Merge branch 'Comfy-Org:master' into ascend/quicker_generate

.ci/update_windows/update.py

@@ -53,6 +53,16 @@ try:
     repo.stash(ident)
 except KeyError:
     print("nothing to stash")  # noqa: T201
+except:
+    print("Could not stash, cleaning index and trying again.")  # noqa: T201
+    repo.state_cleanup()
+    repo.index.read_tree(repo.head.peel().tree)
+    repo.index.write()
+    try:
+        repo.stash(ident)
+    except KeyError:
+        print("nothing to stash.")  # noqa: T201
+
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
 print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:
@@ -66,8 +76,10 @@ if branch is None:
     try:
         ref = repo.lookup_reference('refs/remotes/origin/master')
     except:
-        print("pulling.")  # noqa: T201
-        pull(repo)
+        print("fetching.")  # noqa: T201
+        for remote in repo.remotes:
+            if remote.name == "origin":
+                remote.fetch()
         ref = repo.lookup_reference('refs/remotes/origin/master')
     repo.checkout(ref)
     branch = repo.lookup_branch('master')
@@ -149,3 +161,4 @@ try:
         shutil.copy(stable_update_script, stable_update_script_to)
 except:
     pass
+

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -1,5 +1,5 @@
-As of the time of writing this you need this preview driver for best results:
-https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.html
+As of the time of writing this you need this driver for best results:
+https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
 
 HOW TO RUN:
 
@@ -25,3 +25,4 @@ In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
 Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
 
 
+

.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat

@@ -1,3 +1,3 @@
 ..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
 pause

.ci/windows_nvidia_base_files/run_nvidia_gpu.bat

@@ -1,3 +1,3 @@
 .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
 pause

.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat

@@ -1,3 +1,3 @@
 .\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation
-echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
+echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe
 pause

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

@@ -14,7 +14,7 @@ jobs:
       contents: "write"
       packages: "write"
       pull-requests: "read"
-    name: "Release NVIDIA Default (cu129)"
+    name: "Release NVIDIA Default (cu130)"
     uses: ./.github/workflows/stable-release.yml
     with:
       git_tag: ${{ inputs.git_tag }}
@@ -65,11 +65,11 @@ jobs:
       contents: "write"
       packages: "write"
       pull-requests: "read"
-    name: "Release AMD ROCm 6.4.4"
+    name: "Release AMD ROCm 7.1.1"
     uses: ./.github/workflows/stable-release.yml
     with:
       git_tag: ${{ inputs.git_tag }}
-      cache_tag: "rocm644"
+      cache_tag: "rocm711"
       python_minor: "12"
       python_patch: "10"
       rel_name: "amd"

.github/workflows/stable-release.yml

@@ -117,7 +117,7 @@ jobs:
           ./python.exe get-pip.py
           ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
 
-          grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
+          grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
           ./python.exe -s -m pip install -r requirements_comfyui.txt
           rm requirements_comfyui.txt
 

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

.github/workflows/test-ci.yml

@@ -5,6 +5,7 @@ on:
   push:
     branches:
       - master
+      - release/**
     paths-ignore:
       - 'app/**'
       - 'input/**'
@@ -19,6 +20,7 @@ jobs:
   test-stable:
     strategy:
       fail-fast: false
+      max-parallel: 1  # This forces sequential execution
       matrix:
         # os: [macos, linux, windows]
         # os: [macos, linux]
@@ -73,6 +75,7 @@ jobs:
   test-unix-nightly:
     strategy:
       fail-fast: false
+      max-parallel: 1  # This forces sequential execution
       matrix:
         # os: [macos, linux]
         os: [linux]

.github/workflows/test-execution.yml

@@ -2,9 +2,9 @@ name: Execution Tests
 
 on:
   push:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
   pull_request:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
 
 jobs:
   test:

.github/workflows/test-launch.yml

@@ -2,9 +2,9 @@ name: Test server launches without errors
 
 on:
   push:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
   pull_request:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
 
 jobs:
   test:
@@ -32,7 +32,9 @@ jobs:
       working-directory: ComfyUI
     - name: Check for unhandled exceptions in server log
       run: |
-        if grep -qE "Exception|Error" console_output.log; then
+        grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" console_output.log | grep -v "Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \"ImportError: No module named 'triton'\", 'capabilities': \[\]}" > console_output_filtered.log
+        cat console_output_filtered.log
+        if grep -qE "Exception|Error" console_output_filtered.log; then
           echo "Unhandled exception/error found in server log."
           exit 1
         fi

.github/workflows/test-unit.yml

@@ -2,9 +2,9 @@ name: Unit Tests
 
 on:
   push:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
   pull_request:
-    branches: [ main, master ]
+    branches: [ main, master, release/** ]
 
 jobs:
   test:

.github/workflows/update-version.yml

@@ -6,6 +6,7 @@ on:
       - "pyproject.toml"
     branches:
       - master
+      - release/**
 
 jobs:
   update-version:

CODEOWNERS

@@ -1,3 +1,2 @@
 # Admins
-* @comfyanonymous
-* @kosinkadink
+* @comfyanonymous @kosinkadink @guill

README.md

@@ -67,6 +67,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
    - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
    - [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/)
 - 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)
@@ -79,6 +81,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
    - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
+   - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -116,6 +119,9 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
    - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - 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.
    - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
@@ -206,6 +212,8 @@ Python 3.14 works but you may encounter issues with the torch compile node. The
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
+torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch unless it is less than 2 weeks old.
+
 ### Instructions:
 
 Git clone this repo.
@@ -317,6 +325,32 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step
 1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
 2. Launch ComfyUI by running `python main.py`
 
