Add mochi support to readme.

You can still use models/clip but the folder might get removed eventually
on new installs of ComfyUI.

.github/workflows/pullrequest-ci-run.yml

@@ -23,7 +23,7 @@ jobs:
             runner_label: [self-hosted, Linux]
             flags: ""
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:

.github/workflows/stable-release.yml

@@ -12,17 +12,17 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "121"
+        default: "124"
       python_minor:
         description: 'Python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
       python_patch:
         description: 'Python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 
 
 jobs:

.github/workflows/test-ci.yml

@@ -32,7 +32,7 @@ jobs:
             runner_label: [self-hosted, Linux]
             flags: ""
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:
@@ -55,7 +55,7 @@ jobs:
         torch_version: ["nightly"]
         include:
           - os: windows
-            runner_label: [self-hosted, win]
+            runner_label: [self-hosted, Windows]
             flags: ""
     runs-on: ${{ matrix.runner_label }}
     steps:

.github/workflows/test-unit.yml

@@ -0,0 +1,30 @@
+name: Unit Tests
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python      
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+    - name: Run Unit Tests
+      run: |
+        pip install -r tests-unit/requirements.txt
+        python -m pytest tests-unit

.github/workflows/windows_release_dependencies.yml

@@ -12,7 +12,7 @@ on:
         description: 'extra dependencies'
         required: false
         type: string
-        default: "\"numpy<2\""
+        default: ""
       cu:
         description: 'cuda version'
         required: true
@@ -23,13 +23,13 @@ on:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 #  push:
 #    branches:
 #      - master

.github/workflows/windows_release_package.yml

@@ -13,13 +13,13 @@ on:
         description: 'python minor version'
         required: true
         type: string
-        default: "11"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "9"
+        default: "7"
 #  push:
 #    branches:
 #      - master

.gitignore

@@ -12,6 +12,7 @@ extra_model_paths.yaml
 .vscode/
 .idea/
 venv/
+.venv/
 /web/extensions/*
 !/web/extensions/logging.js.example
 !/web/extensions/core/

README.md

@@ -40,6 +40,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
@@ -94,6 +95,8 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Alt + `+`                          | Canvas Zoom in                                                                                                     |
 | Alt + `-`                          | Canvas Zoom out                                                                                                    |
 | Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| P                                  | Pin/Unpin selected nodes                                                                                           |
+| Ctrl + G                           | Group selected nodes                                                                                               |
 | Q                                  | Toggle visibility of the queue                                                                                     |
 | H                                  | Toggle visibility of history                                                                                       |
 | R                                  | Refresh graph                                                                                                      |
@@ -125,6 +128,8 @@ To run it on services like paperspace, kaggle or colab you can use my [Jupyter N
 
 ## Manual Install (Windows, Linux)
 
+Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+
 Git clone this repo.
 
 Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints

api_server/routes/internal/internal_routes.py

@@ -1,6 +1,6 @@
 from aiohttp import web
 from typing import Optional
-from folder_paths import models_dir, user_directory, output_directory
+from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
 from api_server.services.file_service import FileService
 import app.logger
 
@@ -36,6 +36,13 @@ class InternalRoutes:
         async def get_logs(request):
             return web.json_response(app.logger.get_logs())
 
+        @self.routes.get('/folder_paths')
+        async def get_folder_paths(request):
+            response = {}
+            for key in folder_names_and_paths:
+                response[key] = folder_names_and_paths[key][0]
+            return web.json_response(response)
+
     def get_app(self):
         if self._app is None:
             self._app = web.Application()

app/frontend_management.py

@@ -151,6 +151,15 @@ class FrontendManager:
             return cls.DEFAULT_FRONTEND_PATH
 
         repo_owner, repo_name, version = cls.parse_version_string(version_string)
+
+        if version.startswith("v"):
+            expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v"))
+            if os.path.exists(expected_path):
+                logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}")
+                return expected_path
+
+        logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...")
+
         provider = provider or FrontEndProvider(repo_owner, repo_name)
         release = provider.get_release(version)
 

app/logger.py

@@ -10,14 +10,14 @@ def get_logs():
     return "\n".join([formatter.format(x) for x in logs])
 
 
-def setup_logger(verbose: bool = False, capacity: int = 300):
+def setup_logger(log_level: str = 'INFO', capacity: int = 300):
     global logs
     if logs:
         return
 
     # Setup default global logger
     logger = logging.getLogger()
-    logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    logger.setLevel(log_level)
 
     stream_handler = logging.StreamHandler()
     stream_handler.setFormatter(logging.Formatter("%(message)s"))

app/user_manager.py

@@ -5,17 +5,17 @@ import uuid
 import glob
 import shutil
 from aiohttp import web
+from urllib import parse
 from comfy.cli_args import args
-from folder_paths import user_directory
+import folder_paths
 from .app_settings import AppSettings
 
 default_user = "default"
-users_file = os.path.join(user_directory, "users.json")
 
 
 class UserManager():
     def __init__(self):
-        global user_directory
+        user_directory = folder_paths.get_user_directory()
 
         self.settings = AppSettings(self)
         if not os.path.exists(user_directory):
@@ -25,14 +25,17 @@ class UserManager():
                 print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
 
         if args.multi_user:
-            if os.path.isfile(users_file):
+            if os.path.isfile(self.get_users_file()):
-                with open(users_file) as f:
+                with open(self.get_users_file()) as f:
                     self.users = json.load(f)
             else:
                 self.users = {}
         else:
             self.users = {"default": "default"}
 
+    def get_users_file(self):
+        return os.path.join(folder_paths.get_user_directory(), "users.json")
+
     def get_request_user_id(self, request):
         user = "default"
         if args.multi_user and "comfy-user" in request.headers:
@@ -44,7 +47,7 @@ class UserManager():
         return user
 
     def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        global user_directory
+        user_directory = folder_paths.get_user_directory()
 
         if type == "userdata":
             root_dir = user_directory
@@ -59,6 +62,10 @@ class UserManager():
             return None
 
         if file is not None:
+            # Check if filename is url encoded
+            if "%" in file:
+                file = parse.unquote(file)
+
             # prevent leaving /{type}/{user}
             path = os.path.abspath(os.path.join(user_root, file))
             if os.path.commonpath((user_root, path)) != user_root:
@@ -80,8 +87,7 @@ class UserManager():
 
         self.users[user_id] = name
 
-        global users_file
+        with open(self.get_users_file(), "w") as f:
-        with open(users_file, "w") as f:
             json.dump(self.users, f)
 
         return user_id
@@ -112,25 +118,69 @@ class UserManager():
 
         @routes.get("/userdata")
         async def listuserdata(request):
+            """
+            List user data files in a specified directory.
+
+            This endpoint allows listing files in a user's data directory, with options for recursion,
+            full file information, and path splitting.
+
+            Query Parameters:
+            - dir (required): The directory to list files from.
+            - recurse (optional): If "true", recursively list files in subdirectories.
+            - full_info (optional): If "true", return detailed file information (path, size, modified time).
+            - split (optional): If "true", split file paths into components (only applies when full_info is false).
+
+            Returns:
+            - 400: If 'dir' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 404: If the requested directory does not exist.
+            - 200: JSON response with the list of files or file information.
+
+            The response format depends on the query parameters:
+            - Default: List of relative file paths.
+            - full_info=true: List of dictionaries with file details.
+            - split=true (and full_info=false): List of lists, each containing path components.
+            """
             directory = request.rel_url.query.get('dir', '')
             if not directory:
-                return web.Response(status=400)
+                return web.Response(status=400, text="Directory not provided")
 
             path = self.get_request_user_filepath(request, directory)
             if not path:
-                return web.Response(status=403)
+                return web.Response(status=403, text="Invalid directory")
 
             if not os.path.exists(path):
-                return web.Response(status=404)
+                return web.Response(status=404, text="Directory not found")
 
             recurse = request.rel_url.query.get('recurse', '').lower() == "true"
-            results = glob.glob(os.path.join(
+            full_info = request.rel_url.query.get('full_info', '').lower() == "true"
-                glob.escape(path), '**/*'), recursive=recurse)
+
-            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
+            # Use different patterns based on whether we're recursing or not
+            if recurse:
+                pattern = os.path.join(glob.escape(path), '**', '*')
+            else:
+                pattern = os.path.join(glob.escape(path), '*')
+
+            results = glob.glob(pattern, recursive=recurse)
+
+            if full_info:
+                results = [
+                    {
+                        'path': os.path.relpath(x, path).replace(os.sep, '/'),
+                        'size': os.path.getsize(x),
+                        'modified': os.path.getmtime(x)
+                    } for x in results if os.path.isfile(x)
+                ]
+            else:
+                results = [
+                    os.path.relpath(x, path).replace(os.sep, '/')
+                    for x in results
+                    if os.path.isfile(x)
+                ]
 
             split_path = request.rel_url.query.get('split', '').lower() == "true"
-            if split_path:
+            if split_path and not full_info:
-                results = [[x] + x.split(os.sep) for x in results]
+                results = [[x] + x.split('/') for x in results]
 
             return web.json_response(results)
 

comfy/cldm/mmdit.py

@@ -6,6 +6,7 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
         self,
         num_blocks = None,
+        control_latent_channels = None,
         dtype = None,
         device = None,
         operations = None,
@@ -17,10 +18,13 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
         for _ in range(len(self.joint_blocks)):
             self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
 
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+
         self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
             None,
             self.patch_size,
-            self.in_channels,
+            control_latent_channels,
             self.hidden_size,
             bias=True,
             strict_img_size=False,

comfy/cli_args.py

@@ -36,7 +36,7 @@ class EnumAction(argparse.Action):
 
 parser = argparse.ArgumentParser()
 
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
 parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
 parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
 parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
@@ -92,6 +92,8 @@ class LatentPreviewMethod(enum.Enum):
 
 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.")
+
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
@@ -134,7 +136,7 @@ parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Dis
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
-parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
+parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
 
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
@@ -169,6 +171,8 @@ parser.add_argument(
     help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
 )
 
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.")
+
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:

comfy/clip_vision.py

@@ -109,8 +109,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     keys = list(sd.keys())
     for k in keys:
         if k not in u:
-            t = sd.pop(k)
+            sd.pop(k)
-            del t
     return clip
 
 def load(ckpt_path):

comfy/controlnet.py

@@ -60,7 +60,7 @@ class StrengthType(Enum):
     LINEAR_UP = 2
 
 class ControlBase:
-    def __init__(self, device=None):
+    def __init__(self):
         self.cond_hint_original = None
         self.cond_hint = None
         self.strength = 1.0
@@ -72,20 +72,24 @@ class ControlBase:
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
         self.extra_args = {}
-
-        if device is None:
-            device = comfy.model_management.get_torch_device()
-        self.device = device
         self.previous_controlnet = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
+        self.concat_mask = False
+        self.extra_concat_orig = []
+        self.extra_concat = None
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
         if self.latent_format is not None:
+            if vae is None:
+                logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.")
             self.vae = vae
+        self.extra_concat_orig = extra_concat.copy()
+        if self.concat_mask and len(self.extra_concat_orig) == 0:
+            self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -100,9 +104,9 @@ class ControlBase:
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-        if self.cond_hint is not None:
+
-            del self.cond_hint
+        self.cond_hint = None
-            self.cond_hint = None
+        self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
@@ -123,6 +127,8 @@ class ControlBase:
         c.vae = self.vae
         c.extra_conds = self.extra_conds.copy()
         c.strength_type = self.strength_type
+        c.concat_mask = self.concat_mask
+        c.extra_concat_orig = self.extra_concat_orig.copy()
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -175,8 +181,8 @@ class ControlBase:
 
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False):
-        super().__init__(device)
+        super().__init__()
         self.control_model = control_model
         self.load_device = load_device
         if control_model is not None:
@@ -189,6 +195,7 @@ class ControlNet(ControlBase):
         self.latent_format = latent_format
         self.extra_conds += extra_conds
         self.strength_type = strength_type
+        self.concat_mask = concat_mask
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -213,6 +220,9 @@ class ControlNet(ControlBase):
             compression_ratio = self.compression_ratio
             if self.vae is not None:
                 compression_ratio *= self.vae.downscale_ratio
+            else:
+                if self.latent_format is not None:
+                    raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
             self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -220,7 +230,15 @@ class ControlNet(ControlBase):
                 comfy.model_management.load_models_gpu(loaded_models)
             if self.latent_format is not None:
                 self.cond_hint = self.latent_format.process_in(self.cond_hint)
-            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
+            if len(self.extra_concat_orig) > 0:
+                to_concat = []
+                for c in self.extra_concat_orig:
+                    c = c.to(self.cond_hint.device)
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+                    to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
+                self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
+
+            self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
@@ -319,8 +337,8 @@ class ControlLoraOps:
 
 
 class ControlLora(ControlNet):
-    def __init__(self, control_weights, global_average_pooling=False, device=None):
+    def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
-        ControlBase.__init__(self, device)
+        ControlBase.__init__(self)
         self.control_weights = control_weights
         self.global_average_pooling = global_average_pooling
         self.extra_conds += ["y"]
@@ -376,19 +394,25 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
-def controlnet_config(sd):
+def controlnet_config(sd, model_options={}):
     model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
 
-    supported_inference_dtypes = model_config.supported_inference_dtypes
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(sd)
+
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
 
-    controlnet_config = model_config.unet_config
-    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
+
-        operations = comfy.ops.manual_cast
+    operations = model_options.get("custom_operations", None)
-    else:
+    if operations is None:
-        operations = comfy.ops.disable_weight_init
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
 
     offload_device = comfy.model_management.unet_offload_device()
     return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
@@ -403,24 +427,29 @@ def controlnet_load_state_dict(control_model, sd):
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
     return control_model
 
-def load_controlnet_mmdit(sd):
+def load_controlnet_mmdit(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
     num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
     for k in sd:
         new_sd[k] = sd[k]
 
-    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    concat_mask = False
+    control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1]
+    if control_latent_channels == 17: #inpaint controlnet
+        concat_mask = True
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, new_sd)
 
     latent_format = comfy.latent_formats.SD3()
     latent_format.shift_factor = 0 #SD3 controlnet weirdness
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
 
-def load_controlnet_hunyuandit(controlnet_data):
+def load_controlnet_hunyuandit(controlnet_data, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
 
     control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
     control_model = controlnet_load_state_dict(control_model, controlnet_data)
@@ -430,17 +459,17 @@ def load_controlnet_hunyuandit(controlnet_data):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
     return control
 
-def load_controlnet_flux_xlabs(sd):
+def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
-    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-def load_controlnet_flux_instantx(sd):
+def load_controlnet_flux_instantx(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
     for k in sd:
         new_sd[k] = sd[k]
 
@@ -449,21 +478,30 @@ def load_controlnet_flux_instantx(sd):
     if union_cnet in new_sd:
         num_union_modes = new_sd[union_cnet].shape[0]
 
-    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
+    concat_mask = False
+    if control_latent_channels == 17:
+        concat_mask = True
+
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, new_sd)
 
     latent_format = comfy.latent_formats.Flux()
     extra_conds = ['y', 'guidance']
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-def load_controlnet(ckpt_path, model=None):
+def convert_mistoline(sd):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
+
+
+def load_controlnet_state_dict(state_dict, model=None, model_options={}):
+    controlnet_data = state_dict
     if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
-        return load_controlnet_hunyuandit(controlnet_data)
+        return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)
 
     if "lora_controlnet" in controlnet_data:
-        return ControlLora(controlnet_data)
+        return ControlLora(controlnet_data, model_options=model_options)
 
     controlnet_config = None
     supported_inference_dtypes = None
@@ -518,13 +556,15 @@ def load_controlnet(ckpt_path, model=None):
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
-    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+    elif "controlnet_blocks.0.weight" in controlnet_data:
         if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
-            return load_controlnet_flux_xlabs(controlnet_data)
+            return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
         elif "pos_embed_input.proj.weight" in controlnet_data:
-            return load_controlnet_mmdit(controlnet_data)
+            return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
         elif "controlnet_x_embedder.weight" in controlnet_data:
-            return load_controlnet_flux_instantx(controlnet_data)
+            return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
+    elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
+        return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
@@ -536,25 +576,36 @@ def load_controlnet(ckpt_path, model=None):
     elif key in controlnet_data:
         prefix = ""
     else:
-        net = load_t2i_adapter(controlnet_data)
+        net = load_t2i_adapter(controlnet_data, model_options=model_options)
         if net is None:
-            logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+            logging.error("error could not detect control model type.")
         return net
 
     if controlnet_config is None:
         model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
-        supported_inference_dtypes = model_config.supported_inference_dtypes
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
         controlnet_config = model_config.unet_config
 
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(controlnet_data)
+
+        if supported_inference_dtypes is None:
+            supported_inference_dtypes = [comfy.model_management.unet_dtype()]
+
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+
     load_device = comfy.model_management.get_torch_device()
-    if supported_inference_dtypes is None:
-        unet_dtype = comfy.model_management.unet_dtype()
-    else:
-        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
 
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
+    operations = model_options.get("custom_operations", None)
-        controlnet_config["operations"] = comfy.ops.manual_cast
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)
+
+    controlnet_config["operations"] = operations
     controlnet_config["dtype"] = unet_dtype
     controlnet_config["device"] = comfy.model_management.unet_offload_device()
     controlnet_config.pop("out_channels")
@@ -590,22 +641,32 @@ def load_controlnet(ckpt_path, model=None):
     if len(unexpected) > 0:
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
-    global_average_pooling = False
+    global_average_pooling = model_options.get("global_average_pooling", False)
-    filename = os.path.splitext(ckpt_path)[0]
-    if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
-        global_average_pooling = True
-
     control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
+def load_controlnet(ckpt_path, model=None, model_options={}):
+    if "global_average_pooling" not in model_options:
+        filename = os.path.splitext(ckpt_path)[0]
+        if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
+            model_options["global_average_pooling"] = True
+
+    cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)
+    if cnet is None:
+        logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+    return cnet
+
 class T2IAdapter(ControlBase):
     def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
-        super().__init__(device)
+        super().__init__()
         self.t2i_model = t2i_model
         self.channels_in = channels_in
         self.control_input = None
         self.compression_ratio = compression_ratio
         self.upscale_algorithm = upscale_algorithm
+        if device is None:
+            device = comfy.model_management.get_torch_device()
+        self.device = device
 
     def scale_image_to(self, width, height):
         unshuffle_amount = self.t2i_model.unshuffle_amount
@@ -653,7 +714,7 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
-def load_t2i_adapter(t2i_data):
+def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'
 

comfy/extra_samplers/uni_pc.py

@@ -16,7 +16,7 @@ class NoiseScheduleVP:
             continuous_beta_0=0.1,
             continuous_beta_1=20.,
         ):
-        """Create a wrapper class for the forward SDE (VP type).
+        r"""Create a wrapper class for the forward SDE (VP type).
 
         ***
         Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.

comfy/float.py

@@ -1,5 +1,4 @@
 import torch
-import math
 
 def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):
     mantissa_scaled = torch.where(
@@ -41,9 +40,10 @@ def manual_stochastic_round_to_float8(x, dtype, generator=None):
         (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x),
         (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x
     )
-    del abs_x
 
-    return sign.to(dtype=dtype)
+    inf = torch.finfo(dtype)
+    torch.clamp(sign, min=inf.min, max=inf.max, out=sign)
+    return sign
 
 
 
@@ -57,6 +57,11 @@ def stochastic_rounding(value, dtype, seed=0):
     if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
         generator = torch.Generator(device=value.device)
         generator.manual_seed(seed)
-        return manual_stochastic_round_to_float8(value, dtype, generator=generator)
+        output = torch.empty_like(value, dtype=dtype)
+        num_slices = max(1, (value.numel() / (4096 * 4096)))
+        slice_size = max(1, round(value.shape[0] / num_slices))
+        for i in range(0, value.shape[0], slice_size):
+            output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator))
+        return output
 
     return value.to(dtype=dtype)

comfy/k_diffusion/sampling.py

@@ -44,6 +44,17 @@ def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     return append_zero(sigmas)
 
 
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+    """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+    epsilon = 1e-5 # avoid log(0)
+    x = torch.linspace(0, 1, n, device=device)
+    clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+    lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+    sigmas = clamp(torch.exp(lmb))
+    return sigmas
+
+
+
 def to_d(x, sigma, denoised):
     """Converts a denoiser output to a Karras ODE derivative."""
     return (x - denoised) / utils.append_dims(sigma, x.ndim)
@@ -153,6 +164,8 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
 @torch.no_grad()
 def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+        return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
     """Ancestral sampling with Euler method steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -170,6 +183,29 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
 
+@torch.no_grad()
+def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None):
+    """Ancestral sampling with Euler method steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+        sigma_down = sigmas[i+1] * downstep_ratio
+        alpha_ip1 = 1 - sigmas[i+1]
+        alpha_down = 1 - sigma_down
+        renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        # Euler method
+        sigma_down_i_ratio = sigma_down / sigmas[i]
+        x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised
+        if sigmas[i + 1] > 0 and eta > 0:
+            x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+    return x
 
 @torch.no_grad()
 def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
@@ -1069,7 +1105,6 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
         d = to_d(x, sigma_hat, temp[0])
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
         # Euler method
         x = denoised + d * sigmas[i + 1]
     return x
@@ -1096,8 +1131,81 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         d = to_d(x, sigmas[i], temp[0])
         # Euler method
-        dt = sigma_down - sigmas[i]
         x = denoised + d * sigma_down
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigma_down == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], temp[0])
+            x = denoised + d * sigma_down
+        else:
+            # DPM-Solver++(2S)
+            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+            # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird
+            r = 1 / 2
+            h = t_next - t
+            s = t + r * h
+            x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    t_fn = lambda sigma: sigma.log().neg()
+
+    old_uncond_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+    
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_uncond_denoised is None or sigmas[i + 1] == 0:
+            denoised_mix = -torch.exp(-h) * uncond_denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+        x = denoised + denoised_mix + torch.exp(-h) * x
+        old_uncond_denoised = uncond_denoised
+    return x

comfy/latent_formats.py

@@ -4,6 +4,7 @@ class LatentFormat:
     scale_factor = 1.0
     latent_channels = 4
     latent_rgb_factors = None
+    latent_rgb_factors_bias = None
     taesd_decoder_name = None
 
     def process_in(self, latent):
@@ -30,11 +31,13 @@ class SDXL(LatentFormat):
     def __init__(self):
         self.latent_rgb_factors = [
                     #   R        G        B
-                    [ 0.3920,  0.4054,  0.4549],
+                    [ 0.3651,  0.4232,  0.4341],
-                    [-0.2634, -0.0196,  0.0653],
+                    [-0.2533, -0.0042,  0.1068],
-                    [ 0.0568,  0.1687, -0.0755],
+                    [ 0.1076,  0.1111, -0.0362],
-                    [-0.3112, -0.2359, -0.2076]
+                    [-0.3165, -0.2492, -0.2188]
                 ]
+        self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]
+
         self.taesd_decoder_name = "taesdxl_decoder"
 
 class SDXL_Playground_2_5(LatentFormat):
@@ -112,23 +115,24 @@ class SD3(LatentFormat):
         self.scale_factor = 1.5305
         self.shift_factor = 0.0609
         self.latent_rgb_factors = [
-            [-0.0645,  0.0177,  0.1052],
+            [-0.0922, -0.0175,  0.0749],
-            [ 0.0028,  0.0312,  0.0650],
+            [ 0.0311,  0.0633,  0.0954],
-            [ 0.1848,  0.0762,  0.0360],
+            [ 0.1994,  0.0927,  0.0458],
-            [ 0.0944,  0.0360,  0.0889],
+            [ 0.0856,  0.0339,  0.0902],
-            [ 0.0897,  0.0506, -0.0364],
+            [ 0.0587,  0.0272, -0.0496],
-            [-0.0020,  0.1203,  0.0284],
+            [-0.0006,  0.1104,  0.0309],
-            [ 0.0855,  0.0118,  0.0283],
+            [ 0.0978,  0.0306,  0.0427],
-            [-0.0539,  0.0658,  0.1047],
+            [-0.0042,  0.1038,  0.1358],
-            [-0.0057,  0.0116,  0.0700],
+            [-0.0194,  0.0020,  0.0669],
-            [-0.0412,  0.0281, -0.0039],
+            [-0.0488,  0.0130, -0.0268],
-            [ 0.1106,  0.1171,  0.1220],
+            [ 0.0922,  0.0988,  0.0951],
-            [-0.0248,  0.0682, -0.0481],
+            [-0.0278,  0.0524, -0.0542],
-            [ 0.0815,  0.0846,  0.1207],
+            [ 0.0332,  0.0456,  0.0895],
-            [-0.0120, -0.0055, -0.0867],
+            [-0.0069, -0.0030, -0.0810],
-            [-0.0749, -0.0634, -0.0456],
+            [-0.0596, -0.0465, -0.0293],
-            [-0.1418, -0.1457, -0.1259]
+            [-0.1448, -0.1463, -0.1189]
         ]
+        self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]
         self.taesd_decoder_name = "taesd3_decoder"
 
     def process_in(self, latent):
@@ -146,23 +150,24 @@ class Flux(SD3):
         self.scale_factor = 0.3611
         self.shift_factor = 0.1159
         self.latent_rgb_factors =[
-            [-0.0404,  0.0159,  0.0609],
+            [-0.0346,  0.0244,  0.0681],
-            [ 0.0043,  0.0298,  0.0850],
+            [ 0.0034,  0.0210,  0.0687],
-            [ 0.0328, -0.0749, -0.0503],
+            [ 0.0275, -0.0668, -0.0433],
-            [-0.0245,  0.0085,  0.0549],
+            [-0.0174,  0.0160,  0.0617],
-            [ 0.0966,  0.0894,  0.0530],
+            [ 0.0859,  0.0721,  0.0329],
-            [ 0.0035,  0.0399,  0.0123],
+            [ 0.0004,  0.0383,  0.0115],
-            [ 0.0583,  0.1184,  0.1262],
+            [ 0.0405,  0.0861,  0.0915],
-            [-0.0191, -0.0206, -0.0306],
+            [-0.0236, -0.0185, -0.0259],
-            [-0.0324,  0.0055,  0.1001],
+            [-0.0245,  0.0250,  0.1180],
-            [ 0.0955,  0.0659, -0.0545],
+            [ 0.1008,  0.0755, -0.0421],
-            [-0.0504,  0.0231, -0.0013],
+            [-0.0515,  0.0201,  0.0011],
-            [ 0.0500, -0.0008, -0.0088],
+            [ 0.0428, -0.0012, -0.0036],
-            [ 0.0982,  0.0941,  0.0976],
+            [ 0.0817,  0.0765,  0.0749],
-            [-0.1233, -0.0280, -0.0897],
+            [-0.1264, -0.0522, -0.1103],
-            [-0.0005, -0.0530, -0.0020],
+            [-0.0280, -0.0881, -0.0499],
-            [-0.1273, -0.0932, -0.0680]
+            [-0.1262, -0.0982, -0.0778]
         ]
+        self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
         self.taesd_decoder_name = "taef1_decoder"
 
     def process_in(self, latent):
@@ -170,3 +175,30 @@ class Flux(SD3):
 
     def process_out(self, latent):
         return (latent / self.scale_factor) + self.shift_factor
+
+class Mochi(LatentFormat):
+    latent_channels = 12
+
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141,
+                                          -0.05565264470995561, 0.012767231469026969, -0.04703542746246419,
+                                          0.043896967884726704, -0.09346305707025976, -0.09918314763016893,
+                                          -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1)
+        self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617,
+                                         0.959253732819592, 0.8244560132752793, 0.917259975397747,
+                                         0.9294154431013696, 1.3720942357788521, 0.881393668867029,
+                                         0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)
+
+        self.latent_rgb_factors = None #TODO
+        self.taesd_decoder_name = None #TODO
+
+    def process_in(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return (latent - latents_mean) * self.scale_factor / latents_std
+
+    def process_out(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return latent * latents_std / self.scale_factor + latents_mean

comfy/ldm/common_dit.py

@@ -13,9 +13,15 @@ try:
 except:
     rms_norm_torch = None
 
-def rms_norm(x, weight, eps=1e-6):
+def rms_norm(x, weight=None, eps=1e-6):
-    if rms_norm_torch is not None:
+    if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()):
-        return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps)
+        if weight is None:
+            return rms_norm_torch(x, (x.shape[-1],), eps=eps)
+        else:
+            return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps)
     else:
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
+        r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
-        return (x * rrms) * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device)
+        if weight is None:
+            return r
+        else:
+            return r * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device)

comfy/ldm/flux/controlnet.py

@@ -1,4 +1,5 @@
 #Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+#modified to support different types of flux controlnets
 
 import torch
 import math
@@ -12,22 +13,65 @@ from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
 from .model import Flux
 import comfy.ldm.common_dit
 
