Update warning for old pytorch version. (#11319)

Versions below 2.4 are no longer supported. We will not break support on purpose but will not fix it if we do.

.ci/update_windows/update.py

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

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

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

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

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

CODEOWNERS

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

README.md

@@ -68,6 +68,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
    - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
    - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
+   - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
 - Image Editing Models
    - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
    - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -80,6 +81,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
    - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
+   - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -318,6 +320,32 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step
 1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
 2. Launch ComfyUI by running `python main.py`
 
+
+## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)
+
+**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
+
+### Setup
+
+1. Install the manager dependencies:
+   ```bash
+   pip install -r manager_requirements.txt
+   ```
+
+2. Enable the manager with the `--enable-manager` flag when running ComfyUI:
+   ```bash
+   python main.py --enable-manager
+   ```
+
+### Command Line Options
+
+| Flag | Description |
+|------|-------------|
+| `--enable-manager` | Enable ComfyUI-Manager |
+| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
+| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
+
+
 # Running
 
 ```python main.py```

api_server/routes/internal/internal_routes.py

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

app/user_manager.py

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

comfy/cli_args.py

@@ -121,6 +121,12 @@ upcast.add_argument("--force-upcast-attention", action="store_true", help="Force
 upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
 
 
+parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
+manager_group = parser.add_mutually_exclusive_group()
+manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
+manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
+
+
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
@@ -131,7 +137,8 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e
 
 parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
 
-parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
+parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
+parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -167,6 +174,7 @@ parser.add_argument("--multi-user", action="store_true", help="Enables per-user
 parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
 parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
 
+
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
 

comfy/context_windows.py

@@ -51,26 +51,36 @@ class ContextHandlerABC(ABC):
 
 
 class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0):
+    def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):
         self.index_list = index_list
         self.context_length = len(index_list)
         self.dim = dim
+        self.total_frames = total_frames
+        self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
 
-    def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor:
+    def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
         if dim is None:
             dim = self.dim
         if dim == 0 and full.shape[dim] == 1:
             return full
-        idx = [slice(None)] * dim + [self.index_list]
-        return full[idx].to(device)
+        idx = tuple([slice(None)] * dim + [self.index_list])
+        window = full[idx]
+        if retain_index_list:
+            idx = tuple([slice(None)] * dim + [retain_index_list])
+            window[idx] = full[idx]
+        return window.to(device)
 
     def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
         if dim is None:
             dim = self.dim
-        idx = [slice(None)] * dim + [self.index_list]
+        idx = tuple([slice(None)] * dim + [self.index_list])
         full[idx] += to_add
         return full
 
+    def get_region_index(self, num_regions: int) -> int:
+        region_idx = int(self.center_ratio * num_regions)
+        return min(max(region_idx, 0), num_regions - 1)
+
 
 class IndexListCallbacks:
     EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
@@ -94,7 +104,8 @@ class ContextFuseMethod:
 
 ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
 class IndexListContextHandler(ContextHandlerABC):
-    def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop=False, dim=0):
+    def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
+                 closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
         self.context_schedule = context_schedule
         self.fuse_method = fuse_method
         self.context_length = context_length
@@ -103,13 +114,18 @@ class IndexListContextHandler(ContextHandlerABC):
         self.closed_loop = closed_loop
         self.dim = dim
         self._step = 0
+        self.freenoise = freenoise
+        self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
+        self.split_conds_to_windows = split_conds_to_windows
 
         self.callbacks = {}
 
     def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
         # for now, assume first dim is batch - should have stored on BaseModel in actual implementation
         if x_in.size(self.dim) > self.context_length:
-            logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.")
+            logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
+            if self.cond_retain_index_list:
+                logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
             return True
         return False
 
@@ -123,6 +139,11 @@ class IndexListContextHandler(ContextHandlerABC):
             return None
         # reuse or resize cond items to match context requirements
         resized_cond = []
+        # if multiple conds, split based on primary region
+        if self.split_conds_to_windows and len(cond_in) > 1:
+            region = window.get_region_index(len(cond_in))
+            logging.info(f"Splitting conds to windows; using region {region} for window {window[0]}-{window[-1]} with center ratio {window.center_ratio:.3f}")
+            cond_in = [cond_in[region]]
         # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
         for actual_cond in cond_in:
             resized_actual_cond = actual_cond.copy()
@@ -146,12 +167,19 @@ class IndexListContextHandler(ContextHandlerABC):
                         # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
                         for cond_key, cond_value in new_cond_item.items():
                             if isinstance(cond_value, torch.Tensor):
-                                if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim):
+                                if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
+                                   (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
                                     new_cond_item[cond_key] = window.get_tensor(cond_value, device)
+                            # Handle audio_embed (temporal dim is 1)
+                            elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                                audio_cond = cond_value.cond
+                                if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
+                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
                             # if has cond that is a Tensor, check if needs to be subset
                             elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim):
-                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device))
+                                if  (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \
+                                    (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):
+                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))
                             elif cond_key == "num_video_frames": # for SVD
                                 new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
                                 new_cond_item[cond_key].cond = window.context_length
@@ -164,7 +192,7 @@ class IndexListContextHandler(ContextHandlerABC):
         return resized_cond
 
     def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001)
+        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
         matches = torch.nonzero(mask)
         if torch.numel(matches) == 0:
             raise Exception("No sample_sigmas matched current timestep; something went wrong.")
@@ -173,7 +201,7 @@ class IndexListContextHandler(ContextHandlerABC):
     def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
         full_length = x_in.size(self.dim) # TODO: choose dim based on model
         context_windows = self.context_schedule.func(full_length, self, model_options)
-        context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows]
+        context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]
         return context_windows
 
     def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
@@ -250,8 +278,8 @@ class IndexListContextHandler(ContextHandlerABC):
                     prev_weight = (bias_total / (bias_total + bias))
                     new_weight = (bias / (bias_total + bias))
                     # account for dims of tensors
-                    idx_window = [slice(None)] * self.dim + [idx]
-                    pos_window = [slice(None)] * self.dim + [pos]
+                    idx_window = tuple([slice(None)] * self.dim + [idx])
+                    pos_window = tuple([slice(None)] * self.dim + [pos])
                     # apply new values
                     conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
                     biases_final[i][idx] = bias_total + bias
@@ -287,6 +315,28 @@ def create_prepare_sampling_wrapper(model: ModelPatcher):
     )
 
 
+def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):
+    model_options = extra_args.get("model_options", None)
+    if model_options is None:
+        raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+    handler: IndexListContextHandler = model_options.get("context_handler", None)
+    if handler is None:
+        raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+    if not handler.freenoise:
+        return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+    noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"])
+
+    return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+
+
+def create_sampler_sample_wrapper(model: ModelPatcher):
+    model.add_wrapper_with_key(
+        comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
+        "ContextWindows_sampler_sample",
+        _sampler_sample_wrapper
+    )
+
+
 def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
     total_dims = len(x_in.shape)
     weights_tensor = torch.Tensor(weights).to(device=device)
@@ -538,3 +588,29 @@ def shift_window_to_end(window: list[int], num_frames: int):
     for i in range(len(window)):
         # 2) add end_delta to each val to slide windows to end
         window[i] = window[i] + end_delta
+
+
+# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465
+def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):
+    logging.info("Context windows: Applying FreeNoise")
+    generator = torch.Generator(device='cpu').manual_seed(seed)
+    latent_video_length = noise.shape[dim]
+    delta = context_length - context_overlap
+
+    for start_idx in range(0, latent_video_length - context_length, delta):
+        place_idx = start_idx + context_length
+
+        actual_delta = min(delta, latent_video_length - place_idx)
+        if actual_delta <= 0:
+            break
+
+        list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx
+
+        source_slice = [slice(None)] * noise.ndim
+        source_slice[dim] = list_idx
+        target_slice = [slice(None)] * noise.ndim
+        target_slice[dim] = slice(place_idx, place_idx + actual_delta)
+
+        noise[tuple(target_slice)] = noise[tuple(source_slice)]
+
+    return noise

comfy/k_diffusion/sampling.py

@@ -1557,10 +1557,13 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
 
 
 @torch.no_grad()
-def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
+def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"):
     """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
     arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
+    if solver_type not in {"phi_1", "phi_2"}:
+        raise ValueError("solver_type must be 'phi_1' or 'phi_2'")
+
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@@ -1600,8 +1603,14 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
         denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
         # Step 2
-        denoised_d = torch.lerp(denoised, denoised_2, fac)
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        if solver_type == "phi_1":
+            denoised_d = torch.lerp(denoised, denoised_2, fac)
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        elif solver_type == "phi_2":
+            b2 = ei_h_phi_2(-h_eta) / r
+            b1 = ei_h_phi_1(-h_eta) - b2
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)
+
         if inject_noise:
             segment_factor = (r - 1) * h * eta
             sde_noise = sde_noise * segment_factor.exp()

comfy/latent_formats.py

@@ -6,6 +6,7 @@ class LatentFormat:
     latent_dimensions = 2
     latent_rgb_factors = None
     latent_rgb_factors_bias = None
+    latent_rgb_factors_reshape = None
     taesd_decoder_name = None
 
     def process_in(self, latent):
@@ -181,6 +182,45 @@ class Flux(SD3):
 class Flux2(LatentFormat):
     latent_channels = 128
 
+    def __init__(self):
+        self.latent_rgb_factors =[
+            [0.0058, 0.0113, 0.0073],
+            [0.0495, 0.0443, 0.0836],
+            [-0.0099, 0.0096, 0.0644],
+            [0.2144, 0.3009, 0.3652],
+            [0.0166, -0.0039, -0.0054],
+            [0.0157, 0.0103, -0.0160],
+            [-0.0398, 0.0902, -0.0235],
+            [-0.0052, 0.0095, 0.0109],
+            [-0.3527, -0.2712, -0.1666],
+            [-0.0301, -0.0356, -0.0180],
+            [-0.0107, 0.0078, 0.0013],
+            [0.0746, 0.0090, -0.0941],
+            [0.0156, 0.0169, 0.0070],
+            [-0.0034, -0.0040, -0.0114],
+            [0.0032, 0.0181, 0.0080],
+            [-0.0939, -0.0008, 0.0186],
+            [0.0018, 0.0043, 0.0104],
+            [0.0284, 0.0056, -0.0127],
+            [-0.0024, -0.0022, -0.0030],
+            [0.1207, -0.0026, 0.0065],
+            [0.0128, 0.0101, 0.0142],
+            [0.0137, -0.0072, -0.0007],
+            [0.0095, 0.0092, -0.0059],
+            [0.0000, -0.0077, -0.0049],
+            [-0.0465, -0.0204, -0.0312],
+            [0.0095, 0.0012, -0.0066],
+            [0.0290, -0.0034, 0.0025],
+            [0.0220, 0.0169, -0.0048],
+            [-0.0332, -0.0457, -0.0468],
+            [-0.0085, 0.0389, 0.0609],
+            [-0.0076, 0.0003, -0.0043],
+            [-0.0111, -0.0460, -0.0614],
+        ]
+
+        self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
+        self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
+
     def process_in(self, latent):
         return latent
 
@@ -391,6 +431,7 @@ class HunyuanVideo(LatentFormat):
     ]
 
     latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]
+    taesd_decoder_name = "taehv"
 
 class Cosmos1CV8x8x8(LatentFormat):
     latent_channels = 16
@@ -454,7 +495,7 @@ class Wan21(LatentFormat):
         ]).view(1, self.latent_channels, 1, 1, 1)
 
 
-        self.taesd_decoder_name = None #TODO
+        self.taesd_decoder_name = "lighttaew2_1"
 
     def process_in(self, latent):
         latents_mean = self.latents_mean.to(latent.device, latent.dtype)
@@ -525,6 +566,7 @@ class Wan22(Wan21):
 
     def __init__(self):
         self.scale_factor = 1.0
+        self.taesd_decoder_name = "lighttaew2_2"
         self.latents_mean = torch.tensor([
                 -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
                 -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
@@ -679,6 +721,7 @@ class HunyuanVideo15(LatentFormat):
     latent_channels = 32
     latent_dimensions = 3
     scale_factor = 1.03682
+    taesd_decoder_name = "lighttaehy1_5"
 
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64

comfy/ldm/chroma/model.py

@@ -40,7 +40,8 @@ class ChromaParams:
     out_dim: int
     hidden_dim: int
     n_layers: int
-
+    txt_ids_dims: list
+    vec_in_dim: int
 
 
 

comfy/ldm/chroma_radiance/model.py

@@ -37,7 +37,7 @@ class ChromaRadianceParams(ChromaParams):
     nerf_final_head_type: str
     # None means use the same dtype as the model.
     nerf_embedder_dtype: Optional[torch.dtype]
-
+    use_x0: bool
 
 class ChromaRadiance(Chroma):
     """
@@ -159,6 +159,9 @@ class ChromaRadiance(Chroma):
         self.skip_dit = []
         self.lite = False
 
+        if params.use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
     @property
     def _nerf_final_layer(self) -> nn.Module:
         if self.params.nerf_final_head_type == "linear":
@@ -276,6 +279,12 @@ class ChromaRadiance(Chroma):
         params_dict |= overrides
         return params.__class__(**params_dict)
 
+    def _apply_x0_residual(self, predicted, noisy, timesteps):
+
+        # non zero during training to prevent 0 div
+        eps = 0.0
+        return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)
+
     def _forward(
         self,
         x: Tensor,
@@ -316,4 +325,11 @@ class ChromaRadiance(Chroma):
             transformer_options,
             attn_mask=kwargs.get("attention_mask", None),
         )
-        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        # If x0 variant → v-pred, just return this instead
+        if hasattr(self, "__x0__"):
+            out = self._apply_x0_residual(out, img, timestep)
+        return out
+

comfy/ldm/flux/layers.py

@@ -57,6 +57,35 @@ class MLPEmbedder(nn.Module):
     def forward(self, x: Tensor) -> Tensor:
         return self.out_layer(self.silu(self.in_layer(x)))
 
+class YakMLP(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.act_fn = nn.SiLU()
+
+    def forward(self, x: Tensor) -> Tensor:
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None):
+    if yak_mlp:
+        return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
+    if mlp_silu_act:
+        return nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
+            SiLUActivation(),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
+        )
+    else:
+        return nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, dtype=None, device=None, operations=None):
@@ -140,7 +169,7 @@ class SiLUActivation(nn.Module):
 
 
 class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
@@ -156,18 +185,7 @@ class DoubleStreamBlock(nn.Module):
 
         self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
-        if mlp_silu_act:
-            self.img_mlp = nn.Sequential(
-                operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
-                SiLUActivation(),
-                operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
-            )
-        else:
-            self.img_mlp = nn.Sequential(
-                operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-                nn.GELU(approximate="tanh"),
-                operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-            )
+        self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
 
         if self.modulation:
             self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
@@ -177,18 +195,7 @@ class DoubleStreamBlock(nn.Module):
 
         self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
-        if mlp_silu_act:
-            self.txt_mlp = nn.Sequential(
-                operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
-                SiLUActivation(),
-                operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
-            )
-        else:
-            self.txt_mlp = nn.Sequential(
-                operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-                nn.GELU(approximate="tanh"),
-                operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-            )
+        self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
 
         self.flipped_img_txt = flipped_img_txt
 
@@ -275,6 +282,7 @@ class SingleStreamBlock(nn.Module):
         modulation=True,
         mlp_silu_act=False,
         bias=True,
+        yak_mlp=False,
         dtype=None,
         device=None,
         operations=None
@@ -288,12 +296,17 @@ class SingleStreamBlock(nn.Module):
         self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
 
         self.mlp_hidden_dim_first = self.mlp_hidden_dim
+        self.yak_mlp = yak_mlp
         if mlp_silu_act:
             self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2)
             self.mlp_act = SiLUActivation()
         else:
             self.mlp_act = nn.GELU(approximate="tanh")
 
+        if self.yak_mlp:
+            self.mlp_hidden_dim_first *= 2
+            self.mlp_act = nn.SiLU()
+
         # qkv and mlp_in
         self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device)
         # proj and mlp_out
@@ -325,7 +338,10 @@ class SingleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
         # compute activation in mlp stream, cat again and run second linear layer
-        mlp = self.mlp_act(mlp)
+        if self.yak_mlp:
+            mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2]
+        else:
+            mlp = self.mlp_act(mlp)
         output = self.linear2(torch.cat((attn, mlp), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)
         if x.dtype == torch.float16:

comfy/ldm/flux/model.py

@@ -15,7 +15,8 @@ from .layers import (
     MLPEmbedder,
     SingleStreamBlock,
     timestep_embedding,
-    Modulation
+    Modulation,
+    RMSNorm
 )
 
 @dataclass
@@ -34,11 +35,14 @@ class FluxParams:
     patch_size: int
     qkv_bias: bool
     guidance_embed: bool
+    txt_ids_dims: list
     global_modulation: bool = False
     mlp_silu_act: bool = False
     ops_bias: bool = True
     default_ref_method: str = "offset"
     ref_index_scale: float = 1.0
+    yak_mlp: bool = False
+    txt_norm: bool = False
 
 
 class Flux(nn.Module):
@@ -76,6 +80,11 @@ class Flux(nn.Module):
         )
         self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
 
+        if params.txt_norm:
+            self.txt_norm = RMSNorm(params.context_in_dim, dtype=dtype, device=device, operations=operations)
+        else:
+            self.txt_norm = None
+
         self.double_blocks = nn.ModuleList(
             [
                 DoubleStreamBlock(
@@ -86,6 +95,7 @@ class Flux(nn.Module):
                     modulation=params.global_modulation is False,
                     mlp_silu_act=params.mlp_silu_act,
                     proj_bias=params.ops_bias,
+                    yak_mlp=params.yak_mlp,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -94,7 +104,7 @@ class Flux(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations)
                 for _ in range(params.depth_single_blocks)
             ]
         )
@@ -150,6 +160,8 @@ class Flux(nn.Module):
                 y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
             vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
 
+        if self.txt_norm is not None:
+            txt = self.txt_norm(txt)
         txt = self.txt_in(txt)
 
         vec_orig = vec
@@ -171,7 +183,10 @@ class Flux(nn.Module):
             pe = None
 
         blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.double_blocks)
+        transformer_options["block_type"] = "double"
         for i, block in enumerate(self.double_blocks):
+            transformer_options["block_index"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -215,7 +230,10 @@ class Flux(nn.Module):
         if self.params.global_modulation:
             vec, _ = self.single_stream_modulation(vec_orig)
 
+        transformer_options["total_blocks"] = len(self.single_blocks)
+        transformer_options["block_type"] = "single"
         for i, block in enumerate(self.single_blocks):
+            transformer_options["block_index"] = i
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -326,8 +344,9 @@ class Flux(nn.Module):
 
         txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
 
-        if len(self.params.axes_dim) == 4: # Flux 2
-            txt_ids[:, :, 3] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
+        if len(self.params.txt_ids_dims) > 0:
+            for i in self.params.txt_ids_dims:
+                txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
 
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]

comfy/ldm/hunyuan_video/model.py

@@ -43,6 +43,7 @@ class HunyuanVideoParams:
     meanflow: bool
     use_cond_type_embedding: bool
     vision_in_dim: int
+    meanflow_sum: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -317,7 +318,7 @@ class HunyuanVideo(nn.Module):
                 timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
                 timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
                 vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
-                vec = (vec + vec_r) / 2
+                vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2
 
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)

comfy/ldm/hunyuan_video/upsampler.py

@@ -1,7 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm, ResnetBlock, VideoConv3d
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
+from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
 import model_management, model_patcher
 
 class SRResidualCausalBlock3D(nn.Module):

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,42 +1,12 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed
 import comfy.ops
 import comfy.ldm.models.autoencoder
 import comfy.model_management
 ops = comfy.ops.disable_weight_init
 
-class NoPadConv3d(nn.Module):
-    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
-        super().__init__()
-        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
-
-    x = xl[0]
-    xl.clear()
-
-    if conv_carry_out is not None:
-        to_push = x[:, :, -2:, :, :].clone()
-        conv_carry_out.append(to_push)
-
-    if isinstance(op, NoPadConv3d):
-        if conv_carry_in is None:
-            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
-        else:
-            carry_len = conv_carry_in[0].shape[2]
-            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
-            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
-
-    out = op(x)
-
-    return out
-
 
 class RMS_norm(nn.Module):
     def __init__(self, dim):
@@ -49,7 +19,7 @@ class RMS_norm(nn.Module):
         return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
 
 class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tds, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tds else 1 * 2 * 2
         assert oc % fct == 0
@@ -109,7 +79,7 @@ class DnSmpl(nn.Module):
 
 
 class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
+    def __init__(self, ic, oc, tus, refiner_vae, op):
         super().__init__()
         fct = 2 * 2 * 2 if tus else 1 * 2 * 2
         self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
@@ -163,23 +133,6 @@ class UpSmpl(nn.Module):
 
         return h + x
 
-class HunyuanRefinerResnetBlock(ResnetBlock):
-    def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm):
-        super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-
-    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
-        h = x
-        h = [ self.swish(self.norm1(x)) ]
-        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
-        h = [ self.dropout(self.swish(self.norm2(h))) ]
-        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-
-        return x+h
-
 class Encoder(nn.Module):
     def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
                  ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
@@ -191,7 +144,7 @@ class Encoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = NoPadConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -206,9 +159,10 @@ class Encoder(nn.Module):
 
         for i, tgt in enumerate(block_out_channels):
             stage = nn.Module()
-            stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                                   out_channels=tgt,
-                                                                   conv_op=conv_op, norm_op=norm_op)
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
                                         for j in range(num_res_blocks)])
             ch = tgt
             if i < depth:
@@ -218,9 +172,9 @@ class Encoder(nn.Module):
             self.down.append(stage)
 
         self.mid = nn.Module()
-        self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
 
         self.norm_out = norm_op(ch)
         self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
@@ -246,22 +200,20 @@ class Encoder(nn.Module):
             conv_carry_out = []
             if i == len(x) - 1:
                 conv_carry_out = None
+
             x1 = [ x1 ]
             x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
 
             for stage in self.down:
                 for blk in stage.block:
-                    x1 = blk(x1, conv_carry_in, conv_carry_out)
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
                 if hasattr(stage, 'downsample'):
                     x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
 
             out.append(x1)
             conv_carry_in = conv_carry_out
 
-        if len(out) > 1:
-            out = torch.cat(out, dim=2)
-        else:
-            out = out[0]
+        out = torch_cat_if_needed(out, dim=2)
 
         x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
         del out
@@ -288,7 +240,7 @@ class Decoder(nn.Module):
 
         self.refiner_vae = refiner_vae
         if self.refiner_vae:
-            conv_op = NoPadConv3d
+            conv_op = CarriedConv3d
             norm_op = RMS_norm
         else:
             conv_op = ops.Conv3d
@@ -298,9 +250,9 @@ class Decoder(nn.Module):
         self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
 
         self.mid = nn.Module()
-        self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
         self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch,  conv_op=conv_op, norm_op=norm_op)
 
         self.up = nn.ModuleList()
         depth = (ffactor_spatial >> 1).bit_length()
@@ -308,9 +260,10 @@ class Decoder(nn.Module):
 
         for i, tgt in enumerate(block_out_channels):
             stage = nn.Module()
-            stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                                   out_channels=tgt,
-                                                                   conv_op=conv_op, norm_op=norm_op)
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
                                         for j in range(num_res_blocks + 1)])
             ch = tgt
             if i < depth:
@@ -340,7 +293,7 @@ class Decoder(nn.Module):
                 conv_carry_out = None
             for stage in self.up:
                 for blk in stage.block:
-                    x1 = blk(x1, conv_carry_in, conv_carry_out)
+                    x1 = blk(x1, None, conv_carry_in, conv_carry_out)
                 if hasattr(stage, 'upsample'):
                     x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
 
@@ -350,10 +303,7 @@ class Decoder(nn.Module):
             conv_carry_in = conv_carry_out
         del x
 
-        if len(out) > 1:
-            out = torch.cat(out, dim=2)
-        else:
-            out = out[0]
+        out = torch_cat_if_needed(out, dim=2)
 
         if not self.refiner_vae:
             if z.shape[-3] == 1:

comfy/ldm/kandinsky5/model.py

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

comfy/ldm/lumina/controlnet.py

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

comfy/ldm/lumina/model.py

@@ -22,6 +22,10 @@ def modulate(x, scale):
 #                               Core NextDiT Model                              #
 #############################################################################
 
+def clamp_fp16(x):
+    if x.dtype == torch.float16:
+        return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+    return x
 
 class JointAttention(nn.Module):
     """Multi-head attention module."""