+
+## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)
+
+**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
+
+### Setup
+
+1. Install the manager dependencies:
+   ```bash
+   pip install -r manager_requirements.txt
+   ```
+
+2. Enable the manager with the `--enable-manager` flag when running ComfyUI:
+   ```bash
+   python main.py --enable-manager
+   ```
+
+### Command Line Options
+
+| Flag | Description |
+|------|-------------|
+| `--enable-manager` | Enable ComfyUI-Manager |
+| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
+| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
+
+
 # Running
 
 ```python main.py```

api_server/routes/internal/internal_routes.py

@@ -58,8 +58,13 @@ class InternalRoutes:
                 return web.json_response({"error": "Invalid directory type"}, status=400)
 
             directory = get_directory_by_type(directory_type)
+
+            def is_visible_file(entry: os.DirEntry) -> bool:
+                """Filter out hidden files (e.g., .DS_Store on macOS)."""
+                return entry.is_file() and not entry.name.startswith('.')
+
             sorted_files = sorted(
-                (entry for entry in os.scandir(directory) if entry.is_file()),
+                (entry for entry in os.scandir(directory) if is_visible_file(entry)),
                 key=lambda entry: -entry.stat().st_mtime
             )
             return web.json_response([entry.name for entry in sorted_files], status=200)

app/frontend_management.py

@@ -10,7 +10,8 @@ import importlib
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import TypedDict, Optional
+from typing import Dict, TypedDict, Optional
+from aiohttp import web
 from importlib.metadata import version
 
 import requests
@@ -257,7 +258,54 @@ comfyui-frontend-package is not installed.
             sys.exit(-1)
 
     @classmethod
-    def templates_path(cls) -> str:
+    def template_asset_map(cls) -> Optional[Dict[str, str]]:
+        """Return a mapping of template asset names to their absolute paths."""
+        try:
+            from comfyui_workflow_templates import (
+                get_asset_path,
+                iter_templates,
+            )
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-workflow-templates is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+            return None
+
+        try:
+            template_entries = list(iter_templates())
+        except Exception as exc:
+            logging.error(f"Failed to enumerate workflow templates: {exc}")
+            return None
+
+        asset_map: Dict[str, str] = {}
+        try:
+            for entry in template_entries:
+                for asset in entry.assets:
+                    asset_map[asset.filename] = get_asset_path(
+                        entry.template_id, asset.filename
+                    )
+        except Exception as exc:
+            logging.error(f"Failed to resolve template asset paths: {exc}")
+            return None
+
+        if not asset_map:
+            logging.error("No workflow template assets found. Did the packages install correctly?")
+            return None
+
+        return asset_map
+
+
+    @classmethod
+    def legacy_templates_path(cls) -> Optional[str]:
+        """Return the legacy templates directory shipped inside the meta package."""
         try:
             import comfyui_workflow_templates
 
@@ -276,6 +324,7 @@ comfyui-workflow-templates is not installed.
 ********** ERROR ***********
 """.strip()
             )
+            return None
 
     @classmethod
     def embedded_docs_path(cls) -> str:
@@ -392,3 +441,17 @@ comfyui-workflow-templates is not installed.
             logging.info("Falling back to the default frontend.")
             check_frontend_version()
             return cls.default_frontend_path()
+    @classmethod
+    def template_asset_handler(cls):
+        assets = cls.template_asset_map()
+        if not assets:
+            return None
+
+        async def serve_template(request: web.Request) -> web.StreamResponse:
+            rel_path = request.match_info.get("path", "")
+            target = assets.get(rel_path)
+            if target is None:
+                raise web.HTTPNotFound()
+            return web.FileResponse(target)
+
+        return serve_template

app/model_manager.py

@@ -44,7 +44,7 @@ class ModelFileManager:
         @routes.get("/experiment/models/{folder}")
         async def get_all_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = self.get_model_file_list(folder)
             return web.json_response(files)
@@ -55,7 +55,7 @@ class ModelFileManager:
             path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
-            if not folder_name in folder_paths.folder_names_and_paths:
+            if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
             folders = folder_paths.folder_names_and_paths[folder_name]

app/user_manager.py

@@ -59,6 +59,9 @@ class UserManager():
         user = "default"
         if args.multi_user and "comfy-user" in request.headers:
             user = request.headers["comfy-user"]
+            # Block System Users (use same error message to prevent probing)
+            if user.startswith(folder_paths.SYSTEM_USER_PREFIX):
+                raise KeyError("Unknown user: " + user)
 
         if user not in self.users:
             raise KeyError("Unknown user: " + user)
@@ -66,15 +69,16 @@ class UserManager():
         return user
 
     def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        user_directory = folder_paths.get_user_directory()
-
         if type == "userdata":
-            root_dir = user_directory
+            root_dir = folder_paths.get_user_directory()
         else:
             raise KeyError("Unknown filepath type:" + type)
 
         user = self.get_request_user_id(request)
-        path = user_root = os.path.abspath(os.path.join(root_dir, user))
+        user_root = folder_paths.get_public_user_directory(user)
+        if user_root is None:
+            return None
+        path = user_root
 
         # prevent leaving /{type}
         if os.path.commonpath((root_dir, user_root)) != root_dir:
@@ -101,7 +105,11 @@ class UserManager():
         name = name.strip()
         if not name:
             raise ValueError("username not provided")
+        if name.startswith(folder_paths.SYSTEM_USER_PREFIX):
+            raise ValueError("System User prefix not allowed")
         user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
+        if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX):
+            raise ValueError("System User prefix not allowed")
         user_id = user_id + "_" + str(uuid.uuid4())
 
         self.users[user_id] = name
@@ -132,7 +140,10 @@ class UserManager():
             if username in self.users.values():
                 return web.json_response({"error": "Duplicate username."}, status=400)
 
-            user_id = self.add_user(username)
+            try:
+                user_id = self.add_user(username)
+            except ValueError as e:
+                return web.json_response({"error": str(e)}, status=400)
             return web.json_response(user_id)
 
         @routes.get("/userdata")
@@ -424,7 +435,7 @@ class UserManager():
                 return source
 
             dest = get_user_data_path(request, check_exists=False, param="dest")
-            if not isinstance(source, str):
+            if not isinstance(dest, str):
                 return dest
 
             overwrite = request.query.get("overwrite", 'true') != "false"

comfy/cldm/cldm.py

@@ -413,7 +413,8 @@ class ControlNet(nn.Module):
         out_middle = []
 
         if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
+            if y is None:
+                raise ValueError("y is None, did you try using a controlnet for SDXL on SD1?")
             emb = emb + self.label_emb(y)
 
         h = x

comfy/cli_args.py

@@ -97,6 +97,13 @@ class LatentPreviewMethod(enum.Enum):
     Latent2RGB = "latent2rgb"
     TAESD = "taesd"
 
+    @classmethod
+    def from_string(cls, value: str):
+        for member in cls:
+            if member.value == value:
+                return member
+        return None
+
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
@@ -121,6 +128,12 @@ upcast.add_argument("--force-upcast-attention", action="store_true", help="Force
 upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
 
 
+parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
+manager_group = parser.add_mutually_exclusive_group()
+manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
+manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
+
+
 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.")
@@ -131,7 +144,8 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e
 
 parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
 
-parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
+parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
+parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -160,13 +174,14 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
 parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
 parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.")
-parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.")
+parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes. Also prevents the frontend from communicating with the internet.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
 parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
 parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
 
+
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
 

comfy/clip_model.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
 
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
         super().__init__()

comfy/clip_vision.py

@@ -1,6 +1,5 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
-import torch
 import json
 import logging
 
@@ -17,24 +16,7 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
 
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
@@ -73,7 +55,7 @@ class ClipVisionModel():
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        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=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()

comfy/context_windows.py

@@ -51,32 +51,43 @@ class ContextHandlerABC(ABC):
 
 
 class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0):
+    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):
         self.index_list = index_list
         self.context_length = len(index_list)
         self.dim = dim
+        self.total_frames = total_frames
+        self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
 
-    def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor:
+    def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
         if dim is None:
             dim = self.dim
         if dim == 0 and full.shape[dim] == 1:
             return full
-        idx = [slice(None)] * dim + [self.index_list]
-        return full[idx].to(device)
+        idx = tuple([slice(None)] * dim + [self.index_list])
+        window = full[idx]
+        if retain_index_list:
+            idx = tuple([slice(None)] * dim + [retain_index_list])
+            window[idx] = full[idx]
+        return window.to(device)
 
     def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
         if dim is None:
             dim = self.dim
-        idx = [slice(None)] * dim + [self.index_list]
+        idx = tuple([slice(None)] * dim + [self.index_list])
         full[idx] += to_add
         return full
 
+    def get_region_index(self, num_regions: int) -> int:
+        region_idx = int(self.center_ratio * num_regions)
+        return min(max(region_idx, 0), num_regions - 1)
+
 
 class IndexListCallbacks:
     EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
     COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results"
     EXECUTE_START = "execute_start"
     EXECUTE_CLEANUP = "execute_cleanup"
+    RESIZE_COND_ITEM = "resize_cond_item"
 
     def init_callbacks(self):
         return {}
@@ -94,7 +105,8 @@ 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=False, dim=0):
+    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):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -103,13 +115,18 @@ class IndexListContextHandler(ContextHandlerABC):
         self.closed_loop = closed_loop
         self.dim = dim
         self._step = 0
+        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.callbacks = {}
 
     def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
         # for now, assume first dim is batch - should have stored on BaseModel in actual implementation
         if x_in.size(self.dim) > self.context_length:
-            logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.")
+            logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
+            if self.cond_retain_index_list:
+                logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
             return True
         return False
 
@@ -123,6 +140,11 @@ class IndexListContextHandler(ContextHandlerABC):
             return None
         # reuse or resize cond items to match context requirements
         resized_cond = []
+        # if multiple conds, split based on primary region
+        if self.split_conds_to_windows and len(cond_in) > 1:
+            region = window.get_region_index(len(cond_in))
+            logging.info(f"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}")
+            cond_in = [cond_in[region]]
         # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
         for actual_cond in cond_in:
             resized_actual_cond = actual_cond.copy()
@@ -145,13 +167,38 @@ class IndexListContextHandler(ContextHandlerABC):
                         new_cond_item = cond_item.copy()
                         # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
                         for cond_key, cond_value in new_cond_item.items():
+                            # Allow callbacks to handle custom conditioning items
+                            handled = False
+                            for callback in comfy.patcher_extension.get_all_callbacks(
+                                IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks
+                            ):
+                                result = callback(cond_key, cond_value, window, x_in, device, new_cond_item)
+                                if result is not None:
+                                    new_cond_item[cond_key] = result
+                                    handled = True
+                                    break
+                            if handled:
+                                continue
                             if isinstance(cond_value, torch.Tensor):
-                                if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim):
+                                if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
+                                   (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
                                     new_cond_item[cond_key] = window.get_tensor(cond_value, device)
+                            # Handle audio_embed (temporal dim is 1)
+                            elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                                audio_cond = cond_value.cond
+                                if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
+                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
+                            # Handle vace_context (temporal dim is 3)
+                            elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                                vace_cond = cond_value.cond
+                                if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
+                                    sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
+                                    new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
                             # if has cond that is a Tensor, check if needs to be subset
                             elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim):
-                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device))
+                                if  (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \
+                                    (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):
+                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))
                             elif cond_key == "num_video_frames": # for SVD
                                 new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
                                 new_cond_item[cond_key].cond = window.context_length
@@ -164,7 +211,7 @@ class IndexListContextHandler(ContextHandlerABC):
         return resized_cond
 
     def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001)
+        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
         matches = torch.nonzero(mask)
         if torch.numel(matches) == 0:
             raise Exception("No sample_sigmas matched current timestep; something went wrong.")
@@ -173,7 +220,7 @@ class IndexListContextHandler(ContextHandlerABC):
     def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
         full_length = x_in.size(self.dim) # TODO: choose dim based on model
         context_windows = self.context_schedule.func(full_length, self, model_options)
-        context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows]
+        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]
         return context_windows
 
     def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
@@ -250,8 +297,8 @@ class IndexListContextHandler(ContextHandlerABC):
                     prev_weight = (bias_total / (bias_total + bias))
                     new_weight = (bias / (bias_total + bias))
                     # account for dims of tensors
-                    idx_window = [slice(None)] * self.dim + [idx]
-                    pos_window = [slice(None)] * self.dim + [pos]
+                    idx_window = tuple([slice(None)] * self.dim + [idx])
+                    pos_window = tuple([slice(None)] * self.dim + [pos])
                     # apply new values
                     conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
                     biases_final[i][idx] = bias_total + bias
@@ -287,6 +334,28 @@ def create_prepare_sampling_wrapper(model: ModelPatcher):
     )
 
 
+def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):
+    model_options = extra_args.get("model_options", None)
+    if model_options is None:
+        raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+    handler: IndexListContextHandler = model_options.get("context_handler", None)
+    if handler is None:
+        raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+    if not handler.freenoise:
+        return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+    noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"])
+
+    return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+
+
+def create_sampler_sample_wrapper(model: ModelPatcher):
+    model.add_wrapper_with_key(
+        comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
+        "ContextWindows_sampler_sample",
+        _sampler_sample_wrapper
+    )
+
+
 def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
     total_dims = len(x_in.shape)
     weights_tensor = torch.Tensor(weights).to(device=device)
@@ -538,3 +607,29 @@ def shift_window_to_end(window: list[int], num_frames: int):
     for i in range(len(window)):
         # 2) add end_delta to each val to slide windows to end
         window[i] = window[i] + end_delta
+
+
+# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465
+def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):
+    logging.info("Context windows: Applying FreeNoise")
+    generator = torch.Generator(device='cpu').manual_seed(seed)
+    latent_video_length = noise.shape[dim]
+    delta = context_length - context_overlap
+
+    for start_idx in range(0, latent_video_length - context_length, delta):
+        place_idx = start_idx + context_length
+
+        actual_delta = min(delta, latent_video_length - place_idx)
+        if actual_delta <= 0:
+            break
+
+        list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx
+
+        source_slice = [slice(None)] * noise.ndim
+        source_slice[dim] = list_idx
+        target_slice = [slice(None)] * noise.ndim
+        target_slice[dim] = slice(place_idx, place_idx + actual_delta)
+
+        noise[tuple(target_slice)] = noise[tuple(source_slice)]
+
+    return noise

comfy/hooks.py

@@ -527,7 +527,8 @@ class HookKeyframeGroup:
                         if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
-                    else: break
+                    else:
+                        break
         # update steps current context is used
         self._current_used_steps += 1
         # update current timestep this was performed on

comfy/k_diffusion/sampling.py

@@ -74,6 +74,9 @@ def get_ancestral_step(sigma_from, sigma_to, eta=1.):
 
 def default_noise_sampler(x, seed=None):
     if seed is not None:
+        if x.device == torch.device("cpu"):
+            seed += 1
+
         generator = torch.Generator(device=x.device)
         generator.manual_seed(seed)
     else:
@@ -1557,10 +1560,13 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
 
 
 @torch.no_grad()
-def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
+def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"):
     """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
     arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
+    if solver_type not in {"phi_1", "phi_2"}:
+        raise ValueError("solver_type must be 'phi_1' or 'phi_2'")
+
     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
@@ -1600,8 +1606,14 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
         denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
         # Step 2
-        denoised_d = torch.lerp(denoised, denoised_2, fac)
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        if solver_type == "phi_1":
+            denoised_d = torch.lerp(denoised, denoised_2, fac)
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        elif solver_type == "phi_2":
+            b2 = ei_h_phi_2(-h_eta) / r
+            b1 = ei_h_phi_1(-h_eta) - b2
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)
+
         if inject_noise:
             segment_factor = (r - 1) * h * eta
             sde_noise = sde_noise * segment_factor.exp()
@@ -1609,6 +1621,17 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             x = x + sde_noise * sigmas[i + 1] * s_noise
     return x
 
+@torch.no_grad()
+def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=None, disable=None, solver_type="phi_2"):
+    """Deterministic exponential Heun second order method in data prediction (x0) and logSNR time."""
+    return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None, r=1.0, solver_type=solver_type)
+
+
+@torch.no_grad()
+def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type="phi_2"):
+    """Stochastic exponential Heun second order method in data prediction (x0) and logSNR time."""
+    return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=1.0, solver_type=solver_type)
+
 
 @torch.no_grad()
 def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
@@ -1756,7 +1779,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
         # Predictor
         if sigmas[i + 1] == 0:
             # Denoising step
-            x = denoised
+            x_pred = denoised
         else:
             tau_t = tau_func(sigmas[i + 1])
             curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
@@ -1777,7 +1800,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
             if tau_t > 0 and s_noise > 0:
                 noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
                 x_pred = x_pred + noise
-    return x
+    return x_pred
 
 
 @torch.no_grad()

comfy/latent_formats.py

@@ -6,6 +6,7 @@ class LatentFormat:
     latent_dimensions = 2
     latent_rgb_factors = None
     latent_rgb_factors_bias = None
+    latent_rgb_factors_reshape = None
     taesd_decoder_name = None
 
     def process_in(self, latent):
@@ -178,6 +179,54 @@ class Flux(SD3):
     def process_out(self, latent):
         return (latent / self.scale_factor) + self.shift_factor
 
+class Flux2(LatentFormat):
+    latent_channels = 128
+
+    def __init__(self):
+        self.latent_rgb_factors =[
+            [0.0058, 0.0113, 0.0073],
+            [0.0495, 0.0443, 0.0836],
+            [-0.0099, 0.0096, 0.0644],
+            [0.2144, 0.3009, 0.3652],
+            [0.0166, -0.0039, -0.0054],
+            [0.0157, 0.0103, -0.0160],
+            [-0.0398, 0.0902, -0.0235],
+            [-0.0052, 0.0095, 0.0109],
+            [-0.3527, -0.2712, -0.1666],
+            [-0.0301, -0.0356, -0.0180],
+            [-0.0107, 0.0078, 0.0013],
+            [0.0746, 0.0090, -0.0941],
+            [0.0156, 0.0169, 0.0070],
+            [-0.0034, -0.0040, -0.0114],
+            [0.0032, 0.0181, 0.0080],
+            [-0.0939, -0.0008, 0.0186],
+            [0.0018, 0.0043, 0.0104],
+            [0.0284, 0.0056, -0.0127],
+            [-0.0024, -0.0022, -0.0030],
+            [0.1207, -0.0026, 0.0065],
+            [0.0128, 0.0101, 0.0142],
+            [0.0137, -0.0072, -0.0007],
+            [0.0095, 0.0092, -0.0059],
+            [0.0000, -0.0077, -0.0049],
+            [-0.0465, -0.0204, -0.0312],
+            [0.0095, 0.0012, -0.0066],
+            [0.0290, -0.0034, 0.0025],
+            [0.0220, 0.0169, -0.0048],
+            [-0.0332, -0.0457, -0.0468],
+            [-0.0085, 0.0389, 0.0609],
+            [-0.0076, 0.0003, -0.0043],
+            [-0.0111, -0.0460, -0.0614],
+        ]
+
+        self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
+        self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
+
+    def process_in(self, latent):
+        return latent
+
+    def process_out(self, latent):
+        return latent
+
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3
@@ -358,6 +407,11 @@ class LTXV(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
 
+class LTXAV(LTXV):
+    def __init__(self):
+        self.latent_rgb_factors = None
+        self.latent_rgb_factors_bias = None
+
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3
@@ -382,6 +436,7 @@ class HunyuanVideo(LatentFormat):
     ]
 
     latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]
+    taesd_decoder_name = "taehv"
 
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
@@ -445,7 +500,7 @@ class Wan21(LatentFormat):
         ]).view(1, self.latent_channels, 1, 1, 1)
 
 
-        self.taesd_decoder_name = None #TODO
+        self.taesd_decoder_name = "lighttaew2_1"
 
     def process_in(self, latent):
         latents_mean = self.latents_mean.to(latent.device, latent.dtype)
@@ -516,6 +571,7 @@ class Wan22(Wan21):
 
     def __init__(self):
         self.scale_factor = 1.0
+        self.taesd_decoder_name = "lighttaew2_2"
         self.latents_mean = torch.tensor([
                 -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
                 -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
@@ -611,6 +667,67 @@ class HunyuanImage21Refiner(LatentFormat):
     latent_dimensions = 3
     scale_factor = 1.03682
 
+    def process_in(self, latent):
+        out = latent * self.scale_factor
+        out = torch.cat((out[:, :, :1], out), dim=2)
+        out = out.permute(0, 2, 1, 3, 4)
+        b, f_times_2, c, h, w = out.shape
+        out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
+        out = out.permute(0, 2, 1, 3, 4).contiguous()
+        return out
+
+    def process_out(self, latent):
+        z = latent / self.scale_factor
+        z = z.permute(0, 2, 1, 3, 4)
+        b, f, c, h, w = z.shape
+        z = z.reshape(b, f, 2, c // 2, h, w)
+        z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
+        z = z.permute(0, 2, 1, 3, 4)
+        z = z[:, :, 1:]
+        return z
+
+class HunyuanVideo15(LatentFormat):
+    latent_rgb_factors = [
+        [ 0.0568, -0.0521, -0.0131],
+        [ 0.0014,  0.0735,  0.0326],
+        [ 0.0186,  0.0531, -0.0138],
+        [-0.0031,  0.0051,  0.0288],
+        [ 0.0110,  0.0556,  0.0432],
+        [-0.0041, -0.0023, -0.0485],
+        [ 0.0530,  0.0413,  0.0253],
+        [ 0.0283,  0.0251,  0.0339],
+        [ 0.0277, -0.0372, -0.0093],
+        [ 0.0393,  0.0944,  0.1131],
+        [ 0.0020,  0.0251,  0.0037],
+        [-0.0017,  0.0012,  0.0234],
+        [ 0.0468,  0.0436,  0.0203],
+        [ 0.0354,  0.0439, -0.0233],
+        [ 0.0090,  0.0123,  0.0346],
+        [ 0.0382,  0.0029,  0.0217],
+        [ 0.0261, -0.0300,  0.0030],
+        [-0.0088, -0.0220, -0.0283],
+        [-0.0272, -0.0121, -0.0363],
+        [-0.0664, -0.0622,  0.0144],
+        [ 0.0414,  0.0479,  0.0529],
+        [ 0.0355,  0.0612, -0.0247],
+        [ 0.0147,  0.0264,  0.0174],
+        [ 0.0438,  0.0038,  0.0542],
+        [ 0.0431, -0.0573, -0.0033],
+        [-0.0162, -0.0211, -0.0406],
+        [-0.0487, -0.0295, -0.0393],
+        [ 0.0005, -0.0109,  0.0253],
+        [ 0.0296,  0.0591,  0.0353],
+        [ 0.0119,  0.0181, -0.0306],
+        [-0.0085, -0.0362,  0.0229],
+        [ 0.0005, -0.0106,  0.0242]
+    ]
+
+    latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
+    latent_channels = 32
+    latent_dimensions = 3
+    scale_factor = 1.03682
+    taesd_decoder_name = "lighttaehy1_5"
+
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/chroma/model.py

@@ -40,7 +40,8 @@ class ChromaParams:
     out_dim: int
     hidden_dim: int
     n_layers: int
-
+    txt_ids_dims: list
+    vec_in_dim: int
 
 
 
@@ -179,7 +180,10 @@ class Chroma(nn.Module):
         pe = self.pe_embedder(ids)
 
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.double_blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
+            transformer_options["block_index"] = i
             if i not in self.skip_mmdit:
                 double_mod = (
                     self.get_modulations(mod_vectors, "double_img", idx=i),
@@ -222,7 +226,10 @@ class Chroma(nn.Module):
 
         img = torch.cat((txt, img), 1)
 
+        transformer_options["total_blocks"] = len(self.single_blocks)
+        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
+            transformer_options["block_index"] = i
             if i not in self.skip_dit:
                 single_mod = self.get_modulations(mod_vectors, "single", idx=i)
                 if ("single_block", i) in blocks_replace:

comfy/ldm/chroma_radiance/model.py

@@ -37,7 +37,7 @@ class ChromaRadianceParams(ChromaParams):
     nerf_final_head_type: str
     # None means use the same dtype as the model.
     nerf_embedder_dtype: Optional[torch.dtype]
-
+    use_x0: bool
 
 class ChromaRadiance(Chroma):
     """
@@ -159,6 +159,9 @@ class ChromaRadiance(Chroma):
         self.skip_dit = []
         self.lite = False
 
+        if params.use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
     @property
     def _nerf_final_layer(self) -> nn.Module:
         if self.params.nerf_final_head_type == "linear":
@@ -267,7 +270,7 @@ class ChromaRadiance(Chroma):
         bad_keys = tuple(
             k
             for k, v in overrides.items()
-            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
+            if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)
         )
         if bad_keys:
             e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
@@ -276,6 +279,12 @@ class ChromaRadiance(Chroma):
         params_dict |= overrides
         return params.__class__(**params_dict)
 
+    def _apply_x0_residual(self, predicted, noisy, timesteps):
+
+        # non zero during training to prevent 0 div
+        eps = 0.0
+        return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)
+
     def _forward(
         self,
         x: Tensor,
@@ -316,4 +325,11 @@ class ChromaRadiance(Chroma):
             transformer_options,
             attn_mask=kwargs.get("attention_mask", None),
         )
-        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        # If x0 variant → v-pred, just return this instead
+        if hasattr(self, "__x0__"):
+            out = self._apply_x0_residual(out, img, timestep)
+        return out
+

comfy/ldm/flux/layers.py

@@ -48,15 +48,44 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10
     return embedding
 
 class MLPEmbedder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+    def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None, device=None, operations=None):
         super().__init__()
-        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
         self.silu = nn.SiLU()
-        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=bias, dtype=dtype, device=device)
 
     def forward(self, x: Tensor) -> Tensor:
         return self.out_layer(self.silu(self.in_layer(x)))
 
+class YakMLP(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.act_fn = nn.SiLU()
+
+    def forward(self, x: Tensor) -> Tensor:
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None):
+    if yak_mlp:
+        return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
+    if mlp_silu_act:
+        return nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
+            SiLUActivation(),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
+        )
+    else:
+        return nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, dtype=None, device=None, operations=None):
@@ -80,14 +109,14 @@ class QKNorm(torch.nn.Module):
 
 
 class SelfAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, dtype=None, device=None, operations=None):
         super().__init__()
         self.num_heads = num_heads
         head_dim = dim // num_heads
 
         self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
         self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
-        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim, dim, bias=proj_bias, dtype=dtype, device=device)
 
 
 @dataclass
@@ -98,11 +127,11 @@ class ModulationOut:
 
 
 class Modulation(nn.Module):
-    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, double: bool, bias=True, dtype=None, device=None, operations=None):
         super().__init__()
         self.is_double = double
         self.multiplier = 6 if double else 3
-        self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
+        self.lin = operations.Linear(dim, self.multiplier * dim, bias=bias, dtype=dtype, device=device)
 
     def forward(self, vec: Tensor) -> tuple:
         if vec.ndim == 2:
@@ -129,8 +158,18 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
         return tensor
 
 
+class SiLUActivation(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.gate_fn = nn.SiLU()
+
+    def forward(self, x: Tensor) -> Tensor:
+        x1, x2 = x.chunk(2, dim=-1)
+        return self.gate_fn(x1) * x2
+
+
 class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
@@ -142,27 +181,22 @@ class DoubleStreamBlock(nn.Module):
             self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
 
         self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)
 
         self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.img_mlp = nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-            nn.GELU(approximate="tanh"),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
+
+        self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
 
         if self.modulation:
             self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
 
         self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)
 
         self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.txt_mlp = nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-            nn.GELU(approximate="tanh"),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
+
+        self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
+
         self.flipped_img_txt = flipped_img_txt
 
     def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
@@ -246,6 +280,9 @@ class SingleStreamBlock(nn.Module):
         mlp_ratio: float = 4.0,
         qk_scale: float = None,
         modulation=True,
+        mlp_silu_act=False,
+        bias=True,
+        yak_mlp=False,
         dtype=None,
         device=None,
         operations=None
@@ -257,17 +294,29 @@ class SingleStreamBlock(nn.Module):
         self.scale = qk_scale or head_dim**-0.5
 
         self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+
+        self.mlp_hidden_dim_first = self.mlp_hidden_dim
+        self.yak_mlp = yak_mlp
+        if mlp_silu_act:
+            self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2)
+            self.mlp_act = SiLUActivation()
+        else:
+            self.mlp_act = nn.GELU(approximate="tanh")
+
+        if self.yak_mlp:
+            self.mlp_hidden_dim_first *= 2
+            self.mlp_act = nn.SiLU()
+
         # qkv and mlp_in
-        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device)
         # proj and mlp_out
-        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, bias=bias, dtype=dtype, device=device)
 
         self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
 
         self.hidden_size = hidden_size
         self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
-        self.mlp_act = nn.GELU(approximate="tanh")
         if modulation:
             self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
         else:
@@ -279,7 +328,7 @@ class SingleStreamBlock(nn.Module):
         else:
             mod = vec
 
-        qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+        qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim_first], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         del qkv
@@ -289,7 +338,10 @@ class SingleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
         # compute activation in mlp stream, cat again and run second linear layer
-        mlp = self.mlp_act(mlp)
+        if self.yak_mlp:
+            mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2]
+        else:
+            mlp = self.mlp_act(mlp)
         output = self.linear2(torch.cat((attn, mlp), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)
         if x.dtype == torch.float16:
@@ -298,11 +350,11 @@ class SingleStreamBlock(nn.Module):
 
 
 class LastLayer(nn.Module):
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, bias=True, dtype=None, device=None, operations=None):
         super().__init__()
         self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=bias, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=bias, dtype=dtype, device=device))
 
     def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor:
         if vec.ndim == 2:

comfy/ldm/flux/math.py

@@ -4,15 +4,16 @@ from torch import Tensor
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
+import logging
 
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
-    q, k = apply_rope(q, k, pe)
+    if pe is not None:
+        q, k = apply_rope(q, k, pe)
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
     return x
 
-
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
     if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
@@ -27,13 +28,20 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.to(dtype=torch.float32, device=pos.device)
 
-def apply_rope1(x: Tensor, freqs_cis: Tensor):
-    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
 
-    x_out = freqs_cis[..., 0] * x_[..., 0]
-    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
+try:
+    import comfy.quant_ops
+    apply_rope = comfy.quant_ops.ck.apply_rope
+    apply_rope1 = comfy.quant_ops.ck.apply_rope1
+except:
+    logging.warning("No comfy kitchen, using old apply_rope functions.")
+    def apply_rope1(x: Tensor, freqs_cis: Tensor):
+        x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
 
-    return x_out.reshape(*x.shape).type_as(x)
+        x_out = freqs_cis[..., 0] * x_[..., 0]
+        x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
 
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+        return x_out.reshape(*x.shape).type_as(x)
+
+    def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+        return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)

comfy/ldm/flux/model.py

@@ -15,6 +15,8 @@ from .layers import (
     MLPEmbedder,
     SingleStreamBlock,
     timestep_embedding,
+    Modulation,
+    RMSNorm
 )
 
 @dataclass
@@ -33,6 +35,14 @@ class FluxParams:
     patch_size: int
     qkv_bias: bool
     guidance_embed: bool
+    txt_ids_dims: list
+    global_modulation: bool = False
+    mlp_silu_act: bool = False
+    ops_bias: bool = True
+    default_ref_method: str = "offset"
+    ref_index_scale: float = 1.0
+    yak_mlp: bool = False
+    txt_norm: bool = False
 
 
 class Flux(nn.Module):
@@ -58,13 +68,22 @@ class Flux(nn.Module):
         self.hidden_size = params.hidden_size
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
-        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
+        if params.vec_in_dim is not None:
+            self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            self.vector_in = None
+
         self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
         )
-        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
+
+        if params.txt_norm:
+            self.txt_norm = RMSNorm(params.context_in_dim, dtype=dtype, device=device, operations=operations)
+        else:
+            self.txt_norm = None
 
         self.double_blocks = nn.ModuleList(
             [
@@ -73,6 +92,10 @@ class Flux(nn.Module):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
+                    modulation=params.global_modulation is False,
+                    mlp_silu_act=params.mlp_silu_act,
+                    proj_bias=params.ops_bias,
+                    yak_mlp=params.yak_mlp,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -81,13 +104,30 @@ class Flux(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations)
                 for _ in range(params.depth_single_blocks)
             ]
         )
 
         if final_layer:
-            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
+
+        if params.global_modulation:
+            self.double_stream_modulation_img = Modulation(
+                self.hidden_size,
+                double=True,
+                bias=False,
+                dtype=dtype, device=device, operations=operations
+            )
+            self.double_stream_modulation_txt = Modulation(
+                self.hidden_size,
+                double=True,
+                bias=False,
+                dtype=dtype, device=device, operations=operations
+            )
+            self.single_stream_modulation = Modulation(
+                self.hidden_size, double=False, bias=False, dtype=dtype, device=device, operations=operations
+            )
 
     def forward_orig(
         self,
@@ -103,9 +143,6 @@ class Flux(nn.Module):
         attn_mask: Tensor = None,
     ) -> Tensor:
 
-        if y is None:
-            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
-
         patches = transformer_options.get("patches", {})
         patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
@@ -118,9 +155,19 @@ class Flux(nn.Module):
             if guidance is not None:
                 vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+        if self.vector_in is not None:
+            if y is None:
+                y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
+            vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+
+        if self.txt_norm is not None:
+            txt = self.txt_norm(txt)
         txt = self.txt_in(txt)
 
+        vec_orig = vec
+        if self.params.global_modulation:
+            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
+
         if "post_input" in patches:
             for p in patches["post_input"]:
                 out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
@@ -136,7 +183,10 @@ class Flux(nn.Module):
             pe = None
 
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.double_blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -177,7 +227,13 @@ class Flux(nn.Module):
 
         img = torch.cat((txt, img), 1)
 
+        if self.params.global_modulation:
+            vec, _ = self.single_stream_modulation(vec_orig)
+
+        transformer_options["total_blocks"] = len(self.single_blocks)
+        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
+            transformer_options["block_index"] = i
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -207,7 +263,7 @@ class Flux(nn.Module):
 
         img = img[:, txt.shape[1] :, ...]
 
-        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        img = self.final_layer(img, vec_orig)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
@@ -234,10 +290,10 @@ class Flux(nn.Module):
             h_offset += rope_options.get("shift_y", 0.0)
             w_offset += rope_options.get("shift_x", 0.0)
 
-        img_ids = torch.zeros((steps_h, steps_w, 3), device=x.device, dtype=x.dtype)
+        img_ids = torch.zeros((steps_h, steps_w, len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
         img_ids[:, :, 0] = img_ids[:, :, 1] + index
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=torch.float32).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=torch.float32).unsqueeze(0)
         return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
     def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
@@ -259,10 +315,10 @@ class Flux(nn.Module):
             h = 0
             w = 0
             index = 0
-            ref_latents_method = kwargs.get("ref_latents_method", "offset")
+            ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
             for ref in ref_latents:
                 if ref_latents_method == "index":
-                    index += 1
+                    index += self.params.ref_index_scale
                     h_offset = 0
                     w_offset = 0
                 elif ref_latents_method == "uxo":
@@ -286,7 +342,12 @@ class Flux(nn.Module):
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
 
-        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
+
+        if len(self.params.txt_ids_dims) > 0:
+            for i in self.params.txt_ids_dims:
+                txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
+
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]

comfy/ldm/hunyuan_video/model.py

@@ -6,7 +6,6 @@ import comfy.ldm.flux.layers
 import comfy.ldm.modules.diffusionmodules.mmdit
 from comfy.ldm.modules.attention import optimized_attention
 
-
 from dataclasses import dataclass
 from einops import repeat
 
@@ -42,6 +41,9 @@ class HunyuanVideoParams:
     guidance_embed: bool
     byt5: bool
     meanflow: bool
+    use_cond_type_embedding: bool
+    vision_in_dim: int
+    meanflow_sum: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -157,7 +159,10 @@ class TokenRefiner(nn.Module):
         t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
         # m = mask.float().unsqueeze(-1)
         # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise
-        c = x.sum(dim=1) / x.shape[1]
+        if x.dtype == torch.float16:
+            c = x.float().sum(dim=1) / x.shape[1]
+        else:
+            c = x.sum(dim=1) / x.shape[1]
 
         c = t + self.c_embedder(c.to(x.dtype))
         x = self.input_embedder(x)
@@ -196,11 +201,15 @@ class HunyuanVideo(nn.Module):
     def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
         super().__init__()
         self.dtype = dtype
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
         params = HunyuanVideoParams(**kwargs)
         self.params = params
         self.patch_size = params.patch_size
         self.in_channels = params.in_channels
         self.out_channels = params.out_channels
+        self.use_cond_type_embedding = params.use_cond_type_embedding
+        self.vision_in_dim = params.vision_in_dim
         if params.hidden_size % params.num_heads != 0:
             raise ValueError(
                 f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
@@ -266,6 +275,18 @@ class HunyuanVideo(nn.Module):
         if final_layer:
             self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
 
+        # HunyuanVideo 1.5 specific modules
+        if self.vision_in_dim is not None:
+            from comfy.ldm.wan.model import MLPProj
+            self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings)
+        else:
+            self.vision_in = None
+        if self.use_cond_type_embedding:
+            # 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature
+            self.cond_type_embedding = nn.Embedding(3, self.hidden_size)
+        else:
+            self.cond_type_embedding = None
+
     def forward_orig(
         self,
         img: Tensor,
@@ -276,6 +297,7 @@ class HunyuanVideo(nn.Module):
         timesteps: Tensor,
         y: Tensor = None,
         txt_byt5=None,
+        clip_fea=None,
         guidance: Tensor = None,
         guiding_frame_index=None,
         ref_latent=None,
@@ -296,7 +318,7 @@ class HunyuanVideo(nn.Module):
                 timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
                 timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
                 vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
-                vec = (vec + vec_r) / 2
+                vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2
 
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)
@@ -331,12 +353,31 @@ class HunyuanVideo(nn.Module):
 
         txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)
 
+        if self.cond_type_embedding is not None:
+            self.cond_type_embedding.to(txt.device)
+            cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long))
+            txt = txt + cond_emb.to(txt.dtype)
+
         if self.byt5_in is not None and txt_byt5 is not None:
             txt_byt5 = self.byt5_in(txt_byt5)
+            if self.cond_type_embedding is not None:
+                cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long))
+                txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype)
+                txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5
+            else:
+                txt = torch.cat((txt, txt_byt5), dim=1)
             txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
-            txt = torch.cat((txt, txt_byt5), dim=1)
             txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1)
 
+        if clip_fea is not None:
+            txt_vision_states = self.vision_in(clip_fea)
+            if self.cond_type_embedding is not None:
+                cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device))
+                txt_vision_states = txt_vision_states + cond_emb
+            txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1)
+            extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
+            txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1)
+
         ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
 
@@ -349,7 +390,10 @@ class HunyuanVideo(nn.Module):
             attn_mask = None
 
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.double_blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -371,7 +415,10 @@ class HunyuanVideo(nn.Module):
 
         img = torch.cat((img, txt), 1)
 
+        transformer_options["total_blocks"] = len(self.single_blocks)
+        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
+            transformer_options["block_index"] = i
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -430,14 +477,14 @@ class HunyuanVideo(nn.Module):
         img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         return repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
-    def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=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, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)
+        ).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)
 
-    def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
+    def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
         bs = x.shape[0]
         if len(self.patch_size) == 3:
             img_ids = self.img_ids(x)
@@ -445,5 +492,5 @@ class HunyuanVideo(nn.Module):
         else:
             img_ids = self.img_ids_2d(x)
             txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
         return out

comfy/ldm/hunyuan_video/upsampler.py

@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
+from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
+import model_management
+import model_patcher
+
+class SRResidualCausalBlock3D(nn.Module):
+    def __init__(self, channels: int):
+        super().__init__()
+        self.block = nn.Sequential(
+            VideoConv3d(channels, channels, kernel_size=3),
+            nn.SiLU(inplace=True),
+            VideoConv3d(channels, channels, kernel_size=3),
+            nn.SiLU(inplace=True),
+            VideoConv3d(channels, channels, kernel_size=3),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.block(x)
+
+class SRModel3DV2(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        hidden_channels: int = 64,
+        num_blocks: int = 6,
+        global_residual: bool = False,
+    ):
+        super().__init__()
+        self.in_conv = VideoConv3d(in_channels, hidden_channels, kernel_size=3)
+        self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)])
+        self.out_conv = VideoConv3d(hidden_channels, out_channels, kernel_size=3)
+        self.global_residual = bool(global_residual)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        y = self.in_conv(x)
+        for blk in self.blocks:
+            y = blk(y)
+        y = self.out_conv(y)
+        if self.global_residual and (y.shape == residual.shape):
+            y = y + residual
+        return y
+
+
+class Upsampler(nn.Module):
+    def __init__(
+        self,
+        z_channels: int,
+        out_channels: int,
+        block_out_channels: tuple[int, ...],
+        num_res_blocks: int = 2,
+    ):
+        super().__init__()
+        self.num_res_blocks = num_res_blocks
+        self.block_out_channels = block_out_channels
+        self.z_channels = z_channels
+
+        ch = block_out_channels[0]
+        self.conv_in = VideoConv3d(z_channels, ch, kernel_size=3)
+
+        self.up = nn.ModuleList()
+
+        for i, tgt in enumerate(block_out_channels):
+            stage = nn.Module()
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                    out_channels=tgt,
+                                                    temb_channels=0,
+                                                    conv_shortcut=False,
+                                                    conv_op=VideoConv3d, norm_op=RMS_norm)
+                                        for j in range(num_res_blocks + 1)])
+            ch = tgt
+            self.up.append(stage)
+
+        self.norm_out = RMS_norm(ch)
+        self.conv_out = VideoConv3d(ch, out_channels, kernel_size=3)
+
+    def forward(self, z):
+        """
+        Args:
+            z: (B, C, T, H, W)
+            target_shape: (H, W)
+        """
+        # z to block_in
+        repeats = self.block_out_channels[0] // (self.z_channels)
+        x = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)
+
+        # upsampling
+        for stage in self.up:
+            for blk in stage.block:
+                x = blk(x)
+
+        out = self.conv_out(F.silu(self.norm_out(x)))
+        return out
+
+UPSAMPLERS = {
+    "720p": SRModel3DV2,
+    "1080p": Upsampler,
+}
+
+class HunyuanVideo15SRModel():
+    def __init__(self, model_type, config):
+        self.load_device = model_management.vae_device()
+        offload_device = model_management.vae_offload_device()
+        self.dtype = model_management.vae_dtype(self.load_device)
+        self.model_class = UPSAMPLERS.get(model_type)
+        self.model = self.model_class(**config).eval()
+
+        self.patcher = model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=True)
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def resample_latent(self, latent):
+        model_management.load_model_gpu(self.patcher)
+        return self.model(latent.to(self.load_device))

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,11 +1,13 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed
 import comfy.ops
 import comfy.ldm.models.autoencoder
+import comfy.model_management
 ops = comfy.ops.disable_weight_init
 
+
 class RMS_norm(nn.Module):
     def __init__(self, dim):
         super().__init__()
@@ -14,10 +16,10 @@ class RMS_norm(nn.Module):
         self.gamma = nn.Parameter(torch.empty(shape))
 
     def forward(self, x):
-        return F.normalize(x, dim=1) * self.scale * self.gamma
+        return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
 
 class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tds, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tds else 1 * 2 * 2
         assert oc % fct == 0
@@ -27,11 +29,12 @@ class DnSmpl(nn.Module):
         self.tds = tds
         self.gs = fct * ic // oc
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         r1 = 2 if self.tds else 1
-        h = self.conv(x)
+        h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
+
+        if self.tds and self.refiner_vae and conv_carry_in is None:
 
-        if self.tds and self.refiner_vae:
             hf = h[:, :, :1, :, :]
             b, c, f, ht, wd = hf.shape
             hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
@@ -39,14 +42,7 @@ class DnSmpl(nn.Module):
             hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
             hf = torch.cat([hf, hf], dim=1)
 
-            hn = h[:, :, 1:, :, :]
-            b, c, frms, ht, wd = hn.shape
-            nf = frms // r1
-            hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
-            hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
-
-            h = torch.cat([hf, hn], dim=2)
+            h = h[:, :, 1:, :, :]
 
             xf = x[:, :, :1, :, :]
             b, ci, f, ht, wd = xf.shape
@@ -54,38 +50,36 @@ class DnSmpl(nn.Module):
             xf = xf.permute(0, 4, 6, 1, 2, 3, 5)
             xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)
             B, C, T, H, W = xf.shape
-            xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2)
+            xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2)
 
-            xn = x[:, :, 1:, :, :]
-            b, ci, frms, ht, wd = xn.shape
-            nf = frms // r1
-            xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
-            xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
-            B, C, T, H, W = xn.shape
-            xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
-            sc = torch.cat([xf, xn], dim=2)
-        else:
-            b, c, frms, ht, wd = h.shape
+            x = x[:, :, 1:, :, :]
 
-            nf = frms // r1
-            h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
-            h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
+        if h.shape[2] == 0:
+            return hf + xf
 
-            b, ci, frms, ht, wd = x.shape
-            nf = frms // r1
-            sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
-            sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
-            B, C, T, H, W = sc.shape
-            sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
+        b, c, frms, ht, wd = h.shape
+        nf = frms // r1
+        h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
+        h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
 
-        return h + sc
+        b, ci, frms, ht, wd = x.shape
+        nf = frms // r1
+        x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
+        x = x.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
+        B, C, T, H, W = x.shape
+        x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
+
+        if self.tds and self.refiner_vae and conv_carry_in is None:
+            h = torch.cat([hf, h], dim=2)
+            x = torch.cat([xf, x], dim=2)
+
+        return h + x
 
 
 class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tus, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tus else 1 * 2 * 2
         self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
@@ -94,11 +88,11 @@ class UpSmpl(nn.Module):
         self.tus = tus
         self.rp = fct * oc // ic
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         r1 = 2 if self.tus else 1
-        h = self.conv(x)
+        h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
 
-        if self.tus and self.refiner_vae:
+        if self.tus and self.refiner_vae and conv_carry_in is None:
             hf = h[:, :, :1, :, :]
             b, c, f, ht, wd = hf.shape
             nc = c // (2 * 2)
@@ -107,14 +101,7 @@ class UpSmpl(nn.Module):
             hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
             hf = hf[:, : hf.shape[1] // 2]
 
-            hn = h[:, :, 1:, :, :]
-            b, c, frms, ht, wd = hn.shape
-            nc = c // (r1 * 2 * 2)
-            hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-            h = torch.cat([hf, hn], dim=2)
+            h = h[:, :, 1:, :, :]
 
             xf = x[:, :, :1, :, :]
             b, ci, f, ht, wd = xf.shape
@@ -125,29 +112,26 @@ class UpSmpl(nn.Module):
             xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
             xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
 
-            xn = x[:, :, 1:, :, :]
-            xn = xn.repeat_interleave(repeats=self.rp, dim=1)
-            b, c, frms, ht, wd = xn.shape
-            nc = c // (r1 * 2 * 2)
-            xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-            sc = torch.cat([xf, xn], dim=2)
-        else:
-            b, c, frms, ht, wd = h.shape
-            nc = c // (r1 * 2 * 2)
-            h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+            x = x[:, :, 1:, :, :]
 
-            sc = x.repeat_interleave(repeats=self.rp, dim=1)
-            b, c, frms, ht, wd = sc.shape
-            nc = c // (r1 * 2 * 2)
-            sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+        b, c, frms, ht, wd = h.shape
+        nc = c // (r1 * 2 * 2)
+        h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+        h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
+        h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
 
-        return h + sc
+        x = x.repeat_interleave(repeats=self.rp, dim=1)
+        b, c, frms, ht, wd = x.shape
+        nc = c // (r1 * 2 * 2)
+        x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+        x = x.permute(0, 4, 5, 1, 6, 2, 7, 3)
+        x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+
+        if self.tus and self.refiner_vae and conv_carry_in is None:
+            h = torch.cat([hf, h], dim=2)
+            x = torch.cat([xf, x], dim=2)
+
+        return h + x
 
 class Encoder(nn.Module):
     def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
@@ -160,7 +144,7 @@ class Encoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = VideoConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -188,9 +172,9 @@ class Encoder(nn.Module):
             self.down.append(stage)
 
         self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
 
         self.norm_out = norm_op(ch)
         self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
@@ -201,31 +185,48 @@ class Encoder(nn.Module):
         if not self.refiner_vae and x.shape[2] == 1:
             x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
 
-        x = self.conv_in(x)
+        if self.refiner_vae:
+            xl = [x[:, :, :1, :, :]]
+            if x.shape[2] > self.ffactor_temporal:
+                xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2)
+            x = xl
+        else:
+            x = [x]
+        out = []
 
-        for stage in self.down:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'downsample'):
-                x = stage.downsample(x)
+        conv_carry_in = None
 
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
+        for i, x1 in enumerate(x):
+            conv_carry_out = []
+            if i == len(x) - 1:
+                conv_carry_out = None
+
+            x1 = [ x1 ]
+            x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
+
+            for stage in self.down:
+                for blk in stage.block:
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
+                if hasattr(stage, 'downsample'):
+                    x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
+
+            out.append(x1)
+            conv_carry_in = conv_carry_out
+
+        out = torch_cat_if_needed(out, dim=2)
+
+        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
+        del out
 
         b, c, t, h, w = x.shape
         grp = c // (self.z_channels << 1)
         skip = x.view(b, c // grp, grp, t, h, w).mean(2)
 
-        out = self.conv_out(F.silu(self.norm_out(x))) + skip
+        out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip
 
         if self.refiner_vae:
             out = self.regul(out)[0]
 
-            out = torch.cat((out[:, :, :1], out), dim=2)
-            out = out.permute(0, 2, 1, 3, 4)
-            b, f_times_2, c, h, w = out.shape
-            out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
-            out = out.permute(0, 2, 1, 3, 4).contiguous()
-
         return out
 
 class Decoder(nn.Module):
@@ -239,7 +240,7 @@ class Decoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = VideoConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -249,9 +250,9 @@ class Decoder(nn.Module):
         self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
 
         self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
 
         self.up = nn.ModuleList()
         depth = (ffactor_spatial >> 1).bit_length()
@@ -275,27 +276,38 @@ class Decoder(nn.Module):
         self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
 
     def forward(self, z):
-        if self.refiner_vae:
-            z = z.permute(0, 2, 1, 3, 4)
-            b, f, c, h, w = z.shape
-            z = z.reshape(b, f, 2, c // 2, h, w)
-            z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
-            z = z.permute(0, 2, 1, 3, 4)
-            z = z[:, :, 1:]
-