+class MistolineCondDownsamplBlock(nn.Module):
+    def __init__(self, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.encoder(x)
+
+class MistolineControlnetBlock(nn.Module):
+    def __init__(self, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.linear(x))
+
 
 class ControlNetFlux(Flux):
-    def __init__(self, latent_input=False, num_union_modes=0, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
         super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
 
         self.main_model_double = 19
         self.main_model_single = 38
+
+        self.mistoline = mistoline
         # add ControlNet blocks
+        if self.mistoline:
+            control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
         self.controlnet_blocks = nn.ModuleList([])
         for _ in range(self.params.depth):
-            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+            self.controlnet_blocks.append(control_block())
-            self.controlnet_blocks.append(controlnet_block)
 
         self.controlnet_single_blocks = nn.ModuleList([])
         for _ in range(self.params.depth_single_blocks):
-            self.controlnet_single_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device))
+            self.controlnet_single_blocks.append(control_block())
 
         self.num_union_modes = num_union_modes
         self.controlnet_mode_embedder = None
@@ -36,25 +80,33 @@ class ControlNetFlux(Flux):
 
         self.gradient_checkpointing = False
         self.latent_input = latent_input
-        self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+        else:
+            control_latent_channels *= 2 * 2 #patch size
+
+        self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
         if not self.latent_input:
-            self.input_hint_block = nn.Sequential(
+            if self.mistoline:
-                operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+                self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
-                nn.SiLU(),
+            else:
-                operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                self.input_hint_block = nn.Sequential(
-                nn.SiLU(),
+                    operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
-                nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
-                nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
-                nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
-                nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
-                nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
-                operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+                    nn.SiLU(),
-            )
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+                )
 
     def forward_orig(
         self,
@@ -73,9 +125,6 @@ class ControlNetFlux(Flux):
 
         # running on sequences img
         img = self.img_in(img)
-        if not self.latent_input:
-            controlnet_cond = self.input_hint_block(controlnet_cond)
-            controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
 
         controlnet_cond = self.pos_embed_input(controlnet_cond)
         img = img + controlnet_cond
@@ -131,9 +180,14 @@ class ControlNetFlux(Flux):
         patch_size = 2
         if self.latent_input:
             hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
-            hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+        elif self.mistoline:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_cond_block(hint)
         else:
             hint = hint * 2.0 - 1.0
+            hint = self.input_hint_block(hint)
+
+        hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
 
         bs, c, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/flux/model.py

@@ -108,7 +108,7 @@ class Flux(nn.Module):
                 raise ValueError("Didn't get guidance strength for guidance distilled model.")
             vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y)
+        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
         ids = torch.cat((txt_ids, img_ids), dim=1)
@@ -151,8 +151,8 @@ class Flux(nn.Module):
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[:, :, 1] = torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids[:, :, 2] = torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -0,0 +1,541 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+# from flash_attn import flash_attn_varlen_qkvpacked_func
+from comfy.ldm.modules.attention import optimized_attention
+
+from .layers import (
+    FeedForward,
+    PatchEmbed,
+    RMSNorm,
+    TimestepEmbedder,
+)
+
+from .rope_mixed import (
+    compute_mixed_rotation,
+    create_position_matrix,
+)
+from .temporal_rope import apply_rotary_emb_qk_real
+from .utils import (
+    AttentionPool,
+    modulate,
+)
+
+import comfy.ldm.common_dit
+import comfy.ops
+
+
+def modulated_rmsnorm(x, scale, eps=1e-6):
+    # Normalize and modulate
+    x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps)
+    x_modulated = x_normed * (1 + scale.unsqueeze(1))
+
+    return x_modulated
+
+
+def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6):
+    # Apply tanh to gate
+    tanh_gate = torch.tanh(gate).unsqueeze(1)
+
+    # Normalize and apply gated scaling
+    x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate
+
+    # Apply residual connection
+    output = x + x_normed
+
+    return output
+
+class AsymmetricAttention(nn.Module):
+    def __init__(
+        self,
+        dim_x: int,
+        dim_y: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        attn_drop: float = 0.0,
+        update_y: bool = True,
+        out_bias: bool = True,
+        attend_to_padding: bool = False,
+        softmax_scale: Optional[float] = None,
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dim_x = dim_x
+        self.dim_y = dim_y
+        self.num_heads = num_heads
+        self.head_dim = dim_x // num_heads
+        self.attn_drop = attn_drop
+        self.update_y = update_y
+        self.attend_to_padding = attend_to_padding
+        self.softmax_scale = softmax_scale
+        if dim_x % num_heads != 0:
+            raise ValueError(
+                f"dim_x={dim_x} should be divisible by num_heads={num_heads}"
+            )
+
+        # Input layers.
+        self.qkv_bias = qkv_bias
+        self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)
+        # Project text features to match visual features (dim_y -> dim_x)
+        self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)
+
+        # Query and key normalization for stability.
+        assert qk_norm
+        self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
+        self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
+        self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
+        self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
+
+        # Output layers. y features go back down from dim_x -> dim_y.
+        self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype)
+        self.proj_y = (
+            operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype)
+            if update_y
+            else nn.Identity()
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,  # (B, N, dim_x)
+        y: torch.Tensor,  # (B, L, dim_y)
+        scale_x: torch.Tensor,  # (B, dim_x), modulation for pre-RMSNorm.
+        scale_y: torch.Tensor,  # (B, dim_y), modulation for pre-RMSNorm.
+        crop_y,
+        **rope_rotation,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        rope_cos = rope_rotation.get("rope_cos")
+        rope_sin = rope_rotation.get("rope_sin")
+        # Pre-norm for visual features
+        x = modulated_rmsnorm(x, scale_x)  # (B, M, dim_x) where M = N / cp_group_size
+
+        # Process visual features
+        # qkv_x = self.qkv_x(x)  # (B, M, 3 * dim_x)
+        # assert qkv_x.dtype == torch.bfloat16
+        # qkv_x = all_to_all_collect_tokens(
+        #     qkv_x, self.num_heads
+        # )  # (3, B, N, local_h, head_dim)
+
+        # Process text features
+        y = modulated_rmsnorm(y, scale_y)  # (B, L, dim_y)
+        q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2)  # (B, N, local_h, head_dim)
+
+        q_y = self.q_norm_y(q_y)
+        k_y = self.k_norm_y(k_y)
+
+        # Split qkv_x into q, k, v
+        q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2)  # (B, N, local_h, head_dim)
+        q_x = self.q_norm_x(q_x)
+        q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin)
+        k_x = self.k_norm_x(k_x)
+        k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin)
+
+        q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2)
+        k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2)
+        v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2)
+
+        xy = optimized_attention(q,
+                                 k,
+                                 v, self.num_heads, skip_reshape=True)
+
+        x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
+        x = self.proj_x(x)
+        o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype)
+        o[:, :y.shape[1]] = y
+
+        y = self.proj_y(o)
+        # print("ox", x)
+        # print("oy", y)
+        return x, y
+
+
+class AsymmetricJointBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size_x: int,
+        hidden_size_y: int,
+        num_heads: int,
+        *,
+        mlp_ratio_x: float = 8.0,  # Ratio of hidden size to d_model for MLP for visual tokens.
+        mlp_ratio_y: float = 4.0,  # Ratio of hidden size to d_model for MLP for text tokens.
+        update_y: bool = True,  # Whether to update text tokens in this block.
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+        **block_kwargs,
+    ):
+        super().__init__()
+        self.update_y = update_y
+        self.hidden_size_x = hidden_size_x
+        self.hidden_size_y = hidden_size_y
+        self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype)
+        if self.update_y:
+            self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype)
+        else:
+            self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype)
+
+        # Self-attention:
+        self.attn = AsymmetricAttention(
+            hidden_size_x,
+            hidden_size_y,
+            num_heads=num_heads,
+            update_y=update_y,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+            **block_kwargs,
+        )
+
+        # MLP.
+        mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x)
+        assert mlp_hidden_dim_x == int(1536 * 8)
+        self.mlp_x = FeedForward(
+            in_features=hidden_size_x,
+            hidden_size=mlp_hidden_dim_x,
+            multiple_of=256,
+            ffn_dim_multiplier=None,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+        # MLP for text not needed in last block.
+        if self.update_y:
+            mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y)
+            self.mlp_y = FeedForward(
+                in_features=hidden_size_y,
+                hidden_size=mlp_hidden_dim_y,
+                multiple_of=256,
+                ffn_dim_multiplier=None,
+                device=device,
+                dtype=dtype,
+                operations=operations,
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,
+        y: torch.Tensor,
+        **attn_kwargs,
+    ):
+        """Forward pass of a block.
+
+        Args:
+            x: (B, N, dim) tensor of visual tokens
+            c: (B, dim) tensor of conditioned features
+            y: (B, L, dim) tensor of text tokens
+            num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens
+
+        Returns:
+            x: (B, N, dim) tensor of visual tokens after block
+            y: (B, L, dim) tensor of text tokens after block
+        """
+        N = x.size(1)
+
+        c = F.silu(c)
+        mod_x = self.mod_x(c)
+        scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1)
+
+        mod_y = self.mod_y(c)
+        if self.update_y:
+            scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1)
+        else:
+            scale_msa_y = mod_y
+
+        # Self-attention block.
+        x_attn, y_attn = self.attn(
+            x,
+            y,
+            scale_x=scale_msa_x,
+            scale_y=scale_msa_y,
+            **attn_kwargs,
+        )
+
+        assert x_attn.size(1) == N
+        x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x)
+        if self.update_y:
+            y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y)
+
+        # MLP block.
+        x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x)
+        if self.update_y:
+            y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y)
+
+        return x, y
+
+    def ff_block_x(self, x, scale_x, gate_x):
+        x_mod = modulated_rmsnorm(x, scale_x)
+        x_res = self.mlp_x(x_mod)
+        x = residual_tanh_gated_rmsnorm(x, x_res, gate_x)  # Sandwich norm
+        return x
+
+    def ff_block_y(self, y, scale_y, gate_y):
+        y_mod = modulated_rmsnorm(y, scale_y)
+        y_res = self.mlp_y(y_mod)
+        y = residual_tanh_gated_rmsnorm(y, y_res, gate_y)  # Sandwich norm
+        return y
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        patch_size,
+        out_channels,
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(
+            hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype
+        )
+        self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype)
+        self.linear = operations.Linear(
+            hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype
+        )
+
+    def forward(self, x, c):
+        c = F.silu(c)
+        shift, scale = self.mod(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class AsymmDiTJoint(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+
+    Ingests text embeddings instead of a label.
+    """
+
+    def __init__(
+        self,
+        *,
+        patch_size=2,
+        in_channels=4,
+        hidden_size_x=1152,
+        hidden_size_y=1152,
+        depth=48,
+        num_heads=16,
+        mlp_ratio_x=8.0,
+        mlp_ratio_y=4.0,
+        use_t5: bool = False,
+        t5_feat_dim: int = 4096,
+        t5_token_length: int = 256,
+        learn_sigma=True,
+        patch_embed_bias: bool = True,
+        timestep_mlp_bias: bool = True,
+        attend_to_padding: bool = False,
+        timestep_scale: Optional[float] = None,
+        use_extended_posenc: bool = False,
+        posenc_preserve_area: bool = False,
+        rope_theta: float = 10000.0,
+        image_model=None,
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+        **block_kwargs,
+    ):
+        super().__init__()
+
+        self.dtype = dtype
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size_x = hidden_size_x
+        self.hidden_size_y = hidden_size_y
+        self.head_dim = (
+            hidden_size_x // num_heads
+        )  # Head dimension and count is determined by visual.
+        self.attend_to_padding = attend_to_padding
+        self.use_extended_posenc = use_extended_posenc
+        self.posenc_preserve_area = posenc_preserve_area
+        self.use_t5 = use_t5
+        self.t5_token_length = t5_token_length
+        self.t5_feat_dim = t5_feat_dim
+        self.rope_theta = (
+            rope_theta  # Scaling factor for frequency computation for temporal RoPE.
+        )
+
+        self.x_embedder = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_channels,
+            embed_dim=hidden_size_x,
+            bias=patch_embed_bias,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        # Conditionings
+        # Timestep
+        self.t_embedder = TimestepEmbedder(
+            hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
+        )
+
+        if self.use_t5:
+            # Caption Pooling (T5)
+            self.t5_y_embedder = AttentionPool(
+                t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations
+            )
+
+            # Dense Embedding Projection (T5)
+            self.t5_yproj = operations.Linear(
+                t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device
+            )
+
+        # Initialize pos_frequencies as an empty parameter.
+        self.pos_frequencies = nn.Parameter(
+            torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device)
+        )
+
+        assert not self.attend_to_padding
+
+        # for depth 48:
+        #  b =  0: AsymmetricJointBlock, update_y=True
+        #  b =  1: AsymmetricJointBlock, update_y=True
+        #  ...
+        #  b = 46: AsymmetricJointBlock, update_y=True
+        #  b = 47: AsymmetricJointBlock, update_y=False. No need to update text features.
+        blocks = []
+        for b in range(depth):
+            # Joint multi-modal block
+            update_y = b < depth - 1
+            block = AsymmetricJointBlock(
+                hidden_size_x,
+                hidden_size_y,
+                num_heads,
+                mlp_ratio_x=mlp_ratio_x,
+                mlp_ratio_y=mlp_ratio_y,
+                update_y=update_y,
+                attend_to_padding=attend_to_padding,
+                device=device,
+                dtype=dtype,
+                operations=operations,
+                **block_kwargs,
+            )
+
+            blocks.append(block)
+        self.blocks = nn.ModuleList(blocks)
+
+        self.final_layer = FinalLayer(
+            hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+    def embed_x(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: (B, C=12, T, H, W) tensor of visual tokens
+
+        Returns:
+            x: (B, C=3072, N) tensor of visual tokens with positional embedding.
+        """
+        return self.x_embedder(x)  # Convert BcTHW to BCN
+
+    def prepare(
+        self,
+        x: torch.Tensor,
+        sigma: torch.Tensor,
+        t5_feat: torch.Tensor,
+        t5_mask: torch.Tensor,
+    ):
+        """Prepare input and conditioning embeddings."""
+        # Visual patch embeddings with positional encoding.
+        T, H, W = x.shape[-3:]
+        pH, pW = H // self.patch_size, W // self.patch_size
+        x = self.embed_x(x)  # (B, N, D), where N = T * H * W / patch_size ** 2
+        assert x.ndim == 3
+        B = x.size(0)
+
+
+        pH, pW = H // self.patch_size, W // self.patch_size
+        N = T * pH * pW
+        assert x.size(1) == N
+        pos = create_position_matrix(
+            T, pH=pH, pW=pW, device=x.device, dtype=torch.float32
+        )  # (N, 3)
+        rope_cos, rope_sin = compute_mixed_rotation(
+            freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos
+        )  # Each are (N, num_heads, dim // 2)
+
+        c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype)  # (B, D)
+
+        t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask)  # (B, D)
+
+        c = c_t + t5_y_pool
+
+        y_feat = self.t5_yproj(t5_feat)  # (B, L, t5_feat_dim) --> (B, L, D)
+
+        return x, c, y_feat, rope_cos, rope_sin
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timestep: torch.Tensor,
+        context: List[torch.Tensor],
+        attention_mask: List[torch.Tensor],
+        num_tokens=256,
+        packed_indices: Dict[str, torch.Tensor] = None,
+        rope_cos: torch.Tensor = None,
+        rope_sin: torch.Tensor = None,
+        control=None, **kwargs
+    ):
+        y_feat = context
+        y_mask = attention_mask
+        sigma = timestep
+        """Forward pass of DiT.
+
+        Args:
+            x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images)
+            sigma: (B,) tensor of noise standard deviations
+            y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048)
+            y_mask: List((B, L) boolean tensor indicating which tokens are not padding)
+            packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices.
+        """
+        B, _, T, H, W = x.shape
+
+        x, c, y_feat, rope_cos, rope_sin = self.prepare(
+            x, sigma, y_feat, y_mask
+        )
+        del y_mask
+
+        for i, block in enumerate(self.blocks):
+            x, y_feat = block(
+                x,
+                c,
+                y_feat,
+                rope_cos=rope_cos,
+                rope_sin=rope_sin,
+                crop_y=num_tokens,
+            )  # (B, M, D), (B, L, D)
+        del y_feat  # Final layers don't use dense text features.
+
+        x = self.final_layer(x, c)  # (B, M, patch_size ** 2 * out_channels)
+        x = rearrange(
+            x,
+            "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)",
+            T=T,
+            hp=H // self.patch_size,
+            wp=W // self.patch_size,
+            p1=self.patch_size,
+            p2=self.patch_size,
+            c=self.out_channels,
+        )
+
+        return -x

comfy/ldm/genmo/joint_model/layers.py

@@ -0,0 +1,164 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+import collections.abc
+import math
+from itertools import repeat
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import comfy.ldm.common_dit
+
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return tuple(x)
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_2tuple = _ntuple(2)
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        frequency_embedding_size: int = 256,
+        *,
+        bias: bool = True,
+        timestep_scale: Optional[float] = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+        self.timestep_scale = timestep_scale
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
+        freqs.mul_(-math.log(max_period) / half).exp_()
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    def forward(self, t, out_dtype):
+        if self.timestep_scale is not None:
+            t = t * self.timestep_scale
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_size: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        # keep parameter count and computation constant compared to standard FFN
+        hidden_size = int(2 * hidden_size / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_size = int(ffn_dim_multiplier * hidden_size)
+        hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of)
+
+        self.hidden_dim = hidden_size
+        self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype)
+        self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x, gate = self.w1(x).chunk(2, dim=-1)
+        x = self.w2(F.silu(x) * gate)
+        return x
+
+
+class PatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten: bool = True,
+        bias: bool = True,
+        dynamic_img_pad: bool = False,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.patch_size = to_2tuple(patch_size)
+        self.flatten = flatten
+        self.dynamic_img_pad = dynamic_img_pad
+
+        self.proj = operations.Conv2d(
+            in_chans,
+            embed_dim,
+            kernel_size=patch_size,
+            stride=patch_size,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+        assert norm_layer is None
+        self.norm = (
+            norm_layer(embed_dim, device=device) if norm_layer else nn.Identity()
+        )
+
+    def forward(self, x):
+        B, _C, T, H, W = x.shape
+        if not self.dynamic_img_pad:
+            assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+            assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+        else:
+            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
+            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
+            x = F.pad(x, (0, pad_w, 0, pad_h))
+
+        x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T)
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular')
+        x = self.proj(x)
+
+        # Flatten temporal and spatial dimensions.
+        if not self.flatten:
+            raise NotImplementedError("Must flatten output.")
+        x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T)
+
+        x = self.norm(x)
+        return x
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
+        super().__init__()
+        self.eps = eps
+        self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype))
+        self.register_parameter("bias", None)
+
+    def forward(self, x):
+        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)

comfy/ldm/genmo/joint_model/rope_mixed.py

@@ -0,0 +1,88 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+
+# import functools
+import math
+
+import torch
+
+
+def centers(start: float, stop, num, dtype=None, device=None):
+    """linspace through bin centers.
+
+    Args:
+        start (float): Start of the range.
+        stop (float): End of the range.
+        num (int): Number of points.
+        dtype (torch.dtype): Data type of the points.
+        device (torch.device): Device of the points.
+
+    Returns:
+        centers (Tensor): Centers of the bins. Shape: (num,).
+    """
+    edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device)
+    return (edges[:-1] + edges[1:]) / 2
+
+
+# @functools.lru_cache(maxsize=1)
+def create_position_matrix(
+    T: int,
+    pH: int,
+    pW: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    *,
+    target_area: float = 36864,
+):
+    """
+    Args:
+        T: int - Temporal dimension
+        pH: int - Height dimension after patchify
+        pW: int - Width dimension after patchify
+
+    Returns:
+        pos: [T * pH * pW, 3] - position matrix
+    """
+    # Create 1D tensors for each dimension
+    t = torch.arange(T, dtype=dtype)
+
+    # Positionally interpolate to area 36864.
+    # (3072x3072 frame with 16x16 patches = 192x192 latents).
+    # This automatically scales rope positions when the resolution changes.
+    # We use a large target area so the model is more sensitive
+    # to changes in the learned pos_frequencies matrix.
+    scale = math.sqrt(target_area / (pW * pH))
+    w = centers(-pW * scale / 2, pW * scale / 2, pW)
+    h = centers(-pH * scale / 2, pH * scale / 2, pH)
+
+    # Use meshgrid to create 3D grids
+    grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij")
+
+    # Stack and reshape the grids.
+    pos = torch.stack([grid_t, grid_h, grid_w], dim=-1)  # [T, pH, pW, 3]
+    pos = pos.view(-1, 3)  # [T * pH * pW, 3]
+    pos = pos.to(dtype=dtype, device=device)
+
+    return pos
+
+
+def compute_mixed_rotation(
+    freqs: torch.Tensor,
+    pos: torch.Tensor,
+):
+    """
+    Project each 3-dim position into per-head, per-head-dim 1D frequencies.
+
+    Args:
+        freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position
+        pos: [N, 3] - position of each token
+        num_heads: int
+
+    Returns:
+        freqs_cos: [N, num_heads, num_freqs] - cosine components
+        freqs_sin: [N, num_heads, num_freqs] - sine components
+    """
+    assert freqs.ndim == 3
+    freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs)
+    freqs_cos = torch.cos(freqs_sum)
+    freqs_sin = torch.sin(freqs_sum)
+    return freqs_cos, freqs_sin

comfy/ldm/genmo/joint_model/temporal_rope.py

@@ -0,0 +1,34 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+
+# Based on Llama3 Implementation.
+import torch
+
+
+def apply_rotary_emb_qk_real(
+    xqk: torch.Tensor,
+    freqs_cos: torch.Tensor,
+    freqs_sin: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers.
+
+    Args:
+        xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D)
+                            Can be either just query or just key, or both stacked along some batch or * dim.
+        freqs_cos (torch.Tensor): Precomputed cosine frequency tensor.
+        freqs_sin (torch.Tensor): Precomputed sine frequency tensor.
+
+    Returns:
+        torch.Tensor: The input tensor with rotary embeddings applied.
+    """
+    # Split the last dimension into even and odd parts
+    xqk_even = xqk[..., 0::2]
+    xqk_odd = xqk[..., 1::2]
+
+    # Apply rotation
+    cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk)
+    sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk)
+
+    # Interleave the results back into the original shape
+    out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2)
+    return out

comfy/ldm/genmo/joint_model/utils.py

@@ -0,0 +1,102 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:
+    """
+    Pool tokens in x using mask.
+
+    NOTE: We assume x does not require gradients.
+
+    Args:
+        x: (B, L, D) tensor of tokens.
+        mask: (B, L) boolean tensor indicating which tokens are not padding.
+
+    Returns:
+        pooled: (B, D) tensor of pooled tokens.
+    """
+    assert x.size(1) == mask.size(1)  # Expected mask to have same length as tokens.
+    assert x.size(0) == mask.size(0)  # Expected mask to have same batch size as tokens.
+    mask = mask[:, :, None].to(dtype=x.dtype)
+    mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)
+    pooled = (x * mask).sum(dim=1, keepdim=keepdim)
+    return pooled
+
+
+class AttentionPool(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        output_dim: int = None,
+        device: Optional[torch.device] = None,
+        dtype=None,
+        operations=None,
+    ):
+        """
+        Args:
+            spatial_dim (int): Number of tokens in sequence length.
+            embed_dim (int): Dimensionality of input tokens.
+            num_heads (int): Number of attention heads.
+            output_dim (int): Dimensionality of output tokens. Defaults to embed_dim.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype)
+        self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+        self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x, mask):
+        """
+        Args:
+            x (torch.Tensor): (B, L, D) tensor of input tokens.
+            mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding.
+
+        NOTE: We assume x does not require gradients.
+
+        Returns:
+            x (torch.Tensor): (B, D) tensor of pooled tokens.
+        """
+        D = x.size(2)
+
+        # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
+        attn_mask = mask[:, None, None, :].bool()  # (B, 1, 1, L).
+        attn_mask = F.pad(attn_mask, (1, 0), value=True)  # (B, 1, 1, 1+L).
+
+        # Average non-padding token features. These will be used as the query.
+        x_pool = pool_tokens(x, mask, keepdim=True)  # (B, 1, D)
+
+        # Concat pooled features to input sequence.
+        x = torch.cat([x_pool, x], dim=1)  # (B, L+1, D)
+
+        # Compute queries, keys, values. Only the mean token is used to create a query.
+        kv = self.to_kv(x)  # (B, L+1, 2 * D)
+        q = self.to_q(x[:, 0])  # (B, D)
+
+        # Extract heads.
+        head_dim = D // self.num_heads
+        kv = kv.unflatten(2, (2, self.num_heads, head_dim))  # (B, 1+L, 2, H, head_dim)
+        kv = kv.transpose(1, 3)  # (B, H, 2, 1+L, head_dim)
+        k, v = kv.unbind(2)  # (B, H, 1+L, head_dim)
+        q = q.unflatten(1, (self.num_heads, head_dim))  # (B, H, head_dim)
+        q = q.unsqueeze(2)  # (B, H, 1, head_dim)
+
+        # Compute attention.
+        x = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=attn_mask, dropout_p=0.0
+        )  # (B, H, 1, head_dim)
+
+        # Concatenate heads and run output.
+        x = x.squeeze(2).flatten(1, 2)  # (B, D = H * head_dim)
+        x = self.to_out(x)
+        return x