@@ -169,7 +173,7 @@ class FeedForward(nn.Module):
 
     # @torch.compile
     def _forward_silu_gating(self, x1, x3):
-        return F.silu(x1) * x3
+        return clamp_fp16(F.silu(x1) * x3)
 
     def forward(self, x):
         return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
@@ -273,27 +277,27 @@ class JointTransformerBlock(nn.Module):
             scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
 
             x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
-                self.attention(
+                clamp_fp16(self.attention(
                     modulate(self.attention_norm1(x), scale_msa),
                     x_mask,
                     freqs_cis,
                     transformer_options=transformer_options,
-                )
+                ))
             )
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
-                self.feed_forward(
+                clamp_fp16(self.feed_forward(
                     modulate(self.ffn_norm1(x), scale_mlp),
-                )
+                ))
             )
         else:
             assert adaln_input is None
             x = x + self.attention_norm2(
-                self.attention(
+                clamp_fp16(self.attention(
                     self.attention_norm1(x),
                     x_mask,
                     freqs_cis,
                     transformer_options=transformer_options,
-                )
+                ))
             )
             x = x + self.ffn_norm2(
                 self.feed_forward(
@@ -373,6 +377,7 @@ class NextDiT(nn.Module):
         z_image_modulation=False,
         time_scale=1.0,
         pad_tokens_multiple=None,
+        clip_text_dim=None,
         image_model=None,
         device=None,
         dtype=None,
@@ -443,6 +448,31 @@ class NextDiT(nn.Module):
             ),
         )
 
+        self.clip_text_pooled_proj = None
+
+        if clip_text_dim is not None:
+            self.clip_text_dim = clip_text_dim
+            self.clip_text_pooled_proj = nn.Sequential(
+                operation_settings.get("operations").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+                operation_settings.get("operations").Linear(
+                    clip_text_dim,
+                    clip_text_dim,
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+            self.time_text_embed = nn.Sequential(
+                nn.SiLU(),
+                operation_settings.get("operations").Linear(
+                    min(dim, 1024) + clip_text_dim,
+                    min(dim, 1024),
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+
         self.layers = nn.ModuleList(
             [
                 JointTransformerBlock(
@@ -506,10 +536,11 @@ class NextDiT(nn.Module):
         bsz = len(x)
         pH = pW = self.patch_size
         device = x[0].device
+        orig_x = x
 
         if self.pad_tokens_multiple is not None:
             pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
-            cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1)
+            cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1)
 
         cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)
         cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0
@@ -517,26 +548,46 @@ class NextDiT(nn.Module):
         B, C, H, W = x.shape
         x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
 
+        rope_options = transformer_options.get("rope_options", None)
+        h_scale = 1.0
+        w_scale = 1.0
+        h_start = 0
+        w_start = 0
+        if rope_options is not None:
+            h_scale = rope_options.get("scale_y", 1.0)
+            w_scale = rope_options.get("scale_x", 1.0)
+
+            h_start = rope_options.get("shift_y", 0.0)
+            w_start = rope_options.get("shift_x", 0.0)
+
         H_tokens, W_tokens = H // pH, W // pW
         x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
         x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
-        x_pos_ids[:, :, 1] = torch.arange(H_tokens, dtype=torch.float32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
-        x_pos_ids[:, :, 2] = torch.arange(W_tokens, dtype=torch.float32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+        x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
+        x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
 
         if self.pad_tokens_multiple is not None:
             pad_extra = (-x.shape[1]) % self.pad_tokens_multiple
-            x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1)
+            x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1)
             x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))
 
         freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)
 
+        patches = transformer_options.get("patches", {})
+
         # refine context
         for layer in self.context_refiner:
             cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options)
 
         padded_img_mask = None
-        for layer in self.noise_refiner:
+        x_input = x
+        for i, layer in enumerate(self.noise_refiner):
             x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options)
+            if "noise_refiner" in patches:
+                for p in patches["noise_refiner"]:
+                    out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": freqs_cis[:, cap_pos_ids.shape[1]:], "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"})
+                    if "img" in out:
+                        x = out["img"]
 
         padded_full_embed = torch.cat((cap_feats, x), dim=1)
         mask = None
@@ -552,7 +603,7 @@ class NextDiT(nn.Module):
         ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
 
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -569,16 +620,33 @@ class NextDiT(nn.Module):
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
-        transformer_options = kwargs.get("transformer_options", {})
+        if self.clip_text_pooled_proj is not None:
+            pooled = kwargs.get("clip_text_pooled", None)
+            if pooled is not None:
+                pooled = self.clip_text_pooled_proj(pooled)
+            else:
+                pooled = torch.zeros((1, self.clip_text_dim), device=x.device, dtype=x.dtype)
+
+            adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
+
+        patches = transformer_options.get("patches", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
-        freqs_cis = freqs_cis.to(x.device)
+        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, transformer_options=transformer_options)
+        freqs_cis = freqs_cis.to(img.device)
 
-        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+        img_input = img
+        for i, layer in enumerate(self.layers):
+            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
 
-        x = self.final_layer(x, adaln_input)
-        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+        img = self.final_layer(img, adaln_input)
+        img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
 
-        return -x
+        return -img
 

comfy/ldm/modules/attention.py

@@ -517,6 +517,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
@@ -541,6 +542,8 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
         out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
+        exception_fallback = True
+    if exception_fallback:
         if tensor_layout == "NHD":
             q, k, v = map(
                 lambda t: t.transpose(1, 2),

comfy/ldm/modules/diffusionmodules/model.py

@@ -13,6 +13,12 @@ if model_management.xformers_enabled_vae():
     import xformers
     import xformers.ops
 
+def torch_cat_if_needed(xl, dim):
+    if len(xl) > 1:
+        return torch.cat(xl, dim)
+    else:
+        return xl[0]
+
 def get_timestep_embedding(timesteps, embedding_dim):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
@@ -43,6 +49,37 @@ def Normalize(in_channels, num_groups=32):
     return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
 
+class CarriedConv3d(nn.Module):
+    def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
+        super().__init__()
+        self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
+
+    x = xl[0]
+    xl.clear()
+
+    if isinstance(op, CarriedConv3d):
+        if conv_carry_in is None:
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
+        else:
+            carry_len = conv_carry_in[0].shape[2]
+            x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
+            x = torch.cat([conv_carry_in.pop(0), x], dim=2)
+
+        if conv_carry_out is not None:
+            to_push = x[:, :, -2:, :, :].clone()
+            conv_carry_out.append(to_push)
+
+    out = op(x)
+
+    return out
+
+
 class VideoConv3d(nn.Module):
     def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):
         super().__init__()
@@ -89,29 +126,24 @@ class Upsample(nn.Module):
                                         stride=1,
                                         padding=1)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         scale_factor = self.scale_factor
         if isinstance(scale_factor, (int, float)):
             scale_factor = (scale_factor,) * (x.ndim - 2)
 
         if x.ndim == 5 and scale_factor[0] > 1.0:
-            t = x.shape[2]
-            if t > 1:
-                a, b = x.split((1, t - 1), dim=2)
-                del x
-                b = interpolate_up(b, scale_factor)
-            else:
-                a = x
-
-            a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)
-            if t > 1:
-                x = torch.cat((a, b), dim=2)
-            else:
-                x = a
+            results = []
+            if conv_carry_in is None:
+                first = x[:, :, :1, :, :]
+                results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))
+                x = x[:, :, 1:, :, :]
+            if x.shape[2] > 0:
+                results.append(interpolate_up(x, scale_factor))
+            x = torch_cat_if_needed(results, dim=2)
         else:
             x = interpolate_up(x, scale_factor)
         if self.with_conv:
-            x = self.conv(x)
+            x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
         return x
 
 
@@ -127,17 +159,20 @@ class Downsample(nn.Module):
                                         stride=stride,
                                         padding=0)
 
-    def forward(self, x):
+    def forward(self, x, conv_carry_in=None, conv_carry_out=None):
         if self.with_conv:
-            if x.ndim == 4:
+            if isinstance(self.conv, CarriedConv3d):
+                x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
+            elif x.ndim == 4:
                 pad = (0, 1, 0, 1)
                 mode = "constant"
                 x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
+                x = self.conv(x)
             elif x.ndim == 5:
                 pad = (1, 1, 1, 1, 2, 0)
                 mode = "replicate"
                 x = torch.nn.functional.pad(x, pad, mode=mode)
-            x = self.conv(x)
+                x = self.conv(x)
         else:
             x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
         return x
@@ -183,23 +218,23 @@ class ResnetBlock(nn.Module):
                                                     stride=1,
                                                     padding=0)
 
-    def forward(self, x, temb=None):
+    def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):
         h = x
         h = self.norm1(h)
-        h = self.swish(h)
-        h = self.conv1(h)
+        h = [ self.swish(h) ]
+        h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if temb is not None:
             h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
 
         h = self.norm2(h)
         h = self.swish(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
+        h = [ self.dropout(h) ]
+        h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
 
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
+                x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
             else:
                 x = self.nin_shortcut(x)
 
@@ -279,6 +314,7 @@ def pytorch_attention(q, k, v):
     orig_shape = q.shape
     B = orig_shape[0]
     C = orig_shape[1]
+    oom_fallback = False
     q, k, v = map(
         lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
         (q, k, v),
@@ -289,6 +325,8 @@ def pytorch_attention(q, k, v):
         out = out.transpose(2, 3).reshape(orig_shape)
     except model_management.OOM_EXCEPTION:
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
+        oom_fallback = True
+    if oom_fallback:
         out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
     return out
 
@@ -517,9 +555,14 @@ class Encoder(nn.Module):
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
+            if not attn_resolutions:
+                conv_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -532,6 +575,7 @@ class Encoder(nn.Module):
                                        stride=1,
                                        padding=1)
 
+        self.time_compress = 1
         curr_res = resolution
         in_ch_mult = (1,)+tuple(ch_mult)
         self.in_ch_mult = in_ch_mult
@@ -558,10 +602,15 @@ class Encoder(nn.Module):
                 if time_compress is not None:
                     if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):
                         stride = (1, 2, 2)
+                else:
+                    self.time_compress *= 2
                 down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
                 curr_res = curr_res // 2
             self.down.append(down)
 
+        if time_compress is not None:
+            self.time_compress = time_compress
+
         # middle
         self.mid = nn.Module()
         self.mid.block_1 = ResnetBlock(in_channels=block_in,
@@ -587,15 +636,42 @@ class Encoder(nn.Module):
     def forward(self, x):
         # timestep embedding
         temb = None
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions-1:
-                h = self.down[i_level].downsample(h)
+
+        if self.carried:
+            xl = [x[:, :, :1, :, :]]
+            if x.shape[2] > self.time_compress:
+                tc = self.time_compress
+                xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)
+            x = xl
+        else:
+            x = [x]
+        out = []
+
+        conv_carry_in = None
+
+        for i, x1 in enumerate(x):
+            conv_carry_out = []
+            if i == len(x) - 1:
+                conv_carry_out = None
+
+            # downsampling
+            x1 = [ x1 ]
+            h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
+
+            for i_level in range(self.num_resolutions):
+                for i_block in range(self.num_res_blocks):
+                    h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)
+                    if len(self.down[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.down[i_level].attn[i_block](h1)
+                if i_level != self.num_resolutions-1:
+                    h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)
+
+            out.append(h1)
+            conv_carry_in = conv_carry_out
+
+        h = torch_cat_if_needed(out, dim=2)
+        del out
 
         # middle
         h = self.mid.block_1(h, temb)
@@ -604,15 +680,15 @@ class Encoder(nn.Module):
 
         # end
         h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
+        h = [ nonlinearity(h) ]
+        h = conv_carry_causal_3d(h, self.conv_out)
         return h
 
 
 class Decoder(nn.Module):
     def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+                 resolution, z_channels, tanh_out=False, use_linear_attn=False,
                  conv_out_op=ops.Conv2d,
                  resnet_op=ResnetBlock,
                  attn_op=AttnBlock,
@@ -626,12 +702,18 @@ class Decoder(nn.Module):
         self.num_res_blocks = num_res_blocks
         self.resolution = resolution
         self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
         self.tanh_out = tanh_out
+        self.carried = False
 
         if conv3d:
-            conv_op = VideoConv3d
-            conv_out_op = VideoConv3d
+            if not attn_resolutions and resnet_op == ResnetBlock:
+                conv_op = CarriedConv3d
+                conv_out_op = CarriedConv3d
+                self.carried = True
+            else:
+                conv_op = VideoConv3d
+                conv_out_op = VideoConv3d
+
             mid_attn_conv_op = ops.Conv3d
         else:
             conv_op = ops.Conv2d
@@ -706,29 +788,43 @@ class Decoder(nn.Module):
         temb = None
 
         # z to block_in
-        h = self.conv_in(z)
+        h = conv_carry_causal_3d([z], self.conv_in)
 
         # middle
         h = self.mid.block_1(h, temb, **kwargs)
         h = self.mid.attn_1(h, **kwargs)
         h = self.mid.block_2(h, temb, **kwargs)
 
+        if self.carried:
+            h = torch.split(h, 2, dim=2)
+        else:
+            h = [ h ]
+        out = []
+
+        conv_carry_in = None
+
         # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb, **kwargs)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h, **kwargs)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
+        for i, h1 in enumerate(h):
+            conv_carry_out = []
+            if i == len(h) - 1:
+                conv_carry_out = None
+            for i_level in reversed(range(self.num_resolutions)):
+                for i_block in range(self.num_res_blocks+1):
+                    h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)
+                    if len(self.up[i_level].attn) > 0:
+                        assert i == 0 #carried should not happen if attn exists
+                        h1 = self.up[i_level].attn[i_block](h1, **kwargs)
+                if i_level != 0:
+                    h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)
 
-        # end
-        if self.give_pre_end:
-            return h
+            h1 = self.norm_out(h1)
+            h1 = [ nonlinearity(h1) ]
+            h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)
+            if self.tanh_out:
+                h1 = torch.tanh(h1)
+            out.append(h1)
+            conv_carry_in = conv_carry_out
 
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h, **kwargs)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
+        out = torch_cat_if_needed(out, dim=2)
+
+        return out

comfy/lora.py

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

comfy/model_base.py

@@ -47,6 +47,7 @@ import comfy.ldm.chroma_radiance.model
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
+import comfy.ldm.kandinsky5.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -134,7 +135,7 @@ class BaseModel(torch.nn.Module):
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
                 fp8 = model_config.optimizations.get("fp8", False)
-                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8, model_config=model_config)
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, model_config=model_config)
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -329,18 +330,6 @@ class BaseModel(torch.nn.Module):
             extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
 
         unet_state_dict = self.diffusion_model.state_dict()
-
-        if self.model_config.scaled_fp8 is not None:
-            unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
-
-        # Save mixed precision metadata
-        if hasattr(self.model_config, 'layer_quant_config') and self.model_config.layer_quant_config:
-            metadata = {
-                "format_version": "1.0",
-                "layers": self.model_config.layer_quant_config
-            }
-            unet_state_dict["_quantization_metadata"] = metadata
-
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
         if self.model_type == ModelType.V_PREDICTION:
@@ -1121,6 +1110,10 @@ class Lumina2(BaseModel):
             if 'num_tokens' not in out:
                 out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1])
 
+        clip_text_pooled = kwargs["pooled_output"]  # Newbie
+        if clip_text_pooled is not None:
+            out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
+
         return out
 
 class WAN21(BaseModel):
@@ -1642,3 +1635,49 @@ class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
         out = super().extra_conds(**kwargs)
         out['disable_time_r'] = comfy.conds.CONDConstant(False)
         return out
+
+class Kandinsky5(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.kandinsky5.model.Kandinsky5)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+        image = torch.zeros_like(noise)
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.zeros_like(noise)[:, :1]
+        else:
+            mask = 1.0 - mask
+            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            if mask.shape[-3] < noise.shape[-3]:
+                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+        return torch.cat((image, mask), dim=1)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        time_dim_replace = kwargs.get("time_dim_replace", None)
+        if time_dim_replace is not None:
+            out['time_dim_replace'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_replace))
+
+        return out
+
+class Kandinsky5Image(Kandinsky5):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+
+    def concat_cond(self, **kwargs):
+        return None

comfy/model_detection.py

@@ -6,20 +6,6 @@ import math
 import logging
 import torch
 
-
-def detect_layer_quantization(metadata):
-    quant_key = "_quantization_metadata"
-    if metadata is not None and quant_key in metadata:
-        quant_metadata = metadata.pop(quant_key)
-        quant_metadata = json.loads(quant_metadata)
-        if isinstance(quant_metadata, dict) and "layers" in quant_metadata:
-            logging.info(f"Found quantization metadata (version {quant_metadata.get('format_version', 'unknown')})")
-            return quant_metadata["layers"]
-        else:
-            raise ValueError("Invalid quantization metadata format")
-    return None
-
-
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
     while True:
@@ -194,8 +180,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["use_cond_type_embedding"] = False
         if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
             dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
+            dit_config["meanflow_sum"] = True
         else:
             dit_config["vision_in_dim"] = None
+            dit_config["meanflow_sum"] = False
         return dit_config
 
     if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
@@ -208,12 +196,12 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["theta"] = 2000
             dit_config["out_channels"] = 128
             dit_config["global_modulation"] = True
-            dit_config["vec_in_dim"] = None
             dit_config["mlp_silu_act"] = True
             dit_config["qkv_bias"] = False
             dit_config["ops_bias"] = False
             dit_config["default_ref_method"] = "index"
             dit_config["ref_index_scale"] = 10.0
+            dit_config["txt_ids_dims"] = [3]
             patch_size = 1
         else:
             dit_config["image_model"] = "flux"
@@ -223,6 +211,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["theta"] = 10000
             dit_config["out_channels"] = 16
             dit_config["qkv_bias"] = True
+            dit_config["txt_ids_dims"] = []
             patch_size = 2
 
         dit_config["in_channels"] = 16
@@ -245,6 +234,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)
         if vec_in_key in state_dict_keys:
             dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1]
+        else:
+            dit_config["vec_in_dim"] = None
 
         dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
         dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
@@ -268,8 +259,17 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_tile_size"] = 512
                 dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
+                if "__x0__" in state_dict_keys: # x0 pred
+                    dit_config["use_x0"] = True
+                else:
+                    dit_config["use_x0"] = False
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+            dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
+            dit_config["txt_norm"] = "{}txt_norm.scale".format(key_prefix) in state_dict_keys
+            if dit_config["yak_mlp"] and dit_config["txt_norm"]:  # Ovis model
+                dit_config["txt_ids_dims"] = [1, 2]
+
         return dit_config
 
     if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
@@ -429,6 +429,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["axes_lens"] = [300, 512, 512]
             dit_config["rope_theta"] = 10000.0
             dit_config["ffn_dim_multiplier"] = 4.0
+            ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None)
+            if ctd_weight is not None:
+                dit_config["clip_text_dim"] = ctd_weight.shape[0]
         elif dit_config["dim"] == 3840:  # Z image
             dit_config["n_heads"] = 30
             dit_config["n_kv_heads"] = 30
@@ -617,6 +620,24 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
         return dit_config
 
+    if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
+        dit_config = {}
+        model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+        dit_config["model_dim"] = model_dim
+        if model_dim in [4096, 2560]: # pro video and lite image
+            dit_config["axes_dims"] = (32, 48, 48)
+            if model_dim == 2560: # lite image
+                dit_config["rope_scale_factor"] = (1.0, 1.0, 1.0)
+        elif model_dim == 1792: # lite video
+            dit_config["axes_dims"] = (16, 24, 24)
+        dit_config["time_dim"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+        dit_config["image_model"] = "kandinsky5"
+        dit_config["ff_dim"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0]
+        dit_config["visual_embed_dim"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1]
+        dit_config["num_text_blocks"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.')
+        dit_config["num_visual_blocks"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.')