-        x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
+        x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
         x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
 
-        for stage in self.up:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'upsample'):
-                x = stage.upsample(x)
+        if self.refiner_vae:
+            x = torch.split(x, 2, dim=2)
+        else:
+            x = [ x ]
+        out = []
 
-        out = self.conv_out(F.silu(self.norm_out(x)))
+        conv_carry_in = None
+
+        for i, x1 in enumerate(x):
+            conv_carry_out = []
+            if i == len(x) - 1:
+                conv_carry_out = None
+            for stage in self.up:
+                for blk in stage.block:
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
+                if hasattr(stage, 'upsample'):
+                    x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
+
+            x1 = [ F.silu(self.norm_out(x1)) ]
+            x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out)
+            out.append(x1)
+            conv_carry_in = conv_carry_out
+        del x
+
+        out = torch_cat_if_needed(out, dim=2)
 
         if not self.refiner_vae:
             if z.shape[-3] == 1:
                 out = out[:, :, -1:]
 
         return out
+

comfy/ldm/kandinsky5/model.py

@@ -0,0 +1,413 @@
+import torch
+from torch import nn
+import math
+
+import comfy.ldm.common_dit
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.flux.math import apply_rope1
+from comfy.ldm.flux.layers import EmbedND
+
+def attention(q, k, v, heads, transformer_options={}):
+    return optimized_attention(
+        q.transpose(1, 2),
+        k.transpose(1, 2),
+        v.transpose(1, 2),
+        heads=heads,
+        skip_reshape=True,
+        transformer_options=transformer_options
+    )
+
+def apply_scale_shift_norm(norm, x, scale, shift):
+    return torch.addcmul(shift, norm(x), scale + 1.0)
+
+def apply_gate_sum(x, out, gate):
+    return torch.addcmul(x, gate, out)
+
+def get_shift_scale_gate(params):
+    shift, scale, gate = torch.chunk(params, 3, dim=-1)
+    return tuple(x.unsqueeze(1) for x in (shift, scale, gate))
+
+def get_freqs(dim, max_period=10000.0):
+    return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim)
+
+
+class TimeEmbeddings(nn.Module):
+    def __init__(self, model_dim, time_dim, max_period=10000.0, operation_settings=None):
+        super().__init__()
+        assert model_dim % 2 == 0
+        self.model_dim = model_dim
+        self.max_period = max_period
+        self.register_buffer("freqs", get_freqs(model_dim // 2, max_period), persistent=False)
+        operations = operation_settings.get("operations")
+        self.in_layer = operations.Linear(model_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.activation = nn.SiLU()
+        self.out_layer = operations.Linear(time_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, timestep, dtype):
+        args = torch.outer(timestep, self.freqs.to(device=timestep.device))
+        time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype)
+        time_embed = self.out_layer(self.activation(self.in_layer(time_embed)))
+        return time_embed
+
+
+class TextEmbeddings(nn.Module):
+    def __init__(self, text_dim, model_dim, operation_settings=None):
+        super().__init__()
+        operations = operation_settings.get("operations")
+        self.in_layer = operations.Linear(text_dim, model_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm = operations.LayerNorm(model_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, text_embed):
+        text_embed = self.in_layer(text_embed)
+        return self.norm(text_embed).type_as(text_embed)
+
+
+class VisualEmbeddings(nn.Module):
+    def __init__(self, visual_dim, model_dim, patch_size, operation_settings=None):
+        super().__init__()
+        self.patch_size = patch_size
+        operations = operation_settings.get("operations")
+        self.in_layer = operations.Linear(visual_dim, model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x):
+        x = x.movedim(1, -1)  # B C T H W -> B T H W C
+        B, T, H, W, dim = x.shape
+        pt, ph, pw = self.patch_size
+
+        x = x.view(
+            B,
+            T // pt, pt,
+            H // ph, ph,
+            W // pw, pw,
+            dim,
+        ).permute(0, 1, 3, 5, 2, 4, 6, 7).flatten(4, 7)
+
+        return self.in_layer(x)
+
+
+class Modulation(nn.Module):
+    def __init__(self, time_dim, model_dim, num_params, operation_settings=None):
+        super().__init__()
+        self.activation = nn.SiLU()
+        self.out_layer = operation_settings.get("operations").Linear(time_dim, num_params * model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x):
+        return self.out_layer(self.activation(x))
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, num_channels, head_dim, operation_settings=None):
+        super().__init__()
+        assert num_channels % head_dim == 0
+        self.num_heads = num_channels // head_dim
+        self.head_dim = head_dim
+
+        operations = operation_settings.get("operations")
+        self.to_query = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.to_key = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.to_value = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.query_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.key_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+        self.out_layer = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.num_chunks = 2
+
+    def _compute_qk(self, x, freqs, proj_fn, norm_fn):
+        result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1)
+        return apply_rope1(norm_fn(result), freqs)
+
+    def _forward(self, x, freqs, transformer_options={}):
+        q = self._compute_qk(x, freqs, self.to_query, self.query_norm)
+        k = self._compute_qk(x, freqs, self.to_key, self.key_norm)
+        v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
+        out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
+        return self.out_layer(out)
+
+    def _forward_chunked(self, x, freqs, transformer_options={}):
+        def process_chunks(proj_fn, norm_fn):
+            x_chunks = torch.chunk(x, self.num_chunks, dim=1)
+            freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1)
+            chunks = []
+            for x_chunk, freqs_chunk in zip(x_chunks, freqs_chunks):
+                chunks.append(self._compute_qk(x_chunk, freqs_chunk, proj_fn, norm_fn))
+            return torch.cat(chunks, dim=1)
+
+        q = process_chunks(self.to_query, self.query_norm)
+        k = process_chunks(self.to_key, self.key_norm)
+        v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
+        out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
+        return self.out_layer(out)
+
+    def forward(self, x, freqs, transformer_options={}):
+        if x.shape[1] > 8192:
+            return self._forward_chunked(x, freqs, transformer_options=transformer_options)
+        else:
+            return self._forward(x, freqs, transformer_options=transformer_options)
+
+
+class CrossAttention(SelfAttention):
+    def get_qkv(self, x, context):
+        q = self.to_query(x).view(*x.shape[:-1], self.num_heads, -1)
+        k = self.to_key(context).view(*context.shape[:-1], self.num_heads, -1)
+        v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1)
+        return q, k, v
+
+    def forward(self, x, context, transformer_options={}):
+        q, k, v = self.get_qkv(x, context)
+        out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options)
+        return self.out_layer(out)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, ff_dim, operation_settings=None):
+        super().__init__()
+        operations = operation_settings.get("operations")
+        self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.activation = nn.GELU()
+        self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.num_chunks = 4
+
+    def _forward(self, x):
+        return self.out_layer(self.activation(self.in_layer(x)))
+
+    def _forward_chunked(self, x):
+        chunks = torch.chunk(x, self.num_chunks, dim=1)
+        output_chunks = []
+        for chunk in chunks:
+            output_chunks.append(self._forward(chunk))
+        return torch.cat(output_chunks, dim=1)
+
+    def forward(self, x):
+        if x.shape[1] > 8192:
+            return self._forward_chunked(x)
+        else:
+            return self._forward(x)
+
+
+class OutLayer(nn.Module):
+    def __init__(self, model_dim, time_dim, visual_dim, patch_size, operation_settings=None):
+        super().__init__()
+        self.patch_size = patch_size
+        self.modulation = Modulation(time_dim, model_dim, 2, operation_settings=operation_settings)
+        operations = operation_settings.get("operations")
+        self.norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.out_layer = operations.Linear(model_dim, math.prod(patch_size) * visual_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, visual_embed, time_embed):
+        B, T, H, W, _ = visual_embed.shape
+        shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1)
+        scale = scale[:, None, None, None, :]
+        shift = shift[:, None, None, None, :]
+        visual_embed = apply_scale_shift_norm(self.norm, visual_embed, scale, shift)
+        x = self.out_layer(visual_embed)
+
+        out_dim = x.shape[-1] // (self.patch_size[0] * self.patch_size[1] * self.patch_size[2])
+        x = x.view(
+            B, T, H, W,
+            out_dim,
+            self.patch_size[0], self.patch_size[1], self.patch_size[2]
+        )
+        return x.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(2, 3).flatten(3, 4).flatten(4, 5)
+
+
+class TransformerEncoderBlock(nn.Module):
+    def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
+        super().__init__()
+        self.text_modulation = Modulation(time_dim, model_dim, 6, operation_settings=operation_settings)
+        operations = operation_settings.get("operations")
+
+        self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+        self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
+
+    def forward(self, x, time_embed, freqs, transformer_options={}):
+        self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1)
+        shift, scale, gate = get_shift_scale_gate(self_attn_params)
+        out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift)
+        out = self.self_attention(out, freqs, transformer_options=transformer_options)
+        x = apply_gate_sum(x, out, gate)
+
+        shift, scale, gate = get_shift_scale_gate(ff_params)
+        out = apply_scale_shift_norm(self.feed_forward_norm, x, scale, shift)
+        out = self.feed_forward(out)
+        x = apply_gate_sum(x, out, gate)
+        return x
+
+
+class TransformerDecoderBlock(nn.Module):
+    def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
+        super().__init__()
+        self.visual_modulation = Modulation(time_dim, model_dim, 9, operation_settings=operation_settings)
+
+        operations = operation_settings.get("operations")
+        self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+        self.cross_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.cross_attention = CrossAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+        self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
+
+    def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}):
+        self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1)
+        # self attention
+        shift, scale, gate = get_shift_scale_gate(self_attn_params)
+        visual_out = apply_scale_shift_norm(self.self_attention_norm, visual_embed, scale, shift)
+        visual_out = self.self_attention(visual_out, freqs, transformer_options=transformer_options)
+        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+        # cross attention
+        shift, scale, gate = get_shift_scale_gate(cross_attn_params)
+        visual_out = apply_scale_shift_norm(self.cross_attention_norm, visual_embed, scale, shift)
+        visual_out = self.cross_attention(visual_out, text_embed, transformer_options=transformer_options)
+        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+        # feed forward
+        shift, scale, gate = get_shift_scale_gate(ff_params)
+        visual_out = apply_scale_shift_norm(self.feed_forward_norm, visual_embed, scale, shift)
+        visual_out = self.feed_forward(visual_out)
+        visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+        return visual_embed
+
+
+class Kandinsky5(nn.Module):
+    def __init__(
+        self,
+        in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,
+        model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,
+        axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),
+        dtype=None, device=None, operations=None, **kwargs
+    ):
+        super().__init__()
+        head_dim = sum(axes_dims)
+        self.rope_scale_factor = rope_scale_factor
+        self.in_visual_dim = in_visual_dim
+        self.model_dim = model_dim
+        self.patch_size = patch_size
+        self.visual_embed_dim = visual_embed_dim
+        self.dtype = dtype
+        self.device = device
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.time_embeddings = TimeEmbeddings(model_dim, time_dim, operation_settings=operation_settings)
+        self.text_embeddings = TextEmbeddings(in_text_dim, model_dim, operation_settings=operation_settings)
+        self.pooled_text_embeddings = TextEmbeddings(in_text_dim2, time_dim, operation_settings=operation_settings)
+        self.visual_embeddings = VisualEmbeddings(visual_embed_dim, model_dim, patch_size, operation_settings=operation_settings)
+
+        self.text_transformer_blocks = nn.ModuleList(
+            [TransformerEncoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_text_blocks)]
+        )
+
+        self.visual_transformer_blocks = nn.ModuleList(
+            [TransformerDecoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_visual_blocks)]
+        )
+
+        self.out_layer = OutLayer(model_dim, time_dim, out_visual_dim, patch_size, operation_settings=operation_settings)
+
+        self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims)
+        self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim])
+
+    def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}):
+        steps = seq_len if steps is None else steps
+        seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype)
+        seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0)  # Shape: (1, steps, 1)
+        freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2)
+        return freqs
+
+    def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
+
+        patch_size = self.patch_size
+        t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+        h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+        w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+
+        if steps_t is None:
+            steps_t = t_len
+        if steps_h is None:
+            steps_h = h_len
+        if steps_w is None:
+            steps_w = w_len
+
+        h_start = 0
+        w_start = 0
+        rope_options = transformer_options.get("rope_options", None)
+        if rope_options is not None:
+            t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
+            h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
+            w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
+
+            t_start += rope_options.get("shift_t", 0.0)
+            h_start += rope_options.get("shift_y", 0.0)
+            w_start += rope_options.get("shift_x", 0.0)
+        else:
+            rope_scale_factor = self.rope_scale_factor
+            if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions
+                if h * w >= 14080:
+                    rope_scale_factor = (1.0, 3.16, 3.16)
+
+            t_len = (t_len - 1.0) / rope_scale_factor[0] + 1.0
+            h_len = (h_len - 1.0) / rope_scale_factor[1] + 1.0
+            w_len = (w_len - 1.0) / rope_scale_factor[2] + 1.0
+
+        img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
+        img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
+        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
+        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
+        img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
+
+        freqs = self.rope_embedder_3d(img_ids).movedim(1, 2)
+        return freqs
+
+    def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs):
+        patches_replace = transformer_options.get("patches_replace", {})
+        context = self.text_embeddings(context)
+        time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y)
+
+        for block in self.text_transformer_blocks:
+            context = block(context, time_embed, freqs_text, transformer_options=transformer_options)
+
+        visual_embed = self.visual_embeddings(x)
+        visual_shape = visual_embed.shape[:-1]
+        visual_embed = visual_embed.flatten(1, -2)
+
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.visual_transformer_blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.visual_transformer_blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    return block(x=args["x"], context=args["context"], time_embed=args["time_embed"], freqs=args["freqs"], transformer_options=args.get("transformer_options"))
+                visual_embed = blocks_replace[("double_block", i)]({"x": visual_embed, "context": context, "time_embed": time_embed, "freqs": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})["x"]
+            else:
+                visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options)
+
+        visual_embed = visual_embed.reshape(*visual_shape, -1)
+        return self.out_layer(visual_embed, time_embed)
+
+    def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
+        original_dims = x.ndim
+        if original_dims == 4:
+            x = x.unsqueeze(2)
+        bs, c, t_len, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
+        if time_dim_replace is not None:
+            time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size)
+            x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace
+
+        freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+        freqs_text = self.rope_encode_1d(context.shape[1], device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+
+        out = self.forward_orig(x, timestep, context, y, freqs, freqs_text, transformer_options=transformer_options, **kwargs)
+        if original_dims == 4:
+            out = out.squeeze(2)
+        return out
+
+    def forward(self, x, timestep, context, y, time_dim_replace=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, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs)

comfy/ldm/lightricks/av_model.py

@@ -0,0 +1,837 @@
+from typing import Tuple
+import torch
+import torch.nn as nn
+from comfy.ldm.lightricks.model import (
+    CrossAttention,
+    FeedForward,
+    AdaLayerNormSingle,
+    PixArtAlphaTextProjection,
+    LTXVModel,
+)
+from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+import comfy.ldm.common_dit
+
+class BasicAVTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        a_dim,
+        v_heads,
+        a_heads,
+        vd_head,
+        ad_head,
+        v_context_dim=None,
+        a_context_dim=None,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        self.attn_precision = attn_precision
+
+        self.attn1 = CrossAttention(
+            query_dim=v_dim,
+            heads=v_heads,
+            dim_head=vd_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.audio_attn1 = CrossAttention(
+            query_dim=a_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.attn2 = CrossAttention(
+            query_dim=v_dim,
+            context_dim=v_context_dim,
+            heads=v_heads,
+            dim_head=vd_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.audio_attn2 = CrossAttention(
+            query_dim=a_dim,
+            context_dim=a_context_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Q: Video, K,V: Audio
+        self.audio_to_video_attn = CrossAttention(
+            query_dim=v_dim,
+            context_dim=a_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Q: Audio, K,V: Video
+        self.video_to_audio_attn = CrossAttention(
+            query_dim=a_dim,
+            context_dim=v_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.ff = FeedForward(
+            v_dim, dim_out=v_dim, glu=True, dtype=dtype, device=device, operations=operations
+        )
+        self.audio_ff = FeedForward(
+            a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.empty(6, v_dim, device=device, dtype=dtype))
+        self.audio_scale_shift_table = nn.Parameter(
+            torch.empty(6, a_dim, device=device, dtype=dtype)
+        )
+
+        self.scale_shift_table_a2v_ca_audio = nn.Parameter(
+            torch.empty(5, a_dim, device=device, dtype=dtype)
+        )
+        self.scale_shift_table_a2v_ca_video = nn.Parameter(
+            torch.empty(5, v_dim, device=device, dtype=dtype)
+        )
+
+    def get_ada_values(
+        self, scale_shift_table: torch.Tensor, batch_size: int, timestep: torch.Tensor, indices: slice = slice(None, None)
+    ):
+        num_ada_params = scale_shift_table.shape[0]
+
+        ada_values = (
+            scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=timestep.device, dtype=timestep.dtype)
+            + timestep.reshape(batch_size, timestep.shape[1], num_ada_params, -1)[:, :, indices, :]
+        ).unbind(dim=2)
+        return ada_values
+
+    def get_av_ca_ada_values(
+        self,
+        scale_shift_table: torch.Tensor,
+        batch_size: int,
+        scale_shift_timestep: torch.Tensor,
+        gate_timestep: torch.Tensor,
+        num_scale_shift_values: int = 4,
+    ):
+        scale_shift_ada_values = self.get_ada_values(
+            scale_shift_table[:num_scale_shift_values, :],
+            batch_size,
+            scale_shift_timestep,
+        )
+        gate_ada_values = self.get_ada_values(
+            scale_shift_table[num_scale_shift_values:, :],
+            batch_size,
+            gate_timestep,
+        )
+
+        scale_shift_chunks = [t.squeeze(2) for t in scale_shift_ada_values]
+        gate_ada_values = [t.squeeze(2) for t in gate_ada_values]
+
+        return (*scale_shift_chunks, *gate_ada_values)
+
+    def forward(
+        self,
+        x: Tuple[torch.Tensor, torch.Tensor],
+        v_context=None,
+        a_context=None,
+        attention_mask=None,
+        v_timestep=None,
+        a_timestep=None,
+        v_pe=None,
+        a_pe=None,
+        v_cross_pe=None,
+        a_cross_pe=None,
+        v_cross_scale_shift_timestep=None,
+        a_cross_scale_shift_timestep=None,
+        v_cross_gate_timestep=None,
+        a_cross_gate_timestep=None,
+        transformer_options=None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        run_vx = transformer_options.get("run_vx", True)
+        run_ax = transformer_options.get("run_ax", True)
+
+        vx, ax = x
+        run_ax = run_ax and ax.numel() > 0
+        run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0
+        run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True)
+
+        if run_vx:
+            vshift_msa, vscale_msa, vgate_msa = (
+                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 3))
+            )
+
+            norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa
+            vx += self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options) * vgate_msa
+            vx += self.attn2(
+                comfy.ldm.common_dit.rms_norm(vx),
+                context=v_context,
+                mask=attention_mask,
+                transformer_options=transformer_options,
+            )
+
+            del vshift_msa, vscale_msa, vgate_msa
+
+        if run_ax:
+            ashift_msa, ascale_msa, agate_msa = (
+                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 3))
+            )
+
+            norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa
+            ax += (
+                self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
+                * agate_msa
+            )
+            ax += self.audio_attn2(
+                comfy.ldm.common_dit.rms_norm(ax),
+                context=a_context,
+                mask=attention_mask,
+                transformer_options=transformer_options,
+            )
+
+            del ashift_msa, ascale_msa, agate_msa
+
+        # Audio - Video cross attention.
+        if run_a2v or run_v2a:
+            # norm3
+            vx_norm3 = comfy.ldm.common_dit.rms_norm(vx)
+            ax_norm3 = comfy.ldm.common_dit.rms_norm(ax)
+
+            (
+                scale_ca_audio_hidden_states_a2v,
+                shift_ca_audio_hidden_states_a2v,
+                scale_ca_audio_hidden_states_v2a,
+                shift_ca_audio_hidden_states_v2a,
+                gate_out_v2a,
+            ) = self.get_av_ca_ada_values(
+                self.scale_shift_table_a2v_ca_audio,
+                ax.shape[0],
+                a_cross_scale_shift_timestep,
+                a_cross_gate_timestep,
+            )
+
+            (
+                scale_ca_video_hidden_states_a2v,
+                shift_ca_video_hidden_states_a2v,
+                scale_ca_video_hidden_states_v2a,
+                shift_ca_video_hidden_states_v2a,
+                gate_out_a2v,
+            ) = self.get_av_ca_ada_values(
+                self.scale_shift_table_a2v_ca_video,
+                vx.shape[0],
+                v_cross_scale_shift_timestep,
+                v_cross_gate_timestep,
+            )
+
+            if run_a2v:
+                vx_scaled = (
+                    vx_norm3 * (1 + scale_ca_video_hidden_states_a2v)
+                    + shift_ca_video_hidden_states_a2v
+                )
+                ax_scaled = (
+                    ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v)
+                    + shift_ca_audio_hidden_states_a2v
+                )
+                vx += (
+                    self.audio_to_video_attn(
+                        vx_scaled,
+                        context=ax_scaled,
+                        pe=v_cross_pe,
+                        k_pe=a_cross_pe,
+                        transformer_options=transformer_options,
+                    )
+                    * gate_out_a2v
+                )
+
+                del gate_out_a2v
+                del scale_ca_video_hidden_states_a2v,\
+                    shift_ca_video_hidden_states_a2v,\
+                    scale_ca_audio_hidden_states_a2v,\
+                    shift_ca_audio_hidden_states_a2v,\
+
+            if run_v2a:
+                ax_scaled = (
+                    ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a)
+                    + shift_ca_audio_hidden_states_v2a
+                )
+                vx_scaled = (
+                    vx_norm3 * (1 + scale_ca_video_hidden_states_v2a)
+                    + shift_ca_video_hidden_states_v2a
+                )
+                ax += (
+                    self.video_to_audio_attn(
+                        ax_scaled,
+                        context=vx_scaled,
+                        pe=a_cross_pe,
+                        k_pe=v_cross_pe,
+                        transformer_options=transformer_options,
+                    )
+                    * gate_out_v2a
+                )
+
+                del gate_out_v2a
+                del scale_ca_video_hidden_states_v2a,\
+                    shift_ca_video_hidden_states_v2a,\
+                    scale_ca_audio_hidden_states_v2a,\
+                    shift_ca_audio_hidden_states_v2a
+
+        if run_vx:
+            vshift_mlp, vscale_mlp, vgate_mlp = (
+                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, None))
+            )
+
+            vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp
+            vx += self.ff(vx_scaled) * vgate_mlp
+            del vshift_mlp, vscale_mlp, vgate_mlp
+
+        if run_ax:
+            ashift_mlp, ascale_mlp, agate_mlp = (
+                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, None))
+            )
+
+            ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp
+            ax += self.audio_ff(ax_scaled) * agate_mlp
+
+            del ashift_mlp, ascale_mlp, agate_mlp
+
+
+        return vx, ax
+
+
+class LTXAVModel(LTXVModel):
+    """LTXAV model for audio-video generation."""
+
+    def __init__(
+        self,
+        in_channels=128,
+        audio_in_channels=128,
+        cross_attention_dim=4096,
+        audio_cross_attention_dim=2048,
+        attention_head_dim=128,
+        audio_attention_head_dim=64,
+        num_attention_heads=32,
+        audio_num_attention_heads=32,
+        caption_channels=3840,
+        num_layers=48,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[20, 2048, 2048],
+        audio_positional_embedding_max_pos=[20],
+        causal_temporal_positioning=False,
+        vae_scale_factors=(8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier=1000.0,
+        av_ca_timestep_scale_multiplier=1.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        # Store audio-specific parameters
+        self.audio_in_channels = audio_in_channels
+        self.audio_cross_attention_dim = audio_cross_attention_dim
+        self.audio_attention_head_dim = audio_attention_head_dim
+        self.audio_num_attention_heads = audio_num_attention_heads
+        self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos
+
+        # Calculate audio dimensions
+        self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim
+        self.audio_out_channels = audio_in_channels
+
+        # Audio-specific constants
+        self.num_audio_channels = 8
+        self.audio_frequency_bins = 16
+
+        self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier
+
+        super().__init__(
+            in_channels=in_channels,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            caption_channels=caption_channels,
+            num_layers=num_layers,
+            positional_embedding_theta=positional_embedding_theta,
+            positional_embedding_max_pos=positional_embedding_max_pos,
+            causal_temporal_positioning=causal_temporal_positioning,
+            vae_scale_factors=vae_scale_factors,
+            use_middle_indices_grid=use_middle_indices_grid,
+            timestep_scale_multiplier=timestep_scale_multiplier,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **kwargs,
+        )
+
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize LTXAV-specific components."""
+        # Audio-specific projections
+        self.audio_patchify_proj = self.operations.Linear(
+            self.audio_in_channels, self.audio_inner_dim, bias=True, dtype=dtype, device=device
+        )
+
+        # Audio-specific AdaLN
+        self.audio_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        num_scale_shift_values = 4
+        self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(
+            self.inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=num_scale_shift_values,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle(
+            self.inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=1,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=num_scale_shift_values,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=1,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        # Audio caption projection
+        self.audio_caption_projection = PixArtAlphaTextProjection(
+            in_features=self.caption_channels,
+            hidden_size=self.audio_inner_dim,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks for LTXAV."""
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicAVTransformerBlock(
+                    v_dim=self.inner_dim,
+                    a_dim=self.audio_inner_dim,
+                    v_heads=self.num_attention_heads,
+                    a_heads=self.audio_num_attention_heads,
+                    vd_head=self.attention_head_dim,
+                    ad_head=self.audio_attention_head_dim,
+                    v_context_dim=self.cross_attention_dim,
+                    a_context_dim=self.audio_cross_attention_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+                for _ in range(self.num_layers)
+            ]
+        )
+
+    def _init_output_components(self, device, dtype):
+        """Initialize output components for LTXAV."""
+        # Video output components
+        super()._init_output_components(device, dtype)
+        # Audio output components
+        self.audio_scale_shift_table = nn.Parameter(
+            torch.empty(2, self.audio_inner_dim, dtype=dtype, device=device)
+        )
+        self.audio_norm_out = self.operations.LayerNorm(
+            self.audio_inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+        )
+        self.audio_proj_out = self.operations.Linear(
+            self.audio_inner_dim, self.audio_out_channels, dtype=dtype, device=device
+        )
+        self.a_patchifier = AudioPatchifier(1, start_end=True)
+
+    def separate_audio_and_video_latents(self, x, audio_length):
+        """Separate audio and video latents from combined input."""
+        # vx = x[:, : self.in_channels]
+        # ax = x[:, self.in_channels :]
+        #
+        # ax = ax.reshape(ax.shape[0], -1)
+        # ax = ax[:, : audio_length * self.num_audio_channels * self.audio_frequency_bins]
+        #
+        # ax = ax.reshape(
+        #     ax.shape[0], self.num_audio_channels, audio_length, self.audio_frequency_bins
+        # )
+
+        vx = x[0]
+        ax = x[1] if len(x) > 1 else torch.zeros(
+            (vx.shape[0], self.num_audio_channels, 0, self.audio_frequency_bins),
+            device=vx.device, dtype=vx.dtype
+        )
+        return vx, ax
+
+    def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):
+        if ax.numel() == 0:
+            return vx
+        else:
+            return [vx, ax]
+        """Recombine audio and video latents for output."""
+        # if ax.device != vx.device or ax.dtype != vx.dtype:
+        #     logging.warning("Audio and video latents are on different devices or dtypes.")
+        #     ax = ax.to(device=vx.device, dtype=vx.dtype)
+        #     logging.warning(f"Audio audio latent moved to device: {ax.device}, dtype: {ax.dtype}")
+        #
+        # ax = ax.reshape(ax.shape[0], -1)
+        # # pad to f x h x w of the video latents
+        # divisor = vx.shape[-1] * vx.shape[-2] * vx.shape[-3]
+        # if target_shape is None:
+        #     repetitions = math.ceil(ax.shape[-1] / divisor)
+        # else:
+        #     repetitions = target_shape[1] - vx.shape[1]
+        # padded_len = repetitions * divisor
+        # ax = F.pad(ax, (0, padded_len - ax.shape[-1]))
+        # ax = ax.reshape(ax.shape[0], -1, vx.shape[-3], vx.shape[-2], vx.shape[-1])
+        # return torch.cat([vx, ax], dim=1)
+
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input for LTXAV - separate audio and video, then patchify."""
+        audio_length = kwargs.get("audio_length", 0)
+        # Separate audio and video latents
+        vx, ax = self.separate_audio_and_video_latents(x, audio_length)
+        [vx, v_pixel_coords, additional_args] = super()._process_input(
+            vx, keyframe_idxs, denoise_mask, **kwargs
+        )
+
+        ax, a_latent_coords = self.a_patchifier.patchify(ax)
+        ax = self.audio_patchify_proj(ax)
+
+        # additional_args.update({"av_orig_shape": list(x.shape)})
+        return [vx, ax], [v_pixel_coords, a_latent_coords], additional_args
+
+    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 = timestep * self.timestep_scale_multiplier
+        v_timestep, v_embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        v_timestep = v_timestep.view(batch_size, -1, v_timestep.shape[-1])
+        v_embedded_timestep = v_embedded_timestep.view(
+            batch_size, -1, v_embedded_timestep.shape[-1]
+        )
+
+        # Prepare audio timestep
+        a_timestep = kwargs.get("a_timestep")
+        if a_timestep is not None:
+            a_timestep = a_timestep * self.timestep_scale_multiplier
+            av_ca_factor = self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier
+
+            av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(
+                a_timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single(
+                timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single(
+                timestep.flatten() * av_ca_factor,
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single(
+                a_timestep.flatten() * av_ca_factor,
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+
+            a_timestep, a_embedded_timestep = self.audio_adaln_single(
+                a_timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1])
+            a_embedded_timestep = a_embedded_timestep.view(
+                batch_size, -1, a_embedded_timestep.shape[-1]
+            )
+            cross_av_timestep_ss = [
+                av_ca_audio_scale_shift_timestep,
+                av_ca_video_scale_shift_timestep,
+                av_ca_a2v_gate_noise_timestep,
+                av_ca_v2a_gate_noise_timestep,
+            ]
+            cross_av_timestep_ss = list(
+                [t.view(batch_size, -1, t.shape[-1]) for t in cross_av_timestep_ss]
+            )
+        else:
+            a_timestep = timestep
+            a_embedded_timestep = kwargs.get("embedded_timestep")
+            cross_av_timestep_ss = []
+
+        return [v_timestep, a_timestep, cross_av_timestep_ss], [
+            v_embedded_timestep,
+            a_embedded_timestep,
+        ]
+
+    def _prepare_context(self, context, batch_size, x, attention_mask=None):
+        vx = x[0]
+        ax = x[1]
+        v_context, a_context = torch.split(
+            context, int(context.shape[-1] / 2), len(context.shape) - 1
+        )
+
+        v_context, attention_mask = super()._prepare_context(
+            v_context, batch_size, vx, attention_mask
+        )
+        if self.audio_caption_projection is not None:
+            a_context = self.audio_caption_projection(a_context)
+            a_context = a_context.view(batch_size, -1, ax.shape[-1])
+
+        return [v_context, a_context], attention_mask
+
+    def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
+        v_pixel_coords = pixel_coords[0]
+        v_pe = super()._prepare_positional_embeddings(v_pixel_coords, frame_rate, x_dtype)
+
+        a_latent_coords = pixel_coords[1]
+        a_pe = self._precompute_freqs_cis(
+            a_latent_coords,
+            dim=self.audio_inner_dim,
+            out_dtype=x_dtype,
+            max_pos=self.audio_positional_embedding_max_pos,
+            use_middle_indices_grid=self.use_middle_indices_grid,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+
+        # calculate positional embeddings for the middle of the token duration, to use in av cross attention layers.
+        max_pos = max(
+            self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0]
+        )
+        v_pixel_coords = v_pixel_coords.to(torch.float32)
+        v_pixel_coords[:, 0] = v_pixel_coords[:, 0] * (1.0 / frame_rate)
+        av_cross_video_freq_cis = self._precompute_freqs_cis(
+            v_pixel_coords[:, 0:1, :],
+            dim=self.audio_cross_attention_dim,
+            out_dtype=x_dtype,
+            max_pos=[max_pos],
+            use_middle_indices_grid=True,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+        av_cross_audio_freq_cis = self._precompute_freqs_cis(
+            a_latent_coords[:, 0:1, :],
+            dim=self.audio_cross_attention_dim,
+            out_dtype=x_dtype,
+            max_pos=[max_pos],
+            use_middle_indices_grid=True,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+
+        return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)]
+
+    def _process_transformer_blocks(
+        self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs
+    ):
+        vx = x[0]
+        ax = x[1]
+        v_context = context[0]
+        a_context = context[1]
+        v_timestep = timestep[0]
+        a_timestep = timestep[1]
+        v_pe, av_cross_video_freq_cis = pe[0]
+        a_pe, av_cross_audio_freq_cis = pe[1]
+
+        (
+            av_ca_audio_scale_shift_timestep,
+            av_ca_video_scale_shift_timestep,
+            av_ca_a2v_gate_noise_timestep,
+            av_ca_v2a_gate_noise_timestep,
+        ) = timestep[2]
+
+        """Process transformer blocks for LTXAV."""
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+
+        # Process transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(
+                        args["img"],
+                        v_context=args["v_context"],
+                        a_context=args["a_context"],
+                        attention_mask=args["attention_mask"],
+                        v_timestep=args["v_timestep"],
+                        a_timestep=args["a_timestep"],
+                        v_pe=args["v_pe"],
+                        a_pe=args["a_pe"],
+                        v_cross_pe=args["v_cross_pe"],
+                        a_cross_pe=args["a_cross_pe"],
+                        v_cross_scale_shift_timestep=args["v_cross_scale_shift_timestep"],
+                        a_cross_scale_shift_timestep=args["a_cross_scale_shift_timestep"],
+                        v_cross_gate_timestep=args["v_cross_gate_timestep"],
+                        a_cross_gate_timestep=args["a_cross_gate_timestep"],
+                        transformer_options=args["transformer_options"],
+                    )
+                    return out
+
+                out = blocks_replace[("double_block", i)](
+                    {
+                        "img": (vx, ax),
+                        "v_context": v_context,
+                        "a_context": a_context,
+                        "attention_mask": attention_mask,
+                        "v_timestep": v_timestep,
+                        "a_timestep": a_timestep,
+                        "v_pe": v_pe,
+                        "a_pe": a_pe,
+                        "v_cross_pe": av_cross_video_freq_cis,
+                        "a_cross_pe": av_cross_audio_freq_cis,
+                        "v_cross_scale_shift_timestep": av_ca_video_scale_shift_timestep,
+                        "a_cross_scale_shift_timestep": av_ca_audio_scale_shift_timestep,
+                        "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep,
+                        "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
+                        "transformer_options": transformer_options,
+                    },
+                    {"original_block": block_wrap},
+                )
+                vx, ax = out["img"]
+            else:
+                vx, ax = block(
+                    (vx, ax),
+                    v_context=v_context,
+                    a_context=a_context,
+                    attention_mask=attention_mask,
+                    v_timestep=v_timestep,
+                    a_timestep=a_timestep,
+                    v_pe=v_pe,
+                    a_pe=a_pe,
+                    v_cross_pe=av_cross_video_freq_cis,
+                    a_cross_pe=av_cross_audio_freq_cis,
+                    v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep,
+                    a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep,
+                    v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,
+                    a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
+                    transformer_options=transformer_options,
+                )
+
+        return [vx, ax]
+
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        vx = x[0]
+        ax = x[1]
+        v_embedded_timestep = embedded_timestep[0]
+        a_embedded_timestep = embedded_timestep[1]
+        vx = super()._process_output(vx, v_embedded_timestep, keyframe_idxs, **kwargs)
+
+        # Process audio output
+        a_scale_shift_values = (
+            self.audio_scale_shift_table[None, None].to(device=a_embedded_timestep.device, dtype=a_embedded_timestep.dtype)
+            + a_embedded_timestep[:, :, None]
+        )
+        a_shift, a_scale = a_scale_shift_values[:, :, 0], a_scale_shift_values[:, :, 1]
+
+        ax = self.audio_norm_out(ax)
+        ax = ax * (1 + a_scale) + a_shift
+        ax = self.audio_proj_out(ax)
+
+        # Unpatchify audio
+        ax = self.a_patchifier.unpatchify(
+            ax, channels=self.num_audio_channels, freq=self.audio_frequency_bins
+        )
+
+        # Recombine audio and video
+        original_shape = kwargs.get("av_orig_shape")
+        return self.recombine_audio_and_video_latents(vx, ax, original_shape)
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context,
+        attention_mask=None,
+        frame_rate=25,
+        transformer_options={},
+        keyframe_idxs=None,
+        **kwargs,
+    ):
+        """
+        Forward pass for LTXAV model.
+
+        Args:
+            x: Combined audio-video input tensor
+            timestep: Tuple of (video_timestep, audio_timestep) or single timestep
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments including audio_length
+
+        Returns:
+            Combined audio-video output tensor
+        """
+        # Handle timestep format
+        if isinstance(timestep, (tuple, list)) and len(timestep) == 2:
+            v_timestep, a_timestep = timestep
+            kwargs["a_timestep"] = a_timestep
+            timestep = v_timestep
+        else:
+            kwargs["a_timestep"] = timestep
+
+        # Call parent forward method
+        return super().forward(
+            x,
+            timestep,
+            context,
+            attention_mask,
+            frame_rate,
+            transformer_options,
+            keyframe_idxs,
+            **kwargs,
+        )

comfy/ldm/lightricks/embeddings_connector.py

@@ -0,0 +1,305 @@
+import math
+from typing import Optional
+
+import comfy.ldm.common_dit
+import torch
+from comfy.ldm.lightricks.model import (
+    CrossAttention,
+    FeedForward,
+    generate_freq_grid_np,
+    interleaved_freqs_cis,
+    split_freqs_cis,
+)
+from torch import nn
+
+
+class BasicTransformerBlock1D(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        standardization_norm (`str`, *optional*, defaults to `"layer_norm"`): The type of pre-normalization to use. Can be `"layer_norm"` or `"rms_norm"`.
+        norm_eps (`float`, *optional*, defaults to 1e-5): Epsilon value for normalization layers.
+        qk_norm (`str`, *optional*, defaults to None):
+            Set to 'layer_norm' or `rms_norm` to perform query and key normalization.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*): Dimension of the inner feed-forward layer. If not provided, defaults to `dim * 4`.
+        ff_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the feed-forward layer.
+        attention_out_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the attention output layer.
+        use_rope (`bool`, *optional*, defaults to `False`): Whether to use Rotary Position Embeddings (RoPE).
+        ffn_dim_mult (`int`, *optional*, defaults to 4): Multiplier for the inner dimension of the feed-forward layer.
+    """
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        context_dim=None,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        self.attn1 = CrossAttention(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            context_dim=None,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # 3. Feed-forward
+        self.ff = FeedForward(
+            dim,
+            dim_out=dim,
+            glu=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+    def forward(self, hidden_states, attention_mask=None, pe=None) -> torch.FloatTensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+
+        # 1. Normalization Before Self-Attention
+        norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        norm_hidden_states = norm_hidden_states.squeeze(1)
+
+        # 2. Self-Attention
+        attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe)
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 3. Normalization before Feed-Forward
+        norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class Embeddings1DConnector(nn.Module):
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels=128,
+        cross_attention_dim=2048,
+        attention_head_dim=128,
+        num_attention_heads=30,
+        num_layers=2,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[4096],
+        causal_temporal_positioning=False,
+        num_learnable_registers: Optional[int] = 128,
+        dtype=None,
+        device=None,
+        operations=None,
+        split_rope=False,
+        double_precision_rope=False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.out_channels = in_channels
+        self.num_attention_heads = num_attention_heads
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = positional_embedding_max_pos
+        self.split_rope = split_rope
+        self.double_precision_rope = double_precision_rope
+        self.transformer_1d_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock1D(
+                    self.inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    context_dim=cross_attention_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        inner_dim = num_attention_heads * attention_head_dim
+        self.num_learnable_registers = num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = nn.Parameter(
+                torch.rand(
+                    self.num_learnable_registers, inner_dim, dtype=dtype, device=device
+                )
+                * 2.0
+                - 1.0
+            )
+
+    def get_fractional_positions(self, indices_grid):
+        fractional_positions = torch.stack(
+            [
+                indices_grid[:, i] / self.positional_embedding_max_pos[i]
+                for i in range(1)
+            ],
+            dim=-1,
+        )
+        return fractional_positions
+
+    def precompute_freqs(self, indices_grid, spacing):
+        source_dtype = indices_grid.dtype
+        dtype = (
+            torch.float32
+            if source_dtype in (torch.bfloat16, torch.float16)
+            else source_dtype
+        )
+
+        fractional_positions = self.get_fractional_positions(indices_grid)
+        indices = (
+            generate_freq_grid_np(
+                self.positional_embedding_theta,
+                indices_grid.shape[1],
+                self.inner_dim,
+            )
+            if self.double_precision_rope
+            else self.generate_freq_grid(spacing, dtype, fractional_positions.device)
+        ).to(device=fractional_positions.device)
+
+        if spacing == "exp_2":
+            freqs = (
+                (indices * fractional_positions.unsqueeze(-1))
+                .transpose(-1, -2)
+                .flatten(2)
+            )
+        else:
+            freqs = (
+                (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+                .transpose(-1, -2)
+                .flatten(2)
+            )
+        return freqs
+
+    def generate_freq_grid(self, spacing, dtype, device):
+        dim = self.inner_dim
+        theta = self.positional_embedding_theta
+        n_pos_dims = 1
+        n_elem = 2 * n_pos_dims  # 2 for cos and sin e.g. x 3 = 6
+        start = 1
+        end = theta
+
+        if spacing == "exp":
+            indices = theta ** (torch.arange(0, dim, n_elem, device="cpu", dtype=torch.float32) / (dim - n_elem))
+            indices = indices.to(dtype=dtype, device=device)
+        elif spacing == "exp_2":
+            indices = 1.0 / theta ** (torch.arange(0, dim, n_elem, device=device) / dim)
+            indices = indices.to(dtype=dtype)
+        elif spacing == "linear":
+            indices = torch.linspace(
+                start, end, dim // n_elem, device=device, dtype=dtype
+            )
+        elif spacing == "sqrt":
+            indices = torch.linspace(
+                start**2, end**2, dim // n_elem, device=device, dtype=dtype
+            ).sqrt()
+
+        indices = indices * math.pi / 2
+
+        return indices
+
+    def precompute_freqs_cis(self, indices_grid, spacing="exp"):
+        dim = self.inner_dim
+        n_elem = 2  # 2 because of cos and sin
+        freqs = self.precompute_freqs(indices_grid, spacing)
+        if self.split_rope:
+            expected_freqs = dim // 2
+            current_freqs = freqs.shape[-1]
+            pad_size = expected_freqs - current_freqs
+            cos_freq, sin_freq = split_freqs_cis(
+                freqs, pad_size, self.num_attention_heads
+            )
+        else:
+            cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
+        return cos_freq.to(self.dtype), sin_freq.to(self.dtype), self.split_rope
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        """
+        The [`Transformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            indices_grid (`torch.LongTensor` of shape `(batch size, 3, num latent pixels)`):
+            attention_mask ( `torch.Tensor`, *optional*):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+
+        if self.num_learnable_registers:
+            num_registers_duplications = math.ceil(
+                max(1024, hidden_states.shape[1]) / self.num_learnable_registers
+            )
+            learnable_registers = torch.tile(
+                self.learnable_registers.to(hidden_states), (num_registers_duplications, 1)
+            )
+
+            hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1)
+
+            if attention_mask is not None:
+                attention_mask = torch.zeros([1, 1, 1, hidden_states.shape[1]], dtype=attention_mask.dtype, device=attention_mask.device)
+
+        indices_grid = torch.arange(
+            hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device
+        )
+        indices_grid = indices_grid[None, None, :]
+        freqs_cis = self.precompute_freqs_cis(indices_grid)
+
+        # 2. Blocks
+        for block_idx, block in enumerate(self.transformer_1d_blocks):
+            hidden_states = block(
+                hidden_states, attention_mask=attention_mask, pe=freqs_cis
+            )
+
+        # 3. Output
+        # if self.output_scale is not None:
+        #     hidden_states = hidden_states / self.output_scale
+
+        hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        return hidden_states, attention_mask

comfy/ldm/lightricks/latent_upsampler.py

@@ -0,0 +1,292 @@
+from typing import Optional, Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+def _rational_for_scale(scale: float) -> Tuple[int, int]:
+    mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)}
+    if float(scale) not in mapping:
+        raise ValueError(
+            f"Unsupported spatial_scale {scale}. Choose from {list(mapping.keys())}"
+        )
+    return mapping[float(scale)]
+
+
+class PixelShuffleND(nn.Module):
+    def __init__(self, dims, upscale_factors=(2, 2, 2)):
+        super().__init__()
+        assert dims in [1, 2, 3], "dims must be 1, 2, or 3"
+        self.dims = dims
+        self.upscale_factors = upscale_factors
+
+    def forward(self, x):
+        if self.dims == 3:
+            return rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+                p3=self.upscale_factors[2],
+            )
+        elif self.dims == 2:
+            return rearrange(
+                x,
+                "b (c p1 p2) h w -> b c (h p1) (w p2)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+            )
+        elif self.dims == 1:
+            return rearrange(
+                x,
+                "b (c p1) f h w -> b c (f p1) h w",
+                p1=self.upscale_factors[0],
+            )
+
+
+class BlurDownsample(nn.Module):
+    """
+    Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel.
+    Applies only on H,W. Works for dims=2 or dims=3 (per-frame).
+    """
+
+    def __init__(self, dims: int, stride: int):
+        super().__init__()
+        assert dims in (2, 3)
+        assert stride >= 1 and isinstance(stride, int)
+        self.dims = dims
+        self.stride = stride
+
+        # 5x5 separable binomial kernel [1,4,6,4,1] (outer product), normalized
+        k = torch.tensor([1.0, 4.0, 6.0, 4.0, 1.0])
+        k2d = k[:, None] @ k[None, :]
+        k2d = (k2d / k2d.sum()).float()  # shape (5,5)
+        self.register_buffer("kernel", k2d[None, None, :, :])  # (1,1,5,5)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.stride == 1:
+            return x
+
+        def _apply_2d(x2d: torch.Tensor) -> torch.Tensor:
+            # x2d: (B, C, H, W)
+            B, C, H, W = x2d.shape
+            weight = self.kernel.expand(C, 1, 5, 5)  # depthwise
+            x2d = F.conv2d(
+                x2d, weight=weight, bias=None, stride=self.stride, padding=2, groups=C
+            )
+            return x2d
+
+        if self.dims == 2:
+            return _apply_2d(x)
+        else:
+            # dims == 3: apply per-frame on H,W
+            b, c, f, h, w = x.shape
+            x = rearrange(x, "b c f h w -> (b f) c h w")
+            x = _apply_2d(x)
+            h2, w2 = x.shape[-2:]
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f, h=h2, w=w2)
+            return x
+
+
+class SpatialRationalResampler(nn.Module):
+    """
+    Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased
+    downsample by 'den' using fixed blur + stride. Operates on H,W only.
+
+    For dims==3, work per-frame for spatial scaling (temporal axis untouched).
+    """
+
+    def __init__(self, mid_channels: int, scale: float):
+        super().__init__()
+        self.scale = float(scale)
+        self.num, self.den = _rational_for_scale(self.scale)
+        self.conv = nn.Conv2d(
+            mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1
+        )
+        self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num))
+        self.blur_down = BlurDownsample(dims=2, stride=self.den)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, c, f, h, w = x.shape
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.blur_down(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        return x
+
+
+class ResBlock(nn.Module):
+    def __init__(
+        self, channels: int, mid_channels: Optional[int] = None, dims: int = 3
+    ):
+        super().__init__()
+        if mid_channels is None:
+            mid_channels = channels
+
+        Conv = nn.Conv2d if dims == 2 else nn.Conv3d
+
+        self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1)
+        self.norm1 = nn.GroupNorm(32, mid_channels)
+        self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1)
+        self.norm2 = nn.GroupNorm(32, channels)
+        self.activation = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.activation(x)
+        x = self.conv2(x)
+        x = self.norm2(x)
+        x = self.activation(x + residual)
+        return x
+
+
+class LatentUpsampler(nn.Module):
+    """
+    Model to spatially upsample VAE latents.
+
+    Args:
+        in_channels (`int`): Number of channels in the input latent
+        mid_channels (`int`): Number of channels in the middle layers
+        num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling)
+        dims (`int`): Number of dimensions for convolutions (2 or 3)
+        spatial_upsample (`bool`): Whether to spatially upsample the latent
+        temporal_upsample (`bool`): Whether to temporally upsample the latent
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 128,
+        mid_channels: int = 512,
+        num_blocks_per_stage: int = 4,
+        dims: int = 3,
+        spatial_upsample: bool = True,
+        temporal_upsample: bool = False,
+        spatial_scale: float = 2.0,
+        rational_resampler: bool = False,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.mid_channels = mid_channels
+        self.num_blocks_per_stage = num_blocks_per_stage
+        self.dims = dims
+        self.spatial_upsample = spatial_upsample
+        self.temporal_upsample = temporal_upsample
+        self.spatial_scale = float(spatial_scale)
+        self.rational_resampler = rational_resampler
+
+        Conv = nn.Conv2d if dims == 2 else nn.Conv3d
+
+        self.initial_conv = Conv(in_channels, mid_channels, kernel_size=3, padding=1)
+        self.initial_norm = nn.GroupNorm(32, mid_channels)
+        self.initial_activation = nn.SiLU()
+
+        self.res_blocks = nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        if spatial_upsample and temporal_upsample:
+            self.upsampler = nn.Sequential(
+                nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(3),
+            )
+        elif spatial_upsample:
+            if rational_resampler:
+                self.upsampler = SpatialRationalResampler(
+                    mid_channels=mid_channels, scale=self.spatial_scale
+                )
+            else:
+                self.upsampler = nn.Sequential(
+                    nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
+                    PixelShuffleND(2),
+                )
+        elif temporal_upsample:
+            self.upsampler = nn.Sequential(
+                nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(1),
+            )
+        else:
+            raise ValueError(
+                "Either spatial_upsample or temporal_upsample must be True"
+            )
+
+        self.post_upsample_res_blocks = nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        self.final_conv = Conv(mid_channels, in_channels, kernel_size=3, padding=1)
+
+    def forward(self, latent: torch.Tensor) -> torch.Tensor:
+        b, c, f, h, w = latent.shape
+
+        if self.dims == 2:
+            x = rearrange(latent, "b c f h w -> (b f) c h w")
+            x = self.initial_conv(x)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            x = self.upsampler(x)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        else:
+            x = self.initial_conv(latent)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            if self.temporal_upsample:
+                x = self.upsampler(x)
+                x = x[:, :, 1:, :, :]
+            else:
+                if isinstance(self.upsampler, SpatialRationalResampler):
+                    x = self.upsampler(x)
+                else:
+                    x = rearrange(x, "b c f h w -> (b f) c h w")
+                    x = self.upsampler(x)
+                    x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+
+        return x
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(
+            in_channels=config.get("in_channels", 4),
+            mid_channels=config.get("mid_channels", 128),
+            num_blocks_per_stage=config.get("num_blocks_per_stage", 4),
+            dims=config.get("dims", 2),
+            spatial_upsample=config.get("spatial_upsample", True),
+            temporal_upsample=config.get("temporal_upsample", False),
+            spatial_scale=config.get("spatial_scale", 2.0),
+            rational_resampler=config.get("rational_resampler", False),
+        )
+
+    def config(self):
+        return {
+            "_class_name": "LatentUpsampler",
+            "in_channels": self.in_channels,
+            "mid_channels": self.mid_channels,
+            "num_blocks_per_stage": self.num_blocks_per_stage,
+            "dims": self.dims,
+            "spatial_upsample": self.spatial_upsample,
+            "temporal_upsample": self.temporal_upsample,
+            "spatial_scale": self.spatial_scale,
+            "rational_resampler": self.rational_resampler,
+        }

comfy/ldm/lightricks/model.py

@@ -1,13 +1,47 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+import functools
+import math
+from typing import Dict, Optional, Tuple
+
+from einops import rearrange
+import numpy as np
 import torch
 from torch import nn
 import comfy.patcher_extension
 import comfy.ldm.modules.attention
 import comfy.ldm.common_dit
-import math
-from typing import Dict, Optional, Tuple
 
 from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
-from comfy.ldm.flux.math import apply_rope1
+
+def _log_base(x, base):
+    return np.log(x) / np.log(base)
+
+class LTXRopeType(str, Enum):
+    INTERLEAVED = "interleaved"
+    SPLIT = "split"
+
+    KEY = "rope_type"
+
+    @classmethod
+    def from_dict(cls, kwargs, default=None):
+        if default is None:
+            default = cls.INTERLEAVED
+        return cls(kwargs.get(cls.KEY, default))
+
+
+class LTXFrequenciesPrecision(str, Enum):
+    FLOAT32 = "float32"
+    FLOAT64 = "float64"
+
+    KEY = "frequencies_precision"
+
+    @classmethod
+    def from_dict(cls, kwargs, default=None):
+        if default is None:
+            default = cls.FLOAT32
+        return cls(kwargs.get(cls.KEY, default))
+
 
 def get_timestep_embedding(
     timesteps: torch.Tensor,
@@ -39,9 +73,7 @@ def get_timestep_embedding(
     assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
 
     half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(
-        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
-    )
+    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
     exponent = exponent / (half_dim - downscale_freq_shift)
 
     emb = torch.exp(exponent)
@@ -73,7 +105,9 @@ class TimestepEmbedding(nn.Module):
         post_act_fn: Optional[str] = None,
         cond_proj_dim=None,
         sample_proj_bias=True,
-        dtype=None, device=None, operations=None,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
 
@@ -90,7 +124,9 @@ class TimestepEmbedding(nn.Module):
             time_embed_dim_out = out_dim
         else:
             time_embed_dim_out = time_embed_dim
-        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(
+            time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device
+        )
 
         if post_act_fn is None:
             self.post_act = None
@@ -139,12 +175,22 @@ class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
     https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
     """
 
-    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+    def __init__(
+        self,
+        embedding_dim,
+        size_emb_dim,