comfy/ldm/genmo/vae/model.py

@@ -0,0 +1,711 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Callable, List, Optional, Tuple, Union
+from functools import partial
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from comfy.ldm.modules.attention import optimized_attention
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+# import mochi_preview.dit.joint_model.context_parallel as cp
+# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames
+
+
+def cast_tuple(t, length=1):
+    return t if isinstance(t, tuple) else ((t,) * length)
+
+
+class GroupNormSpatial(ops.GroupNorm):
+    """
+    GroupNorm applied per-frame.
+    """
+
+    def forward(self, x: torch.Tensor, *, chunk_size: int = 8):
+        B, C, T, H, W = x.shape
+        x = rearrange(x, "B C T H W -> (B T) C H W")
+        # Run group norm in chunks.
+        output = torch.empty_like(x)
+        for b in range(0, B * T, chunk_size):
+            output[b : b + chunk_size] = super().forward(x[b : b + chunk_size])
+        return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T)
+
+class PConv3d(ops.Conv3d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: Union[int, Tuple[int, int, int]],
+        causal: bool = True,
+        context_parallel: bool = True,
+        **kwargs,
+    ):
+        self.causal = causal
+        self.context_parallel = context_parallel
+        kernel_size = cast_tuple(kernel_size, 3)
+        stride = cast_tuple(stride, 3)
+        height_pad = (kernel_size[1] - 1) // 2
+        width_pad = (kernel_size[2] - 1) // 2
+
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=(1, 1, 1),
+            padding=(0, height_pad, width_pad),
+            **kwargs,
+        )
+
+    def forward(self, x: torch.Tensor):
+        # Compute padding amounts.
+        context_size = self.kernel_size[0] - 1
+        if self.causal:
+            pad_front = context_size
+            pad_back = 0
+        else:
+            pad_front = context_size // 2
+            pad_back = context_size - pad_front
+
+        # Apply padding.
+        assert self.padding_mode == "replicate"  # DEBUG
+        mode = "constant" if self.padding_mode == "zeros" else self.padding_mode
+        x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode)
+        return super().forward(x)
+
+
+class Conv1x1(ops.Linear):
+    """*1x1 Conv implemented with a linear layer."""
+
+    def __init__(self, in_features: int, out_features: int, *args, **kwargs):
+        super().__init__(in_features, out_features, *args, **kwargs)
+
+    def forward(self, x: torch.Tensor):
+        """Forward pass.
+
+        Args:
+            x: Input tensor. Shape: [B, C, *] or [B, *, C].
+
+        Returns:
+            x: Output tensor. Shape: [B, C', *] or [B, *, C'].
+        """
+        x = x.movedim(1, -1)
+        x = super().forward(x)
+        x = x.movedim(-1, 1)
+        return x
+
+
+class DepthToSpaceTime(nn.Module):
+    def __init__(
+        self,
+        temporal_expansion: int,
+        spatial_expansion: int,
+    ):
+        super().__init__()
+        self.temporal_expansion = temporal_expansion
+        self.spatial_expansion = spatial_expansion
+
+    # When printed, this module should show the temporal and spatial expansion factors.
+    def extra_repr(self):
+        return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}"
+
+    def forward(self, x: torch.Tensor):
+        """Forward pass.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+
+        Returns:
+            x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s].
+        """
+        x = rearrange(
+            x,
+            "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)",
+            st=self.temporal_expansion,
+            sh=self.spatial_expansion,
+            sw=self.spatial_expansion,
+        )
+
+        # cp_rank, _ = cp.get_cp_rank_size()
+        if self.temporal_expansion > 1: # and cp_rank == 0:
+            # Drop the first self.temporal_expansion - 1 frames.
+            # This is because we always want the 3x3x3 conv filter to only apply
+            # to the first frame, and the first frame doesn't need to be repeated.
+            assert all(x.shape)
+            x = x[:, :, self.temporal_expansion - 1 :]
+            assert all(x.shape)
+
+        return x
+
+
+def norm_fn(
+    in_channels: int,
+    affine: bool = True,
+):
+    return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels)
+
+
+class ResBlock(nn.Module):
+    """Residual block that preserves the spatial dimensions."""
+
+    def __init__(
+        self,
+        channels: int,
+        *,
+        affine: bool = True,
+        attn_block: Optional[nn.Module] = None,
+        causal: bool = True,
+        prune_bottleneck: bool = False,
+        padding_mode: str,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.channels = channels
+
+        assert causal
+        self.stack = nn.Sequential(
+            norm_fn(channels, affine=affine),
+            nn.SiLU(inplace=True),
+            PConv3d(
+                in_channels=channels,
+                out_channels=channels // 2 if prune_bottleneck else channels,
+                kernel_size=(3, 3, 3),
+                stride=(1, 1, 1),
+                padding_mode=padding_mode,
+                bias=bias,
+                causal=causal,
+            ),
+            norm_fn(channels, affine=affine),
+            nn.SiLU(inplace=True),
+            PConv3d(
+                in_channels=channels // 2 if prune_bottleneck else channels,
+                out_channels=channels,
+                kernel_size=(3, 3, 3),
+                stride=(1, 1, 1),
+                padding_mode=padding_mode,
+                bias=bias,
+                causal=causal,
+            ),
+        )
+
+        self.attn_block = attn_block if attn_block else nn.Identity()
+
+    def forward(self, x: torch.Tensor):
+        """Forward pass.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+        """
+        residual = x
+        x = self.stack(x)
+        x = x + residual
+        del residual
+
+        return self.attn_block(x)
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        out_bias: bool = True,
+        qk_norm: bool = True,
+    ) -> None:
+        super().__init__()
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.qk_norm = qk_norm
+
+        self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
+        self.out = nn.Linear(dim, dim, bias=out_bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute temporal self-attention.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+            chunk_size: Chunk size for large tensors.
+
+        Returns:
+            x: Output tensor. Shape: [B, C, T, H, W].
+        """
+        B, _, T, H, W = x.shape
+
+        if T == 1:
+            # No attention for single frame.
+            x = x.movedim(1, -1)  # [B, C, T, H, W] -> [B, T, H, W, C]
+            qkv = self.qkv(x)
+            _, _, x = qkv.chunk(3, dim=-1)  # Throw away queries and keys.
+            x = self.out(x)
+            return x.movedim(-1, 1)  # [B, T, H, W, C] -> [B, C, T, H, W]
+
+        # 1D temporal attention.
+        x = rearrange(x, "B C t h w -> (B h w) t C")
+        qkv = self.qkv(x)
+
+        # Input: qkv with shape [B, t, 3 * num_heads * head_dim]
+        # Output: x with shape [B, num_heads, t, head_dim]
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)
+
+        if self.qk_norm:
+            q = F.normalize(q, p=2, dim=-1)
+            k = F.normalize(k, p=2, dim=-1)
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
+
+        assert x.size(0) == q.size(0)
+
+        x = self.out(x)
+        x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
+        return x
+
+
+class AttentionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        **attn_kwargs,
+    ) -> None:
+        super().__init__()
+        self.norm = norm_fn(dim)
+        self.attn = Attention(dim, **attn_kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.attn(self.norm(x))
+
+
+class CausalUpsampleBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_res_blocks: int,
+        *,
+        temporal_expansion: int = 2,
+        spatial_expansion: int = 2,
+        **block_kwargs,
+    ):
+        super().__init__()
+
+        blocks = []
+        for _ in range(num_res_blocks):
+            blocks.append(block_fn(in_channels, **block_kwargs))
+        self.blocks = nn.Sequential(*blocks)
+
+        self.temporal_expansion = temporal_expansion
+        self.spatial_expansion = spatial_expansion
+
+        # Change channels in the final convolution layer.
+        self.proj = Conv1x1(
+            in_channels,
+            out_channels * temporal_expansion * (spatial_expansion**2),
+        )
+
+        self.d2st = DepthToSpaceTime(
+            temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion
+        )
+
+    def forward(self, x):
+        x = self.blocks(x)
+        x = self.proj(x)
+        x = self.d2st(x)
+        return x
+
+
+def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):
+    attn_block = AttentionBlock(channels) if has_attention else None
+    return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)
+
+
+class DownsampleBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_res_blocks,
+        *,
+        temporal_reduction=2,
+        spatial_reduction=2,
+        **block_kwargs,
+    ):
+        """
+        Downsample block for the VAE encoder.
+
+        Args:
+            in_channels: Number of input channels.
+            out_channels: Number of output channels.
+            num_res_blocks: Number of residual blocks.
+            temporal_reduction: Temporal reduction factor.
+            spatial_reduction: Spatial reduction factor.
+        """
+        super().__init__()
+        layers = []
+
+        # Change the channel count in the strided convolution.
+        # This lets the ResBlock have uniform channel count,
+        # as in ConvNeXt.
+        assert in_channels != out_channels
+        layers.append(
+            PConv3d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
+                stride=(temporal_reduction, spatial_reduction, spatial_reduction),
+                # First layer in each block always uses replicate padding
+                padding_mode="replicate",
+                bias=block_kwargs["bias"],
+            )
+        )
+
+        for _ in range(num_res_blocks):
+            layers.append(block_fn(out_channels, **block_kwargs))
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):
+    num_freqs = (stop - start) // step
+    assert inputs.ndim == 5
+    C = inputs.size(1)
+
+    # Create Base 2 Fourier features.
+    freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device)
+    assert num_freqs == len(freqs)
+    w = torch.pow(2.0, freqs) * (2 * torch.pi)  # [num_freqs]
+    C = inputs.shape[1]
+    w = w.repeat(C)[None, :, None, None, None]  # [1, C * num_freqs, 1, 1, 1]
+
+    # Interleaved repeat of input channels to match w.
+    h = inputs.repeat_interleave(num_freqs, dim=1)  # [B, C * num_freqs, T, H, W]
+    # Scale channels by frequency.
+    h = w * h
+
+    return torch.cat(
+        [
+            inputs,
+            torch.sin(h),
+            torch.cos(h),
+        ],
+        dim=1,
+    )
+
+
+class FourierFeatures(nn.Module):
+    def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
+        super().__init__()
+        self.start = start
+        self.stop = stop
+        self.step = step
+
+    def forward(self, inputs):
+        """Add Fourier features to inputs.
+
+        Args:
+            inputs: Input tensor. Shape: [B, C, T, H, W]
+
+        Returns:
+            h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W]
+        """
+        return add_fourier_features(inputs, self.start, self.stop, self.step)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        out_channels: int = 3,
+        latent_dim: int,
+        base_channels: int,
+        channel_multipliers: List[int],
+        num_res_blocks: List[int],
+        temporal_expansions: Optional[List[int]] = None,
+        spatial_expansions: Optional[List[int]] = None,
+        has_attention: List[bool],
+        output_norm: bool = True,
+        nonlinearity: str = "silu",
+        output_nonlinearity: str = "silu",
+        causal: bool = True,
+        **block_kwargs,
+    ):
+        super().__init__()
+        self.input_channels = latent_dim
+        self.base_channels = base_channels
+        self.channel_multipliers = channel_multipliers
+        self.num_res_blocks = num_res_blocks
+        self.output_nonlinearity = output_nonlinearity
+        assert nonlinearity == "silu"
+        assert causal
+
+        ch = [mult * base_channels for mult in channel_multipliers]
+        self.num_up_blocks = len(ch) - 1
+        assert len(num_res_blocks) == self.num_up_blocks + 2
+
+        blocks = []
+
+        first_block = [
+            ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
+        ]  # Input layer.
+        # First set of blocks preserve channel count.
+        for _ in range(num_res_blocks[-1]):
+            first_block.append(
+                block_fn(
+                    ch[-1],
+                    has_attention=has_attention[-1],
+                    causal=causal,
+                    **block_kwargs,
+                )
+            )
+        blocks.append(nn.Sequential(*first_block))
+
+        assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks
+        assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2
+
+        upsample_block_fn = CausalUpsampleBlock
+
+        for i in range(self.num_up_blocks):
+            block = upsample_block_fn(
+                ch[-i - 1],
+                ch[-i - 2],
+                num_res_blocks=num_res_blocks[-i - 2],
+                has_attention=has_attention[-i - 2],
+                temporal_expansion=temporal_expansions[-i - 1],
+                spatial_expansion=spatial_expansions[-i - 1],
+                causal=causal,
+                **block_kwargs,
+            )
+            blocks.append(block)
+
+        assert not output_norm
+
+        # Last block. Preserve channel count.
+        last_block = []
+        for _ in range(num_res_blocks[0]):
+            last_block.append(
+                block_fn(
+                    ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs
+                )
+            )
+        blocks.append(nn.Sequential(*last_block))
+
+        self.blocks = nn.ModuleList(blocks)
+        self.output_proj = Conv1x1(ch[0], out_channels)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1].
+
+        Returns:
+            x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1].
+               T + 1 = (t - 1) * 4.
+               H = h * 16, W = w * 16.
+        """
+        for block in self.blocks:
+            x = block(x)
+
+        if self.output_nonlinearity == "silu":
+            x = F.silu(x, inplace=not self.training)
+        else:
+            assert (
+                not self.output_nonlinearity
+            )  # StyleGAN3 omits the to-RGB nonlinearity.
+
+        return self.output_proj(x).contiguous()
+
+class LatentDistribution:
+    def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+        """Initialize latent distribution.
+
+        Args:
+            mean: Mean of the distribution. Shape: [B, C, T, H, W].
+            logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].
+        """
+        assert mean.shape == logvar.shape
+        self.mean = mean
+        self.logvar = logvar
+
+    def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):
+        if temperature == 0.0:
+            return self.mean
+
+        if noise is None:
+            noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)
+        else:
+            assert noise.device == self.mean.device
+            noise = noise.to(self.mean.dtype)
+
+        if temperature != 1.0:
+            raise NotImplementedError(f"Temperature {temperature} is not supported.")
+
+        # Just Gaussian sample with no scaling of variance.
+        return noise * torch.exp(self.logvar * 0.5) + self.mean
+
+    def mode(self):
+        return self.mean
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        base_channels: int,
+        channel_multipliers: List[int],
+        num_res_blocks: List[int],
+        latent_dim: int,
+        temporal_reductions: List[int],
+        spatial_reductions: List[int],
+        prune_bottlenecks: List[bool],
+        has_attentions: List[bool],
+        affine: bool = True,
+        bias: bool = True,
+        input_is_conv_1x1: bool = False,
+        padding_mode: str,
+    ):
+        super().__init__()
+        self.temporal_reductions = temporal_reductions
+        self.spatial_reductions = spatial_reductions
+        self.base_channels = base_channels
+        self.channel_multipliers = channel_multipliers
+        self.num_res_blocks = num_res_blocks
+        self.latent_dim = latent_dim
+
+        self.fourier_features = FourierFeatures()
+        ch = [mult * base_channels for mult in channel_multipliers]
+        num_down_blocks = len(ch) - 1
+        assert len(num_res_blocks) == num_down_blocks + 2
+
+        layers = (
+            [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]
+            if not input_is_conv_1x1
+            else [Conv1x1(in_channels, ch[0])]
+        )
+
+        assert len(prune_bottlenecks) == num_down_blocks + 2
+        assert len(has_attentions) == num_down_blocks + 2
+        block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)
+
+        for _ in range(num_res_blocks[0]):
+            layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))
+        prune_bottlenecks = prune_bottlenecks[1:]
+        has_attentions = has_attentions[1:]
+
+        assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1
+        for i in range(num_down_blocks):
+            layer = DownsampleBlock(
+                ch[i],
+                ch[i + 1],
+                num_res_blocks=num_res_blocks[i + 1],
+                temporal_reduction=temporal_reductions[i],
+                spatial_reduction=spatial_reductions[i],
+                prune_bottleneck=prune_bottlenecks[i],
+                has_attention=has_attentions[i],
+                affine=affine,
+                bias=bias,
+                padding_mode=padding_mode,
+            )
+
+            layers.append(layer)
+
+        # Additional blocks.
+        for _ in range(num_res_blocks[-1]):
+            layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))
+
+        self.layers = nn.Sequential(*layers)
+
+        # Output layers.
+        self.output_norm = norm_fn(ch[-1])
+        self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)
+
+    @property
+    def temporal_downsample(self):
+        return math.prod(self.temporal_reductions)
+
+    @property
+    def spatial_downsample(self):
+        return math.prod(self.spatial_reductions)
+
+    def forward(self, x) -> LatentDistribution:
+        """Forward pass.
+
+        Args:
+            x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]
+
+        Returns:
+            means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].
+                   h = H // 8, w = W // 8, t - 1 = (T - 1) // 6
+            logvar: Shape: [B, latent_dim, t, h, w].
+        """
+        assert x.ndim == 5, f"Expected 5D input, got {x.shape}"
+        x = self.fourier_features(x)
+
+        x = self.layers(x)
+
+        x = self.output_norm(x)
+        x = F.silu(x, inplace=True)
+        x = self.output_proj(x)
+
+        means, logvar = torch.chunk(x, 2, dim=1)
+
+        assert means.ndim == 5
+        assert logvar.shape == means.shape
+        assert means.size(1) == self.latent_dim
+
+        return LatentDistribution(means, logvar)
+
+
+class VideoVAE(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = Encoder(
+            in_channels=15,
+            base_channels=64,
+            channel_multipliers=[1, 2, 4, 6],
+            num_res_blocks=[3, 3, 4, 6, 3],
+            latent_dim=12,
+            temporal_reductions=[1, 2, 3],
+            spatial_reductions=[2, 2, 2],
+            prune_bottlenecks=[False, False, False, False, False],
+            has_attentions=[False, True, True, True, True],
+            affine=True,
+            bias=True,
+            input_is_conv_1x1=True,
+            padding_mode="replicate"
+        )
+        self.decoder = Decoder(
+            out_channels=3,
+            base_channels=128,
+            channel_multipliers=[1, 2, 4, 6],
+            temporal_expansions=[1, 2, 3],
+            spatial_expansions=[2, 2, 2],
+            num_res_blocks=[3, 3, 4, 6, 3],
+            latent_dim=12,
+            has_attention=[False, False, False, False, False],
+            padding_mode="replicate",
+            output_norm=False,
+            nonlinearity="silu",
+            output_nonlinearity="silu",
+            causal=True,
+        )
+
+    def encode(self, x):
+        return self.encoder(x).mode()
+
+    def decode(self, x):
+        return self.decoder(x)

comfy/ldm/modules/attention.py

@@ -393,6 +393,13 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     return out
 
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+    SDP_BATCH_LIMIT = 2**15
+else:
+    #TODO: other GPUs ?
+    SDP_BATCH_LIMIT = 2**31
+
+
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
@@ -404,10 +411,15 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             (q, k, v),
         )
 
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    if SDP_BATCH_LIMIT >= q.shape[0]:
-    out = (
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        out = (
-    )
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+        for i in range(0, q.shape[0], SDP_BATCH_LIMIT):
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
 

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -1,11 +1,11 @@
 import logging
 import math
-from typing import Dict, Optional
+from typing import Dict, Optional, List
 
 import numpy as np
 import torch
 import torch.nn as nn
-from .. import attention
+from ..attention import optimized_attention
 from einops import rearrange, repeat
 from .util import timestep_embedding
 import comfy.ops
@@ -97,7 +97,7 @@ class PatchEmbed(nn.Module):
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
     def forward(self, x):
-        B, C, H, W = x.shape
+        # B, C, H, W = x.shape
         # if self.img_size is not None:
         #     if self.strict_img_size:
         #         _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
@@ -266,8 +266,6 @@ def split_qkv(qkv, head_dim):
     qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
     return qkv[0], qkv[1], qkv[2]
 
-def optimized_attention(qkv, num_heads):
-    return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads)
 
 class SelfAttention(nn.Module):
     ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
@@ -326,9 +324,9 @@ class SelfAttention(nn.Module):
         return x
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        qkv = self.pre_attention(x)
+        q, k, v = self.pre_attention(x)
         x = optimized_attention(
-            qkv, num_heads=self.num_heads
+            q, k, v, heads=self.num_heads
         )
         x = self.post_attention(x)
         return x
@@ -417,6 +415,7 @@ class DismantledBlock(nn.Module):
         scale_mod_only: bool = False,
         swiglu: bool = False,
         qk_norm: Optional[str] = None,
+        x_block_self_attn: bool = False,
         dtype=None,
         device=None,
         operations=None,
@@ -440,6 +439,24 @@ class DismantledBlock(nn.Module):
             device=device,
             operations=operations
         )
+        if x_block_self_attn:
+            assert not pre_only
+            assert not scale_mod_only
+            self.x_block_self_attn = True
+            self.attn2 = SelfAttention(
+                dim=hidden_size,
+                num_heads=num_heads,
+                qkv_bias=qkv_bias,
+                attn_mode=attn_mode,
+                pre_only=False,
+                qk_norm=qk_norm,
+                rmsnorm=rmsnorm,
+                dtype=dtype,
+                device=device,
+                operations=operations
+            )
+        else:
+            self.x_block_self_attn = False
         if not pre_only:
             if not rmsnorm:
                 self.norm2 = operations.LayerNorm(
@@ -466,7 +483,11 @@ class DismantledBlock(nn.Module):
                     multiple_of=256,
                 )
         self.scale_mod_only = scale_mod_only
-        if not scale_mod_only:
+        if x_block_self_attn:
+            assert not pre_only
+            assert not scale_mod_only
+            n_mods = 9
+        elif not scale_mod_only:
             n_mods = 6 if not pre_only else 2
         else:
             n_mods = 4 if not pre_only else 1
@@ -527,14 +548,64 @@ class DismantledBlock(nn.Module):
         )
         return x
 
+    def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        assert self.x_block_self_attn
+        (
+            shift_msa,
+            scale_msa,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            shift_msa2,
+            scale_msa2,
+            gate_msa2,
+        ) = self.adaLN_modulation(c).chunk(9, dim=1)
+        x_norm = self.norm1(x)
+        qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa))
+        qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2))
+        return qkv, qkv2, (
+            x,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            gate_msa2,
+        )
+
+    def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2):
+        assert not self.pre_only
+        attn1 = self.attn.post_attention(attn)
+        attn2 = self.attn2.post_attention(attn2)
+        out1 = gate_msa.unsqueeze(1) * attn1
+        out2 = gate_msa2.unsqueeze(1) * attn2
+        x = x + out1
+        x = x + out2
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp)
+        )
+        return x
+
     def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
         assert not self.pre_only
-        qkv, intermediates = self.pre_attention(x, c)
+        if self.x_block_self_attn:
-        attn = optimized_attention(
+            qkv, qkv2, intermediates = self.pre_attention_x(x, c)
-            qkv,
+            attn, _ = optimized_attention(
-            num_heads=self.attn.num_heads,
+                qkv[0], qkv[1], qkv[2],
-        )
+                num_heads=self.attn.num_heads,
-        return self.post_attention(attn, *intermediates)
+            )
+            attn2, _ = optimized_attention(
+                qkv2[0], qkv2[1], qkv2[2],
+                num_heads=self.attn2.num_heads,
+            )
+            return self.post_attention_x(attn, attn2, *intermediates)
+        else:
+            qkv, intermediates = self.pre_attention(x, c)
+            attn = optimized_attention(
+                qkv[0], qkv[1], qkv[2],
+                heads=self.attn.num_heads,
+            )
+            return self.post_attention(attn, *intermediates)
 
 
 def block_mixing(*args, use_checkpoint=True, **kwargs):
@@ -549,7 +620,10 @@ def block_mixing(*args, use_checkpoint=True, **kwargs):
 def _block_mixing(context, x, context_block, x_block, c):
     context_qkv, context_intermediates = context_block.pre_attention(context, c)
 
-    x_qkv, x_intermediates = x_block.pre_attention(x, c)
+    if x_block.x_block_self_attn:
+        x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)
+    else:
+        x_qkv, x_intermediates = x_block.pre_attention(x, c)
 
     o = []
     for t in range(3):
@@ -557,8 +631,8 @@ def _block_mixing(context, x, context_block, x_block, c):
     qkv = tuple(o)
 
     attn = optimized_attention(
-        qkv,
+        qkv[0], qkv[1], qkv[2],
-        num_heads=x_block.attn.num_heads,
+        heads=x_block.attn.num_heads,
     )
     context_attn, x_attn = (
         attn[:, : context_qkv[0].shape[1]],
@@ -570,7 +644,14 @@ def _block_mixing(context, x, context_block, x_block, c):
 
     else:
         context = None
-    x = x_block.post_attention(x_attn, *x_intermediates)
+    if x_block.x_block_self_attn:
+        attn2 = optimized_attention(
+                x_qkv2[0], x_qkv2[1], x_qkv2[2],
+                heads=x_block.attn2.num_heads,
+            )
+        x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
+    else:
+        x = x_block.post_attention(x_attn, *x_intermediates)
     return context, x
 
 
@@ -585,8 +666,13 @@ class JointBlock(nn.Module):
         super().__init__()
         pre_only = kwargs.pop("pre_only")
         qk_norm = kwargs.pop("qk_norm", None)
+        x_block_self_attn = kwargs.pop("x_block_self_attn", False)
         self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
-        self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
+        self.x_block = DismantledBlock(*args,
+                                       pre_only=False,
+                                       qk_norm=qk_norm,
+                                       x_block_self_attn=x_block_self_attn,
+                                       **kwargs)
 
     def forward(self, *args, **kwargs):
         return block_mixing(
@@ -642,7 +728,7 @@ class SelfAttentionContext(nn.Module):
     def forward(self, x):
         qkv = self.qkv(x)
         q, k, v = split_qkv(qkv, self.dim_head)
-        x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads)
+        x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads)
         return self.proj(x)
 
 class ContextProcessorBlock(nn.Module):
@@ -701,9 +787,12 @@ class MMDiT(nn.Module):
         qk_norm: Optional[str] = None,
         qkv_bias: bool = True,
         context_processor_layers = None,
+        x_block_self_attn: bool = False,
+        x_block_self_attn_layers: Optional[List[int]] = [],
         context_size = 4096,
         num_blocks = None,
         final_layer = True,
+        skip_blocks = False,
         dtype = None, #TODO
         device = None,
         operations = None,
@@ -718,6 +807,7 @@ class MMDiT(nn.Module):
         self.pos_embed_scaling_factor = pos_embed_scaling_factor
         self.pos_embed_offset = pos_embed_offset
         self.pos_embed_max_size = pos_embed_max_size
+        self.x_block_self_attn_layers = x_block_self_attn_layers
 