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -759,22 +780,11 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 
-    scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
-    if scaled_fp8_key in state_dict:
-        scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
-        model_config.scaled_fp8 = scaled_fp8_weight.dtype
-        if model_config.scaled_fp8 == torch.float32:
-            model_config.scaled_fp8 = torch.float8_e4m3fn
-        if scaled_fp8_weight.nelement() == 2:
-            model_config.optimizations["fp8"] = False
-        else:
-            model_config.optimizations["fp8"] = True
-
     # Detect per-layer quantization (mixed precision)
-    layer_quant_config = detect_layer_quantization(metadata)
-    if layer_quant_config:
-        model_config.layer_quant_config = layer_quant_config
-        logging.info(f"Detected mixed precision quantization: {len(layer_quant_config)} layers quantized")
+    quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix)
+    if quant_config:
+        model_config.quant_config = quant_config
+        logging.info("Detected mixed precision quantization")
 
     return model_config
 

comfy/model_management.py

@@ -689,7 +689,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             loaded_memory = loaded_model.model_loaded_memory()
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
-            lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
+            lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
             lowvram_model_memory = lowvram_model_memory - loaded_memory
 
             if lowvram_model_memory == 0:
@@ -1012,9 +1012,18 @@ def force_channels_last():
 
 
 STREAMS = {}
-NUM_STREAMS = 1
-if args.async_offload:
-    NUM_STREAMS = 2
+NUM_STREAMS = 0
+if args.async_offload is not None:
+    NUM_STREAMS = args.async_offload
+else:
+    #  Enable by default on Nvidia
+    if is_nvidia():
+        NUM_STREAMS = 2
+
+if args.disable_async_offload:
+    NUM_STREAMS = 0
+
+if NUM_STREAMS > 0:
     logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS))
 
 def current_stream(device):
@@ -1030,7 +1039,10 @@ def current_stream(device):
 stream_counters = {}
 def get_offload_stream(device):
     stream_counter = stream_counters.get(device, 0)
-    if NUM_STREAMS <= 1:
+    if NUM_STREAMS == 0:
+        return None
+
+    if torch.compiler.is_compiling():
         return None
 
     if device in STREAMS:
@@ -1043,7 +1055,9 @@ def get_offload_stream(device):
     elif is_device_cuda(device):
         ss = []
         for k in range(NUM_STREAMS):
-            ss.append(torch.cuda.Stream(device=device, priority=0))
+            s1 = torch.cuda.Stream(device=device, priority=0)
+            s1.as_context = torch.cuda.stream
+            ss.append(s1)
         STREAMS[device] = ss
         s = ss[stream_counter]
         stream_counters[device] = stream_counter
@@ -1051,7 +1065,9 @@ def get_offload_stream(device):
     elif is_device_xpu(device):
         ss = []
         for k in range(NUM_STREAMS):
-            ss.append(torch.xpu.Stream(device=device, priority=0))
+            s1 = torch.xpu.Stream(device=device, priority=0)
+            s1.as_context = torch.xpu.stream
+            ss.append(s1)
         STREAMS[device] = ss
         s = ss[stream_counter]
         stream_counters[device] = stream_counter
@@ -1069,12 +1085,19 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
             if dtype is None or weight.dtype == dtype:
                 return weight
         if stream is not None:
-            with stream:
+            wf_context = stream
+            if hasattr(wf_context, "as_context"):
+                wf_context = wf_context.as_context(stream)
+            with wf_context:
                 return weight.to(dtype=dtype, copy=copy)
         return weight.to(dtype=dtype, copy=copy)
 
+
     if stream is not None:
-        with stream:
+        wf_context = stream
+        if hasattr(wf_context, "as_context"):
+            wf_context = wf_context.as_context(stream)
+        with wf_context:
             r = torch.empty_like(weight, dtype=dtype, device=device)
             r.copy_(weight, non_blocking=non_blocking)
     else:
@@ -1469,6 +1492,20 @@ def extended_fp16_support():
 
     return True
 
+LORA_COMPUTE_DTYPES = {}
+def lora_compute_dtype(device):
+    dtype = LORA_COMPUTE_DTYPES.get(device, None)
+    if dtype is not None:
+        return dtype
+
+    if should_use_fp16(device):
+        dtype = torch.float16
+    else:
+        dtype = torch.float32
+
+    LORA_COMPUTE_DTYPES[device] = dtype
+    return dtype
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS:

comfy/model_patcher.py

@@ -35,6 +35,7 @@ import comfy.model_management
 import comfy.patcher_extension
 import comfy.utils
 from comfy.comfy_types import UnetWrapperFunction
+from comfy.quant_ops import QuantizedTensor
 from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
 
 
@@ -126,27 +127,23 @@ class LowVramPatch:
     def __init__(self, key, patches, convert_func=None, set_func=None):
         self.key = key
         self.patches = patches
-        self.convert_func = convert_func
+        self.convert_func = convert_func # TODO: remove
         self.set_func = set_func
 
     def __call__(self, weight):
-        intermediate_dtype = weight.dtype
-        if self.convert_func is not None:
-            weight = self.convert_func(weight, inplace=False)
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
 
-        if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
-            intermediate_dtype = torch.float32
-            out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
-            if self.set_func is None:
-                return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
-            else:
-                return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
+LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2
 
-        out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
-        if self.set_func is not None:
-            return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
-        else:
-            return out
+def low_vram_patch_estimate_vram(model, key):
+    weight, set_func, convert_func = get_key_weight(model, key)
+    if weight is None:
+        return 0
+    model_dtype = getattr(model, "manual_cast_dtype", torch.float32)
+    if model_dtype is None:
+        model_dtype = weight.dtype
+
+    return weight.numel() * model_dtype.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR
 
 def get_key_weight(model, key):
     set_func = None
@@ -269,6 +266,9 @@ class ModelPatcher:
         if not hasattr(self.model, 'current_weight_patches_uuid'):
             self.model.current_weight_patches_uuid = None
 
+        if not hasattr(self.model, 'model_offload_buffer_memory'):
+            self.model.model_offload_buffer_memory = 0
+
     def model_size(self):
         if self.size > 0:
             return self.size
@@ -454,6 +454,9 @@ class ModelPatcher:
     def set_model_post_input_patch(self, patch):
         self.set_model_patch(patch, "post_input")
 
+    def set_model_noise_refiner_patch(self, patch):
+        self.set_model_patch(patch, "noise_refiner")
+
     def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
         rope_options = self.model_options["transformer_options"].get("rope_options", {})
         rope_options["scale_x"] = scale_x
@@ -618,10 +621,11 @@ class ModelPatcher:
         if key not in self.backup:
             self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
 
+        temp_dtype = comfy.model_management.lora_compute_dtype(device_to)
         if device_to is not None:
-            temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
         else:
-            temp_weight = weight.to(torch.float32, copy=True)
+            temp_weight = weight.to(temp_dtype, copy=True)
         if convert_func is not None:
             temp_weight = convert_func(temp_weight, inplace=True)
 
@@ -662,7 +666,22 @@ class ModelPatcher:
                     skip = True # skip random weights in non leaf modules
                     break
             if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
-                loading.append((comfy.model_management.module_size(m), n, m, params))
+                module_mem = comfy.model_management.module_size(m)
+                module_offload_mem = module_mem
+                if hasattr(m, "comfy_cast_weights"):
+                    def check_module_offload_mem(key):
+                        if key in self.patches:
+                            return low_vram_patch_estimate_vram(self.model, key)
+                        model_dtype = getattr(self.model, "manual_cast_dtype", None)
+                        weight, _, _ = get_key_weight(self.model, key)
+                        if model_dtype is None or weight is None:
+                            return 0
+                        if (weight.dtype != model_dtype or isinstance(weight, QuantizedTensor)):
+                            return weight.numel() * model_dtype.itemsize
+                        return 0
+                    module_offload_mem += check_module_offload_mem("{}.weight".format(n))
+                    module_offload_mem += check_module_offload_mem("{}.bias".format(n))
+                loading.append((module_offload_mem, module_mem, n, m, params))
         return loading
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
@@ -676,20 +695,22 @@ class ModelPatcher:
 
             load_completely = []
             offloaded = []
+            offload_buffer = 0
             loading.sort(reverse=True)
-            for x in loading:
-                n = x[1]
-                m = x[2]
-                params = x[3]
-                module_mem = x[0]
+            for i, x in enumerate(loading):
+                module_offload_mem, module_mem, n, m, params = x
 
                 lowvram_weight = False
 
+                potential_offload = max(offload_buffer, module_offload_mem + sum([ x1[1] for x1 in loading[i+1:i+1+comfy.model_management.NUM_STREAMS]]))
+                lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory
+
                 weight_key = "{}.weight".format(n)
                 bias_key = "{}.bias".format(n)
 
                 if not full_load and hasattr(m, "comfy_cast_weights"):
-                    if mem_counter + module_mem >= lowvram_model_memory:
+                    if not lowvram_fits:
+                        offload_buffer = potential_offload
                         lowvram_weight = True
                         lowvram_counter += 1
                         lowvram_mem_counter += module_mem
@@ -723,9 +744,11 @@ class ModelPatcher:
                     if hasattr(m, "comfy_cast_weights"):
                         wipe_lowvram_weight(m)
 
-                    if full_load or mem_counter + module_mem < lowvram_model_memory:
+                    if full_load or lowvram_fits:
                         mem_counter += module_mem
                         load_completely.append((module_mem, n, m, params))
+                    else:
+                        offload_buffer = potential_offload
 
                 if cast_weight and hasattr(m, "comfy_cast_weights"):
                     m.prev_comfy_cast_weights = m.comfy_cast_weights
@@ -752,6 +775,8 @@ class ModelPatcher:
                     key = "{}.{}".format(n, param)
                     self.unpin_weight(key)
                     self.patch_weight_to_device(key, device_to=device_to)
+                if comfy.model_management.is_device_cuda(device_to):
+                    torch.cuda.synchronize()
 
                 logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
                 m.comfy_patched_weights = True
@@ -766,7 +791,7 @@ class ModelPatcher:
                     self.pin_weight_to_device("{}.{}".format(n, param))
 
             if lowvram_counter > 0:
-                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
             else:
                 logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
@@ -778,6 +803,7 @@ class ModelPatcher:
             self.model.lowvram_patch_counter += patch_counter
             self.model.device = device_to
             self.model.model_loaded_weight_memory = mem_counter
+            self.model.model_offload_buffer_memory = offload_buffer
             self.model.current_weight_patches_uuid = self.patches_uuid
 
             for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):
@@ -831,6 +857,7 @@ class ModelPatcher:
                 self.model.to(device_to)
                 self.model.device = device_to
             self.model.model_loaded_weight_memory = 0
+            self.model.model_offload_buffer_memory = 0
 
             for m in self.model.modules():
                 if hasattr(m, "comfy_patched_weights"):
@@ -849,13 +876,18 @@ class ModelPatcher:
             patch_counter = 0
             unload_list = self._load_list()
             unload_list.sort()
+
+            offload_buffer = self.model.model_offload_buffer_memory
+            if len(unload_list) > 0:
+                NS = comfy.model_management.NUM_STREAMS
+                offload_weight_factor = [ min(offload_buffer / (NS + 1), unload_list[0][1]) ] * NS
+
             for unload in unload_list:
-                if memory_to_free < memory_freed:
+                if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed:
                     break
-                module_mem = unload[0]
-                n = unload[1]
-                m = unload[2]
-                params = unload[3]
+                module_offload_mem, module_mem, n, m, params = unload
+
+                potential_offload = module_offload_mem + sum(offload_weight_factor)
 
                 lowvram_possible = hasattr(m, "comfy_cast_weights")
                 if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
@@ -901,20 +933,25 @@ class ModelPatcher:
                                     patch_counter += 1
                             cast_weight = True
 
-                        if cast_weight:
+                        if cast_weight and hasattr(m, "comfy_cast_weights"):
                             m.prev_comfy_cast_weights = m.comfy_cast_weights
                             m.comfy_cast_weights = True
                         m.comfy_patched_weights = False
                         memory_freed += module_mem
+                        offload_buffer = max(offload_buffer, potential_offload)
+                        offload_weight_factor.append(module_mem)
+                        offload_weight_factor.pop(0)
                         logging.debug("freed {}".format(n))
 
                         for param in params:
                             self.pin_weight_to_device("{}.{}".format(n, param))
 
+
             self.model.model_lowvram = True
             self.model.lowvram_patch_counter += patch_counter
             self.model.model_loaded_weight_memory -= memory_freed
-            logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter))
+            self.model.model_offload_buffer_memory = offload_buffer
+            logging.info("Unloaded partially: {:.2f} MB freed, {:.2f} MB remains loaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(memory_freed / (1024 * 1024), self.model.model_loaded_weight_memory / (1024 * 1024), offload_buffer / (1024 * 1024), self.model.lowvram_patch_counter))
             return memory_freed
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):

comfy/ops.py

@@ -22,7 +22,7 @@ import comfy.model_management
 from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 import comfy.rmsnorm
-import contextlib
+import json
 
 def run_every_op():
     if torch.compiler.is_compiling():
@@ -93,11 +93,6 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     else:
         offload_stream = None
 
-    if offload_stream is not None:
-        wf_context = offload_stream
-    else:
-        wf_context = contextlib.nullcontext()
-
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     weight_has_function = len(s.weight_function) > 0
@@ -109,22 +104,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     if s.bias is not None:
         bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
 
-        if bias_has_function:
-            with wf_context:
-                for f in s.bias_function:
-                    bias = f(bias)
+    comfy.model_management.sync_stream(device, offload_stream)
+
+    bias_a = bias
+    weight_a = weight
+
+    if s.bias is not None:
+        for f in s.bias_function:
+            bias = f(bias)
 
     if weight_has_function or weight.dtype != dtype:
-        with wf_context:
-            weight = weight.to(dtype=dtype)
-            if isinstance(weight, QuantizedTensor):
-                weight = weight.dequantize()
-            for f in s.weight_function:
-                weight = f(weight)
+        weight = weight.to(dtype=dtype)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        for f in s.weight_function:
+            weight = f(weight)
 
-    comfy.model_management.sync_stream(device, offload_stream)
     if offloadable:
-        return weight, bias, offload_stream
+        return weight, bias, (offload_stream, weight_a, bias_a)
     else:
         #Legacy function signature
         return weight, bias
@@ -133,13 +130,16 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
 def uncast_bias_weight(s, weight, bias, offload_stream):
     if offload_stream is None:
         return
-    if weight is not None:
-        device = weight.device
+    os, weight_a, bias_a = offload_stream
+    if os is None:
+        return
+    if weight_a is not None:
+        device = weight_a.device
     else:
-        if bias is None:
+        if bias_a is None:
             return
-        device = bias.device
-    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+        device = bias_a.device
+    os.wait_stream(comfy.model_management.current_stream(device))
 
 
 class CastWeightBiasOp:
@@ -415,22 +415,12 @@ def fp8_linear(self, input):
 
     if input.ndim == 3 or input.ndim == 2:
         w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+        scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
 
-        scale_weight = self.scale_weight
-        scale_input = self.scale_input
-        if scale_weight is None:
-            scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
-        else:
-            scale_weight = scale_weight.to(input.device)
-
-        if scale_input is None:
-            scale_input = torch.ones((), device=input.device, dtype=torch.float32)
-            input = torch.clamp(input, min=-448, max=448, out=input)
-            layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
-            quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
-        else:
-            scale_input = scale_input.to(input.device)
-            quantized_input = QuantizedTensor.from_float(input, "TensorCoreFP8Layout", scale=scale_input, dtype=dtype)
+        scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+        input = torch.clamp(input, min=-448, max=448, out=input)
+        layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
+        quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
 
         # Wrap weight in QuantizedTensor - this enables unified dispatch
         # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
@@ -451,7 +441,7 @@ class fp8_ops(manual_cast):
             return None
 
         def forward_comfy_cast_weights(self, input):
-            if not self.training:
+            if len(self.weight_function) == 0 and len(self.bias_function) == 0:
                 try:
                     out = fp8_linear(self, input)
                     if out is not None:
@@ -464,59 +454,6 @@ class fp8_ops(manual_cast):
             uncast_bias_weight(self, weight, bias, offload_stream)
             return x
 
-def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
-    logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
-    class scaled_fp8_op(manual_cast):
-        class Linear(manual_cast.Linear):
-            def __init__(self, *args, **kwargs):
-                if override_dtype is not None:
-                    kwargs['dtype'] = override_dtype
-                super().__init__(*args, **kwargs)
-
-            def reset_parameters(self):
-                if not hasattr(self, 'scale_weight'):
-                    self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
-
-                if not scale_input:
-                    self.scale_input = None
-
-                if not hasattr(self, 'scale_input'):
-                    self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
-                return None
-
-            def forward_comfy_cast_weights(self, input):
-                if fp8_matrix_mult:
-                    out = fp8_linear(self, input)
-                    if out is not None:
-                        return out
-
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-
-                if weight.numel() < input.numel(): #TODO: optimize
-                    x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
-                else:
-                    x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
-                uncast_bias_weight(self, weight, bias, offload_stream)
-                return x
-
-            def convert_weight(self, weight, inplace=False, **kwargs):
-                if inplace:
-                    weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
-                    return weight
-                else:
-                    return weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32)
-
-            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
-                weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
-                if return_weight:
-                    return weight
-                if inplace_update:
-                    self.weight.data.copy_(weight)
-                else:
-                    self.weight = torch.nn.Parameter(weight, requires_grad=False)
-
-    return scaled_fp8_op
-
 CUBLAS_IS_AVAILABLE = False
 try:
     from cublas_ops import CublasLinear
@@ -543,9 +480,9 @@ if CUBLAS_IS_AVAILABLE:
 from .quant_ops import QuantizedTensor, QUANT_ALGOS
 
 
-def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
+def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
     class MixedPrecisionOps(manual_cast):
-        _layer_quant_config = layer_quant_config
+        _quant_config = quant_config
         _compute_dtype = compute_dtype
         _full_precision_mm = full_precision_mm
 
@@ -560,15 +497,14 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
             ) -> None:
                 super().__init__()
 
-                self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
-                # self.factory_kwargs = {"device": device, "dtype": dtype}
+                if dtype is None:
+                    dtype = MixedPrecisionOps._compute_dtype
+
+                self.factory_kwargs = {"device": device, "dtype": dtype}
 
                 self.in_features = in_features
                 self.out_features = out_features
-                if bias:
-                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
-                else:
-                    self.register_parameter("bias", None)
+                self._has_bias = bias
 
                 self.tensor_class = None
                 self._full_precision_mm = MixedPrecisionOps._full_precision_mm
@@ -588,36 +524,59 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
 
                 manually_loaded_keys = [weight_key]
 
-                if layer_name not in MixedPrecisionOps._layer_quant_config:
-                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
+                layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
+                if layer_conf is not None:
+                    layer_conf = json.loads(layer_conf.numpy().tobytes())
+
+                if layer_conf is None:
+                    dtype = self.factory_kwargs["dtype"]
+                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=dtype), requires_grad=False)
+                    if dtype != MixedPrecisionOps._compute_dtype:
+                        self.comfy_cast_weights = True
+                    if self._has_bias:
+                        self.bias = torch.nn.Parameter(torch.empty(self.out_features, device=device, dtype=dtype))
+                    else:
+                        self.register_parameter("bias", None)
                 else:
-                    quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None)
-                    if quant_format is None:
+                    self.quant_format = layer_conf.get("format", None)
+                    if not self._full_precision_mm:
+                        self._full_precision_mm = layer_conf.get("full_precision_matrix_mult", False)
+
+                    if self.quant_format is None:
                         raise ValueError(f"Unknown quantization format for layer {layer_name}")
 
-                    qconfig = QUANT_ALGOS[quant_format]
+                    qconfig = QUANT_ALGOS[self.quant_format]
                     self.layout_type = qconfig["comfy_tensor_layout"]
 
                     weight_scale_key = f"{prefix}weight_scale"
+                    scale = state_dict.pop(weight_scale_key, None)
+                    if scale is not None:
+                        scale = scale.to(device)
                     layout_params = {
-                        'scale': state_dict.pop(weight_scale_key, None),
+                        'scale': scale,
                         'orig_dtype': MixedPrecisionOps._compute_dtype,
                         'block_size': qconfig.get("group_size", None),
                     }
-                    if layout_params['scale'] is not None:
+
+                    if scale is not None:
                         manually_loaded_keys.append(weight_scale_key)
 
                     self.weight = torch.nn.Parameter(
-                        QuantizedTensor(weight.to(device=device), self.layout_type, layout_params),
+                        QuantizedTensor(weight.to(device=device, dtype=qconfig.get("storage_t", None)), self.layout_type, layout_params),
                         requires_grad=False
                     )
 
+                    if self._has_bias:
+                        self.bias = torch.nn.Parameter(torch.empty(self.out_features, device=device, dtype=MixedPrecisionOps._compute_dtype))
+                    else:
+                        self.register_parameter("bias", None)
+
                     for param_name in qconfig["parameters"]:
                         param_key = f"{prefix}{param_name}"
                         _v = state_dict.pop(param_key, None)
                         if _v is None:
                             continue
-                        setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+                        self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
                         manually_loaded_keys.append(param_key)
 
                 super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
@@ -626,6 +585,16 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
                     if key in missing_keys:
                         missing_keys.remove(key)
 
+            def state_dict(self, *args, destination=None, prefix="", **kwargs):
+                sd = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
+                if isinstance(self.weight, QuantizedTensor):
+                    sd["{}weight_scale".format(prefix)] = self.weight._layout_params['scale']
+                    quant_conf = {"format": self.quant_format}
+                    if self._full_precision_mm:
+                        quant_conf["full_precision_matrix_mult"] = True
+                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
+                return sd
+
             def _forward(self, input, weight, bias):
                 return torch.nn.functional.linear(input, weight, bias)
 
@@ -641,9 +610,8 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
                 if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                     return self.forward_comfy_cast_weights(input, *args, **kwargs)
                 if (getattr(self, 'layout_type', None) is not None and
-                    getattr(self, 'input_scale', None) is not None and
                     not isinstance(input, QuantizedTensor)):
-                    input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype)
+                    input = QuantizedTensor.from_float(input, self.layout_type, scale=getattr(self, 'input_scale', None), dtype=self.weight.dtype)
                 return self._forward(input, self.weight, self.bias)
 
             def convert_weight(self, weight, inplace=False, **kwargs):
@@ -654,7 +622,7 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
 
             def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
                 if getattr(self, 'layout_type', None) is not None:
-                    weight = QuantizedTensor.from_float(weight, self.layout_type, scale=None, dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True)
+                    weight = QuantizedTensor.from_float(weight, self.layout_type, scale="recalculate", dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True)
                 else:
                     weight = weight.to(self.weight.dtype)
                 if return_weight:
@@ -663,17 +631,28 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
                 assert inplace_update is False  # TODO: eventually remove the inplace_update stuff
                 self.weight = torch.nn.Parameter(weight, requires_grad=False)
 
+            def _apply(self, fn, recurse=True):  # This is to get torch.compile + moving weights to another device working
+                if recurse:
+                    for module in self.children():
+                        module._apply(fn)
+
+                for key, param in self._parameters.items():
+                    if param is None:
+                        continue
+                    self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False))
+                for key, buf in self._buffers.items():
+                    if buf is not None:
+                        self._buffers[key] = fn(buf)
+                return self
+
     return MixedPrecisionOps
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
 
-    if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
-        logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
-        return mixed_precision_ops(model_config.layer_quant_config, compute_dtype, full_precision_mm=not fp8_compute)
-
-    if scaled_fp8 is not None:
-        return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)
+    if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
+        logging.info("Using mixed precision operations")
+        return mixed_precision_ops(model_config.quant_config, compute_dtype, full_precision_mm=not fp8_compute)
 
     if (
         fp8_compute and

comfy/quant_ops.py

@@ -235,8 +235,11 @@ class QuantizedTensor(torch.Tensor):
     def is_pinned(self):
         return self._qdata.is_pinned()
 
-    def is_contiguous(self):
-        return self._qdata.is_contiguous()
+    def is_contiguous(self, *arg, **kwargs):
+        return self._qdata.is_contiguous(*arg, **kwargs)
+
+    def storage(self):
+        return self._qdata.storage()
 
 # ==============================================================================
 # Generic Utilities (Layout-Agnostic Operations)
@@ -249,12 +252,6 @@ def _create_transformed_qtensor(qt, transform_fn):
 
 
 def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
-    if target_dtype is not None and target_dtype != qt.dtype:
-        logging.warning(
-            f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
-            f"but not supported for quantized tensors. Ignoring dtype."