+        use_additional_conditions: bool = False,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
         super().__init__()
 
         self.outdim = size_emb_dim
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+        self.timestep_embedder = TimestepEmbedding(
+            in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations
+        )
 
     def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
         timesteps_proj = self.time_proj(timestep)
@@ -163,15 +209,22 @@ class AdaLayerNormSingle(nn.Module):
         use_additional_conditions (`bool`): To use additional conditions for normalization or not.
     """
 
-    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+    def __init__(
+        self, embedding_dim: int, embedding_coefficient: int = 6, use_additional_conditions: bool = False, dtype=None, device=None, operations=None
+    ):
         super().__init__()
 
         self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
-            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+            embedding_dim,
+            size_emb_dim=embedding_dim // 3,
+            use_additional_conditions=use_additional_conditions,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
 
         self.silu = nn.SiLU()
-        self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+        self.linear = operations.Linear(embedding_dim, embedding_coefficient * embedding_dim, bias=True, dtype=dtype, device=device)
 
     def forward(
         self,
@@ -185,6 +238,7 @@ class AdaLayerNormSingle(nn.Module):
         embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
         return self.linear(self.silu(embedded_timestep)), embedded_timestep
 
+
 class PixArtAlphaTextProjection(nn.Module):
     """
     Projects caption embeddings. Also handles dropout for classifier-free guidance.
@@ -192,18 +246,24 @@ class PixArtAlphaTextProjection(nn.Module):
     Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
     """
 
-    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+    def __init__(
+        self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None
+    ):
         super().__init__()
         if out_features is None:
             out_features = hidden_size
-        self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+        self.linear_1 = operations.Linear(
+            in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device
+        )
         if act_fn == "gelu_tanh":
             self.act_1 = nn.GELU(approximate="tanh")
         elif act_fn == "silu":
             self.act_1 = nn.SiLU()
         else:
             raise ValueError(f"Unknown activation function: {act_fn}")
-        self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(
+            in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device
+        )
 
     def forward(self, caption):
         hidden_states = self.linear_1(caption)
@@ -222,23 +282,68 @@ class GELU_approx(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = int(dim * mult)
         project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
 
         self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+            project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
         )
 
     def forward(self, x):
         return self.net(x)
 
+def apply_rotary_emb(input_tensor, freqs_cis):
+    cos_freqs, sin_freqs = freqs_cis[0], freqs_cis[1]
+    split_pe = freqs_cis[2] if len(freqs_cis) > 2 else False
+    return (
+        apply_split_rotary_emb(input_tensor, cos_freqs, sin_freqs)
+        if split_pe else
+        apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs)
+    )
+
+def apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs):  # TODO: remove duplicate funcs and pick the best/fastest one
+    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+    t1, t2 = t_dup.unbind(dim=-1)
+    t_dup = torch.stack((-t2, t1), dim=-1)
+    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+    return out
+
+def apply_split_rotary_emb(input_tensor, cos, sin):
+    needs_reshape = False
+    if input_tensor.ndim != 4 and cos.ndim == 4:
+        B, H, T, _ = cos.shape
+        input_tensor = input_tensor.reshape(B, T, H, -1).swapaxes(1, 2)
+        needs_reshape = True
+    split_input = rearrange(input_tensor, "... (d r) -> ... d r", d=2)
+    first_half_input = split_input[..., :1, :]
+    second_half_input = split_input[..., 1:, :]
+    output = split_input * cos.unsqueeze(-2)
+    first_half_output = output[..., :1, :]
+    second_half_output = output[..., 1:, :]
+    first_half_output.addcmul_(-sin.unsqueeze(-2), second_half_input)
+    second_half_output.addcmul_(sin.unsqueeze(-2), first_half_input)
+    output = rearrange(output, "... d r -> ... (d r)")
+    return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output
+
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = query_dim if context_dim is None else context_dim
@@ -254,9 +359,11 @@ class CrossAttention(nn.Module):
         self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
         self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
 
-        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)
+        )
 
-    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}):
         q = self.to_q(x)
         context = x if context is None else context
         k = self.to_k(context)
@@ -266,8 +373,8 @@ class CrossAttention(nn.Module):
         k = self.k_norm(k)
 
         if pe is not None:
-            q = apply_rope1(q.unsqueeze(1), pe).squeeze(1)
-            k = apply_rope1(k.unsqueeze(1), pe).squeeze(1)
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, pe if k_pe is None else k_pe)
 
         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)
@@ -277,14 +384,34 @@ class CrossAttention(nn.Module):
 
 
 class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+    def __init__(
+        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None
+    ):
         super().__init__()
 
         self.attn_precision = attn_precision
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.attn1 = CrossAttention(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
         self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
 
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.attn2 = CrossAttention(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
@@ -306,116 +433,446 @@ class BasicTransformerBlock(nn.Module):
         return x
 
 def get_fractional_positions(indices_grid, max_pos):
+    n_pos_dims = indices_grid.shape[1]
+    assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})'
     fractional_positions = torch.stack(
-        [
-            indices_grid[:, i] / max_pos[i]
-            for i in range(3)
-        ],
-        dim=-1,
+        [indices_grid[:, i] / max_pos[i] for i in range(n_pos_dims)],
+        axis=-1,
     )
     return fractional_positions
 
 
-def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
-    dtype = torch.float32
-    device = indices_grid.device
+@functools.lru_cache(maxsize=5)
+def generate_freq_grid_np(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, _ = None):
+    theta = positional_embedding_theta
+    start = 1
+    end = theta
+
+    n_elem = 2 * positional_embedding_max_pos_count
+    pow_indices = np.power(
+        theta,
+        np.linspace(
+            _log_base(start, theta),
+            _log_base(end, theta),
+            inner_dim // n_elem,
+            dtype=np.float64,
+        ),
+    )
+    return torch.tensor(pow_indices * math.pi / 2, dtype=torch.float32)
+
+def generate_freq_grid_pytorch(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, device):
+    theta = positional_embedding_theta
+    start = 1
+    end = theta
+    n_elem = 2 * positional_embedding_max_pos_count
+
+    indices = theta ** (
+        torch.linspace(
+            math.log(start, theta),
+            math.log(end, theta),
+            inner_dim // n_elem,
+            device=device,
+            dtype=torch.float32,
+        )
+    )
+    indices = indices.to(dtype=torch.float32)
+
+    indices = indices * math.pi / 2
+
+    return indices
+
+def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid):
+    if use_middle_indices_grid:
+        assert(len(indices_grid.shape) == 4 and indices_grid.shape[-1] ==2)
+        indices_grid_start, indices_grid_end = indices_grid[..., 0], indices_grid[..., 1]
+        indices_grid = (indices_grid_start + indices_grid_end) / 2.0
+    elif len(indices_grid.shape) == 4:
+        indices_grid = indices_grid[..., 0]
 
     # Get fractional positions and compute frequency indices
     fractional_positions = get_fractional_positions(indices_grid, max_pos)
-    indices = theta ** torch.linspace(0, 1, dim // 6, device=device, dtype=dtype) * math.pi / 2
+    indices = indices.to(device=fractional_positions.device)
 
-    # Compute frequencies and apply cos/sin
-    freqs = (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)).transpose(-1, -2).flatten(2)
-    cos_vals = freqs.cos().repeat_interleave(2, dim=-1)
-    sin_vals = freqs.sin().repeat_interleave(2, dim=-1)
+    freqs = (
+        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+        .transpose(-1, -2)
+        .flatten(2)
+    )
+    return freqs
 
-    # Pad if dim is not divisible by 6
-    if dim % 6 != 0:
-        padding_size = dim % 6
-        cos_vals = torch.cat([torch.ones_like(cos_vals[:, :, :padding_size]), cos_vals], dim=-1)
-        sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
+def interleaved_freqs_cis(freqs, pad_size):
+    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    if pad_size != 0:
+        cos_padding = torch.ones_like(cos_freq[:, :, : pad_size])
+        sin_padding = torch.zeros_like(cos_freq[:, :, : pad_size])
+        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+    return cos_freq, sin_freq
 
-    # Reshape and extract one value per pair (since repeat_interleave duplicates each value)
-    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
-    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+def split_freqs_cis(freqs, pad_size, num_attention_heads):
+    cos_freq = freqs.cos()
+    sin_freq = freqs.sin()
 
-    # Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
-    freqs_cis = torch.stack([
-        torch.stack([cos_vals, -sin_vals], dim=-1),
-        torch.stack([sin_vals, cos_vals], dim=-1)
-    ], dim=-2).unsqueeze(1)  # [B, 1, N, dim//2, 2, 2]
+    if pad_size != 0:
+        cos_padding = torch.ones_like(cos_freq[:, :, :pad_size])
+        sin_padding = torch.zeros_like(sin_freq[:, :, :pad_size])
 
-    return freqs_cis
+        cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
+        sin_freq = torch.concatenate([sin_padding, sin_freq], axis=-1)
 
+    # Reshape freqs to be compatible with multi-head attention
+    B , T, half_HD = cos_freq.shape
 
-class LTXVModel(torch.nn.Module):
-    def __init__(self,
-                 in_channels=128,
-                 cross_attention_dim=2048,
-                 attention_head_dim=64,
-                 num_attention_heads=32,
+    cos_freq = cos_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
+    sin_freq = sin_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
 
-                 caption_channels=4096,
-                 num_layers=28,
+    cos_freq = torch.swapaxes(cos_freq, 1, 2)  # (B,H,T,D//2)
+    sin_freq = torch.swapaxes(sin_freq, 1, 2)  # (B,H,T,D//2)
+    return cos_freq, sin_freq
 
+class LTXBaseModel(torch.nn.Module, ABC):
+    """
+    Abstract base class for LTX models (Lightricks Transformer models).
 
-                 positional_embedding_theta=10000.0,
-                 positional_embedding_max_pos=[20, 2048, 2048],
-                 causal_temporal_positioning=False,
-                 vae_scale_factors=(8, 32, 32),
-                 dtype=None, device=None, operations=None, **kwargs):
+    This class defines the common interface and shared functionality for all LTX models,
+    including LTXV (video) and LTXAV (audio-video) variants.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        cross_attention_dim: int,
+        attention_head_dim: int,
+        num_attention_heads: int,
+        caption_channels: int,
+        num_layers: int,
+        positional_embedding_theta: float = 10000.0,
+        positional_embedding_max_pos: list = [20, 2048, 2048],
+        causal_temporal_positioning: bool = False,
+        vae_scale_factors: tuple = (8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier = 1000.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
         super().__init__()
         self.generator = None
         self.vae_scale_factors = vae_scale_factors
+        self.use_middle_indices_grid = use_middle_indices_grid
         self.dtype = dtype
-        self.out_channels = in_channels
-        self.inner_dim = num_attention_heads * attention_head_dim
+        self.in_channels = in_channels
+        self.cross_attention_dim = cross_attention_dim
+        self.attention_head_dim = attention_head_dim
+        self.num_attention_heads = num_attention_heads
+        self.caption_channels = caption_channels
+        self.num_layers = num_layers
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = positional_embedding_max_pos
+        self.split_positional_embedding = LTXRopeType.from_dict(kwargs)
+        self.freq_grid_generator = (
+            generate_freq_grid_np if LTXFrequenciesPrecision.from_dict(kwargs) == LTXFrequenciesPrecision.FLOAT64
+            else generate_freq_grid_pytorch
+        )
         self.causal_temporal_positioning = causal_temporal_positioning
+        self.operations = operations
+        self.timestep_scale_multiplier = timestep_scale_multiplier
 
-        self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+        # Common dimensions
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.out_channels = in_channels
+
+        # Initialize common components
+        self._init_common_components(device, dtype)
+
+        # Initialize model-specific components
+        self._init_model_components(device, dtype, **kwargs)
+
+        # Initialize transformer blocks
+        self._init_transformer_blocks(device, dtype, **kwargs)
+
+        # Initialize output components
+        self._init_output_components(device, dtype)
+
+    def _init_common_components(self, device, dtype):
+        """Initialize components common to all LTX models
+        - patchify_proj: Linear projection for patchifying input
+        - adaln_single: AdaLN layer for timestep embedding
+        - caption_projection: Linear projection for caption embedding
+        """
+        self.patchify_proj = self.operations.Linear(
+            self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device
+        )
 
         self.adaln_single = AdaLayerNormSingle(
-            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
         )
 
-        # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
-
         self.caption_projection = PixArtAlphaTextProjection(
-            in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+            in_features=self.caption_channels,
+            hidden_size=self.inner_dim,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
         )
 
+    @abstractmethod
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize model-specific components. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _init_output_components(self, device, dtype):
+        """Initialize output components. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input data. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, **kwargs):
+        """Process transformer blocks. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        """Process output data. Must be implemented by subclasses."""
+        pass
+
+    def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
+        """Prepare timestep embeddings."""
+        grid_mask = kwargs.get("grid_mask", None)
+        if grid_mask is not None:
+            timestep = timestep[:, grid_mask]
+
+        timestep = timestep * self.timestep_scale_multiplier
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.shape[-1])
+
+        return timestep, embedded_timestep
+
+    def _prepare_context(self, context, batch_size, x, attention_mask=None):
+        """Prepare context for transformer blocks."""
+        if self.caption_projection is not None:
+            context = self.caption_projection(context)
+            context = context.view(batch_size, -1, x.shape[-1])
+
+        return context, attention_mask
+
+    def _precompute_freqs_cis(
+        self,
+        indices_grid,
+        dim,
+        out_dtype,
+        theta=10000.0,
+        max_pos=[20, 2048, 2048],
+        use_middle_indices_grid=False,
+        num_attention_heads=32,
+    ):
+        split_mode = self.split_positional_embedding == LTXRopeType.SPLIT
+        indices = self.freq_grid_generator(theta, indices_grid.shape[1], dim, indices_grid.device)
+        freqs = generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid)
+
+        if split_mode:
+            expected_freqs = dim // 2
+            current_freqs = freqs.shape[-1]
+            pad_size = expected_freqs - current_freqs
+            cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)
+        else:
+            # 2 because of cos and sin by 3 for (t, x, y), 1 for temporal only
+            n_elem = 2 * indices_grid.shape[1]
+            cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
+        return cos_freq.to(out_dtype), sin_freq.to(out_dtype), split_mode
+
+    def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
+        """Prepare positional embeddings."""
+        fractional_coords = pixel_coords.to(torch.float32)
+        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+        pe = self._precompute_freqs_cis(
+            fractional_coords,
+            dim=self.inner_dim,
+            out_dtype=x_dtype,
+            max_pos=self.positional_embedding_max_pos,
+            use_middle_indices_grid=self.use_middle_indices_grid,
+            num_attention_heads=self.num_attention_heads,
+        )
+        return pe
+
+    def _prepare_attention_mask(self, attention_mask, x_dtype):
+        """Prepare attention mask."""
+        if attention_mask is not None and not torch.is_floating_point(attention_mask):
+            attention_mask = (attention_mask - 1).to(x_dtype).reshape(
+                (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
+            ) * torch.finfo(x_dtype).max
+        return attention_mask
+
+    def forward(
+        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+    ):
+        """
+        Forward pass for LTX models.
+
+        Args:
+            x: Input tensor
+            timestep: Timestep tensor
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed output tensor
+        """
+        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, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)
+
+    def _forward(
+        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+    ):
+        """
+        Internal forward pass for LTX models.
+
+        Args:
+            x: Input tensor
+            timestep: Timestep tensor
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed output tensor
+        """
+        if isinstance(x, list):
+            input_dtype = x[0].dtype
+            batch_size = x[0].shape[0]
+        else:
+            input_dtype = x.dtype
+            batch_size = x.shape[0]
+        # Process input
+        merged_args = {**transformer_options, **kwargs}
+        x, pixel_coords, additional_args = self._process_input(x, keyframe_idxs, denoise_mask, **merged_args)
+        merged_args.update(additional_args)
+
+        # Prepare timestep and context
+        timestep, embedded_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)
+
+        # Prepare attention mask and positional embeddings
+        attention_mask = self._prepare_attention_mask(attention_mask, input_dtype)
+        pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype)
+
+        # Process transformer blocks
+        x = self._process_transformer_blocks(
+            x, context, attention_mask, timestep, pe, transformer_options=transformer_options, **merged_args
+        )
+
+        # Process output
+        x = self._process_output(x, embedded_timestep, keyframe_idxs, **merged_args)
+        return x
+
+
+class LTXVModel(LTXBaseModel):
+    """LTXV model for video generation."""
+
+    def __init__(
+        self,
+        in_channels=128,
+        cross_attention_dim=2048,
+        attention_head_dim=64,
+        num_attention_heads=32,
+        caption_channels=4096,
+        num_layers=28,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[20, 2048, 2048],
+        causal_temporal_positioning=False,
+        vae_scale_factors=(8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier = 1000.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            caption_channels=caption_channels,
+            num_layers=num_layers,
+            positional_embedding_theta=positional_embedding_theta,
+            positional_embedding_max_pos=positional_embedding_max_pos,
+            causal_temporal_positioning=causal_temporal_positioning,
+            vae_scale_factors=vae_scale_factors,
+            use_middle_indices_grid=use_middle_indices_grid,
+            timestep_scale_multiplier=timestep_scale_multiplier,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **kwargs,
+        )
+
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize LTXV-specific components."""
+        # No additional components needed for LTXV beyond base class
+        pass
+
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks for LTXV."""
         self.transformer_blocks = nn.ModuleList(
             [
                 BasicTransformerBlock(
                     self.inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    context_dim=cross_attention_dim,
-                    # attn_precision=attn_precision,
-                    dtype=dtype, device=device, operations=operations
+                    self.num_attention_heads,
+                    self.attention_head_dim,
+                    context_dim=self.cross_attention_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
                 )
-                for d in range(num_layers)
+                for _ in range(self.num_layers)
             ]
         )
 
+    def _init_output_components(self, device, dtype):
+        """Initialize output components for LTXV."""
         self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
-        self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
-
-        self.patchifier = SymmetricPatchifier(1)
-
-    def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **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, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs)
-
-    def _forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
-        patches_replace = transformer_options.get("patches_replace", {})
-
-        orig_shape = list(x.shape)
+        self.norm_out = self.operations.LayerNorm(
+            self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+        )
+        self.proj_out = self.operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+        self.patchifier = SymmetricPatchifier(1, start_end=True)
 
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input for LTXV."""
+        additional_args = {"orig_shape": list(x.shape)}
         x, latent_coords = self.patchifier.patchify(x)
         pixel_coords = latent_to_pixel_coords(
             latent_coords=latent_coords,
@@ -423,44 +880,30 @@ class LTXVModel(torch.nn.Module):
             causal_fix=self.causal_temporal_positioning,
         )
 
+        grid_mask = None
         if keyframe_idxs is not None:
-            pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs
+            additional_args.update({ "orig_patchified_shape": list(x.shape)})
+            denoise_mask = self.patchifier.patchify(denoise_mask)[0]
+            grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0]
+            additional_args.update({"grid_mask": grid_mask})
+            x = x[:, grid_mask, :]
+            pixel_coords = pixel_coords[:, :, grid_mask, ...]
 
-        fractional_coords = pixel_coords.to(torch.float32)
-        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+            kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:]
+            keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]
+            pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
 
         x = self.patchify_proj(x)
-        timestep = timestep * 1000.0
-
-        if attention_mask is not None and not torch.is_floating_point(attention_mask):
-            attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
-
-        pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
-
-        batch_size = x.shape[0]
-        timestep, embedded_timestep = self.adaln_single(
-            timestep.flatten(),
-            {"resolution": None, "aspect_ratio": None},
-            batch_size=batch_size,
-            hidden_dtype=x.dtype,
-        )
-        # Second dimension is 1 or number of tokens (if timestep_per_token)
-        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
-        embedded_timestep = embedded_timestep.view(
-            batch_size, -1, embedded_timestep.shape[-1]
-        )
-
-        # 2. Blocks
-        if self.caption_projection is not None:
-            batch_size = x.shape[0]
-            context = self.caption_projection(context)
-            context = context.view(
-                batch_size, -1, x.shape[-1]
-            )
+        return x, pixel_coords, additional_args
 
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs):
+        """Process transformer blocks for LTXV."""
+        patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+
         for i, block in enumerate(self.transformer_blocks):
             if ("double_block", i) in blocks_replace:
+
                 def block_wrap(args):
                     out = {}
                     out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
@@ -478,16 +921,28 @@ class LTXVModel(torch.nn.Module):
                     transformer_options=transformer_options,
                 )
 
-        # 3. Output
+        return x
+
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        """Process output for LTXV."""
+        # Apply scale-shift modulation
         scale_shift_values = (
             self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
         )
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+
         x = self.norm_out(x)
-        # Modulation
-        x = torch.addcmul(x, x, scale).add_(shift)
+        x = x * (1 + scale) + shift
         x = self.proj_out(x)
 
+        if keyframe_idxs is not None:
+            grid_mask = kwargs["grid_mask"]
+            orig_patchified_shape = kwargs["orig_patchified_shape"]
+            full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device)
+            full_x[:, grid_mask, :] = x
+            x = full_x
+        # Unpatchify to restore original dimensions
+        orig_shape = kwargs["orig_shape"]
         x = self.patchifier.unpatchify(
             latents=x,
             output_height=orig_shape[3],

comfy/ldm/lightricks/symmetric_patchifier.py

@@ -21,20 +21,23 @@ def latent_to_pixel_coords(
     Returns:
         Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates.
     """
+    shape = [1] * latent_coords.ndim
+    shape[1] = -1
     pixel_coords = (
         latent_coords
-        * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None]
+        * torch.tensor(scale_factors, device=latent_coords.device).view(*shape)
     )
     if causal_fix:
         # Fix temporal scale for first frame to 1 due to causality
-        pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0)
+        pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)
     return pixel_coords
 
 
 class Patchifier(ABC):
-    def __init__(self, patch_size: int):
+    def __init__(self, patch_size: int, start_end: bool=False):
         super().__init__()
         self._patch_size = (1, patch_size, patch_size)
+        self.start_end = start_end
 
     @abstractmethod
     def patchify(
@@ -71,11 +74,23 @@ class Patchifier(ABC):
             torch.arange(0, latent_width, self._patch_size[2], device=device),
             indexing="ij",
         )
-        latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
-        latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
-        latent_coords = rearrange(
-            latent_coords, "b c f h w -> b c (f h w)", b=batch_size
+        latent_sample_coords_start = torch.stack(latent_sample_coords, dim=0)
+        delta = torch.tensor(self._patch_size, device=latent_sample_coords_start.device, dtype=latent_sample_coords_start.dtype)[:, None, None, None]
+        latent_sample_coords_end = latent_sample_coords_start + delta
+
+        latent_sample_coords_start = latent_sample_coords_start.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+        latent_sample_coords_start = rearrange(
+            latent_sample_coords_start, "b c f h w -> b c (f h w)", b=batch_size
         )
+        if self.start_end:
+            latent_sample_coords_end = latent_sample_coords_end.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+            latent_sample_coords_end = rearrange(
+                latent_sample_coords_end, "b c f h w -> b c (f h w)", b=batch_size
+            )
+
+            latent_coords = torch.stack((latent_sample_coords_start, latent_sample_coords_end), dim=-1)
+        else:
+            latent_coords = latent_sample_coords_start
         return latent_coords
 
 
@@ -115,3 +130,61 @@ class SymmetricPatchifier(Patchifier):
             q=self._patch_size[2],
         )
         return latents
+
+
+class AudioPatchifier(Patchifier):
+    def __init__(self, patch_size: int,
+        sample_rate=16000,
+        hop_length=160,
+        audio_latent_downsample_factor=4,
+        is_causal=True,
+        start_end=False,
+        shift = 0
+    ):
+        super().__init__(patch_size, start_end=start_end)
+        self.hop_length = hop_length
+        self.sample_rate = sample_rate
+        self.audio_latent_downsample_factor = audio_latent_downsample_factor
+        self.is_causal = is_causal
+        self.shift = shift
+
+    def copy_with_shift(self, shift):
+        return AudioPatchifier(
+            self.patch_size, self.sample_rate, self.hop_length, self.audio_latent_downsample_factor,
+            self.is_causal, self.start_end, shift
+        )
+
+    def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int, dtype: torch.dtype, device=torch.device):
+        audio_latent_frame = torch.arange(start_latent, end_latent, dtype=dtype, device=device)
+        audio_mel_frame = audio_latent_frame * self.audio_latent_downsample_factor
+        if self.is_causal:
+            audio_mel_frame = (audio_mel_frame + 1 - self.audio_latent_downsample_factor).clip(min=0)
+        return audio_mel_frame * self.hop_length / self.sample_rate
+
+
+    def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        # audio_latents: (batch, channels, time, freq)
+        b, _, t, _ = audio_latents.shape
+        audio_latents = rearrange(
+            audio_latents,
+            "b c t f -> b t (c f)",
+        )
+
+        audio_latents_start_timings = self._get_audio_latent_time_in_sec(self.shift, t + self.shift, torch.float32, audio_latents.device)
+        audio_latents_start_timings = audio_latents_start_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)
+
+        if self.start_end:
+            audio_latents_end_timings = self._get_audio_latent_time_in_sec(self.shift + 1, t + self.shift + 1, torch.float32, audio_latents.device)
+            audio_latents_end_timings = audio_latents_end_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)
+
+            audio_latents_timings = torch.stack([audio_latents_start_timings, audio_latents_end_timings], dim=-1)
+        else:
+            audio_latents_timings = audio_latents_start_timings
+        return audio_latents, audio_latents_timings
+
+    def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq: int) -> torch.Tensor:
+        # audio_latents: (batch, time, freq * channels)
+        audio_latents = rearrange(
+            audio_latents, "b t (c f) -> b c t f", c=channels, f=freq
+        )
+        return audio_latents

comfy/ldm/lightricks/vae/audio_vae.py

@@ -0,0 +1,286 @@
+import json
+from dataclasses import dataclass
+import math
+import torch
+import torchaudio
+
+import comfy.model_management
+import comfy.model_patcher
+import comfy.utils as utils
+from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution
+from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
+    CausalityAxis,
+    CausalAudioAutoencoder,
+)
+from comfy.ldm.lightricks.vocoders.vocoder import Vocoder
+
+LATENT_DOWNSAMPLE_FACTOR = 4
+
+
+@dataclass(frozen=True)
+class AudioVAEComponentConfig:
+    """Container for model component configuration extracted from metadata."""
+
+    autoencoder: dict
+    vocoder: dict
+
+    @classmethod
+    def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig":
+        assert metadata is not None and "config" in metadata, "Metadata is required for audio VAE"
+
+        raw_config = metadata["config"]
+        if isinstance(raw_config, str):
+            parsed_config = json.loads(raw_config)
+        else:
+            parsed_config = raw_config
+
+        audio_config = parsed_config.get("audio_vae")
+        vocoder_config = parsed_config.get("vocoder")
+
+        assert audio_config is not None, "Audio VAE config is required for audio VAE"
+        assert vocoder_config is not None, "Vocoder config is required for audio VAE"
+
+        return cls(autoencoder=audio_config, vocoder=vocoder_config)
+
+
+class ModelDeviceManager:
+    """Manages device placement and GPU residency for the composed model."""
+
+    def __init__(self, module: torch.nn.Module):
+        load_device = comfy.model_management.get_torch_device()
+        offload_device = comfy.model_management.vae_offload_device()
+        self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device)
+
+    def ensure_model_loaded(self) -> None:
+        comfy.model_management.free_memory(
+            self.patcher.model_size(),
+            self.patcher.load_device,
+        )
+        comfy.model_management.load_model_gpu(self.patcher)
+
+    def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
+        return tensor.to(self.patcher.load_device)
+
+    @property
+    def load_device(self):
+        return self.patcher.load_device
+
+
+class AudioLatentNormalizer:
+    """Applies per-channel statistics in patch space and restores original layout."""
+
+    def __init__(self, patchfier: AudioPatchifier, statistics_processor: torch.nn.Module):
+        self.patchifier = patchfier
+        self.statistics = statistics_processor
+
+    def normalize(self, latents: torch.Tensor) -> torch.Tensor:
+        channels = latents.shape[1]
+        freq = latents.shape[3]
+        patched, _ = self.patchifier.patchify(latents)
+        normalized = self.statistics.normalize(patched)
+        return self.patchifier.unpatchify(normalized, channels=channels, freq=freq)
+
+    def denormalize(self, latents: torch.Tensor) -> torch.Tensor:
+        channels = latents.shape[1]
+        freq = latents.shape[3]
+        patched, _ = self.patchifier.patchify(latents)
+        denormalized = self.statistics.un_normalize(patched)
+        return self.patchifier.unpatchify(denormalized, channels=channels, freq=freq)
+
+
+class AudioPreprocessor:
+    """Prepares raw waveforms for the autoencoder by matching training conditions."""
+
+    def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_length: int, n_fft: int):
+        self.target_sample_rate = target_sample_rate
+        self.mel_bins = mel_bins
+        self.mel_hop_length = mel_hop_length
+        self.n_fft = n_fft
+
+    def resample(self, waveform: torch.Tensor, source_rate: int) -> torch.Tensor:
+        if source_rate == self.target_sample_rate:
+            return waveform
+        return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)
+
+    @staticmethod
+    def normalize_amplitude(
+        waveform: torch.Tensor, max_amplitude: float = 0.5, eps: float = 1e-5
+    ) -> torch.Tensor:
+        waveform = waveform - waveform.mean(dim=2, keepdim=True)
+        peak = torch.max(torch.abs(waveform)) + eps
+        scale = peak.clamp(max=max_amplitude) / peak
+        return waveform * scale
+
+    def waveform_to_mel(
+        self, waveform: torch.Tensor, waveform_sample_rate: int, device
+    ) -> torch.Tensor:
+        waveform = self.resample(waveform, waveform_sample_rate)
+        waveform = self.normalize_amplitude(waveform)
+
+        mel_transform = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.target_sample_rate,
+            n_fft=self.n_fft,
+            win_length=self.n_fft,
+            hop_length=self.mel_hop_length,
+            f_min=0.0,
+            f_max=self.target_sample_rate / 2.0,
+            n_mels=self.mel_bins,
+            window_fn=torch.hann_window,
+            center=True,
+            pad_mode="reflect",
+            power=1.0,
+            mel_scale="slaney",
+            norm="slaney",
+        ).to(device)
+
+        mel = mel_transform(waveform)
+        mel = torch.log(torch.clamp(mel, min=1e-5))
+        return mel.permute(0, 1, 3, 2).contiguous()
+
+
+class AudioVAE(torch.nn.Module):
+    """High-level Audio VAE wrapper exposing encode and decode entry points."""
+
+    def __init__(self, state_dict: dict, metadata: dict):
+        super().__init__()
+
+        component_config = AudioVAEComponentConfig.from_metadata(metadata)
+
+        vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True)
+        vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True)
+
+        self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
+        self.vocoder = Vocoder(config=component_config.vocoder)
+
+        self.autoencoder.load_state_dict(vae_sd, strict=False)
+        self.vocoder.load_state_dict(vocoder_sd, strict=False)
+
+        autoencoder_config = self.autoencoder.get_config()
+        self.normalizer = AudioLatentNormalizer(
+            AudioPatchifier(
+                patch_size=1,
+                audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR,
+                sample_rate=autoencoder_config["sampling_rate"],
+                hop_length=autoencoder_config["mel_hop_length"],
+                is_causal=autoencoder_config["is_causal"],
+            ),
+            self.autoencoder.per_channel_statistics,
+        )
+
+        self.preprocessor = AudioPreprocessor(
+            target_sample_rate=autoencoder_config["sampling_rate"],
+            mel_bins=autoencoder_config["mel_bins"],
+            mel_hop_length=autoencoder_config["mel_hop_length"],
+            n_fft=autoencoder_config["n_fft"],
+        )
+
+        self.device_manager = ModelDeviceManager(self)
+
+    def encode(self, audio: dict) -> torch.Tensor:
+        """Encode a waveform dictionary into normalized latent tensors."""
+
+        waveform = audio["waveform"]
+        waveform_sample_rate = audio["sample_rate"]
+        input_device = waveform.device
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        waveform = self.device_manager.move_to_load_device(waveform)
+        expected_channels = self.autoencoder.encoder.in_channels
+        if waveform.shape[1] != expected_channels:
+            raise ValueError(
+                f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}"
+            )
+
+        mel_spec = self.preprocessor.waveform_to_mel(
+            waveform, waveform_sample_rate, device=self.device_manager.load_device
+        )
+
+        latents = self.autoencoder.encode(mel_spec)
+        posterior = DiagonalGaussianDistribution(latents)
+        latent_mode = posterior.mode()
+
+        normalized = self.normalizer.normalize(latent_mode)
+        return normalized.to(input_device)
+
+    def decode(self, latents: torch.Tensor) -> torch.Tensor:
+        """Decode normalized latent tensors into an audio waveform."""
+        original_shape = latents.shape
+
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        latents = self.device_manager.move_to_load_device(latents)
+        latents = self.normalizer.denormalize(latents)
+
+        target_shape = self.target_shape_from_latents(original_shape)
+        mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)
+
+        waveform = self.run_vocoder(mel_spec)
+        return self.device_manager.move_to_load_device(waveform)
+
+    def target_shape_from_latents(self, latents_shape):
+        batch, _, time, _ = latents_shape
+        target_length = time * LATENT_DOWNSAMPLE_FACTOR
+        if self.autoencoder.causality_axis != CausalityAxis.NONE:
+            target_length -= LATENT_DOWNSAMPLE_FACTOR - 1
+        return (
+            batch,
+            self.autoencoder.decoder.out_ch,
+            target_length,
+            self.autoencoder.mel_bins,
+        )
+
+    def num_of_latents_from_frames(self, frames_number: int, frame_rate: int) -> int:
+        return math.ceil((float(frames_number) / frame_rate) * self.latents_per_second)
+
+    def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:
+        audio_channels = self.autoencoder.decoder.out_ch
+        vocoder_input = mel_spec.transpose(2, 3)
+
+        if audio_channels == 1:
+            vocoder_input = vocoder_input.squeeze(1)
+        elif audio_channels != 2:
+            raise ValueError(f"Unsupported audio_channels: {audio_channels}")
+
+        return self.vocoder(vocoder_input)
+
+    @property
+    def sample_rate(self) -> int:
+        return int(self.autoencoder.sampling_rate)
+
+    @property
+    def mel_hop_length(self) -> int:
+        return int(self.autoencoder.mel_hop_length)
+
+    @property
+    def mel_bins(self) -> int:
+        return int(self.autoencoder.mel_bins)
+
+    @property
+    def latent_channels(self) -> int:
+        return int(self.autoencoder.decoder.z_channels)
+
+    @property
+    def latent_frequency_bins(self) -> int:
+        return int(self.mel_bins // LATENT_DOWNSAMPLE_FACTOR)
+
+    @property
+    def latents_per_second(self) -> float:
+        return self.sample_rate / self.mel_hop_length / LATENT_DOWNSAMPLE_FACTOR
+
+    @property
+    def output_sample_rate(self) -> int:
+        output_rate = getattr(self.vocoder, "output_sample_rate", None)
+        if output_rate is not None:
+            return int(output_rate)
+        upsample_factor = getattr(self.vocoder, "upsample_factor", None)
+        if upsample_factor is None:
+            raise AttributeError(
+                "Vocoder is missing upsample_factor; cannot infer output sample rate"
+            )
+        return int(self.sample_rate * upsample_factor / self.mel_hop_length)
+
+    def memory_required(self, input_shape):
+        return self.device_manager.patcher.model_size()

comfy/ldm/lightricks/vae/causal_audio_autoencoder.py

@@ -0,0 +1,909 @@
+from __future__ import annotations
+import torch
+from torch import nn
+from torch.nn import functional as F
+from typing import Optional
+from enum import Enum
+from .pixel_norm import PixelNorm
+import comfy.ops
+import logging
+
+ops = comfy.ops.disable_weight_init
+
+
+class StringConvertibleEnum(Enum):
+    """
+    Base enum class that provides string-to-enum conversion functionality.
+
+    This mixin adds a str_to_enum() class method that handles conversion from
+    strings, None, or existing enum instances with case-insensitive matching.
+    """
+
+    @classmethod
+    def str_to_enum(cls, value):
+        """
+        Convert a string, enum instance, or None to the appropriate enum member.
+
+        Args:
+            value: Can be an enum instance of this class, a string, or None
+
+        Returns:
+            Enum member of this class
+
+        Raises:
+            ValueError: If the value cannot be converted to a valid enum member
+        """
+        # Already an enum instance of this class
+        if isinstance(value, cls):
+            return value
+
+        # None maps to NONE member if it exists
+        if value is None:
+            if hasattr(cls, "NONE"):
+                return cls.NONE
+            raise ValueError(f"{cls.__name__} does not have a NONE member to map None to")
+
+        # String conversion (case-insensitive)
+        if isinstance(value, str):
+            value_lower = value.lower()
+
+            # Try to match against enum values
+            for member in cls:
+                # Handle members with None values
+                if member.value is None:
+                    if value_lower == "none":
+                        return member
+                # Handle members with string values
+                elif isinstance(member.value, str) and member.value.lower() == value_lower:
+                    return member
+
+            # Build helpful error message with valid values
+            valid_values = []
+            for member in cls:
+                if member.value is None:
+                    valid_values.append("none")
+                elif isinstance(member.value, str):
+                    valid_values.append(member.value)
+
+            raise ValueError(f"Invalid {cls.__name__} string: '{value}'. " f"Valid values are: {valid_values}")
+
+        raise ValueError(
+            f"Cannot convert type {type(value).__name__} to {cls.__name__} enum. "
+            f"Expected string, None, or {cls.__name__} instance."
+        )
+
+
+class AttentionType(StringConvertibleEnum):
+    """Enum for specifying the attention mechanism type."""
+
+    VANILLA = "vanilla"
+    LINEAR = "linear"
+    NONE = "none"
+
+
+class CausalityAxis(StringConvertibleEnum):
+    """Enum for specifying the causality axis in causal convolutions."""
+
+    NONE = None
+    WIDTH = "width"
+    HEIGHT = "height"
+    WIDTH_COMPATIBILITY = "width-compatibility"
+
+
+def Normalize(in_channels, *, num_groups=32, normtype="group"):
+    if normtype == "group":
+        return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    elif normtype == "pixel":
+        return PixelNorm(dim=1, eps=1e-6)
+    else:
+        raise ValueError(f"Invalid normalization type: {normtype}")
+
+
+class CausalConv2d(nn.Module):
+    """
+    A causal 2D convolution.
+
+    This layer ensures that the output at time `t` only depends on inputs
+    at time `t` and earlier. It achieves this by applying asymmetric padding
+    to the time dimension (width) before the convolution.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        dilation=1,
+        groups=1,
+        bias=True,
+        causality_axis: CausalityAxis = CausalityAxis.HEIGHT,
+    ):
+        super().__init__()
+
+        self.causality_axis = causality_axis
+
+        # Ensure kernel_size and dilation are tuples
+        kernel_size = nn.modules.utils._pair(kernel_size)
+        dilation = nn.modules.utils._pair(dilation)
+
+        # Calculate padding dimensions
+        pad_h = (kernel_size[0] - 1) * dilation[0]
+        pad_w = (kernel_size[1] - 1) * dilation[1]
+
+        # The padding tuple for F.pad is (pad_left, pad_right, pad_top, pad_bottom)
+        match self.causality_axis:
+            case CausalityAxis.NONE:
+                self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
+            case CausalityAxis.WIDTH | CausalityAxis.WIDTH_COMPATIBILITY:
+                self.padding = (pad_w, 0, pad_h // 2, pad_h - pad_h // 2)
+            case CausalityAxis.HEIGHT:
+                self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h, 0)
+            case _:
+                raise ValueError(f"Invalid causality_axis: {causality_axis}")
+
+        # The internal convolution layer uses no padding, as we handle it manually
+        self.conv = ops.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=0,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        # Apply causal padding before convolution
+        x = F.pad(x, self.padding)
+        return self.conv(x)
+
+
+def make_conv2d(
+    in_channels,
+    out_channels,
+    kernel_size,
+    stride=1,
+    padding=None,
+    dilation=1,
+    groups=1,
+    bias=True,
+    causality_axis: Optional[CausalityAxis] = None,
+):
+    """
+    Create a 2D convolution layer that can be either causal or non-causal.
+
+    Args:
+        in_channels: Number of input channels
+        out_channels: Number of output channels
+        kernel_size: Size of the convolution kernel
+        stride: Convolution stride
+        padding: Padding (if None, will be calculated based on causal flag)
+        dilation: Dilation rate
+        groups: Number of groups for grouped convolution
+        bias: Whether to use bias
+        causality_axis: Dimension along which to apply causality.
+
+    Returns:
+        Either a regular Conv2d or CausalConv2d layer
+    """
+    if causality_axis is not None:
+        # For causal convolution, padding is handled internally by CausalConv2d
+        return CausalConv2d(in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis)
+    else:
+        # For non-causal convolution, use symmetric padding if not specified
+        if padding is None:
+            if isinstance(kernel_size, int):
+                padding = kernel_size // 2
+            else:
+                padding = tuple(k // 2 for k in kernel_size)
+        return ops.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+        )
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.HEIGHT):
+        super().__init__()
+        self.with_conv = with_conv
+        self.causality_axis = causality_axis
+        if self.with_conv:
+            self.conv = make_conv2d(in_channels, in_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+            # Drop FIRST element in the causal axis to undo encoder's padding, while keeping the length 1 + 2 * n.
+            # For example, if the input is [0, 1, 2], after interpolation, the output is [0, 0, 1, 1, 2, 2].
+            # The causal convolution will pad the first element as [-, -, 0, 0, 1, 1, 2, 2],
+            # So the output elements rely on the following windows:
+            # 0: [-,-,0]
+            # 1: [-,0,0]
+            # 2: [0,0,1]
+            # 3: [0,1,1]
+            # 4: [1,1,2]
+            # 5: [1,2,2]
+            # Notice that the first and second elements in the output rely only on the first element in the input,
+            # while all other elements rely on two elements in the input.
+            # So we can drop the first element to undo the padding (rather than the last element).
+            # This is a no-op for non-causal convolutions.
+            match self.causality_axis:
+                case CausalityAxis.NONE:
+                    pass  # x remains unchanged
+                case CausalityAxis.HEIGHT:
+                    x = x[:, :, 1:, :]
+                case CausalityAxis.WIDTH:
+                    x = x[:, :, :, 1:]
+                case CausalityAxis.WIDTH_COMPATIBILITY:
+                    pass  # x remains unchanged
+                case _:
+                    raise ValueError(f"Invalid causality_axis: {self.causality_axis}")
+
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer that can use either a strided convolution
+    or average pooling. Supports standard and causal padding for the
+    convolutional mode.
+    """
+
+    def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.WIDTH):
+        super().__init__()
+        self.with_conv = with_conv
+        self.causality_axis = causality_axis
+
+        if self.causality_axis != CausalityAxis.NONE and not self.with_conv:
+            raise ValueError("causality is only supported when `with_conv=True`.")
+
+        if self.with_conv:
+            # Do time downsampling here
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x):
+        if self.with_conv:
+            # (pad_left, pad_right, pad_top, pad_bottom)
+            match self.causality_axis:
+                case CausalityAxis.NONE:
+                    pad = (0, 1, 0, 1)
+                case CausalityAxis.WIDTH:
+                    pad = (2, 0, 0, 1)
+                case CausalityAxis.HEIGHT:
+                    pad = (0, 1, 2, 0)
+                case CausalityAxis.WIDTH_COMPATIBILITY:
+                    pad = (1, 0, 0, 1)
+                case _:
+                    raise ValueError(f"Invalid causality_axis: {self.causality_axis}")
+
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            # This branch is only taken if with_conv=False, which implies causality_axis is NONE.
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout,
+        temb_channels=512,
+        norm_type="group",
+        causality_axis: CausalityAxis = CausalityAxis.HEIGHT,
+    ):
+        super().__init__()
+        self.causality_axis = causality_axis
+
+        if self.causality_axis != CausalityAxis.NONE and norm_type == "group":
+            raise ValueError("Causal ResnetBlock with GroupNorm is not supported.")
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels, normtype=norm_type)
+        self.non_linearity = nn.SiLU()
+        self.conv1 = make_conv2d(in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+        if temb_channels > 0:
+            self.temb_proj = ops.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels, normtype=norm_type)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = make_conv2d(out_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = make_conv2d(
+                    in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis
+                )
+            else:
+                self.nin_shortcut = make_conv2d(
+                    in_channels, out_channels, kernel_size=1, stride=1, causality_axis=causality_axis
+                )
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = self.non_linearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(self.non_linearity(temb))[:, :, None, None]
+
+        h = self.norm2(h)
+        h = self.non_linearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels, norm_type="group"):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels, normtype=norm_type)
+        self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h * w).contiguous()
+        q = q.permute(0, 2, 1).contiguous()  # b,hw,c
+        k = k.reshape(b, c, h * w).contiguous()  # b,c,hw
+        w_ = torch.bmm(q, k).contiguous()  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w).contiguous()
+        w_ = w_.permute(0, 2, 1).contiguous()  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_).contiguous()  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, h, w).contiguous()
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+def make_attn(in_channels, attn_type="vanilla", norm_type="group"):
+    # Convert string to enum if needed
+    attn_type = AttentionType.str_to_enum(attn_type)
+
+    if attn_type != AttentionType.NONE:
+        logging.info(f"making attention of type '{attn_type.value}' with {in_channels} in_channels")
+    else:
+        logging.info(f"making identity attention with {in_channels} in_channels")
+
+    match attn_type:
+        case AttentionType.VANILLA:
+            return AttnBlock(in_channels, norm_type=norm_type)
+        case AttentionType.NONE:
+            return nn.Identity(in_channels)
+        case AttentionType.LINEAR:
+            raise NotImplementedError(f"Attention type {attn_type.value} is not supported yet.")
+        case _:
+            raise ValueError(f"Unknown attention type: {attn_type}")
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        double_z=True,
+        attn_type="vanilla",
+        mid_block_add_attention=True,
+        norm_type="group",
+        causality_axis=CausalityAxis.WIDTH.value,
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+        self.double_z = double_z
+        self.norm_type = norm_type
+        # Convert string to enum if needed (for config loading)
+        causality_axis = CausalityAxis.str_to_enum(causality_axis)
+        self.attn_type = AttentionType.str_to_enum(attn_type)
+
+        # downsampling
+        self.conv_in = make_conv2d(
+            in_channels,
+            self.ch,
+            kernel_size=3,
+            stride=1,
+            causality_axis=causality_axis,
+        )
+
+        self.non_linearity = nn.SiLU()
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+
+            for _ in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                        norm_type=self.norm_type,
+                        causality_axis=causality_axis,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type))
+
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv, causality_axis=causality_axis)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+        if mid_block_add_attention:
+            self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)
+        else:
+            self.mid.attn_1 = nn.Identity()
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+
+        # end
+        self.norm_out = Normalize(block_in, normtype=self.norm_type)
+        self.conv_out = make_conv2d(
+            block_in,
+            2 * z_channels if double_z else z_channels,
+            kernel_size=3,
+            stride=1,
+            causality_axis=causality_axis,
+        )
+
+    def forward(self, x):
+        """
+        Forward pass through the encoder.
+
+        Args:
+            x: Input tensor of shape [batch, channels, time, n_mels]
+
+        Returns:
+            Encoded latent representation
+        """
+        feature_maps = [self.conv_in(x)]
+
+        # Process each resolution level (from high to low resolution)
+        for resolution_level in range(self.num_resolutions):
+            # Apply residual blocks at current resolution level
+            for block_idx in range(self.num_res_blocks):
+                # Apply ResNet block with optional timestep embedding
+                current_features = self.down[resolution_level].block[block_idx](feature_maps[-1], temb=None)
+
+                # Apply attention if configured for this resolution level
+                if len(self.down[resolution_level].attn) > 0:
+                    current_features = self.down[resolution_level].attn[block_idx](current_features)
+
+                # Store processed features
+                feature_maps.append(current_features)
+
+            # Downsample spatial dimensions (except at the final resolution level)
+            if resolution_level != self.num_resolutions - 1:
+                downsampled_features = self.down[resolution_level].downsample(feature_maps[-1])
+                feature_maps.append(downsampled_features)
+
+        # === MIDDLE PROCESSING PHASE ===
+        # Take the lowest resolution features for middle processing
+        bottleneck_features = feature_maps[-1]
+
+        # Apply first middle ResNet block
+        bottleneck_features = self.mid.block_1(bottleneck_features, temb=None)
+
+        # Apply middle attention block
+        bottleneck_features = self.mid.attn_1(bottleneck_features)
+
+        # Apply second middle ResNet block
+        bottleneck_features = self.mid.block_2(bottleneck_features, temb=None)
+
+        # === OUTPUT PHASE ===
+        # Normalize the bottleneck features
+        output_features = self.norm_out(bottleneck_features)
+
+        # Apply non-linearity (SiLU activation)
+        output_features = self.non_linearity(output_features)
+
+        # Final convolution to produce latent representation
+        # [batch, channels, time, n_mels] -> [batch, 2 * z_channels if double_z else z_channels, time, n_mels]
+        return self.conv_out(output_features)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        give_pre_end=False,
+        tanh_out=False,
+        attn_type="vanilla",
+        mid_block_add_attention=True,
+        norm_type="group",
+        causality_axis=CausalityAxis.WIDTH.value,
+        **ignorekwargs,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.out_ch = out_ch
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+        self.norm_type = norm_type
+        self.z_channels = z_channels
+        # Convert string to enum if needed (for config loading)
+        causality_axis = CausalityAxis.str_to_enum(causality_axis)
+        self.attn_type = AttentionType.str_to_enum(attn_type)
+
+        # compute block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = make_conv2d(z_channels, block_in, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+        self.non_linearity = nn.SiLU()
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+        if mid_block_add_attention:
+            self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)
+        else:
+            self.mid.attn_1 = nn.Identity()
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                        norm_type=self.norm_type,
+                        causality_axis=causality_axis,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv, causality_axis=causality_axis)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in, normtype=self.norm_type)
+        self.conv_out = make_conv2d(block_in, out_ch, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+    def _adjust_output_shape(self, decoded_output, target_shape):
+        """
+        Adjust output shape to match target dimensions for variable-length audio.
+
+        This function handles the common case where decoded audio spectrograms need to be
+        resized to match a specific target shape.
+
+        Args:
+            decoded_output: Tensor of shape (batch, channels, time, frequency)
+            target_shape: Target shape tuple (batch, channels, time, frequency)
+
+        Returns:
+            Tensor adjusted to match target_shape exactly
+        """
+        # Current output shape: (batch, channels, time, frequency)
+        _, _, current_time, current_freq = decoded_output.shape
+        _, target_channels, target_time, target_freq = target_shape
+
+        # Step 1: Crop first to avoid exceeding target dimensions
+        decoded_output = decoded_output[
+            :, :target_channels, : min(current_time, target_time), : min(current_freq, target_freq)
+        ]
+
+        # Step 2: Calculate padding needed for time and frequency dimensions
+        time_padding_needed = target_time - decoded_output.shape[2]
+        freq_padding_needed = target_freq - decoded_output.shape[3]
+
+        # Step 3: Apply padding if needed
+        if time_padding_needed > 0 or freq_padding_needed > 0:
+            # PyTorch padding format: (pad_left, pad_right, pad_top, pad_bottom)
+            # For audio: pad_left/right = frequency, pad_top/bottom = time
+            padding = (
+                0,
+                max(freq_padding_needed, 0),  # frequency padding (left, right)
+                0,
+                max(time_padding_needed, 0),  # time padding (top, bottom)
+            )
+            decoded_output = F.pad(decoded_output, padding)
+
+        # Step 4: Final safety crop to ensure exact target shape
+        decoded_output = decoded_output[:, :target_channels, :target_time, :target_freq]
+
+        return decoded_output
+
+    def get_config(self):
+        return {
+            "ch": self.ch,
+            "out_ch": self.out_ch,
+            "ch_mult": self.ch_mult,
+            "num_res_blocks": self.num_res_blocks,
+            "in_channels": self.in_channels,
+            "resolution": self.resolution,
+            "z_channels": self.z_channels,
+        }
+
+    def forward(self, latent_features, target_shape=None):
+        """
+        Decode latent features back to audio spectrograms.
+
+        Args:
+            latent_features: Encoded latent representation of shape (batch, channels, height, width)
+            target_shape: Optional target output shape (batch, channels, time, frequency)
+                         If provided, output will be cropped/padded to match this shape
+
+        Returns:
+            Reconstructed audio spectrogram of shape (batch, channels, time, frequency)
+        """
+        assert target_shape is not None, "Target shape is required for CausalAudioAutoencoder Decoder"
+
+        # Transform latent features to decoder's internal feature dimension
+        hidden_features = self.conv_in(latent_features)
+
+        # Middle processing
+        hidden_features = self.mid.block_1(hidden_features, temb=None)
+        hidden_features = self.mid.attn_1(hidden_features)
+        hidden_features = self.mid.block_2(hidden_features, temb=None)
+
+        # Upsampling
+        # Progressively increase spatial resolution from lowest to highest
+        for resolution_level in reversed(range(self.num_resolutions)):
+            # Apply residual blocks at current resolution level
+            for block_index in range(self.num_res_blocks + 1):
+                hidden_features = self.up[resolution_level].block[block_index](hidden_features, temb=None)
+
+                if len(self.up[resolution_level].attn) > 0:
+                    hidden_features = self.up[resolution_level].attn[block_index](hidden_features)
+
+            if resolution_level != 0:
+                hidden_features = self.up[resolution_level].upsample(hidden_features)
+
+        # Output
+        if self.give_pre_end:
+            # Return intermediate features before final processing (for debugging/analysis)
+            decoded_output = hidden_features
+        else:
+            # Standard output path: normalize, activate, and convert to output channels
+            # Final normalization layer
+            hidden_features = self.norm_out(hidden_features)
+
+            # Apply SiLU (Swish) activation function
+            hidden_features = self.non_linearity(hidden_features)
+
+            # Final convolution to map to output channels (typically 2 for stereo audio)
+            decoded_output = self.conv_out(hidden_features)
+
+            # Optional tanh activation to bound output values to [-1, 1] range
+            if self.tanh_out:
+                decoded_output = torch.tanh(decoded_output)
+
+        # Adjust shape for audio data
+        if target_shape is not None:
+            decoded_output = self._adjust_output_shape(decoded_output, target_shape)
+
+        return decoded_output
+
+
+class processor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.register_buffer("std-of-means", torch.empty(128))
+        self.register_buffer("mean-of-means", torch.empty(128))
+
+    def un_normalize(self, x):
+        return (x * self.get_buffer("std-of-means").to(x)) + self.get_buffer("mean-of-means").to(x)
+