         # hidden_size = default(hidden_size, 64 * depth)
         # num_heads = default(num_heads, hidden_size // 64)
@@ -775,26 +865,28 @@ class MMDiT(nn.Module):
             self.pos_embed = None
 
         self.use_checkpoint = use_checkpoint
-        self.joint_blocks = nn.ModuleList(
+        if not skip_blocks:
-            [
+            self.joint_blocks = nn.ModuleList(
-                JointBlock(
+                [
-                    self.hidden_size,
+                    JointBlock(
-                    num_heads,
+                        self.hidden_size,
-                    mlp_ratio=mlp_ratio,
+                        num_heads,
-                    qkv_bias=qkv_bias,
+                        mlp_ratio=mlp_ratio,
-                    attn_mode=attn_mode,
+                        qkv_bias=qkv_bias,
-                    pre_only=(i == num_blocks - 1) and final_layer,
+                        attn_mode=attn_mode,
-                    rmsnorm=rmsnorm,
+                        pre_only=(i == num_blocks - 1) and final_layer,
-                    scale_mod_only=scale_mod_only,
+                        rmsnorm=rmsnorm,
-                    swiglu=swiglu,
+                        scale_mod_only=scale_mod_only,
-                    qk_norm=qk_norm,
+                        swiglu=swiglu,
-                    dtype=dtype,
+                        qk_norm=qk_norm,
-                    device=device,
+                        x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn,
-                    operations=operations
+                        dtype=dtype,
-                )
+                        device=device,
-                for i in range(num_blocks)
+                        operations=operations,
-            ]
+                    )
-        )
+                    for i in range(num_blocks)
+                ]
+            )
 
         if final_layer:
             self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
@@ -857,7 +949,9 @@ class MMDiT(nn.Module):
         c_mod: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         control = None,
+        transformer_options = {},
     ) -> torch.Tensor:
+        patches_replace = transformer_options.get("patches_replace", {})
         if self.register_length > 0:
             context = torch.cat(
                 (
@@ -869,14 +963,25 @@ class MMDiT(nn.Module):
 
         # context is B, L', D
         # x is B, L, D
+        blocks_replace = patches_replace.get("dit", {})
         blocks = len(self.joint_blocks)
         for i in range(blocks):
-            context, x = self.joint_blocks[i](
+            if ("double_block", i) in blocks_replace:
-                context,
+                def block_wrap(args):
-                x,
+                    out = {}
-                c=c_mod,
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
-                use_checkpoint=self.use_checkpoint,
+                    return out
-            )
+
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
+                context = out["txt"]
+                x = out["img"]
+            else:
+                context, x = self.joint_blocks[i](
+                    context,
+                    x,
+                    c=c_mod,
+                    use_checkpoint=self.use_checkpoint,
+                )
             if control is not None:
                 control_o = control.get("output")
                 if i < len(control_o):
@@ -894,6 +999,7 @@ class MMDiT(nn.Module):
         y: Optional[torch.Tensor] = None,
         context: Optional[torch.Tensor] = None,
         control = None,
+        transformer_options = {},
     ) -> torch.Tensor:
         """
         Forward pass of DiT.
@@ -915,7 +1021,7 @@ class MMDiT(nn.Module):
         if context is not None:
             context = self.context_embedder(context)
 
-        x = self.forward_core_with_concat(x, c, context, control)
+        x = self.forward_core_with_concat(x, c, context, control, transformer_options)
 
         x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
         return x[:,:,:hw[-2],:hw[-1]]
@@ -929,7 +1035,8 @@ class OpenAISignatureMMDITWrapper(MMDiT):
         context: Optional[torch.Tensor] = None,
         y: Optional[torch.Tensor] = None,
         control = None,
+        transformer_options = {},
         **kwargs,
     ) -> torch.Tensor:
-        return super().forward(x, timesteps, context=context, y=y, control=control)
+        return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options)
 

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -842,6 +842,11 @@ class UNetModel(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
+        if "emb_patch" in transformer_patches:
+            patch = transformer_patches["emb_patch"]
+            for p in patch:
+                emb = p(emb, self.model_channels, transformer_options)
+
         if self.num_classes is not None:
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)

comfy/lora.py

@@ -201,9 +201,13 @@ def load_lora(lora, to_load):
 
 def model_lora_keys_clip(model, key_map={}):
     sdk = model.state_dict().keys()
+    for k in sdk:
+        if k.endswith(".weight"):
+            key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
 
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
     clip_l_present = False
+    clip_g_present = False
     for b in range(32): #TODO: clean up
         for c in LORA_CLIP_MAP:
             k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
@@ -227,6 +231,7 @@ def model_lora_keys_clip(model, key_map={}):
 
             k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
+                clip_g_present = True
                 if clip_l_present:
                     lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                     key_map[lora_key] = k
@@ -242,10 +247,18 @@ def model_lora_keys_clip(model, key_map={}):
 
     for k in sdk:
         if k.endswith(".weight"):
-            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 lora
+            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
                 l_key = k[len("t5xxl.transformer."):-len(".weight")]
-                lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
+                t5_index = 1
-                key_map[lora_key] = k
+                if clip_g_present:
+                    t5_index += 1
+                if clip_l_present:
+                    t5_index += 1
+                    if t5_index == 2:
+                        key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
+                        t5_index += 1
+
+                key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
             elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
                 l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
                 lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
@@ -281,6 +294,7 @@ def model_lora_keys_unet(model, key_map={}):
             unet_key = "diffusion_model.{}".format(diffusers_keys[k])
             key_lora = k[:-len(".weight")].replace(".", "_")
             key_map["lora_unet_{}".format(key_lora)] = unet_key
+            key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format
 
             diffusers_lora_prefix = ["", "unet."]
             for p in diffusers_lora_prefix:
@@ -303,6 +317,10 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
                 key_map[key_lora] = to
 
+                key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format
+                key_map[key_lora] = to
+
+
     if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
         diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:
@@ -324,14 +342,15 @@ def model_lora_keys_unet(model, key_map={}):
                 to = diffusers_keys[k]
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
+                key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
 
     return key_map
 
 
-def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function):
     dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
     lora_diff *= alpha
-    weight_calc = weight + lora_diff.type(weight.dtype)
+    weight_calc = weight + function(lora_diff).type(weight.dtype)
     weight_norm = (
         weight_calc.transpose(0, 1)
         .reshape(weight_calc.shape[1], -1)
@@ -400,7 +419,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             weight *= strength_model
 
         if isinstance(v, list):
-            v = (calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
+            v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), )
 
         if len(v) == 1:
             patch_type = "diff"
@@ -438,7 +457,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
@@ -484,7 +503,7 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
@@ -521,28 +540,48 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             try:
                 lora_diff = (m1 * m2).reshape(weight.shape)
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 logging.error("ERROR {} {} {}".format(patch_type, key, e))
         elif patch_type == "glora":
-            if v[4] is not None:
-                alpha = v[4] / v[0].shape[0]
-            else:
-                alpha = 1.0
-
             dora_scale = v[5]
 
+            old_glora = False
+            if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
+                rank = v[0].shape[0]
+                old_glora = True
+
+            if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
+                if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
+                    pass
+                else:
+                    old_glora = False
+                    rank = v[1].shape[0]
+
             a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
             a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
             b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
             b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
 
+            if v[4] is not None:
+                alpha = v[4] / rank
+            else:
+                alpha = 1.0
+
             try:
-                lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape)
+                if old_glora:
+                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
+                else:
+                    if weight.dim() > 2:
+                        lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    else:
+                        lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    lora_diff += torch.mm(b1, b2).reshape(weight.shape)
+
                 if dora_scale is not None:
-                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                    weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
                 else:
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:

comfy/model_base.py

@@ -24,6 +24,7 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.genmo.joint_model.asymm_models_joint
 import comfy.ldm.aura.mmdit
 import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
@@ -96,7 +97,8 @@ class BaseModel(torch.nn.Module):
 
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype)
+                fp8 = model_config.optimizations.get("fp8", model_config.scaled_fp8 is not None)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -244,6 +246,10 @@ 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)
+
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:
@@ -713,3 +719,18 @@ class Flux(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out
+
+class GenmoMochi(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
+
+    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)
+            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)
+        return out

comfy/model_detection.py

@@ -70,6 +70,11 @@ def detect_unet_config(state_dict, key_prefix):
         context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
         if context_processor in state_dict_keys:
             unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
+        unet_config["x_block_self_attn_layers"] = []
+        for key in state_dict_keys:
+            if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'):
+                layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')]
+                unet_config["x_block_self_attn_layers"].append(int(layer))
         return unet_config
 
     if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
@@ -145,6 +150,34 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
 
+    if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
+        dit_config = {}
+        dit_config["image_model"] = "mochi_preview"
+        dit_config["depth"] = 48
+        dit_config["patch_size"] = 2
+        dit_config["num_heads"] = 24
+        dit_config["hidden_size_x"] = 3072
+        dit_config["hidden_size_y"] = 1536
+        dit_config["mlp_ratio_x"] = 4.0
+        dit_config["mlp_ratio_y"] = 4.0
+        dit_config["learn_sigma"] = False
+        dit_config["in_channels"] = 12
+        dit_config["qk_norm"] = True
+        dit_config["qkv_bias"] = False
+        dit_config["out_bias"] = True
+        dit_config["attn_drop"] = 0.0
+        dit_config["patch_embed_bias"] = True
+        dit_config["posenc_preserve_area"] = True
+        dit_config["timestep_mlp_bias"] = True
+        dit_config["attend_to_padding"] = False
+        dit_config["timestep_scale"] = 1000.0
+        dit_config["use_t5"] = True
+        dit_config["t5_feat_dim"] = 4096
+        dit_config["t5_token_length"] = 256
+        dit_config["rope_theta"] = 10000.0
+        return dit_config
+
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -286,9 +319,15 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         return None
     model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
-        return comfy.supported_models_base.BASE(unet_config)
+        model_config = comfy.supported_models_base.BASE(unet_config)
-    else:
+
-        return model_config
+    scaled_fp8_weight = state_dict.get("{}scaled_fp8".format(unet_key_prefix), None)
+    if scaled_fp8_weight is not None:
+        model_config.scaled_fp8 = scaled_fp8_weight.dtype
+        if model_config.scaled_fp8 == torch.float32:
+            model_config.scaled_fp8 = torch.float8_e4m3fn
+
+    return model_config
 
 def unet_prefix_from_state_dict(state_dict):
     candidates = ["model.diffusion_model.", #ldm/sgm models
@@ -501,7 +540,11 @@ def model_config_from_diffusers_unet(state_dict):
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
     out_sd = {}
 
-    if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
+    if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
         hidden_size = state_dict["x_embedder.bias"].shape[0]
@@ -510,10 +553,6 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
         sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
-    elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
-        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
-        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
-        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
     else:
         return None
 

comfy/model_management.py

@@ -145,7 +145,7 @@ total_ram = psutil.virtual_memory().total / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
 try:
-    logging.info("pytorch version: {}".format(torch.version.__version__))
+    logging.info("pytorch version: {}".format(torch_version))
 except:
     pass
 
@@ -326,7 +326,7 @@ class LoadedModel:
                 self.model_unload()
                 raise e
 
-        if is_intel_xpu() and not args.disable_ipex_optimize and self.real_model is not None:
+        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and self.real_model is not None:
             with torch.no_grad():
                 self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
 
@@ -426,7 +426,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         shift_model = current_loaded_models[i]
         if shift_model.device == device:
             if shift_model not in keep_loaded:
-                can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+                can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
     for x in sorted(can_unload):
@@ -626,6 +626,8 @@ def maximum_vram_for_weights(device=None):
     return (get_total_memory(device) * 0.88 - minimum_inference_memory())
 
 def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
+    if model_params < 0:
+        model_params = 1000000000000000000000
     if args.bf16_unet:
         return torch.bfloat16
     if args.fp16_unet:
@@ -645,6 +647,9 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
         pass
 
     if fp8_dtype is not None:
+        if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive
+            return fp8_dtype
+
         free_model_memory = maximum_vram_for_weights(device)
         if model_params * 2 > free_model_memory:
             return fp8_dtype
@@ -838,27 +843,21 @@ def force_channels_last():
     #TODO
     return False
 
+def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
+    if device is None or weight.device == device:
+        if not copy:
+            if dtype is None or weight.dtype == dtype:
+                return weight
+        return weight.to(dtype=dtype, copy=copy)
+
+    r = torch.empty_like(weight, dtype=dtype, device=device)
+    r.copy_(weight, non_blocking=non_blocking)
+    return r
+
 def cast_to_device(tensor, device, dtype, copy=False):
-    device_supports_cast = False
+    non_blocking = device_supports_non_blocking(device)
-    if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
+    return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
-        device_supports_cast = True
-    elif tensor.dtype == torch.bfloat16:
-        if hasattr(device, 'type') and device.type.startswith("cuda"):
-            device_supports_cast = True
-        elif is_intel_xpu():
-            device_supports_cast = True
 
-    non_blocking = device_should_use_non_blocking(device)
-
-    if device_supports_cast:
-        if copy:
-            if tensor.device == device:
-                return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
-            return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-        else:
-            return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-    else:
-        return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)
 
 def xformers_enabled():
     global directml_enabled
@@ -897,7 +896,7 @@ def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
     try:
         macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
-        if (14, 5) <= macos_version < (14, 7):  # black image bug on recent versions of MacOS
+        if (14, 5) <= macos_version <= (15, 2):  # black image bug on recent versions of macOS
             upcast = True
     except:
         pass
@@ -1063,6 +1062,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     return False
 
 def supports_fp8_compute(device=None):
+    if not is_nvidia():
+        return False
+
     props = torch.cuda.get_device_properties(device)
     if props.major >= 9:
         return True
@@ -1070,6 +1072,14 @@ def supports_fp8_compute(device=None):
         return False
     if props.minor < 9:
         return False
+
+    if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+        return False
+
+    if WINDOWS:
+        if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+            return False
+
     return True
 
 def soft_empty_cache(force=False):

comfy/model_patcher.py

@@ -28,7 +28,7 @@ import comfy.utils
 import comfy.float
 import comfy.model_management
 import comfy.lora
-from comfy.types import UnetWrapperFunction
+from comfy.comfy_types import UnetWrapperFunction
 
 def string_to_seed(data):
     crc = 0xFFFFFFFF
@@ -88,7 +88,36 @@ class LowVramPatch:
         self.key = key
         self.patches = patches
     def __call__(self, weight):
-        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
+        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
+            return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+
+def get_key_weight(model, key):
+    set_func = None
+    convert_func = None
+    op_keys = key.rsplit('.', 1)
+    if len(op_keys) < 2:
+        weight = comfy.utils.get_attr(model, key)
+    else:
+        op = comfy.utils.get_attr(model, op_keys[0])
+        try:
+            set_func = getattr(op, "set_{}".format(op_keys[1]))
+        except AttributeError:
+            pass
+
+        try:
+            convert_func = getattr(op, "convert_{}".format(op_keys[1]))
+        except AttributeError:
+            pass
+
+        weight = getattr(op, op_keys[1])
+        if convert_func is not None:
+            weight = comfy.utils.get_attr(model, key)
+
+    return weight, set_func, convert_func
 
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
@@ -283,17 +312,23 @@ class ModelPatcher:
         return list(p)
 
     def get_key_patches(self, filter_prefix=None):
-        comfy.model_management.unload_model_clones(self)
         model_sd = self.model_state_dict()
         p = {}
         for k in model_sd:
             if filter_prefix is not None:
                 if not k.startswith(filter_prefix):
                     continue
+            bk = self.backup.get(k, None)
+            weight, set_func, convert_func = get_key_weight(self.model, k)
+            if bk is not None:
+                weight = bk.weight
+            if convert_func is None:
+                convert_func = lambda a, **kwargs: a
+
             if k in self.patches:
-                p[k] = [model_sd[k]] + self.patches[k]
+                p[k] = [(weight, convert_func)] + self.patches[k]
             else:
-                p[k] = (model_sd[k],)
+                p[k] = [(weight, convert_func)]
         return p
 
     def model_state_dict(self, filter_prefix=None):
@@ -309,8 +344,7 @@ class ModelPatcher:
         if key not in self.patches:
             return
 
-        weight = comfy.utils.get_attr(self.model, key)
+        weight, set_func, convert_func = get_key_weight(self.model, key)
-
         inplace_update = self.weight_inplace_update or inplace_update
 
         if key not in self.backup:
@@ -320,12 +354,18 @@ class ModelPatcher:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
         else:
             temp_weight = weight.to(torch.float32, copy=True)
+        if convert_func is not None:
+            temp_weight = convert_func(temp_weight, inplace=True)
+
         out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
-        out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
+        if set_func is None:
-        if inplace_update:
+            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
-            comfy.utils.copy_to_param(self.model, key, out_weight)
+            if inplace_update:
+                comfy.utils.copy_to_param(self.model, key, out_weight)
+            else:
+                comfy.utils.set_attr_param(self.model, key, out_weight)
         else:
-            comfy.utils.set_attr_param(self.model, key, out_weight)
+            set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
         mem_counter = 0

comfy/ops.py

@@ -19,20 +19,12 @@
 import torch
 import comfy.model_management
 from comfy.cli_args import args
+import comfy.float
 
-def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
+cast_to = comfy.model_management.cast_to #TODO: remove once no more references
-    if device is None or weight.device == device:
-        if not copy:
-            if dtype is None or weight.dtype == dtype:
-                return weight
-        return weight.to(dtype=dtype, copy=copy)
-
-    r = torch.empty_like(weight, dtype=dtype, device=device)
-    r.copy_(weight, non_blocking=non_blocking)
-    return r
 
 def cast_to_input(weight, input, non_blocking=False, copy=True):
-    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
+    return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     if input is not None:
@@ -47,12 +39,12 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
     if s.bias is not None:
         has_function = s.bias_function is not None
-        bias = cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
+        bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
         if has_function:
             bias = s.bias_function(bias)
 
     has_function = s.weight_function is not None
-    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
+    weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
     if has_function:
         weight = s.weight_function(weight)
     return weight, bias
@@ -258,20 +250,29 @@ def fp8_linear(self, input):
     if dtype not in [torch.float8_e4m3fn]:
         return None
 
+    tensor_2d = False
+    if len(input.shape) == 2:
+        tensor_2d = True
+        input = input.unsqueeze(1)
+
+
     if len(input.shape) == 3:
-        inn = input.reshape(-1, input.shape[2]).to(dtype)
-        non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
         w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
         w = w.t()
 
         scale_weight = self.scale_weight
         scale_input = self.scale_input
         if scale_weight is None:
-            scale_weight = torch.ones((1), device=input.device, dtype=torch.float32)
+            scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
-            if scale_input is None:
+        else:
-                scale_input = scale_weight
+            scale_weight = scale_weight.to(input.device)
+
         if scale_input is None:
-            scale_input = torch.ones((1), device=input.device, dtype=torch.float32)
+            scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+            inn = input.reshape(-1, input.shape[2]).to(dtype)
+        else:
+            scale_input = scale_input.to(input.device)
+            inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype)
 
         if bias is not None:
             o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
@@ -281,7 +282,11 @@ def fp8_linear(self, input):
         if isinstance(o, tuple):
             o = o[0]
 
+        if tensor_2d:
+            return o.reshape(input.shape[0], -1)
+
         return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+
     return None
 
 class fp8_ops(manual_cast):
@@ -299,11 +304,63 @@ class fp8_ops(manual_cast):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)
 
+def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
+    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 = cast_bias_weight(self, input)
+
+                if weight.numel() < input.numel(): #TODO: optimize
+                    return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+                else:
+                    return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+
+            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, **kwargs):
+                weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+                if inplace_update:
+                    self.weight.data.copy_(weight)
+                else:
+                    self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+    return scaled_fp8_op
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+    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, scale_input=True, override_dtype=scaled_fp8)
+
+    if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8:
+        return fp8_ops
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None):
     if compute_dtype is None or weight_dtype == compute_dtype:
         return disable_weight_init
-    if args.fast:
+
-        if comfy.model_management.supports_fp8_compute(load_device):
-            return fp8_ops
     return manual_cast

comfy/samplers.py

@@ -6,7 +6,7 @@ from comfy import model_management
 import math
 import logging
 import comfy.sampler_helpers
-import scipy
+import scipy.stats
 import numpy
 
 def get_area_and_mult(conds, x_in, timestep_in):
@@ -358,11 +358,35 @@ def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
     ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
 
     sigs = []
+    last_t = -1
     for t in ts:
-        sigs += [float(model_sampling.sigmas[int(t)])]
+        if t != last_t:
+            sigs += [float(model_sampling.sigmas[int(t)])]
+        last_t = t
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
+# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41
+def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None):
+    if steps == 1:
+        sigma_schedule = [1.0, 0.0]
+    else:
+        if linear_steps is None:
+            linear_steps = steps // 2
+        linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
+        threshold_noise_step_diff = linear_steps - threshold_noise * steps
+        quadratic_steps = steps - linear_steps
+        quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2)
+        linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2)
+        const = quadratic_coef * (linear_steps ** 2)
+        quadratic_sigma_schedule = [
+            quadratic_coef * (i ** 2) + linear_coef * i + const
+            for i in range(linear_steps, steps)
+        ]
+        sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
+        sigma_schedule = [1.0 - x for x in sigma_schedule]
+    return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()
+
 def get_mask_aabb(masks):
     if masks.numel() == 0:
         return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
@@ -570,8 +594,8 @@ class Sampler:
         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",
-                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
+                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+                  "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis"]
 
 class KSAMPLER(Sampler):
@@ -729,7 +753,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 def calculate_sigmas(model_sampling, scheduler_name, steps):
@@ -747,6 +771,8 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
         sigmas = normal_scheduler(model_sampling, steps, sgm=True)
     elif scheduler_name == "beta":
         sigmas = beta_scheduler(model_sampling, steps)
+    elif scheduler_name == "linear_quadratic":
+        sigmas = linear_quadratic_schedule(model_sampling, steps)
     else:
         logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas

comfy/sd.py

@@ -7,6 +7,7 @@ from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
 from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
+import comfy.ldm.genmo.vae.model
 import yaml
 
 import comfy.utils
@@ -25,11 +26,11 @@ import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 import comfy.text_encoders.long_clipl
+import comfy.text_encoders.genmo
 
 import comfy.model_patcher
 import comfy.lora
 import comfy.t2i_adapter.adapter
-import comfy.supported_models_base
 import comfy.taesd.taesd
 
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
@@ -70,14 +71,14 @@ class CLIP:
         clip = target.clip
         tokenizer = target.tokenizer
 
-        load_device = model_management.text_encoder_device()
+        load_device = model_options.get("load_device", model_management.text_encoder_device())
-        offload_device = model_management.text_encoder_offload_device()
+        offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device())
         dtype = model_options.get("dtype", None)
         if dtype is None:
             dtype = model_management.text_encoder_dtype(load_device)
 
         params['dtype'] = dtype
-        params['device'] = model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))
+        params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype)))
         params['model_options'] = model_options
 
         self.cond_stage_model = clip(**(params))
@@ -170,6 +171,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.latent_dim = 2
         self.output_channels = 3
         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)
@@ -239,9 +241,22 @@ class VAE:
                 self.output_channels = 2
                 self.upscale_ratio = 2048
                 self.downscale_ratio =  2048
+                self.latent_dim = 1
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight": #genmo mochi vae
+                if "blocks.2.blocks.3.stack.5.weight" in sd:
+                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
+                if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
+                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
+                self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
+                self.latent_channels = 12
+                self.latent_dim = 3
+                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
+                self.working_dtypes = [torch.float16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -297,6 +312,10 @@ class VAE:
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
 
+    def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -315,6 +334,7 @@ class VAE:
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
 
     def decode(self, samples_in):
+        pixel_samples = None
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
@@ -322,16 +342,21 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
+                out = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
+                if pixel_samples is None:
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                pixel_samples[x:x+batch_number] = out
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
-            if len(samples_in.shape) == 3:
+            dims = samples_in.ndim - 2
+            if dims == 1:
                 pixel_samples = self.decode_tiled_1d(samples_in)
-            else:
+            elif dims == 2:
                 pixel_samples = self.decode_tiled_(samples_in)
+            elif dims == 3:
+                pixel_samples = self.decode_tiled_3d(samples_in)
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -343,17 +368,22 @@ class VAE:
 
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        pixel_samples = pixel_samples.movedim(-1,1)
+        pixel_samples = pixel_samples.movedim(-1, 1)
+        if self.latent_dim == 3:
+            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
+            batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
+            samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                if samples is None:
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                samples[x:x + batch_number] = out
 
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
@@ -399,6 +429,7 @@ class CLIPType(Enum):
     STABLE_AUDIO = 4
     HUNYUAN_DIT = 5
     FLUX = 6
+    MOCHI = 7
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
@@ -406,8 +437,46 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
     return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
 
+
+class TEModel(Enum):
+    CLIP_L = 1
+    CLIP_H = 2
+    CLIP_G = 3
+    T5_XXL = 4
+    T5_XL = 5
+    T5_BASE = 6
+
+def detect_te_model(sd):
+    if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_G
+    if "text_model.encoder.layers.22.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_H
+    if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_L
+    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:
+            return TEModel.T5_XXL
+        elif weight.shape[-1] == 2048:
+            return TEModel.T5_XL
+    if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
+        return TEModel.T5_BASE
+    return None
+
+
+def t5xxl_detect(clip_data):
+    weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+
+    for sd in clip_data:
+        if weight_name in sd:
+            return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)
+
+    return {}
+
+
 def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = state_dicts
+
     class EmptyClass:
         pass
 
@@ -421,64 +490,65 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
+        te_model = detect_te_model(clip_data[0])
+        if te_model == TEModel.CLIP_G:
             if clip_type == CLIPType.STABLE_CASCADE:
                 clip_target.clip = sdxl_clip.StableCascadeClipModel
                 clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
+            elif clip_type == CLIPType.SD3:
+                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
             else:
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                 clip_target.tokenizer = sdxl_clip.SDXLTokenizer
-        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
+        elif te_model == TEModel.CLIP_H:
             clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
             clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
-        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
+        elif te_model == TEModel.T5_XXL:
-            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+            if clip_type == CLIPType.SD3:
-            dtype_t5 = weight.dtype
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
-            if weight.shape[-1] == 4096:
-                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
-            elif weight.shape[-1] == 2048:
+            else: #CLIPType.MOCHI
-                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+                clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
-                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
+                clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
-        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
+        elif te_model == TEModel.T5_XL:
+            clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+            clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
+        elif te_model == TEModel.T5_BASE:
             clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
             clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
         else:
-            w = clip_data[0].get("text_model.embeddings.position_embedding.weight", None)
+            if clip_type == CLIPType.SD3:
-            if w is not None and w.shape[0] == 248:
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
-                clip_target.clip = comfy.text_encoders.long_clipl.LongClipModel
+                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
-                clip_target.tokenizer = comfy.text_encoders.long_clipl.LongClipTokenizer
             else:
                 clip_target.clip = sd1_clip.SD1ClipModel
                 clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
         if clip_type == CLIPType.SD3:
-            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
+            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
         elif clip_type == CLIPType.HUNYUAN_DIT:
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
         elif clip_type == CLIPType.FLUX:
-            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+            clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))
-            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
-            dtype_t5 = None
-            if weight is not None:
-                dtype_t5 = weight.dtype
-
-            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
             clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
-        clip_target.clip = comfy.text_encoders.sd3_clip.SD3ClipModel
+        clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))
         clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
     parameters = 0
+    tokenizer_data = {}
     for c in clip_data:
         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, model_options=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:
@@ -544,11 +614,11 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         return None
 