-        )
-
     if target_layout is not None and target_layout != torch.strided:
         logging.warning(
             f"QuantizedTensor: layout change requested to {target_layout}, "
@@ -274,6 +271,8 @@ def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=
             logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
             new_q_data = qt._qdata.to(device=target_device)
             new_params = _move_layout_params_to_device(qt._layout_params, target_device)
+            if target_dtype is not None:
+                new_params["orig_dtype"] = target_dtype
             new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
             logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
             return new_qt
@@ -339,7 +338,9 @@ def generic_copy_(func, args, kwargs):
             # Copy from another quantized tensor
             qt_dest._qdata.copy_(src._qdata, non_blocking=non_blocking)
             qt_dest._layout_type = src._layout_type
+            orig_dtype = qt_dest._layout_params["orig_dtype"]
             _copy_layout_params_inplace(src._layout_params, qt_dest._layout_params, non_blocking=non_blocking)
+            qt_dest._layout_params["orig_dtype"] = orig_dtype
         else:
             # Copy from regular tensor - just copy raw data
             qt_dest._qdata.copy_(src)
@@ -397,17 +398,23 @@ class TensorCoreFP8Layout(QuantizedLayout):
     def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False):
         orig_dtype = tensor.dtype
 
-        if scale is None:
-            scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
+        if isinstance(scale, str) and scale == "recalculate":
+            scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(dtype).max
+            if tensor.dtype not in [torch.float32, torch.bfloat16]:  # Prevent scale from being too small
+                tensor_info = torch.finfo(tensor.dtype)
+                scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max))
 
-        if not isinstance(scale, torch.Tensor):
-            scale = torch.tensor(scale)
-        scale = scale.to(device=tensor.device, dtype=torch.float32)
+        if scale is not None:
+            if not isinstance(scale, torch.Tensor):
+                scale = torch.tensor(scale)
+            scale = scale.to(device=tensor.device, dtype=torch.float32)
 
-        if inplace_ops:
-            tensor *= (1.0 / scale).to(tensor.dtype)
+            if inplace_ops:
+                tensor *= (1.0 / scale).to(tensor.dtype)
+            else:
+                tensor = tensor * (1.0 / scale).to(tensor.dtype)
         else:
-            tensor = tensor * (1.0 / scale).to(tensor.dtype)
+            scale = torch.ones((), device=tensor.device, dtype=torch.float32)
 
         if stochastic_rounding > 0:
             tensor = comfy.float.stochastic_rounding(tensor, dtype=dtype, seed=stochastic_rounding)
@@ -425,7 +432,8 @@ class TensorCoreFP8Layout(QuantizedLayout):
     @staticmethod
     def dequantize(qdata, scale, orig_dtype, **kwargs):
         plain_tensor = torch.ops.aten._to_copy.default(qdata, dtype=orig_dtype)
-        return plain_tensor * scale
+        plain_tensor.mul_(scale)
+        return plain_tensor
 
     @classmethod
     def get_plain_tensors(cls, qtensor):

comfy/sd.py

@@ -53,6 +53,8 @@ import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.z_image
+import comfy.text_encoders.ovis
+import comfy.text_encoders.kandinsky5
 
 import comfy.model_patcher
 import comfy.lora
@@ -60,6 +62,8 @@ import comfy.lora_convert
 import comfy.hooks
 import comfy.t2i_adapter.adapter
 import comfy.taesd.taesd
+import comfy.taesd.taehv
+import comfy.latent_formats
 
 import comfy.ldm.flux.redux
 
@@ -95,7 +99,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
         if no_init:
             return
         params = target.params.copy()
@@ -123,9 +127,32 @@ class CLIP:
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+        #Match torch.float32 hardcode upcast in TE implemention
+        self.patcher.set_model_compute_dtype(torch.float32)
         self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
         self.patcher.is_clip = True
         self.apply_hooks_to_conds = None
+        if len(state_dict) > 0:
+            if isinstance(state_dict, list):
+                for c in state_dict:
+                    m, u = self.load_sd(c)
+                    if len(m) > 0:
+                        logging.warning("clip missing: {}".format(m))
+
+                    if len(u) > 0:
+                        logging.debug("clip unexpected: {}".format(u))
+            else:
+                m, u = self.load_sd(state_dict, full_model=True)
+                if len(m) > 0:
+                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+                    if len(m_filter) > 0:
+                        logging.warning("clip missing: {}".format(m))
+                    else:
+                        logging.debug("clip missing: {}".format(m))
+
+                if len(u) > 0:
+                    logging.debug("clip unexpected {}:".format(u))
+
         if params['device'] == load_device:
             model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
@@ -190,6 +217,7 @@ class CLIP:
                 self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
             self.load_model()
+            self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
             all_hooks.reset()
             self.patcher.patch_hooks(None)
             if show_pbar:
@@ -237,6 +265,7 @@ class CLIP:
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
+        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         o = self.cond_stage_model.encode_token_weights(tokens)
         cond, pooled = o[:2]
         if return_dict:
@@ -466,7 +495,7 @@ class VAE:
                                                             decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
 
                 self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
-                self.memory_used_decode = lambda shape, dtype: (2800 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
             elif "decoder.conv_in.conv.weight" in sd:
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                 ddconfig["conv3d"] = True
@@ -478,8 +507,10 @@ class VAE:
                 self.latent_dim = 3
                 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
-                self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                #This is likely to significantly over-estimate with single image or low frame counts as the
+                #implementation is able to completely skip caching. Rework if used as an image only VAE
+                self.memory_used_decode = lambda shape, dtype: (2800 * min(8, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1400 * min(9, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
             elif "decoder.unpatcher3d.wavelets" in sd:
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)
@@ -508,17 +539,20 @@ class VAE:
                     self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
                     self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
                 else:  # Wan 2.1 VAE
+                    dim = sd["decoder.head.0.gamma"].shape[0]
                     self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                     self.upscale_index_formula = (4, 8, 8)
                     self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                     self.downscale_index_formula = (4, 8, 8)
                     self.latent_dim = 3
                     self.latent_channels = 16
-                    ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
                     self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
-                    self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
-                    self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
+                    self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype)
+
+
             # Hunyuan 3d v2 2.0 & 2.1
             elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
 
@@ -584,6 +618,35 @@ class VAE:
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float32]
                 self.crop_input = False
+            elif "decoder.22.bias" in sd: # taehv, taew and lighttae
+                self.latent_channels = sd["decoder.1.weight"].shape[1]
+                self.latent_dim = 3
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                self.upscale_index_formula = (4, 16, 16)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                self.downscale_index_formula = (4, 16, 16)
+                if self.latent_channels == 48: # Wan 2.2
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=None) # taehv doesn't need scaling
+                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
+                    self.process_output = lambda image: image
+                    self.memory_used_decode = lambda shape, dtype: (1800 * (max(1, (shape[-3] ** 0.7 * 0.1)) * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype))
+                elif self.latent_channels == 32 and sd["decoder.22.bias"].shape[0] == 12: # lighttae_hv15
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=comfy.latent_formats.HunyuanVideo15)
+                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
+                    self.memory_used_decode = lambda shape, dtype: (1200 * (max(1, (shape[-3] ** 0.7 * 0.05)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
+                else:
+                    if sd["decoder.1.weight"].dtype == torch.float16: # taehv currently only available in float16, so assume it's not lighttaew2_1 as otherwise state dicts are identical
+                        latent_format=comfy.latent_formats.HunyuanVideo
+                    else:
+                        latent_format=None # lighttaew2_1 doesn't need scaling
+                    self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=latent_format)
+                    self.process_input = self.process_output = lambda image: image
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                    self.upscale_index_formula = (4, 8, 8)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                    self.downscale_index_formula = (4, 8, 8)
+                    self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype))
+                    self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -708,6 +771,8 @@ class VAE:
         self.throw_exception_if_invalid()
         pixel_samples = None
         do_tile = False
+        if self.latent_dim == 2 and samples_in.ndim == 5:
+            samples_in = samples_in[:, :, 0]
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -924,16 +989,17 @@ class CLIPType(Enum):
     QWEN_IMAGE = 18
     HUNYUAN_IMAGE = 19
     HUNYUAN_VIDEO_15 = 20
+    OVIS = 21
+    KANDINSKY5 = 22
+    KANDINSKY5_IMAGE = 23
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
     for p in ckpt_paths:
         sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)
-        if metadata is not None:
-            quant_metadata = metadata.get("_quantization_metadata", None)
-            if quant_metadata is not None:
-                sd["_quantization_metadata"] = quant_metadata
+        if model_options.get("custom_operations", None) is None:
+            sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata)
         clip_data.append(sd)
     return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
 
@@ -955,6 +1021,7 @@ class TEModel(Enum):
     MISTRAL3_24B = 14
     MISTRAL3_24B_PRUNED_FLUX2 = 15
     QWEN3_4B = 16
+    QWEN3_2B = 17
 
 
 def detect_te_model(sd):
@@ -988,9 +1055,12 @@ def detect_te_model(sd):
         if weight.shape[0] == 512:
             return TEModel.QWEN25_7B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
-        if 'model.layers.0.self_attn.q_norm.weight' in sd:
-            return TEModel.QWEN3_4B
         weight = sd['model.layers.0.post_attention_layernorm.weight']
+        if 'model.layers.0.self_attn.q_norm.weight' in sd:
+            if weight.shape[0] == 2560:
+                return TEModel.QWEN3_4B
+            elif weight.shape[0] == 2048:
+                return TEModel.QWEN3_2B
         if weight.shape[0] == 5120:
             if "model.layers.39.post_attention_layernorm.weight" in sd:
                 return TEModel.MISTRAL3_24B
@@ -1046,7 +1116,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             elif clip_type == CLIPType.HIDREAM:
-                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else:
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
@@ -1070,7 +1140,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
             elif clip_type == CLIPType.HIDREAM:
                 clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
-                                                                        clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
+                                                                        clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
@@ -1099,7 +1169,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
-                                                                        clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
+                                                                        clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
         elif te_model == TEModel.QWEN25_3B:
             clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
@@ -1118,13 +1188,16 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.QWEN3_4B:
             clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer
+        elif te_model == TEModel.QWEN3_2B:
+            clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
         else:
             # clip_l
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             elif clip_type == CLIPType.HIDREAM:
-                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else:
                 clip_target.clip = sd1_clip.SD1ClipModel
@@ -1167,6 +1240,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif clip_type == CLIPType.HUNYUAN_VIDEO_15:
             clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer
+        elif clip_type == CLIPType.KANDINSKY5:
+            clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer
+        elif clip_type == CLIPType.KANDINSKY5_IMAGE:
+            clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
@@ -1179,19 +1258,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
 
     parameters = 0
     for c in clip_data:
-        if "_quantization_metadata" in c:
-            c.pop("_quantization_metadata")
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
-    for c in clip_data:
-        m, u = clip.load_sd(c)
-        if len(m) > 0:
-            logging.warning("clip missing: {}".format(m))
-
-        if len(u) > 0:
-            logging.debug("clip unexpected: {}".format(u))
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options)
     return clip
 
 def load_gligen(ckpt_path):
@@ -1250,6 +1320,10 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, diffusion_model_prefix, metadata=metadata)
+
     model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
     if model_config is None:
         logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.")
@@ -1258,18 +1332,22 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
             return None
         return (diffusion_model, None, VAE(sd={}), None)  # The VAE object is there to throw an exception if it's actually used'
 
-
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if model_config.scaled_fp8 is not None:
+    if model_config.quant_config is not None:
         weight_dtype = None
 
-    model_config.custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is not None:
+        model_config.custom_operations = custom_operations
+
     unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
 
     if unet_dtype is None:
         unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
 
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    if model_config.quant_config is not None:
+        manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
+    else:
+        manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
     if model_config.clip_vision_prefix is not None:
@@ -1287,22 +1365,33 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         vae = VAE(sd=vae_sd, metadata=metadata)
 
     if output_clip:
+        if te_model_options.get("custom_operations", None) is None:
+            scaled_fp8_list = []
+            for k in list(sd.keys()):  # Convert scaled fp8 to mixed ops
+                if k.endswith(".scaled_fp8"):
+                    scaled_fp8_list.append(k[:-len("scaled_fp8")])
+
+            if len(scaled_fp8_list) > 0:
+                out_sd = {}
+                for k in sd:
+                    skip = False
+                    for pref in scaled_fp8_list:
+                        skip = skip or k.startswith(pref)
+                    if not skip:
+                        out_sd[k] = sd[k]
+
+                for pref in scaled_fp8_list:
+                    quant_sd, qmetadata = comfy.utils.convert_old_quants(sd, pref, metadata={})
+                    for k in quant_sd:
+                        out_sd[k] = quant_sd[k]
+                    sd = out_sd
+
         clip_target = model_config.clip_target(state_dict=sd)
         if clip_target is not None:
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
                 parameters = comfy.utils.calculate_parameters(clip_sd)
-                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options)
-                m, u = clip.load_sd(clip_sd, full_model=True)
-                if len(m) > 0:
-                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
-                    if len(m_filter) > 0:
-                        logging.warning("clip missing: {}".format(m))
-                    else:
-                        logging.debug("clip missing: {}".format(m))
-
-                if len(u) > 0:
-                    logging.debug("clip unexpected {}:".format(u))
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options)
             else:
                 logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 
@@ -1349,6 +1438,9 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     if len(temp_sd) > 0:
         sd = temp_sd
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
     parameters = comfy.utils.calculate_parameters(sd)
     weight_dtype = comfy.utils.weight_dtype(sd)
 
@@ -1379,7 +1471,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
 
     offload_device = model_management.unet_offload_device()
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if model_config.scaled_fp8 is not None:
+    if model_config.quant_config is not None:
         weight_dtype = None
 
     if dtype is None:
@@ -1387,12 +1479,15 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
     else:
         unet_dtype = dtype
 
-    if model_config.layer_quant_config is not None:
+    if model_config.quant_config is not None:
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+
+    if custom_operations is not None:
+        model_config.custom_operations = custom_operations
+
     if model_options.get("fp8_optimizations", False):
         model_config.optimizations["fp8"] = True
 
@@ -1431,6 +1526,9 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
     if vae is not None:
         vae_sd = vae.get_sd()
 
+    if metadata is None:
+        metadata = {}
+
     model_management.load_models_gpu(load_models, force_patch_weights=True)
     clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
     sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)

comfy/sd1_clip.py

@@ -107,29 +107,17 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             config[k] = v
 
         operations = model_options.get("custom_operations", None)
-        scaled_fp8 = None
-        quantization_metadata = model_options.get("quantization_metadata", None)
+        quant_config = model_options.get("quantization_metadata", None)
 
         if operations is None:
-            layer_quant_config = None
-            if quantization_metadata is not None:
-                layer_quant_config = json.loads(quantization_metadata).get("layers", None)
-
-            if layer_quant_config is not None:
-                operations = comfy.ops.mixed_precision_ops(layer_quant_config, dtype, full_precision_mm=True)
-                logging.info(f"Using MixedPrecisionOps for text encoder: {len(layer_quant_config)} quantized layers")
+            if quant_config is not None:
+                operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
+                logging.info("Using MixedPrecisionOps for text encoder")
             else:
-                # Fallback to scaled_fp8_ops for backward compatibility
-                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
+                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
 
@@ -147,6 +135,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
         self.return_attention_masks = return_attention_masks
+        self.execution_device = None
 
         if layer == "hidden":
             assert layer_idx is not None
@@ -163,6 +152,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def set_clip_options(self, options):
         layer_idx = options.get("layer", self.layer_idx)
         self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+        self.execution_device = options.get("execution_device", self.execution_device)
         if isinstance(self.layer, list) or self.layer == "all":
             pass
         elif layer_idx is None or abs(layer_idx) > self.num_layers:
@@ -175,6 +165,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer = self.options_default[0]
         self.layer_idx = self.options_default[1]
         self.return_projected_pooled = self.options_default[2]
+        self.execution_device = None
 
     def process_tokens(self, tokens, device):
         end_token = self.special_tokens.get("end", None)
@@ -258,7 +249,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info
 
     def forward(self, tokens):
-        device = self.transformer.get_input_embeddings().weight.device
+        if self.execution_device is None:
+            device = self.transformer.get_input_embeddings().weight.device
+        else:
+            device = self.execution_device
+
         embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
 
         attention_mask_model = None

comfy/supported_models.py

@@ -21,6 +21,7 @@ import comfy.text_encoders.ace
 import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
+import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.z_image
 
 from . import supported_models_base
@@ -540,7 +541,7 @@ class SD3(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.SD3
 
-    memory_usage_factor = 1.2
+    memory_usage_factor = 1.6
 
     text_encoder_key_prefix = ["text_encoders."]
 
@@ -964,7 +965,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
-        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.CosmosPredict2(self, device=device)
@@ -1025,7 +1026,9 @@ class ZImage(Lumina2):
         "shift": 3.0,
     }
 
-    memory_usage_factor = 1.7
+    memory_usage_factor = 2.0
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
     def clip_target(self, state_dict={}):
         pref = self.text_encoder_key_prefix[0]
@@ -1286,7 +1289,7 @@ class ChromaRadiance(Chroma):
     latent_format = comfy.latent_formats.ChromaRadiance
 
     # Pixel-space model, no spatial compression for model input.
-    memory_usage_factor = 0.038
+    memory_usage_factor = 0.044
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.ChromaRadiance(self, device=device)
@@ -1329,7 +1332,7 @@ class Omnigen2(supported_models_base.BASE):
         "shift": 2.6,
     }
 
-    memory_usage_factor = 1.65 #TODO
+    memory_usage_factor = 1.95 #TODO
 
     unet_extra_config = {}
     latent_format = latent_formats.Flux
@@ -1394,7 +1397,7 @@ class HunyuanImage21(HunyuanVideo):
 
     latent_format = latent_formats.HunyuanImage21
 
-    memory_usage_factor = 7.7
+    memory_usage_factor = 8.7
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -1472,7 +1475,60 @@ class HunyuanVideo15_SR_Distilled(HunyuanVideo):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2]
 
+class Kandinsky5(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "kandinsky5",
+    }
+
+    sampling_settings = {
+        "shift": 10.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.HunyuanVideo
+
+    memory_usage_factor = 1.25 #TODO
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Kandinsky5(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+class Kandinsky5Image(Kandinsky5):
+    unet_config = {
+        "image_model": "kandinsky5",
+        "model_dim": 2560,
+        "visual_embed_dim": 64,
+    }
+
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    latent_format = latent_formats.Flux
+    memory_usage_factor = 1.25 #TODO
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Kandinsky5Image(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
 
 models += [SVD_img2vid]

comfy/supported_models_base.py

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

comfy/taesd/taehv.py

@@ -0,0 +1,171 @@
+# Tiny AutoEncoder for HunyuanVideo and WanVideo https://github.com/madebyollin/taehv
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm.auto import tqdm
+from collections import namedtuple, deque
+
+import comfy.ops
+operations=comfy.ops.disable_weight_init
+
+DecoderResult = namedtuple("DecoderResult", ("frame", "memory"))
+TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index"))
+
+def conv(n_in, n_out, **kwargs):
+    return operations.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+class Clamp(nn.Module):
+    def forward(self, x):
+        return torch.tanh(x / 3) * 3
+
+class MemBlock(nn.Module):
+    def __init__(self, n_in, n_out, act_func):
+        super().__init__()
+        self.conv = nn.Sequential(conv(n_in * 2, n_out), act_func, conv(n_out, n_out), act_func, conv(n_out, n_out))
+        self.skip = operations.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.act = act_func
+    def forward(self, x, past):
+        return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x))
+
+class TPool(nn.Module):
+    def __init__(self, n_f, stride):
+        super().__init__()
+        self.stride = stride
+        self.conv = operations.Conv2d(n_f*stride,n_f, 1, bias=False)
+    def forward(self, x):
+        _NT, C, H, W = x.shape
+        return self.conv(x.reshape(-1, self.stride * C, H, W))
+
+class TGrow(nn.Module):
+    def __init__(self, n_f, stride):
+        super().__init__()
+        self.stride = stride
+        self.conv = operations.Conv2d(n_f, n_f*stride, 1, bias=False)
+    def forward(self, x):
+        _NT, C, H, W = x.shape
+        x = self.conv(x)
+        return x.reshape(-1, C, H, W)
+
+def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
+
+    B, T, C, H, W = x.shape
+    if parallel:
+        x = x.reshape(B*T, C, H, W)
+        # parallel over input timesteps, iterate over blocks
+        for b in tqdm(model, disable=not show_progress_bar):
+            if isinstance(b, MemBlock):
+                BT, C, H, W = x.shape
+                T = BT // B
+                _x = x.reshape(B, T, C, H, W)
+                mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)
+                x = b(x, mem)
+            else:
+                x = b(x)
+        BT, C, H, W = x.shape
+        T = BT // B
+        x = x.view(B, T, C, H, W)
+    else:
+        out = []
+        work_queue = deque([TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(B, T * C, H, W).chunk(T, dim=1))])
+        progress_bar = tqdm(range(T), disable=not show_progress_bar)
+        mem = [None] * len(model)
+        while work_queue:
+            xt, i = work_queue.popleft()
+            if i == 0:
+                progress_bar.update(1)
+            if i == len(model):
+                out.append(xt)
+                del xt
+            else:
+                b = model[i]
+                if isinstance(b, MemBlock):
+                    if mem[i] is None:
+                        xt_new = b(xt, xt * 0)
+                        mem[i] = xt.detach().clone()
+                    else:
+                        xt_new = b(xt, mem[i])
+                        mem[i] = xt.detach().clone()
+                    del xt
+                    work_queue.appendleft(TWorkItem(xt_new, i+1))
+                elif isinstance(b, TPool):
+                    if mem[i] is None:
+                        mem[i] = []
+                    mem[i].append(xt.detach().clone())
+                    if len(mem[i]) == b.stride:
+                        B, C, H, W = xt.shape
+                        xt = b(torch.cat(mem[i], 1).view(B*b.stride, C, H, W))
+                        mem[i] = []
+                        work_queue.appendleft(TWorkItem(xt, i+1))
+                elif isinstance(b, TGrow):
+                    xt = b(xt)
+                    NT, C, H, W = xt.shape
+                    for xt_next in reversed(xt.view(B, b.stride*C, H, W).chunk(b.stride, 1)):
+                        work_queue.appendleft(TWorkItem(xt_next, i+1))
+                    del xt
+                else:
+                    xt = b(xt)
+                    work_queue.appendleft(TWorkItem(xt, i+1))
+        progress_bar.close()
+        x = torch.stack(out, 1)
+    return x
+
+
+class TAEHV(nn.Module):
+    def __init__(self, latent_channels, parallel=False, decoder_time_upscale=(True, True), decoder_space_upscale=(True, True, True), latent_format=None, show_progress_bar=True):
+        super().__init__()
+        self.image_channels = 3
+        self.patch_size = 1
+        self.latent_channels = latent_channels
+        self.parallel = parallel
+        self.latent_format = latent_format
+        self.show_progress_bar = show_progress_bar
+        self.process_in = latent_format().process_in if latent_format is not None else (lambda x: x)
+        self.process_out = latent_format().process_out if latent_format is not None else (lambda x: x)
+        if self.latent_channels in [48, 32]: # Wan 2.2 and HunyuanVideo1.5
+            self.patch_size = 2
+        if self.latent_channels == 32: # HunyuanVideo1.5
+            act_func = nn.LeakyReLU(0.2, inplace=True)
+        else: # HunyuanVideo, Wan 2.1
+            act_func = nn.ReLU(inplace=True)
+
+        self.encoder = nn.Sequential(
+            conv(self.image_channels*self.patch_size**2, 64), act_func,
+            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            conv(64, self.latent_channels),
+        )
+        n_f = [256, 128, 64, 64]
+        self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
+        self.decoder = nn.Sequential(
+            Clamp(), conv(self.latent_channels, n_f[0]), act_func,
+            MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
+            MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
+            MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+            act_func, conv(n_f[3], self.image_channels*self.patch_size**2),
+        )
+        @property
+        def show_progress_bar(self):
+            return self._show_progress_bar
+
+        @show_progress_bar.setter
+        def show_progress_bar(self, value):
+            self._show_progress_bar = value
+
+    def encode(self, x, **kwargs):
+        if self.patch_size > 1: x = F.pixel_unshuffle(x, self.patch_size)
+        x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
+        if x.shape[1] % 4 != 0:
+            # pad at end to multiple of 4
+            n_pad = 4 - x.shape[1] % 4
+            padding = x[:, -1:].repeat_interleave(n_pad, dim=1)
+            x = torch.cat([x, padding], 1)
+        x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1)
+        return self.process_out(x)
+
+    def decode(self, x, **kwargs):
+        x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
+        x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
+        if self.patch_size > 1: x = F.pixel_shuffle(x, self.patch_size)
+        return x[:, self.frames_to_trim:].movedim(2, 1)

comfy/text_encoders/cosmos.py

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

comfy/text_encoders/flux.py

@@ -63,12 +63,12 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
-def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
+def flux_clip(dtype_t5=None, t5_quantization_metadata=None):
     class FluxClipModel_(FluxClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+            if t5_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+                model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
             super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
     return FluxClipModel_
 
@@ -159,15 +159,13 @@ class Flux2TEModel(sd1_clip.SD1ClipModel):
         out = out.reshape(out.shape[0], out.shape[1], -1)
         return out, pooled, extra
 
-def flux2_te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None, pruned=False):
+def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False):
     class Flux2TEModel_(Flux2TEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
-                model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
             if dtype_llama is not None:
                 dtype = dtype_llama
             if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
                 model_options["quantization_metadata"] = llama_quantization_metadata
             if pruned:
                 model_options = model_options.copy()

comfy/text_encoders/genmo.py

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

comfy/text_encoders/hidream.py

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

comfy/text_encoders/hunyuan_image.py

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

comfy/text_encoders/hunyuan_video.py

@@ -6,7 +6,7 @@ from transformers import LlamaTokenizerFast
 import torch
 import os
 import numbers
-
+import comfy.utils
 
 def llama_detect(state_dict, prefix=""):
     out = {}
@@ -14,12 +14,9 @@ def llama_detect(state_dict, prefix=""):
     if t5_key in state_dict:
         out["dtype_llama"] = state_dict[t5_key].dtype
 
-    scaled_fp8_key = "{}scaled_fp8".format(prefix)
-    if scaled_fp8_key in state_dict:
-        out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
-
-    if "_quantization_metadata" in state_dict:
-        out["llama_quantization_metadata"] = state_dict["_quantization_metadata"]
+    quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+    if quant is not None:
+        out["llama_quantization_metadata"] = quant
 
     return out
 
@@ -31,10 +28,10 @@ class LLAMA3Tokenizer(sd1_clip.SDTokenizer):
 
 class LLAMAModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}):
-        llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
-        if llama_scaled_fp8 is not None:
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
             model_options = model_options.copy()
-            model_options["scaled_fp8"] = llama_scaled_fp8
+            model_options["quantization_metadata"] = llama_quantization_metadata
 
         textmodel_json_config = {}
         vocab_size = model_options.get("vocab_size", None)
@@ -161,11 +158,11 @@ class HunyuanVideoClipModel(torch.nn.Module):
             return self.llama.load_sd(sd)
 
 
-def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None):
+def hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None):
     class HunyuanVideoClipModel_(HunyuanVideoClipModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
             super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
     return HunyuanVideoClipModel_

comfy/text_encoders/kandinsky5.py

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

comfy/text_encoders/llama.py

@@ -100,6 +100,28 @@ class Qwen3_4BConfig:
     rope_scale = None
     final_norm: bool = True
 
+@dataclass
+class Ovis25_2BConfig:
+    vocab_size: int = 151936
+    hidden_size: int = 2048
+    intermediate_size: int = 6144
+    num_hidden_layers: int = 28
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 40960
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 1000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    rope_scale = None
+    final_norm: bool = True
+
 @dataclass
 class Qwen25_7BVLI_Config:
     vocab_size: int = 152064
@@ -542,6 +564,15 @@ class Qwen3_4B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+class Ovis25_2B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Ovis25_2BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
 class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()

comfy/text_encoders/lumina2.py

@@ -40,7 +40,7 @@ class LuminaModel(sd1_clip.SD1ClipModel):
         super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b"):
     if model_type == "gemma2_2b":
         model = Gemma2_2BModel
     elif model_type == "gemma3_4b":
@@ -48,9 +48,9 @@ def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
 
     class LuminaTEModel_(LuminaModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
+                model_options["quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)

comfy/text_encoders/omnigen2.py

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

comfy/text_encoders/ovis.py

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

comfy/text_encoders/pixart_t5.py

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

comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json

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

comfy/text_encoders/qwen_image.py

@@ -85,12 +85,12 @@ class QwenImageTEModel(sd1_clip.SD1ClipModel):
         return out, pooled, extra
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
     class QwenImageTEModel_(QwenImageTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                 model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
+                model_options["quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(device=device, dtype=dtype, model_options=model_options)

comfy/text_encoders/sd3_clip.py

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

comfy/text_encoders/wan.py

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

comfy/text_encoders/z_image.py

@@ -34,12 +34,9 @@ class ZImageTEModel(sd1_clip.SD1ClipModel):
         super().__init__(device=device, dtype=dtype, name="qwen3_4b", clip_model=Qwen3_4BModel, model_options=model_options)
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
     class ZImageTEModel_(ZImageTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
-                model_options = model_options.copy()
-                model_options["scaled_fp8"] = llama_scaled_fp8
             if dtype_llama is not None:
                 dtype = dtype_llama
             if llama_quantization_metadata is not None:

comfy/utils.py

@@ -29,6 +29,7 @@ import itertools
 from torch.nn.functional import interpolate
 from einops import rearrange
 from comfy.cli_args import args
+import json
 
 MMAP_TORCH_FILES = args.mmap_torch_files
 DISABLE_MMAP = args.disable_mmap
@@ -52,7 +53,7 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
     ALWAYS_SAFE_LOAD = True
     logging.info("Checkpoint files will always be loaded safely.")