+    def normalize(self, x):
+        return (x - self.get_buffer("mean-of-means").to(x)) / self.get_buffer("std-of-means").to(x)
+
+
+class CausalAudioAutoencoder(nn.Module):
+    def __init__(self, config=None):
+        super().__init__()
+
+        if config is None:
+            config = self._guess_config()
+
+        # Extract encoder and decoder configs from the new format
+        model_config = config.get("model", {}).get("params", {})
+        variables_config = config.get("variables", {})
+
+        self.sampling_rate = variables_config.get(
+            "sampling_rate",
+            model_config.get("sampling_rate", config.get("sampling_rate", 16000)),
+        )
+        encoder_config = model_config.get("encoder", model_config.get("ddconfig", {}))
+        decoder_config = model_config.get("decoder", encoder_config)
+
+        # Load mel spectrogram parameters
+        self.mel_bins = encoder_config.get("mel_bins", 64)
+        self.mel_hop_length = model_config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160)
+        self.n_fft = model_config.get("preprocessing", {}).get("stft", {}).get("filter_length", 1024)
+
+        # Store causality configuration at VAE level (not just in encoder internals)
+        causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.WIDTH.value)
+        self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value)
+        self.is_causal = self.causality_axis == CausalityAxis.HEIGHT
+
+        self.encoder = Encoder(**encoder_config)
+        self.decoder = Decoder(**decoder_config)
+
+        self.per_channel_statistics = processor()
+
+    def _guess_config(self):
+        encoder_config = {
+            # Required parameters - based on ltx-video-av-1679000 model metadata
+            "ch": 128,
+            "out_ch": 8,
+            "ch_mult": [1, 2, 4],  # Based on metadata: [1, 2, 4] not [1, 2, 4, 8]
+            "num_res_blocks": 2,
+            "attn_resolutions": [],  # Based on metadata: empty list, no attention
+            "dropout": 0.0,
+            "resamp_with_conv": True,
+            "in_channels": 2,  # stereo
+            "resolution": 256,
+            "z_channels": 8,
+            "double_z": True,
+            "attn_type": "vanilla",
+            "mid_block_add_attention": False,  # Based on metadata: false
+            "norm_type": "pixel",
+            "causality_axis": "height",  # Based on metadata
+            "mel_bins": 64,  # Based on metadata: mel_bins = 64
+        }
+
+        decoder_config = {
+            # Inherits encoder config, can override specific params
+            **encoder_config,
+            "out_ch": 2,  # Stereo audio output (2 channels)
+            "give_pre_end": False,
+            "tanh_out": False,
+        }
+
+        config = {
+            "_class_name": "CausalAudioAutoencoder",
+            "sampling_rate": 16000,
+            "model": {
+                "params": {
+                    "encoder": encoder_config,
+                    "decoder": decoder_config,
+                }
+            },
+        }
+
+        return config
+
+    def get_config(self):
+        return {
+            "sampling_rate": self.sampling_rate,
+            "mel_bins": self.mel_bins,
+            "mel_hop_length": self.mel_hop_length,
+            "n_fft": self.n_fft,
+            "causality_axis": self.causality_axis.value,
+            "is_causal": self.is_causal,
+        }
+
+    def encode(self, x):
+        return self.encoder(x)
+
+    def decode(self, x, target_shape=None):
+        return self.decoder(x, target_shape=target_shape)

comfy/ldm/lightricks/vocoders/vocoder.py

@@ -0,0 +1,213 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+import comfy.ops
+import numpy as np
+
+ops = comfy.ops.disable_weight_init
+
+LRELU_SLOPE = 0.1
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[2],
+                    padding=get_padding(kernel_size, dilation[2]),
+                ),
+            ]
+        )
+
+        self.convs2 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+
+class Vocoder(torch.nn.Module):
+    """
+    Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan.
+
+    """
+
+    def __init__(self, config=None):
+        super(Vocoder, self).__init__()
+
+        if config is None:
+            config = self.get_default_config()
+
+        resblock_kernel_sizes = config.get("resblock_kernel_sizes", [3, 7, 11])
+        upsample_rates = config.get("upsample_rates", [6, 5, 2, 2, 2])
+        upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 15, 8, 4, 4])
+        resblock_dilation_sizes = config.get("resblock_dilation_sizes", [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
+        upsample_initial_channel = config.get("upsample_initial_channel", 1024)
+        stereo = config.get("stereo", True)
+        resblock = config.get("resblock", "1")
+
+        self.output_sample_rate = config.get("output_sample_rate")
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        in_channels = 128 if stereo else 64
+        self.conv_pre = ops.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)
+        resblock_class = ResBlock1 if resblock == "1" else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                ops.ConvTranspose1d(
+                    upsample_initial_channel // (2**i),
+                    upsample_initial_channel // (2 ** (i + 1)),
+                    k,
+                    u,
+                    padding=(k - u) // 2,
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock_class(ch, k, d))
+
+        out_channels = 2 if stereo else 1
+        self.conv_post = ops.Conv1d(ch, out_channels, 7, 1, padding=3)
+
+        self.upsample_factor = np.prod([self.ups[i].stride[0] for i in range(len(self.ups))])
+
+    def get_default_config(self):
+        """Generate default configuration for the vocoder."""
+
+        config = {
+            "resblock_kernel_sizes": [3, 7, 11],
+            "upsample_rates": [6, 5, 2, 2, 2],
+            "upsample_kernel_sizes": [16, 15, 8, 4, 4],
+            "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            "upsample_initial_channel": 1024,
+            "stereo": True,
+            "resblock": "1",
+        }
+
+        return config
+
+    def forward(self, x):
+        """
+        Forward pass of the vocoder.
+
+        Args:
+            x: Input spectrogram tensor. Can be:
+               - 3D: (batch_size, channels, time_steps) for mono
+               - 4D: (batch_size, 2, channels, time_steps) for stereo
+
+        Returns:
+            Audio tensor of shape (batch_size, out_channels, audio_length)
+        """
+        if x.dim() == 4:  # stereo
+            assert x.shape[1] == 2, "Input must have 2 channels for stereo"
+            x = torch.cat((x[:, 0, :, :], x[:, 1, :, :]), dim=1)
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x

comfy/ldm/lumina/controlnet.py

@@ -0,0 +1,160 @@
+import torch
+from torch import nn
+
+from .model import JointTransformerBlock
+
+class ZImageControlTransformerBlock(JointTransformerBlock):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        block_id=0,
+        operation_settings=None,
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, c, x, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        return c_skip, c
+
+class ZImage_Control(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int = 3840,
+        n_heads: int = 30,
+        n_kv_heads: int = 30,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: float = (8.0 / 3.0),
+        norm_eps: float = 1e-5,
+        qk_norm: bool = True,
+        n_control_layers=6,
+        control_in_dim=16,
+        additional_in_dim=0,
+        broken=False,
+        refiner_control=False,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+    ):
+        super().__init__()
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.broken = broken
+        self.additional_in_dim = additional_in_dim
+        self.control_in_dim = control_in_dim
+        n_refiner_layers = 2
+        self.n_control_layers = n_control_layers
+        self.control_layers = nn.ModuleList(
+            [
+                ZImageControlTransformerBlock(
+                    i,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    block_id=i,
+                    operation_settings=operation_settings,
+                )
+                for i in range(self.n_control_layers)
+            ]
+        )
+
+        all_x_embedder = {}
+        patch_size = 2
+        f_patch_size = 1
+        x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * (self.control_in_dim + self.additional_in_dim), dim, bias=True, device=device, dtype=dtype)
+        all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+        self.refiner_control = refiner_control
+
+        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+        if self.refiner_control:
+            self.control_noise_refiner = nn.ModuleList(
+                [
+                    ZImageControlTransformerBlock(
+                        layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        multiple_of,
+                        ffn_dim_multiplier,
+                        norm_eps,
+                        qk_norm,
+                        block_id=layer_id,
+                        operation_settings=operation_settings,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+        else:
+            self.control_noise_refiner = nn.ModuleList(
+                [
+                    JointTransformerBlock(
+                        layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        multiple_of,
+                        ffn_dim_multiplier,
+                        norm_eps,
+                        qk_norm,
+                        modulation=True,
+                        z_image_modulation=True,
+                        operation_settings=operation_settings,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+
+    def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
+        patch_size = 2
+        f_patch_size = 1
+        pH = pW = patch_size
+        B, C, H, W = control_context.shape
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
+
+        x_attn_mask = None
+        if not self.refiner_control:
+            for layer in self.control_noise_refiner:
+                control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+
+        return control_context
+
+    def forward_noise_refiner_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+        if self.refiner_control:
+            if self.broken:
+                if layer_id == 0:
+                    return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+                if layer_id > 0:
+                    out = None
+                    for i in range(1, len(self.control_layers)):
+                        o, control_context = self.control_layers[i](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+                        if out is None:
+                            out = o
+
+                    return (out, control_context)
+            else:
+                return self.control_noise_refiner[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+        else:
+            return (None, control_context)
+
+    def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+        return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)

comfy/ldm/lumina/model.py

@@ -11,6 +11,7 @@ import comfy.ldm.common_dit
 from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
 from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.math import apply_rope
 import comfy.patcher_extension
 
 
@@ -21,6 +22,10 @@ def modulate(x, scale):
 #                               Core NextDiT Model                              #
 #############################################################################
 
+def clamp_fp16(x):
+    if x.dtype == torch.float16:
+        return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+    return x
 
 class JointAttention(nn.Module):
     """Multi-head attention module."""
@@ -31,6 +36,7 @@ class JointAttention(nn.Module):
         n_heads: int,
         n_kv_heads: Optional[int],
         qk_norm: bool,
+        out_bias: bool = False,
         operation_settings={},
     ):
         """
@@ -59,7 +65,7 @@ class JointAttention(nn.Module):
         self.out = operation_settings.get("operations").Linear(
             n_heads * self.head_dim,
             dim,
-            bias=False,
+            bias=out_bias,
             device=operation_settings.get("device"),
             dtype=operation_settings.get("dtype"),
         )
@@ -70,35 +76,6 @@ class JointAttention(nn.Module):
         else:
             self.q_norm = self.k_norm = nn.Identity()
 
-    @staticmethod
-    def apply_rotary_emb(
-        x_in: torch.Tensor,
-        freqs_cis: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Apply rotary embeddings to input tensors using the given frequency
-        tensor.
-
-        This function applies rotary embeddings to the given query 'xq' and
-        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
-        input tensors are reshaped as complex numbers, and the frequency tensor
-        is reshaped for broadcasting compatibility. The resulting tensors
-        contain rotary embeddings and are returned as real tensors.
-
-        Args:
-            x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
-            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
-                exponentials.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
-                and key tensor with rotary embeddings.
-        """
-
-        t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2)
-        t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
-        return t_out.reshape(*x_in.shape)
-
     def forward(
         self,
         x: torch.Tensor,
@@ -134,8 +111,7 @@ class JointAttention(nn.Module):
         xq = self.q_norm(xq)
         xk = self.k_norm(xk)
 
-        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
-        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
+        xq, xk = apply_rope(xq, xk, freqs_cis)
 
         n_rep = self.n_local_heads // self.n_local_kv_heads
         if n_rep >= 1:
@@ -197,7 +173,7 @@ class FeedForward(nn.Module):
 
     # @torch.compile
     def _forward_silu_gating(self, x1, x3):
-        return F.silu(x1) * x3
+        return clamp_fp16(F.silu(x1) * x3)
 
     def forward(self, x):
         return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
@@ -215,6 +191,8 @@ class JointTransformerBlock(nn.Module):
         norm_eps: float,
         qk_norm: bool,
         modulation=True,
+        z_image_modulation=False,
+        attn_out_bias=False,
         operation_settings={},
     ) -> None:
         """
@@ -235,10 +213,10 @@ class JointTransformerBlock(nn.Module):
         super().__init__()
         self.dim = dim
         self.head_dim = dim // n_heads
-        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings)
+        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings)
         self.feed_forward = FeedForward(
             dim=dim,
-            hidden_dim=4 * dim,
+            hidden_dim=dim,
             multiple_of=multiple_of,
             ffn_dim_multiplier=ffn_dim_multiplier,
             operation_settings=operation_settings,
@@ -252,16 +230,27 @@ class JointTransformerBlock(nn.Module):
 
         self.modulation = modulation
         if modulation:
-            self.adaLN_modulation = nn.Sequential(
-                nn.SiLU(),
-                operation_settings.get("operations").Linear(
-                    min(dim, 1024),
-                    4 * dim,
-                    bias=True,
-                    device=operation_settings.get("device"),
-                    dtype=operation_settings.get("dtype"),
-                ),
-            )
+            if z_image_modulation:
+                self.adaLN_modulation = nn.Sequential(
+                    operation_settings.get("operations").Linear(
+                        min(dim, 256),
+                        4 * dim,
+                        bias=True,
+                        device=operation_settings.get("device"),
+                        dtype=operation_settings.get("dtype"),
+                    ),
+                )
+            else:
+                self.adaLN_modulation = nn.Sequential(
+                    nn.SiLU(),
+                    operation_settings.get("operations").Linear(
+                        min(dim, 1024),
+                        4 * dim,
+                        bias=True,
+                        device=operation_settings.get("device"),
+                        dtype=operation_settings.get("dtype"),
+                    ),
+                )
 
     def forward(
         self,
@@ -288,27 +277,27 @@ class JointTransformerBlock(nn.Module):
             scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
 
             x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
-                self.attention(
+                clamp_fp16(self.attention(
                     modulate(self.attention_norm1(x), scale_msa),
                     x_mask,
                     freqs_cis,
                     transformer_options=transformer_options,
-                )
+                ))
             )
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
-                self.feed_forward(
+                clamp_fp16(self.feed_forward(
                     modulate(self.ffn_norm1(x), scale_mlp),
-                )
+                ))
             )
         else:
             assert adaln_input is None
             x = x + self.attention_norm2(
-                self.attention(
+                clamp_fp16(self.attention(
                     self.attention_norm1(x),
                     x_mask,
                     freqs_cis,
                     transformer_options=transformer_options,
-                )
+                ))
             )
             x = x + self.ffn_norm2(
                 self.feed_forward(
@@ -323,7 +312,7 @@ class FinalLayer(nn.Module):
     The final layer of NextDiT.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}):
+    def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}):
         super().__init__()
         self.norm_final = operation_settings.get("operations").LayerNorm(
             hidden_size,
@@ -340,10 +329,15 @@ class FinalLayer(nn.Module):
             dtype=operation_settings.get("dtype"),
         )
 
+        if z_image_modulation:
+            min_mod = 256
+        else:
+            min_mod = 1024
+
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
             operation_settings.get("operations").Linear(
-                min(hidden_size, 1024),
+                min(hidden_size, min_mod),
                 hidden_size,
                 bias=True,
                 device=operation_settings.get("device"),
@@ -373,12 +367,17 @@ class NextDiT(nn.Module):
         n_heads: int = 32,
         n_kv_heads: Optional[int] = None,
         multiple_of: int = 256,
-        ffn_dim_multiplier: Optional[float] = None,
+        ffn_dim_multiplier: float = 4.0,
         norm_eps: float = 1e-5,
         qk_norm: bool = False,
         cap_feat_dim: int = 5120,
         axes_dims: List[int] = (16, 56, 56),
         axes_lens: List[int] = (1, 512, 512),
+        rope_theta=10000.0,
+        z_image_modulation=False,
+        time_scale=1.0,
+        pad_tokens_multiple=None,
+        clip_text_dim=None,
         image_model=None,
         device=None,
         dtype=None,
@@ -390,6 +389,8 @@ class NextDiT(nn.Module):
         self.in_channels = in_channels
         self.out_channels = in_channels
         self.patch_size = patch_size
+        self.time_scale = time_scale
+        self.pad_tokens_multiple = pad_tokens_multiple
 
         self.x_embedder = operation_settings.get("operations").Linear(
             in_features=patch_size * patch_size * in_channels,
@@ -411,6 +412,7 @@ class NextDiT(nn.Module):
                     norm_eps,
                     qk_norm,
                     modulation=True,
+                    z_image_modulation=z_image_modulation,
                     operation_settings=operation_settings,
                 )
                 for layer_id in range(n_refiner_layers)
@@ -434,7 +436,7 @@ class NextDiT(nn.Module):
             ]
         )
 
-        self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
+        self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings)
         self.cap_embedder = nn.Sequential(
             operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
             operation_settings.get("operations").Linear(
@@ -446,6 +448,31 @@ class NextDiT(nn.Module):
             ),
         )
 
+        self.clip_text_pooled_proj = None
+
+        if clip_text_dim is not None:
+            self.clip_text_dim = clip_text_dim
+            self.clip_text_pooled_proj = nn.Sequential(
+                operation_settings.get("operations").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+                operation_settings.get("operations").Linear(
+                    clip_text_dim,
+                    clip_text_dim,
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+            self.time_text_embed = nn.Sequential(
+                nn.SiLU(),
+                operation_settings.get("operations").Linear(
+                    min(dim, 1024) + clip_text_dim,
+                    min(dim, 1024),
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+
         self.layers = nn.ModuleList(
             [
                 JointTransformerBlock(
@@ -457,18 +484,25 @@ class NextDiT(nn.Module):
                     ffn_dim_multiplier,
                     norm_eps,
                     qk_norm,
+                    z_image_modulation=z_image_modulation,
+                    attn_out_bias=False,
                     operation_settings=operation_settings,
                 )
                 for layer_id in range(n_layers)
             ]
         )
-        self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
+        # This norm final is in the lumina 2.0 code but isn't actually used for anything.
+        # self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)
+
+        if self.pad_tokens_multiple is not None:
+            self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
+            self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
 
         assert (dim // n_heads) == sum(axes_dims)
         self.axes_dims = axes_dims
         self.axes_lens = axes_lens
-        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
+        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims)
         self.dim = dim
         self.n_heads = n_heads
 
@@ -503,108 +537,63 @@ class NextDiT(nn.Module):
         bsz = len(x)
         pH = pW = self.patch_size
         device = x[0].device
-        dtype = x[0].dtype
+        orig_x = x
 
-        if cap_mask is not None:
-            l_effective_cap_len = cap_mask.sum(dim=1).tolist()
-        else:
-            l_effective_cap_len = [num_tokens] * bsz
+        if self.pad_tokens_multiple is not None:
+            pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
+            cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1)
 
-        if cap_mask is not None and not torch.is_floating_point(cap_mask):
-            cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max
+        cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)
+        cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0
 
-        img_sizes = [(img.size(1), img.size(2)) for img in x]
-        l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
+        B, C, H, W = x.shape
+        x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
 
-        max_seq_len = max(
-            (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
-        )
-        max_cap_len = max(l_effective_cap_len)
-        max_img_len = max(l_effective_img_len)
+        rope_options = transformer_options.get("rope_options", None)
+        h_scale = 1.0
+        w_scale = 1.0
+        h_start = 0
+        w_start = 0
+        if rope_options is not None:
+            h_scale = rope_options.get("scale_y", 1.0)
+            w_scale = rope_options.get("scale_x", 1.0)
 
-        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.float32, device=device)
+            h_start = rope_options.get("shift_y", 0.0)
+            w_start = rope_options.get("shift_x", 0.0)
 
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            H, W = img_sizes[i]
-            H_tokens, W_tokens = H // pH, W // pW
-            assert H_tokens * W_tokens == img_len
+        H_tokens, W_tokens = H // pH, W // pW
+        x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
+        x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
+        x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
+        x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
 
-            rope_options = transformer_options.get("rope_options", None)
-            h_scale = 1.0
-            w_scale = 1.0
-            h_start = 0
-            w_start = 0
-            if rope_options is not None:
-                h_scale = rope_options.get("scale_y", 1.0)
-                w_scale = rope_options.get("scale_x", 1.0)
+        if self.pad_tokens_multiple is not None:
+            pad_extra = (-x.shape[1]) % self.pad_tokens_multiple
+            x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1)
+            x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))
 
-                h_start = rope_options.get("shift_y", 0.0)
-                w_start = rope_options.get("shift_x", 0.0)
+        freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)
 
-            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.float32, device=device)
-            position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
-            row_ids = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
-            col_ids = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
-            position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
-            position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
-
-        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype)
-
-        # build freqs_cis for cap and image individually
-        cap_freqs_cis_shape = list(freqs_cis.shape)
-        # cap_freqs_cis_shape[1] = max_cap_len
-        cap_freqs_cis_shape[1] = cap_feats.shape[1]
-        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        img_freqs_cis_shape = list(freqs_cis.shape)
-        img_freqs_cis_shape[1] = max_img_len
-        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
-            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
+        patches = transformer_options.get("patches", {})
 
         # refine context
         for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)
+            cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options)
 
-        # refine image
-        flat_x = []
-        for i in range(bsz):
-            img = x[i]
-            C, H, W = img.size()
-            img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
-            flat_x.append(img)
-        x = flat_x
-        padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
-        padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device)
-        for i in range(bsz):
-            padded_img_embed[i, :l_effective_img_len[i]] = x[i]
-            padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max
-
-        padded_img_embed = self.x_embedder(padded_img_embed)
-        padded_img_mask = padded_img_mask.unsqueeze(1)
-        for layer in self.noise_refiner:
-            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options)
-
-        if cap_mask is not None:
-            mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
-            mask[:, :max_cap_len] = cap_mask[:, :max_cap_len]
-        else:
-            mask = None
-
-        padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-
-            padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
-            padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
+        padded_img_mask = None
+        x_input = x
+        for i, layer in enumerate(self.noise_refiner):
+            x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options)
+            if "noise_refiner" in patches:
+                for p in patches["noise_refiner"]:
+                    out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": freqs_cis[:, cap_pos_ids.shape[1]:], "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"})
+                    if "img" in out:
+                        x = out["img"]
 
+        padded_full_embed = torch.cat((cap_feats, x), dim=1)
+        mask = None
+        img_sizes = [(H, W)] * bsz
+        l_effective_cap_len = [cap_feats.shape[1]] * bsz
         return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
 
     def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
@@ -615,7 +604,7 @@ class NextDiT(nn.Module):
         ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
 
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -627,21 +616,41 @@ class NextDiT(nn.Module):
         y: (N,) tensor of text tokens/features
         """
 
-        t = self.t_embedder(t, dtype=x.dtype)  # (N, D)
+        t = self.t_embedder(t * self.time_scale, dtype=x.dtype)  # (N, D)
         adaln_input = t
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
-        transformer_options = kwargs.get("transformer_options", {})
+        if self.clip_text_pooled_proj is not None:
+            pooled = kwargs.get("clip_text_pooled", None)
+            if pooled is not None:
+                pooled = self.clip_text_pooled_proj(pooled)
+            else:
+                pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
+
+            adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
+
+        patches = transformer_options.get("patches", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
-        freqs_cis = freqs_cis.to(x.device)
+        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, transformer_options=transformer_options)
+        freqs_cis = freqs_cis.to(img.device)
 
-        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+        transformer_options["total_blocks"] = len(self.layers)
+        transformer_options["block_type"] = "double"
+        img_input = img
+        for i, layer in enumerate(self.layers):
+            transformer_options["block_index"] = i
+            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
 
-        x = self.final_layer(x, adaln_input)
-        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+        img = self.final_layer(img, adaln_input)
+        img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
 
-        return -x
+        return -img
 

comfy/ldm/models/autoencoder.py

@@ -9,6 +9,8 @@ from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistri
 from comfy.ldm.util import get_obj_from_str, instantiate_from_config
 from comfy.ldm.modules.ema import LitEma
 import comfy.ops
+from einops import rearrange
+import comfy.model_management
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
     def __init__(self, sample: bool = False):
@@ -179,6 +181,21 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
         self.post_quant_conv = conv_op(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
+        if ddconfig.get("batch_norm_latent", False):
+            self.bn_eps = 1e-4
+            self.bn_momentum = 0.1
+            self.ps = [2, 2]
+            self.bn = torch.nn.BatchNorm2d(math.prod(self.ps) * ddconfig["z_channels"],
+                                           eps=self.bn_eps,
+                                           momentum=self.bn_momentum,
+                                           affine=False,
+                                           track_running_stats=True,
+                                           )
+            self.bn.eval()
+        else:
+            self.bn = None
+
+
     def get_autoencoder_params(self) -> list:
         params = super().get_autoencoder_params()
         return params
@@ -201,11 +218,36 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             z = torch.cat(z, 0)
 
         z, reg_log = self.regularization(z)
+
+        if self.bn is not None:
+            z = rearrange(z,
+                          "... c (i pi) (j pj)  -> ... (c pi pj) i j",
+                          pi=self.ps[0],
+                          pj=self.ps[1],
+                          )
+
+            z = torch.nn.functional.batch_norm(z,
+                                               comfy.model_management.cast_to(self.bn.running_mean, dtype=z.dtype, device=z.device),
+                                               comfy.model_management.cast_to(self.bn.running_var, dtype=z.dtype, device=z.device),
+                                               momentum=self.bn_momentum,
+                                               eps=self.bn_eps)
+
         if return_reg_log:
             return z, reg_log
         return z
 
     def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
+        if self.bn is not None:
+            s = torch.sqrt(comfy.model_management.cast_to(self.bn.running_var.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) + self.bn_eps)
+            m = comfy.model_management.cast_to(self.bn.running_mean.view(1, -1, 1, 1), dtype=z.dtype, device=z.device)
+            z = z * s + m
+            z = rearrange(
+                z,
+                "... (c pi pj) i j -> ... c (i pi) (j pj)",
+                pi=self.ps[0],
+                pj=self.ps[1],
+            )
+
         if self.max_batch_size is None:
             dec = self.post_quant_conv(z)
             dec = self.decoder(dec, **decoder_kwargs)

comfy/ldm/modules/attention.py

@@ -30,6 +30,13 @@ except ImportError as e:
             raise e
         exit(-1)
 
+SAGE_ATTENTION3_IS_AVAILABLE = False
+try:
+    from sageattn3 import sageattn3_blackwell
+    SAGE_ATTENTION3_IS_AVAILABLE = True
+except ImportError:
+    pass
+
 FLASH_ATTENTION_IS_AVAILABLE = False
 try:
     from flash_attn import flash_attn_func
@@ -517,6 +524,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
@@ -541,6 +549,8 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
         out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
+        exception_fallback = True
+    if exception_fallback:
         if tensor_layout == "NHD":
             q, k, v = map(
                 lambda t: t.transpose(1, 2),
@@ -560,6 +570,93 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
             out = out.reshape(b, -1, heads * dim_head)
     return out
 
+@wrap_attn
+def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    exception_fallback = False
+    if (q.device.type != "cuda" or
+        q.dtype not in (torch.float16, torch.bfloat16) or
+        mask is not None):
+        return attention_pytorch(
+            q, k, v, heads,
+            mask=mask,
+            attn_precision=attn_precision,
+            skip_reshape=skip_reshape,
+            skip_output_reshape=skip_output_reshape,
+            **kwargs
+        )
+
+    if skip_reshape:
+        B, H, L, D = q.shape
+        if H != heads:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=True,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        q_s, k_s, v_s = q, k, v
+        N = q.shape[2]
+        dim_head = D
+    else:
+        B, N, inner_dim = q.shape
+        if inner_dim % heads != 0:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        dim_head = inner_dim // heads
+
+    if dim_head >= 256 or N <= 1024:
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=skip_reshape,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if not skip_reshape:
+        q_s, k_s, v_s = map(
+            lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),
+            (q, k, v),
+        )
+        B, H, L, D = q_s.shape
+
+    try:
+        out = sageattn3_blackwell(q_s, k_s, v_s, is_causal=False)
+    except Exception as e:
+        exception_fallback = True
+        logging.error("Error running SageAttention3: %s, falling back to pytorch attention.", e)
+
+    if exception_fallback:
+        if not skip_reshape:
+            del q_s, k_s, v_s
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if skip_reshape:
+        if not skip_output_reshape:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+    else:
+        if skip_output_reshape:
+            pass
+        else:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+
+    return out
 
 try:
     @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
@@ -647,6 +744,8 @@ optimized_attention_masked = optimized_attention
 # register core-supported attention functions
 if SAGE_ATTENTION_IS_AVAILABLE:
     register_attention_function("sage", attention_sage)
+if SAGE_ATTENTION3_IS_AVAILABLE:
+    register_attention_function("sage3", attention3_sage)
 if FLASH_ATTENTION_IS_AVAILABLE:
     register_attention_function("flash", attention_flash)
 if model_management.xformers_enabled():

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -211,12 +211,14 @@ class TimestepEmbedder(nn.Module):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None):
         super().__init__()
+        if output_size is None:
+            output_size = hidden_size
         self.mlp = nn.Sequential(
             operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+            operations.Linear(hidden_size, output_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
 

comfy/ldm/modules/diffusionmodules/model.py

@@ -13,6 +13,12 @@ if model_management.xformers_enabled_vae():
     import xformers
     import xformers.ops
 
+def torch_cat_if_needed(xl, dim):
+    if len(xl) > 1:
+        return torch.cat(xl, dim)
+    else:
+        return xl[0]
+
 def get_timestep_embedding(timesteps, embedding_dim):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
@@ -43,6 +49,37 @@ def Normalize(in_channels, num_groups=32):
     return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
 
+class CarriedConv3d(nn.Module):
+    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
+        super().__init__()
+        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
+
+    x = xl[0]
+    xl.clear()
+
+    if isinstance(op, CarriedConv3d):
+        if conv_carry_in is None:
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
+        else:
+            carry_len = conv_carry_in[0].shape[2]
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
+            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
+
+        if conv_carry_out is not None:
+            to_push = x[:, :, -2:, :, :].clone()
+            conv_carry_out.append(to_push)
+
+    out = op(x)
+
+    return out
+
+
 class VideoConv3d(nn.Module):
     def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):
         super().__init__()
@@ -89,29 +126,24 @@ class Upsample(nn.Module):
                                         stride=1,
                                         padding=1)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         scale_factor = self.scale_factor
         if isinstance(scale_factor, (int, float)):
             scale_factor = (scale_factor,) * (x.ndim - 2)
 
         if x.ndim == 5 and scale_factor[0] > 1.0:
-            t = x.shape[2]
-            if t > 1:
-                a, b = x.split((1, t - 1), dim=2)
-                del x
-                b = interpolate_up(b, scale_factor)
-            else:
-                a = x
-
-            a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)
-            if t > 1:
-                x = torch.cat((a, b), dim=2)
-            else:
-                x = a
+            results = []
+            if conv_carry_in is None:
+                first = x[:, :, :1, :, :]
+                results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))
+                x = x[:, :, 1:, :, :]
+            if x.shape[2] > 0:
+                results.append(interpolate_up(x, scale_factor))
+            x = torch_cat_if_needed(results, dim=2)
         else:
             x = interpolate_up(x, scale_factor)
         if self.with_conv:
-            x = self.conv(x)
+            x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
         return x
 
 
@@ -127,17 +159,20 @@ class Downsample(nn.Module):
                                         stride=stride,
                                         padding=0)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         if self.with_conv:
-            if x.ndim == 4:
+            if isinstance(self.conv, CarriedConv3d):
+                x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
+            elif x.ndim == 4:
                 pad = (0, 1, 0, 1)
                 mode = "constant"
                 x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
+                x = self.conv(x)
             elif x.ndim == 5:
                 pad = (1, 1, 1, 1, 2, 0)
                 mode = "replicate"
                 x = torch.nn.functional.pad(x, pad, mode=mode)
-            x = self.conv(x)
+                x = self.conv(x)
         else:
             x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
         return x
@@ -183,23 +218,23 @@ class ResnetBlock(nn.Module):
                                                     stride=1,
                                                     padding=0)
 
-    def forward(self, x, temb=None):
+    def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):
         h = x
         h = self.norm1(h)
-        h = self.swish(h)
-        h = self.conv1(h)
+        h = [ self.swish(h) ]
+        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if temb is not None:
             h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
 
         h = self.norm2(h)
         h = self.swish(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
+        h = [ self.dropout(h) ]
+        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
+                x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
             else:
                 x = self.nin_shortcut(x)
 
@@ -279,6 +314,7 @@ def pytorch_attention(q, k, v):
     orig_shape = q.shape
     B = orig_shape[0]
     C = orig_shape[1]
+    oom_fallback = False
     q, k, v = map(
         lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
         (q, k, v),
@@ -289,6 +325,8 @@ def pytorch_attention(q, k, v):
         out = out.transpose(2, 3).reshape(orig_shape)
     except model_management.OOM_EXCEPTION:
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
+        oom_fallback = True
+    if oom_fallback:
         out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
     return out
 
@@ -356,7 +394,8 @@ class Model(nn.Module):
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                  resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = self.ch*4
         self.num_resolutions = len(ch_mult)
@@ -510,16 +549,22 @@ class Encoder(nn.Module):
                  conv3d=False, time_compress=None,
                  **ignore_kwargs):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = 0
         self.num_resolutions = len(ch_mult)
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
+            if not attn_resolutions:
+                conv_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -532,6 +577,7 @@ class Encoder(nn.Module):
                                        stride=1,
                                        padding=1)
 
+        self.time_compress = 1
         curr_res = resolution
         in_ch_mult = (1,)+tuple(ch_mult)
         self.in_ch_mult = in_ch_mult
@@ -558,10 +604,15 @@ class Encoder(nn.Module):
                 if time_compress is not None:
                     if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):
                         stride = (1, 2, 2)
+                else:
+                    self.time_compress *= 2
                 down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
                 curr_res = curr_res // 2
             self.down.append(down)
 
+        if time_compress is not None:
+            self.time_compress = time_compress
+
         # middle
         self.mid = nn.Module()
         self.mid.block_1 = ResnetBlock(in_channels=block_in,
@@ -587,15 +638,42 @@ class Encoder(nn.Module):
     def forward(self, x):
         # timestep embedding
         temb = None
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions-1:
-                h = self.down[i_level].downsample(h)
+
+        if self.carried:
+            xl = [x[:, :, :1, :, :]]
+            if x.shape[2] > self.time_compress:
+                tc = self.time_compress
+                xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)
+            x = xl
+        else:
+            x = [x]
+        out = []
+
+        conv_carry_in = None
+
+        for i, x1 in enumerate(x):
+            conv_carry_out = []
+            if i == len(x) - 1:
+                conv_carry_out = None
+
+            # downsampling
+            x1 = [ x1 ]
+            h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
+
+            for i_level in range(self.num_resolutions):
+                for i_block in range(self.num_res_blocks):
+                    h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)
+                    if len(self.down[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.down[i_level].attn[i_block](h1)
+                if i_level != self.num_resolutions-1:
+                    h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)
+
+            out.append(h1)
+            conv_carry_in = conv_carry_out
+
+        h = torch_cat_if_needed(out, dim=2)
+        del out
 
         # middle
         h = self.mid.block_1(h, temb)
@@ -604,15 +682,15 @@ class Encoder(nn.Module):
 
         # end
         h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
+        h = [ nonlinearity(h) ]
+        h = conv_carry_causal_3d(h, self.conv_out)
         return h
 
 
 class Decoder(nn.Module):
     def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+                 resolution, z_channels, tanh_out=False, use_linear_attn=False,
                  conv_out_op=ops.Conv2d,
                  resnet_op=ResnetBlock,
                  attn_op=AttnBlock,
@@ -626,12 +704,18 @@ class Decoder(nn.Module):
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
         self.tanh_out = tanh_out
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
-            conv_out_op = VideoConv3d
+            if not attn_resolutions and resnet_op == ResnetBlock:
+                conv_op = CarriedConv3d
+                conv_out_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
+                conv_out_op = VideoConv3d
+
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -706,29 +790,43 @@ class Decoder(nn.Module):
         temb = None
 
         # z to block_in
-        h = self.conv_in(z)
+        h = conv_carry_causal_3d([z], self.conv_in)
 
         # middle
         h = self.mid.block_1(h, temb, **kwargs)
         h = self.mid.attn_1(h, **kwargs)
         h = self.mid.block_2(h, temb, **kwargs)
 
+        if self.carried:
+            h = torch.split(h, 2, dim=2)
+        else:
+            h = [ h ]
+        out = []
+
+        conv_carry_in = None
+
         # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb, **kwargs)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h, **kwargs)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
+        for i, h1 in enumerate(h):
+            conv_carry_out = []
+            if i == len(h) - 1:
+                conv_carry_out = None
+            for i_level in reversed(range(self.num_resolutions)):
+                for i_block in range(self.num_res_blocks+1):
+                    h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)
+                    if len(self.up[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.up[i_level].attn[i_block](h1, **kwargs)
+                if i_level != 0:
+                    h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)
 
-        # end
-        if self.give_pre_end:
-            return h
+            h1 = self.norm_out(h1)
+            h1 = [ nonlinearity(h1) ]
+            h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)
+            if self.tanh_out:
+                h1 = torch.tanh(h1)
+            out.append(h1)
+            conv_carry_in = conv_carry_out
 
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h, **kwargs)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
+        out = torch_cat_if_needed(out, dim=2)
+
+        return out

comfy/ldm/modules/ema.py

@@ -45,7 +45,7 @@ class LitEma(nn.Module):
                     shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                     shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                 else:
-                    assert not key in self.m_name2s_name
+                    assert key not in self.m_name2s_name
 
     def copy_to(self, model):
         m_param = dict(model.named_parameters())
@@ -54,7 +54,7 @@ class LitEma(nn.Module):
             if m_param[key].requires_grad:
                 m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
-                assert not key in self.m_name2s_name
+                assert key not in self.m_name2s_name
 
     def store(self, parameters):
         """

comfy/ldm/qwen_image/model.py

@@ -61,7 +61,7 @@ def apply_rotary_emb(x, freqs_cis):
 
 
 class QwenTimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim, pooled_projection_dim, dtype=None, device=None, operations=None):
+    def __init__(self, embedding_dim, pooled_projection_dim, use_additional_t_cond=False, dtype=None, device=None, operations=None):
         super().__init__()
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)
         self.timestep_embedder = TimestepEmbedding(
@@ -72,9 +72,19 @@ class QwenTimestepProjEmbeddings(nn.Module):
             operations=operations
         )
 
-    def forward(self, timestep, hidden_states):
+        self.use_additional_t_cond = use_additional_t_cond
+        if self.use_additional_t_cond:
+            self.addition_t_embedding = operations.Embedding(2, embedding_dim, device=device, dtype=dtype)
+
+    def forward(self, timestep, hidden_states, addition_t_cond=None):
         timesteps_proj = self.time_proj(timestep)
         timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))
+
+        if self.use_additional_t_cond:
+            if addition_t_cond is None:
+                addition_t_cond = torch.zeros((timesteps_emb.shape[0]), device=timesteps_emb.device, dtype=torch.long)
+            timesteps_emb += self.addition_t_embedding(addition_t_cond, out_dtype=timesteps_emb.dtype)
+
         return timesteps_emb
 
 
@@ -218,9 +228,24 @@ class QwenImageTransformerBlock(nn.Module):
             operations=operations,
         )
 
-    def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def _apply_gate(self, x, y, gate, timestep_zero_index=None):
+        if timestep_zero_index is not None:
+            return y + torch.cat((x[:, :timestep_zero_index] * gate[0], x[:, timestep_zero_index:] * gate[1]), dim=1)
+        else:
+            return torch.addcmul(y, gate, x)
+
+    def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor, timestep_zero_index=None) -> Tuple[torch.Tensor, torch.Tensor]:
         shift, scale, gate = torch.chunk(mod_params, 3, dim=-1)
-        return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)
+        if timestep_zero_index is not None:
+            actual_batch = shift.size(0) // 2
+            shift, shift_0 = shift[:actual_batch], shift[actual_batch:]
+            scale, scale_0 = scale[:actual_batch], scale[actual_batch:]
+            gate, gate_0 = gate[:actual_batch], gate[actual_batch:]
+            reg = torch.addcmul(shift.unsqueeze(1), x[:, :timestep_zero_index], 1 + scale.unsqueeze(1))
+            zero = torch.addcmul(shift_0.unsqueeze(1), x[:, timestep_zero_index:], 1 + scale_0.unsqueeze(1))
+            return torch.cat((reg, zero), dim=1), (gate.unsqueeze(1), gate_0.unsqueeze(1))
+        else:
+            return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)
 
     def forward(
         self,
@@ -229,14 +254,19 @@ class QwenImageTransformerBlock(nn.Module):
         encoder_hidden_states_mask: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        timestep_zero_index=None,
         transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         img_mod_params = self.img_mod(temb)
+
+        if timestep_zero_index is not None:
+            temb = temb.chunk(2, dim=0)[0]
+
         txt_mod_params = self.txt_mod(temb)
         img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)
         txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)
 
-        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1)
+        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1, timestep_zero_index)
         del img_mod1
         txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)
         del txt_mod1
@@ -251,15 +281,15 @@ class QwenImageTransformerBlock(nn.Module):
         del img_modulated
         del txt_modulated
 
-        hidden_states = hidden_states + img_gate1 * img_attn_output
+        hidden_states = self._apply_gate(img_attn_output, hidden_states, img_gate1, timestep_zero_index)
         encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
         del img_attn_output
         del txt_attn_output
         del img_gate1
         del txt_gate1
 
-        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2)
-        hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2))
+        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2, timestep_zero_index)
+        hidden_states = self._apply_gate(self.img_mlp(img_modulated2), hidden_states, img_gate2, timestep_zero_index)
 
         txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)
         encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))
@@ -300,10 +330,11 @@ class QwenImageTransformer2DModel(nn.Module):
         num_attention_heads: int = 24,
         joint_attention_dim: int = 3584,
         pooled_projection_dim: int = 768,
-        guidance_embeds: bool = False,
         axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
+        default_ref_method="index",
         image_model=None,
         final_layer=True,
+        use_additional_t_cond=False,
         dtype=None,
         device=None,
         operations=None,
@@ -314,12 +345,14 @@ class QwenImageTransformer2DModel(nn.Module):
         self.in_channels = in_channels
         self.out_channels = out_channels or in_channels
         self.inner_dim = num_attention_heads * attention_head_dim
+        self.default_ref_method = default_ref_method
 
         self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope))
 
         self.time_text_embed = QwenTimestepProjEmbeddings(
             embedding_dim=self.inner_dim,
             pooled_projection_dim=pooled_projection_dim,
+            use_additional_t_cond=use_additional_t_cond,
             dtype=dtype,
             device=device,
             operations=operations
@@ -341,6 +374,9 @@ class QwenImageTransformer2DModel(nn.Module):
             for _ in range(num_layers)
         ])
 
+        if self.default_ref_method == "index_timestep_zero":
+            self.register_buffer("__index_timestep_zero__", torch.tensor([]))
+
         if final_layer:
             self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations)
             self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device)
@@ -350,27 +386,33 @@ class QwenImageTransformer2DModel(nn.Module):
         patch_size = self.patch_size
         hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size))
         orig_shape = hidden_states.shape
-        hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2)
-        hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5)
-        hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4)
+        hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-3], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2)
+        hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6)
+        hidden_states = hidden_states.reshape(orig_shape[0], orig_shape[-3] * (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4)
+        t_len = t
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
 
         h_offset = ((h_offset + (patch_size // 2)) // patch_size)
         w_offset = ((w_offset + (patch_size // 2)) // patch_size)
 
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device)
-        img_ids[:, :, 0] = img_ids[:, :, 1] + index
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - (h_len // 2)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - (w_len // 2)
-        return hidden_states, repeat(img_ids, "h w c -> b (h w) c", b=bs), orig_shape
+        img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device)
 
-    def forward(self, x, timestep, context, attention_mask=None, guidance=None, ref_latents=None, transformer_options={}, **kwargs):
+        if t_len > 1:
+            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(1)
+        else:
+            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + index
+
+        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(0) - (h_len // 2)
+        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0).unsqueeze(0) - (w_len // 2)
+        return hidden_states, repeat(img_ids, "t h w c -> b (t h w) c", b=bs), orig_shape
+
+    def forward(self, x, timestep, context, attention_mask=None, ref_latents=None, additional_t_cond=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, timestep, context, attention_mask, guidance, ref_latents, transformer_options, **kwargs)
+        ).execute(x, timestep, context, attention_mask, ref_latents, additional_t_cond, transformer_options, **kwargs)
 
     def _forward(
         self,
@@ -378,8 +420,8 @@ class QwenImageTransformer2DModel(nn.Module):
         timesteps,
         context,
         attention_mask=None,
-        guidance: torch.Tensor = None,
         ref_latents=None,
+        additional_t_cond=None,
         transformer_options={},
         control=None,
         **kwargs
@@ -391,16 +433,24 @@ class QwenImageTransformer2DModel(nn.Module):
         hidden_states, img_ids, orig_shape = self.process_img(x)
         num_embeds = hidden_states.shape[1]
 
+        timestep_zero_index = None
         if ref_latents is not None:
             h = 0
             w = 0
             index = 0
-            index_ref_method = kwargs.get("ref_latents_method", "index") == "index"
+            ref_method = kwargs.get("ref_latents_method", self.default_ref_method)
+            index_ref_method = (ref_method == "index") or (ref_method == "index_timestep_zero")
+            negative_ref_method = ref_method == "negative_index"
+            timestep_zero = ref_method == "index_timestep_zero"
             for ref in ref_latents:
                 if index_ref_method:
                     index += 1
                     h_offset = 0
                     w_offset = 0
+                elif negative_ref_method:
+                    index -= 1
+                    h_offset = 0
+                    w_offset = 0
                 else:
                     index = 1
                     h_offset = 0
@@ -415,6 +465,10 @@ class QwenImageTransformer2DModel(nn.Module):
                 kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 hidden_states = torch.cat([hidden_states, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+            if timestep_zero:
+                if index > 0:
+                    timestep = torch.cat([timestep, timestep * 0], dim=0)
+                    timestep_zero_index = num_embeds
 
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
@@ -426,24 +480,20 @@ class QwenImageTransformer2DModel(nn.Module):
         encoder_hidden_states = self.txt_norm(encoder_hidden_states)
         encoder_hidden_states = self.txt_in(encoder_hidden_states)
 
-        if guidance is not None:
-            guidance = guidance * 1000
-
-        temb = (
-            self.time_text_embed(timestep, hidden_states)
-            if guidance is None
-            else self.time_text_embed(timestep, guidance, hidden_states)
-        )
+        temb = self.time_text_embed(timestep, hidden_states, additional_t_cond)
 
         patches_replace = transformer_options.get("patches_replace", {})
         patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
 
+        transformer_options["total_blocks"] = len(self.transformer_blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.transformer_blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
+                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], timestep_zero_index=timestep_zero_index, transformer_options=args["transformer_options"])
                     return out
                 out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 hidden_states = out["img"]
@@ -455,6 +505,7 @@ class QwenImageTransformer2DModel(nn.Module):
                     encoder_hidden_states_mask=encoder_hidden_states_mask,
                     temb=temb,
                     image_rotary_emb=image_rotary_emb,
+                    timestep_zero_index=timestep_zero_index,
                     transformer_options=transformer_options,
                 )
 
@@ -471,9 +522,12 @@ class QwenImageTransformer2DModel(nn.Module):
                     if add is not None:
                         hidden_states[:, :add.shape[1]] += add
 
+        if timestep_zero_index is not None:
+            temb = temb.chunk(2, dim=0)[0]
+
         hidden_states = self.norm_out(hidden_states, temb)
         hidden_states = self.proj_out(hidden_states)
 
-        hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2)
-        hidden_states = hidden_states.permute(0, 3, 1, 4, 2, 5)
+        hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-3], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2)
+        hidden_states = hidden_states.permute(0, 4, 1, 2, 5, 3, 6)
         return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]]

comfy/ldm/util.py

@@ -71,7 +71,7 @@ def count_params(model, verbose=False):
 
 
 def instantiate_from_config(config):
-    if not "target" in config:
+    if "target" not in config:
         if config == '__is_first_stage__':
             return None
         elif config == "__is_unconditional__":

comfy/ldm/wan/model.py

@@ -568,7 +568,10 @@ class WanModel(torch.nn.Module):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -763,7 +766,10 @@ class VaceWanModel(WanModel):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -862,7 +868,10 @@ class CameraWanModel(WanModel):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -1326,16 +1335,19 @@ class WanModel_S2V(WanModel):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], transformer_options=args["transformer_options"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context)
+                x = block(x, e=e0, freqs=freqs, context=context, transformer_options=transformer_options)
             if audio_emb is not None:
                 x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len)
         # head
@@ -1574,7 +1586,10 @@ class HumoWanModel(WanModel):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}

comfy/ldm/wan/model_animate.py

@@ -523,7 +523,10 @@ class AnimateWanModel(WanModel):
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}

comfy/ldm/wan/vae.py

@@ -227,6 +227,7 @@ class Encoder3d(nn.Module):
     def __init__(self,
                  dim=128,
                  z_dim=4,
+                 input_channels=3,
                  dim_mult=[1, 2, 4, 4],
                  num_res_blocks=2,
                  attn_scales=[],
@@ -245,7 +246,7 @@ class Encoder3d(nn.Module):
         scale = 1.0
 
         # init block
-        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
+        self.conv1 = CausalConv3d(input_channels, dims[0], 3, padding=1)
 
         # downsample blocks
         downsamples = []
@@ -331,6 +332,7 @@ class Decoder3d(nn.Module):
     def __init__(self,
                  dim=128,
                  z_dim=4,
+                 output_channels=3,
                  dim_mult=[1, 2, 4, 4],
                  num_res_blocks=2,
                  attn_scales=[],
@@ -378,7 +380,7 @@ class Decoder3d(nn.Module):
         # output blocks
         self.head = nn.Sequential(
             RMS_norm(out_dim, images=False), nn.SiLU(),
-            CausalConv3d(out_dim, 3, 3, padding=1))
+            CausalConv3d(out_dim, output_channels, 3, padding=1))
 
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         ## conv1
@@ -449,6 +451,7 @@ class WanVAE(nn.Module):
                  num_res_blocks=2,
                  attn_scales=[],
                  temperal_downsample=[True, True, False],
+                 image_channels=3,
                  dropout=0.0):
         super().__init__()
         self.dim = dim
@@ -460,11 +463,11 @@ class WanVAE(nn.Module):
         self.temperal_upsample = temperal_downsample[::-1]
 
         # modules
-        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
+        self.encoder = Encoder3d(dim, z_dim * 2, image_channels, dim_mult, num_res_blocks,
                                  attn_scales, self.temperal_downsample, dropout)
         self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.conv2 = CausalConv3d(z_dim, z_dim, 1)
-        self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
+        self.decoder = Decoder3d(dim, z_dim, image_channels, dim_mult, num_res_blocks,
                                  attn_scales, self.temperal_upsample, dropout)
 
     def encode(self, x):

comfy/lora.py

@@ -313,6 +313,23 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["transformer.{}".format(key_lora)] = k
                 key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format
 
+    if isinstance(model, comfy.model_base.Lumina2):
+        diffusers_keys = comfy.utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = k[:-len(".weight")]
+                key_map["diffusion_model.{}".format(key_lora)] = to
+                key_map["transformer.{}".format(key_lora)] = to
+                key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
+
+    if isinstance(model, comfy.model_base.Kandinsky5):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+                key_map["transformer.{}".format(key_lora)] = k
+
     return key_map
 
 

comfy/model_base.py

@@ -20,6 +20,7 @@ import comfy.ldm.hunyuan3dv2_1
 import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
+import comfy.ldm.lightricks.av_model
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