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if weight_dtype is not None:
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
         unet_weight_dtype.append(weight_dtype)
 
     model_config.custom_operations = model_options.get("custom_operations", None)
-    unet_dtype = model_options.get("weight_dtype", None)
+    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)
@@ -562,7 +632,6 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
 
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
-        offload_device = model_management.unet_offload_device()
         model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
         model.load_model_weights(sd, diffusion_model_prefix)
 
@@ -614,6 +683,8 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
         sd = temp_sd
 
     parameters = comfy.utils.calculate_parameters(sd)
+    weight_dtype = comfy.utils.weight_dtype(sd)
+
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -640,14 +711,21 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
+        unet_weight_dtype.append(weight_dtype)
+
     if dtype is None:
-        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
     else:
         unet_dtype = dtype
 
     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", None)
+    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+    if model_options.get("fp8_optimizations", False):
+        model_config.optimizations["fp8"] = True
+
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")

comfy/sd1_clip.py

@@ -80,7 +80,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         "pooled",
         "hidden"
     ]
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
+    def __init__(self, device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
                  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
@@ -94,11 +94,20 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             config = json.load(f)
 
         operations = model_options.get("custom_operations", None)
+        scaled_fp8 = None
+
         if operations is None:
-            operations = comfy.ops.manual_cast
+            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)
+            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
 
         self.max_length = max_length
@@ -542,6 +551,7 @@ class SD1Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
         self.clip_name = clip_name
         self.clip = "clip_{}".format(self.clip_name)
+        tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -570,6 +580,7 @@ class SD1ClipModel(torch.nn.Module):
             self.clip_name = clip_name
             self.clip = "clip_{}".format(self.clip_name)
 
+        clip_model = model_options.get("{}_class".format(self.clip), clip_model)
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
 
         self.dtypes = set()

comfy/sdxl_clip.py

@@ -22,7 +22,8 @@ class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
 
 class SDXLTokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -40,7 +41,8 @@ class SDXLTokenizer:
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None, model_options={}):
         super().__init__()
-        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)
+        clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+        self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)
         self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options)
         self.dtypes = set([dtype])
 
@@ -57,7 +59,8 @@ class SDXLClipModel(torch.nn.Module):
         token_weight_pairs_l = token_weight_pairs["l"]
         g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
-        return torch.cat([l_out, g_out], dim=-1), g_pooled
+        cut_to = min(l_out.shape[1], g_out.shape[1])
+        return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled
 
     def load_sd(self, sd):
         if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:

comfy/supported_models.py

@@ -10,6 +10,7 @@ import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
+import comfy.text_encoders.genmo
 
 from . import supported_models_base
 from . import latent_formats
@@ -529,12 +530,11 @@ class SD3(supported_models_base.BASE):
             clip_l = True
         if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
             clip_g = True
-        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        if t5_key in state_dict:
+        if "dtype_t5" in t5_detect:
             t5 = True
-            dtype_t5 = state_dict[t5_key].dtype
 
-        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect))
 
 class StableAudio(supported_models_base.BASE):
     unet_config = {
@@ -653,11 +653,8 @@ class Flux(supported_models_base.BASE):
 
     def clip_target(self, state_dict={}):
         pref = self.text_encoder_key_prefix[0]
-        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        dtype_t5 = None
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
-        if t5_key in state_dict:
-            dtype_t5 = state_dict[t5_key].dtype
-        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
 
 class FluxSchnell(Flux):
     unet_config = {
@@ -674,7 +671,36 @@ class FluxSchnell(Flux):
         out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
         return out
 
+class GenmoMochi(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "mochi_preview",
+    }
 
-models = [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, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 6.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Mochi
+
+    memory_usage_factor = 2.0 #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.GenmoMochi(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        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.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))
+
+
+models = [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, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell, GenmoMochi]
 
 models += [SVD_img2vid]

comfy/supported_models_base.py

@@ -49,6 +49,8 @@ class BASE:
 
     manual_cast_dtype = None
     custom_operations = None
+    scaled_fp8 = None
+    optimizations = {"fp8": False}
 
     @classmethod
     def matches(s, unet_config, state_dict=None):
@@ -71,6 +73,7 @@ class BASE:
         self.unet_config = unet_config.copy()
         self.sampling_settings = self.sampling_settings.copy()
         self.latent_format = self.latent_format()
+        self.optimizations = self.optimizations.copy()
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
 

comfy/text_encoders/flux.py

@@ -1,24 +1,21 @@
 from comfy import sd1_clip
 import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
 import comfy.model_management
 from transformers import T5TokenizerFast
 import torch
 import os
 
-class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        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, model_options=model_options)
-
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
 
 
 class FluxTokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -38,8 +35,9 @@ class FluxClipModel(torch.nn.Module):
     def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}):
         super().__init__()
         dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
-        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+        clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
-        self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+        self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+        self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
         self.dtypes = set([dtype, dtype_t5])
 
     def set_clip_options(self, options):
@@ -64,8 +62,11 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def flux_clip(dtype_t5=None):
+def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=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:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
             super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
     return FluxClipModel_

comfy/text_encoders/genmo.py

@@ -0,0 +1,38 @@
+from comfy import sd1_clip
+import comfy.text_encoders.sd3_clip
+import os
+from transformers import T5TokenizerFast
+
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+    def __init__(self, **kwargs):
+        kwargs["attention_mask"] = True
+        super().__init__(**kwargs)
+
+
+class MochiT5XXL(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+
+class MochiT5Tokenizer(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 mochi_te(dtype_t5=None, t5xxl_scaled_fp8=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:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            if dtype is None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return MochiTEModel_

comfy/text_encoders/long_clipl.py

@@ -6,9 +6,9 @@ class LongClipTokenizer_(sd1_clip.SDTokenizer):
         super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
 
 class LongClipModel_(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
+    def __init__(self, *args, **kwargs):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json")
-        super().__init__(device=device, textmodel_json_config=textmodel_json_config, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+        super().__init__(*args, textmodel_json_config=textmodel_json_config, **kwargs)
 
 class LongClipTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -17,3 +17,14 @@ class LongClipTokenizer(sd1_clip.SD1Tokenizer):
 class LongClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
         super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs)
+
+def model_options_long_clip(sd, tokenizer_data, model_options):
+    w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None)
+    if w is None:
+        w = sd.get("text_model.embeddings.position_embedding.weight", None)
+    if w is not None and w.shape[0] == 248:
+        tokenizer_data = tokenizer_data.copy()
+        model_options = model_options.copy()
+        tokenizer_data["clip_l_tokenizer_class"] = LongClipTokenizer_
+        model_options["clip_l_class"] = LongClipModel_
+    return tokenizer_data, model_options

comfy/text_encoders/sd3_clip.py

@@ -8,9 +8,27 @@ import comfy.model_management
 import logging
 
 class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+    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")
-        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, model_options=model_options)
+        t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+        if t5xxl_scaled_fp8 is not None:
+            model_options = model_options.copy()
+            model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+        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)
+
+
+def t5_xxl_detect(state_dict, prefix=""):
+    out = {}
+    t5_key = "{}encoder.final_layer_norm.weight".format(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
+
+    return out
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -20,7 +38,8 @@ class T5XXLTokenizer(sd1_clip.SDTokenizer):
 
 class SD3Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+        self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
@@ -38,11 +57,12 @@ class SD3Tokenizer:
         return {}
 
 class SD3ClipModel(torch.nn.Module):
-    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None, model_options={}):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
         super().__init__()
         self.dtypes = set()
         if clip_l:
-            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
+            clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+            self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
             self.dtypes.add(dtype)
         else:
             self.clip_l = None
@@ -55,7 +75,8 @@ class SD3ClipModel(torch.nn.Module):
 
         if t5:
             dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
-            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+            self.t5_attention_mask = t5_attention_mask
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
             self.dtypes.add(dtype_t5)
         else:
             self.t5xxl = None
@@ -85,6 +106,7 @@ class SD3ClipModel(torch.nn.Module):
         lg_out = None
         pooled = None
         out = None
+        extra = {}
 
         if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
             if self.clip_l is not None:
@@ -95,7 +117,8 @@ class SD3ClipModel(torch.nn.Module):
             if self.clip_g is not None:
                 g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
                 if lg_out is not None:
-                    lg_out = torch.cat([lg_out, g_out], dim=-1)
+                    cut_to = min(lg_out.shape[1], g_out.shape[1])
+                    lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
                 else:
                     lg_out = torch.nn.functional.pad(g_out, (768, 0))
             else:
@@ -108,7 +131,11 @@ class SD3ClipModel(torch.nn.Module):
             pooled = torch.cat((l_pooled, g_pooled), dim=-1)
 
         if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_out, t5_pooled = t5_output[:2]
+            if self.t5_attention_mask:
+                extra["attention_mask"] = t5_output[2]["attention_mask"]
+
             if lg_out is not None:
                 out = torch.cat([lg_out, t5_out], dim=-2)
             else:
@@ -120,7 +147,7 @@ class SD3ClipModel(torch.nn.Module):
         if pooled is None:
             pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
 
-        return out, pooled
+        return out, pooled, extra
 
     def load_sd(self, sd):
         if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -130,8 +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):
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
     class SD3ClipModel_(SD3ClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            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/utils.py

@@ -68,7 +68,7 @@ def weight_dtype(sd, prefix=""):
     for k in sd.keys():
         if k.startswith(prefix):
             w = sd[k]
-            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
+            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel()
 
     if len(dtypes) == 0:
         return None
@@ -316,10 +316,18 @@ MMDIT_MAP_BLOCK = {
     ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
     ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
     ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"),
+    ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"),
     ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
     ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
     ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
     ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.attn.ln_q.weight", "attn.norm_q.weight"),
+    ("x_block.attn.ln_k.weight", "attn.norm_k.weight"),
+    ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"),
+    ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"),
+    ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"),
+    ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"),
     ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
     ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
     ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
@@ -349,6 +357,12 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
             key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
             key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
 
+            k = "{}.attn2.".format(block_from)
+            qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
         for k in MMDIT_MAP_BLOCK:
             key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
 
@@ -690,9 +704,14 @@ def lanczos(samples, width, height):
     return result.to(samples.device, samples.dtype)
 
 def common_upscale(samples, width, height, upscale_method, crop):
+        orig_shape = tuple(samples.shape)
+        if len(orig_shape) > 4:
+            samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1])
+            samples = samples.movedim(2, 1)
+            samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1])
         if crop == "center":
-            old_width = samples.shape[3]
+            old_width = samples.shape[-1]
-            old_height = samples.shape[2]
+            old_height = samples.shape[-2]
             old_aspect = old_width / old_height
             new_aspect = width / height
             x = 0
@@ -701,48 +720,87 @@ def common_upscale(samples, width, height, upscale_method, crop):
                 x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
             elif old_aspect < new_aspect:
                 y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
-            s = samples[:,:,y:old_height-y,x:old_width-x]
+            s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2)
         else:
             s = samples
 
         if upscale_method == "bislerp":
-            return bislerp(s, width, height)
+            out = bislerp(s, width, height)
         elif upscale_method == "lanczos":
-            return lanczos(s, width, height)
+            out = lanczos(s, width, height)
         else:
-            return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
+            out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
+
+        if len(orig_shape) == 4:
+            return out
+
+        out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width))
+        return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width))
 
 def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
-    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
+    rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap))
+    cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap))
+    return rows * cols
 
 @torch.inference_mode()
 def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
     dims = len(tile)
-    output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
+
+    if not (isinstance(upscale_amount, (tuple, list))):
+        upscale_amount = [upscale_amount] * dims
+
+    if not (isinstance(overlap, (tuple, list))):
+        overlap = [overlap] * dims
+
+    def get_upscale(dim, val):
+        up = upscale_amount[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return up * val
+
+    def mult_list_upscale(a):
+        out = []
+        for i in range(len(a)):
+            out.append(round(get_upscale(i, a[i])))
+        return out
+
+    output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device)
 
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
-        out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
-        out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
 
-        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
+        # handle entire input fitting in a single tile
+        if all(s.shape[d+2] <= tile[d] for d in range(dims)):
+            output[b:b+1] = function(s).to(output_device)
+            if pbar is not None:
+                pbar.update(1)
+            continue
+
+        out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+        out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+
+        positions = [range(0, s.shape[d+2], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
+
+        for it in itertools.product(*positions):
             s_in = s
             upscaled = []
 
             for d in range(dims):
-                pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
+                pos = max(0, min(s.shape[d + 2] - (overlap[d] + 1), it[d]))
                 l = min(tile[d], s.shape[d + 2] - pos)
                 s_in = s_in.narrow(d + 2, pos, l)
-                upscaled.append(round(pos * upscale_amount))
+                upscaled.append(round(get_upscale(d, pos)))
+
             ps = function(s_in).to(output_device)
             mask = torch.ones_like(ps)
-            feather = round(overlap * upscale_amount)
+
-            for t in range(feather):
+            for d in range(2, dims + 2):
-                for d in range(2, dims + 2):
+                feather = round(get_upscale(d - 2, overlap[d - 2]))
-                    m = mask.narrow(d, t, 1)
+                for t in range(feather):
-                    m *= ((1.0/feather) * (t + 1))
+                    a = (t + 1) / feather
-                    m = mask.narrow(d, mask.shape[d] -1 -t, 1)
+                    mask.narrow(d, t, 1).mul_(a)
-                    m *= ((1.0/feather) * (t + 1))
+                    mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a)
 
             o = out
             o_d = out_div
@@ -750,8 +808,8 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
                 o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
                 o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
 
-            o += ps * mask
+            o.add_(ps * mask)
-            o_d += mask
+            o_d.add_(mask)
 
             if pbar is not None:
                 pbar.update(1)

comfy_execution/caching.py

@@ -1,11 +1,21 @@
 import itertools
-from typing import Sequence, Mapping
+from typing import Sequence, Mapping, Dict
 from comfy_execution.graph import DynamicPrompt
 
 import nodes
 
 from comfy_execution.graph_utils import is_link
 
+NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
+
+
+def include_unique_id_in_input(class_type: str) -> bool:
+    if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
+        return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
+    return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+
 class CacheKeySet:
     def __init__(self, dynprompt, node_ids, is_changed_cache):
         self.keys = {}
@@ -98,7 +108,7 @@ class CacheKeySetInputSignature(CacheKeySet):
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
         signature = [class_type, self.is_changed_cache.get(node_id)]
-        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT):
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
         inputs = node["inputs"]
         for key in sorted(inputs.keys()):

comfy_execution/graph.py

@@ -99,30 +99,44 @@ class TopologicalSort:
         self.add_strong_link(from_node_id, from_socket, to_node_id)
 
     def add_strong_link(self, from_node_id, from_socket, to_node_id):
-        self.add_node(from_node_id)
+        if not self.is_cached(from_node_id):
-        if to_node_id not in self.blocking[from_node_id]:
+            self.add_node(from_node_id)
-            self.blocking[from_node_id][to_node_id] = {}
+            if to_node_id not in self.blocking[from_node_id]:
-            self.blockCount[to_node_id] += 1
+                self.blocking[from_node_id][to_node_id] = {}
-        self.blocking[from_node_id][to_node_id][from_socket] = True
+                self.blockCount[to_node_id] += 1
+            self.blocking[from_node_id][to_node_id][from_socket] = True
 
-    def add_node(self, unique_id, include_lazy=False, subgraph_nodes=None):
+    def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None):
-        if unique_id in self.pendingNodes:
+        node_ids = [node_unique_id]
-            return
+        links = []
-        self.pendingNodes[unique_id] = True
-        self.blockCount[unique_id] = 0
-        self.blocking[unique_id] = {}
 
-        inputs = self.dynprompt.get_node(unique_id)["inputs"]
+        while len(node_ids) > 0:
-        for input_name in inputs:
+            unique_id = node_ids.pop()
-            value = inputs[input_name]
+            if unique_id in self.pendingNodes:
-            if is_link(value):
+                continue
-                from_node_id, from_socket = value
+
-                if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+            self.pendingNodes[unique_id] = True
-                    continue
+            self.blockCount[unique_id] = 0
-                input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+            self.blocking[unique_id] = {}
-                is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+
-                if include_lazy or not is_lazy:
+            inputs = self.dynprompt.get_node(unique_id)["inputs"]
-                    self.add_strong_link(from_node_id, from_socket, unique_id)
+            for input_name in inputs:
+                value = inputs[input_name]
+                if is_link(value):
+                    from_node_id, from_socket = value
+                    if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+                        continue
+                    input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+                    is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+                    if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
+                        node_ids.append(from_node_id)
+                        links.append((from_node_id, from_socket, unique_id))
+                        
+        for link in links:
+            self.add_strong_link(*link)
+
+    def is_cached(self, node_id):
+        return False
 
     def get_ready_nodes(self):
         return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
@@ -146,11 +160,8 @@ class ExecutionList(TopologicalSort):
         self.output_cache = output_cache
         self.staged_node_id = None
 
-    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+    def is_cached(self, node_id):
-        if self.output_cache.get(from_node_id) is not None:
+        return self.output_cache.get(node_id) is not None
-            # Nothing to do
-            return
-        super().add_strong_link(from_node_id, from_socket, to_node_id)
 
     def stage_node_execution(self):
         assert self.staged_node_id is None

comfy_extras/nodes_audio.py

@@ -16,14 +16,15 @@ class EmptyLatentAudio:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
+                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
     CATEGORY = "latent/audio"
 
-    def generate(self, seconds):
+    def generate(self, seconds, batch_size):
-        batch_size = 1
         length = round((seconds * 44100 / 2048) / 2) * 2
         latent = torch.zeros([batch_size, 64, length], device=self.device)
         return ({"samples":latent, "type": "audio"}, )
@@ -58,6 +59,9 @@ class VAEDecodeAudio:
 
     def decode(self, vae, samples):
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
+        std[std < 1.0] = 1.0
+        audio /= std
         return ({"waveform": audio, "sample_rate": 44100}, )
 
 
@@ -183,17 +187,10 @@ class PreviewAudio(SaveAudio):
                 }
 
 class LoadAudio:
-    SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
-
     @classmethod
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
-        files = [
+        files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
-            f for f in os.listdir(input_dir)
-            if (os.path.isfile(os.path.join(input_dir, f))
-                and f.endswith(LoadAudio.SUPPORTED_FORMATS)
-            )
-        ]
         return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
 
     CATEGORY = "audio"

comfy_extras/nodes_controlnet.py

@@ -1,4 +1,6 @@
 from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+import nodes
+import comfy.utils
 
 class SetUnionControlNetType:
     @classmethod
@@ -22,6 +24,37 @@ class SetUnionControlNetType:
 
         return (control_net,)
 
+class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "vae": ("VAE", ),
+                             "image": ("IMAGE", ),
+                             "mask": ("MASK", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+
+    FUNCTION = "apply_inpaint_controlnet"
+
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent):
+        extra_concat = []
+        if control_net.concat_mask:
+            mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+            mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round()
+            image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
+            extra_concat = [mask]
+
+        return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat)
+
+
+
 NODE_CLASS_MAPPINGS = {
     "SetUnionControlNetType": SetUnionControlNetType,
+    "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply,
 }

comfy_extras/nodes_custom_sampler.py

@@ -90,6 +90,27 @@ class PolyexponentialScheduler:
         sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
         return (sigmas, )
 
+class LaplaceScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}),
+                     "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta):
+        sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
+        return (sigmas, )
+
+
 class SDTurboScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -673,6 +694,7 @@ NODE_CLASS_MAPPINGS = {
     "KarrasScheduler": KarrasScheduler,
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
+    "LaplaceScheduler": LaplaceScheduler,
     "VPScheduler": VPScheduler,
     "BetaSamplingScheduler": BetaSamplingScheduler,
     "SDTurboScheduler": SDTurboScheduler,

comfy_extras/nodes_hypernetwork.py

@@ -107,7 +107,7 @@ class HypernetworkLoader:
     CATEGORY = "loaders"
 
     def load_hypernetwork(self, model, hypernetwork_name, strength):
-        hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
+        hypernetwork_path = folder_paths.get_full_path_or_raise("hypernetworks", hypernetwork_name)
         model_hypernetwork = model.clone()
         patch = load_hypernetwork_patch(hypernetwork_path, strength)
         if patch is not None:

comfy_extras/nodes_latent.py

@@ -1,4 +1,5 @@
 import comfy.utils
+import comfy_extras.nodes_post_processing
 import torch
 
 def reshape_latent_to(target_shape, latent):
@@ -145,6 +146,131 @@ class LatentBatchSeedBehavior:
 
         return (samples_out,)
 
+class LatentApplyOperation:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                             "operation": ("LATENT_OPERATION",),
+                             }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, samples, operation):
+        samples_out = samples.copy()
+
+        s1 = samples["samples"]
+        samples_out["samples"] = operation(latent=s1)
+        return (samples_out,)
+
+class LatentApplyOperationCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "operation": ("LATENT_OPERATION",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def patch(self, model, operation):
+        m = model.clone()
+
+        def pre_cfg_function(args):
+            conds_out = args["conds_out"]
+            if len(conds_out) == 2:
+                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
+            else:
+                conds_out[0] = operation(latent=conds_out[0])
+            return conds_out
+
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return (m, )
+
+class LatentOperationTonemapReinhard:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              }}
+
+    RETURN_TYPES = ("LATENT_OPERATION",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, multiplier):
+        def tonemap_reinhard(latent, **kwargs):
+            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
+            normalized_latent = latent / latent_vector_magnitude
+
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+
+            top = (std * 5 + mean) * multiplier
+
+            #reinhard
+            latent_vector_magnitude *= (1.0 / top)
+            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            return normalized_latent * new_magnitude
+        return (tonemap_reinhard,)
+
+class LatentOperationSharpen:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "sharpen_radius": ("INT", {
+                    "default": 9,
+                    "min": 1,
+                    "max": 31,
+                    "step": 1
+                }),
+                "sigma": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.1,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+                "alpha": ("FLOAT", {
+                    "default": 0.1,
+                    "min": 0.0,
+                    "max": 5.0,
+                    "step": 0.01
+                }),
+                              }}
+
+    RETURN_TYPES = ("LATENT_OPERATION",)
+    FUNCTION = "op"
+
+    CATEGORY = "latent/advanced/operations"
+    EXPERIMENTAL = True
+
+    def op(self, sharpen_radius, sigma, alpha):
+        def sharpen(latent, **kwargs):
+            luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
+            normalized_latent = latent / luminance
+            channels = latent.shape[1]
+
+            kernel_size = sharpen_radius * 2 + 1
+            kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
+            center = kernel_size // 2
+
+            kernel *= alpha * -10
+            kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+
+            padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
+            sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
+
+            return luminance * sharpened
+        return (sharpen,)
+
 NODE_CLASS_MAPPINGS = {
     "LatentAdd": LatentAdd,
     "LatentSubtract": LatentSubtract,
@@ -152,4 +278,8 @@ NODE_CLASS_MAPPINGS = {
     "LatentInterpolate": LatentInterpolate,
     "LatentBatch": LatentBatch,
     "LatentBatchSeedBehavior": LatentBatchSeedBehavior,
+    "LatentApplyOperation": LatentApplyOperation,
+    "LatentApplyOperationCFG": LatentApplyOperationCFG,
+    "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard,
+    "LatentOperationSharpen": LatentOperationSharpen,
 }

comfy_extras/nodes_lora_extract.py

@@ -0,0 +1,119 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+from enum import Enum
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, rank):
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+    rank = min(rank, in_dim, out_dim)
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+
+    U, S, Vh = torch.linalg.svd(diff.float())
+    U = U[:, :rank]
+    S = S[:rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:rank, :]
+
+    dist = torch.cat([U.flatten(), Vh.flatten()])
+    hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+    low_val = -hi_val
+
+    U = U.clamp(low_val, hi_val)
+    Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, rank, 1, 1)
+        Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+class LORAType(Enum):
+    STANDARD = 0
+    FULL_DIFF = 1
+
+LORA_TYPES = {"standard": LORAType.STANDARD,
+              "full_diff": LORAType.FULL_DIFF}
+
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False):
+    comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
+    sd = model_diff.model_state_dict(filter_prefix=prefix_model)
+
+    for k in sd:
+        if k.endswith(".weight"):
+            weight_diff = sd[k]
+            if lora_type == LORAType.STANDARD:
+                if weight_diff.ndim < 2:
+                    if bias_diff:
+                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+                    continue
+                try:
+                    out = extract_lora(weight_diff, rank)
+                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu()
+                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu()
+                except:
+                    logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
+            elif lora_type == LORAType.FULL_DIFF:
+                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu()
+
+        elif bias_diff and k.endswith(".bias"):
+            output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
+    return output_sd
+
+class LoraSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
+                              "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
+                              "lora_type": (tuple(LORA_TYPES.keys()),),
+                              "bias_diff": ("BOOLEAN", {"default": True}),
+                            },
+                "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}),
+                             "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})},
+    }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
+        if model_diff is None and text_encoder_diff is None:
+            return {}
+
+        lora_type = LORA_TYPES.get(lora_type)
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        output_sd = {}
+        if model_diff is not None:
+            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff)
+        if text_encoder_diff is not None:
+            output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff)
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return {}
+
+NODE_CLASS_MAPPINGS = {
+    "LoraSave": LoraSave
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraSave": "Extract and Save Lora"
+}

comfy_extras/nodes_mochi.py

@@ -0,0 +1,26 @@
+import nodes
+import torch
+import comfy.model_management
+
+class EmptyMochiLatentVideo:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                              "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                              "length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/mochi"
+
+    def generate(self, width, height, length, batch_size=1):
+        latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=self.device)
+        return ({"samples":latent}, )
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyMochiLatentVideo": EmptyMochiLatentVideo,
+}

comfy_extras/nodes_model_downscale.py

@@ -17,7 +17,7 @@ class PatchModelAddDownscale:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
         model_sampling = model.get_model_object("model_sampling")

comfy_extras/nodes_model_merging_model_specific.py

@@ -101,10 +101,34 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_embed."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["context_embedder."] = argument
+        arg_dict["y_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+
+        for i in range(38):
+            arg_dict["joint_blocks.{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
     "ModelMergeSDXL": ModelMergeSDXL,
     "ModelMergeSD3_2B": ModelMergeSD3_2B,
     "ModelMergeFlux1": ModelMergeFlux1,
+    "ModelMergeSD35_Large": ModelMergeSD35_Large,
 }

comfy_extras/nodes_perpneg.py

@@ -26,6 +26,7 @@ class PerpNeg:
     FUNCTION = "patch"
 
