool)
+    if atol is not None:
+        edge |= diff > atol
+    if rtol is not None:
+        edge |= (diff / d.clamp_min(1e-6)).nan_to_num_() > rtol
+    return edge.reshape(*shape)
+
+
+def triangulate_grid_mesh(points: torch.Tensor, mask: Optional[torch.Tensor] = None, decimation: int = 1, discontinuity_threshold: float = 0.04,
+                          depth: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Triangulate a (H, W, 3) point map into (vertices, faces, uvs) on CPU.
+
+    Vertices: pixels with finite coords (passing optional mask).  Quads with four valid corners
+    become two triangles.  depth overrides the scalar used for the rtol edge check; pass radial
+    depth for panoramas (the default points[..., 2] goes negative below the equator).
+    """
+    points = points.detach().cpu()
+    finite = torch.isfinite(points).all(dim=-1)
+    if mask is None:
+        mask = finite
+    else:
+        mask = mask.detach().cpu().to(torch.bool) & finite
+
+    if discontinuity_threshold > 0:
+        d = depth.detach().cpu() if depth is not None else points[..., 2]
+        # Replace inf with 0 so max-pool doesn't poison neighbourhoods (mask above already excludes those pixels).
+        d_finite = torch.where(finite, d, torch.zeros_like(d))
+        edge = depth_map_edge(d_finite, rtol=discontinuity_threshold)
+        mask = mask & ~edge
+
+    if decimation > 1:
+        points = points[::decimation, ::decimation].contiguous()
+        mask = mask[::decimation, ::decimation].contiguous()
+    H, W = points.shape[:2]
+
+    flat_mask = mask.reshape(-1)
+    idx = torch.full((H * W,), -1, dtype=torch.long)
+    n_valid = int(flat_mask.sum().item())
+    idx[flat_mask] = torch.arange(n_valid, dtype=torch.long)
+    idx = idx.reshape(H, W)
+
+    vertices = points.reshape(-1, 3)[flat_mask].contiguous()
+
+    yy, xx = torch.meshgrid(torch.arange(H), torch.arange(W), indexing="ij")
+    u = xx.float() / max(W - 1, 1)
+    v = yy.float() / max(H - 1, 1)
+    uvs = torch.stack([u, v], dim=-1).reshape(-1, 2)[flat_mask].contiguous()
+
+    a, b, c, d = idx[:-1, :-1], idx[:-1, 1:], idx[1:, 1:], idx[1:, :-1]
+    quad_ok = (a >= 0) & (b >= 0) & (c >= 0) & (d >= 0)
+    a, b, c, d = a[quad_ok], b[quad_ok], c[quad_ok], d[quad_ok]
+    faces = torch.cat([torch.stack([a, b, c], dim=-1), torch.stack([a, c, d], dim=-1)], dim=0).contiguous()
+    return vertices, faces, uvs