 from comfy.ldm.cascade.stage_b import StageB
@@ -47,6 +48,7 @@ import comfy.ldm.chroma_radiance.model
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
+import comfy.ldm.kandinsky5.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -134,7 +136,7 @@ class BaseModel(torch.nn.Module):
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
                 fp8 = model_config.optimizations.get("fp8", False)
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8, model_config=model_config)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, model_config=model_config)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -329,18 +331,6 @@ class BaseModel(torch.nn.Module):
             extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
 
         unet_state_dict = self.diffusion_model.state_dict()
-
-        if self.model_config.scaled_fp8 is not None:
-            unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
-
-        # Save mixed precision metadata
-        if hasattr(self.model_config, 'layer_quant_config') and self.model_config.layer_quant_config:
-            metadata = {
-                "format_version": "1.0",
-                "layers": self.model_config.layer_quant_config
-            }
-            unet_state_dict["_quantization_metadata"] = metadata
-
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:
@@ -898,12 +888,13 @@ class Flux(BaseModel):
         attention_mask = kwargs.get("attention_mask", None)
         if attention_mask is not None:
             shape = kwargs["noise"].shape
-            mask_ref_size = kwargs["attention_mask_img_shape"]
-            # the model will pad to the patch size, and then divide
-            # essentially dividing and rounding up
-            (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
-            attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
-            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+            mask_ref_size = kwargs.get("attention_mask_img_shape", None)
+            if mask_ref_size is not None:
+                # the model will pad to the patch size, and then divide
+                # essentially dividing and rounding up
+                (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
+                attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
 
         guidance = kwargs.get("guidance", 3.5)
         if guidance is not None:
@@ -925,9 +916,19 @@ class Flux(BaseModel):
         out = {}
         ref_latents = kwargs.get("reference_latents", None)
         if ref_latents is not None:
-            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
         return out
 
+class Flux2(Flux):
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            target_text_len = 512
+            if cross_attn.shape[1] < target_text_len:
+                cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, target_text_len - cross_attn.shape[1], 0))
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
 
 class GenmoMochi(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
@@ -946,7 +947,7 @@ class GenmoMochi(BaseModel):
 
 class LTXV(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -977,6 +978,60 @@ class LTXV(BaseModel):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return latent_image
 
+class LTXAV(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.av_model.LTXAVModel) #TODO
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+
+        audio_denoise_mask = None
+        if denoise_mask is not None and "latent_shapes" in kwargs:
+            denoise_mask = utils.unpack_latents(denoise_mask, kwargs["latent_shapes"])
+            if len(denoise_mask) > 1:
+                audio_denoise_mask = denoise_mask[1]
+            denoise_mask = denoise_mask[0]
+
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+
+        if audio_denoise_mask is not None:
+            out["audio_denoise_mask"] = comfy.conds.CONDRegular(audio_denoise_mask)
+
+        keyframe_idxs = kwargs.get("keyframe_idxs", None)
+        if keyframe_idxs is not None:
+            out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)
+
+        latent_shapes = kwargs.get("latent_shapes", None)
+        if latent_shapes is not None:
+            out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)
+
+        return out
+
+    def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
+        v_timestep = timestep
+        a_timestep = timestep
+
+        if denoise_mask is not None:
+            v_timestep = self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0]
+        if audio_denoise_mask is not None:
+            a_timestep = self.diffusion_model.a_patchifier.patchify(((audio_denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (audio_denoise_mask.ndim - 1)))[:, :1, :, :1])[0]
+
+        return v_timestep, a_timestep
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return latent_image
+
 class HunyuanVideo(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)
@@ -1103,9 +1158,17 @@ class Lumina2(BaseModel):
             if torch.numel(attention_mask) != attention_mask.sum():
                 out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
             out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
+
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+            if 'num_tokens' not in out:
+                out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1])
+
+        clip_text_pooled = kwargs.get("pooled_output", None)  # NewBie
+        if clip_text_pooled is not None:
+            out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
+
         return out
 
 class WAN21(BaseModel):
@@ -1536,3 +1599,140 @@ class HunyuanImage21Refiner(HunyuanImage21):
         out = super().extra_conds(**kwargs)
         out['disable_time_r'] = comfy.conds.CONDConstant(True)
         return out
+
+class HunyuanVideo15(HunyuanVideo):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        extra_channels = self.diffusion_model.img_in.proj.weight.shape[1] - noise.shape[1] - 1 #noise 32 img cond 32 + mask 1
+        if extra_channels == 0:
+            return None
+
+        image = kwargs.get("concat_latent_image", None)
+        device = kwargs["device"]
+
+        if image is None:
+            shape_image = list(noise.shape)
+            shape_image[1] = extra_channels
+            image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
+        else:
+            latent_dim = self.latent_format.latent_channels
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            for i in range(0, image.shape[1], latent_dim):
+                image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim])
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.zeros_like(noise)[:, :1]
+        else:
+            mask = 1.0 - mask
+            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            if mask.shape[-3] < noise.shape[-3]:
+                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+        return torch.cat((image, mask), dim=1)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        conditioning_byt5small = kwargs.get("conditioning_byt5small", None)
+        if conditioning_byt5small is not None:
+            out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small)
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+
+        clip_vision_output = kwargs.get("clip_vision_output", None)
+        if clip_vision_output is not None:
+            out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.last_hidden_state)
+
+        return out
+
+class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        image = kwargs.get("concat_latent_image", None)
+        noise_augmentation = kwargs.get("noise_augmentation", 0.0)
+        device = kwargs["device"]
+
+        if image is None:
+            image = torch.zeros([noise.shape[0], noise.shape[1] * 2 + 2, noise.shape[-3], noise.shape[-2], noise.shape[-1]], device=comfy.model_management.intermediate_device())
+        else:
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            #image = self.process_latent_in(image) # scaling wasn't applied in reference code
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+            lq_image_slice = slice(noise.shape[1] + 1, 2 * noise.shape[1] + 1)
+            if noise_augmentation > 0:
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(kwargs.get("seed", 0) - 10)
+                noise = torch.randn(image[:, lq_image_slice].shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device)
+                image[:, lq_image_slice] = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image[:, lq_image_slice]
+            else:
+                image[:, lq_image_slice] = 0.75 * image[:, lq_image_slice]
+        return image
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        out['disable_time_r'] = comfy.conds.CONDConstant(False)
+        return out
+
+class Kandinsky5(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.kandinsky5.model.Kandinsky5)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+        image = torch.zeros_like(noise)
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.zeros_like(noise)[:, :1]
+        else:
+            mask = 1.0 - mask
+            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            if mask.shape[-3] < noise.shape[-3]:
+                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+        return torch.cat((image, mask), dim=1)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        time_dim_replace = kwargs.get("time_dim_replace", None)
+        if time_dim_replace is not None:
+            out['time_dim_replace'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_replace))
+
+        return out
+
+class Kandinsky5Image(Kandinsky5):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        return None

comfy/model_detection.py

@@ -6,20 +6,6 @@ import math
 import logging
 import torch
 
-
-def detect_layer_quantization(metadata):
-    quant_key = "_quantization_metadata"
-    if metadata is not None and quant_key in metadata:
-        quant_metadata = metadata.pop(quant_key)
-        quant_metadata = json.loads(quant_metadata)
-        if isinstance(quant_metadata, dict) and "layers" in quant_metadata:
-            logging.info(f"Found quantization metadata (version {quant_metadata.get('format_version', 'unknown')})")
-            return quant_metadata["layers"]
-        else:
-            raise ValueError("Invalid quantization metadata format")
-    return None
-
-
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
     while True:
@@ -186,30 +172,73 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys))
         dit_config["guidance_embed"] = len(guidance_keys) > 0
+
+        # HunyuanVideo 1.5
+        if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["use_cond_type_embedding"] = True
+        else:
+            dit_config["use_cond_type_embedding"] = False
+        if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
+            dit_config["meanflow_sum"] = True
+        else:
+            dit_config["vision_in_dim"] = None
+            dit_config["meanflow_sum"] = False
         return dit_config
 
     if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
         dit_config = {}
-        dit_config["image_model"] = "flux"
+        if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["image_model"] = "flux2"
+            dit_config["axes_dim"] = [32, 32, 32, 32]
+            dit_config["num_heads"] = 48
+            dit_config["mlp_ratio"] = 3.0
+            dit_config["theta"] = 2000
+            dit_config["out_channels"] = 128
+            dit_config["global_modulation"] = True
+            dit_config["mlp_silu_act"] = True
+            dit_config["qkv_bias"] = False
+            dit_config["ops_bias"] = False
+            dit_config["default_ref_method"] = "index"
+            dit_config["ref_index_scale"] = 10.0
+            dit_config["txt_ids_dims"] = [3]
+            patch_size = 1
+        else:
+            dit_config["image_model"] = "flux"
+            dit_config["axes_dim"] = [16, 56, 56]
+            dit_config["num_heads"] = 24
+            dit_config["mlp_ratio"] = 4.0
+            dit_config["theta"] = 10000
+            dit_config["out_channels"] = 16
+            dit_config["qkv_bias"] = True
+            dit_config["txt_ids_dims"] = []
+            patch_size = 2
+
         dit_config["in_channels"] = 16
-        patch_size = 2
+        dit_config["hidden_size"] = 3072
+        dit_config["context_in_dim"] = 4096
+
         dit_config["patch_size"] = patch_size
         in_key = "{}img_in.weight".format(key_prefix)
         if in_key in state_dict_keys:
-            dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size)
-        dit_config["out_channels"] = 16
+            w = state_dict[in_key]
+            dit_config["in_channels"] = w.shape[1] // (patch_size * patch_size)
+            dit_config["hidden_size"] = w.shape[0]
+
+        txt_in_key = "{}txt_in.weight".format(key_prefix)
+        if txt_in_key in state_dict_keys:
+            w = state_dict[txt_in_key]
+            dit_config["context_in_dim"] = w.shape[1]
+            dit_config["hidden_size"] = w.shape[0]
+
         vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)
         if vec_in_key in state_dict_keys:
             dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1]
-        dit_config["context_in_dim"] = 4096
-        dit_config["hidden_size"] = 3072
-        dit_config["mlp_ratio"] = 4.0
-        dit_config["num_heads"] = 24
+        else:
+            dit_config["vec_in_dim"] = None
+
         dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
         dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
-        dit_config["axes_dim"] = [16, 56, 56]
-        dit_config["theta"] = 10000
-        dit_config["qkv_bias"] = True
         if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma
             dit_config["image_model"] = "chroma"
             dit_config["in_channels"] = 64
@@ -230,8 +259,17 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_tile_size"] = 512
                 dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
+                if "{}__x0__".format(key_prefix) in state_dict_keys: # x0 pred
+                    dit_config["use_x0"] = True
+                else:
+                    dit_config["use_x0"] = False
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+            dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
+            dit_config["txt_norm"] = "{}txt_norm.scale".format(key_prefix) in state_dict_keys
+            if dit_config["yak_mlp"] and dit_config["txt_norm"]:  # Ovis model
+                dit_config["txt_ids_dims"] = [1, 2]
+
         return dit_config
 
     if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
@@ -267,7 +305,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
         dit_config = {}
-        dit_config["image_model"] = "ltxv"
+        dit_config["image_model"] = "ltxav" if f'{key_prefix}audio_adaln_single.linear.weight' in state_dict_keys else "ltxv"
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
         shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape
         dit_config["attention_head_dim"] = shape[0] // 32
@@ -378,14 +416,35 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["image_model"] = "lumina2"
         dit_config["patch_size"] = 2
         dit_config["in_channels"] = 16
-        dit_config["dim"] = 2304
-        dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1]
+        w = state_dict['{}cap_embedder.1.weight'.format(key_prefix)]
+        dit_config["dim"] = w.shape[0]
+        dit_config["cap_feat_dim"] = w.shape[1]
         dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
-        dit_config["n_heads"] = 24
-        dit_config["n_kv_heads"] = 8
         dit_config["qk_norm"] = True
-        dit_config["axes_dims"] = [32, 32, 32]
-        dit_config["axes_lens"] = [300, 512, 512]
+
+        if dit_config["dim"] == 2304: # Original Lumina 2
+            dit_config["n_heads"] = 24
+            dit_config["n_kv_heads"] = 8
+            dit_config["axes_dims"] = [32, 32, 32]
+            dit_config["axes_lens"] = [300, 512, 512]
+            dit_config["rope_theta"] = 10000.0
+            dit_config["ffn_dim_multiplier"] = 4.0
+            ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None)
+            if ctd_weight is not None:  # NewBie
+                dit_config["clip_text_dim"] = ctd_weight.shape[0]
+                # NewBie also sets axes_lens = [1024, 512, 512] but it's not used in ComfyUI
+        elif dit_config["dim"] == 3840:  # Z image
+            dit_config["n_heads"] = 30
+            dit_config["n_kv_heads"] = 30
+            dit_config["axes_dims"] = [32, 48, 48]
+            dit_config["axes_lens"] = [1536, 512, 512]
+            dit_config["rope_theta"] = 256.0
+            dit_config["ffn_dim_multiplier"] = (8.0 / 3.0)
+            dit_config["z_image_modulation"] = True
+            dit_config["time_scale"] = 1000.0
+            if '{}cap_pad_token'.format(key_prefix) in state_dict_keys:
+                dit_config["pad_tokens_multiple"] = 32
+
         return dit_config
 
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
@@ -560,6 +619,29 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["image_model"] = "qwen_image"
         dit_config["in_channels"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1]
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
+        if "{}__index_timestep_zero__".format(key_prefix) in state_dict_keys:  # 2511
+            dit_config["default_ref_method"] = "index_timestep_zero"
+        if "{}time_text_embed.addition_t_embedding.weight".format(key_prefix) in state_dict_keys:  # Layered
+            dit_config["use_additional_t_cond"] = True
+            dit_config["default_ref_method"] = "negative_index"
+        return dit_config
+
+    if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
+        dit_config = {}
+        model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+        dit_config["model_dim"] = model_dim
+        if model_dim in [4096, 2560]: # pro video and lite image
+            dit_config["axes_dims"] = (32, 48, 48)
+            if model_dim == 2560: # lite image
+                dit_config["rope_scale_factor"] = (1.0, 1.0, 1.0)
+        elif model_dim == 1792: # lite video
+            dit_config["axes_dims"] = (16, 24, 24)
+        dit_config["time_dim"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+        dit_config["image_model"] = "kandinsky5"
+        dit_config["ff_dim"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0]
+        dit_config["visual_embed_dim"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1]
+        dit_config["num_text_blocks"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.')
+        dit_config["num_visual_blocks"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.')
         return dit_config
 
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
@@ -704,22 +786,11 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 
-    scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
-    if scaled_fp8_key in state_dict:
-        scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
-        model_config.scaled_fp8 = scaled_fp8_weight.dtype
-        if model_config.scaled_fp8 == torch.float32:
-            model_config.scaled_fp8 = torch.float8_e4m3fn
-        if scaled_fp8_weight.nelement() == 2:
-            model_config.optimizations["fp8"] = False
-        else:
-            model_config.optimizations["fp8"] = True
-
     # Detect per-layer quantization (mixed precision)
-    layer_quant_config = detect_layer_quantization(metadata)
-    if layer_quant_config:
-        model_config.layer_quant_config = layer_quant_config
-        logging.info(f"Detected mixed precision quantization: {len(layer_quant_config)} layers quantized")
+    quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix)
+    if quant_config:
+        model_config.quant_config = quant_config
+        logging.info("Detected mixed precision quantization")
 
     return model_config
 

comfy/model_management.py

@@ -26,6 +26,7 @@ import importlib
 import platform
 import weakref
 import gc
+import os
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -334,13 +335,15 @@ except:
 SUPPORT_FP8_OPS = args.supports_fp8_compute
 
 AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]
+AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'
 
 try:
     if is_amd():
         arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
         if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
-            torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
-            logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.")
+            if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':
+                torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
+                logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.")
 
         try:
             rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
@@ -454,7 +457,7 @@ def module_size(module):
     sd = module.state_dict()
     for k in sd:
         t = sd[k]
-        module_mem += t.nelement() * t.element_size()
+        module_mem += t.nbytes
     return module_mem
 
 class LoadedModel:
@@ -690,7 +693,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             loaded_memory = loaded_model.model_loaded_memory()
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
-            lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
+            lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
             lowvram_model_memory = lowvram_model_memory - loaded_memory
 
             if lowvram_model_memory == 0:
@@ -1013,9 +1016,18 @@ def force_channels_last():
 
 
 STREAMS = {}
-NUM_STREAMS = 1
-if args.async_offload:
-    NUM_STREAMS = 2
+NUM_STREAMS = 0
+if args.async_offload is not None:
+    NUM_STREAMS = args.async_offload
+else:
+    #  Enable by default on Nvidia and AMD
+    if is_nvidia() or is_amd():
+        NUM_STREAMS = 2
+
+if args.disable_async_offload:
+    NUM_STREAMS = 0
+
+if NUM_STREAMS > 0:
     logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS))
 
 def current_stream(device):
@@ -1031,7 +1043,10 @@ def current_stream(device):
 stream_counters = {}
 def get_offload_stream(device):
     stream_counter = stream_counters.get(device, 0)
-    if NUM_STREAMS <= 1:
+    if NUM_STREAMS == 0:
+        return None
+
+    if torch.compiler.is_compiling():
         return None
 
     if device in STREAMS:
@@ -1044,7 +1059,9 @@ def get_offload_stream(device):
     elif is_device_cuda(device):
         ss = []
         for k in range(NUM_STREAMS):
-            ss.append(torch.cuda.Stream(device=device, priority=0))
+            s1 = torch.cuda.Stream(device=device, priority=0)
+            s1.as_context = torch.cuda.stream
+            ss.append(s1)
         STREAMS[device] = ss
         s = ss[stream_counter]
         stream_counters[device] = stream_counter
@@ -1052,7 +1069,9 @@ def get_offload_stream(device):
     elif is_device_xpu(device):
         ss = []
         for k in range(NUM_STREAMS):
-            ss.append(torch.xpu.Stream(device=device, priority=0))
+            s1 = torch.xpu.Stream(device=device, priority=0)
+            s1.as_context = torch.xpu.stream
+            ss.append(s1)
         STREAMS[device] = ss
         s = ss[stream_counter]
         stream_counters[device] = stream_counter
@@ -1070,12 +1089,19 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
             if dtype is None or weight.dtype == dtype:
                 return weight
         if stream is not None:
-            with stream:
+            wf_context = stream
+            if hasattr(wf_context, "as_context"):
+                wf_context = wf_context.as_context(stream)
+            with wf_context:
                 return weight.to(dtype=dtype, copy=copy)
         return weight.to(dtype=dtype, copy=copy)
 
+
     if stream is not None:
-        with stream:
+        wf_context = stream
+        if hasattr(wf_context, "as_context"):
+            wf_context = wf_context.as_context(stream)
+        with wf_context:
             r = torch.empty_like(weight, dtype=dtype, device=device)
             r.copy_(weight, non_blocking=non_blocking)
     else:
@@ -1099,13 +1125,24 @@ if not args.disable_pinned_memory:
             MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
         logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
 
+PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])
+
+def discard_cuda_async_error():
+    try:
+        a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        _ = a + b
+        torch.cuda.synchronize()
+    except torch.AcceleratorError:
+        #Dump it! We already know about it from the synchronous return
+        pass
 
 def pin_memory(tensor):
     global TOTAL_PINNED_MEMORY
     if MAX_PINNED_MEMORY <= 0:
         return False
 
-    if type(tensor) is not torch.nn.parameter.Parameter:
+    if type(tensor).__name__ not in PINNING_ALLOWED_TYPES:
         return False
 
     if not is_device_cpu(tensor.device):
@@ -1120,15 +1157,21 @@ def pin_memory(tensor):
     if not tensor.is_contiguous():
         return False
 
-    size = tensor.numel() * tensor.element_size()
+    size = tensor.nbytes
     if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
         return False
 
     ptr = tensor.data_ptr()
+    if ptr == 0:
+        return False
+
     if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:
         PINNED_MEMORY[ptr] = size
         TOTAL_PINNED_MEMORY += size
         return True
+    else:
+        logging.warning("Pin error.")
+        discard_cuda_async_error()
 
     return False
 
@@ -1141,7 +1184,7 @@ def unpin_memory(tensor):
         return False
 
     ptr = tensor.data_ptr()
-    size = tensor.numel() * tensor.element_size()
+    size = tensor.nbytes
 
     size_stored = PINNED_MEMORY.get(ptr, None)
     if size_stored is None:
@@ -1157,6 +1200,9 @@ def unpin_memory(tensor):
         if len(PINNED_MEMORY) == 0:
             TOTAL_PINNED_MEMORY = 0
         return True
+    else:
+        logging.warning("Unpin error.")
+        discard_cuda_async_error()
 
     return False
 
@@ -1459,6 +1505,16 @@ def supports_fp8_compute(device=None):
 
     return True
 
+def supports_nvfp4_compute(device=None):
+    if not is_nvidia():
+        return False
+
+    props = torch.cuda.get_device_properties(device)
+    if props.major < 10:
+        return False
+
+    return True
+
 def extended_fp16_support():
     # TODO: check why some models work with fp16 on newer torch versions but not on older
     if torch_version_numeric < (2, 7):
@@ -1466,6 +1522,20 @@ def extended_fp16_support():
 
     return True
 
+LORA_COMPUTE_DTYPES = {}
+def lora_compute_dtype(device):
+    dtype = LORA_COMPUTE_DTYPES.get(device, None)
+    if dtype is not None:
+        return dtype
+
+    if should_use_fp16(device):
+        dtype = torch.float16
+    else:
+        dtype = torch.float32
+
+    LORA_COMPUTE_DTYPES[device] = dtype
+    return dtype
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS:
@@ -1483,6 +1553,10 @@ def soft_empty_cache(force=False):
 def unload_all_models():
     free_memory(1e30, get_torch_device())
 
+def debug_memory_summary():
+    if is_amd() or is_nvidia():
+        return torch.cuda.memory.memory_summary()
+    return ""
 
 #TODO: might be cleaner to put this somewhere else
 import threading

comfy/model_patcher.py

@@ -35,6 +35,7 @@ import comfy.model_management
 import comfy.patcher_extension
 import comfy.utils
 from comfy.comfy_types import UnetWrapperFunction
+from comfy.quant_ops import QuantizedTensor
 from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
 
 
@@ -126,27 +127,23 @@ class LowVramPatch:
     def __init__(self, key, patches, convert_func=None, set_func=None):
         self.key = key
         self.patches = patches
-        self.convert_func = convert_func
+        self.convert_func = convert_func # TODO: remove
         self.set_func = set_func
 
     def __call__(self, weight):
-        intermediate_dtype = weight.dtype
-        if self.convert_func is not None:
-            weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True)
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
 
-        if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
-            intermediate_dtype = torch.float32
-            out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
-            if self.set_func is None:
-                return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
-            else:
-                return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
+LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2
 
-        out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
-        if self.set_func is not None:
-            return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
-        else:
-            return out
+def low_vram_patch_estimate_vram(model, key):
+    weight, set_func, convert_func = get_key_weight(model, key)
+    if weight is None:
+        return 0
+    model_dtype = getattr(model, "manual_cast_dtype", torch.float32)
+    if model_dtype is None:
+        model_dtype = weight.dtype
+
+    return weight.numel() * model_dtype.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR
 
 def get_key_weight(model, key):
     set_func = None
@@ -231,7 +228,6 @@ class ModelPatcher:
         self.object_patches_backup = {}
         self.weight_wrapper_patches = {}
         self.model_options = {"transformer_options":{}}
-        self.model_size()
         self.load_device = load_device
         self.offload_device = offload_device
         self.weight_inplace_update = weight_inplace_update
@@ -270,6 +266,9 @@ class ModelPatcher:
         if not hasattr(self.model, 'current_weight_patches_uuid'):
             self.model.current_weight_patches_uuid = None
 
+        if not hasattr(self.model, 'model_offload_buffer_memory'):
+            self.model.model_offload_buffer_memory = 0
+
     def model_size(self):
         if self.size > 0:
             return self.size
@@ -286,7 +285,7 @@ class ModelPatcher:
         return self.model.lowvram_patch_counter
 
     def clone(self):
-        n = self.__class__(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
+        n = self.__class__(self.model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -455,6 +454,9 @@ class ModelPatcher:
     def set_model_post_input_patch(self, patch):
         self.set_model_patch(patch, "post_input")
 
+    def set_model_noise_refiner_patch(self, patch):
+        self.set_model_patch(patch, "noise_refiner")
+
     def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
         rope_options = self.model_options["transformer_options"].get("rope_options", {})
         rope_options["scale_x"] = scale_x
@@ -619,10 +621,11 @@ class ModelPatcher:
         if key not in self.backup:
             self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
 
+        temp_dtype = comfy.model_management.lora_compute_dtype(device_to)
         if device_to is not None:
-            temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
         else:
-            temp_weight = weight.to(torch.float32, copy=True)
+            temp_weight = weight.to(temp_dtype, copy=True)
         if convert_func is not None:
             temp_weight = convert_func(temp_weight, inplace=True)
 
@@ -663,7 +666,22 @@ class ModelPatcher:
                     skip = True # skip random weights in non leaf modules
                     break
             if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
-                loading.append((comfy.model_management.module_size(m), n, m, params))
+                module_mem = comfy.model_management.module_size(m)
+                module_offload_mem = module_mem
+                if hasattr(m, "comfy_cast_weights"):
+                    def check_module_offload_mem(key):
+                        if key in self.patches:
+                            return low_vram_patch_estimate_vram(self.model, key)
+                        model_dtype = getattr(self.model, "manual_cast_dtype", None)
+                        weight, _, _ = get_key_weight(self.model, key)
+                        if model_dtype is None or weight is None:
+                            return 0
+                        if (weight.dtype != model_dtype or isinstance(weight, QuantizedTensor)):
+                            return weight.numel() * model_dtype.itemsize
+                        return 0
+                    module_offload_mem += check_module_offload_mem("{}.weight".format(n))
+                    module_offload_mem += check_module_offload_mem("{}.bias".format(n))
+                loading.append((module_offload_mem, module_mem, n, m, params))
         return loading
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
@@ -677,20 +695,22 @@ class ModelPatcher:
 
             load_completely = []
             offloaded = []
+            offload_buffer = 0
             loading.sort(reverse=True)
-            for x in loading:
-                n = x[1]
-                m = x[2]
-                params = x[3]
-                module_mem = x[0]
+            for i, x in enumerate(loading):
+                module_offload_mem, module_mem, n, m, params = x
 
                 lowvram_weight = False
 
+                potential_offload = max(offload_buffer, module_offload_mem + sum([ x1[1] for x1 in loading[i+1:i+1+comfy.model_management.NUM_STREAMS]]))
+                lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory
+
                 weight_key = "{}.weight".format(n)
                 bias_key = "{}.bias".format(n)
 
                 if not full_load and hasattr(m, "comfy_cast_weights"):
-                    if mem_counter + module_mem >= lowvram_model_memory:
+                    if not lowvram_fits:
+                        offload_buffer = potential_offload
                         lowvram_weight = True
                         lowvram_counter += 1
                         lowvram_mem_counter += module_mem
@@ -724,9 +744,11 @@ class ModelPatcher:
                     if hasattr(m, "comfy_cast_weights"):
                         wipe_lowvram_weight(m)
 
-                    if full_load or mem_counter + module_mem < lowvram_model_memory:
+                    if full_load or lowvram_fits:
                         mem_counter += module_mem
                         load_completely.append((module_mem, n, m, params))
+                    else:
+                        offload_buffer = potential_offload
 
                 if cast_weight and hasattr(m, "comfy_cast_weights"):
                     m.prev_comfy_cast_weights = m.comfy_cast_weights
@@ -753,6 +775,8 @@ class ModelPatcher:
                     key = "{}.{}".format(n, param)
                     self.unpin_weight(key)
                     self.patch_weight_to_device(key, device_to=device_to)
+                if comfy.model_management.is_device_cuda(device_to):
+                    torch.cuda.synchronize()
 
                 logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
                 m.comfy_patched_weights = True
@@ -767,7 +791,7 @@ class ModelPatcher:
                     self.pin_weight_to_device("{}.{}".format(n, param))
 
             if lowvram_counter > 0:
-                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
             else:
                 logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
@@ -779,6 +803,7 @@ class ModelPatcher:
             self.model.lowvram_patch_counter += patch_counter
             self.model.device = device_to
             self.model.model_loaded_weight_memory = mem_counter
+            self.model.model_offload_buffer_memory = offload_buffer
             self.model.current_weight_patches_uuid = self.patches_uuid
 
             for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):
@@ -832,6 +857,7 @@ class ModelPatcher:
                 self.model.to(device_to)
                 self.model.device = device_to
             self.model.model_loaded_weight_memory = 0
+            self.model.model_offload_buffer_memory = 0
 
             for m in self.model.modules():
                 if hasattr(m, "comfy_patched_weights"):
@@ -850,13 +876,18 @@ class ModelPatcher:
             patch_counter = 0
             unload_list = self._load_list()
             unload_list.sort()
+
+            offload_buffer = self.model.model_offload_buffer_memory
+            if len(unload_list) > 0:
+                NS = comfy.model_management.NUM_STREAMS
+                offload_weight_factor = [ min(offload_buffer / (NS + 1), unload_list[0][1]) ] * NS
+
             for unload in unload_list:
-                if memory_to_free < memory_freed:
+                if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed:
                     break
-                module_mem = unload[0]
-                n = unload[1]
-                m = unload[2]
-                params = unload[3]
+                module_offload_mem, module_mem, n, m, params = unload
+
+                potential_offload = module_offload_mem + sum(offload_weight_factor)
 
                 lowvram_possible = hasattr(m, "comfy_cast_weights")
                 if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
@@ -902,20 +933,25 @@ class ModelPatcher:
                                     patch_counter += 1
                             cast_weight = True
 
-                        if cast_weight:
+                        if cast_weight and hasattr(m, "comfy_cast_weights"):
                             m.prev_comfy_cast_weights = m.comfy_cast_weights
                             m.comfy_cast_weights = True
                         m.comfy_patched_weights = False
                         memory_freed += module_mem
+                        offload_buffer = max(offload_buffer, potential_offload)
+                        offload_weight_factor.append(module_mem)
+                        offload_weight_factor.pop(0)
                         logging.debug("freed {}".format(n))
 
                         for param in params:
                             self.pin_weight_to_device("{}.{}".format(n, param))
 
+
             self.model.model_lowvram = True
             self.model.lowvram_patch_counter += patch_counter
             self.model.model_loaded_weight_memory -= memory_freed
-            logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter))
+            self.model.model_offload_buffer_memory = offload_buffer
+            logging.info("Unloaded partially: {:.2f} MB freed, {:.2f} MB remains loaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(memory_freed / (1024 * 1024), self.model.model_loaded_weight_memory / (1024 * 1024), offload_buffer / (1024 * 1024), self.model.lowvram_patch_counter))
             return memory_freed
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):

comfy/ops.py

@@ -22,7 +22,7 @@ import comfy.model_management
 from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 import comfy.rmsnorm
-import contextlib
+import json
 
 def run_every_op():
     if torch.compiler.is_compiling():
@@ -58,7 +58,8 @@ except (ModuleNotFoundError, TypeError):
 NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
 try:
     if comfy.model_management.is_nvidia():
-        if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
+        cudnn_version = torch.backends.cudnn.version()
+        if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
             #TODO: change upper bound version once it's fixed'
             NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
             logging.info("working around nvidia conv3d memory bug.")
@@ -78,7 +79,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     if input is not None:
         if dtype is None:
             if isinstance(input, QuantizedTensor):
-                dtype = input._layout_params["orig_dtype"]
+                dtype = input.params.orig_dtype
             else:
                 dtype = input.dtype
         if bias_dtype is None:
@@ -92,11 +93,6 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     else:
         offload_stream = None
 
-    if offload_stream is not None:
-        wf_context = offload_stream
-    else:
-        wf_context = contextlib.nullcontext()
-
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     weight_has_function = len(s.weight_function) > 0
@@ -108,20 +104,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     if s.bias is not None:
         bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
 
-        if bias_has_function:
-            with wf_context:
-                for f in s.bias_function:
-                    bias = f(bias)
+    comfy.model_management.sync_stream(device, offload_stream)
+
+    bias_a = bias
+    weight_a = weight
+
+    if s.bias is not None:
+        for f in s.bias_function:
+            bias = f(bias)
 
     if weight_has_function or weight.dtype != dtype:
-        with wf_context:
-            weight = weight.to(dtype=dtype)
-            for f in s.weight_function:
-                weight = f(weight)
+        weight = weight.to(dtype=dtype)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        for f in s.weight_function:
+            weight = f(weight)
 
-    comfy.model_management.sync_stream(device, offload_stream)
     if offloadable:
-        return weight, bias, offload_stream
+        return weight, bias, (offload_stream, weight_a, bias_a)
     else:
         #Legacy function signature
         return weight, bias
@@ -130,13 +130,16 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
 def uncast_bias_weight(s, weight, bias, offload_stream):
     if offload_stream is None:
         return
-    if weight is not None:
-        device = weight.device
+    os, weight_a, bias_a = offload_stream
+    if os is None:
+        return
+    if weight_a is not None:
+        device = weight_a.device
     else:
-        if bias is None:
+        if bias_a is None:
             return
-        device = bias.device
-    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+        device = bias_a.device
+    os.wait_stream(comfy.model_management.current_stream(device))
 
 
 class CastWeightBiasOp:
@@ -409,36 +412,34 @@ def fp8_linear(self, input):
         return None
 
     input_dtype = input.dtype
+    input_shape = input.shape
+    tensor_3d = input.ndim == 3
 
-    if input.ndim == 3 or input.ndim == 2:
-        w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+    if tensor_3d:
+        input = input.reshape(-1, input_shape[2])
 
-        scale_weight = self.scale_weight
-        scale_input = self.scale_input
-        if scale_weight is None:
-            scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
-        else:
-            scale_weight = scale_weight.to(input.device)
+    if input.ndim != 2:
+        return None
+    w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+    scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
 
-        if scale_input is None:
-            scale_input = torch.ones((), device=input.device, dtype=torch.float32)
-            input = torch.clamp(input, min=-448, max=448, out=input)
-            layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
-            quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
-        else:
-            scale_input = scale_input.to(input.device)
-            quantized_input = QuantizedTensor.from_float(input, "TensorCoreFP8Layout", scale=scale_input, dtype=dtype)
+    scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+    input = torch.clamp(input, min=-448, max=448, out=input)
+    input_fp8 = input.to(dtype).contiguous()
+    layout_params_input = TensorCoreFP8Layout.Params(scale=scale_input, orig_dtype=input_dtype, orig_shape=tuple(input_fp8.shape))
+    quantized_input = QuantizedTensor(input_fp8, "TensorCoreFP8Layout", layout_params_input)
 
-        # Wrap weight in QuantizedTensor - this enables unified dispatch
-        # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
-        layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
-        quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight)
-        o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
+    # Wrap weight in QuantizedTensor - this enables unified dispatch
+    # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
+    layout_params_weight = TensorCoreFP8Layout.Params(scale=scale_weight, orig_dtype=input_dtype, orig_shape=tuple(w.shape))
+    quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight)
+    o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
-        uncast_bias_weight(self, w, bias, offload_stream)
-        return o
+    uncast_bias_weight(self, w, bias, offload_stream)
+    if tensor_3d:
+        o = o.reshape((input_shape[0], input_shape[1], w.shape[0]))
 
-    return None
+    return o
 
 class fp8_ops(manual_cast):
     class Linear(manual_cast.Linear):
@@ -448,7 +449,7 @@ class fp8_ops(manual_cast):
             return None
 
         def forward_comfy_cast_weights(self, input):
-            if not self.training:
+            if len(self.weight_function) == 0 and len(self.bias_function) == 0:
                 try:
                     out = fp8_linear(self, input)
                     if out is not None:
@@ -461,59 +462,6 @@ class fp8_ops(manual_cast):
             uncast_bias_weight(self, weight, bias, offload_stream)
             return x
 
-def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
-    logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
-    class scaled_fp8_op(manual_cast):
-        class Linear(manual_cast.Linear):
-            def __init__(self, *args, **kwargs):
-                if override_dtype is not None:
-                    kwargs['dtype'] = override_dtype
-                super().__init__(*args, **kwargs)
-
-            def reset_parameters(self):
-                if not hasattr(self, 'scale_weight'):
-                    self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
-
-                if not scale_input:
-                    self.scale_input = None
-
-                if not hasattr(self, 'scale_input'):
-                    self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
-                return None
-
-            def forward_comfy_cast_weights(self, input):
-                if fp8_matrix_mult:
-                    out = fp8_linear(self, input)
-                    if out is not None:
-                        return out
-
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-
-                if weight.numel() < input.numel(): #TODO: optimize
-                    x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
-                else:
-                    x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
-                uncast_bias_weight(self, weight, bias, offload_stream)
-                return x
-
-            def convert_weight(self, weight, inplace=False, **kwargs):
-                if inplace:
-                    weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
-                    return weight
-                else:
-                    return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
-
-            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
-                weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
-                if return_weight:
-                    return weight
-                if inplace_update:
-                    self.weight.data.copy_(weight)
-                else:
-                    self.weight = torch.nn.Parameter(weight, requires_grad=False)
-
-    return scaled_fp8_op
-
 CUBLAS_IS_AVAILABLE = False
 try:
     from cublas_ops import CublasLinear
@@ -537,119 +485,249 @@ if CUBLAS_IS_AVAILABLE:
 # ==============================================================================
 # Mixed Precision Operations
 # ==============================================================================
-from .quant_ops import QuantizedTensor, QUANT_ALGOS
+from .quant_ops import (
+    QuantizedTensor,
+    QUANT_ALGOS,
+    TensorCoreFP8Layout,
+    get_layout_class,
+)
 
-class MixedPrecisionOps(disable_weight_init):
-    _layer_quant_config = {}
-    _compute_dtype = torch.bfloat16
 
-    class Linear(torch.nn.Module, CastWeightBiasOp):
-        def __init__(
-            self,
-            in_features: int,
-            out_features: int,
-            bias: bool = True,
-            device=None,
-            dtype=None,
-        ) -> None:
-            super().__init__()
+def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
+    class MixedPrecisionOps(manual_cast):
+        _quant_config = quant_config
+        _compute_dtype = compute_dtype
+        _full_precision_mm = full_precision_mm
+        _disabled = disabled
 
-            self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
-            # self.factory_kwargs = {"device": device, "dtype": dtype}
+        class Linear(torch.nn.Module, CastWeightBiasOp):
+            def __init__(
+                self,
+                in_features: int,
+                out_features: int,
+                bias: bool = True,
+                device=None,
+                dtype=None,
+            ) -> None:
+                super().__init__()
 
-            self.in_features = in_features
-            self.out_features = out_features
-            if bias:
-                self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
-            else:
-                self.register_parameter("bias", None)
+                self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
+                # self.factory_kwargs = {"device": device, "dtype": dtype}
 
-            self.tensor_class = None
+                self.in_features = in_features
+                self.out_features = out_features
+                if bias:
+                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
+                else:
+                    self.register_parameter("bias", None)
 
-        def reset_parameters(self):
-            return None
+                self.tensor_class = None
+                self._full_precision_mm = MixedPrecisionOps._full_precision_mm
+                self._full_precision_mm_config = False
 
-        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
-                                  strict, missing_keys, unexpected_keys, error_msgs):
+            def reset_parameters(self):
+                return None
 
-            device = self.factory_kwargs["device"]
-            layer_name = prefix.rstrip('.')
-            weight_key = f"{prefix}weight"
-            weight = state_dict.pop(weight_key, None)
-            if weight is None:
-                raise ValueError(f"Missing weight for layer {layer_name}")
+            def _load_scale_param(self, state_dict, prefix, param_name, device, manually_loaded_keys, dtype=None):
+                key = f"{prefix}{param_name}"
+                value = state_dict.pop(key, None)
+                if value is not None:
+                    value = value.to(device=device)
+                    if dtype is not None:
+                        value = value.view(dtype=dtype)
+                    manually_loaded_keys.append(key)
+                return value
 
-            manually_loaded_keys = [weight_key]
+            def _load_from_state_dict(self, state_dict, prefix, local_metadata,
+                                    strict, missing_keys, unexpected_keys, error_msgs):
 
-            if layer_name not in MixedPrecisionOps._layer_quant_config:
-                self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
-            else:
-                quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None)
-                if quant_format is None:
-                    raise ValueError(f"Unknown quantization format for layer {layer_name}")
+                device = self.factory_kwargs["device"]
+                layer_name = prefix.rstrip('.')
+                weight_key = f"{prefix}weight"
+                weight = state_dict.pop(weight_key, None)
+                if weight is None:
+                    raise ValueError(f"Missing weight for layer {layer_name}")
 
-                qconfig = QUANT_ALGOS[quant_format]
-                self.layout_type = qconfig["comfy_tensor_layout"]
+                manually_loaded_keys = [weight_key]
 
-                weight_scale_key = f"{prefix}weight_scale"
-                layout_params = {
-                    'scale': state_dict.pop(weight_scale_key, None),
-                    'orig_dtype': MixedPrecisionOps._compute_dtype,
-                    'block_size': qconfig.get("group_size", None),
-                }
-                if layout_params['scale'] is not None:
-                    manually_loaded_keys.append(weight_scale_key)
+                layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
+                if layer_conf is not None:
+                    layer_conf = json.loads(layer_conf.numpy().tobytes())
 
-                self.weight = torch.nn.Parameter(
-                    QuantizedTensor(weight.to(device=device), self.layout_type, layout_params),
-                    requires_grad=False
-                )
+                if layer_conf is None:
+                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
+                else:
+                    self.quant_format = layer_conf.get("format", None)
+                    self._full_precision_mm_config = layer_conf.get("full_precision_matrix_mult", False)
+                    if not self._full_precision_mm:
+                        self._full_precision_mm = self._full_precision_mm_config
 
-                for param_name in qconfig["parameters"]:
-                    param_key = f"{prefix}{param_name}"
-                    _v = state_dict.pop(param_key, None)
-                    if _v is None:
+                    if self.quant_format in MixedPrecisionOps._disabled:
+                        self._full_precision_mm = True
+
+                    if self.quant_format is None:
+                        raise ValueError(f"Unknown quantization format for layer {layer_name}")
+
+                    qconfig = QUANT_ALGOS[self.quant_format]
+                    self.layout_type = qconfig["comfy_tensor_layout"]
+                    layout_cls = get_layout_class(self.layout_type)
+
+                    # Load format-specific parameters
+                    if self.quant_format in ["float8_e4m3fn", "float8_e5m2"]:
+                        # FP8: single tensor scale
+                        scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys)
+
+                        params = layout_cls.Params(
+                            scale=scale,
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                        )
+
+                    elif self.quant_format == "nvfp4":
+                        # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)
+                        tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys)
+                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
+                                                             dtype=torch.float8_e4m3fn)
+
+                        if tensor_scale is None or block_scale is None:
+                            raise ValueError(f"Missing NVFP4 scales for layer {layer_name}")
+
+                        params = layout_cls.Params(
+                            scale=tensor_scale,
+                            block_scale=block_scale,
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                        )
+                    else:
+                        raise ValueError(f"Unsupported quantization format: {self.quant_format}")
+
+                    self.weight = torch.nn.Parameter(
+                        QuantizedTensor(weight.to(device=device, dtype=qconfig["storage_t"]), self.layout_type, params),
+                        requires_grad=False
+                    )
+
+                    for param_name in qconfig["parameters"]:
+                        if param_name in {"weight_scale", "weight_scale_2"}:
+                            continue  # Already handled above
+
+                        param_key = f"{prefix}{param_name}"
+                        _v = state_dict.pop(param_key, None)
+                        if _v is None:
+                            continue
+                        self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+                        manually_loaded_keys.append(param_key)
+
+                super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+                for key in manually_loaded_keys:
+                    if key in missing_keys:
+                        missing_keys.remove(key)
+
+            def state_dict(self, *args, destination=None, prefix="", **kwargs):
+                sd = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
+                if isinstance(self.weight, QuantizedTensor):
+                    layout_cls = self.weight._layout_cls
+
+                    # Check if it's any FP8 variant (E4M3 or E5M2)
+                    if layout_cls in ("TensorCoreFP8E4M3Layout", "TensorCoreFP8E5M2Layout", "TensorCoreFP8Layout"):
+                        sd["{}weight_scale".format(prefix)] = self.weight._params.scale
+                    elif layout_cls == "TensorCoreNVFP4Layout":
+                        sd["{}weight_scale_2".format(prefix)] = self.weight._params.scale
+                        sd["{}weight_scale".format(prefix)] = self.weight._params.block_scale
+
+                    quant_conf = {"format": self.quant_format}
+                    if self._full_precision_mm_config:
+                        quant_conf["full_precision_matrix_mult"] = True
+                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
+                return sd
+
+            def _forward(self, input, weight, bias):
+                return torch.nn.functional.linear(input, weight, bias)
+
+            def forward_comfy_cast_weights(self, input):
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+                x = self._forward(input, weight, bias)
+                uncast_bias_weight(self, weight, bias, offload_stream)
+                return x
+
+            def forward(self, input, *args, **kwargs):
+                run_every_op()
+
+                input_shape = input.shape
+                tensor_3d = input.ndim == 3
+
+                if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                    return self.forward_comfy_cast_weights(input, *args, **kwargs)
+
+                if (getattr(self, 'layout_type', None) is not None and
+                    not isinstance(input, QuantizedTensor)):
+
+                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
+                    if tensor_3d:
+                        input = input.reshape(-1, input_shape[2])
+
+                    if input.ndim != 2:
+                        # Fall back to comfy_cast_weights for non-2D tensors
+                        return self.forward_comfy_cast_weights(input.reshape(input_shape), *args, **kwargs)
+
+                    # dtype is now implicit in the layout class
+                    input = QuantizedTensor.from_float(input, self.layout_type, scale=getattr(self, 'input_scale', None))
+
+                output = self._forward(input, self.weight, self.bias)
+
+                # Reshape output back to 3D if input was 3D
+                if tensor_3d:
+                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
+
+                return output
+
+            def convert_weight(self, weight, inplace=False, **kwargs):
+                if isinstance(weight, QuantizedTensor):
+                    return weight.dequantize()
+                else:
+                    return weight
+
+            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
+                if getattr(self, 'layout_type', None) is not None:
+                    # dtype is now implicit in the layout class
+                    weight = QuantizedTensor.from_float(weight, self.layout_type, scale="recalculate", stochastic_rounding=seed, inplace_ops=True)
+                else:
+                    weight = weight.to(self.weight.dtype)
+                if return_weight:
+                    return weight
+
+                assert inplace_update is False  # TODO: eventually remove the inplace_update stuff
+                self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+            def _apply(self, fn, recurse=True):  # This is to get torch.compile + moving weights to another device working
+                if recurse:
+                    for module in self.children():
+                        module._apply(fn)
+
+                for key, param in self._parameters.items():
+                    if param is None:
                         continue
-                    setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
-                    manually_loaded_keys.append(param_key)
+                    self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False))
+                for key, buf in self._buffers.items():
+                    if buf is not None:
+                        self._buffers[key] = fn(buf)
+                return self
 
-            super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+    return MixedPrecisionOps
 
-            for key in manually_loaded_keys:
-                if key in missing_keys:
-                    missing_keys.remove(key)
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
+    fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
+    nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)
 
-        def _forward(self, input, weight, bias):
-            return torch.nn.functional.linear(input, weight, bias)
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-            x = self._forward(input, weight, bias)
-            uncast_bias_weight(self, weight, bias, offload_stream)
-            return x
-
-        def forward(self, input, *args, **kwargs):
-            run_every_op()
-
-            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
-                return self.forward_comfy_cast_weights(input, *args, **kwargs)
-            if (getattr(self, 'layout_type', None) is not None and
-                getattr(self, 'input_scale', None) is not None and
-                not isinstance(input, QuantizedTensor)):
-                input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype)
-            return self._forward(input, self.weight, self.bias)
-
-
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
-    if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
-        MixedPrecisionOps._layer_quant_config = model_config.layer_quant_config
-        MixedPrecisionOps._compute_dtype = compute_dtype
-        logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
-        return MixedPrecisionOps
-
-    fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
-    if scaled_fp8 is not None:
-        return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)
+    if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
+        logging.info("Using mixed precision operations")
+        disabled = set()
+        if not nvfp4_compute:
+            disabled.add("nvfp4")
+        if not fp8_compute:
+            disabled.add("float8_e4m3fn")
+            disabled.add("float8_e5m2")
+        return mixed_precision_ops(model_config.quant_config, compute_dtype, disabled=disabled)
 
     if (
         fp8_compute and

comfy/quant_ops.py

@@ -1,545 +1,140 @@
 import torch
 import logging
-from typing import Tuple, Dict
 
-_LAYOUT_REGISTRY = {}
-_GENERIC_UTILS = {}
+try:
+    import comfy_kitchen as ck
+    from comfy_kitchen.tensor import (
+        QuantizedTensor,
+        QuantizedLayout,
+        TensorCoreFP8Layout as _CKFp8Layout,
+        TensorCoreNVFP4Layout,  # Direct import, no wrapper needed
+        register_layout_op,
+        register_layout_class,
+        get_layout_class,
+    )
+    _CK_AVAILABLE = True
+    if torch.version.cuda is None:
+        ck.registry.disable("cuda")
+    else:
+        cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))
+        if cuda_version < (13,):
+            ck.registry.disable("cuda")
 
+    ck.registry.disable("triton")
+    for k, v in ck.list_backends().items():
+        logging.info(f"Found comfy_kitchen backend {k}: {v}")
+except ImportError as e:
+    logging.error(f"Failed to import comfy_kitchen, Error: {e}, fp8 and fp4 support will not be available.")
+    _CK_AVAILABLE = False
 
-def register_layout_op(torch_op, layout_type):
-    """
-    Decorator to register a layout-specific operation handler.
-    Args:
-        torch_op: PyTorch operation (e.g., torch.ops.aten.linear.default)
-        layout_type: Layout class (e.g., TensorCoreFP8Layout)
-    Example:
-        @register_layout_op(torch.ops.aten.linear.default, TensorCoreFP8Layout)
-        def fp8_linear(func, args, kwargs):
-            # FP8-specific linear implementation
-            ...
-    """
-    def decorator(handler_func):
-        if torch_op not in _LAYOUT_REGISTRY:
-            _LAYOUT_REGISTRY[torch_op] = {}
-        _LAYOUT_REGISTRY[torch_op][layout_type] = handler_func
-        return handler_func
-    return decorator
+    class QuantizedTensor:
+        pass
 
+    class _CKFp8Layout:
+        pass
 
-def register_generic_util(torch_op):
-    """
-    Decorator to register a generic utility that works for all layouts.
-    Args:
-        torch_op: PyTorch operation (e.g., torch.ops.aten.detach.default)
+    class TensorCoreNVFP4Layout:
+        pass
 
-    Example:
-        @register_generic_util(torch.ops.aten.detach.default)
-        def generic_detach(func, args, kwargs):
-            # Works for any layout
-            ...
-    """
-    def decorator(handler_func):
-        _GENERIC_UTILS[torch_op] = handler_func
-        return handler_func
-    return decorator
+    def register_layout_class(name, cls):
+        pass
 
+    def get_layout_class(name):
+        return None
 
-def _get_layout_from_args(args):
-    for arg in args:
-        if isinstance(arg, QuantizedTensor):
-            return arg._layout_type
-        elif isinstance(arg, (list, tuple)):
-            for item in arg:
-                if isinstance(item, QuantizedTensor):
-                    return item._layout_type
-    return None
-
-
-def _move_layout_params_to_device(params, device):
-    new_params = {}
-    for k, v in params.items():
-        if isinstance(v, torch.Tensor):
-            new_params[k] = v.to(device=device)
-        else:
-            new_params[k] = v
-    return new_params
-
-
-def _copy_layout_params(params):
-    new_params = {}