 else:
-    logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
+    logging.warning("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended as older versions of pytorch are no longer supported.")
 
 def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     if device is None:
@@ -675,6 +676,72 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+def z_image_to_diffusers(mmdit_config, output_prefix=""):
+    n_layers = mmdit_config.get("n_layers", 0)
+    hidden_size = mmdit_config.get("dim", 0)
+    n_context_refiner = mmdit_config.get("n_refiner_layers", 2)
+    n_noise_refiner = mmdit_config.get("n_refiner_layers", 2)
+    key_map = {}
+
+    def add_block_keys(prefix_from, prefix_to, has_adaln=True):
+        for end in ("weight", "bias"):
+            k = "{}.attention.".format(prefix_from)
+            qkv = "{}.attention.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {
+            "attention.norm_q.weight": "attention.q_norm.weight",
+            "attention.norm_k.weight": "attention.k_norm.weight",
+            "attention.to_out.0.weight": "attention.out.weight",
+            "attention.to_out.0.bias": "attention.out.bias",
+            "attention_norm1.weight": "attention_norm1.weight",
+            "attention_norm2.weight": "attention_norm2.weight",
+            "feed_forward.w1.weight": "feed_forward.w1.weight",
+            "feed_forward.w2.weight": "feed_forward.w2.weight",
+            "feed_forward.w3.weight": "feed_forward.w3.weight",
+            "ffn_norm1.weight": "ffn_norm1.weight",
+            "ffn_norm2.weight": "ffn_norm2.weight",
+        }
+        if has_adaln:
+            block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight"
+            block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias"
+        for k, v in block_map.items():
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v)
+
+    for i in range(n_layers):
+        add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i))
+
+    for i in range(n_context_refiner):
+        add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i))
+
+    for i in range(n_noise_refiner):
+        add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i))
+
+    MAP_BASIC = [
+        ("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"),
+        ("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"),
+        ("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"),
+        ("x_embedder.weight", "all_x_embedder.2-1.weight"),
+        ("x_embedder.bias", "all_x_embedder.2-1.bias"),
+        ("x_pad_token", "x_pad_token"),
+        ("cap_embedder.0.weight", "cap_embedder.0.weight"),
+        ("cap_embedder.1.weight", "cap_embedder.1.weight"),
+        ("cap_embedder.1.bias", "cap_embedder.1.bias"),
+        ("cap_pad_token", "cap_pad_token"),
+        ("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"),
+        ("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"),
+        ("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"),
+        ("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"),
+    ]
+
+    for c, diffusers in MAP_BASIC:
+        key_map[diffusers] = "{}{}".format(output_prefix, c)
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)
@@ -736,12 +803,17 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
             return None
         return f.read(length_of_header)
 
+ATTR_UNSET={}
+
 def set_attr(obj, attr, value):
     attrs = attr.split(".")
     for name in attrs[:-1]:
         obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], value)
+    prev = getattr(obj, attrs[-1], ATTR_UNSET)
+    if value is ATTR_UNSET:
+        delattr(obj, attrs[-1])
+    else:
+        setattr(obj, attrs[-1], value)
     return prev
 
 def set_attr_param(obj, attr, value):
@@ -1128,3 +1200,68 @@ def unpack_latents(combined_latent, latent_shapes):
     else:
         output_tensors = combined_latent
     return output_tensors
+
+def detect_layer_quantization(state_dict, prefix):
+    for k in state_dict:
+        if k.startswith(prefix) and k.endswith(".comfy_quant"):
+            logging.info("Found quantization metadata version 1")
+            return {"mixed_ops": True}
+    return None
+
+def convert_old_quants(state_dict, model_prefix="", metadata={}):
+    if metadata is None:
+        metadata = {}
+
+    quant_metadata = None
+    if "_quantization_metadata" not in metadata:
+        scaled_fp8_key = "{}scaled_fp8".format(model_prefix)
+
+        if scaled_fp8_key in state_dict:
+            scaled_fp8_weight = state_dict[scaled_fp8_key]
+            scaled_fp8_dtype = scaled_fp8_weight.dtype
+            if scaled_fp8_dtype == torch.float32:
+                scaled_fp8_dtype = torch.float8_e4m3fn
+
+            if scaled_fp8_weight.nelement() == 2:
+                full_precision_matrix_mult = True
+            else:
+                full_precision_matrix_mult = False
+
+            out_sd = {}
+            layers = {}
+            for k in list(state_dict.keys()):
+                if not k.startswith(model_prefix):
+                    out_sd[k] = state_dict[k]
+                    continue
+                k_out = k
+                w = state_dict.pop(k)
+                layer = None
+                if k_out.endswith(".scale_weight"):
+                    layer = k_out[:-len(".scale_weight")]
+                    k_out = "{}.weight_scale".format(layer)
+
+                if layer is not None:
+                    layer_conf = {"format": "float8_e4m3fn"}  # TODO: check if anyone did some non e4m3fn scaled checkpoints
+                    if full_precision_matrix_mult:
+                        layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult
+                    layers[layer] = layer_conf
+
+                if k_out.endswith(".scale_input"):
+                    layer = k_out[:-len(".scale_input")]
+                    k_out = "{}.input_scale".format(layer)
+                    if w.item() == 1.0:
+                        continue
+
+                out_sd[k_out] = w
+
+            state_dict = out_sd
+            quant_metadata = {"layers": layers}
+    else:
+        quant_metadata = json.loads(metadata["_quantization_metadata"])
+
+    if quant_metadata is not None:
+        layers = quant_metadata["layers"]
+        for k, v in layers.items():
+            state_dict["{}.comfy_quant".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8)
+
+    return state_dict, metadata

comfy_api/feature_flags.py

@@ -13,6 +13,7 @@ from comfy.cli_args import args
 SERVER_FEATURE_FLAGS: Dict[str, Any] = {
     "supports_preview_metadata": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+    "extension": {"manager": {"supports_v4": True}},
 }
 
 

comfy_api/latest/__init__.py

@@ -5,11 +5,11 @@ from typing import Type, TYPE_CHECKING
 from comfy_api.internal import ComfyAPIBase
 from comfy_api.internal.singleton import ProxiedSingleton
 from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
-from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
-from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
-from . import _io as io
-from . import _ui as ui
+from ._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
+from ._input_impl import VideoFromFile, VideoFromComponents
+from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
+from . import _io_public as io
+from . import _ui_public as ui
 # from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
@@ -80,7 +80,7 @@ class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
         Called when an extension is loaded.
-        This should be used to initialize any global resources neeeded by the extension.
+        This should be used to initialize any global resources needed by the extension.
         """
 
     @abstractmethod

comfy_api/latest/_input/video_types.py

@@ -4,7 +4,7 @@ from fractions import Fraction
 from typing import Optional, Union, IO
 import io
 import av
-from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
+from .._util import VideoContainer, VideoCodec, VideoComponents
 
 class VideoInput(ABC):
     """

comfy_api/latest/_input_impl/video_types.py

@@ -3,14 +3,14 @@ from av.container import InputContainer
 from av.subtitles.stream import SubtitleStream
 from fractions import Fraction
 from typing import Optional
-from comfy_api.latest._input import AudioInput, VideoInput
+from .._input import AudioInput, VideoInput
 import av
 import io
 import json
 import numpy as np
 import math
 import torch
-from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents
+from .._util import VideoContainer, VideoCodec, VideoComponents
 
 
 def container_to_output_format(container_format: str | None) -> str | None:
@@ -336,7 +336,10 @@ class VideoFromComponents(VideoInput):
             raise ValueError("Only MP4 format is supported for now")
         if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
             raise ValueError("Only H264 codec is supported for now")
-        with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output:
+        extra_kwargs = {}
+        if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
+            extra_kwargs["format"] = format.value
+        with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
             # Add metadata before writing any streams
             if metadata is not None:
                 for key, value in metadata.items():

comfy_api/latest/_io.py

@@ -4,7 +4,8 @@ import copy
 import inspect
 from abc import ABC, abstractmethod
 from collections import Counter
-from dataclasses import asdict, dataclass
+from collections.abc import Iterable
+from dataclasses import asdict, dataclass, field
 from enum import Enum
 from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING
 from typing_extensions import NotRequired, final
@@ -25,7 +26,7 @@ if TYPE_CHECKING:
     from comfy_api.input import VideoInput
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
-from comfy_api.latest._resources import Resources, ResourcesLocal
+from ._resources import Resources, ResourcesLocal
 from comfy_execution.graph_utils import ExecutionBlocker
 from ._util import MESH, VOXEL
 
@@ -150,6 +151,9 @@ class _IO_V3:
     def __init__(self):
         pass
 
+    def validate(self):
+        pass
+
     @property
     def io_type(self):
         return self.Parent.io_type
@@ -182,6 +186,9 @@ class Input(_IO_V3):
     def get_io_type(self):
         return _StringIOType(self.io_type)
 
+    def get_all(self) -> list[Input]:
+        return [self]
+
 class WidgetInput(Input):
     '''
     Base class for a V3 Input with widget.
@@ -561,6 +568,8 @@ class Conditioning(ComfyTypeIO):
         '''Used by WAN Camera.'''
         time_dim_concat: NotRequired[torch.Tensor]
         '''Used by WAN Phantom Subject.'''
+        time_dim_replace: NotRequired[torch.Tensor]
+        '''Used by Kandinsky5 I2V.'''
 
     CondList = list[tuple[torch.Tensor, PooledDict]]
     Type = CondList
@@ -765,6 +774,13 @@ class AudioEncoder(ComfyTypeIO):
 class AudioEncoderOutput(ComfyTypeIO):
     Type = Any
 
+@comfytype(io_type="TRACKS")
+class Tracks(ComfyTypeIO):
+    class TrackDict(TypedDict):
+        track_path: torch.Tensor
+        track_visibility: torch.Tensor
+    Type = TrackDict
+
 @comfytype(io_type="COMFY_MULTITYPED_V3")
 class MultiType:
     Type = Any
@@ -814,13 +830,61 @@ class MultiType:
             else:
                 return super().as_dict()
 
+@comfytype(io_type="COMFY_MATCHTYPE_V3")
+class MatchType(ComfyTypeIO):
+    class Template:
+        def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType] = AnyType):
+            self.template_id = template_id
+            # account for syntactic sugar
+            if not isinstance(allowed_types, Iterable):
+                allowed_types = [allowed_types]
+            for t in allowed_types:
+                if not isinstance(t, type):
+                    if not isinstance(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__class__.__name__}")
+                else:
+                    if not issubclass(t, _ComfyType):
+                        raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__name__}")
+            self.allowed_types = allowed_types
+
+        def as_dict(self):
+            return {
+                "template_id": self.template_id,
+                "allowed_types": ",".join([t.io_type for t in self.allowed_types]),
+            }
+
+    class Input(Input):
+        def __init__(self, id: str, template: MatchType.Template,
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
+    class Output(Output):
+        def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
+                     is_output_list=False):
+            super().__init__(id, display_name, tooltip, is_output_list)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
+
 class DynamicInput(Input, ABC):
     '''
     Abstract class for dynamic input registration.
     '''
-    @abstractmethod
     def get_dynamic(self) -> list[Input]:
-        ...
+        return []
+
+    def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+        pass
+
 
 class DynamicOutput(Output, ABC):
     '''
@@ -830,99 +894,223 @@ class DynamicOutput(Output, ABC):
                  is_output_list=False):
         super().__init__(id, display_name, tooltip, is_output_list)
 
-    @abstractmethod
     def get_dynamic(self) -> list[Output]:
-        ...
+        return []
 
 
 @comfytype(io_type="COMFY_AUTOGROW_V3")
-class AutogrowDynamic(ComfyTypeI):
-    Type = list[Any]
-    class Input(DynamicInput):
-        def __init__(self, id: str, template_input: Input, min: int=1, max: int=None,
-                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
-            self.template_input = template_input
-            if min is not None:
-                assert(min >= 1)
-            if max is not None:
-                assert(max >= 1)
+class Autogrow(ComfyTypeI):
+    Type = dict[str, Any]
+    _MaxNames = 100  # NOTE: max 100 names for sanity
+
+    class _AutogrowTemplate:
+        def __init__(self, input: Input):
+            # dynamic inputs are not allowed as the template input
+            assert(not isinstance(input, DynamicInput))
+            self.input = copy.copy(input)
+            if isinstance(self.input, WidgetInput):
+                self.input.force_input = True
+            self.names: list[str] = []
+            self.cached_inputs = {}
+
+        def _create_input(self, input: Input, name: str):
+            new_input = copy.copy(self.input)
+            new_input.id = name
+            return new_input
+
+        def _create_cached_inputs(self):
+            for name in self.names:
+                self.cached_inputs[name] = self._create_input(self.input, name)
+
+        def get_all(self) -> list[Input]:
+            return list(self.cached_inputs.values())
+
+        def as_dict(self):
+            return prune_dict({
+                "input": create_input_dict_v1([self.input]),
+            })
+
+        def validate(self):
+            self.input.validate()
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            real_inputs = []
+            for name, input in self.cached_inputs.items():
+                if name in live_inputs:
+                    real_inputs.append(input)
+            add_to_input_dict_v1(d, real_inputs, live_inputs, curr_prefix)
+            add_dynamic_id_mapping(d, real_inputs, curr_prefix)
+
+    class TemplatePrefix(_AutogrowTemplate):
+        def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
+            super().__init__(input)
+            self.prefix = prefix
+            assert(min >= 0)
+            assert(max >= 1)
+            assert(max <= Autogrow._MaxNames)
             self.min = min
             self.max = max
+            self.names = [f"{self.prefix}{i}" for i in range(self.max)]
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "prefix": self.prefix,
+                "min": self.min,
+                "max": self.max,
+            })
+
+    class TemplateNames(_AutogrowTemplate):
+        def __init__(self, input: Input, names: list[str], min: int=1):
+            super().__init__(input)
+            self.names = names[:Autogrow._MaxNames]
+            assert(min >= 0)
+            self.min = min
+            self._create_cached_inputs()
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "names": self.names,
+                "min": self.min,
+            })
+
+    class Input(DynamicInput):
+        def __init__(self, id: str, template: Autogrow.TemplatePrefix | Autogrow.TemplateNames,
+                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.template = template
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": self.template.as_dict(),
+            })
 
         def get_dynamic(self) -> list[Input]:
-            curr_count = 1
-            new_inputs = []
-            for i in range(self.min):
-                new_input = copy.copy(self.template_input)
-                new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
-                if new_input.display_name is not None:
-                    new_input.display_name = f"{new_input.display_name}{curr_count}"
-                new_input.optional = self.optional or new_input.optional
-                if isinstance(self.template_input, WidgetInput):
-                    new_input.force_input = True
-                new_inputs.append(new_input)
-                curr_count += 1
-            # pretend to expand up to max
-            for i in range(curr_count-1, self.max):
-                new_input = copy.copy(self.template_input)
-                new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
-                if new_input.display_name is not None:
-                    new_input.display_name = f"{new_input.display_name}{curr_count}"
-                new_input.optional = True
-                if isinstance(self.template_input, WidgetInput):
-                    new_input.force_input = True
-                new_inputs.append(new_input)
-                curr_count += 1
-            return new_inputs
+            return self.template.get_all()
 
-@comfytype(io_type="COMFY_COMBODYNAMIC_V3")
-class ComboDynamic(ComfyTypeI):
-    class Input(DynamicInput):
-        def __init__(self, id: str):
-            pass
+        def get_all(self) -> list[Input]:
+            return [self] + self.template.get_all()
 
-@comfytype(io_type="COMFY_MATCHTYPE_V3")
-class MatchType(ComfyTypeIO):
-    class Template:
-        def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType]):
-            self.template_id = template_id
-            self.allowed_types = [allowed_types] if isinstance(allowed_types, _ComfyType) else allowed_types
+        def validate(self):
+            self.template.validate()
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            curr_prefix = f"{curr_prefix}{self.id}."
+            # need to remove self from expected inputs dictionary; replaced by template inputs in frontend
+            for inner_dict in d.values():
+                if self.id in inner_dict:
+                    del inner_dict[self.id]
+            self.template.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+
+@comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
+class DynamicCombo(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Option:
+        def __init__(self, key: str, inputs: list[Input]):
+            self.key = key
+            self.inputs = inputs
 
         def as_dict(self):
             return {
-                "template_id": self.template_id,
-                "allowed_types": "".join(t.io_type for t in self.allowed_types),
+                "key": self.key,
+                "inputs": create_input_dict_v1(self.inputs),
             }
 
     class Input(DynamicInput):
-        def __init__(self, id: str, template: MatchType.Template,
+        def __init__(self, id: str, options: list[DynamicCombo.Option],
                     display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
             super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
-            self.template = template
+            self.options = options
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            # check if dynamic input's id is in live_inputs
+            if self.id in live_inputs:
+                curr_prefix = f"{curr_prefix}{self.id}."
+                key = live_inputs[self.id]
+                selected_option = None
+                for option in self.options:
+                    if option.key == key:
+                        selected_option = option
+                        break
+                if selected_option is not None:
+                    add_to_input_dict_v1(d, selected_option.inputs, live_inputs, curr_prefix)
+                    add_dynamic_id_mapping(d, selected_option.inputs, curr_prefix, self)
 
         def get_dynamic(self) -> list[Input]:
-            return [self]
+            return [input for option in self.options for input in option.inputs]
+
+        def get_all(self) -> list[Input]:
+            return [self] + [input for option in self.options for input in option.inputs]
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
-                "template": self.template.as_dict(),
+                "options": [o.as_dict() for o in self.options],
             })
 
-    class Output(DynamicOutput):
-        def __init__(self, id: str, template: MatchType.Template, display_name: str=None, tooltip: str=None,
-                     is_output_list=False):
-            super().__init__(id, display_name, tooltip, is_output_list)
-            self.template = template
+        def validate(self):
+            # make sure all nested inputs are validated
+            for option in self.options:
+                for input in option.inputs:
+                    input.validate()
 
-        def get_dynamic(self) -> list[Output]:
-            return [self]
+@comfytype(io_type="COMFY_DYNAMICSLOT_V3")
+class DynamicSlot(ComfyTypeI):
+    Type = dict[str, Any]
+
+    class Input(DynamicInput):
+        def __init__(self, slot: Input, inputs: list[Input],
+                    display_name: str=None, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            assert(not isinstance(slot, DynamicInput))
+            self.slot = copy.copy(slot)
+            self.slot.display_name = slot.display_name if slot.display_name is not None else display_name
+            optional = True
+            self.slot.tooltip = slot.tooltip if slot.tooltip is not None else tooltip
+            self.slot.lazy = slot.lazy if slot.lazy is not None else lazy
+            self.slot.extra_dict = slot.extra_dict if slot.extra_dict is not None else extra_dict
+            super().__init__(slot.id, self.slot.display_name, optional, self.slot.tooltip, self.slot.lazy, self.slot.extra_dict)
+            self.inputs = inputs
+            self.force_input = None
+            # force widget inputs to have no widgets, otherwise this would be awkward
+            if isinstance(self.slot, WidgetInput):
+                self.force_input = True
+                self.slot.force_input = True
+
+        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+            if self.id in live_inputs:
+                curr_prefix = f"{curr_prefix}{self.id}."