     CATEGORY = "_for_testing"
+    DEPRECATED = True
 
     def patch(self, model, empty_conditioning, neg_scale):
         m = model.clone()

comfy_extras/nodes_photomaker.py

@@ -126,7 +126,7 @@ class PhotoMakerLoader:
     CATEGORY = "_for_testing/photomaker"
 
     def load_photomaker_model(self, photomaker_model_name):
-        photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name)
+        photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name)
         photomaker_model = PhotoMakerIDEncoder()
         data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
         if "id_encoder" in data:

comfy_extras/nodes_sd3.py

@@ -3,11 +3,11 @@ import comfy.sd
 import comfy.model_management
 import nodes
 import torch
-
+import re
 class TripleCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), )
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -15,9 +15,9 @@ class TripleCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, clip_name3):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
-        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
@@ -36,7 +36,7 @@ class EmptySD3LatentImage:
     CATEGORY = "latent/sd3"
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609
+        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )
 
 class CLIPTextEncodeSD3:
@@ -93,15 +93,75 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
                              "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                              }}
     CATEGORY = "conditioning/controlnet"
+    DEPRECATED = True
+
+class SkipLayerGuidanceSD3:
+    '''
+    Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
+    Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
+    Experimental implementation by Dango233@StabilityAI.
+    '''
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "skip_guidance"
+
+    CATEGORY = "advanced/guidance"
+
+
+    def skip_guidance(self, model, layers, scale, start_percent, end_percent):
+        if layers == "" or layers == None:
+            return (model, )
+        # check if layer is comma separated integers
+        def skip(args, extra_args):
+            return args
+
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            model = args["model"]
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            x = args["input"]
+            model_options = args["model_options"].copy()
+        
+            for layer in layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
+            model_sampling.percent_to_sigma(start_percent)
+
+            sigma_ = sigma[0].item()
+            if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
+                (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
+                cfg_result = cfg_result + (cond_pred - slg) * scale
+            return cfg_result
+
+        layers = re.findall(r'\d+', layers)
+        layers = [int(i) for i in layers]
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m, )
+
 
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
     "EmptySD3LatentImage": EmptySD3LatentImage,
     "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
     "ControlNetApplySD3": ControlNetApplySD3,
+    "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     # Sampling
-    "ControlNetApplySD3": "ControlNetApply SD3 and HunyuanDiT",
+    "ControlNetApplySD3": "Apply Controlnet with VAE",
 }

comfy_extras/nodes_stable_cascade.py

@@ -116,6 +116,7 @@ class StableCascade_SuperResolutionControlnet:
     RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
     FUNCTION = "generate"
 
+    EXPERIMENTAL = True
     CATEGORY = "_for_testing/stable_cascade"
 
     def generate(self, image, vae):

comfy_extras/nodes_tomesd.py

@@ -154,7 +154,7 @@ class TomePatchModel:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, ratio):
         self.u = None

comfy_extras/nodes_torch_compile.py

@@ -0,0 +1,22 @@
+import torch
+
+class TorchCompileModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "backend": (["inductor", "cudagraphs"],),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+    EXPERIMENTAL = True
+
+    def patch(self, model, backend):
+        m = model.clone()
+        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend))
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "TorchCompileModel": TorchCompileModel,
+}

comfy_extras/nodes_upscale_model.py

@@ -25,7 +25,7 @@ class UpscaleModelLoader:
     CATEGORY = "loaders"
 
     def load_model(self, model_name):
-        model_path = folder_paths.get_full_path("upscale_models", model_name)
+        model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name)
         sd = comfy.utils.load_torch_file(model_path, safe_load=True)
         if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})

comfy_extras/nodes_video_model.py

@@ -17,7 +17,7 @@ class ImageOnlyCheckpointLoader:
     CATEGORY = "loaders/video_models"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (out[0], out[3], out[2])
 
@@ -107,7 +107,7 @@ class VideoTriangleCFGGuidance:
         return (m, )
 
 class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
-    CATEGORY = "_for_testing"
+    CATEGORY = "advanced/model_merging"
 
     @classmethod
     def INPUT_TYPES(s):

custom_nodes/websocket_image_save.py

@@ -37,6 +37,7 @@ class SaveImageWebsocket:
 
         return {}
 
+    @classmethod
     def IS_CHANGED(s, images):
         return time.time()
 

execution.py

@@ -179,7 +179,13 @@ def merge_result_data(results, obj):
     # merge node execution results
     for i, is_list in zip(range(len(results[0])), output_is_list):
         if is_list:
-            output.append([x for o in results for x in o[i]])
+            value = []
+            for o in results:
+                if isinstance(o[i], ExecutionBlocker):
+                    value.append(o[i])
+                else:
+                    value.extend(o[i])
+            output.append(value)
         else:
             output.append([o[i] for o in results])
     return output

extra_model_paths.yaml.example

@@ -25,11 +25,16 @@ a111:
 
 #comfyui:
 #     base_path: path/to/comfyui/
+#     # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads
+#     #is_default: true
 #     checkpoints: models/checkpoints/
 #     clip: models/clip/
 #     clip_vision: models/clip_vision/
 #     configs: models/configs/
 #     controlnet: models/controlnet/
+#     diffusion_models: |
+#                  models/diffusion_models
+#                  models/unet
 #     embeddings: models/embeddings/
 #     loras: models/loras/
 #     upscale_models: models/upscale_models/

folder_paths.py

@@ -2,7 +2,9 @@ from __future__ import annotations
 
 import os
 import time
+import mimetypes
 import logging
+from typing import Set, List, Dict, Tuple, Literal
 from collections.abc import Collection
 
 supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
@@ -16,7 +18,7 @@ folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".y
 
 folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
 folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions)
 folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions)
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
@@ -44,8 +46,43 @@ user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 
+class CacheHelper:
+    """
+    Helper class for managing file list cache data.
+    """
+    def __init__(self):
+        self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
+        self.active = False
+
+    def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]:
+        if not self.active:
+            return default
+        return self.cache.get(key, default)
+    
+    def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None:
+        if self.active:
+            self.cache[key] = value
+
+    def clear(self):
+        self.cache.clear()
+
+    def __enter__(self):
+        self.active = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.active = False
+        self.clear()
+
+cache_helper = CacheHelper()
+
+extension_mimetypes_cache = {
+    "webp" : "image",
+}
+
 def map_legacy(folder_name: str) -> str:
-    legacy = {"unet": "diffusion_models"}
+    legacy = {"unet": "diffusion_models",
+              "clip": "text_encoders"}
     return legacy.get(folder_name, folder_name)
 
 if not os.path.exists(input_directory):
@@ -78,6 +115,13 @@ def get_input_directory() -> str:
     global input_directory
     return input_directory
 
+def get_user_directory() -> str:
+    return user_directory
+
+def set_user_directory(user_dir: str) -> None:
+    global user_directory
+    user_directory = user_dir
+
 
 #NOTE: used in http server so don't put folders that should not be accessed remotely
 def get_directory_by_type(type_name: str) -> str | None:
@@ -89,6 +133,28 @@ def get_directory_by_type(type_name: str) -> str | None:
         return get_input_directory()
     return None
 
+def filter_files_content_types(files: List[str], content_types: Literal["image", "video", "audio"]) -> List[str]:
+    """
+    Example:
+        files = os.listdir(folder_paths.get_input_directory())
+        filter_files_content_types(files, ["image", "audio", "video"])
+    """
+    global extension_mimetypes_cache
+    result = []
+    for file in files:
+        extension = file.split('.')[-1]
+        if extension not in extension_mimetypes_cache:
+            mime_type, _ = mimetypes.guess_type(file, strict=False)
+            if not mime_type:
+                continue
+            content_type = mime_type.split('/')[0]
+            extension_mimetypes_cache[extension] = content_type
+        else:
+            content_type = extension_mimetypes_cache[extension]
+
+        if content_type in content_types:
+            result.append(file)
+    return result
 
 # determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
 # otherwise use default_path as base_dir
@@ -130,11 +196,14 @@ def exists_annotated_filepath(name) -> bool:
     return os.path.exists(filepath)
 
 
-def add_model_folder_path(folder_name: str, full_folder_path: str) -> None:
+def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None:
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
     if folder_name in folder_names_and_paths:
-        folder_names_and_paths[folder_name][0].append(full_folder_path)
+        if is_default:
+            folder_names_and_paths[folder_name][0].insert(0, full_folder_path)
+        else:
+            folder_names_and_paths[folder_name][0].append(full_folder_path)
     else:
         folder_names_and_paths[folder_name] = ([full_folder_path], set())
 
@@ -166,8 +235,12 @@ def recursive_search(directory: str, excluded_dir_names: list[str] | None=None)
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
-            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            try:
-            result.append(relative_path)
+                relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                result.append(relative_path)
+            except:
+                logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
+                continue
 
         for d in subdirs:
             path: str = os.path.join(dirpath, d)
@@ -200,6 +273,14 @@ def get_full_path(folder_name: str, filename: str) -> str | None:
 
     return None
 
+
+def get_full_path_or_raise(folder_name: str, filename: str) -> str:
+    full_path = get_full_path(folder_name, filename)
+    if full_path is None:
+        raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.")
+    return full_path
+
+
 def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]:
     folder_name = map_legacy(folder_name)
     global folder_names_and_paths
@@ -214,6 +295,10 @@ def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], f
     return sorted(list(output_list)), output_folders, time.perf_counter()
 
 def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None:
+    strong_cache = cache_helper.get(folder_name)
+    if strong_cache is not None:
+        return strong_cache
+    
     global filename_list_cache
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
@@ -242,6 +327,7 @@ def get_filename_list(folder_name: str) -> list[str]:
         out = get_filename_list_(folder_name)
         global filename_list_cache
         filename_list_cache[folder_name] = out
+    cache_helper.set(folder_name, out)
     return list(out[0])
 
 def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]:
@@ -257,9 +343,17 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im
     def compute_vars(input: str, image_width: int, image_height: int) -> str:
         input = input.replace("%width%", str(image_width))
         input = input.replace("%height%", str(image_height))
+        now = time.localtime()
+        input = input.replace("%year%", str(now.tm_year))
+        input = input.replace("%month%", str(now.tm_mon).zfill(2))
+        input = input.replace("%day%", str(now.tm_mday).zfill(2))
+        input = input.replace("%hour%", str(now.tm_hour).zfill(2))
+        input = input.replace("%minute%", str(now.tm_min).zfill(2))
+        input = input.replace("%second%", str(now.tm_sec).zfill(2))
         return input
 
-    filename_prefix = compute_vars(filename_prefix, image_width, image_height)
+    if "%" in filename_prefix:
+        filename_prefix = compute_vars(filename_prefix, image_width, image_height)
 
     subfolder = os.path.dirname(os.path.normpath(filename_prefix))
     filename = os.path.basename(os.path.normpath(filename_prefix))

latent_preview.py

@@ -9,7 +9,7 @@ import folder_paths
 import comfy.utils
 import logging
 
-MAX_PREVIEW_RESOLUTION = 512
+MAX_PREVIEW_RESOLUTION = args.preview_size
 
 def preview_to_image(latent_image):
         latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
@@ -36,12 +36,20 @@ class TAESDPreviewerImpl(LatentPreviewer):
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self, latent_rgb_factors):
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
-        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        if latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
 
     def decode_latent_to_preview(self, x0):
         self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
-        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+        if self.latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+
+        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+
         return preview_to_image(latent_image)
 
 
@@ -71,7 +79,7 @@ def get_previewer(device, latent_format):
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:
-                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
     return previewer
 
 def prepare_callback(model, steps, x0_output_dict=None):

main.py

@@ -9,7 +9,7 @@ from comfy.cli_args import args
 from app.logger import setup_logger
 
 
-setup_logger(verbose=args.verbose)
+setup_logger(log_level=args.verbose)
 
 
 def execute_prestartup_script():
@@ -63,6 +63,7 @@ import threading
 import gc
 
 import logging
+import utils.extra_config
 
 if os.name == "nt":
     logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
@@ -85,7 +86,6 @@ if args.windows_standalone_build:
         pass
 
 import comfy.utils
-import yaml
 
 import execution
 import server
@@ -160,7 +160,10 @@ def prompt_worker(q, server):
                 need_gc = False
 
 async def run(server, address='', port=8188, verbose=True, call_on_start=None):
-    await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
+    addresses = []
+    for addr in address.split(","):
+        addresses.append((addr, port))
+    await asyncio.gather(server.start_multi_address(addresses, call_on_start), server.publish_loop())
 
 
 def hijack_progress(server):
@@ -180,27 +183,6 @@ def cleanup_temp():
         shutil.rmtree(temp_dir, ignore_errors=True)
 
 
-def load_extra_path_config(yaml_path):
-    with open(yaml_path, 'r') as stream:
-        config = yaml.safe_load(stream)
-    for c in config:
-        conf = config[c]
-        if conf is None:
-            continue
-        base_path = None
-        if "base_path" in conf:
-            base_path = conf.pop("base_path")
-        for x in conf:
-            for y in conf[x].split("\n"):
-                if len(y) == 0:
-                    continue
-                full_path = y
-                if base_path is not None:
-                    full_path = os.path.join(base_path, full_path)
-                logging.info("Adding extra search path {} {}".format(x, full_path))
-                folder_paths.add_model_folder_path(x, full_path)
-
-
 if __name__ == "__main__":
     if args.temp_directory:
         temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
@@ -222,11 +204,11 @@ if __name__ == "__main__":
 
     extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
     if os.path.isfile(extra_model_paths_config_path):
-        load_extra_path_config(extra_model_paths_config_path)
+        utils.extra_config.load_extra_path_config(extra_model_paths_config_path)
 
     if args.extra_model_paths_config:
         for config_path in itertools.chain(*args.extra_model_paths_config):
-            load_extra_path_config(config_path)
+            utils.extra_config.load_extra_path_config(config_path)
 
     nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes)
 
@@ -247,21 +229,30 @@ if __name__ == "__main__":
     folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
     folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
     folder_paths.add_model_folder_path("diffusion_models", os.path.join(folder_paths.get_output_directory(), "diffusion_models"))
+    folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras"))
 
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)
         logging.info(f"Setting input directory to: {input_dir}")
         folder_paths.set_input_directory(input_dir)
     
+    if args.user_directory:
+        user_dir = os.path.abspath(args.user_directory)
+        logging.info(f"Setting user directory to: {user_dir}")
+        folder_paths.set_user_directory(user_dir)
+
     if args.quick_test_for_ci:
         exit(0)
 
+    os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
     call_on_start = None
     if args.auto_launch:
         def startup_server(scheme, address, port):
             import webbrowser
             if os.name == 'nt' and address == '0.0.0.0':
                 address = '127.0.0.1'
+            if ':' in address:
+                address = "[{}]".format(address)
             webbrowser.open(f"{scheme}://{address}:{port}")
         call_on_start = startup_server
 

model_filemanager/__init__.py

@@ -1,2 +1,2 @@
 # model_manager/__init__.py
-from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_model_subdirectory, validate_filename
+from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_filename

model_filemanager/download_models.py

@@ -1,9 +1,10 @@
+#NOTE: This was an experiment and WILL BE REMOVED
 from __future__ import annotations
 import aiohttp
 import os
 import traceback
 import logging
-from folder_paths import models_dir
+from folder_paths import folder_names_and_paths, get_folder_paths
 import re
 from typing import Callable, Any, Optional, Awaitable, Dict
 from enum import Enum
@@ -17,6 +18,7 @@ class DownloadStatusType(Enum):
     COMPLETED = "completed"
     ERROR = "error"
 
+
 @dataclass
 class DownloadModelStatus():
     status: str
@@ -38,10 +40,12 @@ class DownloadModelStatus():
             "already_existed": self.already_existed
         }
 
+
 async def download_model(model_download_request: Callable[[str], Awaitable[aiohttp.ClientResponse]],
                          model_name: str,
                          model_url: str,
-                         model_sub_directory: str,
+                         model_directory: str,
+                         folder_path: str,
                          progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
                          progress_interval: float = 1.0) -> DownloadModelStatus:
     """
@@ -54,23 +58,17 @@ async def download_model(model_download_request: Callable[[str], Awaitable[aioht
             The name of the model file to be downloaded. This will be the filename on disk.
         model_url (str):
             The URL from which to download the model.
-        model_sub_directory (str): 
+        model_directory (str):
             The subdirectory within the main models directory where the model
             should be saved (e.g., 'checkpoints', 'loras', etc.).
         progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]):
             An asynchronous function to call with progress updates.
+        folder_path (str);
+            Path to which model folder should be used as the root.
 
     Returns:
         DownloadModelStatus: The result of the download operation.
     """
-    if not validate_model_subdirectory(model_sub_directory):
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR, 
-            0,
-            "Invalid model subdirectory", 
-            False
-        )
-
     if not validate_filename(model_name):
         return DownloadModelStatus(
             DownloadStatusType.ERROR,
@@ -79,52 +77,67 @@ async def download_model(model_download_request: Callable[[str], Awaitable[aioht
             False
         )
 
-    file_path, relative_path = create_model_path(model_name, model_sub_directory, models_dir)
+    if not model_directory in folder_names_and_paths:
-    existing_file = await check_file_exists(file_path, model_name, progress_callback, relative_path)
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            "Invalid or unrecognized model directory. model_directory must be a known model type (eg 'checkpoints'). If you are seeing this error for a custom model type, ensure the relevant custom nodes are installed and working.",
+            False
+        )
+
+    if not folder_path in get_folder_paths(model_directory):
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            f"Invalid folder path '{folder_path}', does not match the list of known directories ({get_folder_paths(model_directory)}). If you're seeing this in the downloader UI, you may need to refresh the page.",
+            False
+        )
+
+    file_path = create_model_path(model_name, folder_path)
+    existing_file = await check_file_exists(file_path, model_name, progress_callback)
     if existing_file:
         return existing_file
 
     try:
+        logging.info(f"Downloading {model_name} from {model_url}")
         status = DownloadModelStatus(DownloadStatusType.PENDING, 0, f"Starting download of {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         response = await model_download_request(model_url)
         if response.status != 200:
             error_message = f"Failed to download {model_name}. Status code: {response.status}"
             logging.error(error_message)
             status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             return DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
 
-        return await track_download_progress(response, file_path, model_name, progress_callback, relative_path, progress_interval)
+        return await track_download_progress(response, file_path, model_name, progress_callback, progress_interval)
 
     except Exception as e:
         logging.error(f"Error in downloading model: {e}")
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
 
 
-def create_model_path(model_name: str, model_directory: str, models_base_dir: str) -> tuple[str, str]:
+def create_model_path(model_name: str, folder_path: str) -> tuple[str, str]:
-    full_model_dir = os.path.join(models_base_dir, model_directory)
+    os.makedirs(folder_path, exist_ok=True)
-    os.makedirs(full_model_dir, exist_ok=True)
+    file_path = os.path.join(folder_path, model_name)
-    file_path = os.path.join(full_model_dir, model_name)
 
     # Ensure the resulting path is still within the base directory
     abs_file_path = os.path.abspath(file_path)
-    abs_base_dir = os.path.abspath(str(models_base_dir))
+    abs_base_dir = os.path.abspath(folder_path)
     if os.path.commonprefix([abs_file_path, abs_base_dir]) != abs_base_dir:
-        raise Exception(f"Invalid model directory: {model_directory}/{model_name}")
+        raise Exception(f"Invalid model directory: {folder_path}/{model_name}")
 
+    return file_path
 
-    relative_path = '/'.join([model_directory, model_name])
-    return file_path, relative_path
 
 async def check_file_exists(file_path: str,
                             model_name: str,
-                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]], 
+                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]]
-                            relative_path: str) -> Optional[DownloadModelStatus]:
+                            ) -> Optional[DownloadModelStatus]:
     if os.path.exists(file_path):
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"{model_name} already exists", True)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
         return status
     return None
 
@@ -133,7 +146,6 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                                   file_path: str,
                                   model_name: str,
                                   progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                                  relative_path: str, 
                                   interval: float = 1.0) -> DownloadModelStatus:
     try:
         total_size = int(response.headers.get('Content-Length', 0))
@@ -144,10 +156,11 @@ async def track_download_progress(response: aiohttp.ClientResponse,
             nonlocal last_update_time
             progress = (downloaded / total_size) * 100 if total_size > 0 else 0
             status = DownloadModelStatus(DownloadStatusType.IN_PROGRESS, progress, f"Downloading {model_name}", False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             last_update_time = time.time()
 
-        with open(file_path, 'wb') as f:
+        temp_file_path = file_path + '.tmp'
+        with open(temp_file_path, 'wb') as f:
             chunk_iterator = response.content.iter_chunked(8192)
             while True:
                 try:
@@ -160,49 +173,30 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                 if time.time() - last_update_time >= interval:
                     await update_progress()
 
+        os.rename(temp_file_path, file_path)
+
         await update_progress()
 
         logging.info(f"Successfully downloaded {model_name}. Total downloaded: {downloaded}")
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"Successfully downloaded {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         return status
     except Exception as e:
         logging.error(f"Error in track_download_progress: {e}")
         logging.error(traceback.format_exc())
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
+
 
 async def handle_download_error(e: Exception,
                                 model_name: str,
-                                progress_callback: Callable[[str, DownloadModelStatus], Any], 
+                                progress_callback: Callable[[str, DownloadModelStatus], Any]
-                                relative_path: str) -> DownloadModelStatus:
+                                ) -> DownloadModelStatus:
     error_message = f"Error downloading {model_name}: {str(e)}"
     status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-    await progress_callback(relative_path, status)
+    await progress_callback(model_name, status)
     return status
 
-def validate_model_subdirectory(model_subdirectory: str) -> bool:
-    """
-    Validate that the model subdirectory is safe to install into. 
-    Must not contain relative paths, nested paths or special characters
-    other than underscores and hyphens.
-
-    Args:
-        model_subdirectory (str): The subdirectory for the specific model type.
-
-    Returns:
-        bool: True if the subdirectory is safe, False otherwise.
-    """
-    if len(model_subdirectory) > 50:
-        return False
-
-    if '..' in model_subdirectory or '/' in model_subdirectory:
-        return False
-
-    if not re.match(r'^[a-zA-Z0-9_-]+$', model_subdirectory):
-        return False
-
-    return True
 
 def validate_filename(filename: str)-> bool:
     """

nodes.py

@@ -281,7 +281,10 @@ class VAEDecode:
     DESCRIPTION = "Decodes latent images back into pixel space images."
 
     def decode(self, vae, samples):
-        return (vae.decode(samples["samples"]), )
+        images = vae.decode(samples["samples"])
+        if len(images.shape) == 5: #Combine batches
+            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+        return (images, )
 
 class VAEDecodeTiled:
     @classmethod
@@ -511,10 +514,11 @@ class CheckpointLoader:
     FUNCTION = "load_checkpoint"
 
     CATEGORY = "advanced/loaders"
+    DEPRECATED = True
 
     def load_checkpoint(self, config_name, ckpt_name):
         config_path = folder_paths.get_full_path("configs", config_name)
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
 
 class CheckpointLoaderSimple:
@@ -535,7 +539,7 @@ class CheckpointLoaderSimple:
     DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
 
     def load_checkpoint(self, ckpt_name):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out[:3]
 
@@ -577,7 +581,7 @@ class unCLIPCheckpointLoader:
     CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out
 
@@ -624,7 +628,7 @@ class LoraLoader:
         if strength_model == 0 and strength_clip == 0:
             return (model, clip)
 
-        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         lora = None
         if self.loaded_lora is not None:
             if self.loaded_lora[0] == lora_path:
@@ -703,11 +707,11 @@ class VAELoader:
         encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
         decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))
 
-        enc = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder))
+        enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder))
         for k in enc:
             sd["taesd_encoder.{}".format(k)] = enc[k]
 
-        dec = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder))
+        dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder))
         for k in dec:
             sd["taesd_decoder.{}".format(k)] = dec[k]
 
@@ -738,7 +742,7 @@ class VAELoader:
         if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]:
             sd = self.load_taesd(vae_name)
         else:
-            vae_path = folder_paths.get_full_path("vae", vae_name)
+            vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
             sd = comfy.utils.load_torch_file(vae_path)
         vae = comfy.sd.VAE(sd=sd)
         return (vae,)
@@ -754,7 +758,7 @@ class ControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
         controlnet = comfy.controlnet.load_controlnet(controlnet_path)
         return (controlnet,)
 
@@ -770,7 +774,7 @@ class DiffControlNetLoader:
     CATEGORY = "loaders"
 
     def load_controlnet(self, model, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
         controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
         return (controlnet,)
 
@@ -786,6 +790,7 @@ class ControlNetApply:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_controlnet"
 
+    DEPRECATED = True
     CATEGORY = "conditioning/controlnet"
 
     def apply_controlnet(self, conditioning, control_net, image, strength):
@@ -815,7 +820,10 @@ class ControlNetApplyAdvanced:
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                              "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-                             }}
+                             },
+                "optional": {"vae": ("VAE", ),
+                             }
+    }
 
     RETURN_TYPES = ("CONDITIONING","CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -823,7 +831,7 @@ class ControlNetApplyAdvanced:
 
     CATEGORY = "conditioning/controlnet"
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
         if strength == 0:
             return (positive, negative)
 
@@ -840,7 +848,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 
@@ -856,7 +864,7 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
-                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
@@ -867,18 +875,21 @@ class UNETLoader:
         model_options = {}
         if weight_dtype == "fp8_e4m3fn":
             model_options["dtype"] = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
         elif weight_dtype == "fp8_e5m2":
             model_options["dtype"] = torch.float8_e5m2
 
-        unet_path = folder_paths.get_full_path("diffusion_models", unet_name)
+        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name)
         model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
         return (model,)
 
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -892,18 +903,20 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.SD3
         elif type == "stable_audio":
             clip_type = comfy.sd.CLIPType.STABLE_AUDIO
+        elif type == "mochi":
+            clip_type = comfy.sd.CLIPType.MOCHI
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
-        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -912,8 +925,8 @@ class DualCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         if type == "sdxl":
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
@@ -935,7 +948,7 @@ class CLIPVisionLoader:
     CATEGORY = "loaders"
 
     def load_clip(self, clip_name):
-        clip_path = folder_paths.get_full_path("clip_vision", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name)
         clip_vision = comfy.clip_vision.load(clip_path)
         return (clip_vision,)
 
@@ -965,7 +978,7 @@ class StyleModelLoader:
     CATEGORY = "loaders"
 
     def load_style_model(self, style_model_name):
-        style_model_path = folder_paths.get_full_path("style_models", style_model_name)
+        style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name)
         style_model = comfy.sd.load_style_model(style_model_path)
         return (style_model,)
 