-    for k, v in params.items():
-        if isinstance(v, torch.Tensor):
-            new_params[k] = v.clone()
-        else:
-            new_params[k] = v
-    return new_params
-
-def _copy_layout_params_inplace(src, dst, non_blocking=False):
-    for k, v in src.items():
-        if isinstance(v, torch.Tensor):
-            dst[k].copy_(v, non_blocking=non_blocking)
-        else:
-            dst[k] = v
-
-class QuantizedLayout:
-    """
-    Base class for quantization layouts.
-
-    A layout encapsulates the format-specific logic for quantization/dequantization
-    and provides a uniform interface for extracting raw tensors needed for computation.
-
-    New quantization formats should subclass this and implement the required methods.
-    """
-    @classmethod
-    def quantize(cls, tensor, **kwargs) -> Tuple[torch.Tensor, Dict]:
-        raise NotImplementedError(f"{cls.__name__} must implement quantize()")
-
-    @staticmethod
-    def dequantize(qdata, **layout_params) -> torch.Tensor:
-        raise NotImplementedError("TensorLayout must implement dequantize()")
-
-    @classmethod
-    def get_plain_tensors(cls, qtensor) -> torch.Tensor:
-        raise NotImplementedError(f"{cls.__name__} must implement get_plain_tensors()")
-
-
-class QuantizedTensor(torch.Tensor):
-    """
-    Universal quantized tensor that works with any layout.
-
-    This tensor subclass uses a pluggable layout system to support multiple
-    quantization formats (FP8, INT4, INT8, etc.) without code duplication.
-
-    The layout_type determines format-specific behavior, while common operations
-    (detach, clone, to) are handled generically.
-
-    Attributes:
-        _qdata: The quantized tensor data
-        _layout_type: Layout class (e.g., TensorCoreFP8Layout)
-        _layout_params: Dict with layout-specific params (scale, zero_point, etc.)
-    """
-
-    @staticmethod
-    def __new__(cls, qdata, layout_type, layout_params):
-        """
-        Create a quantized tensor.
-
-        Args:
-            qdata: The quantized data tensor
-            layout_type: Layout class (subclass of QuantizedLayout)
-            layout_params: Dict with layout-specific parameters
-        """
-        return torch.Tensor._make_wrapper_subclass(cls, qdata.shape, device=qdata.device, dtype=qdata.dtype, requires_grad=False)
-
-    def __init__(self, qdata, layout_type, layout_params):
-        self._qdata = qdata
-        self._layout_type = layout_type
-        self._layout_params = layout_params
-
-    def __repr__(self):
-        layout_name = self._layout_type
-        param_str = ", ".join(f"{k}={v}" for k, v in list(self._layout_params.items())[:2])
-        return f"QuantizedTensor(shape={self.shape}, layout={layout_name}, {param_str})"
-
-    @property
-    def layout_type(self):
-        return self._layout_type
-
-    def __tensor_flatten__(self):
-        """
-        Tensor flattening protocol for proper device movement.
-        """
-        inner_tensors = ["_qdata"]
-        ctx = {
-            "layout_type": self._layout_type,
-        }
-
-        tensor_params = {}
-        non_tensor_params = {}
-        for k, v in self._layout_params.items():
-            if isinstance(v, torch.Tensor):
-                tensor_params[k] = v
-            else:
-                non_tensor_params[k] = v
-
-        ctx["tensor_param_keys"] = list(tensor_params.keys())
-        ctx["non_tensor_params"] = non_tensor_params
-
-        for k, v in tensor_params.items():
-            attr_name = f"_layout_param_{k}"
-            object.__setattr__(self, attr_name, v)
-            inner_tensors.append(attr_name)
-
-        return inner_tensors, ctx
-
-    @staticmethod
-    def __tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride):
-        """
-        Tensor unflattening protocol for proper device movement.
-        Reconstructs the QuantizedTensor after device movement.
-        """
-        layout_type = ctx["layout_type"]
-        layout_params = dict(ctx["non_tensor_params"])
-
-        for key in ctx["tensor_param_keys"]:
-            attr_name = f"_layout_param_{key}"
-            layout_params[key] = inner_tensors[attr_name]
-
-        return QuantizedTensor(inner_tensors["_qdata"], layout_type, layout_params)
-
-    @classmethod
-    def from_float(cls, tensor, layout_type, **quantize_kwargs) -> 'QuantizedTensor':
-        qdata, layout_params = LAYOUTS[layout_type].quantize(tensor, **quantize_kwargs)
-        return cls(qdata, layout_type, layout_params)
-
-    def dequantize(self) -> torch.Tensor:
-        return LAYOUTS[self._layout_type].dequantize(self._qdata, **self._layout_params)
-
-    @classmethod
-    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
-        kwargs = kwargs or {}
-
-        # Step 1: Check generic utilities first (detach, clone, to, etc.)
-        if func in _GENERIC_UTILS:
-            return _GENERIC_UTILS[func](func, args, kwargs)
-
-        # Step 2: Check layout-specific handlers (linear, matmul, etc.)
-        layout_type = _get_layout_from_args(args)
-        if layout_type and func in _LAYOUT_REGISTRY:
-            handler = _LAYOUT_REGISTRY[func].get(layout_type)
-            if handler:
-                return handler(func, args, kwargs)
-
-        # Step 3: Fallback to dequantization
-        if isinstance(args[0] if args else None, QuantizedTensor):
-            logging.info(f"QuantizedTensor: Unhandled operation {func}, falling back to dequantization. kwargs={kwargs}")
-        return cls._dequant_and_fallback(func, args, kwargs)
-
-    @classmethod
-    def _dequant_and_fallback(cls, func, args, kwargs):
-        def dequant_arg(arg):
-            if isinstance(arg, QuantizedTensor):
-                return arg.dequantize()
-            elif isinstance(arg, (list, tuple)):
-                return type(arg)(dequant_arg(a) for a in arg)
-            return arg
-
-        new_args = dequant_arg(args)
-        new_kwargs = dequant_arg(kwargs)
-        return func(*new_args, **new_kwargs)
-
+import comfy.float
 
 # ==============================================================================
-# Generic Utilities (Layout-Agnostic Operations)
+# FP8 Layouts with Comfy-Specific Extensions
 # ==============================================================================
 
-def _create_transformed_qtensor(qt, transform_fn):
-    new_data = transform_fn(qt._qdata)
-    new_params = _copy_layout_params(qt._layout_params)
-    return QuantizedTensor(new_data, qt._layout_type, new_params)
+class _TensorCoreFP8LayoutBase(_CKFp8Layout):
+    FP8_DTYPE = None  # Must be overridden in subclass
 
-
-def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
-    if target_dtype is not None and target_dtype != qt.dtype:
-        logging.warning(
-            f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
-            f"but not supported for quantized tensors. Ignoring dtype."
-        )
-
-    if target_layout is not None and target_layout != torch.strided:
-        logging.warning(
-            f"QuantizedTensor: layout change requested to {target_layout}, "
-            f"but not supported. Ignoring layout."
-        )
-
-    # Handle device transfer
-    current_device = qt._qdata.device
-    if target_device is not None:
-        # Normalize device for comparison
-        if isinstance(target_device, str):
-            target_device = torch.device(target_device)
-        if isinstance(current_device, str):
-            current_device = torch.device(current_device)
-
-        if target_device != current_device:
-            logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
-            new_q_data = qt._qdata.to(device=target_device)
-            new_params = _move_layout_params_to_device(qt._layout_params, target_device)
-            new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
-            logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
-            return new_qt
-
-    logging.debug(f"QuantizedTensor.{op_name}: No device change needed, returning original")
-    return qt
-
-
-@register_generic_util(torch.ops.aten.detach.default)
-def generic_detach(func, args, kwargs):
-    """Detach operation - creates a detached copy of the quantized tensor."""
-    qt = args[0]
-    if isinstance(qt, QuantizedTensor):
-        return _create_transformed_qtensor(qt, lambda x: x.detach())
-    return func(*args, **kwargs)
-
-
-@register_generic_util(torch.ops.aten.clone.default)
-def generic_clone(func, args, kwargs):
-    """Clone operation - creates a deep copy of the quantized tensor."""
-    qt = args[0]
-    if isinstance(qt, QuantizedTensor):
-        return _create_transformed_qtensor(qt, lambda x: x.clone())
-    return func(*args, **kwargs)
-
-
-@register_generic_util(torch.ops.aten._to_copy.default)
-def generic_to_copy(func, args, kwargs):
-    """Device/dtype transfer operation - handles .to(device) calls."""
-    qt = args[0]
-    if isinstance(qt, QuantizedTensor):
-        return _handle_device_transfer(
-            qt,
-            target_device=kwargs.get('device', None),
-            target_dtype=kwargs.get('dtype', None),
-            op_name="_to_copy"
-        )
-    return func(*args, **kwargs)
-
-
-@register_generic_util(torch.ops.aten.to.dtype_layout)
-def generic_to_dtype_layout(func, args, kwargs):
-    """Handle .to(device) calls using the dtype_layout variant."""
-    qt = args[0]
-    if isinstance(qt, QuantizedTensor):
-        return _handle_device_transfer(
-            qt,
-            target_device=kwargs.get('device', None),
-            target_dtype=kwargs.get('dtype', None),
-            target_layout=kwargs.get('layout', None),
-            op_name="to"
-        )
-    return func(*args, **kwargs)
-
-
-@register_generic_util(torch.ops.aten.copy_.default)
-def generic_copy_(func, args, kwargs):
-    qt_dest = args[0]
-    src = args[1]
-    non_blocking = args[2] if len(args) > 2 else False
-    if isinstance(qt_dest, QuantizedTensor):
-        if isinstance(src, QuantizedTensor):
-            # Copy from another quantized tensor
-            qt_dest._qdata.copy_(src._qdata, non_blocking=non_blocking)
-            qt_dest._layout_type = src._layout_type
-            _copy_layout_params_inplace(src._layout_params, qt_dest._layout_params, non_blocking=non_blocking)
-        else:
-            # Copy from regular tensor - just copy raw data
-            qt_dest._qdata.copy_(src)
-        return qt_dest
-    return func(*args, **kwargs)
-
-
-@register_generic_util(torch.ops.aten._has_compatible_shallow_copy_type.default)
-def generic_has_compatible_shallow_copy_type(func, args, kwargs):
-    return True
-
-
-@register_generic_util(torch.ops.aten.empty_like.default)
-def generic_empty_like(func, args, kwargs):
-    """Empty_like operation - creates an empty tensor with the same quantized structure."""
-    qt = args[0]
-    if isinstance(qt, QuantizedTensor):
-        # Create empty tensor with same shape and dtype as the quantized data
-        hp_dtype = kwargs.pop('dtype', qt._layout_params["orig_dtype"])
-        new_qdata = torch.empty_like(qt._qdata, **kwargs)
-
-        # Handle device transfer for layout params
-        target_device = kwargs.get('device', new_qdata.device)
-        new_params = _move_layout_params_to_device(qt._layout_params, target_device)
-
-        # Update orig_dtype if dtype is specified
-        new_params['orig_dtype'] = hp_dtype
-
-        return QuantizedTensor(new_qdata, qt._layout_type, new_params)
-    return func(*args, **kwargs)
-
-# ==============================================================================
-# FP8 Layout + Operation Handlers
-# ==============================================================================
-class TensorCoreFP8Layout(QuantizedLayout):
-    """
-    Storage format:
-    - qdata: FP8 tensor (torch.float8_e4m3fn or torch.float8_e5m2)
-    - scale: Scalar tensor (float32) for dequantization
-    - orig_dtype: Original dtype before quantization (for casting back)
-    """
     @classmethod
-    def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn):
+    def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
+        if cls.FP8_DTYPE is None:
+            raise NotImplementedError(f"{cls.__name__} must define FP8_DTYPE")
+
         orig_dtype = tensor.dtype
+        orig_shape = tuple(tensor.shape)
+
+        if isinstance(scale, str) and scale == "recalculate":
+            scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(cls.FP8_DTYPE).max
+            if tensor.dtype not in [torch.float32, torch.bfloat16]:  # Prevent scale from being too small
+                tensor_info = torch.finfo(tensor.dtype)
+                scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max))
 
         if scale is None:
-            scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
-
+            scale = torch.ones((), device=tensor.device, dtype=torch.float32)
         if not isinstance(scale, torch.Tensor):
-            scale = torch.tensor(scale)
-        scale = scale.to(device=tensor.device, dtype=torch.float32)
+            scale = torch.tensor(scale, device=tensor.device, dtype=torch.float32)
 
-        tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
-        # TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
-        # lp_amax = torch.finfo(dtype).max
-        # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
-        qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
+        if stochastic_rounding > 0:
+            if inplace_ops:
+                tensor *= (1.0 / scale).to(tensor.dtype)
+            else:
+                tensor = tensor * (1.0 / scale).to(tensor.dtype)
+            qdata = comfy.float.stochastic_rounding(tensor, dtype=cls.FP8_DTYPE, seed=stochastic_rounding)
+        else:
+            qdata = ck.quantize_per_tensor_fp8(tensor, scale, cls.FP8_DTYPE)
 
-        layout_params = {
-            'scale': scale,
-            'orig_dtype': orig_dtype
-        }
-        return qdata, layout_params
+        params = cls.Params(scale=scale.float(), orig_dtype=orig_dtype, orig_shape=orig_shape)
+        return qdata, params
 
-    @staticmethod
-    def dequantize(qdata, scale, orig_dtype, **kwargs):
-        plain_tensor = torch.ops.aten._to_copy.default(qdata, dtype=orig_dtype)
-        return plain_tensor * scale
 
-    @classmethod
-    def get_plain_tensors(cls, qtensor):
-        return qtensor._qdata, qtensor._layout_params['scale']
+class TensorCoreFP8E4M3Layout(_TensorCoreFP8LayoutBase):
+    FP8_DTYPE = torch.float8_e4m3fn
+
+
+class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
+    FP8_DTYPE = torch.float8_e5m2
+
+
+# Backward compatibility alias - default to E4M3
+TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
+
+
+# ==============================================================================
+# Registry
+# ==============================================================================
+
+register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
+register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
+register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
+register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
 
 QUANT_ALGOS = {
     "float8_e4m3fn": {
         "storage_t": torch.float8_e4m3fn,
         "parameters": {"weight_scale", "input_scale"},
-        "comfy_tensor_layout": "TensorCoreFP8Layout",
+        "comfy_tensor_layout": "TensorCoreFP8E4M3Layout",
+    },
+    "float8_e5m2": {
+        "storage_t": torch.float8_e5m2,
+        "parameters": {"weight_scale", "input_scale"},
+        "comfy_tensor_layout": "TensorCoreFP8E5M2Layout",
+    },
+    "nvfp4": {
+        "storage_t": torch.uint8,
+        "parameters": {"weight_scale", "weight_scale_2", "input_scale"},
+        "comfy_tensor_layout": "TensorCoreNVFP4Layout",
+        "group_size": 16,
     },
 }
 
-LAYOUTS = {
-    "TensorCoreFP8Layout": TensorCoreFP8Layout,
-}
 
+# ==============================================================================
+# Re-exports for backward compatibility
+# ==============================================================================
 
-@register_layout_op(torch.ops.aten.linear.default, "TensorCoreFP8Layout")
-def fp8_linear(func, args, kwargs):
-    input_tensor = args[0]
-    weight = args[1]
-    bias = args[2] if len(args) > 2 else None
-
-    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
-        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
-        plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
-
-        out_dtype = kwargs.get("out_dtype")
-        if out_dtype is None:
-            out_dtype = input_tensor._layout_params['orig_dtype']
-
-        weight_t = plain_weight.t()
-
-        tensor_2d = False
-        if len(plain_input.shape) == 2:
-            tensor_2d = True
-            plain_input = plain_input.unsqueeze(1)
-
-        input_shape = plain_input.shape
-        if len(input_shape) != 3:
-            return None
-
-        try:
-            output = torch._scaled_mm(
-                plain_input.reshape(-1, input_shape[2]).contiguous(),
-                weight_t,
-                bias=bias,
-                scale_a=scale_a,
-                scale_b=scale_b,
-                out_dtype=out_dtype,
-            )
-
-            if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
-                output = output[0]
-
-            if not tensor_2d:
-                output = output.reshape((-1, input_shape[1], weight.shape[0]))
-
-            if output.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
-                output_scale = scale_a * scale_b
-                output_params = {
-                    'scale': output_scale,
-                    'orig_dtype': input_tensor._layout_params['orig_dtype']
-                }
-                return QuantizedTensor(output, "TensorCoreFP8Layout", output_params)
-            else:
-                return output
-
-        except Exception as e:
-            raise RuntimeError(f"FP8 _scaled_mm failed, falling back to dequantization: {e}")
-
-    # Case 2: DQ Fallback
-    if isinstance(weight, QuantizedTensor):
-        weight = weight.dequantize()
-    if isinstance(input_tensor, QuantizedTensor):
-        input_tensor = input_tensor.dequantize()
-
-    return torch.nn.functional.linear(input_tensor, weight, bias)
-
-def fp8_mm_(input_tensor, weight, bias=None, out_dtype=None):
-    if out_dtype is None:
-        out_dtype = input_tensor._layout_params['orig_dtype']
-
-    plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
-    plain_weight, scale_b = TensorCoreFP8Layout.get_plain_tensors(weight)
-
-    output = torch._scaled_mm(
-        plain_input.contiguous(),
-        plain_weight,
-        bias=bias,
-        scale_a=scale_a,
-        scale_b=scale_b,
-        out_dtype=out_dtype,
-    )
-
-    if isinstance(output, tuple):  # TODO: remove when we drop support for torch 2.4
-        output = output[0]
-    return output
-
-@register_layout_op(torch.ops.aten.addmm.default, "TensorCoreFP8Layout")
-def fp8_addmm(func, args, kwargs):
-    input_tensor = args[1]
-    weight = args[2]
-    bias = args[0]
-
-    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
-        return fp8_mm_(input_tensor, weight, bias=bias, out_dtype=kwargs.get("out_dtype", None))
-
-    a = list(args)
-    if isinstance(args[0], QuantizedTensor):
-        a[0] = args[0].dequantize()
-    if isinstance(args[1], QuantizedTensor):
-        a[1] = args[1].dequantize()
-    if isinstance(args[2], QuantizedTensor):
-        a[2] = args[2].dequantize()
-
-    return func(*a, **kwargs)
-
-@register_layout_op(torch.ops.aten.mm.default, "TensorCoreFP8Layout")
-def fp8_mm(func, args, kwargs):
-    input_tensor = args[0]
-    weight = args[1]
-
-    if isinstance(input_tensor, QuantizedTensor) and isinstance(weight, QuantizedTensor):
-        return fp8_mm_(input_tensor, weight, bias=None, out_dtype=kwargs.get("out_dtype", None))
-
-    a = list(args)
-    if isinstance(args[0], QuantizedTensor):
-        a[0] = args[0].dequantize()
-    if isinstance(args[1], QuantizedTensor):
-        a[1] = args[1].dequantize()
-    return func(*a, **kwargs)
-
-@register_layout_op(torch.ops.aten.view.default, "TensorCoreFP8Layout")
-@register_layout_op(torch.ops.aten.t.default, "TensorCoreFP8Layout")
-def fp8_func(func, args, kwargs):
-    input_tensor = args[0]
-    if isinstance(input_tensor, QuantizedTensor):
-        plain_input, scale_a = TensorCoreFP8Layout.get_plain_tensors(input_tensor)
-        ar = list(args)
-        ar[0] = plain_input
-        return QuantizedTensor(func(*ar, **kwargs), "TensorCoreFP8Layout", input_tensor._layout_params)
-    return func(*args, **kwargs)
+__all__ = [
+    "QuantizedTensor",
+    "QuantizedLayout",
+    "TensorCoreFP8Layout",
+    "TensorCoreFP8E4M3Layout",
+    "TensorCoreFP8E5M2Layout",
+    "TensorCoreNVFP4Layout",
+    "QUANT_ALGOS",
+    "register_layout_op",
+]

comfy/sampler_helpers.py

@@ -122,20 +122,20 @@ def estimate_memory(model, noise_shape, conds):
     minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
     return memory_required, minimum_memory_required
 
-def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
         _prepare_sampling,
         comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
     )
-    return executor.execute(model, noise_shape, conds, model_options=model_options)
+    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load)
 
-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
+def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
     memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory, force_full_load=force_full_load)
     real_model = model.model
 
     return real_model, conds, models

comfy/samplers.py

@@ -720,7 +720,7 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2", "exp_heun_2_x0", "exp_heun_2_x0_sde", "dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
@@ -984,9 +984,6 @@ class CFGGuider:
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
-        if denoise_mask is not None:
-            denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
-
         noise = noise.to(device)
         latent_image = latent_image.to(device)
         sigmas = sigmas.to(device)
@@ -1013,6 +1010,24 @@ class CFGGuider:
         else:
             latent_shapes = [latent_image.shape]
 
+        if denoise_mask is not None:
+            if denoise_mask.is_nested:
+                denoise_masks = denoise_mask.unbind()
+                denoise_masks = denoise_masks[:len(latent_shapes)]
+            else:
+                denoise_masks = [denoise_mask]
+
+            for i in range(len(denoise_masks), len(latent_shapes)):
+                denoise_masks.append(torch.ones(latent_shapes[i]))
+
+            for i in range(len(denoise_masks)):
+                denoise_masks[i] = comfy.sampler_helpers.prepare_mask(denoise_masks[i], latent_shapes[i], self.model_patcher.load_device)
+
+            if len(denoise_masks) > 1:
+                denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)
+            else:
+                denoise_mask = denoise_masks[0]
+
         self.conds = {}
         for k in self.original_conds:
             self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))

comfy/sd.py

@@ -52,6 +52,11 @@ import comfy.text_encoders.ace
 import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
+import comfy.text_encoders.z_image
+import comfy.text_encoders.ovis
+import comfy.text_encoders.kandinsky5
+import comfy.text_encoders.jina_clip_2
+import comfy.text_encoders.newbie
 
 import comfy.model_patcher
 import comfy.lora
@@ -59,6 +64,8 @@ import comfy.lora_convert
 import comfy.hooks
 import comfy.t2i_adapter.adapter
 import comfy.taesd.taesd
+import comfy.taesd.taehv
+import comfy.latent_formats
 
 import comfy.ldm.flux.redux
 
@@ -94,7 +101,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
         if no_init:
             return
         params = target.params.copy()
@@ -122,9 +129,32 @@ class CLIP:
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+        #Match torch.float32 hardcode upcast in TE implemention
+        self.patcher.set_model_compute_dtype(torch.float32)
         self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
         self.patcher.is_clip = True
         self.apply_hooks_to_conds = None
+        if len(state_dict) > 0:
+            if isinstance(state_dict, list):
+                for c in state_dict:
+                    m, u = self.load_sd(c)
+                    if len(m) > 0:
+                        logging.warning("clip missing: {}".format(m))
+
+                    if len(u) > 0:
+                        logging.debug("clip unexpected: {}".format(u))
+            else:
+                m, u = self.load_sd(state_dict, full_model=True)
+                if len(m) > 0:
+                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+                    if len(m_filter) > 0:
+                        logging.warning("clip missing: {}".format(m))
+                    else:
+                        logging.debug("clip missing: {}".format(m))
+
+                if len(u) > 0:
+                    logging.debug("clip unexpected {}:".format(u))
+
         if params['device'] == load_device:
             model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
@@ -189,6 +219,7 @@ class CLIP:
                 self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
             self.load_model()
+            self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
             all_hooks.reset()
             self.patcher.patch_hooks(None)
             if show_pbar:
@@ -236,6 +267,7 @@ class CLIP:
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
+        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         o = self.cond_stage_model.encode_token_weights(tokens)
         cond, pooled = o[:2]
         if return_dict:
@@ -291,6 +323,7 @@ class VAE:
         self.latent_channels = 4
         self.latent_dim = 2
         self.output_channels = 3
+        self.pad_channel_value = None
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
@@ -356,7 +389,7 @@ class VAE:
 
                     self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
                     self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype)
-                elif sd['decoder.conv_in.weight'].shape[1] == 32:
+                elif sd['decoder.conv_in.weight'].shape[1] == 32 and sd['decoder.conv_in.weight'].ndim == 5:
                     ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True, "refiner_vae": False}
                     self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
                     self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
@@ -382,6 +415,17 @@ class VAE:
                         self.upscale_ratio = 4
 
                     self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    if 'decoder.post_quant_conv.weight' in sd:
+                        sd = comfy.utils.state_dict_prefix_replace(sd, {"decoder.post_quant_conv.": "post_quant_conv.", "encoder.quant_conv.": "quant_conv."})
+
+                    if 'bn.running_mean' in sd:
+                        ddconfig["batch_norm_latent"] = True
+                        self.downscale_ratio *= 2
+                        self.upscale_ratio *= 2
+                        self.latent_channels *= 4
+                        old_memory_used_decode = self.memory_used_decode
+                        self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) *  4.0
+
                     if 'post_quant_conv.weight' in sd:
                         self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
                     else:
@@ -394,6 +438,7 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
                 self.latent_channels = 64
                 self.output_channels = 2
+                self.pad_channel_value = "replicate"
                 self.upscale_ratio = 2048
                 self.downscale_ratio =  2048
                 self.latent_dim = 1
@@ -441,20 +486,20 @@ class VAE:
             elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32:
                 ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True}
                 ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
-                self.latent_channels = 64
+                self.latent_channels = 32
                 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
                 self.upscale_index_formula = (4, 16, 16)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
                 self.downscale_index_formula = (4, 16, 16)
                 self.latent_dim = 3
-                self.not_video = True
+                self.not_video = False
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
                 self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"},
                                                             encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig},
                                                             decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
 
-                self.memory_used_encode = lambda shape, dtype: (1400 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
-                self.memory_used_decode = lambda shape, dtype: (1400 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
             elif "decoder.conv_in.conv.weight" in sd:
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                 ddconfig["conv3d"] = True
@@ -466,8 +511,10 @@ class VAE:
                 self.latent_dim = 3
                 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
-                self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                #This is likely to significantly over-estimate with single image or low frame counts as the
+                #implementation is able to completely skip caching. Rework if used as an image only VAE
+                self.memory_used_decode = lambda shape, dtype: (2800 * min(8, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1400 * min(9, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
             elif "decoder.unpatcher3d.wavelets" in sd:
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)
@@ -496,17 +543,22 @@ class VAE:
                     self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
                     self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
                 else:  # Wan 2.1 VAE
+                    dim = sd["decoder.head.0.gamma"].shape[0]
                     self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                     self.upscale_index_formula = (4, 8, 8)
                     self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                     self.downscale_index_formula = (4, 8, 8)
                     self.latent_dim = 3
                     self.latent_channels = 16
-                    ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    self.output_channels = sd["encoder.conv1.weight"].shape[1]
+                    self.pad_channel_value = 1.0
+                    ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "image_channels": self.output_channels, "dropout": 0.0}
                     self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
-                    self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
-                    self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
+                    self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype)
+
+
             # Hunyuan 3d v2 2.0 & 2.1
             elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
 
@@ -536,6 +588,7 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype)
                 self.latent_channels = 8
                 self.output_channels = 2
+                self.pad_channel_value = "replicate"
                 self.upscale_ratio = 4096
                 self.downscale_ratio = 4096
                 self.latent_dim = 2
@@ -572,6 +625,35 @@ class VAE:
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float32]
                 self.crop_input = False
+            elif "decoder.22.bias" in sd: # taehv, taew and lighttae
+                self.latent_channels = sd["decoder.1.weight"].shape[1]
+                self.latent_dim = 3
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                self.upscale_index_formula = (4, 16, 16)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                self.downscale_index_formula = (4, 16, 16)
+                if self.latent_channels == 48: # Wan 2.2
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=None) # taehv doesn't need scaling
+                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
+                    self.process_output = lambda image: image
+                    self.memory_used_decode = lambda shape, dtype: (1800 * (max(1, (shape[-3] ** 0.7 * 0.1)) * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype))
+                elif self.latent_channels == 32 and sd["decoder.22.bias"].shape[0] == 12: # lighttae_hv15
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=comfy.latent_formats.HunyuanVideo15)
+                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
+                    self.memory_used_decode = lambda shape, dtype: (1200 * (max(1, (shape[-3] ** 0.7 * 0.05)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
+                else:
+                    if sd["decoder.1.weight"].dtype == torch.float16: # taehv currently only available in float16, so assume it's not lighttaew2_1 as otherwise state dicts are identical
+                        latent_format=comfy.latent_formats.HunyuanVideo
+                    else:
+                        latent_format=None # lighttaew2_1 doesn't need scaling
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=latent_format)
+                    self.process_input = self.process_output = lambda image: image
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                    self.upscale_index_formula = (4, 8, 8)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                    self.downscale_index_formula = (4, 8, 8)
+                    self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype))
+                    self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -615,17 +697,28 @@ class VAE:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
 
     def vae_encode_crop_pixels(self, pixels):
-        if not self.crop_input:
-            return pixels
+        if self.crop_input:
+            downscale_ratio = self.spacial_compression_encode()
 
-        downscale_ratio = self.spacial_compression_encode()
+            dims = pixels.shape[1:-1]
+            for d in range(len(dims)):
+                x = (dims[d] // downscale_ratio) * downscale_ratio
+                x_offset = (dims[d] % downscale_ratio) // 2
+                if x != dims[d]:
+                    pixels = pixels.narrow(d + 1, x_offset, x)
 
-        dims = pixels.shape[1:-1]
-        for d in range(len(dims)):
-            x = (dims[d] // downscale_ratio) * downscale_ratio
-            x_offset = (dims[d] % downscale_ratio) // 2
-            if x != dims[d]:
-                pixels = pixels.narrow(d + 1, x_offset, x)
+        if pixels.shape[-1] > self.output_channels:
+            pixels = pixels[..., :self.output_channels]
+        elif pixels.shape[-1] < self.output_channels:
+            if self.pad_channel_value is not None:
+                if isinstance(self.pad_channel_value, str):
+                    mode = self.pad_channel_value
+                    value = None
+                else:
+                    mode = "constant"
+                    value = self.pad_channel_value
+
+                pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value)
         return pixels
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
@@ -696,6 +789,8 @@ class VAE:
         self.throw_exception_if_invalid()
         pixel_samples = None
         do_tile = False
+        if self.latent_dim == 2 and samples_in.ndim == 5:
+            samples_in = samples_in[:, :, 0]
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -911,12 +1006,20 @@ class CLIPType(Enum):
     OMNIGEN2 = 17
     QWEN_IMAGE = 18
     HUNYUAN_IMAGE = 19
+    HUNYUAN_VIDEO_15 = 20
+    OVIS = 21
+    KANDINSKY5 = 22
+    KANDINSKY5_IMAGE = 23
+    NEWBIE = 24
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
     for p in ckpt_paths:
-        clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
+        sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)
+        if model_options.get("custom_operations", None) is None:
+            sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata)
+        clip_data.append(sd)
     return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
 
 
@@ -934,6 +1037,13 @@ class TEModel(Enum):
     QWEN25_7B = 11
     BYT5_SMALL_GLYPH = 12
     GEMMA_3_4B = 13
+    MISTRAL3_24B = 14
+    MISTRAL3_24B_PRUNED_FLUX2 = 15
+    QWEN3_4B = 16
+    QWEN3_2B = 17
+    GEMMA_3_12B = 18
+    JINA_CLIP_2 = 19
+
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -942,6 +1052,8 @@ def detect_te_model(sd):
         return TEModel.CLIP_H
     if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
         return TEModel.CLIP_L
+    if "model.encoder.layers.0.mixer.Wqkv.weight" in sd:
+        return TEModel.JINA_CLIP_2
     if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
         weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
         if weight.shape[-1] == 4096:
@@ -956,6 +1068,8 @@ def detect_te_model(sd):
             return TEModel.BYT5_SMALL_GLYPH
         return TEModel.T5_BASE
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
+        if 'model.layers.47.self_attn.q_norm.weight' in sd:
+            return TEModel.GEMMA_3_12B
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
             return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
@@ -966,6 +1080,18 @@ def detect_te_model(sd):
         if weight.shape[0] == 512:
             return TEModel.QWEN25_7B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
+        weight = sd['model.layers.0.post_attention_layernorm.weight']
+        if 'model.layers.0.self_attn.q_norm.weight' in sd:
+            if weight.shape[0] == 2560:
+                return TEModel.QWEN3_4B
+            elif weight.shape[0] == 2048:
+                return TEModel.QWEN3_2B
+        if weight.shape[0] == 5120:
+            if "model.layers.39.post_attention_layernorm.weight" in sd:
+                return TEModel.MISTRAL3_24B
+            else:
+                return TEModel.MISTRAL3_24B_PRUNED_FLUX2
+
         return TEModel.LLAMA3_8
     return None
 
@@ -1015,7 +1141,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             elif clip_type == CLIPType.HIDREAM:
-                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else:
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
@@ -1039,7 +1165,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
             elif clip_type == CLIPType.HIDREAM:
                 clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
-                                                                        clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
+                                                                        clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
@@ -1068,7 +1194,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
-                                                                        clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
+                                                                        clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
         elif te_model == TEModel.QWEN25_3B:
             clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
@@ -1080,13 +1206,26 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             else:
                 clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer
+        elif te_model == TEModel.MISTRAL3_24B or te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2:
+            clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2)
+            clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer
+            tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None)
+        elif te_model == TEModel.QWEN3_4B:
+            clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer
+        elif te_model == TEModel.QWEN3_2B:
+            clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
+        elif te_model == TEModel.JINA_CLIP_2:
+            clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
+            clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
         else:
             # clip_l
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             elif clip_type == CLIPType.HIDREAM:
-                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else:
                 clip_target.clip = sd1_clip.SD1ClipModel
@@ -1126,6 +1265,30 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif clip_type == CLIPType.HUNYUAN_IMAGE:
             clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
+        elif clip_type == CLIPType.HUNYUAN_VIDEO_15:
+            clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer
+        elif clip_type == CLIPType.KANDINSKY5:
+            clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer
+        elif clip_type == CLIPType.KANDINSKY5_IMAGE:
+            clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
+        elif clip_type == CLIPType.LTXV:
+            clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif clip_type == CLIPType.NEWBIE:
+            clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer
+            if "model.layers.0.self_attn.q_norm.weight" in clip_data[0]:
+                clip_data_gemma = clip_data[0]
+                clip_data_jina = clip_data[1]
+            else:
+                clip_data_gemma = clip_data[1]
+                clip_data_jina = clip_data[0]
+            tokenizer_data["gemma_spiece_model"] = clip_data_gemma.get("spiece_model", None)
+            tokenizer_data["jina_spiece_model"] = clip_data_jina.get("spiece_model", None)
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
@@ -1141,14 +1304,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
-    for c in clip_data:
-        m, u = clip.load_sd(c)
-        if len(m) > 0:
-            logging.warning("clip missing: {}".format(m))
-
-        if len(u) > 0:
-            logging.debug("clip unexpected: {}".format(u))
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options)
     return clip
 
 def load_gligen(ckpt_path):
@@ -1207,6 +1363,10 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, diffusion_model_prefix, metadata=metadata)
+
     model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
     if model_config is None:
         logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.")
@@ -1215,18 +1375,22 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
             return None
         return (diffusion_model, None, VAE(sd={}), None)  # The VAE object is there to throw an exception if it's actually used'
 
-
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if model_config.scaled_fp8 is not None:
+    if model_config.quant_config is not None:
         weight_dtype = None
 
-    model_config.custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is not None:
+        model_config.custom_operations = custom_operations
+
     unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
 
     if unet_dtype is None:
         unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
 
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    if model_config.quant_config is not None:
+        manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
+    else:
+        manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
     if model_config.clip_vision_prefix is not None:
@@ -1244,22 +1408,33 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         vae = VAE(sd=vae_sd, metadata=metadata)
 
     if output_clip:
+        if te_model_options.get("custom_operations", None) is None:
+            scaled_fp8_list = []
+            for k in list(sd.keys()):  # Convert scaled fp8 to mixed ops
+                if k.endswith(".scaled_fp8"):
+                    scaled_fp8_list.append(k[:-len("scaled_fp8")])
+
+            if len(scaled_fp8_list) > 0:
+                out_sd = {}
+                for k in sd:
+                    skip = False
+                    for pref in scaled_fp8_list:
+                        skip = skip or k.startswith(pref)
+                    if not skip:
+                        out_sd[k] = sd[k]
+
+                for pref in scaled_fp8_list:
+                    quant_sd, qmetadata = comfy.utils.convert_old_quants(sd, pref, metadata={})
+                    for k in quant_sd:
+                        out_sd[k] = quant_sd[k]
+                    sd = out_sd
+
         clip_target = model_config.clip_target(state_dict=sd)
         if clip_target is not None:
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
                 parameters = comfy.utils.calculate_parameters(clip_sd)
-                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options)
-                m, u = clip.load_sd(clip_sd, full_model=True)
-                if len(m) > 0:
-                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
-                    if len(m_filter) > 0:
-                        logging.warning("clip missing: {}".format(m))
-                    else:
-                        logging.debug("clip missing: {}".format(m))
-
-                if len(u) > 0:
-                    logging.debug("clip unexpected {}:".format(u))
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options)
             else:
                 logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 
@@ -1306,6 +1481,9 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     if len(temp_sd) > 0:
         sd = temp_sd
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
     parameters = comfy.utils.calculate_parameters(sd)
     weight_dtype = comfy.utils.weight_dtype(sd)
 
@@ -1336,7 +1514,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
 
     offload_device = model_management.unet_offload_device()
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if model_config.scaled_fp8 is not None:
+    if model_config.quant_config is not None:
         weight_dtype = None
 
     if dtype is None:
@@ -1344,12 +1522,15 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     else:
         unet_dtype = dtype
 
-    if model_config.layer_quant_config is not None:
+    if model_config.quant_config is not None:
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+
+    if custom_operations is not None:
+        model_config.custom_operations = custom_operations
+
     if model_options.get("fp8_optimizations", False):
         model_config.optimizations["fp8"] = True
 
@@ -1388,6 +1569,9 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
     if vae is not None:
         vae_sd = vae.get_sd()
 
+    if metadata is None:
+        metadata = {}
+
     model_management.load_models_gpu(load_models, force_patch_weights=True)
     clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
     sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)

comfy/sd1_clip.py

@@ -90,7 +90,6 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
                  special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
                  return_projected_pooled=True, return_attention_masks=False, model_options={}):  # clip-vit-base-patch32
         super().__init__()
-        assert layer in self.LAYERS
 
         if textmodel_json_config is None:
             textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
@@ -108,19 +107,17 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             config[k] = v
 
         operations = model_options.get("custom_operations", None)
-        scaled_fp8 = None
+        quant_config = model_options.get("quantization_metadata", None)
 
         if operations is None:
-            scaled_fp8 = model_options.get("scaled_fp8", None)
-            if scaled_fp8 is not None:
-                operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
+            if quant_config is not None:
+                operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
+                logging.info("Using MixedPrecisionOps for text encoder")
             else:
                 operations = comfy.ops.manual_cast
 
         self.operations = operations
         self.transformer = model_class(config, dtype, device, self.operations)
-        if scaled_fp8 is not None:
-            self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
 
         self.num_layers = self.transformer.num_layers
 
@@ -138,6 +135,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
         self.return_attention_masks = return_attention_masks
+        self.execution_device = None
 
         if layer == "hidden":
             assert layer_idx is not None
@@ -154,7 +152,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def set_clip_options(self, options):
         layer_idx = options.get("layer", self.layer_idx)
         self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
-        if self.layer == "all":
+        self.execution_device = options.get("execution_device", self.execution_device)
+        if isinstance(self.layer, list) or self.layer == "all":
             pass
         elif layer_idx is None or abs(layer_idx) > self.num_layers:
             self.layer = "last"
@@ -166,6 +165,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer = self.options_default[0]
         self.layer_idx = self.options_default[1]
         self.return_projected_pooled = self.options_default[2]
+        self.execution_device = None
 
     def process_tokens(self, tokens, device):
         end_token = self.special_tokens.get("end", None)
@@ -249,14 +249,20 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info
 
     def forward(self, tokens):
-        device = self.transformer.get_input_embeddings().weight.device
+        if self.execution_device is None:
+            device = self.transformer.get_input_embeddings().weight.device
+        else:
+            device = self.execution_device
+
         embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
 
         attention_mask_model = None
         if self.enable_attention_masks:
             attention_mask_model = attention_mask
 
-        if self.layer == "all":
+        if isinstance(self.layer, list):
+            intermediate_output = self.layer
+        elif self.layer == "all":
             intermediate_output = "all"
         else:
             intermediate_output = self.layer_idx
@@ -460,7 +466,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, disable_weights=False, tokenizer_data={}, tokenizer_args={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
@@ -507,6 +513,8 @@ class SDTokenizer:
         self.embedding_size = embedding_size
         self.embedding_key = embedding_key
 
+        self.disable_weights = disable_weights
+
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
@@ -541,7 +549,7 @@ class SDTokenizer:
         min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
 
         text = escape_important(text)
-        if kwargs.get("disable_weights", False):
+        if kwargs.get("disable_weights", self.disable_weights):
             parsed_weights = [(text, 1.0)]
         else:
             parsed_weights = token_weights(text, 1.0)

comfy/supported_models.py

@@ -21,11 +21,14 @@ import comfy.text_encoders.ace
 import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
+import comfy.text_encoders.kandinsky5
+import comfy.text_encoders.z_image
 
 from . import supported_models_base
 from . import latent_formats
 
 from . import diffusers_convert
+import comfy.model_management
 
 class SD15(supported_models_base.BASE):
     unet_config = {
@@ -539,7 +542,7 @@ class SD3(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.SD3
 
-    memory_usage_factor = 1.2
+    memory_usage_factor = 1.6
 
     text_encoder_key_prefix = ["text_encoders."]
 
@@ -741,6 +744,37 @@ class FluxSchnell(Flux):
         out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
         return out
 
+class Flux2(Flux):
+    unet_config = {
+        "image_model": "flux2",
+    }
+
+    sampling_settings = {
+        "shift": 2.02,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux2
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = self.memory_usage_factor * (2.0 * 2.0) * 2.36
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return None # TODO
+        pref = self.text_encoder_key_prefix[0]
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
+
 class GenmoMochi(supported_models_base.BASE):
     unet_config = {
         "image_model": "mochi_preview",
@@ -802,6 +836,21 @@ class LTXV(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
 
+class LTXAV(LTXV):
+    unet_config = {
+        "image_model": "ltxav",
+    }
+
+    latent_format = latent_formats.LTXAV
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = 0.055  # TODO
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.LTXAV(self, device=device)
+        return out
+
 class HunyuanVideo(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan_video",
@@ -932,7 +981,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
-        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.CosmosPredict2(self, device=device)
@@ -963,7 +1012,7 @@ class Lumina2(supported_models_base.BASE):
         "shift": 6.0,
     }
 
-    memory_usage_factor = 1.2
+    memory_usage_factor = 1.4
 
     unet_extra_config = {}
     latent_format = latent_formats.Flux
@@ -982,6 +1031,32 @@ class Lumina2(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect))
 
+class ZImage(Lumina2):
+    unet_config = {
+        "image_model": "lumina2",
+        "dim": 3840,
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 3.0,
+    }
+
+    memory_usage_factor = 2.0
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        if comfy.model_management.extended_fp16_support():
+            self.supported_inference_dtypes = self.supported_inference_dtypes.copy()
+            self.supported_inference_dtypes.insert(1, torch.float16)
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))
+
 class WAN21_T2V(supported_models_base.BASE):
     unet_config = {
         "image_model": "wan2.1",
@@ -1236,7 +1311,7 @@ class ChromaRadiance(Chroma):
     latent_format = comfy.latent_formats.ChromaRadiance
 
     # Pixel-space model, no spatial compression for model input.
-    memory_usage_factor = 0.038
+    memory_usage_factor = 0.044
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.ChromaRadiance(self, device=device)
@@ -1279,7 +1354,7 @@ class Omnigen2(supported_models_base.BASE):
         "shift": 2.6,
     }
 
-    memory_usage_factor = 1.65 #TODO
+    memory_usage_factor = 1.95 #TODO
 
     unet_extra_config = {}
     latent_format = latent_formats.Flux
@@ -1344,7 +1419,7 @@ class HunyuanImage21(HunyuanVideo):
 
     latent_format = latent_formats.HunyuanImage21
 
-    memory_usage_factor = 7.7
+    memory_usage_factor = 8.7
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -1374,6 +1449,108 @@ class HunyuanImage21Refiner(HunyuanVideo):
         out = model_base.HunyuanImage21Refiner(self, device=device)
         return out
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage]
+class HunyuanVideo15(HunyuanVideo):
+    unet_config = {
+        "image_model": "hunyuan_video",
+        "vision_in_dim": 1152,
+    }
+
+    sampling_settings = {
+        "shift": 7.0,
+    }
+    memory_usage_factor = 4.0 #TODO
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    latent_format = latent_formats.HunyuanVideo15
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanVideo15(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))
+
+
+class HunyuanVideo15_SR_Distilled(HunyuanVideo):
+    unet_config = {
+        "image_model": "hunyuan_video",
+        "vision_in_dim": 1152,
+        "in_channels": 98,
+    }
+
+    sampling_settings = {
+        "shift": 2.0,
+    }
+    memory_usage_factor = 4.0 #TODO
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    latent_format = latent_formats.HunyuanVideo15
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanVideo15_SR_Distilled(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))
+
+
+class Kandinsky5(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "kandinsky5",
+    }
+
+    sampling_settings = {
+        "shift": 10.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.HunyuanVideo
+
+    memory_usage_factor = 1.25 #TODO
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Kandinsky5(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+class Kandinsky5Image(Kandinsky5):
+    unet_config = {
+        "image_model": "kandinsky5",
+        "model_dim": 2560,
+        "visual_embed_dim": 64,
+    }
+
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    latent_format = latent_formats.Flux
+    memory_usage_factor = 1.25 #TODO
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Kandinsky5Image(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
 
 models += [SVD_img2vid]

comfy/supported_models_base.py

@@ -17,6 +17,7 @@
 """
 
 import torch
+import logging
 from . import model_base
 from . import utils
 from . import latent_formats
@@ -49,8 +50,7 @@ class BASE:
 
     manual_cast_dtype = None
     custom_operations = None
-    scaled_fp8 = None
-    layer_quant_config = None  # Per-layer quantization configuration for mixed precision
+    quant_config = None  # quantization configuration for mixed precision
     optimizations = {"fp8": False}
 
     @classmethod
@@ -118,3 +118,7 @@ class BASE:
     def set_inference_dtype(self, dtype, manual_cast_dtype):
         self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
+
+    def __getattr__(self, name):
+        logging.warning("\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\n".format(name))
+        return None

comfy/taesd/taehv.py

@@ -0,0 +1,173 @@
+# Tiny AutoEncoder for HunyuanVideo and WanVideo https://github.com/madebyollin/taehv
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm.auto import tqdm
+from collections import namedtuple, deque
+
+import comfy.ops
+operations=comfy.ops.disable_weight_init
+
+DecoderResult = namedtuple("DecoderResult", ("frame", "memory"))
+TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index"))
+
+def conv(n_in, n_out, **kwargs):
+    return operations.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+class Clamp(nn.Module):
+    def forward(self, x):
+        return torch.tanh(x / 3) * 3
+
+class MemBlock(nn.Module):
+    def __init__(self, n_in, n_out, act_func):
+        super().__init__()
+        self.conv = nn.Sequential(conv(n_in * 2, n_out), act_func, conv(n_out, n_out), act_func, conv(n_out, n_out))
+        self.skip = operations.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.act = act_func
+    def forward(self, x, past):
+        return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x))
+
+class TPool(nn.Module):
+    def __init__(self, n_f, stride):
+        super().__init__()
+        self.stride = stride
+        self.conv = operations.Conv2d(n_f*stride,n_f, 1, bias=False)
+    def forward(self, x):
+        _NT, C, H, W = x.shape
+        return self.conv(x.reshape(-1, self.stride * C, H, W))
+
+class TGrow(nn.Module):
+    def __init__(self, n_f, stride):
+        super().__init__()
+        self.stride = stride
+        self.conv = operations.Conv2d(n_f, n_f*stride, 1, bias=False)
+    def forward(self, x):
+        _NT, C, H, W = x.shape
+        x = self.conv(x)
+        return x.reshape(-1, C, H, W)
+
+def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
+
+    B, T, C, H, W = x.shape
+    if parallel:
+        x = x.reshape(B*T, C, H, W)
+        # parallel over input timesteps, iterate over blocks
+        for b in tqdm(model, disable=not show_progress_bar):
+            if isinstance(b, MemBlock):
+                BT, C, H, W = x.shape
+                T = BT // B
+                _x = x.reshape(B, T, C, H, W)
+                mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)
+                x = b(x, mem)
+            else:
+                x = b(x)
+        BT, C, H, W = x.shape
+        T = BT // B
+        x = x.view(B, T, C, H, W)
+    else:
+        out = []
+        work_queue = deque([TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(B, T * C, H, W).chunk(T, dim=1))])
+        progress_bar = tqdm(range(T), disable=not show_progress_bar)
+        mem = [None] * len(model)
+        while work_queue:
+            xt, i = work_queue.popleft()
+            if i == 0:
+                progress_bar.update(1)
+            if i == len(model):
+                out.append(xt)
+                del xt
+            else:
+                b = model[i]
+                if isinstance(b, MemBlock):
+                    if mem[i] is None:
+                        xt_new = b(xt, xt * 0)
+                        mem[i] = xt.detach().clone()
+                    else:
+                        xt_new = b(xt, mem[i])
+                        mem[i] = xt.detach().clone()
+                    del xt
+                    work_queue.appendleft(TWorkItem(xt_new, i+1))
+                elif isinstance(b, TPool):
+                    if mem[i] is None:
+                        mem[i] = []
+                    mem[i].append(xt.detach().clone())
+                    if len(mem[i]) == b.stride:
+                        B, C, H, W = xt.shape
+                        xt = b(torch.cat(mem[i], 1).view(B*b.stride, C, H, W))
+                        mem[i] = []
+                        work_queue.appendleft(TWorkItem(xt, i+1))
+                elif isinstance(b, TGrow):
+                    xt = b(xt)
+                    NT, C, H, W = xt.shape
+                    for xt_next in reversed(xt.view(B, b.stride*C, H, W).chunk(b.stride, 1)):
+                        work_queue.appendleft(TWorkItem(xt_next, i+1))
+                    del xt
+                else:
+                    xt = b(xt)
+                    work_queue.appendleft(TWorkItem(xt, i+1))
+        progress_bar.close()
+        x = torch.stack(out, 1)
+    return x
+
+
+class TAEHV(nn.Module):
+    def __init__(self, latent_channels, parallel=False, decoder_time_upscale=(True, True), decoder_space_upscale=(True, True, True), latent_format=None, show_progress_bar=True):
+        super().__init__()
+        self.image_channels = 3
+        self.patch_size = 1
+        self.latent_channels = latent_channels
+        self.parallel = parallel
+        self.latent_format = latent_format
+        self.show_progress_bar = show_progress_bar
+        self.process_in = latent_format().process_in if latent_format is not None else (lambda x: x)
+        self.process_out = latent_format().process_out if latent_format is not None else (lambda x: x)
+        if self.latent_channels in [48, 32]: # Wan 2.2 and HunyuanVideo1.5
+            self.patch_size = 2
+        if self.latent_channels == 32: # HunyuanVideo1.5
+            act_func = nn.LeakyReLU(0.2, inplace=True)
+        else: # HunyuanVideo, Wan 2.1
+            act_func = nn.ReLU(inplace=True)
+
+        self.encoder = nn.Sequential(
+            conv(self.image_channels*self.patch_size**2, 64), act_func,
+            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            conv(64, self.latent_channels),
+        )
+        n_f = [256, 128, 64, 64]
+        self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
+        self.decoder = nn.Sequential(
+            Clamp(), conv(self.latent_channels, n_f[0]), act_func,
+            MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
+            MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
+            MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+            act_func, conv(n_f[3], self.image_channels*self.patch_size**2),
+        )
+        @property
+        def show_progress_bar(self):
+            return self._show_progress_bar
+
+        @show_progress_bar.setter
+        def show_progress_bar(self, value):
+            self._show_progress_bar = value
+
+    def encode(self, x, **kwargs):
+        if self.patch_size > 1:
+            x = F.pixel_unshuffle(x, self.patch_size)
+        x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
+        if x.shape[1] % 4 != 0:
+            # pad at end to multiple of 4
+            n_pad = 4 - x.shape[1] % 4
+            padding = x[:, -1:].repeat_interleave(n_pad, dim=1)
+            x = torch.cat([x, padding], 1)
+        x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1)
+        return self.process_out(x)
+
+    def decode(self, x, **kwargs):
+        x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
+        x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
+        if self.patch_size > 1:
+            x = F.pixel_shuffle(x, self.patch_size)
+        return x[:, self.frames_to_trim:].movedim(2, 1)

comfy/text_encoders/cosmos.py

@@ -7,10 +7,10 @@ from transformers import T5TokenizerFast
 class T5XXLModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json")
-        t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
-        if t5xxl_scaled_fp8 is not None:
+        t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
+        if t5xxl_quantization_metadata is not None:
             model_options = model_options.copy()
-            model_options["scaled_fp8"] = t5xxl_scaled_fp8
+            model_options["quantization_metadata"] = t5xxl_quantization_metadata
 
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)
 
@@ -30,12 +30,12 @@ class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
 
 
-def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def te(dtype_t5=None, t5_quantization_metadata=None):
     class CosmosTEModel_(CosmosT5XXL):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             if dtype is None:
                 dtype = dtype_t5
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/flux.py

@@ -1,10 +1,13 @@
 from comfy import sd1_clip
 import comfy.text_encoders.t5
 import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.llama
 import comfy.model_management
-from transformers import T5TokenizerFast
+from transformers import T5TokenizerFast, LlamaTokenizerFast
 import torch
 import os
+import json
+import base64
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -60,11 +63,112 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
+def flux_clip(dtype_t5=None, t5_quantization_metadata=None):
     class FluxClipModel_(FluxClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