+                add_to_input_dict_v1(d, self.inputs, live_inputs, curr_prefix)
+                add_dynamic_id_mapping(d, [self.slot] + self.inputs, curr_prefix)
+
+        def get_dynamic(self) -> list[Input]:
+            return [self.slot] + self.inputs
+
+        def get_all(self) -> list[Input]:
+            return [self] + [self.slot] + self.inputs
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
-                "template": self.template.as_dict(),
+                "slotType": str(self.slot.get_io_type()),
+                "inputs": create_input_dict_v1(self.inputs),
+                "forceInput": self.force_input,
             })
 
+        def validate(self):
+            self.slot.validate()
+            for input in self.inputs:
+                input.validate()
+
+def add_dynamic_id_mapping(d: dict[str, Any], inputs: list[Input], curr_prefix: str, self: DynamicInput=None):
+    dynamic = d.setdefault("dynamic_paths", {})
+    if self is not None:
+        dynamic[self.id] = f"{curr_prefix}{self.id}"
+    for i in inputs:
+        if not isinstance(i, DynamicInput):
+            dynamic[f"{i.id}"] = f"{curr_prefix}{i.id}"
+
+class V3Data(TypedDict):
+    hidden_inputs: dict[str, Any]
+    dynamic_paths: dict[str, Any]
 
 class HiddenHolder:
     def __init__(self, unique_id: str, prompt: Any,
@@ -984,6 +1172,7 @@ class NodeInfoV1:
     output_is_list: list[bool]=None
     output_name: list[str]=None
     output_tooltips: list[str]=None
+    output_matchtypes: list[str]=None
     name: str=None
     display_name: str=None
     description: str=None
@@ -1019,9 +1208,9 @@ class Schema:
     """Display name of node."""
     category: str = "sd"
     """The category of the node, as per the "Add Node" menu."""
-    inputs: list[Input]=None
-    outputs: list[Output]=None
-    hidden: list[Hidden]=None
+    inputs: list[Input] = field(default_factory=list)
+    outputs: list[Output] = field(default_factory=list)
+    hidden: list[Hidden] = field(default_factory=list)
     description: str=""
     """Node description, shown as a tooltip when hovering over the node."""
     is_input_list: bool = False
@@ -1061,7 +1250,11 @@ class Schema:
         '''Validate the schema:
         - verify ids on inputs and outputs are unique - both internally and in relation to each other
         '''
-        input_ids = [i.id for i in self.inputs] if self.inputs is not None else []
+        nested_inputs: list[Input] = []
+        if self.inputs is not None:
+            for input in self.inputs:
+                nested_inputs.extend(input.get_all())
+        input_ids = [i.id for i in nested_inputs] if nested_inputs is not None else []
         output_ids = [o.id for o in self.outputs] if self.outputs is not None else []
         input_set = set(input_ids)
         output_set = set(output_ids)
@@ -1077,6 +1270,13 @@ class Schema:
             issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.")
         if len(issues) > 0:
             raise ValueError("\n".join(issues))
+        # validate inputs and outputs
+        if self.inputs is not None:
+            for input in self.inputs:
+                input.validate()
+        if self.outputs is not None:
+            for output in self.outputs:
+                output.validate()
 
     def finalize(self):
         """Add hidden based on selected schema options, and give outputs without ids default ids."""
@@ -1102,19 +1302,10 @@ class Schema:
                 if output.id is None:
                     output.id = f"_{i}_{output.io_type}_"
 
-    def get_v1_info(self, cls) -> NodeInfoV1:
+    def get_v1_info(self, cls, live_inputs: dict[str, Any]=None) -> NodeInfoV1:
+        # NOTE: live_inputs will not be used anymore very soon and this will be done another way
         # get V1 inputs
-        input = {
-            "required": {}
-        }
-        if self.inputs:
-            for i in self.inputs:
-                if isinstance(i, DynamicInput):
-                    dynamic_inputs = i.get_dynamic()
-                    for d in dynamic_inputs:
-                        add_to_dict_v1(d, input)
-                else:
-                    add_to_dict_v1(i, input)
+        input = create_input_dict_v1(self.inputs, live_inputs)
         if self.hidden:
             for hidden in self.hidden:
                 input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
@@ -1123,12 +1314,24 @@ class Schema:
         output_is_list = []
         output_name = []
         output_tooltips = []
+        output_matchtypes = []
+        any_matchtypes = False
         if self.outputs:
             for o in self.outputs:
                 output.append(o.io_type)
                 output_is_list.append(o.is_output_list)
                 output_name.append(o.display_name if o.display_name else o.io_type)
                 output_tooltips.append(o.tooltip if o.tooltip else None)
+                # special handling for MatchType
+                if isinstance(o, MatchType.Output):
+                    output_matchtypes.append(o.template.template_id)
+                    any_matchtypes = True
+                else:
+                    output_matchtypes.append(None)
+
+        # clear out lists that are all None
+        if not any_matchtypes:
+            output_matchtypes = None
 
         info = NodeInfoV1(
             input=input,
@@ -1137,6 +1340,7 @@ class Schema:
             output_is_list=output_is_list,
             output_name=output_name,
             output_tooltips=output_tooltips,
+            output_matchtypes=output_matchtypes,
             name=self.node_id,
             display_name=self.display_name,
             category=self.category,
@@ -1182,16 +1386,57 @@ class Schema:
         return info
 
 
-def add_to_dict_v1(i: Input, input: dict):
+def create_input_dict_v1(inputs: list[Input], live_inputs: dict[str, Any]=None) -> dict:
+    input = {
+        "required": {}
+    }
+    add_to_input_dict_v1(input, inputs, live_inputs)
+    return input
+
+def add_to_input_dict_v1(d: dict[str, Any], inputs: list[Input], live_inputs: dict[str, Any]=None, curr_prefix=''):
+    for i in inputs:
+        if isinstance(i, DynamicInput):
+            add_to_dict_v1(i, d)
+            if live_inputs is not None:
+                i.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+        else:
+            add_to_dict_v1(i, d)
+
+def add_to_dict_v1(i: Input, d: dict, dynamic_dict: dict=None):
     key = "optional" if i.optional else "required"
     as_dict = i.as_dict()
     # for v1, we don't want to include the optional key
     as_dict.pop("optional", None)
-    input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
+    if dynamic_dict is None:
+        value = (i.get_io_type(), as_dict)
+    else:
+        value = (i.get_io_type(), as_dict, dynamic_dict)
+    d.setdefault(key, {})[i.id] = value
 
 def add_to_dict_v3(io: Input | Output, d: dict):
     d[io.id] = (io.get_io_type(), io.as_dict())
 
+def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
+    paths = v3_data.get("dynamic_paths", None)
+    if paths is None:
+        return values
+    values = values.copy()
+    result = {}
+
+    for key, path in paths.items():
+        parts = path.split(".")
+        current = result
+
+        for i, p in enumerate(parts):
+            is_last = (i == len(parts) - 1)
+
+            if is_last:
+                current[p] = values.pop(key, None)
+            else:
+                current = current.setdefault(p, {})
+
+    values.update(result)
+    return values
 
 
 class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@@ -1311,12 +1556,12 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNode]:
+    def PREPARE_CLASS_CLONE(cls, v3_data: V3Data) -> type[ComfyNode]:
         """Creates clone of real node class to prevent monkey-patching."""
         c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
         type_clone: type[ComfyNode] = shallow_clone_class(c_type)
         # set hidden
-        type_clone.hidden = HiddenHolder.from_dict(hidden_inputs)
+        type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"])
         return type_clone
 
     @final
@@ -1433,14 +1678,18 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], Schema]:
+    def INPUT_TYPES(cls, include_hidden=True, return_schema=False, live_inputs=None) -> dict[str, dict] | tuple[dict[str, dict], Schema, V3Data]:
         schema = cls.FINALIZE_SCHEMA()
-        info = schema.get_v1_info(cls)
+        info = schema.get_v1_info(cls, live_inputs)
         input = info.input
         if not include_hidden:
             input.pop("hidden", None)
         if return_schema:
-            return input, schema
+            v3_data: V3Data = {}
+            dynamic = input.pop("dynamic_paths", None)
+            if dynamic is not None:
+                v3_data["dynamic_paths"] = dynamic
+            return input, schema, v3_data
         return input
 
     @final
@@ -1513,7 +1762,7 @@ class ComfyNode(_ComfyNodeBaseInternal):
         raise NotImplementedError
 
     @classmethod
-    def validate_inputs(cls, **kwargs) -> bool:
+    def validate_inputs(cls, **kwargs) -> bool | str:
         """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS."""
         raise NotImplementedError
 
@@ -1573,7 +1822,7 @@ class NodeOutput(_NodeOutputInternal):
             ui = data["ui"]
         if "expand" in data:
             expand = data["expand"]
-        return cls(args=args, ui=ui, expand=expand)
+        return cls(*args, ui=ui, expand=expand)
 
     def __getitem__(self, index) -> Any:
         return self.args[index]
@@ -1628,6 +1877,7 @@ __all__ = [
     "StyleModel",
     "Gligen",
     "UpscaleModel",
+    "LatentUpscaleModel",
     "Audio",
     "Video",
     "SVG",
@@ -1651,6 +1901,11 @@ __all__ = [
     "SEGS",
     "AnyType",
     "MultiType",
+    "Tracks",
+    # Dynamic Types
+    "MatchType",
+    # "DynamicCombo",
+    # "Autogrow",
     # Other classes
     "HiddenHolder",
     "Hidden",
@@ -1661,4 +1916,5 @@ __all__ = [
     "NodeOutput",
     "add_to_dict_v1",
     "add_to_dict_v3",
+    "V3Data",
 ]

comfy_api/latest/_io_public.py

@@ -0,0 +1 @@
+from ._io import *  # noqa: F403

comfy_api/latest/_ui.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import json
 import os
 import random
+import uuid
 from io import BytesIO
 from typing import Type
 
@@ -21,7 +22,7 @@ import folder_paths
 
 # used for image preview
 from comfy.cli_args import args
-from comfy_api.latest._io import ComfyNode, FolderType, Image, _UIOutput
+from ._io import ComfyNode, FolderType, Image, _UIOutput
 
 
 class SavedResult(dict):
@@ -318,9 +319,10 @@ class AudioSaveHelper:
             for key, value in metadata.items():
                 output_container.metadata[key] = value
 
+            layout = "mono" if waveform.shape[0] == 1 else "stereo"
             # Set up the output stream with appropriate properties
             if format == "opus":
-                out_stream = output_container.add_stream("libopus", rate=sample_rate)
+                out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
                 if quality == "64k":
                     out_stream.bit_rate = 64000
                 elif quality == "96k":
@@ -332,7 +334,7 @@ class AudioSaveHelper:
                 elif quality == "320k":
                     out_stream.bit_rate = 320000
             elif format == "mp3":
-                out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+                out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
                 if quality == "V0":
                     # TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
                     out_stream.codec_context.qscale = 1
@@ -341,12 +343,12 @@ class AudioSaveHelper:
                 elif quality == "320k":
                     out_stream.bit_rate = 320000
             else:  # format == "flac":
-                out_stream = output_container.add_stream("flac", rate=sample_rate)
+                out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
 
             frame = av.AudioFrame.from_ndarray(
                 waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
                 format="flt",
-                layout="mono" if waveform.shape[0] == 1 else "stereo",
+                layout=layout,
             )
             frame.sample_rate = sample_rate
             frame.pts = 0
@@ -436,9 +438,19 @@ class PreviewUI3D(_UIOutput):
     def __init__(self, model_file, camera_info, **kwargs):
         self.model_file = model_file
         self.camera_info = camera_info
+        self.bg_image_path = None
+        bg_image = kwargs.get("bg_image", None)
+        if bg_image is not None:
+            img_array = (bg_image[0].cpu().numpy() * 255).astype(np.uint8)
+            img = PILImage.fromarray(img_array)
+            temp_dir = folder_paths.get_temp_directory()
+            filename = f"bg_{uuid.uuid4().hex}.png"
+            bg_image_path = os.path.join(temp_dir, filename)
+            img.save(bg_image_path, compress_level=1)
+            self.bg_image_path = f"temp/{filename}"
 
     def as_dict(self):
-        return {"result": [self.model_file, self.camera_info]}
+        return {"result": [self.model_file, self.camera_info, self.bg_image_path]}
 
 
 class PreviewText(_UIOutput):

comfy_api/latest/_ui_public.py

@@ -0,0 +1 @@
+from ._ui import *  # noqa: F403

comfy_api/latest/_util/video_types.py

@@ -3,7 +3,7 @@ from dataclasses import dataclass
 from enum import Enum
 from fractions import Fraction
 from typing import Optional
-from comfy_api.latest._input import ImageInput, AudioInput
+from .._input import ImageInput, AudioInput
 
 class VideoCodec(str, Enum):
     AUTO = "auto"

comfy_api/v0_0_2/__init__.py

@@ -6,7 +6,7 @@ from comfy_api.latest import (
 )
 from typing import Type, TYPE_CHECKING
 from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest import io, ui, ComfyExtension  #noqa: F401
+from comfy_api.latest import io, ui, IO, UI, ComfyExtension  #noqa: F401
 
 
 class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
@@ -42,4 +42,8 @@ __all__ = [
     "InputImpl",
     "Types",
     "ComfyExtension",
+    "io",
+    "IO",
+    "ui",
+    "UI",
 ]

comfy_api_nodes/apis/bytedance_api.py

@@ -0,0 +1,144 @@
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    response_format: str | None = Field("url")
+    size: str | None = Field(None)
+    seed: int | None = Field(0, ge=0, le=2147483647)
+    guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
+    watermark: bool | None = Field(True)
+
+
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    response_format: str | None = Field("url")
+    image: str = Field(..., description="Base64 encoded string or image URL")
+    size: str | None = Field("adaptive")
+    seed: int | None = Field(..., ge=0, le=2147483647)
+    guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
+    watermark: bool | None = Field(True)
+
+
+class Seedream4Options(BaseModel):
+    max_images: int = Field(15)
+
+
+class Seedream4TaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    prompt: str = Field(...)
+    response_format: str = Field("url")
+    image: list[str] | None = Field(None, description="Image URLs")
+    size: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    sequential_image_generation: str = Field("disabled")
+    sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
+    watermark: bool = Field(True)
+
+
+class ImageTaskCreationResponse(BaseModel):
+    model: str = Field(...)
+    created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")
+    data: list = Field([], description="Contains information about the generated image(s).")
+    error: dict = Field({}, description="Contains `code` and `message` fields in case of error.")
+
+
+class TaskTextContent(BaseModel):
+    type: str = Field("text")
+    text: str = Field(...)
+
+
+class TaskImageContentUrl(BaseModel):
+    url: str = Field(...)
+
+
+class TaskImageContent(BaseModel):
+    type: str = Field("image_url")
+    image_url: TaskImageContentUrl = Field(...)
+    role: Literal["first_frame", "last_frame", "reference_image"] | None = Field(None)
+
+
+class Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[TaskTextContent] = Field(..., min_length=1)
+
+
+class Image2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    content: list[TaskTextContent | TaskImageContent] = Field(..., min_length=2)
+
+
+class TaskCreationResponse(BaseModel):
+    id: str = Field(...)
+
+
+class TaskStatusError(BaseModel):
+    code: str = Field(...)
+    message: str = Field(...)
+
+
+class TaskStatusResult(BaseModel):
+    video_url: str = Field(...)
+
+
+class TaskStatusResponse(BaseModel):
+    id: str = Field(...)
+    model: str = Field(...)
+    status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...)
+    error: TaskStatusError | None = Field(None)
+    content: TaskStatusResult | None = Field(None)
+
+
+RECOMMENDED_PRESETS = [
+    ("1024x1024 (1:1)", 1024, 1024),
+    ("864x1152 (3:4)", 864, 1152),
+    ("1152x864 (4:3)", 1152, 864),
+    ("1280x720 (16:9)", 1280, 720),
+    ("720x1280 (9:16)", 720, 1280),
+    ("832x1248 (2:3)", 832, 1248),
+    ("1248x832 (3:2)", 1248, 832),
+    ("1512x648 (21:9)", 1512, 648),
+    ("2048x2048 (1:1)", 2048, 2048),
+    ("Custom", None, None),
+]
+
+RECOMMENDED_PRESETS_SEEDREAM_4 = [
+    ("2048x2048 (1:1)", 2048, 2048),
+    ("2304x1728 (4:3)", 2304, 1728),
+    ("1728x2304 (3:4)", 1728, 2304),
+    ("2560x1440 (16:9)", 2560, 1440),
+    ("1440x2560 (9:16)", 1440, 2560),
+    ("2496x1664 (3:2)", 2496, 1664),
+    ("1664x2496 (2:3)", 1664, 2496),
+    ("3024x1296 (21:9)", 3024, 1296),
+    ("4096x4096 (1:1)", 4096, 4096),
+    ("Custom", None, None),
+]
+
+# The time in this dictionary are given for 10 seconds duration.
+VIDEO_TASKS_EXECUTION_TIME = {
+    "seedance-1-0-lite-t2v-250428": {
+        "480p": 40,
+        "720p": 60,
+        "1080p": 90,
+    },
+    "seedance-1-0-lite-i2v-250428": {
+        "480p": 40,
+        "720p": 60,
+        "1080p": 90,
+    },
+    "seedance-1-0-pro-250528": {
+        "480p": 70,
+        "720p": 85,
+        "1080p": 115,
+    },
+    "seedance-1-0-pro-fast-251015": {
+        "480p": 50,
+        "720p": 65,
+        "1080p": 100,
+    },
+}

comfy_api_nodes/apis/gemini_api.py

@@ -58,8 +58,14 @@ class GeminiInlineData(BaseModel):
     mimeType: GeminiMimeType | None = Field(None)
 
 
+class GeminiFileData(BaseModel):
+    fileUri: str | None = Field(None)
+    mimeType: GeminiMimeType | None = Field(None)
+
+
 class GeminiPart(BaseModel):
     inlineData: GeminiInlineData | None = Field(None)
+    fileData: GeminiFileData | None = Field(None)
     text: str | None = Field(None)
 
 
@@ -78,15 +84,7 @@ class GeminiSystemInstructionContent(BaseModel):
         description="A list of ordered parts that make up a single message. "
         "Different parts may have different IANA MIME types.",
     )
-    role: GeminiRole = Field(
-        ...,
-        description="The identity of the entity that creates the message. "
-        "The following values are supported: "
-        "user: This indicates that the message is sent by a real person, typically a user-generated message. "
-        "model: This indicates that the message is generated by the model. "
-        "The model value is used to insert messages from model into the conversation during multi-turn conversations. "
-        "For non-multi-turn conversations, this field can be left blank or unset.",
-    )
+    role: GeminiRole | None = Field(..., description="The role field of systemInstruction may be ignored.")
 
 
 class GeminiFunctionDeclaration(BaseModel):

comfy_api_nodes/apis/kling_api.py

@@ -0,0 +1,104 @@
+from pydantic import BaseModel, Field
+
+
+class OmniProText2VideoRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+
+
+class OmniParamImage(BaseModel):
+    image_url: str = Field(...)
+    type: str | None = Field(None, description="Can be 'first_frame' or 'end_frame'")
+
+
+class OmniParamVideo(BaseModel):
+    video_url: str = Field(...)
+    refer_type: str | None = Field(..., description="Can be 'base' or 'feature'")
+    keep_original_sound: str = Field(..., description="'yes' or 'no'")
+
+
+class OmniProFirstLastFrameRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    image_list: list[OmniParamImage] = Field(..., min_length=1, max_length=7)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+
+
+class OmniProReferences2VideoRequest(BaseModel):
+    model_name: str = Field(..., description="kling-video-o1")
+    aspect_ratio: str | None = Field(..., description="'16:9', '9:16' or '1:1'")
+    image_list: list[OmniParamImage] | None = Field(
+        None, max_length=7, description="Max length 4 when video is present."
+    )
+    video_list: list[OmniParamVideo] | None = Field(None, max_length=1)
+    duration: str | None = Field(..., description="From 3 to 10.")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+
+
+class TaskStatusVideoResult(BaseModel):
+    duration: str | None = Field(None, description="Total video duration")
+    id: str | None = Field(None, description="Generated video ID")
+    url: str | None = Field(None, description="URL for generated video")
+
+
+class TaskStatusImageResult(BaseModel):
+    index: int = Field(..., description="Image Number，0-9")
+    url: str = Field(..., description="URL for generated image")
+
+
+class TaskStatusResults(BaseModel):
+    videos: list[TaskStatusVideoResult] | None = Field(None)
+    images: list[TaskStatusImageResult] | None = Field(None)
+
+
+class TaskStatusResponseData(BaseModel):
+    created_at: int | None = Field(None, description="Task creation time")
+    updated_at: int | None = Field(None, description="Task update time")
+    task_status: str | None = None
+    task_status_msg: str | None = Field(None, description="Additional failure reason. Only for polling endpoint.")
+    task_id: str | None = Field(None, description="Task ID")
+    task_result: TaskStatusResults | None = Field(None)
+
+
+class TaskStatusResponse(BaseModel):
+    code: int | None = Field(None, description="Error code")
+    message: str | None = Field(None, description="Error message")
+    request_id: str | None = Field(None, description="Request ID")
+    data: TaskStatusResponseData | None = Field(None)
+
+
+class OmniImageParamImage(BaseModel):
+    image: str = Field(...)
+
+
+class OmniProImageRequest(BaseModel):
+    model_name: str = Field(..., description="kling-image-o1")
+    resolution: str = Field(..., description="'1k' or '2k'")
+    aspect_ratio: str | None = Field(...)