@@ -1030,7 +1043,7 @@ class GLIGENLoader:
     CATEGORY = "loaders"
 
     def load_gligen(self, gligen_name):
-        gligen_path = folder_paths.get_full_path("gligen", gligen_name)
+        gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name)
         gligen = comfy.sd.load_gligen(gligen_path)
         return (gligen,)
 
@@ -1171,10 +1184,10 @@ class LatentUpscale:
 
             if width == 0:
                 height = max(64, height)
-                width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2]))
+                width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2]))
             elif height == 0:
                 width = max(64, width)
-                height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3]))
+                height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1]))
             else:
                 width = max(64, width)
                 height = max(64, height)
@@ -1196,8 +1209,8 @@ class LatentUpscaleBy:
 
     def upscale(self, samples, upscale_method, scale_by):
         s = samples.copy()
-        width = round(samples["samples"].shape[3] * scale_by)
+        width = round(samples["samples"].shape[-1] * scale_by)
-        height = round(samples["samples"].shape[2] * scale_by)
+        height = round(samples["samples"].shape[-2] * scale_by)
         s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
         return (s,)
 
@@ -1916,8 +1929,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
-    "ControlNetApply": "Apply ControlNet",
+    "ControlNetApply": "Apply ControlNet (OLD)",
-    "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)",
+    "ControlNetApplyAdvanced": "Apply ControlNet",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
@@ -1944,6 +1957,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageInvert": "Invert Image",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
     "ImageBatch": "Batch Images",
+    "ImageCrop": "Image Crop",
+    "ImageBlend": "Image Blend",
+    "ImageBlur": "Image Blur",
+    "ImageQuantize": "Image Quantize",
+    "ImageSharpen": "Image Sharpen",
+    "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
@@ -2101,6 +2120,9 @@ def init_builtin_extra_nodes():
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
         "nodes_flux.py",
+        "nodes_lora_extract.py",
+        "nodes_torch_compile.py",
+        "nodes_mochi.py",
     ]
 
     import_failed = []

script_examples/websockets_api_example.py

@@ -38,6 +38,9 @@ def get_images(ws, prompt):
                 if data['node'] is None and data['prompt_id'] == prompt_id:
                     break #Execution is done
         else:
+            # If you want to be able to decode the binary stream for latent previews, here is how you can do it:
+            # bytesIO = BytesIO(out[8:])
+            # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global
             continue #previews are binary data
 
     history = get_history(prompt_id)[prompt_id]
@@ -151,7 +154,7 @@ prompt["3"]["inputs"]["seed"] = 5
 ws = websocket.WebSocket()
 ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
 images = get_images(ws, prompt)
-
+ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
 #Commented out code to display the output images:
 
 # for node_id in images:

script_examples/websockets_api_example_ws_images.py

@@ -147,7 +147,7 @@ prompt["3"]["inputs"]["seed"] = 5
 ws = websocket.WebSocket()
 ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
 images = get_images(ws, prompt)
-
+ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
 #Commented out code to display the output images:
 
 # for node_id in images:

server.py

@@ -12,6 +12,8 @@ import json
 import glob
 import struct
 import ssl
+import socket
+import ipaddress
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
@@ -38,7 +40,7 @@ class BinaryEventTypes:
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err:
         logging.warning("send error: {}".format(err))
 
 def get_comfyui_version():
@@ -80,6 +82,68 @@ def create_cors_middleware(allowed_origin: str):
 
     return cors_middleware
 
+def is_loopback(host):
+    if host is None:
+        return False
+    try:
+        if ipaddress.ip_address(host).is_loopback:
+            return True
+        else:
+            return False
+    except:
+        pass
+
+    loopback = False
+    for family in (socket.AF_INET, socket.AF_INET6):
+        try:
+            r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM)
+            for family, _, _, _, sockaddr in r:
+                if not ipaddress.ip_address(sockaddr[0]).is_loopback:
+                    return loopback
+                else:
+                    loopback = True
+        except socket.gaierror:
+            pass
+
+    return loopback
+
+
+def create_origin_only_middleware():
+    @web.middleware
+    async def origin_only_middleware(request: web.Request, handler):
+        #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason.
+        #in that case the Host and Origin hostnames won't match
+        #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime
+        if 'Host' in request.headers and 'Origin' in request.headers:
+            host = request.headers['Host']
+            origin = request.headers['Origin']
+            host_domain = host.lower()
+            parsed = urllib.parse.urlparse(origin)
+            origin_domain = parsed.netloc.lower()
+            host_domain_parsed = urllib.parse.urlsplit('//' + host_domain)
+
+            #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit
+            loopback = is_loopback(host_domain_parsed.hostname)
+
+            if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host
+                host_domain = host_domain_parsed.hostname
+            if host_domain_parsed.port is None:
+                origin_domain = parsed.hostname
+
+            if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0:
+                if host_domain != origin_domain:
+                    logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain))
+                    return web.Response(status=403)
+
+        if request.method == "OPTIONS":
+            response = web.Response()
+        else:
+            response = await handler(request)
+
+        return response
+
+    return origin_only_middleware
+
 class PromptServer():
     def __init__(self, loop):
         PromptServer.instance = self
@@ -99,6 +163,8 @@ class PromptServer():
         middlewares = [cache_control]
         if args.enable_cors_header:
             middlewares.append(create_cors_middleware(args.enable_cors_header))
+        else:
+            middlewares.append(create_origin_only_middleware())
 
         max_upload_size = round(args.max_upload_size * 1024 * 1024)
         self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
@@ -156,6 +222,12 @@ class PromptServer():
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
         
+        @routes.get("/models")
+        def list_model_types(request):
+            model_types = list(folder_paths.folder_names_and_paths.keys())
+
+            return web.json_response(model_types)
+
         @routes.get("/models/{folder}")
         async def get_models(request):
             folder = request.match_info.get("folder", None)
@@ -418,12 +490,17 @@ class PromptServer():
         async def system_stats(request):
             device = comfy.model_management.get_torch_device()
             device_name = comfy.model_management.get_torch_device_name(device)
+            cpu_device = comfy.model_management.torch.device("cpu")
+            ram_total = comfy.model_management.get_total_memory(cpu_device)
+            ram_free = comfy.model_management.get_free_memory(cpu_device)
             vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True)
             vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True)
 
             system_stats = {
                 "system": {
                     "os": os.name,
+                    "ram_total": ram_total,
+                    "ram_free": ram_free,
                     "comfyui_version": get_comfyui_version(),
                     "python_version": sys.version,
                     "pytorch_version": comfy.model_management.torch_version,
@@ -480,14 +557,15 @@ class PromptServer():
 
         @routes.get("/object_info")
         async def get_object_info(request):
-            out = {}
+            with folder_paths.cache_helper:
-            for x in nodes.NODE_CLASS_MAPPINGS:
+                out = {}
-                try:
+                for x in nodes.NODE_CLASS_MAPPINGS:
-                    out[x] = node_info(x)
+                    try:
-                except Exception as e:
+                        out[x] = node_info(x)
-                    logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
+                    except Exception as e:
-                    logging.error(traceback.format_exc())
+                        logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
-            return web.json_response(out)
+                        logging.error(traceback.format_exc())
+                return web.json_response(out)
 
         @routes.get("/object_info/{node_class}")
         async def get_object_info_node(request):
@@ -601,6 +679,7 @@ class PromptServer():
         
         # Internal route. Should not be depended upon and is subject to change at any time.
         # TODO(robinhuang): Move to internal route table class once we refactor PromptServer to pass around Websocket.
+        # NOTE: This was an experiment and WILL BE REMOVED
         @routes.post("/internal/models/download")
         async def download_handler(request):
             async def report_progress(filename: str, status: DownloadModelStatus):
@@ -611,10 +690,11 @@ class PromptServer():
             data = await request.json()
             url = data.get('url')
             model_directory = data.get('model_directory')
+            folder_path = data.get('folder_path')
             model_filename = data.get('model_filename')
             progress_interval = data.get('progress_interval', 1.0) # In seconds, how often to report download progress.
 
-            if not url or not model_directory or not model_filename:
+            if not url or not model_directory or not model_filename or not folder_path:
                 return web.json_response({"status": "error", "message": "Missing URL or folder path or filename"}, status=400)
 
             session = self.client_session
@@ -622,7 +702,7 @@ class PromptServer():
                 logging.error("Client session is not initialized")
                 return web.Response(status=500)
             
-            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, report_progress, progress_interval))
+            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, folder_path, report_progress, progress_interval))
             await task
 
             return web.json_response(task.result().to_dict())
@@ -739,6 +819,9 @@ class PromptServer():
             await self.send(*msg)
 
     async def start(self, address, port, verbose=True, call_on_start=None):
+        await self.start_multi_address([(address, port)], call_on_start=call_on_start)
+
+    async def start_multi_address(self, addresses, call_on_start=None):
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
         ssl_ctx = None
@@ -749,17 +832,26 @@ class PromptServer():
                                 keyfile=args.tls_keyfile)
                 scheme = "https"
 
-        site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
+        logging.info("Starting server\n")
-        await site.start()
+        for addr in addresses:
+            address = addr[0]
+            port = addr[1]
+            site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
+            await site.start()
 
-        self.address = address
+            if not hasattr(self, 'address'):
-        self.port = port
+                self.address = address #TODO: remove this
+                self.port = port
+
+            if ':' in address:
+                address_print = "[{}]".format(address)
+            else:
+                address_print = address
+
+            logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port))
 
-        if verbose:
-            logging.info("Starting server\n")
-            logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address, port))
         if call_on_start is not None:
-            call_on_start(scheme, address, port)
+            call_on_start(scheme, self.address, self.port)
 
     def add_on_prompt_handler(self, handler):
         self.on_prompt_handlers.append(handler)

tests-unit/README.md

@@ -2,7 +2,7 @@
 
 ## Install test dependencies
 
-`pip install -r tests-units/requirements.txt`
+`pip install -r tests-unit/requirements.txt`
 
 ## Run tests
-`pytest tests-units/`
+`pytest tests-unit/`

tests-unit/comfy_test/folder_path_test.py

@@ -0,0 +1,66 @@
+### 🗻 This file is created through the spirit of Mount Fuji at its peak
+# TODO(yoland): clean up this after I get back down
+import pytest
+import os
+import tempfile
+from unittest.mock import patch
+
+import folder_paths
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
+
+
+def test_get_directory_by_type():
+    test_dir = "/test/dir"
+    folder_paths.set_output_directory(test_dir)
+    assert folder_paths.get_directory_by_type("output") == test_dir
+    assert folder_paths.get_directory_by_type("invalid") is None
+
+def test_annotated_filepath():
+    assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None)
+    assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory())
+    assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory())
+    assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory())
+
+def test_get_annotated_filepath():
+    default_dir = "/default/dir"
+    assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt")
+    assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt")
+
+def test_add_model_folder_path():
+    folder_paths.add_model_folder_path("test_folder", "/test/path")
+    assert "/test/path" in folder_paths.get_folder_paths("test_folder")
+
+def test_recursive_search(temp_dir):
+    os.makedirs(os.path.join(temp_dir, "subdir"))
+    open(os.path.join(temp_dir, "file1.txt"), "w").close()
+    open(os.path.join(temp_dir, "subdir", "file2.txt"), "w").close()
+
+    files, dirs = folder_paths.recursive_search(temp_dir)
+    assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")}
+    assert len(dirs) == 2  # temp_dir and subdir
+
+def test_filter_files_extensions():
+    files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"]
+    assert folder_paths.filter_files_extensions(files, []) == files
+
+@patch("folder_paths.recursive_search")
+@patch("folder_paths.folder_names_and_paths")
+def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search):
+    mock_folder_names_and_paths.__getitem__.return_value = (["/test/path"], {".txt"})
+    mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {})
+    assert folder_paths.get_filename_list("test_folder") == ["file1.txt"]
+
+def test_get_save_image_path(temp_dir):
+    with patch("folder_paths.output_directory", temp_dir):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100)
+        assert os.path.samefile(full_output_folder, temp_dir)
+        assert filename == "test"
+        assert counter == 1
+        assert subfolder == ""
+        assert filename_prefix == "test"

tests-unit/folder_paths_test/filter_by_content_types_test.py

@@ -0,0 +1,52 @@
+import pytest
+import os
+import tempfile
+from folder_paths import filter_files_content_types
+
+@pytest.fixture(scope="module")
+def file_extensions():
+    return {
+        'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'], 
+        'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'], 
+        'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv']
+    }
+
+
+@pytest.fixture(scope="module")
+def mock_dir(file_extensions):
+    with tempfile.TemporaryDirectory() as directory:
+        for content_type, extensions in file_extensions.items():
+            for extension in extensions:
+                with open(f"{directory}/sample_{content_type}.{extension}", "w") as f:
+                    f.write(f"Sample {content_type} file in {extension} format")
+        yield directory
+
+
+def test_categorizes_all_correctly(mock_dir, file_extensions):
+    files = os.listdir(mock_dir)
+    for content_type, extensions in file_extensions.items():
+        filtered_files = filter_files_content_types(files, [content_type])
+        for extension in extensions:
+            assert f"sample_{content_type}.{extension}" in filtered_files
+
+
+def test_categorizes_all_uniquely(mock_dir, file_extensions):
+    files = os.listdir(mock_dir)
+    for content_type, extensions in file_extensions.items():
+        filtered_files = filter_files_content_types(files, [content_type])
+        assert len(filtered_files) == len(extensions)
+
+
+def test_handles_bad_extensions():
+    files = ["file1.txt", "file2.py", "file3.example", "file4.pdf", "file5.ini", "file6.doc", "file7.md"]
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []
+
+
+def test_handles_no_extension():
+    files = ["file1", "file2", "file3", "file4", "file5", "file6", "file7"]
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []
+
+
+def test_handles_no_files():
+    files = []
+    assert filter_files_content_types(files, ["image", "audio", "video"]) == []

tests-unit/prompt_server_test/download_models_test.py

@@ -1,10 +1,17 @@
 import pytest
+import tempfile
 import aiohttp
 from aiohttp import ClientResponse
 import itertools
 import os
 from unittest.mock import AsyncMock, patch, MagicMock
-from model_filemanager import download_model, validate_model_subdirectory, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
+from model_filemanager import download_model, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
+import folder_paths
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
 
 class AsyncIteratorMock:
     """
@@ -42,7 +49,7 @@ class ContentMock:
         return AsyncIteratorMock(self.chunks)
 
 @pytest.mark.asyncio
-async def test_download_model_success():
+async def test_download_model_success(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.status = 200
     mock_response.headers = {'Content-Length': '1000'}
@@ -53,15 +60,13 @@ async def test_download_model_success():
     mock_make_request = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
 
-    # Mock file operations
-    mock_open = MagicMock()
-    mock_file = MagicMock()
-    mock_open.return_value.__enter__.return_value = mock_file
     time_values = itertools.count(0, 0.1)
 
-    with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'checkpoints/model.sft')), \
+    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
+
+    with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'model.sft')), \
          patch('model_filemanager.check_file_exists', return_value=None), \
-         patch('builtins.open', mock_open), \
+         patch('folder_paths.folder_names_and_paths', fake_paths), \
          patch('time.time', side_effect=time_values):  # Simulate time passing
 
         result = await download_model(
@@ -69,6 +74,7 @@ async def test_download_model_success():
             'model.sft',
             'http://example.com/model.sft',
             'checkpoints',
+            temp_dir,
             mock_progress_callback
         )
 
@@ -83,44 +89,48 @@ async def test_download_model_success():
     
     # Check initial call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.PENDING, 0, "Starting download of model.sft", False)
     )
 
     # Check final call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "Successfully downloaded model.sft", False)
     )
 
-    # Verify file writing
+    mock_file_path = os.path.join(temp_dir, 'model.sft')
-    mock_file.write.assert_any_call(b'a' * 500)
+    assert os.path.exists(mock_file_path)
-    mock_file.write.assert_any_call(b'b' * 300)
+    with open(mock_file_path, 'rb') as mock_file:
-    mock_file.write.assert_any_call(b'c' * 200)
+        assert mock_file.read() == b''.join(chunks)
+    os.remove(mock_file_path)
 
     # Verify request was made
     mock_make_request.assert_called_once_with('http://example.com/model.sft')
 
 @pytest.mark.asyncio
-async def test_download_model_url_request_failure():
+async def test_download_model_url_request_failure(temp_dir):
     # Mock dependencies
     mock_response = AsyncMock(spec=ClientResponse)
     mock_response.status = 404  # Simulate a "Not Found" error
     mock_get = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
     
+    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
+
     # Mock the create_model_path function
-    with patch('model_filemanager.create_model_path', return_value=('/mock/path/model.safetensors', 'mock/path/model.safetensors')):
+    with patch('model_filemanager.create_model_path', return_value='/mock/path/model.safetensors'), \
-        # Mock the check_file_exists function to return None (file doesn't exist)
+         patch('model_filemanager.check_file_exists', return_value=None), \
-        with patch('model_filemanager.check_file_exists', return_value=None):
+         patch('folder_paths.folder_names_and_paths', fake_paths):
-            # Call the function
+        # Call the function
-            result = await download_model(
+        result = await download_model(
-                mock_get,
+            mock_get,
-                'model.safetensors',
+            'model.safetensors',
-                'http://example.com/model.safetensors',
+            'http://example.com/model.safetensors',
-                'mock_directory',
+            'checkpoints',
-                mock_progress_callback
+            temp_dir,
-            )
+            mock_progress_callback
+        )
 
     # Assert the expected behavior
     assert isinstance(result, DownloadModelStatus)
@@ -130,7 +140,7 @@ async def test_download_model_url_request_failure():
 
     # Check that progress_callback was called with the correct arguments
     mock_progress_callback.assert_any_call(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.PENDING,
             progress_percentage=0,
@@ -139,7 +149,7 @@ async def test_download_model_url_request_failure():
         )
     )
     mock_progress_callback.assert_called_with(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.ERROR,
             progress_percentage=0,
@@ -153,40 +163,59 @@ async def test_download_model_url_request_failure():
 
 @pytest.mark.asyncio
 async def test_download_model_invalid_model_subdirectory():
-    
     mock_make_request = AsyncMock()
     mock_progress_callback = AsyncMock()
 
-    
     result = await download_model(
         mock_make_request,
         'model.sft',
         'http://example.com/model.sft',
         '../bad_path',
+        '../bad_path',
         mock_progress_callback
     )
 
     # Assert the result
     assert isinstance(result, DownloadModelStatus)
-    assert result.message == 'Invalid model subdirectory'
+    assert result.message.startswith('Invalid or unrecognized model directory')
     assert result.status == 'error'
     assert result.already_existed is False
 
+@pytest.mark.asyncio
+async def test_download_model_invalid_folder_path():
+    mock_make_request = AsyncMock()
+    mock_progress_callback = AsyncMock()
+
+    result = await download_model(
+        mock_make_request,
+        'model.sft',
+        'http://example.com/model.sft',
+        'checkpoints',
+        'invalid_path',
+        mock_progress_callback
+    )
+
+    # Assert the result
+    assert isinstance(result, DownloadModelStatus)
+    assert result.message.startswith("Invalid folder path")
+    assert result.status == 'error'
+    assert result.already_existed is False
 
-# For create_model_path function
 def test_create_model_path(tmp_path, monkeypatch):
-    mock_models_dir = tmp_path / "models"
+    model_name = "model.safetensors"
-    monkeypatch.setattr('folder_paths.models_dir', str(mock_models_dir))
+    folder_path = os.path.join(tmp_path, "mock_dir")
 
-    model_name = "test_model.sft"
+    file_path = create_model_path(model_name, folder_path)
-    model_directory = "test_dir"
 
-    file_path, relative_path = create_model_path(model_name, model_directory, mock_models_dir)
+    assert file_path == os.path.join(folder_path, "model.safetensors")
-    
-    assert file_path == str(mock_models_dir / model_directory / model_name)
-    assert relative_path == f"{model_directory}/{model_name}"
     assert os.path.exists(os.path.dirname(file_path))
 
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("../path_traversal.safetensors", folder_path)
+
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("/etc/some_root_path", folder_path)
+
 
 @pytest.mark.asyncio
 async def test_check_file_exists_when_file_exists(tmp_path):
@@ -195,7 +224,7 @@ async def test_check_file_exists_when_file_exists(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback, "test/existing_model.sft")
+    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback)
 
     assert result is not None
     assert result.status == "completed"
@@ -203,7 +232,7 @@ async def test_check_file_exists_when_file_exists(tmp_path):
     assert result.already_existed is True
 
     mock_callback.assert_called_once_with(
-        "test/existing_model.sft",
+        "existing_model.sft",
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "existing_model.sft already exists", already_existed=True)
     )
 
@@ -213,38 +242,46 @@ async def test_check_file_exists_when_file_does_not_exist(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback, "test/non_existing_model.sft")
+    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback)
 
     assert result is None
     mock_callback.assert_not_called()
 
 @pytest.mark.asyncio
-async def test_track_download_progress_no_content_length():
+async def test_track_download_progress_no_content_length(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {}  # No Content-Length header
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 500, b'b' * 500])
+    chunks = [b'a' * 500, b'b' * 500]
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
-    mock_open = MagicMock(return_value=MagicMock())
 
-    with patch('builtins.open', mock_open):
+    full_path = os.path.join(temp_dir, 'model.sft')
-        result = await track_download_progress(
+
-            mock_response, '/mock/path/model.sft', 'model.sft',
+    result = await track_download_progress(
-            mock_callback, 'models/model.sft', interval=0.1
+        mock_response, full_path, 'model.sft',
-        )
+        mock_callback, interval=0.1
+    )
 
     assert result.status == "completed"
+
+    assert os.path.exists(full_path)
+    with open(full_path, 'rb') as f:
+        assert f.read() == b''.join(chunks)
+    os.remove(full_path)
+
     # Check that progress was reported even without knowing the total size
     mock_callback.assert_any_call(
-        'models/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.IN_PROGRESS, 0, "Downloading model.sft", already_existed=False)
     )
 
 @pytest.mark.asyncio
-async def test_track_download_progress_interval():
+async def test_track_download_progress_interval(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {'Content-Length': '1000'}
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 100] * 10)
+    chunks = [b'a' * 100] * 10
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
     mock_open = MagicMock(return_value=MagicMock())
@@ -253,18 +290,18 @@ async def test_track_download_progress_interval():
     mock_time = MagicMock()
     mock_time.side_effect = [i * 0.5 for i in range(30)]  # This should be enough for 10 chunks
 
-    with patch('builtins.open', mock_open), \
+    full_path = os.path.join(temp_dir, 'model.sft')
-         patch('time.time', mock_time):
+
+    with patch('time.time', mock_time):
         await track_download_progress(
-            mock_response, '/mock/path/model.sft', 'model.sft',
+            mock_response, full_path, 'model.sft',
-            mock_callback, 'models/model.sft', interval=1.0
+            mock_callback, interval=1.0
         )
     
-    # Print out the actual call count and the arguments of each call for debugging
+    assert os.path.exists(full_path)
-    print(f"mock_callback was called {mock_callback.call_count} times")
+    with open(full_path, 'rb') as f:
-    for i, call in enumerate(mock_callback.call_args_list):
+        assert f.read() == b''.join(chunks)
-        args, kwargs = call
+    os.remove(full_path)
-        print(f"Call {i + 1}: {args[1].status}, Progress: {args[1].progress_percentage:.2f}%")
 
     # Assert that progress was updated at least 3 times (start, at least one interval, and end)
     assert mock_callback.call_count >= 3, f"Expected at least 3 calls, but got {mock_callback.call_count}"
@@ -279,27 +316,6 @@ async def test_track_download_progress_interval():
     assert last_call[0][1].status == "completed"
     assert last_call[0][1].progress_percentage == 100
 