     return FluxClipModel_
+
+def load_mistral_tokenizer(data):
+    if torch.is_tensor(data):
+        data = data.numpy().tobytes()
+
+    try:
+        from transformers.integrations.mistral import MistralConverter
+    except ModuleNotFoundError:
+        from transformers.models.pixtral.convert_pixtral_weights_to_hf import MistralConverter
+
+    mistral_vocab = json.loads(data)
+
+    special_tokens = {}
+    vocab = {}
+
+    max_vocab = mistral_vocab["config"]["default_vocab_size"]
+    max_vocab -= len(mistral_vocab["special_tokens"])
+
+    for w in mistral_vocab["vocab"]:
+        r = w["rank"]
+        if r >= max_vocab:
+            continue
+
+        vocab[base64.b64decode(w["token_bytes"])] = r
+
+    for w in mistral_vocab["special_tokens"]:
+        if "token_bytes" in w:
+            special_tokens[base64.b64decode(w["token_bytes"])] = w["rank"]
+        else:
+            special_tokens[w["token_str"]] = w["rank"]
+
+    all_special = []
+    for v in special_tokens:
+        all_special.append(v)
+
+    special_tokens.update(vocab)
+    vocab = special_tokens
+    return {"tokenizer_object": MistralConverter(vocab=vocab, additional_special_tokens=all_special).converted(), "legacy": False}
+
+class MistralTokenizerClass:
+    @staticmethod
+    def from_pretrained(path, **kwargs):
+        return LlamaTokenizerFast(**kwargs)
+
+class Mistral3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.tekken_data = tokenizer_data.get("tekken_model", None)
+        super().__init__("", pad_with_end=False, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, max_length=99999999, min_length=1, pad_left=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"tekken_model": self.tekken_data}
+
+class Flux2Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="mistral3_24b", tokenizer=Mistral3Tokenizer)
+        self.llama_template = '[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]{}[/INST]'
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        return tokens
+
+class Mistral3_24BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer=[10, 20, 30], layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        textmodel_json_config = {}
+        num_layers = model_options.get("num_layers", None)
+        if num_layers is not None:
+            textmodel_json_config["num_hidden_layers"] = num_layers
+            if num_layers < 40:
+                textmodel_json_config["final_norm"] = False
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+class Flux2TEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, name="mistral3_24b", clip_model=Mistral3_24BModel):
+        super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+
+        out = torch.stack((out[:, 0], out[:, 1], out[:, 2]), dim=1)
+        out = out.movedim(1, 2)
+        out = out.reshape(out.shape[0], out.shape[1], -1)
+        return out, pooled, extra
+
+def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False):
+    class Flux2TEModel_(Flux2TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            if pruned:
+                model_options = model_options.copy()
+                model_options["num_layers"] = 30
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Flux2TEModel_

comfy/text_encoders/genmo.py

@@ -26,12 +26,12 @@ class MochiT5Tokenizer(sd1_clip.SD1Tokenizer):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
 
 
-def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def mochi_te(dtype_t5=None, t5_quantization_metadata=None):
     class MochiTEModel_(MochiT5XXL):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             if dtype is None:
                 dtype = dtype_t5
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/hidream.py

@@ -142,14 +142,14 @@ class HiDreamTEModel(torch.nn.Module):
             return self.llama.load_sd(sd)
 
 
-def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None):
+def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5_quantization_metadata=None, llama_quantization_metadata=None):
     class HiDreamTEModel_(HiDreamTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
-            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
             super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
     return HiDreamTEModel_

comfy/text_encoders/hunyuan_image.py

@@ -40,10 +40,10 @@ class HunyuanImageTokenizer(QwenImageTokenizer):
 
 class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}):
-        llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None)
-        if llama_scaled_fp8 is not None:
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
             model_options = model_options.copy()
-            model_options["scaled_fp8"] = llama_scaled_fp8
+            model_options["quantization_metadata"] = llama_quantization_metadata
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
 
@@ -91,12 +91,12 @@ class HunyuanImageTEModel(QwenImageTEModel):
         else:
             return super().load_sd(sd)
 
-def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None):
+def te(byt5=True, dtype_llama=None, llama_quantization_metadata=None):
     class QwenImageTEModel_(HunyuanImageTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["qwen_scaled_fp8"] = llama_scaled_fp8
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/hunyuan_video.py

@@ -1,11 +1,12 @@
 from comfy import sd1_clip
 import comfy.model_management
 import comfy.text_encoders.llama
+from .hunyuan_image import HunyuanImageTokenizer
 from transformers import LlamaTokenizerFast
 import torch
 import os
 import numbers
-
+import comfy.utils
 
 def llama_detect(state_dict, prefix=""):
     out = {}
@@ -13,9 +14,9 @@ def llama_detect(state_dict, prefix=""):
     if t5_key in state_dict:
         out["dtype_llama"] = state_dict[t5_key].dtype
 
-    scaled_fp8_key = "{}scaled_fp8".format(prefix)
-    if scaled_fp8_key in state_dict:
-        out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+    quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+    if quant is not None:
+        out["llama_quantization_metadata"] = quant
 
     return out
 
@@ -27,10 +28,10 @@ class LLAMA3Tokenizer(sd1_clip.SDTokenizer):
 
 class LLAMAModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}):
-        llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
-        if llama_scaled_fp8 is not None:
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
             model_options = model_options.copy()
-            model_options["scaled_fp8"] = llama_scaled_fp8
+            model_options["quantization_metadata"] = llama_quantization_metadata
 
         textmodel_json_config = {}
         vocab_size = model_options.get("vocab_size", None)
@@ -73,6 +74,14 @@ class HunyuanVideoTokenizer:
         return {}
 
 
+class HunyuanVideo15Tokenizer(HunyuanImageTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.llama_template = "<|im_start|>system\nYou are a helpful assistant. Describe the video by detailing the following aspects:\n1. The main content and theme of the video.\n2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.\n3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.\n4. background environment, light, style and atmosphere.\n5. camera angles, movements, and transitions used in the video.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        return super().tokenize_with_weights(text, return_word_ids, prevent_empty_text=True, **kwargs)
+
 class HunyuanVideoClipModel(torch.nn.Module):
     def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
         super().__init__()
@@ -149,11 +158,11 @@ class HunyuanVideoClipModel(torch.nn.Module):
             return self.llama.load_sd(sd)
 
 
-def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None):
+def hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None):
     class HunyuanVideoClipModel_(HunyuanVideoClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
             super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
     return HunyuanVideoClipModel_

comfy/text_encoders/jina_clip_2.py

@@ -0,0 +1,219 @@
+# Jina CLIP v2 and Jina Embeddings v3 both use their modified XLM-RoBERTa architecture. Reference implementation:
+# Jina CLIP v2 (both text and vision): https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/modeling_clip.py
+# Jina XLM-RoBERTa (text only): http://huggingface.co/jinaai/xlm-roberta-flash-implementation/blob/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_xlm_roberta.py
+
+from dataclasses import dataclass
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+import comfy.model_management
+import comfy.ops
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+
+class JinaClip2Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        # The official NewBie uses max_length=8000, but Jina Embeddings v3 actually supports 8192
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='jina_clip_2', tokenizer_class=SPieceTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=False, max_length=8192, min_length=1, pad_token=1, end_token=2, tokenizer_args={"add_bos": True, "add_eos": True}, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+class JinaClip2TokenizerWrapper(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=JinaClip2Tokenizer, name="jina_clip_2")
+
+# https://huggingface.co/jinaai/jina-embeddings-v3/blob/343dbf534c76fe845f304fa5c2d1fd87e1e78918/config.json
+@dataclass
+class XLMRobertaConfig:
+    vocab_size: int = 250002
+    type_vocab_size: int = 1
+    hidden_size: int = 1024
+    num_hidden_layers: int = 24
+    num_attention_heads: int = 16
+    rotary_emb_base: float = 20000.0
+    intermediate_size: int = 4096
+    hidden_act: str = "gelu"
+    hidden_dropout_prob: float = 0.1
+    attention_probs_dropout_prob: float = 0.1
+    layer_norm_eps: float = 1e-05
+    bos_token_id: int = 0
+    eos_token_id: int = 2
+    pad_token_id: int = 1
+
+class XLMRobertaEmbeddings(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        embed_dim = config.hidden_size
+        self.word_embeddings = ops.Embedding(config.vocab_size, embed_dim, padding_idx=config.pad_token_id, device=device, dtype=dtype)
+        self.token_type_embeddings = ops.Embedding(config.type_vocab_size, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, input_ids=None, embeddings=None):
+        if input_ids is not None and embeddings is None:
+            embeddings = self.word_embeddings(input_ids)
+
+        if embeddings is not None:
+            token_type_ids = torch.zeros(embeddings.shape[1], device=embeddings.device, dtype=torch.int32)
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings = embeddings + token_type_embeddings
+        return embeddings
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, base, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
+        if seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype:
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=torch.float32)
+            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
+            emb = torch.cat((freqs, freqs), dim=-1)
+            self._cos_cached = emb.cos().to(dtype)
+            self._sin_cached = emb.sin().to(dtype)
+
+    def forward(self, q, k):
+        batch, seqlen, heads, head_dim = q.shape
+        self._update_cos_sin_cache(seqlen, device=q.device, dtype=q.dtype)
+
+        cos = self._cos_cached[:seqlen].view(1, seqlen, 1, head_dim)
+        sin = self._sin_cached[:seqlen].view(1, seqlen, 1, head_dim)
+
+        def rotate_half(x):
+            size = x.shape[-1] // 2
+            x1, x2 = x[..., :size], x[..., size:]
+            return torch.cat((-x2, x1), dim=-1)
+
+        q_embed = (q * cos) + (rotate_half(q) * sin)
+        k_embed = (k * cos) + (rotate_half(k) * sin)
+        return q_embed, k_embed
+
+class MHA(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = embed_dim // config.num_attention_heads
+
+        self.rotary_emb = RotaryEmbedding(self.head_dim, config.rotary_emb_base, device=device)
+        self.Wqkv = ops.Linear(embed_dim, 3 * embed_dim, device=device, dtype=dtype)
+        self.out_proj = ops.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x, mask=None, optimized_attention=None):
+        qkv = self.Wqkv(x)
+        batch_size, seq_len, _ = qkv.shape
+        qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
+        q, k, v = qkv.unbind(2)
+
+        q, k = self.rotary_emb(q, k)
+
+        # NHD -> HND
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        out = optimized_attention(q, k, v, heads=self.num_heads, mask=mask, skip_reshape=True)
+        return self.out_proj(out)
+
+class MLP(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.fc1 = ops.Linear(config.hidden_size, config.intermediate_size, device=device, dtype=dtype)
+        self.activation = F.gelu
+        self.fc2 = ops.Linear(config.intermediate_size, config.hidden_size, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.activation(x)
+        x = self.fc2(x)
+        return x
+
+class Block(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.mixer = MHA(config, device=device, dtype=dtype, ops=ops)
+        self.dropout1 = nn.Dropout(config.hidden_dropout_prob)
+        self.norm1 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.dropout2 = nn.Dropout(config.hidden_dropout_prob)
+        self.norm2 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+
+    def forward(self, hidden_states, mask=None, optimized_attention=None):
+        mixer_out = self.mixer(hidden_states, mask=mask, optimized_attention=optimized_attention)
+        hidden_states = self.norm1(self.dropout1(mixer_out) + hidden_states)
+        mlp_out = self.mlp(hidden_states)
+        hidden_states = self.norm2(self.dropout2(mlp_out) + hidden_states)
+        return hidden_states
+
+class XLMRobertaEncoder(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.layers = nn.ModuleList([Block(config, device=device, dtype=dtype, ops=ops) for _ in range(config.num_hidden_layers)])
+
+    def forward(self, hidden_states, attention_mask=None):
+        optimized_attention = comfy.ldm.modules.attention.optimized_attention_for_device(hidden_states.device, mask=attention_mask is not None, small_input=True)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, mask=attention_mask, optimized_attention=optimized_attention)
+        return hidden_states
+
+class XLMRobertaModel_(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.embeddings = XLMRobertaEmbeddings(config, device=device, dtype=dtype, ops=ops)
+        self.emb_ln = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+        self.emb_drop = nn.Dropout(config.hidden_dropout_prob)
+        self.encoder = XLMRobertaEncoder(config, device=device, dtype=dtype, ops=ops)
+
+    def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):
+        x = self.embeddings(input_ids=input_ids, embeddings=embeds)
+        x = self.emb_ln(x)
+        x = self.emb_drop(x)
+
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, 1, attention_mask.shape[-1]))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        sequence_output = self.encoder(x, attention_mask=mask)
+
+        # Mean pool, see https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/hf_model.py
+        pooled_output = None
+        if attention_mask is None:
+            pooled_output = sequence_output.mean(dim=1)
+        else:
+            attention_mask = attention_mask.to(sequence_output.dtype)
+            pooled_output = (sequence_output * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=-1, keepdim=True)
+
+        # Intermediate output is not yet implemented, use None for placeholder
+        return sequence_output, None, pooled_output
+
+class XLMRobertaModel(nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.config = XLMRobertaConfig(**config_dict)
+        self.model = XLMRobertaModel_(self.config, device=device, dtype=dtype, ops=operations)
+        self.num_layers = self.config.num_hidden_layers
+
+    def get_input_embeddings(self):
+        return self.model.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embeddings):
+        self.model.embeddings.word_embeddings = embeddings
+
+    def forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
+
+class JinaClip2TextModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, textmodel_json_config={}, model_class=XLMRobertaModel, special_tokens={"start": 0, "end": 2, "pad": 1}, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class JinaClip2TextModelWrapper(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, clip_model=JinaClip2TextModel, name="jina_clip_2", model_options=model_options)

comfy/text_encoders/kandinsky5.py

@@ -0,0 +1,68 @@
+from comfy import sd1_clip
+from .qwen_image import QwenImageTokenizer, QwenImageTEModel
+from .llama import Qwen25_7BVLI
+
+
+class Kandinsky5Tokenizer(QwenImageTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.llama_template = "<|im_start|>system\nYou are a prompt engineer. Describe the video in detail.\nDescribe how the camera moves or shakes, describe the zoom and view angle, whether it follows the objects.\nDescribe the location of the video, main characters or objects and their action.\nDescribe the dynamism of the video and presented actions.\nName the visual style of the video: whether it is a professional footage, user generated content, some kind of animation, video game or screen content.\nDescribe the visual effects, postprocessing and transitions if they are presented in the video.\nPay attention to the order of key actions shown in the scene.<|im_end|>\n<|im_start|>user\n{}<|im_end|>"
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        out = super().tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+
+        return out
+
+
+class Kandinsky5TokenizerImage(Kandinsky5Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.llama_template = "<|im_start|>system\nYou are a promt engineer. Describe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>"
+
+
+class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Kandinsky5TEModel(QwenImageTEModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
+        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=-1)
+        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs["l"])
+
+        return cond, l_pooled, extra
+
+    def set_clip_options(self, options):
+        super().set_clip_options(options)
+        self.clip_l.set_clip_options(options)
+
+    def reset_clip_options(self):
+        super().reset_clip_options()
+        self.clip_l.reset_clip_options()
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return super().load_sd(sd)
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class Kandinsky5TEModel_(Kandinsky5TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Kandinsky5TEModel_

comfy/text_encoders/llama.py

@@ -3,13 +3,12 @@ import torch.nn as nn
 from dataclasses import dataclass
 from typing import Optional, Any
 import math
-import logging
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
 import comfy.ldm.common_dit
+import comfy.clip_model
 
-import comfy.model_management
 from . import qwen_vl
 
 @dataclass
@@ -32,6 +31,29 @@ class Llama2Config:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
+
+@dataclass
+class Mistral3Small24BConfig:
+    vocab_size: int = 131072
+    hidden_size: int = 5120
+    intermediate_size: int = 32768
+    num_hidden_layers: int = 40
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 8192
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 1000000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = None
+    k_norm = None
+    rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Qwen25_3BConfig:
@@ -53,6 +75,51 @@ class Qwen25_3BConfig:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
+
+@dataclass
+class Qwen3_4BConfig:
+    vocab_size: int = 151936
+    hidden_size: int = 2560
+    intermediate_size: int = 9728
+    num_hidden_layers: int = 36
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 40960
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 1000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    rope_scale = None
+    final_norm: bool = True
+
+@dataclass
+class Ovis25_2BConfig:
+    vocab_size: int = 151936
+    hidden_size: int = 2048
+    intermediate_size: int = 6144
+    num_hidden_layers: int = 28
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 40960
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 1000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -74,6 +141,7 @@ class Qwen25_7BVLI_Config:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Gemma2_2B_Config:
@@ -96,6 +164,7 @@ class Gemma2_2B_Config:
     k_norm = None
     sliding_attention = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Gemma3_4B_Config:
@@ -107,7 +176,7 @@ class Gemma3_4B_Config:
     num_key_value_heads: int = 4
     max_position_embeddings: int = 131072
     rms_norm_eps: float = 1e-6
-    rope_theta = [10000.0, 1000000.0]
+    rope_theta = [1000000.0, 10000.0]
     transformer_type: str = "gemma3"
     head_dim = 256
     rms_norm_add = True
@@ -116,8 +185,34 @@ class Gemma3_4B_Config:
     rope_dims = None
     q_norm = "gemma3"
     k_norm = "gemma3"
-    sliding_attention = [False, False, False, False, False, 1024]
-    rope_scale = [1.0, 8.0]
+    sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
+    rope_scale = [8.0, 1.0]
+    final_norm: bool = True
+
+@dataclass
+class Gemma3_12B_Config:
+    vocab_size: int = 262208
+    hidden_size: int = 3840
+    intermediate_size: int = 15360
+    num_hidden_layers: int = 48
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 131072
+    rms_norm_eps: float = 1e-6
+    rope_theta = [1000000.0, 10000.0]
+    transformer_type: str = "gemma3"
+    head_dim = 256
+    rms_norm_add = True
+    mlp_activation = "gelu_pytorch_tanh"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
+    rope_scale = [8.0, 1.0]
+    final_norm: bool = True
+    vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+    mm_tokens_per_image = 256
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -299,7 +394,7 @@ class TransformerBlockGemma2(nn.Module):
         self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
 
-        if config.sliding_attention is not None:  # TODO: implement. (Not that necessary since models are trained on less than 1024 tokens)
+        if config.sliding_attention is not None:
             self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)]
         else:
             self.sliding_attention = False
@@ -316,7 +411,12 @@ class TransformerBlockGemma2(nn.Module):
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
                 if x.shape[1] > self.sliding_attention:
-                    logging.warning("Warning: sliding attention not implemented, results may be incorrect")
+                    sliding_mask = torch.full((x.shape[1], x.shape[1]), float("-inf"), device=x.device, dtype=x.dtype)
+                    sliding_mask.tril_(diagonal=-self.sliding_attention)
+                    if attention_mask is not None:
+                        attention_mask = attention_mask + sliding_mask
+                    else:
+                        attention_mask = sliding_mask
                 freqs_cis = freqs_cis[1]
             else:
                 freqs_cis = freqs_cis[0]
@@ -366,7 +466,12 @@ class Llama2_(nn.Module):
             transformer(config, index=i, device=device, dtype=dtype, ops=ops)
             for i in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+        if config.final_norm:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]):
@@ -402,8 +507,12 @@ class Llama2_(nn.Module):
 
         intermediate = None
         all_intermediate = None
+        only_layers = None
         if intermediate_output is not None:
-            if intermediate_output == "all":
+            if isinstance(intermediate_output, list):
+                all_intermediate = []
+                only_layers = set(intermediate_output)
+            elif intermediate_output == "all":
                 all_intermediate = []
                 intermediate_output = None
             elif intermediate_output < 0:
@@ -411,7 +520,8 @@ class Llama2_(nn.Module):
 
         for i, layer in enumerate(self.layers):
             if all_intermediate is not None:
-                all_intermediate.append(x.unsqueeze(1).clone())
+                if only_layers is None or (i in only_layers):
+                    all_intermediate.append(x.unsqueeze(1).clone())
             x = layer(
                 x=x,
                 attention_mask=mask,
@@ -421,18 +531,56 @@ class Llama2_(nn.Module):
             if i == intermediate_output:
                 intermediate = x.clone()
 
-        x = self.norm(x)
+        if self.norm is not None:
+            x = self.norm(x)
+
         if all_intermediate is not None:
-            all_intermediate.append(x.unsqueeze(1).clone())
+            if only_layers is None or ((i + 1) in only_layers):
+                all_intermediate.append(x.unsqueeze(1).clone())
 
         if all_intermediate is not None:
             intermediate = torch.cat(all_intermediate, dim=1)
 
-        if intermediate is not None and final_layer_norm_intermediate:
+        if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
             intermediate = self.norm(intermediate)
 
         return x, intermediate
 
+
+class Gemma3MultiModalProjector(torch.nn.Module):
+    def __init__(self, config, dtype, device, operations):
+        super().__init__()
+
+        self.mm_input_projection_weight = nn.Parameter(
+            torch.empty(config.vision_config["hidden_size"], config.hidden_size, device=device, dtype=dtype)
+        )
+
+        self.mm_soft_emb_norm = RMSNorm(config.vision_config["hidden_size"], eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+        self.patches_per_image = int(config.vision_config["image_size"] // config.vision_config["patch_size"])
+        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
+        self.kernel_size = self.patches_per_image // self.tokens_per_side
+        self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)
+
+    def forward(self, vision_outputs: torch.Tensor):
+        batch_size, _, seq_length = vision_outputs.shape
+
+        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
+        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
+            batch_size, seq_length, self.patches_per_image, self.patches_per_image
+        )
+        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
+
+        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
+        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
+        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
+
+        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
+
+        projected_vision_outputs = torch.matmul(normed_vision_outputs, comfy.model_management.cast_to_device(self.mm_input_projection_weight, device=normed_vision_outputs.device, dtype=normed_vision_outputs.dtype))
+        return projected_vision_outputs.type_as(vision_outputs)
+
+
 class BaseLlama:
     def get_input_embeddings(self):
         return self.model.embed_tokens
@@ -453,6 +601,15 @@ class Llama2(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+class Mistral3Small24B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Mistral3Small24BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
 class Qwen25_3B(BaseLlama, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
@@ -462,6 +619,24 @@ class Qwen25_3B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+class Qwen3_4B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Qwen3_4BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+class Ovis25_2B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Ovis25_2BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
 class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
@@ -522,3 +697,21 @@ class Gemma3_4B(BaseLlama, torch.nn.Module):
 
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
+
+class Gemma3_12B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma3_12B_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)
+        self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)
+        self.dtype = dtype
+        self.image_size = config.vision_config["image_size"]
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)
+            return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None
+        return None, None

comfy/text_encoders/lt.py

@@ -1,7 +1,11 @@
 from comfy import sd1_clip
 import os
 from transformers import T5TokenizerFast
+from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.genmo
+from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
+import torch
+import comfy.utils
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -16,3 +20,112 @@ class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
 
 def ltxv_te(*args, **kwargs):
     return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
+
+
+class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_12b", tokenizer=Gemma3_12BTokenizer)
+
+class Gemma3_12BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template="{}", image_embeds=None, **kwargs):
+        text = llama_template.format(text)
+        text_tokens = super().tokenize_with_weights(text, return_word_ids)
+        embed_count = 0
+        for k in text_tokens:
+            tt = text_tokens[k]
+            for r in tt:
+                for i in range(len(r)):
+                    if r[i][0] == 262144:
+                        if image_embeds is not None and embed_count < image_embeds.shape[0]:
+                            r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
+                            embed_count += 1
+        return text_tokens
+
+class LTXAVTEModel(torch.nn.Module):
+    def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        self.dtypes = set()
+        self.dtypes.add(dtype)
+
+        self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None)
+        self.dtypes.add(dtype_llama)
+
+        operations = self.gemma3_12b.operations # TODO
+        self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
+
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+    def set_clip_options(self, options):
+        self.execution_device = options.get("execution_device", self.execution_device)
+        self.gemma3_12b.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.gemma3_12b.reset_clip_options()
+        self.execution_device = None
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs = token_weight_pairs["gemma3_12b"]
+
+        out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
+        out_device = out.device
+        out = out.movedim(1, -1).to(self.execution_device)
+        out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
+        out = out.reshape((out.shape[0], out.shape[1], -1))
+        out = self.text_embedding_projection(out)
+        out_vid = self.video_embeddings_connector(out)[0]
+        out_audio = self.audio_embeddings_connector(out)[0]
+        out = torch.concat((out_vid, out_audio), dim=-1)
+
+        return out.to(out_device), pooled
+
+    def load_sd(self, sd):
+        if "model.layers.47.self_attn.q_norm.weight" in sd:
+            return self.gemma3_12b.load_sd(sd)
+        else:
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "model.diffusion_model.video_embeddings_connector.": "video_embeddings_connector.", "model.diffusion_model.audio_embeddings_connector.": "audio_embeddings_connector."}, filter_keys=True)
+            if len(sdo) == 0:
+                sdo = sd
+
+            return self.load_state_dict(sdo, strict=False)
+
+
+def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
+    class LTXAVTEModel_(LTXAVTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+    return LTXAVTEModel_

comfy/text_encoders/lumina2.py

@@ -14,7 +14,7 @@ class Gemma2BTokenizer(sd1_clip.SDTokenizer):
 class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
@@ -33,6 +33,11 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
 
 class Gemma3_4BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
 class LuminaModel(sd1_clip.SD1ClipModel):
@@ -40,7 +45,7 @@ class LuminaModel(sd1_clip.SD1ClipModel):
         super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b"):
     if model_type == "gemma2_2b":
         model = Gemma2_2BModel
     elif model_type == "gemma3_4b":
@@ -48,9 +53,9 @@ def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
 
     class LuminaTEModel_(LuminaModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
+                model_options["quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)

comfy/text_encoders/newbie.py

@@ -0,0 +1,62 @@
+import torch
+
+import comfy.model_management
+import comfy.text_encoders.jina_clip_2
+import comfy.text_encoders.lumina2
+
+class NewBieTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BTokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["gemma_spiece_model"]})
+        self.jina = comfy.text_encoders.jina_clip_2.JinaClip2Tokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["jina_spiece_model"]})
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        out = {}
+        out["gemma"] = self.gemma.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["jina"] = self.jina.tokenize_with_weights(text, return_word_ids, **kwargs)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        raise NotImplementedError
+
+    def state_dict(self):
+        return {}
+
+class NewBieTEModel(torch.nn.Module):
+    def __init__(self, dtype_gemma=None, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        dtype_gemma = comfy.model_management.pick_weight_dtype(dtype_gemma, dtype, device)
+        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype_gemma, model_options=model_options)
+        self.jina = comfy.text_encoders.jina_clip_2.JinaClip2TextModel(device=device, dtype=dtype, model_options=model_options)
+        self.dtypes = {dtype, dtype_gemma}
+
+    def set_clip_options(self, options):
+        self.gemma.set_clip_options(options)
+        self.jina.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.gemma.reset_clip_options()
+        self.jina.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_gemma = token_weight_pairs["gemma"]
+        token_weight_pairs_jina = token_weight_pairs["jina"]
+
+        gemma_out, gemma_pooled, gemma_extra = self.gemma.encode_token_weights(token_weight_pairs_gemma)
+        jina_out, jina_pooled, jina_extra = self.jina.encode_token_weights(token_weight_pairs_jina)
+
+        return gemma_out, jina_pooled, gemma_extra
+
+    def load_sd(self, sd):
+        if "model.layers.0.self_attn.q_norm.weight" in sd:
+            return self.gemma.load_sd(sd)
+        else:
+            return self.jina.load_sd(sd)
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class NewBieTEModel_(NewBieTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            super().__init__(dtype_gemma=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+    return NewBieTEModel_

comfy/text_encoders/omnigen2.py

@@ -32,12 +32,12 @@ class Omnigen2Model(sd1_clip.SD1ClipModel):
         super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
     class Omnigen2TEModel_(Omnigen2Model):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
+                model_options["quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/ovis.py

@@ -0,0 +1,66 @@
+from transformers import Qwen2Tokenizer
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+import os
+import torch
+import numbers
+
+class Qwen3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class OvisTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_2b", tokenizer=Qwen3Tokenizer)
+        self.llama_template = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background: {}<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        return tokens
+
+class Ovis25_2BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
+
+
+class OvisTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3_2b", clip_model=Ovis25_2BModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs, template_end=-1):
+        out, pooled = super().encode_token_weights(token_weight_pairs)
+        tok_pairs = token_weight_pairs["qwen3_2b"][0]
+        count_im_start = 0
+        if template_end == -1:
+            for i, v in enumerate(tok_pairs):
+                elem = v[0]
+                if not torch.is_tensor(elem):
+                    if isinstance(elem, numbers.Integral):
+                        if elem == 4004 and count_im_start < 1:
+                            template_end = i
+                            count_im_start += 1
+
+            if out.shape[1] > (template_end + 1):
+                if tok_pairs[template_end + 1][0] == 25:
+                    template_end += 1
+
+        out = out[:, template_end:]
+        return out, pooled, {}
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class OvisTEModel_(OvisTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return OvisTEModel_

comfy/text_encoders/pixart_t5.py

@@ -30,12 +30,12 @@ class PixArtTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
 
-def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def pixart_te(dtype_t5=None, t5_quantization_metadata=None):
     class PixArtTEModel_(PixArtT5XXL):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             if dtype is None:
                 dtype = dtype_t5
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json

@@ -179,36 +179,36 @@
       "special": false
     },
     "151665": {
-      "content": "<|img|>",
+      "content": "<tool_response>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
-      "special": true
+      "special": false
     },
     "151666": {
-      "content": "<|endofimg|>",
+      "content": "</tool_response>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
-      "special": true
+      "special": false
     },
     "151667": {
-      "content": "<|meta|>",
+      "content": "<think>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
-      "special": true
+      "special": false
     },
     "151668": {
-      "content": "<|endofmeta|>",
+      "content": "</think>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
-      "special": true
+      "special": false
     }
   },
   "additional_special_tokens": [

comfy/text_encoders/qwen_image.py

@@ -17,12 +17,14 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
         self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
         self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
 
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs):
         skip_template = False
         if text.startswith('<|im_start|>'):
             skip_template = True
         if text.startswith('<|start_header_id|>'):
             skip_template = True
+        if prevent_empty_text and text == '':
+            text = ' '
 
         if skip_template:
             llama_text = text
@@ -83,12 +85,12 @@ class QwenImageTEModel(sd1_clip.SD1ClipModel):
         return out, pooled, extra
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
     class QwenImageTEModel_(QwenImageTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
+                model_options["quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/sd3_clip.py

@@ -6,14 +6,15 @@ import torch
 import os
 import comfy.model_management
 import logging
+import comfy.utils
 
 class T5XXLModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
-        if t5xxl_scaled_fp8 is not None:
+        t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
+        if t5xxl_quantization_metadata is not None:
             model_options = model_options.copy()
-            model_options["scaled_fp8"] = t5xxl_scaled_fp8
+            model_options["quantization_metadata"] = t5xxl_quantization_metadata
 
         model_options = {**model_options, "model_name": "t5xxl"}
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
@@ -25,9 +26,9 @@ def t5_xxl_detect(state_dict, prefix=""):
     if t5_key in state_dict:
         out["dtype_t5"] = state_dict[t5_key].dtype
 
-    scaled_fp8_key = "{}scaled_fp8".format(prefix)
-    if scaled_fp8_key in state_dict:
-        out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+    quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+    if quant is not None:
+        out["t5_quantization_metadata"] = quant
 
     return out
 
@@ -156,11 +157,11 @@ class SD3ClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quantization_metadata=None, t5_attention_mask=False):
     class SD3ClipModel_(SD3ClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
     return SD3ClipModel_

comfy/text_encoders/wan.py

@@ -25,12 +25,12 @@ class WanT5Model(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
         super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs)
 
-def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def te(dtype_t5=None, t5_quantization_metadata=None):
     class WanTEModel(WanT5Model):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["quantization_metadata"] = t5_quantization_metadata
             if dtype_t5 is not None:
                 dtype = dtype_t5
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/z_image.py

@@ -0,0 +1,45 @@
+from transformers import Qwen2Tokenizer
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+import os
+
+class Qwen3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class ZImageTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_4b", tokenizer=Qwen3Tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        return tokens
+
+
+class Qwen3_4BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class ZImageTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3_4b", clip_model=Qwen3_4BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class ZImageTEModel_(ZImageTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return ZImageTEModel_

comfy/utils.py

@@ -29,6 +29,7 @@ import itertools
 from torch.nn.functional import interpolate
 from einops import rearrange
 from comfy.cli_args import args
+import json
 
 MMAP_TORCH_FILES = args.mmap_torch_files
 DISABLE_MMAP = args.disable_mmap
@@ -52,7 +53,7 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
     ALWAYS_SAFE_LOAD = True
     logging.info("Checkpoint files will always be loaded safely.")
 else:
-    logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
+    logging.warning("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended as older versions of pytorch are no longer supported.")
 
 def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     if device is None:
@@ -675,6 +676,72 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+def z_image_to_diffusers(mmdit_config, output_prefix=""):
+    n_layers = mmdit_config.get("n_layers", 0)
+    hidden_size = mmdit_config.get("dim", 0)
+    n_context_refiner = mmdit_config.get("n_refiner_layers", 2)
+    n_noise_refiner = mmdit_config.get("n_refiner_layers", 2)
+    key_map = {}
+
+    def add_block_keys(prefix_from, prefix_to, has_adaln=True):
+        for end in ("weight", "bias"):
+            k = "{}.attention.".format(prefix_from)
+            qkv = "{}.attention.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {
+            "attention.norm_q.weight": "attention.q_norm.weight",
+            "attention.norm_k.weight": "attention.k_norm.weight",
+            "attention.to_out.0.weight": "attention.out.weight",
+            "attention.to_out.0.bias": "attention.out.bias",
+            "attention_norm1.weight": "attention_norm1.weight",
+            "attention_norm2.weight": "attention_norm2.weight",
+            "feed_forward.w1.weight": "feed_forward.w1.weight",
+            "feed_forward.w2.weight": "feed_forward.w2.weight",
+            "feed_forward.w3.weight": "feed_forward.w3.weight",
+            "ffn_norm1.weight": "ffn_norm1.weight",
+            "ffn_norm2.weight": "ffn_norm2.weight",
+        }
+        if has_adaln:
+            block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight"
+            block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias"
+        for k, v in block_map.items():
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v)
+
+    for i in range(n_layers):
+        add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i))
+
+    for i in range(n_context_refiner):
+        add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i))
+
+    for i in range(n_noise_refiner):
+        add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i))
+
+    MAP_BASIC = [
+        ("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"),
+        ("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"),
+        ("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"),
+        ("x_embedder.weight", "all_x_embedder.2-1.weight"),
+        ("x_embedder.bias", "all_x_embedder.2-1.bias"),
+        ("x_pad_token", "x_pad_token"),
+        ("cap_embedder.0.weight", "cap_embedder.0.weight"),
+        ("cap_embedder.1.weight", "cap_embedder.1.weight"),
+        ("cap_embedder.1.bias", "cap_embedder.1.bias"),
+        ("cap_pad_token", "cap_pad_token"),
+        ("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"),
+        ("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"),
+        ("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"),
+        ("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"),
+    ]
+
+    for c, diffusers in MAP_BASIC:
+        key_map[diffusers] = "{}{}".format(output_prefix, c)
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)
@@ -736,12 +803,17 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
             return None
         return f.read(length_of_header)
 
+ATTR_UNSET={}
+
 def set_attr(obj, attr, value):
     attrs = attr.split(".")
     for name in attrs[:-1]:
         obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], value)
+    prev = getattr(obj, attrs[-1], ATTR_UNSET)
+    if value is ATTR_UNSET:
+        delattr(obj, attrs[-1])
+    else:
+        setattr(obj, attrs[-1], value)
     return prev
 
 def set_attr_param(obj, attr, value):
@@ -1126,5 +1198,72 @@ def unpack_latents(combined_latent, latent_shapes):
             combined_latent = combined_latent[:, :, cut:]
             output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:]))
     else:
-        output_tensors = combined_latent
+        output_tensors = [combined_latent]
     return output_tensors
+
+def detect_layer_quantization(state_dict, prefix):
+    for k in state_dict:
+        if k.startswith(prefix) and k.endswith(".comfy_quant"):
+            logging.info("Found quantization metadata version 1")
+            return {"mixed_ops": True}
+    return None
+
+def convert_old_quants(state_dict, model_prefix="", metadata={}):
+    if metadata is None:
+        metadata = {}
+
+    quant_metadata = None
+    if "_quantization_metadata" not in metadata:
+        scaled_fp8_key = "{}scaled_fp8".format(model_prefix)
+
+        if scaled_fp8_key in state_dict:
+            scaled_fp8_weight = state_dict[scaled_fp8_key]
+            scaled_fp8_dtype = scaled_fp8_weight.dtype
+            if scaled_fp8_dtype == torch.float32:
+                scaled_fp8_dtype = torch.float8_e4m3fn
+
+            if scaled_fp8_weight.nelement() == 2:
+                full_precision_matrix_mult = True
+            else:
+                full_precision_matrix_mult = False
+
+            out_sd = {}
+            layers = {}
+            for k in list(state_dict.keys()):
+                if k == scaled_fp8_key:
+                    continue
+                if not k.startswith(model_prefix):
+                    out_sd[k] = state_dict[k]
+                    continue
+                k_out = k
+                w = state_dict.pop(k)
+                layer = None
+                if k_out.endswith(".scale_weight"):
+                    layer = k_out[:-len(".scale_weight")]
+                    k_out = "{}.weight_scale".format(layer)
+
+                if layer is not None:
+                    layer_conf = {"format": "float8_e4m3fn"}  # TODO: check if anyone did some non e4m3fn scaled checkpoints
+                    if full_precision_matrix_mult:
+                        layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult
+                    layers[layer] = layer_conf
+
+                if k_out.endswith(".scale_input"):
+                    layer = k_out[:-len(".scale_input")]
+                    k_out = "{}.input_scale".format(layer)
+                    if w.item() == 1.0:
+                        continue
+
+                out_sd[k_out] = w
+
+            state_dict = out_sd
+            quant_metadata = {"layers": layers}
+    else:
+        quant_metadata = json.loads(metadata["_quantization_metadata"])
+
+    if quant_metadata is not None:
+        layers = quant_metadata["layers"]
+        for k, v in layers.items():
+            state_dict["{}.comfy_quant".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8)
+
+    return state_dict, metadata

comfy/weight_adapter/lora.py

@@ -194,6 +194,7 @@ class LoRAAdapter(WeightAdapterBase):
             lora_diff = torch.mm(
                 mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)
             ).reshape(weight.shape)
+            del mat1, mat2
             if dora_scale is not None:
                 weight = weight_decompose(
                     dora_scale,

comfy_api/feature_flags.py

@@ -5,19 +5,20 @@ This module handles capability negotiation between frontend and backend,
 allowing graceful protocol evolution while maintaining backward compatibility.
 """
 
-from typing import Any, Dict
+from typing import Any
 
 from comfy.cli_args import args
 
 # Default server capabilities
-SERVER_FEATURE_FLAGS: Dict[str, Any] = {
+SERVER_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+    "extension": {"manager": {"supports_v4": True}},
 }
 
 
 def get_connection_feature(
-    sockets_metadata: Dict[str, Dict[str, Any]],
+    sockets_metadata: dict[str, dict[str, Any]],
     sid: str,
     feature_name: str,
     default: Any = False
@@ -41,7 +42,7 @@ def get_connection_feature(
 
 
 def supports_feature(
-    sockets_metadata: Dict[str, Dict[str, Any]],
+    sockets_metadata: dict[str, dict[str, Any]],
     sid: str,
     feature_name: str
 ) -> bool:
@@ -59,7 +60,7 @@ def supports_feature(
     return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
 
 
-def get_server_features() -> Dict[str, Any]:
+def get_server_features() -> dict[str, Any]:
     """
     Get the server's feature flags.
 

comfy_api/internal/api_registry.py

@@ -1,4 +1,4 @@
-from typing import Type, List, NamedTuple
+from typing import NamedTuple
 from comfy_api.internal.singleton import ProxiedSingleton
 from packaging import version as packaging_version
 
@@ -10,7 +10,7 @@ class ComfyAPIBase(ProxiedSingleton):
 
 class ComfyAPIWithVersion(NamedTuple):
     version: str
-    api_class: Type[ComfyAPIBase]
+    api_class: type[ComfyAPIBase]
 
 
 def parse_version(version_str: str) -> packaging_version.Version:
@@ -23,16 +23,16 @@ def parse_version(version_str: str) -> packaging_version.Version:
     return packaging_version.parse(version_str)
 
 
-registered_versions: List[ComfyAPIWithVersion] = []
+registered_versions: list[ComfyAPIWithVersion] = []
 
 
-def register_versions(versions: List[ComfyAPIWithVersion]):
+def register_versions(versions: list[ComfyAPIWithVersion]):
     versions.sort(key=lambda x: parse_version(x.version))
     global registered_versions
     registered_versions = versions
 
 
-def get_all_versions() -> List[ComfyAPIWithVersion]:
+def get_all_versions() -> list[ComfyAPIWithVersion]:
     """
     Returns a list of all registered ComfyAPI versions.
     """

comfy_api/internal/async_to_sync.py

@@ -8,7 +8,7 @@ import os
 import textwrap
 import threading
 from enum import Enum
-from typing import Optional, Type, get_origin, get_args
+from typing import Optional, get_origin, get_args, get_type_hints
 
 
 class TypeTracker:
@@ -193,7 +193,7 @@ class AsyncToSyncConverter:
         return result_container["result"]
 
     @classmethod
-    def create_sync_class(cls, async_class: Type, thread_pool_size=10) -> Type:
+    def create_sync_class(cls, async_class: type, thread_pool_size=10) -> type:
         """
         Creates a new class with synchronous versions of all async methods.
 
@@ -220,11 +220,18 @@ class AsyncToSyncConverter:
             self._async_instance = async_class(*args, **kwargs)
 
             # Handle annotated class attributes (like execution: Execution)
-            # Get all annotations from the class hierarchy
-            all_annotations = {}
-            for base_class in reversed(inspect.getmro(async_class)):
-                if hasattr(base_class, "__annotations__"):
-                    all_annotations.update(base_class.__annotations__)
+            # Get all annotations from the class hierarchy and resolve string annotations
+            try:
+                # get_type_hints resolves string annotations to actual type objects
+                # This handles classes using 'from __future__ import annotations'
+                all_annotations = get_type_hints(async_class)
+            except Exception:
+                # Fallback to raw annotations if get_type_hints fails
+                # (e.g., for undefined forward references)
+                all_annotations = {}
+                for base_class in reversed(inspect.getmro(async_class)):
+                    if hasattr(base_class, "__annotations__"):
+                        all_annotations.update(base_class.__annotations__)
 
             # For each annotated attribute, check if it needs to be created or wrapped
             for attr_name, attr_type in all_annotations.items():
@@ -556,7 +563,7 @@ class AsyncToSyncConverter:
 
     @classmethod
     def _generate_imports(
-        cls, async_class: Type, type_tracker: TypeTracker
+        cls, async_class: type, type_tracker: TypeTracker
     ) -> list[str]:
         """Generate import statements for the stub file."""
         imports = []
@@ -621,19 +628,23 @@ class AsyncToSyncConverter:
         return imports
 
     @classmethod
-    def _get_class_attributes(cls, async_class: Type) -> list[tuple[str, Type]]:
+    def _get_class_attributes(cls, async_class: type) -> list[tuple[str, type]]:
         """Extract class attributes that are classes themselves."""
         class_attributes = []
 
+        # Get resolved type hints to handle string annotations
+        try:
+            type_hints = get_type_hints(async_class)
+        except Exception:
+            type_hints = {}
+
         # Look for class attributes that are classes
         for name, attr in sorted(inspect.getmembers(async_class)):
             if isinstance(attr, type) and not name.startswith("_"):
                 class_attributes.append((name, attr))
-            elif (
-                hasattr(async_class, "__annotations__")
-                and name in async_class.__annotations__
-            ):
-                annotation = async_class.__annotations__[name]
+            elif name in type_hints:
+                # Use resolved type hint instead of raw annotation
+                annotation = type_hints[name]
                 if isinstance(annotation, type):
                     class_attributes.append((name, annotation))
 
@@ -643,7 +654,7 @@ class AsyncToSyncConverter:
     def _generate_inner_class_stub(
         cls,
         name: str,
-        attr: Type,
+        attr: type,
         indent: str = "    ",
         type_tracker: Optional[TypeTracker] = None,
     ) -> list[str]:
@@ -771,7 +782,7 @@ class AsyncToSyncConverter:
         return processed
 
     @classmethod
-    def generate_stub_file(cls, async_class: Type, sync_class: Type) -> None:
+    def generate_stub_file(cls, async_class: type, sync_class: type) -> None:
         """
         Generate a .pyi stub file for the sync class to help IDEs with type checking.
         """
@@ -908,11 +919,15 @@ class AsyncToSyncConverter:
             attribute_mappings = {}
 
             # First check annotations for typed attributes (including from parent classes)
-            # Collect all annotations from the class hierarchy
-            all_annotations = {}
-            for base_class in reversed(inspect.getmro(async_class)):
-                if hasattr(base_class, "__annotations__"):
-                    all_annotations.update(base_class.__annotations__)
+            # Resolve string annotations to actual types
+            try:
+                all_annotations = get_type_hints(async_class)
+            except Exception:
+                # Fallback to raw annotations
+                all_annotations = {}
+                for base_class in reversed(inspect.getmro(async_class)):
+                    if hasattr(base_class, "__annotations__"):
+                        all_annotations.update(base_class.__annotations__)
 
             for attr_name, attr_type in sorted(all_annotations.items()):
                 for class_name, class_type in class_attributes:
@@ -973,7 +988,7 @@ class AsyncToSyncConverter:
             logging.error(traceback.format_exc())
 
 
-def create_sync_class(async_class: Type, thread_pool_size=10) -> Type:
+def create_sync_class(async_class: type, thread_pool_size=10) -> type:
     """
     Creates a sync version of an async class
 

comfy_api/internal/singleton.py

@@ -1,4 +1,4 @@
-from typing import Type, TypeVar
+from typing import TypeVar
 
 class SingletonMetaclass(type):
     T = TypeVar("T", bound="SingletonMetaclass")
@@ -11,13 +11,13 @@ class SingletonMetaclass(type):
             )
         return cls._instances[cls]
 
-    def inject_instance(cls: Type[T], instance: T) -> None:
+    def inject_instance(cls: type[T], instance: T) -> None:
         assert cls not in SingletonMetaclass._instances, (
             "Cannot inject instance after first instantiation"
         )
         SingletonMetaclass._instances[cls] = instance
 
-    def get_instance(cls: Type[T], *args, **kwargs) -> T:
+    def get_instance(cls: type[T], *args, **kwargs) -> T:
         """
         Gets the singleton instance of the class, creating it if it doesn't exist.
         """

comfy_api/latest/__init__.py

@@ -1,16 +1,15 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Type, TYPE_CHECKING
+from typing import TYPE_CHECKING
 from comfy_api.internal import ComfyAPIBase
 from comfy_api.internal.singleton import ProxiedSingleton
 from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
-from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
-from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
-from . import _io as io
-from . import _ui as ui
-# from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
+from ._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
+from ._input_impl import VideoFromFile, VideoFromComponents
+from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
+from . import _io_public as io
+from . import _ui_public as ui
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
 from PIL import Image
@@ -80,7 +79,7 @@ class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
         Called when an extension is loaded.
-        This should be used to initialize any global resources neeeded by the extension.
+        This should be used to initialize any global resources needed by the extension.
         """
 
     @abstractmethod
@@ -104,6 +103,8 @@ class Types:
     VideoCodec = VideoCodec
     VideoContainer = VideoContainer
     VideoComponents = VideoComponents
+    MESH = MESH
+    VOXEL = VOXEL
 
 ComfyAPI = ComfyAPI_latest
 
@@ -111,7 +112,7 @@ ComfyAPI = ComfyAPI_latest
 if TYPE_CHECKING:
     import comfy_api.latest.generated.ComfyAPISyncStub  # type: ignore
 
-    ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
+    ComfyAPISync: type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
 ComfyAPISync = create_sync_class(ComfyAPI_latest)
 
 # create new aliases for io and ui

comfy_api/latest/_input/basic_types.py

@@ -1,5 +1,5 @@
 import torch
-from typing import TypedDict, List, Optional
+from typing import TypedDict, Optional
 
 ImageInput = torch.Tensor
 """
@@ -39,4 +39,4 @@ class LatentInput(TypedDict):
     Optional noise mask tensor in the same format as samples.
     """
 
-    batch_index: Optional[List[int]]
+    batch_index: Optional[list[int]]

comfy_api/latest/_input/video_types.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 from abc import ABC, abstractmethod
+from fractions import Fraction
 from typing import Optional, Union, IO
 import io
 import av
-from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
+from .._util import VideoContainer, VideoCodec, VideoComponents
 
 class VideoInput(ABC):
     """
@@ -72,6 +73,33 @@ class VideoInput(ABC):
         frame_count = components.images.shape[0]
         return float(frame_count / components.frame_rate)
 
+    def get_frame_count(self) -> int:
+        """
+        Returns the number of frames in the video.
+
+        Default implementation uses :meth:`get_components`, which may require
+        loading all frames into memory. File-based implementations should
+        override this method and use container/stream metadata instead.
+
+        Returns:
+            Total number of frames as an integer.
+        """
+        return int(self.get_components().images.shape[0])
+
+    def get_frame_rate(self) -> Fraction:
+        """
+        Returns the frame rate of the video.
+
+        Default implementation materializes the video into memory via
+        `get_components()`. Subclasses that can inspect the underlying
+        container (e.g. `VideoFromFile`) should override this with a more
+        efficient implementation.
+
+        Returns:
+            Frame rate as a Fraction.
+        """
+        return self.get_components().frame_rate
+
     def get_container_format(self) -> str:
         """
         Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').

comfy_api/latest/_input_impl/video_types.py

@@ -3,14 +3,14 @@ from av.container import InputContainer
 from av.subtitles.stream import SubtitleStream
 from fractions import Fraction
 from typing import Optional
-from comfy_api.latest._input import AudioInput, VideoInput
+from .._input import AudioInput, VideoInput
 import av
 import io
 import json
 import numpy as np
 import math
 import torch
-from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents
+from .._util import VideoContainer, VideoCodec, VideoComponents
 
 
 def container_to_output_format(container_format: str | None) -> str | None:
@@ -121,6 +121,71 @@ class VideoFromFile(VideoInput):
 
         raise ValueError(f"Could not determine duration for file '{self.__file}'")
 
+    def get_frame_count(self) -> int:
+        """
+        Returns the number of frames in the video without materializing them as
+        torch tensors.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+
+        with av.open(self.__file, mode="r") as container:
+            video_stream = self._get_first_video_stream(container)
+            # 1. Prefer the frames field if available
+            if video_stream.frames and video_stream.frames > 0:
+                return int(video_stream.frames)
+
+            # 2. Try to estimate from duration and average_rate using only metadata
+            if container.duration is not None and video_stream.average_rate:
+                duration_seconds = float(container.duration / av.time_base)
+                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
+                if estimated_frames > 0:
+                    return estimated_frames
+
+            if (
+                getattr(video_stream, "duration", None) is not None
+                and getattr(video_stream, "time_base", None) is not None
+                and video_stream.average_rate
+            ):
+                duration_seconds = float(video_stream.duration * video_stream.time_base)
+                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
+                if estimated_frames > 0:
+                    return estimated_frames
+
+            # 3. Last resort: decode frames and count them (streaming)
+            frame_count = 0
+            container.seek(0)
+            for packet in container.demux(video_stream):
+                for _ in packet.decode():
+                    frame_count += 1
+
+            if frame_count == 0:
+                raise ValueError(f"Could not determine frame count for file '{self.__file}'")
+            return frame_count
+
+    def get_frame_rate(self) -> Fraction:
+        """
+        Returns the average frame rate of the video using container metadata
+        without decoding all frames.
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+
+        with av.open(self.__file, mode="r") as container:
+            video_stream = self._get_first_video_stream(container)
+            # Preferred: use PyAV's average_rate (usually already a Fraction-like)
+            if video_stream.average_rate:
+                return Fraction(video_stream.average_rate)
+
+            # Fallback: estimate from frames + duration if available
+            if video_stream.frames and container.duration:
+                duration_seconds = float(container.duration / av.time_base)
+                if duration_seconds > 0:
+                    return Fraction(video_stream.frames / duration_seconds).limit_denominator()
+
+            # Last resort: match get_components_internal default
+            return Fraction(1)
+
     def get_container_format(self) -> str:
         """
         Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
@@ -238,6 +303,13 @@ class VideoFromFile(VideoInput):
                         packet.stream = stream_map[packet.stream]
                         output_container.mux(packet)
 
+    def _get_first_video_stream(self, container: InputContainer):
+        video_stream = next((s for s in container.streams if s.type == "video"), None)
+        if video_stream is None:
+            raise ValueError(f"No video stream found in file '{self.__file}'")
+        return video_stream
+
+
 class VideoFromComponents(VideoInput):
     """
     Class representing video input from tensors.
@@ -264,7 +336,10 @@ class VideoFromComponents(VideoInput):
             raise ValueError("Only MP4 format is supported for now")
         if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
             raise ValueError("Only H264 codec is supported for now")
-        with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output:
+        extra_kwargs = {}
+        if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
+            extra_kwargs["format"] = format.value
+        with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
             # Add metadata before writing any streams
             if metadata is not None:
                 for key, value in metadata.items():