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    n: int | None = Field(1, le=9)
+    image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
+
+
+class TextToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(..., description="kling-v2-6")
+    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")
+
+
+class ImageToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(..., description="kling-v2-6")
+    image: str = Field(...)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")

comfy_api_nodes/apis/veo_api.py

@@ -1,34 +1,21 @@
-from typing import Optional, Union
-from enum import Enum
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
 
-class Image2(BaseModel):
-    bytesBase64Encoded: str
-    gcsUri: Optional[str] = None
-    mimeType: Optional[str] = None
+class VeoRequestInstanceImage(BaseModel):
+    bytesBase64Encoded: str | None = Field(None)
+    gcsUri: str | None = Field(None)
+    mimeType: str | None = Field(None)
 
 
-class Image3(BaseModel):
-    bytesBase64Encoded: Optional[str] = None
-    gcsUri: str
-    mimeType: Optional[str] = None
-
-
-class Instance1(BaseModel):
-    image: Optional[Union[Image2, Image3]] = Field(
-        None, description='Optional image to guide video generation'
-    )
+class VeoRequestInstance(BaseModel):
+    image: VeoRequestInstanceImage | None = Field(None)
+    lastFrame: VeoRequestInstanceImage | None = Field(None)
     prompt: str = Field(..., description='Text description of the video')
 
 
-class PersonGeneration1(str, Enum):
-    ALLOW = 'ALLOW'
-    BLOCK = 'BLOCK'
-
-
-class Parameters1(BaseModel):
+class VeoRequestParameters(BaseModel):
     aspectRatio: Optional[str] = Field(None, examples=['16:9'])
     durationSeconds: Optional[int] = None
     enhancePrompt: Optional[bool] = None
@@ -37,17 +24,18 @@ class Parameters1(BaseModel):
         description='Generate audio for the video. Only supported by veo 3 models.',
     )
     negativePrompt: Optional[str] = None
-    personGeneration: Optional[PersonGeneration1] = None
+    personGeneration: str | None = Field(None, description="ALLOW or BLOCK")
     sampleCount: Optional[int] = None
     seed: Optional[int] = None
     storageUri: Optional[str] = Field(
         None, description='Optional Cloud Storage URI to upload the video'
     )
+    resolution: str | None = Field(None)
 
 
 class VeoGenVidRequest(BaseModel):
-    instances: Optional[list[Instance1]] = None
-    parameters: Optional[Parameters1] = None
+    instances: list[VeoRequestInstance] | None = Field(None)
+    parameters: VeoRequestParameters | None = Field(None)
 
 
 class VeoGenVidResponse(BaseModel):
@@ -97,7 +85,7 @@ class Response1(BaseModel):
     raiMediaFilteredReasons: Optional[list[str]] = Field(
         None, description='Reasons why media was filtered by responsible AI policies'
     )
-    videos: Optional[list[Video]] = None
+    videos: Optional[list[Video]] = Field(None)
 
 
 class VeoGenVidPollResponse(BaseModel):

comfy_api_nodes/nodes_bytedance.py

@@ -1,13 +1,27 @@
 import logging
 import math
-from enum import Enum
-from typing import Literal, Optional, Union
 
 import torch
-from pydantic import BaseModel, Field
 from typing_extensions import override
 
-from comfy_api.latest import IO, ComfyExtension
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.bytedance_api import (
+    RECOMMENDED_PRESETS,
+    RECOMMENDED_PRESETS_SEEDREAM_4,
+    VIDEO_TASKS_EXECUTION_TIME,
+    Image2ImageTaskCreationRequest,
+    Image2VideoTaskCreationRequest,
+    ImageTaskCreationResponse,
+    Seedream4Options,
+    Seedream4TaskCreationRequest,
+    TaskCreationResponse,
+    TaskImageContent,
+    TaskImageContentUrl,
+    TaskStatusResponse,
+    TaskTextContent,
+    Text2ImageTaskCreationRequest,
+    Text2VideoTaskCreationRequest,
+)
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
@@ -29,162 +43,6 @@ BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
 BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
 
 
-class Text2ImageModelName(str, Enum):
-    seedream_3 = "seedream-3-0-t2i-250415"
-
-
-class Image2ImageModelName(str, Enum):
-    seededit_3 = "seededit-3-0-i2i-250628"
-
-
-class Text2VideoModelName(str, Enum):
-    seedance_1_pro = "seedance-1-0-pro-250528"
-    seedance_1_lite = "seedance-1-0-lite-t2v-250428"
-
-
-class Image2VideoModelName(str, Enum):
-    """note(August 31): Pro model only supports FirstFrame: https://docs.byteplus.com/en/docs/ModelArk/1520757"""
-
-    seedance_1_pro = "seedance-1-0-pro-250528"
-    seedance_1_lite = "seedance-1-0-lite-i2v-250428"
-
-
-class Text2ImageTaskCreationRequest(BaseModel):
-    model: Text2ImageModelName = Text2ImageModelName.seedream_3
-    prompt: str = Field(...)
-    response_format: Optional[str] = Field("url")
-    size: Optional[str] = Field(None)
-    seed: Optional[int] = Field(0, ge=0, le=2147483647)
-    guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0)
-    watermark: Optional[bool] = Field(True)
-
-
-class Image2ImageTaskCreationRequest(BaseModel):
-    model: Image2ImageModelName = Image2ImageModelName.seededit_3
-    prompt: str = Field(...)
-    response_format: Optional[str] = Field("url")
-    image: str = Field(..., description="Base64 encoded string or image URL")
-    size: Optional[str] = Field("adaptive")
-    seed: Optional[int] = Field(..., ge=0, le=2147483647)
-    guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0)
-    watermark: Optional[bool] = Field(True)
-
-
-class Seedream4Options(BaseModel):
-    max_images: int = Field(15)
-
-
-class Seedream4TaskCreationRequest(BaseModel):
-    model: str = Field("seedream-4-0-250828")
-    prompt: str = Field(...)
-    response_format: str = Field("url")
-    image: Optional[list[str]] = Field(None, description="Image URLs")
-    size: str = Field(...)
-    seed: int = Field(..., ge=0, le=2147483647)
-    sequential_image_generation: str = Field("disabled")
-    sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
-    watermark: bool = Field(True)
-
-
-class ImageTaskCreationResponse(BaseModel):
-    model: str = Field(...)
-    created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")
-    data: list = Field([], description="Contains information about the generated image(s).")
-    error: dict = Field({}, description="Contains `code` and `message` fields in case of error.")
-
-
-class TaskTextContent(BaseModel):
-    type: str = Field("text")
-    text: str = Field(...)
-
-
-class TaskImageContentUrl(BaseModel):
-    url: str = Field(...)
-
-
-class TaskImageContent(BaseModel):
-    type: str = Field("image_url")
-    image_url: TaskImageContentUrl = Field(...)
-    role: Optional[Literal["first_frame", "last_frame", "reference_image"]] = Field(None)
-
-
-class Text2VideoTaskCreationRequest(BaseModel):
-    model: Text2VideoModelName = Text2VideoModelName.seedance_1_pro
-    content: list[TaskTextContent] = Field(..., min_length=1)
-
-
-class Image2VideoTaskCreationRequest(BaseModel):
-    model: Image2VideoModelName = Image2VideoModelName.seedance_1_pro
-    content: list[Union[TaskTextContent, TaskImageContent]] = Field(..., min_length=2)
-
-
-class TaskCreationResponse(BaseModel):
-    id: str = Field(...)
-
-
-class TaskStatusError(BaseModel):
-    code: str = Field(...)
-    message: str = Field(...)
-
-
-class TaskStatusResult(BaseModel):
-    video_url: str = Field(...)
-
-
-class TaskStatusResponse(BaseModel):
-    id: str = Field(...)
-    model: str = Field(...)
-    status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...)
-    error: Optional[TaskStatusError] = Field(None)
-    content: Optional[TaskStatusResult] = Field(None)
-
-
-RECOMMENDED_PRESETS = [
-    ("1024x1024 (1:1)", 1024, 1024),
-    ("864x1152 (3:4)", 864, 1152),
-    ("1152x864 (4:3)", 1152, 864),
-    ("1280x720 (16:9)", 1280, 720),
-    ("720x1280 (9:16)", 720, 1280),
-    ("832x1248 (2:3)", 832, 1248),
-    ("1248x832 (3:2)", 1248, 832),
-    ("1512x648 (21:9)", 1512, 648),
-    ("2048x2048 (1:1)", 2048, 2048),
-    ("Custom", None, None),
-]
-
-RECOMMENDED_PRESETS_SEEDREAM_4 = [
-    ("2048x2048 (1:1)", 2048, 2048),
-    ("2304x1728 (4:3)", 2304, 1728),
-    ("1728x2304 (3:4)", 1728, 2304),
-    ("2560x1440 (16:9)", 2560, 1440),
-    ("1440x2560 (9:16)", 1440, 2560),
-    ("2496x1664 (3:2)", 2496, 1664),
-    ("1664x2496 (2:3)", 1664, 2496),
-    ("3024x1296 (21:9)", 3024, 1296),
-    ("4096x4096 (1:1)", 4096, 4096),
-    ("Custom", None, None),
-]
-
-# The time in this dictionary are given for 10 seconds duration.
-VIDEO_TASKS_EXECUTION_TIME = {
-    "seedance-1-0-lite-t2v-250428": {
-        "480p": 40,
-        "720p": 60,
-        "1080p": 90,
-    },
-    "seedance-1-0-lite-i2v-250428": {
-        "480p": 40,
-        "720p": 60,
-        "1080p": 90,
-    },
-    "seedance-1-0-pro-250528": {
-        "480p": 70,
-        "720p": 85,
-        "1080p": 115,
-    },
-}
-
-
 def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
     if response.error:
         error_msg = f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}"
@@ -194,13 +52,6 @@ def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
     return response.data[0]["url"]
 
 
-def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
-    """Returns the video URL from the task status response if it exists."""
-    if hasattr(response, "content") and response.content:
-        return response.content.video_url
-    return None
-
-
 class ByteDanceImageNode(IO.ComfyNode):
 
     @classmethod
@@ -211,12 +62,7 @@ class ByteDanceImageNode(IO.ComfyNode):
             category="api node/image/ByteDance",
             description="Generate images using ByteDance models via api based on prompt",
             inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=Text2ImageModelName,
-                    default=Text2ImageModelName.seedream_3,
-                    tooltip="Model name",
-                ),
+                IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -335,12 +181,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
             category="api node/image/ByteDance",
             description="Edit images using ByteDance models via api based on prompt",
             inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=Image2ImageModelName,
-                    default=Image2ImageModelName.seededit_3,
-                    tooltip="Model name",
-                ),
+                IO.Combo.Input("model", options=["seededit-3-0-i2i-250628"]),
                 IO.Image.Input(
                     "image",
                     tooltip="The base image to edit",
@@ -394,7 +235,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
     async def execute(
         cls,
         model: str,
-        image: torch.Tensor,
+        image: Input.Image,
         prompt: str,
         seed: int,
         guidance_scale: float,
@@ -434,7 +275,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedream-4-0-250828"],
+                    options=["seedream-4-5-251128", "seedream-4-0-250828"],
                     tooltip="Model name",
                 ),
                 IO.String.Input(
@@ -459,7 +300,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     default=2048,
                     min=1024,
                     max=4096,
-                    step=64,
+                    step=8,
                     tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -468,7 +309,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     default=2048,
                     min=1024,
                     max=4096,
-                    step=64,
+                    step=8,
                     tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -532,7 +373,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         cls,
         model: str,
         prompt: str,
-        image: torch.Tensor = None,
+        image: Input.Image | None = None,
         size_preset: str = RECOMMENDED_PRESETS_SEEDREAM_4[0][0],
         width: int = 2048,
         height: int = 2048,
@@ -555,6 +396,18 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 raise ValueError(
                     f"Custom size out of range: {w}x{h}. " "Both width and height must be between 1024 and 4096 pixels."
                 )
+        out_num_pixels = w * h
+        mp_provided = out_num_pixels / 1_000_000.0
+        if "seedream-4-5" in model and out_num_pixels < 3686400:
+            raise ValueError(
+                f"Minimum image resolution that Seedream 4.5 can generate is 3.68MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
+        if "seedream-4-0" in model and out_num_pixels < 921600:
+            raise ValueError(
+                f"Minimum image resolution that the selected model can generate is 0.92MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
         n_input_images = get_number_of_images(image) if image is not None else 0
         if n_input_images > 10:
             raise ValueError(f"Maximum of 10 reference images are supported, but {n_input_images} received.")
@@ -607,9 +460,8 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=Text2VideoModelName,
-                    default=Text2VideoModelName.seedance_1_pro,
-                    tooltip="Model name",
+                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-t2v-250428", "seedance-1-0-pro-fast-251015"],
+                    default="seedance-1-0-pro-fast-251015",
                 ),
                 IO.String.Input(
                     "prompt",
@@ -714,9 +566,8 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=Image2VideoModelName,
-                    default=Image2VideoModelName.seedance_1_pro,
-                    tooltip="Model name",
+                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-t2v-250428", "seedance-1-0-pro-fast-251015"],
+                    default="seedance-1-0-pro-fast-251015",
                 ),
                 IO.String.Input(
                     "prompt",
@@ -787,7 +638,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
         cls,
         model: str,
         prompt: str,
-        image: torch.Tensor,
+        image: Input.Image,
         resolution: str,
         aspect_ratio: str,
         duration: int,
@@ -833,9 +684,8 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=[model.value for model in Image2VideoModelName],
-                    default=Image2VideoModelName.seedance_1_lite.value,
-                    tooltip="Model name",
+                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
+                    default="seedance-1-0-lite-i2v-250428",
                 ),
                 IO.String.Input(
                     "prompt",
@@ -910,8 +760,8 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
         cls,
         model: str,
         prompt: str,
-        first_frame: torch.Tensor,
-        last_frame: torch.Tensor,
+        first_frame: Input.Image,
+        last_frame: Input.Image,
         resolution: str,
         aspect_ratio: str,
         duration: int,
@@ -968,9 +818,8 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=[Image2VideoModelName.seedance_1_lite.value],
-                    default=Image2VideoModelName.seedance_1_lite.value,
-                    tooltip="Model name",
+                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
+                    default="seedance-1-0-lite-i2v-250428",
                 ),
                 IO.String.Input(
                     "prompt",
@@ -1034,7 +883,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         cls,
         model: str,
         prompt: str,
-        images: torch.Tensor,
+        images: Input.Image,
         resolution: str,
         aspect_ratio: str,
         duration: int,
@@ -1069,8 +918,8 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
 
 async def process_video_task(
     cls: type[IO.ComfyNode],
-    payload: Union[Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest],
-    estimated_duration: Optional[int],
+    payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest,
+    estimated_duration: int | None,
 ) -> IO.NodeOutput:
     initial_response = await sync_op(
         cls,
@@ -1085,7 +934,7 @@ async def process_video_task(
         estimated_duration=estimated_duration,
         response_model=TaskStatusResponse,
     )
-    return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(response)))
+    return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
 
 
 def raise_if_text_params(prompt: str, text_params: list[str]) -> None:

comfy_api_nodes/nodes_gemini.py

@@ -4,10 +4,7 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
 """
 
 import base64
-import json
 import os
-import time
-import uuid
 from enum import Enum
 from io import BytesIO
 from typing import Literal
@@ -16,10 +13,10 @@ import torch
 from typing_extensions import override
 
 import folder_paths
-from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api.util import VideoCodec, VideoContainer
+from comfy_api.latest import IO, ComfyExtension, Input, Types
 from comfy_api_nodes.apis.gemini_api import (
     GeminiContent,
+    GeminiFileData,
     GeminiGenerateContentRequest,
     GeminiGenerateContentResponse,
     GeminiImageConfig,
@@ -29,6 +26,8 @@ from comfy_api_nodes.apis.gemini_api import (
     GeminiMimeType,
     GeminiPart,
     GeminiRole,
+    GeminiSystemInstructionContent,
+    GeminiTextPart,
     Modality,
 )
 from comfy_api_nodes.util import (
@@ -38,13 +37,21 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
+    upload_images_to_comfyapi,
     validate_string,
     video_to_base64_string,
 )
-from server import PromptServer
 
 GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
 GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+GEMINI_IMAGE_SYS_PROMPT = (
+    "You are an expert image-generation engine. You must ALWAYS produce an image.\n"
+    "Interpret all user input—regardless of "
+    "format, intent, or abstraction—as literal visual directives for image composition.\n"
+    "If a prompt is conversational or lacks specific visual details, "
+    "you must creatively invent a concrete visual scenario that depicts the concept.\n"
+    "Prioritize generating the visual representation above any text, formatting, or conversational requests."
+)
 
 
 class GeminiModel(str, Enum):
@@ -68,24 +75,43 @@ class GeminiImageModel(str, Enum):
     gemini_2_5_flash_image = "gemini-2.5-flash-image"
 
 
-def create_image_parts(image_input: torch.Tensor) -> list[GeminiPart]:
-    """
-    Convert image tensor input to Gemini API compatible parts.
-
-    Args:
-        image_input: Batch of image tensors from ComfyUI.
-
-    Returns:
-        List of GeminiPart objects containing the encoded images.
-    """
+async def create_image_parts(
+    cls: type[IO.ComfyNode],
+    images: Input.Image,
+    image_limit: int = 0,
+) -> list[GeminiPart]:
     image_parts: list[GeminiPart] = []
-    for image_index in range(image_input.shape[0]):
-        image_as_b64 = tensor_to_base64_string(image_input[image_index].unsqueeze(0))
+    if image_limit < 0:
+        raise ValueError("image_limit must be greater than or equal to 0 when creating Gemini image parts.")
+    total_images = get_number_of_images(images)
+    if total_images <= 0:
+        raise ValueError("No images provided to create_image_parts; at least one image is required.")
+
+    # If image_limit == 0 --> use all images; otherwise clamp to image_limit.
+    effective_max = total_images if image_limit == 0 else min(total_images, image_limit)
+
+    # Number of images we'll send as URLs (fileData)
+    num_url_images = min(effective_max, 10)  # Vertex API max number of image links
+    reference_images_urls = await upload_images_to_comfyapi(
+        cls,
+        images,
+        max_images=num_url_images,
+    )
+    for reference_image_url in reference_images_urls:
+        image_parts.append(
+            GeminiPart(
+                fileData=GeminiFileData(
+                    mimeType=GeminiMimeType.image_png,
+                    fileUri=reference_image_url,
+                )
+            )
+        )
+    for idx in range(num_url_images, effective_max):
         image_parts.append(
             GeminiPart(
                 inlineData=GeminiInlineData(
                     mimeType=GeminiMimeType.image_png,
-                    data=image_as_b64,
+                    data=tensor_to_base64_string(images[idx]),
                 )
             )
         )
@@ -137,8 +163,8 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
     return "\n".join([part.text for part in parts])
 
 
-def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Tensor:
-    image_tensors: list[torch.Tensor] = []
+def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
+    image_tensors: list[Input.Image] = []
     parts = get_parts_by_type(response, "image/png")
     for part in parts:
         image_data = base64.b64decode(part.inlineData.data)
@@ -260,6 +286,13 @@ class GeminiNode(IO.ComfyNode):
                     tooltip="Optional file(s) to use as context for the model. "
                     "Accepts inputs from the Gemini Generate Content Input Files node.",
                 ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                ),
             ],
             outputs=[
                 IO.String.Output(),
@@ -276,7 +309,9 @@ class GeminiNode(IO.ComfyNode):
     def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]:
         """Convert video input to Gemini API compatible parts."""
 
-        base_64_string = video_to_base64_string(video_input, container_format=VideoContainer.MP4, codec=VideoCodec.H264)
+        base_64_string = video_to_base64_string(
+            video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+        )
         return [
             GeminiPart(
                 inlineData=GeminiInlineData(
@@ -326,10 +361,11 @@ class GeminiNode(IO.ComfyNode):
         prompt: str,
         model: str,
         seed: int,
-        images: torch.Tensor | None = None,
+        images: Input.Image | None = None,
         audio: Input.Audio | None = None,
         video: Input.Video | None = None,
         files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
@@ -338,8 +374,7 @@ class GeminiNode(IO.ComfyNode):
 
         # Add other modal parts
         if images is not None:
-            image_parts = create_image_parts(images)
-            parts.extend(image_parts)
+            parts.extend(await create_image_parts(cls, images))
         if audio is not None:
             parts.extend(cls.create_audio_parts(audio))
         if video is not None:
@@ -347,7 +382,10 @@ class GeminiNode(IO.ComfyNode):
         if files is not None:
             parts.extend(files)
 
-        # Create response
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
         response = await sync_op(
             cls,
             endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
@@ -357,36 +395,14 @@ class GeminiNode(IO.ComfyNode):
                         role=GeminiRole.user,
                         parts=parts,
                     )
-                ]
+                ],
+                systemInstruction=gemini_system_prompt,
             ),
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
 
         output_text = get_text_from_response(response)
-        if output_text:
-            # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button.
-            render_spec = {
-                "node_id": cls.hidden.unique_id,
-                "component": "ChatHistoryWidget",
-                "props": {
-                    "history": json.dumps(
-                        [
-                            {
-                                "prompt": prompt,
-                                "response": output_text,
-                                "response_id": str(uuid.uuid4()),
-                                "timestamp": time.time(),
-                            }
-                        ]
-                    ),
-                },
-            }
-            PromptServer.instance.send_sync(
-                "display_component",
-                render_spec,
-            )
-
         return IO.NodeOutput(output_text or "Empty response from Gemini model...")
 
 
@@ -530,6 +546,13 @@ class GeminiImage(IO.ComfyNode):
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
                     optional=True,
                 ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -549,10 +572,11 @@ class GeminiImage(IO.ComfyNode):
         prompt: str,
         model: str,
         seed: int,
-        images: torch.Tensor | None = None,
+        images: Input.Image | None = None,
         files: list[GeminiPart] | None = None,
         aspect_ratio: str = "auto",
         response_modalities: str = "IMAGE+TEXT",
+        system_prompt: str = "",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
@@ -562,11 +586,14 @@ class GeminiImage(IO.ComfyNode):
         image_config = GeminiImageConfig(aspectRatio=aspect_ratio)
 
         if images is not None:
-            image_parts = create_image_parts(images)
-            parts.extend(image_parts)
+            parts.extend(await create_image_parts(cls, images))
         if files is not None:
             parts.extend(files)
 
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
         response = await sync_op(
             cls,
             endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
@@ -578,34 +605,12 @@ class GeminiImage(IO.ComfyNode):
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
                     imageConfig=None if aspect_ratio == "auto" else image_config,
                 ),
+                systemInstruction=gemini_system_prompt,
             ),
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-
-        output_text = get_text_from_response(response)
-        if output_text:
-            render_spec = {
-                "node_id": cls.hidden.unique_id,
-                "component": "ChatHistoryWidget",
-                "props": {
-                    "history": json.dumps(
-                        [
-                            {
-                                "prompt": prompt,
-                                "response": output_text,
-                                "response_id": str(uuid.uuid4()),
-                                "timestamp": time.time(),
-                            }
-                        ]
-                    ),
-                },
-            }
-            PromptServer.instance.send_sync(
-                "display_component",
-                render_spec,
-            )
-        return IO.NodeOutput(get_image_from_response(response), output_text)
+        return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiImage2(IO.ComfyNode):
@@ -671,6 +676,13 @@ class GeminiImage2(IO.ComfyNode):
                     tooltip="Optional file(s) to use as context for the model. "
                     "Accepts inputs from the Gemini Generate Content Input Files node.",
                 ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -693,8 +705,9 @@ class GeminiImage2(IO.ComfyNode):
         aspect_ratio: str,
         resolution: str,
         response_modalities: str,
-        images: torch.Tensor | None = None,
+        images: Input.Image | None = None,
         files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
 
@@ -702,7 +715,7 @@ class GeminiImage2(IO.ComfyNode):
         if images is not None:
             if get_number_of_images(images) > 14:
                 raise ValueError("The current maximum number of supported images is 14.")