-def test_valid_subdirectory():
-    assert validate_model_subdirectory("valid-model123") is True
-
-def test_subdirectory_too_long():
-    assert validate_model_subdirectory("a" * 51) is False
-
-def test_subdirectory_with_double_dots():
-    assert validate_model_subdirectory("model/../unsafe") is False
-
-def test_subdirectory_with_slash():
-    assert validate_model_subdirectory("model/unsafe") is False
-
-def test_subdirectory_with_special_characters():
-    assert validate_model_subdirectory("model@unsafe") is False
-
-def test_subdirectory_with_underscore_and_dash():
-    assert validate_model_subdirectory("valid_model-name") is True
-
-def test_empty_subdirectory():
-    assert validate_model_subdirectory("") is False
-
 @pytest.mark.parametrize("filename, expected", [
     ("valid_model.safetensors", True),
     ("valid_model.sft", True),

tests-unit/prompt_server_test/user_manager_test.py

@@ -0,0 +1,120 @@
+import pytest
+import os
+from aiohttp import web
+from app.user_manager import UserManager
+from unittest.mock import patch
+
+pytestmark = (
+    pytest.mark.asyncio
+)  # This applies the asyncio mark to all test functions in the module
+
+
+@pytest.fixture
+def user_manager(tmp_path):
+    um = UserManager()
+    um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join(
+        tmp_path, file
+    )
+    return um
+
+
+@pytest.fixture
+def app(user_manager):
+    app = web.Application()
+    routes = web.RouteTableDef()
+    user_manager.add_routes(routes)
+    app.add_routes(routes)
+    return app
+
+
+async def test_listuserdata_empty_directory(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir")
+    assert resp.status == 404
+
+
+async def test_listuserdata_with_files(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir")
+    assert resp.status == 200
+    assert await resp.json() == ["file1.txt"]
+
+
+async def test_listuserdata_recursive(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir" / "subdir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+    with open(tmp_path / "test_dir" / "subdir" / "file2.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&recurse=true")
+    assert resp.status == 200
+    assert set(await resp.json()) == {"file1.txt", "subdir/file2.txt"}
+
+
+async def test_listuserdata_full_info(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir")
+    with open(tmp_path / "test_dir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&full_info=true")
+    assert resp.status == 200
+    result = await resp.json()
+    assert len(result) == 1
+    assert result[0]["path"] == "file1.txt"
+    assert "size" in result[0]
+    assert "modified" in result[0]
+
+
+async def test_listuserdata_split_path(aiohttp_client, app, tmp_path):
+    os.makedirs(tmp_path / "test_dir" / "subdir")
+    with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true")
+    assert resp.status == 200
+    assert await resp.json() == [
+        ["subdir/file1.txt", "subdir", "file1.txt"]
+    ]
+
+
+async def test_listuserdata_invalid_directory(aiohttp_client, app):
+    client = await aiohttp_client(app)
+    resp = await client.get("/userdata?dir=")
+    assert resp.status == 400
+
+
+async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path):
+    os_sep = "\\"
+    with patch("os.sep", os_sep):
+        with patch("os.path.sep", os_sep):
+            os.makedirs(tmp_path / "test_dir" / "subdir")
+            with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f:
+                f.write("test content")
+
+            client = await aiohttp_client(app)
+            resp = await client.get("/userdata?dir=test_dir&recurse=true")
+            assert resp.status == 200
+            result = await resp.json()
+            assert len(result) == 1
+            assert "/" in result[0]  # Ensure forward slash is used
+            assert "\\" not in result[0]  # Ensure backslash is not present
+            assert result[0] == "subdir/file1.txt"
+
+            # Test with full_info
+            resp = await client.get(
+                "/userdata?dir=test_dir&recurse=true&full_info=true"
+            )
+            assert resp.status == 200
+            result = await resp.json()
+            assert len(result) == 1
+            assert "/" in result[0]["path"]  # Ensure forward slash is used
+            assert "\\" not in result[0]["path"]  # Ensure backslash is not present
+            assert result[0]["path"] == "subdir/file1.txt"

tests-unit/utils/extra_config_test.py

@@ -0,0 +1,126 @@
+import pytest
+import yaml
+import os
+from unittest.mock import Mock, patch, mock_open
+
+from utils.extra_config import load_extra_path_config
+import folder_paths
+
+@pytest.fixture
+def mock_yaml_content():
+    return {
+        'test_config': {
+            'base_path': '~/App/',
+            'checkpoints': 'subfolder1',
+        }
+    }
+
+@pytest.fixture
+def mock_expanded_home():
+    return '/home/user'
+
+@pytest.fixture
+def yaml_config_with_appdata():
+    return """
+    test_config:
+      base_path: '%APPDATA%/ComfyUI'
+      checkpoints: 'models/checkpoints'
+    """
+
+@pytest.fixture
+def mock_yaml_content_appdata(yaml_config_with_appdata):
+    return yaml.safe_load(yaml_config_with_appdata)
+
+@pytest.fixture
+def mock_expandvars_appdata():
+    mock = Mock()
+    mock.side_effect = lambda path: path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming')
+    return mock
+
+@pytest.fixture
+def mock_add_model_folder_path():
+    return Mock()
+
+@pytest.fixture
+def mock_expanduser(mock_expanded_home):
+    def _expanduser(path):
+        if path.startswith('~/'):
+            return os.path.join(mock_expanded_home, path[2:])
+        return path
+    return _expanduser
+
+@pytest.fixture
+def mock_yaml_safe_load(mock_yaml_content):
+    return Mock(return_value=mock_yaml_content)
+
+@patch('builtins.open', new_callable=mock_open, read_data="dummy file content")
+def test_load_extra_model_paths_expands_userpath(
+    mock_file,
+    monkeypatch,
+    mock_add_model_folder_path, 
+    mock_expanduser, 
+    mock_yaml_safe_load,
+    mock_expanded_home
+):
+    # Attach mocks used by load_extra_path_config
+    monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path)
+    monkeypatch.setattr(os.path, 'expanduser', mock_expanduser)
+    monkeypatch.setattr(yaml, 'safe_load', mock_yaml_safe_load)
+
+    dummy_yaml_file_name = 'dummy_path.yaml'
+    load_extra_path_config(dummy_yaml_file_name)
+
+    expected_calls = [
+        ('checkpoints', os.path.join(mock_expanded_home, 'App', 'subfolder1'), False),
+    ]
+
+    assert mock_add_model_folder_path.call_count == len(expected_calls)
+    
+    # Check if add_model_folder_path was called with the correct arguments
+    for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls):
+        assert actual_call.args[0] == expected_call[0] 
+        assert os.path.normpath(actual_call.args[1]) == os.path.normpath(expected_call[1])  # Normalize and check the path to check on multiple OS.
+        assert actual_call.args[2] == expected_call[2]
+
+    # Check if yaml.safe_load was called
+    mock_yaml_safe_load.assert_called_once()
+
+    # Check if open was called with the correct file path
+    mock_file.assert_called_once_with(dummy_yaml_file_name, 'r')
+
+@patch('builtins.open', new_callable=mock_open)
+def test_load_extra_model_paths_expands_appdata(
+    mock_file,
+    monkeypatch,
+    mock_add_model_folder_path,
+    mock_expandvars_appdata,
+    yaml_config_with_appdata,
+    mock_yaml_content_appdata
+):
+    # Set the mock_file to return yaml with appdata as a variable 
+    mock_file.return_value.read.return_value = yaml_config_with_appdata
+
+    # Attach mocks
+    monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path)
+    monkeypatch.setattr(os.path, 'expandvars', mock_expandvars_appdata)
+    monkeypatch.setattr(yaml, 'safe_load', Mock(return_value=mock_yaml_content_appdata))
+
+    # Mock expanduser to do nothing (since we're not testing it here)
+    monkeypatch.setattr(os.path, 'expanduser', lambda x: x)
+
+    dummy_yaml_file_name = 'dummy_path.yaml'
+    load_extra_path_config(dummy_yaml_file_name)
+
+    expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI'
+    expected_calls = [
+        ('checkpoints', os.path.join(expected_base_path, 'models/checkpoints'), False),
+    ]
+
+    assert mock_add_model_folder_path.call_count == len(expected_calls)
+    
+    # Check the base path variable was expanded
+    for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls):
+        assert actual_call.args == expected_call
+
+    # Verify that expandvars was called
+    assert mock_expandvars_appdata.called

tests/inference/test_execution.py

@@ -496,3 +496,29 @@ class TestExecution:
         assert len(images) == 1, "Should have 1 image"
         assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
         assert not result.did_run(test_node), "The execution should have been cached"
+
+    # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
+    # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
+    # only that one entry in the list is blocked.
+    def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0))
+        int1 = g.node("StubInt", value=1)
+        int2 = g.node("StubInt", value=2)
+        int3 = g.node("StubInt", value=3)
+        int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0))
+        compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==")
+        blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
+        
+        list_output = g.node("TestMakeListNode", value1=blocker.out(0))
+        output = g.node("PreviewImage", images=list_output.out(0))
+
+        result = client.run(g)
+        assert result.did_run(output), "The execution should have run"
+        images = result.get_images(output)
+        assert len(images) == 2, "Should have 2 images"
+        assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black"
+        assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black"

utils/extra_config.py

@@ -0,0 +1,28 @@
+import os
+import yaml
+import folder_paths
+import logging
+
+def load_extra_path_config(yaml_path):
+    with open(yaml_path, 'r') as stream:
+        config = yaml.safe_load(stream)
+    for c in config:
+        conf = config[c]
+        if conf is None:
+            continue
+        base_path = None
+        if "base_path" in conf:
+            base_path = conf.pop("base_path")
+            base_path = os.path.expandvars(os.path.expanduser(base_path))
+        is_default = False
+        if "is_default" in conf:
+            is_default = conf.pop("is_default")
+        for x in conf:
+            for y in conf[x].split("\n"):
+                if len(y) == 0:
+                    continue
+                full_path = y
+                if base_path is not None:
+                    full_path = os.path.join(base_path, full_path)
+                logging.info("Adding extra search path {} {}".format(x, full_path))
+                folder_paths.add_model_folder_path(x, full_path, is_default)

web/assets/CREDIT.txt

@@ -0,0 +1 @@
+Thanks to OpenArt (https://openart.ai) for providing the sorted-custom-node-map data, captured in September 2024.

web/assets/ExtensionPanel-BmKi_NKS.js

@@ -0,0 +1,103 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, bQ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bR as script$1, A as createBaseVNode, x as createBlock, N as Fragment, O as renderList, a6 as toDisplayString, aw as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-BHayQCxv.js";
+import { s as script, a as script$2, b as script$3 } from "./index-CwRXxFdA.js";
+import "./index-C_wOqB0f.js";
+const _hoisted_1 = { class: "extension-panel" };
+const _hoisted_2 = { class: "mt-4" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ExtensionPanel",
+  setup(__props) {
+    const extensionStore = useExtensionStore();
+    const settingStore = useSettingStore();
+    const editingEnabledExtensions = ref({});
+    onMounted(() => {
+      extensionStore.extensions.forEach((ext) => {
+        editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name);
+      });
+    });
+    const changedExtensions = computed(() => {
+      return extensionStore.extensions.filter(
+        (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name)
+      );
+    });
+    const hasChanges = computed(() => {
+      return changedExtensions.value.length > 0;
+    });
+    const updateExtensionStatus = /* @__PURE__ */ __name(() => {
+      const editingDisabledExtensionNames = Object.entries(
+        editingEnabledExtensions.value
+      ).filter(([_, enabled]) => !enabled).map(([name]) => name);
+      settingStore.set("Comfy.Extension.Disabled", [
+        ...extensionStore.inactiveDisabledExtensionNames,
+        ...editingDisabledExtensionNames
+      ]);
+    }, "updateExtensionStatus");
+    const applyChanges = /* @__PURE__ */ __name(() => {
+      window.location.reload();
+    }, "applyChanges");
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createVNode(unref(script$2), {
+          value: unref(extensionStore).extensions,
+          stripedRows: "",
+          size: "small"
+        }, {
+          default: withCtx(() => [
+            createVNode(unref(script), {
+              field: "name",
+              header: _ctx.$t("extensionName"),
+              sortable: ""
+            }, null, 8, ["header"]),
+            createVNode(unref(script), { pt: {
+              bodyCell: "flex items-center justify-end"
+            } }, {
+              body: withCtx((slotProps) => [
+                createVNode(unref(script$1), {
+                  modelValue: editingEnabledExtensions.value[slotProps.data.name],
+                  "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"),
+                  onChange: updateExtensionStatus
+                }, null, 8, ["modelValue", "onUpdate:modelValue"])
+              ]),
+              _: 1
+            })
+          ]),
+          _: 1
+        }, 8, ["value"]),
+        createBaseVNode("div", _hoisted_2, [
+          hasChanges.value ? (openBlock(), createBlock(unref(script$3), {
+            key: 0,
+            severity: "info"
+          }, {
+            default: withCtx(() => [
+              createBaseVNode("ul", null, [
+                (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => {
+                  return openBlock(), createElementBlock("li", {
+                    key: ext.name
+                  }, [
+                    createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1),
+                    createTextVNode(" " + toDisplayString(ext.name), 1)
+                  ]);
+                }), 128))
+              ])
+            ]),
+            _: 1
+          })) : createCommentVNode("", true),
+          createVNode(unref(script$4), {
+            label: _ctx.$t("reloadToApplyChanges"),
+            icon: "pi pi-refresh",
+            onClick: applyChanges,
+            disabled: !hasChanges.value,
+            text: "",
+            fluid: "",
+            severity: "danger"
+          }, null, 8, ["label", "disabled"])
+        ])
+      ]);
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ExtensionPanel-BmKi_NKS.js.map

web/assets/ExtensionPanel-BmKi_NKS.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"ExtensionPanel-BmKi_NKS.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <div class=\"extension-panel\">\n    <DataTable :value=\"extensionStore.extensions\" stripedRows size=\"small\">\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n    <div class=\"mt-4\">\n      <Message v-if=\"hasChanges\" severity=\"info\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n      </Message>\n      <Button\n        :label=\"$t('reloadToApplyChanges')\"\n        icon=\"pi pi-refresh\"\n        @click=\"applyChanges\"\n        :disabled=\"!hasChanges\"\n        text\n        fluid\n        severity=\"danger\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/GraphView-Cf7ubG48.css

@@ -0,0 +1,281 @@
+
+.group-title-editor.node-title-editor[data-v-8a100d5a] {
+  z-index: 9999;
+  padding: 0.25rem;
+}
+[data-v-8a100d5a] .editable-text {
+  width: 100%;
+  height: 100%;
+}
+[data-v-8a100d5a] .editable-text input {
+  width: 100%;
+  height: 100%;
+  /* Override the default font size */
+  font-size: inherit;
+}
+
+.side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+}
+.side-bar-button-selected .side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+  font-weight: bold;
+}
+
+.side-bar-button[data-v-caa3ee9c] {
+  width: var(--sidebar-width);
+  height: var(--sidebar-width);
+  border-radius: 0;
+}
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
+  border-left: 4px solid var(--p-button-text-primary-color);
+}
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
+  border-right: 4px solid var(--p-button-text-primary-color);
+}
+
+:root {
+  --sidebar-width: 64px;
+  --sidebar-icon-size: 1.5rem;
+}
+:root .small-sidebar {
+  --sidebar-width: 40px;
+  --sidebar-icon-size: 1rem;
+}
+
+.side-tool-bar-container[data-v-37fd2fa4] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+
+  pointer-events: auto;
+
+  width: var(--sidebar-width);
+  height: 100%;
+
+  background-color: var(--comfy-menu-bg);
+  color: var(--fg-color);
+}
+.side-tool-bar-end[data-v-37fd2fa4] {
+  align-self: flex-end;
+  margin-top: auto;
+}
+
+[data-v-b49f20b1] .p-splitter-gutter {
+  pointer-events: auto;
+}
+.side-bar-panel[data-v-b49f20b1] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.bottom-panel[data-v-b49f20b1] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.splitter-overlay[data-v-b49f20b1] {
+  pointer-events: none;
+  border-style: none;
+  background-color: transparent;
+}
+.splitter-overlay-root[data-v-b49f20b1] {
+  position: absolute;
+  top: 0px;
+  left: 0px;
+  height: 100%;
+  width: 100%;
+
+  /* Set it the same as the ComfyUI menu */
+  /* Note: Lite-graph DOM widgets have the same z-index as the node id, so
+  999 should be sufficient to make sure splitter overlays on node's DOM
+  widgets */
+  z-index: 999;
+}
+
+[data-v-37f672ab] .highlight {
+  background-color: var(--p-primary-color);
+  color: var(--p-primary-contrast-color);
+  font-weight: bold;
+  border-radius: 0.25rem;
+  padding: 0rem 0.125rem;
+  margin: -0.125rem 0.125rem;
+}
+
+.comfy-vue-node-search-container[data-v-2d409367] {
+  display: flex;
+  width: 100%;
+  min-width: 26rem;
+  align-items: center;
+  justify-content: center;
+}
+.comfy-vue-node-search-container[data-v-2d409367] * {
+  pointer-events: auto;
+}
+.comfy-vue-node-preview-container[data-v-2d409367] {
+  position: absolute;
+  left: -350px;
+  top: 50px;
+}
+.comfy-vue-node-search-box[data-v-2d409367] {
+  z-index: 10;
+  flex-grow: 1;
+}
+._filter-button[data-v-2d409367] {
+  z-index: 10;
+}
+._dialog[data-v-2d409367] {
+  min-width: 26rem;
+}
+
+.invisible-dialog-root {
+  width: 60%;
+  min-width: 24rem;
+  max-width: 48rem;
+  border: 0 !important;
+  background-color: transparent !important;
+  margin-top: 25vh;
+  margin-left: 400px;
+}
+@media all and (max-width: 768px) {
+.invisible-dialog-root {
+    margin-left: 0px;
+}
+}
+.node-search-box-dialog-mask {
+  align-items: flex-start !important;
+}
+
+.node-tooltip[data-v-79ec8c53] {
+  background: var(--comfy-input-bg);
+  border-radius: 5px;
+  box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
+  color: var(--input-text);
+  font-family: sans-serif;
+  left: 0;
+  max-width: 30vw;
+  padding: 4px 8px;
+  position: absolute;
+  top: 0;
+  transform: translate(5px, calc(-100% - 5px));
+  white-space: pre-wrap;
+  z-index: 99999;
+}
+
+.p-buttongroup-vertical[data-v-444d3768] {
+  display: flex;
+  flex-direction: column;
+  border-radius: var(--p-button-border-radius);
+  overflow: hidden;
+  border: 1px solid var(--p-panel-border-color);
+}
+.p-buttongroup-vertical .p-button[data-v-444d3768] {
+  margin: 0;
+  border-radius: 0;
+}
+
+[data-v-84e785b8] .p-togglebutton::before {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  background-color: transparent;
+  padding-left: 0.5rem;
+  padding-right: 0.5rem
+}
+[data-v-84e785b8] .p-togglebutton.p-togglebutton-checked {
+  border-bottom-width: 2px;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-84e785b8] .p-togglebutton-checked .close-button,[data-v-84e785b8] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+.status-indicator[data-v-84e785b8] {
+  position: absolute;
+  font-weight: 700;
+  font-size: 1.5rem;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%)
+}
+[data-v-84e785b8] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton .close-button {
+  visibility: hidden
+}
+
+.top-menubar[data-v-9646ca0a] .p-menubar-item-link svg {
+  display: none;
+}
+[data-v-9646ca0a] .p-menubar-submenu.dropdown-direction-up {
+  top: auto;
+  bottom: 100%;
+  flex-direction: column-reverse;
+}
+.keybinding-tag[data-v-9646ca0a] {
+  background: var(--p-content-hover-background);
+  border-color: var(--p-content-border-color);
+  border-style: solid;
+}
+
+[data-v-713442be] .p-inputtext {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.comfyui-queue-button[data-v-2b80bf74] .p-splitbutton-dropdown {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.actionbar[data-v-2e54db00] {
+  pointer-events: all;
+  position: fixed;
+  z-index: 1000;
+}
+.actionbar.is-docked[data-v-2e54db00] {
+  position: static;
+  border-style: none;
+  background-color: transparent;
+  padding: 0px;
+}
+.actionbar.is-dragging[data-v-2e54db00] {
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+}
+[data-v-2e54db00] .p-panel-content {
+  padding: 0.25rem;
+}
+[data-v-2e54db00] .p-panel-header {
+  display: none;
+}
+
+.comfyui-menu[data-v-ad2c662b] {
+  width: 100vw;
+  background: var(--comfy-menu-bg);
+  color: var(--fg-color);
+  font-family: Arial, Helvetica, sans-serif;
+  font-size: 0.8em;
+  box-sizing: border-box;
+  z-index: 1000;
+  order: 0;
+  grid-column: 1/-1;
+  max-height: 90vh;
+}
+.comfyui-menu.dropzone[data-v-ad2c662b] {
+  background: var(--p-highlight-background);
+}
+.comfyui-menu.dropzone-active[data-v-ad2c662b] {
+  background: var(--p-highlight-background-focus);
+}
+.comfyui-logo[data-v-ad2c662b] {
+  font-size: 1.2em;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+  cursor: default;
+}

web/assets/KeybindingPanel-BNYKhW1k.css

@@ -0,0 +1,8 @@
+
+[data-v-e5724e4d] .p-datatable-tbody > tr > td {
+  padding: 1px;
+  min-height: 2rem;
+}
+[data-v-e5724e4d] .p-datatable-row-selected .actions,[data-v-e5724e4d] .p-datatable-selectable-row:hover .actions {
+  visibility: visible;
+}

web/assets/KeybindingPanel-Dm_3sBT5.js

@@ -0,0 +1,264 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, bN as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, b9 as useToast, t as resolveDirective, bO as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ao as script$4, be as withModifiers, aH as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, bJ as KeyComboImpl, bP as KeybindingImpl, _ as _export_sfc } from "./index-BHayQCxv.js";
+import { s as script$1, a as script$3, b as script$5 } from "./index-CwRXxFdA.js";
+import "./index-C_wOqB0f.js";
+const _hoisted_1$1 = {
+  key: 0,
+  class: "px-2"
+};
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "KeyComboDisplay",
+  props: {
+    keyCombo: {},
+    isModified: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    const keySequences = computed(() => props.keyCombo.getKeySequences());
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("span", null, [
+        (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => {
+          return openBlock(), createElementBlock(Fragment, { key: index }, [
+            createVNode(unref(script), {
+              severity: _ctx.isModified ? "info" : "secondary"
+            }, {
+              default: withCtx(() => [
+                createTextVNode(toDisplayString(sequence), 1)
+              ]),
+              _: 2
+            }, 1032, ["severity"]),
+            index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true)
+          ], 64);
+        }), 128))
+      ]);
+    };
+  }
+});
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-e5724e4d"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "keybinding-panel" };
+const _hoisted_2 = { class: "actions invisible" };
+const _hoisted_3 = { key: 1 };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "KeybindingPanel",
+  setup(__props) {
+    const filters = ref({
+      global: { value: "", matchMode: FilterMatchMode.CONTAINS }
+    });
+    const keybindingStore = useKeybindingStore();
+    const commandStore = useCommandStore();
+    const commandsData = computed(() => {
+      return Object.values(commandStore.commands).map((command) => ({
+        id: command.id,
+        keybinding: keybindingStore.getKeybindingByCommandId(command.id)
+      }));
+    });
+    const selectedCommandData = ref(null);
+    const editDialogVisible = ref(false);
+    const newBindingKeyCombo = ref(null);
+    const currentEditingCommand = ref(null);
+    const keybindingInput = ref(null);
+    const existingKeybindingOnCombo = computed(() => {
+      if (!currentEditingCommand.value) {
+        return null;
+      }
+      if (currentEditingCommand.value.keybinding?.combo?.equals(
+        newBindingKeyCombo.value
+      )) {
+        return null;
+      }
+      if (!newBindingKeyCombo.value) {
+        return null;
+      }
+      return keybindingStore.getKeybinding(newBindingKeyCombo.value);
+    });
+    function editKeybinding(commandData) {
+      currentEditingCommand.value = commandData;
+      newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null;
+      editDialogVisible.value = true;
+    }
+    __name(editKeybinding, "editKeybinding");
+    watchEffect(() => {
+      if (editDialogVisible.value) {
+        setTimeout(() => {
+          keybindingInput.value?.$el?.focus();
+        }, 300);
+      }
+    });
+    function removeKeybinding(commandData) {
+      if (commandData.keybinding) {
+        keybindingStore.unsetKeybinding(commandData.keybinding);
+        keybindingStore.persistUserKeybindings();
+      }
+    }
+    __name(removeKeybinding, "removeKeybinding");
+    function captureKeybinding(event) {
+      const keyCombo = KeyComboImpl.fromEvent(event);
+      newBindingKeyCombo.value = keyCombo;
+    }
+    __name(captureKeybinding, "captureKeybinding");
+    function cancelEdit() {
+      editDialogVisible.value = false;
+      currentEditingCommand.value = null;
+      newBindingKeyCombo.value = null;
+    }
+    __name(cancelEdit, "cancelEdit");
+    function saveKeybinding() {
+      if (currentEditingCommand.value && newBindingKeyCombo.value) {
+        const updated = keybindingStore.updateKeybindingOnCommand(
+          new KeybindingImpl({
+            commandId: currentEditingCommand.value.id,
+            combo: newBindingKeyCombo.value
+          })
+        );
+        if (updated) {
+          keybindingStore.persistUserKeybindings();
+        }
+      }
+      cancelEdit();
+    }
+    __name(saveKeybinding, "saveKeybinding");
+    const toast = useToast();
+    async function resetKeybindings() {
+      keybindingStore.resetKeybindings();
+      await keybindingStore.persistUserKeybindings();
+      toast.add({
+        severity: "info",
+        summary: "Info",
+        detail: "Keybindings reset",
+        life: 3e3
+      });
+    }
+    __name(resetKeybindings, "resetKeybindings");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createVNode(unref(script$3), {
+          value: commandsData.value,
+          selection: selectedCommandData.value,
+          "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
+          "global-filter-fields": ["id"],
+          filters: filters.value,
+          selectionMode: "single",
+          stripedRows: "",
+          pt: {
+            header: "px-0"
+          }
+        }, {
+          header: withCtx(() => [
+            createVNode(SearchBox, {
+              modelValue: filters.value["global"].value,
+              "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
+              placeholder: _ctx.$t("searchKeybindings") + "..."
+            }, null, 8, ["modelValue", "placeholder"])
+          ]),
+          default: withCtx(() => [
+            createVNode(unref(script$1), {
+              field: "actions",
+              header: ""
+            }, {
+              body: withCtx((slotProps) => [
+                createBaseVNode("div", _hoisted_2, [
+                  createVNode(unref(script$2), {
+                    icon: "pi pi-pencil",
+                    class: "p-button-text",
+                    onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick")
+                  }, null, 8, ["onClick"]),
+                  createVNode(unref(script$2), {
+                    icon: "pi pi-trash",
+                    class: "p-button-text p-button-danger",
+                    onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"),
+                    disabled: !slotProps.data.keybinding
+                  }, null, 8, ["onClick", "disabled"])
+                ])
+              ]),
+              _: 1
+            }),
+            createVNode(unref(script$1), {
+              field: "id",
+              header: "Command ID",
+              sortable: ""
+            }),
+            createVNode(unref(script$1), {
+              field: "keybinding",
+              header: "Keybinding"
+            }, {
+              body: withCtx((slotProps) => [
+                slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
+                  key: 0,
+                  keyCombo: slotProps.data.keybinding.combo,
+                  isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
+                }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
+              ]),
+              _: 1
+            })
+          ]),
+          _: 1
+        }, 8, ["value", "selection", "filters"]),
+        createVNode(unref(script$6), {
+          class: "min-w-96",
+          visible: editDialogVisible.value,
+          "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
+          modal: "",
+          header: currentEditingCommand.value?.id,
+          onHide: cancelEdit
+        }, {
+          footer: withCtx(() => [
+            createVNode(unref(script$2), {
+              label: "Save",
+              icon: "pi pi-check",
+              onClick: saveKeybinding,
+              disabled: !!existingKeybindingOnCombo.value,
+              autofocus: ""
+            }, null, 8, ["disabled"])
+          ]),
+          default: withCtx(() => [
+            createBaseVNode("div", null, [
+              createVNode(unref(script$4), {
+                class: "mb-2 text-center",
+                ref_key: "keybindingInput",
+                ref: keybindingInput,
+                modelValue: newBindingKeyCombo.value?.toString() ?? "",
+                placeholder: "Press keys for new binding",
+                onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]),
+                autocomplete: "off",
+                fluid: "",
+                invalid: !!existingKeybindingOnCombo.value
+              }, null, 8, ["modelValue", "invalid"]),
+              existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), {
+                key: 0,
+                severity: "error"
+              }, {
+                default: withCtx(() => [
+                  createTextVNode(" Keybinding already exists on "),
+                  createVNode(unref(script), {
+                    severity: "secondary",
+                    value: existingKeybindingOnCombo.value.commandId
+                  }, null, 8, ["value"])
+                ]),
+                _: 1
+              })) : createCommentVNode("", true)
+            ])
+          ]),
+          _: 1
+        }, 8, ["visible", "header"]),
+        withDirectives(createVNode(unref(script$2), {
+          class: "mt-4",
+          label: _ctx.$t("reset"),
+          icon: "pi pi-trash",
+          severity: "danger",
+          fluid: "",
+          text: "",
+          onClick: resetKeybindings
+        }, null, 8, ["label"]), [
+          [_directive_tooltip, _ctx.$t("resetKeybindingsTooltip")]
+        ])
+      ]);
+    };
+  }
+});
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e5724e4d"]]);
+export {
+  KeybindingPanel as default
+};
+//# sourceMappingURL=KeybindingPanel-Dm_3sBT5.js.map