-            parts.extend(create_image_parts(images))
+            parts.extend(await create_image_parts(cls, images))
         if files is not None:
             parts.extend(files)
 
@@ -710,6 +723,10 @@ class GeminiImage2(IO.ComfyNode):
         if aspect_ratio != "auto":
             image_config.aspectRatio = aspect_ratio
 
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
         response = await sync_op(
             cls,
             ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
@@ -721,34 +738,12 @@ class GeminiImage2(IO.ComfyNode):
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
                     imageConfig=image_config,
                 ),
+                systemInstruction=gemini_system_prompt,
             ),
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-
-        output_text = get_text_from_response(response)
-        if output_text:
-            render_spec = {
-                "node_id": cls.hidden.unique_id,
-                "component": "ChatHistoryWidget",
-                "props": {
-                    "history": json.dumps(
-                        [
-                            {
-                                "prompt": prompt,
-                                "response": output_text,
-                                "response_id": str(uuid.uuid4()),
-                                "timestamp": time.time(),
-                            }
-                        ]
-                    ),
-                },
-            }
-            PromptServer.instance.send_sync(
-                "display_component",
-                render_spec,
-            )
-        return IO.NodeOutput(get_image_from_response(response), output_text)
+        return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiExtension(ComfyExtension):

comfy_api_nodes/nodes_kling.py

@@ -4,15 +4,14 @@ For source of truth on the allowed permutations of request fields, please refere
 - [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
 """
 
-from __future__ import annotations
-from typing import Optional, TypeVar
-import math
 import logging
-
-from typing_extensions import override
+import math
+import re
 
 import torch
+from typing_extensions import override
 
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
 from comfy_api_nodes.apis import (
     KlingCameraControl,
     KlingCameraConfig,
@@ -50,25 +49,35 @@ from comfy_api_nodes.apis import (
     KlingCharacterEffectModelName,
     KlingSingleImageEffectModelName,
 )
+from comfy_api_nodes.apis.kling_api import (
+    ImageToVideoWithAudioRequest,
+    OmniImageParamImage,
+    OmniParamImage,
+    OmniParamVideo,
+    OmniProFirstLastFrameRequest,
+    OmniProImageRequest,
+    OmniProReferences2VideoRequest,
+    OmniProText2VideoRequest,
+    TaskStatusResponse,
+    TextToVideoWithAudioRequest,
+)
 from comfy_api_nodes.util import (
-    validate_image_dimensions,
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
     validate_image_aspect_ratio,
+    validate_image_dimensions,
+    validate_string,
     validate_video_dimensions,
     validate_video_duration,
-    tensor_to_base64_string,
-    validate_string,
-    upload_audio_to_comfyapi,
-    download_url_to_image_tensor,
-    upload_video_to_comfyapi,
-    download_url_to_video_output,
-    sync_op,
-    ApiEndpoint,
-    poll_op,
 )
-from comfy_api.input_impl import VideoFromFile
-from comfy_api.input.basic_types import AudioInput
-from comfy_api.input.video_types import VideoInput
-from comfy_api.latest import ComfyExtension, IO
 
 KLING_API_VERSION = "v1"
 PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
@@ -94,8 +103,6 @@ AVERAGE_DURATION_IMAGE_GEN = 32
 AVERAGE_DURATION_VIDEO_EFFECTS = 320
 AVERAGE_DURATION_VIDEO_EXTEND = 320
 
-R = TypeVar("R")
-
 
 MODE_TEXT2VIDEO = {
     "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
@@ -130,6 +137,8 @@ MODE_START_END_FRAME = {
     "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"),
     "pro mode / 5s duration / kling-v2-1": ("pro", "5", "kling-v2-1"),
     "pro mode / 10s duration / kling-v2-1": ("pro", "10", "kling-v2-1"),
+    "pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"),
+    "pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"),
 }
 """
 Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
@@ -206,6 +215,50 @@ VOICES_CONFIG = {
 }
 
 
+def normalize_omni_prompt_references(prompt: str) -> str:
+    """
+    Rewrites Kling Omni-style placeholders used in the app, like:
+
+        @image, @image1, @image2, ... @imageN
+        @video, @video1, @video2, ... @videoN
+
+    into the API-compatible form:
+
+        <<<image_1>>>, <<<image_2>>>, ...
+        <<<video_1>>>, <<<video_2>>>, ...
+
+    This is a UX shim for ComfyUI so users can type the same syntax as in the Kling app.
+    """
+    if not prompt:
+        return prompt
+
+    def _image_repl(match):
+        return f"<<<image_{match.group('idx') or '1'}>>>"
+
+    def _video_repl(match):
+        return f"<<<video_{match.group('idx') or '1'}>>>"
+
+    # (?<!\w) avoids matching e.g. "test@image.com"
+    # (?!\w) makes sure we only match @image / @image<digits> and not @imageFoo
+    prompt = re.sub(r"(?<!\w)@image(?P<idx>\d*)(?!\w)", _image_repl, prompt)
+    return re.sub(r"(?<!\w)@video(?P<idx>\d*)(?!\w)", _video_repl, prompt)
+
+
+async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusResponse) -> IO.NodeOutput:
+    if response.code:
+        raise RuntimeError(
+            f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+        )
+    final_response = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
+        response_model=TaskStatusResponse,
+        status_extractor=lambda r: (r.data.task_status if r.data else None),
+        max_poll_attempts=160,
+    )
+    return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 def is_valid_camera_control_configs(configs: list[float]) -> bool:
     """Verifies that at least one camera control configuration is non-zero."""
     return any(not math.isclose(value, 0.0) for value in configs)
@@ -296,7 +349,7 @@ def get_video_from_response(response) -> KlingVideoResult:
     return video
 
 
-def get_video_url_from_response(response) -> Optional[str]:
+def get_video_url_from_response(response) -> str | None:
     """Returns the first video url from the Kling video generation task result.
     Will not raise an error if the response is not valid.
     """
@@ -315,7 +368,7 @@ def get_images_from_response(response) -> list[KlingImageResult]:
     return images
 
 
-def get_images_urls_from_response(response) -> Optional[str]:
+def get_images_urls_from_response(response) -> str | None:
     """Returns the list of image urls from the Kling image generation task result.
     Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
     """
@@ -349,7 +402,7 @@ async def execute_text2video(
     model_mode: str,
     duration: str,
     aspect_ratio: str,
-    camera_control: Optional[KlingCameraControl] = None,
+    camera_control: KlingCameraControl | None = None,
 ) -> IO.NodeOutput:
     validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
     task_creation_response = await sync_op(
@@ -394,8 +447,8 @@ async def execute_image2video(
     model_mode: str,
     aspect_ratio: str,
     duration: str,
-    camera_control: Optional[KlingCameraControl] = None,
-    end_frame: Optional[torch.Tensor] = None,
+    camera_control: KlingCameraControl | None = None,
+    end_frame: torch.Tensor | None = None,
 ) -> IO.NodeOutput:
     validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
     validate_input_image(start_frame)
@@ -432,12 +485,12 @@ async def execute_image2video(
     task_id = task_creation_response.data.task_id
 
     final_response = await poll_op(
-            cls,
-            ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
-            response_model=KlingImage2VideoResponse,
-            estimated_duration=AVERAGE_DURATION_I2V,
-            status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
-        )
+        cls,
+        ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
+        response_model=KlingImage2VideoResponse,
+        estimated_duration=AVERAGE_DURATION_I2V,
+        status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+    )
     validate_video_result_response(final_response)
 
     video = get_video_from_response(final_response)
@@ -451,9 +504,9 @@ async def execute_video_effect(
     model_name: str,
     duration: KlingVideoGenDuration,
     image_1: torch.Tensor,
-    image_2: Optional[torch.Tensor] = None,
-    model_mode: Optional[KlingVideoGenMode] = None,
-) -> tuple[VideoFromFile, str, str]:
+    image_2: torch.Tensor | None = None,
+    model_mode: KlingVideoGenMode | None = None,
+) -> tuple[InputImpl.VideoFromFile, str, str]:
     if dual_character:
         request_input_field = KlingDualCharacterEffectInput(
             model_name=model_name,
@@ -499,13 +552,13 @@ async def execute_video_effect(
 
 async def execute_lipsync(
     cls: type[IO.ComfyNode],
-    video: VideoInput,
-    audio: Optional[AudioInput] = None,
-    voice_language: Optional[str] = None,
-    model_mode: Optional[str] = None,
-    text: Optional[str] = None,
-    voice_speed: Optional[float] = None,
-    voice_id: Optional[str] = None,
+    video: Input.Video,
+    audio: Input.Audio | None = None,
+    voice_language: str | None = None,
+    model_mode: str | None = None,
+    text: str | None = None,
+    voice_speed: float | None = None,
+    voice_id: str | None = None,
 ) -> IO.NodeOutput:
     if text:
         validate_string(text, field_name="Text", max_length=MAX_PROMPT_LENGTH_LIP_SYNC)
@@ -740,6 +793,474 @@ class KlingTextToVideoNode(IO.ComfyNode):
         )
 
 
+class OmniProTextToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProTextToVideoNode",
+            display_name="Kling Omni Text to Video (Pro)",
+            category="api node/video/Kling",
+            description="Use text prompts to generate videos with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProText2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProFirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProFirstLastFrameNode",
+            display_name="Kling Omni First-Last-Frame to Video (Pro)",
+            category="api node/video/Kling",
+            description="Use a start frame, an optional end frame, or reference images with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("duration", options=["5", "10"]),
+                IO.Image.Input("first_frame"),
+                IO.Image.Input(
+                    "end_frame",
+                    optional=True,
+                    tooltip="An optional end frame for the video. "
+                    "This cannot be used simultaneously with 'reference_images'.",
+                ),
+                IO.Image.Input(
+                    "reference_images",
+                    optional=True,
+                    tooltip="Up to 6 additional reference images.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        duration: int,
+        first_frame: Input.Image,
+        end_frame: Input.Image | None = None,
+        reference_images: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        if end_frame is not None and reference_images is not None:
+            raise ValueError("The 'end_frame' input cannot be used simultaneously with 'reference_images'.")
+        validate_image_dimensions(first_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1))
+        image_list: list[OmniParamImage] = [
+            OmniParamImage(
+                image_url=(await upload_images_to_comfyapi(cls, first_frame, wait_label="Uploading first frame"))[0],
+                type="first_frame",
+            )
+        ]
+        if end_frame is not None:
+            validate_image_dimensions(end_frame, min_width=300, min_height=300)
+            validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1))
+            image_list.append(
+                OmniParamImage(
+                    image_url=(await upload_images_to_comfyapi(cls, end_frame, wait_label="Uploading end frame"))[0],
+                    type="end_frame",
+                )
+            )
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 6:
+                raise ValueError("The maximum number of reference images allowed is 6.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference frame(s)"):
+                image_list.append(OmniParamImage(image_url=i))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProFirstLastFrameRequest(
+                model_name=model_name,
+                prompt=prompt,
+                duration=str(duration),
+                image_list=image_list,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProImageToVideoNode",
+            display_name="Kling Omni Image to Video (Pro)",
+            category="api node/video/Kling",
+            description="Use up to 7 reference images to generate a video with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Int.Input("duration", default=3, min=3, max=10, display_mode=IO.NumberDisplay.slider),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 7 reference images.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+        reference_images: Input.Image,
+    ) -> IO.NodeOutput:
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        if get_number_of_images(reference_images) > 7:
+            raise ValueError("The maximum number of reference images is 7.")
+        for i in reference_images:
+            validate_image_dimensions(i, min_width=300, min_height=300)
+            validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+        image_list: list[OmniParamImage] = []
+        for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+            image_list.append(OmniParamImage(image_url=i))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                image_list=image_list,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProVideoToVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProVideoToVideoNode",
+            display_name="Kling Omni Video to Video (Pro)",
+            category="api node/video/Kling",
+            description="Use a video and up to 4 reference images to generate a video with the latest Kling model.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Int.Input("duration", default=3, min=3, max=10, display_mode=IO.NumberDisplay.slider),
+                IO.Video.Input("reference_video", tooltip="Video to use as a reference."),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 4 additional reference images.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        aspect_ratio: str,
+        duration: int,
+        reference_video: Input.Video,
+        keep_original_sound: bool,
+        reference_images: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_video_duration(reference_video, min_duration=3.0, max_duration=10.05)
+        validate_video_dimensions(reference_video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+        image_list: list[OmniParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 4:
+                raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniParamImage(image_url=i))
+        video_list = [
+            OmniParamVideo(
+                video_url=await upload_video_to_comfyapi(cls, reference_video, wait_label="Uploading reference video"),
+                refer_type="feature",
+                keep_original_sound="yes" if keep_original_sound else "no",
+            )
+        ]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                image_list=image_list if image_list else None,
+                video_list=video_list,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProEditVideoNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProEditVideoNode",
+            display_name="Kling Omni Edit Video (Pro)",
+            category="api node/video/Kling",
+            description="Edit an existing video with the latest model from Kling.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-video-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the video content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Video.Input("video", tooltip="Video for editing. The output video length will be the same."),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 4 additional reference images.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        video: Input.Video,
+        keep_original_sound: bool,
+        reference_images: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_video_duration(video, min_duration=3.0, max_duration=10.05)
+        validate_video_dimensions(video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+        image_list: list[OmniParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 4:
+                raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniParamImage(image_url=i))
+        video_list = [
+            OmniParamVideo(
+                video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading base video"),
+                refer_type="base",
+                keep_original_sound="yes" if keep_original_sound else "no",
+            )
+        ]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProReferences2VideoRequest(
+                model_name=model_name,
+                prompt=prompt,
+                aspect_ratio=None,
+                duration=None,
+                image_list=image_list if image_list else None,
+                video_list=video_list,
+            ),
+        )
+        return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingOmniProImageNode",
+            display_name="Kling Omni Image (Pro)",
+            category="api node/image/Kling",
+            description="Create or edit images with the latest model from Kling.",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-image-o1"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="A text prompt describing the image content. "
+                    "This can include both positive and negative descriptions.",
+                ),
+                IO.Combo.Input("resolution", options=["1K", "2K"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16", "1:1", "4:3", "3:4", "3:2", "2:3", "21:9"],
+                ),
+                IO.Image.Input(
+                    "reference_images",
+                    tooltip="Up to 10 additional reference images.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        reference_images: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        prompt = normalize_omni_prompt_references(prompt)
+        validate_string(prompt, min_length=1, max_length=2500)
+        image_list: list[OmniImageParamImage] = []
+        if reference_images is not None:
+            if get_number_of_images(reference_images) > 10:
+                raise ValueError("The maximum number of reference images is 10.")
+            for i in reference_images:
+                validate_image_dimensions(i, min_width=300, min_height=300)
+                validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+            for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+                image_list.append(OmniImageParamImage(image=i))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/images/omni-image", method="POST"),
+            response_model=TaskStatusResponse,
+            data=OmniProImageRequest(
+                model_name=model_name,
+                prompt=prompt,
+                resolution=resolution.lower(),
+                aspect_ratio=aspect_ratio,
+                image_list=image_list if image_list else None,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/images/omni-image/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.task_result.images[0].url))
+
+
 class KlingCameraControlT2VNode(IO.ComfyNode):
     """
     Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera.
@@ -787,7 +1308,7 @@ class KlingCameraControlT2VNode(IO.ComfyNode):
         negative_prompt: str,
         cfg_scale: float,
         aspect_ratio: str,
-        camera_control: Optional[KlingCameraControl] = None,
+        camera_control: KlingCameraControl | None = None,
     ) -> IO.NodeOutput:
         return await execute_text2video(
             cls,
@@ -809,9 +1330,8 @@ class KlingImage2VideoNode(IO.ComfyNode):
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="KlingImage2VideoNode",
-            display_name="Kling Image to Video",
+            display_name="Kling Image(First Frame) to Video",
             category="api node/video/Kling",
-            description="Kling Image to Video Node",
             inputs=[
                 IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -854,8 +1374,8 @@ class KlingImage2VideoNode(IO.ComfyNode):
         mode: str,
         aspect_ratio: str,
         duration: str,
-        camera_control: Optional[KlingCameraControl] = None,
-        end_frame: Optional[torch.Tensor] = None,
+        camera_control: KlingCameraControl | None = None,
+        end_frame: torch.Tensor | None = None,
     ) -> IO.NodeOutput:
         return await execute_image2video(
             cls,
@@ -965,15 +1485,11 @@ class KlingStartEndFrameNode(IO.ComfyNode):
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
                 IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
                 IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=[i.value for i in KlingVideoGenAspectRatio],
-                    default="16:9",
-                ),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input(
                     "mode",
                     options=modes,
-                    default=modes[2],
+                    default=modes[8],
                     tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
                 ),
             ],
@@ -1170,7 +1686,10 @@ class KlingSingleImageVideoEffectNode(IO.ComfyNode):
             category="api node/video/Kling",
             description="Achieve different special effects when generating a video based on the effect_scene.",
             inputs=[
-                IO.Image.Input("image", tooltip=" Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1"),
+                IO.Image.Input(
+                    "image",
+                    tooltip=" Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1",
+                ),
                 IO.Combo.Input(
                     "effect_scene",
                     options=[i.value for i in KlingSingleImageEffectsScene],
@@ -1254,8 +1773,8 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
     @classmethod
     async def execute(
         cls,
-        video: VideoInput,
-        audio: AudioInput,
+        video: Input.Video,
+        audio: Input.Audio,
         voice_language: str,
     ) -> IO.NodeOutput:
         return await execute_lipsync(
@@ -1314,7 +1833,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
     @classmethod
     async def execute(
         cls,
-        video: VideoInput,
+        video: Input.Video,
         text: str,
         voice: str,
         voice_speed: float,
@@ -1471,7 +1990,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
         human_fidelity: float,
         n: int,
         aspect_ratio: KlingImageGenAspectRatio,
-        image: Optional[torch.Tensor] = None,
+        image: torch.Tensor | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, field_name="prompt", min_length=1, max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
         validate_string(negative_prompt, field_name="negative_prompt", max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
@@ -1516,6 +2035,136 @@ class KlingImageGenerationNode(IO.ComfyNode):
         return IO.NodeOutput(await image_result_to_node_output(images))
 
 
+class TextToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingTextToVideoWithAudio",
+            display_name="Kling Text to Video with Audio",
+            category="api node/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        mode: str,
+        aspect_ratio: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/text2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=TextToVideoWithAudioRequest(
+                model_name=model_name,
+                prompt=prompt,
+                mode=mode,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/text2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class ImageToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingImageToVideoWithAudio",
+            display_name="Kling Image(First Frame) to Video with Audio",
+            category="api node/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.Image.Input("start_frame"),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        start_frame: Input.Image,
+        prompt: str,
+        mode: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=ImageToVideoWithAudioRequest(
+                model_name=model_name,
+                image=(await upload_images_to_comfyapi(cls, start_frame))[0],
+                prompt=prompt,
+                mode=mode,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 class KlingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1533,6 +2182,14 @@ class KlingExtension(ComfyExtension):
             KlingImageGenerationNode,
             KlingSingleImageVideoEffectNode,
             KlingDualCharacterVideoEffectNode,
+            OmniProTextToVideoNode,
+            OmniProFirstLastFrameNode,
+            OmniProImageToVideoNode,
+            OmniProVideoToVideoNode,
+            OmniProEditVideoNode,
+            OmniProImageNode,
+            TextToVideoWithAudio,
+            ImageToVideoWithAudio,
         ]
 
 

comfy_api_nodes/nodes_ltxv.py

@@ -1,12 +1,9 @@
 from io import BytesIO
-from typing import Optional
 
-import torch
 from pydantic import BaseModel, Field
 from typing_extensions import override
 
-from comfy_api.input_impl import VideoFromFile
-from comfy_api.latest import IO, ComfyExtension
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
 from comfy_api_nodes.util import (
     ApiEndpoint,
     get_number_of_images,
@@ -26,9 +23,9 @@ class ExecuteTaskRequest(BaseModel):
     model: str = Field(...)
     duration: int = Field(...)
     resolution: str = Field(...)
-    fps: Optional[int] = Field(25)
-    generate_audio: Optional[bool] = Field(True)
-    image_uri: Optional[str] = Field(None)
+    fps: int | None = Field(25)
+    generate_audio: bool | None = Field(True)
+    image_uri: str | None = Field(None)
 
 
 class TextToVideoNode(IO.ComfyNode):
@@ -103,7 +100,7 @@ class TextToVideoNode(IO.ComfyNode):
             as_binary=True,
             max_retries=1,
         )
-        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+        return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
 
 
 class ImageToVideoNode(IO.ComfyNode):
@@ -153,7 +150,7 @@ class ImageToVideoNode(IO.ComfyNode):
     @classmethod
     async def execute(
         cls,
-        image: torch.Tensor,
+        image: Input.Image,
         model: str,
         prompt: str,
         duration: int,
@@ -183,7 +180,7 @@ class ImageToVideoNode(IO.ComfyNode):
             as_binary=True,
             max_retries=1,
         )
-        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+        return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
 
 
 class LtxvApiExtension(ComfyExtension):

comfy_api_nodes/nodes_moonvalley.py

@@ -1,11 +1,8 @@
 import logging
-from typing import Optional
 
-import torch
 from typing_extensions import override
 
-from comfy_api.input import VideoInput
-from comfy_api.latest import IO, ComfyExtension
+from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis import (
     MoonvalleyPromptResponse,
     MoonvalleyTextToVideoInferenceParams,
@@ -61,7 +58,7 @@ def validate_task_creation_response(response) -> None:
         raise RuntimeError(error_msg)
 
 
-def validate_video_to_video_input(video: VideoInput) -> VideoInput:
+def validate_video_to_video_input(video: Input.Video) -> Input.Video:
     """
     Validates and processes video input for Moonvalley Video-to-Video generation.
 
@@ -82,7 +79,7 @@ def validate_video_to_video_input(video: VideoInput) -> VideoInput:
     return _validate_and_trim_duration(video)
 
 
-def _get_video_dimensions(video: VideoInput) -> tuple[int, int]:
+def _get_video_dimensions(video: Input.Video) -> tuple[int, int]:
     """Extracts video dimensions with error handling."""
     try:
         return video.get_dimensions()
@@ -106,7 +103,7 @@ def _validate_video_dimensions(width: int, height: int) -> None:
         raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}")
 
 
-def _validate_and_trim_duration(video: VideoInput) -> VideoInput:
+def _validate_and_trim_duration(video: Input.Video) -> Input.Video:
     """Validates video duration and trims to 5 seconds if needed."""
     duration = video.get_duration()
     _validate_minimum_duration(duration)
@@ -119,7 +116,7 @@ def _validate_minimum_duration(duration: float) -> None:
         raise ValueError("Input video must be at least 5 seconds long.")
 
 
-def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput:
+def _trim_if_too_long(video: Input.Video, duration: float) -> Input.Video:
     """Trims video to 5 seconds if longer."""
     if duration > 5:
         return trim_video(video, 5)
@@ -241,7 +238,7 @@ class MoonvalleyImg2VideoNode(IO.ComfyNode):
     @classmethod
     async def execute(
         cls,
-        image: torch.Tensor,
+        image: Input.Image,
         prompt: str,
         negative_prompt: str,
         resolution: str,
@@ -362,9 +359,9 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
         prompt: str,
         negative_prompt: str,
         seed: int,
-        video: Optional[VideoInput] = None,
+        video: Input.Video | None = None,
         control_type: str = "Motion Transfer",
-        motion_intensity: Optional[int] = 100,
+        motion_intensity: int | None = 100,
         steps=33,
         prompt_adherence=4.5,
     ) -> IO.NodeOutput:

comfy_api_nodes/nodes_openai.py

@@ -1,15 +1,10 @@
 from io import BytesIO
-from typing import Optional, Union
-import json
 import os
-import time
-import uuid
 from enum import Enum
 from inspect import cleandoc
 import numpy as np
 import torch
 from PIL import Image
-from server import PromptServer
 import folder_paths
 import base64
 from comfy_api.latest import IO, ComfyExtension
@@ -587,11 +582,11 @@ class OpenAIChatNode(IO.ComfyNode):
     def create_input_message_contents(
         cls,
         prompt: str,
-        image: Optional[torch.Tensor] = None,
-        files: Optional[list[InputFileContent]] = None,
+        image: torch.Tensor | None = None,
+        files: list[InputFileContent] | None = None,
     ) -> InputMessageContentList:
         """Create a list of input message contents from prompt and optional image."""
-        content_list: list[Union[InputContent, InputTextContent, InputImageContent, InputFileContent]] = [
+        content_list: list[InputContent | InputTextContent | InputImageContent | InputFileContent] = [
             InputTextContent(text=prompt, type="input_text"),
         ]
         if image is not None:
@@ -617,9 +612,9 @@ class OpenAIChatNode(IO.ComfyNode):
         prompt: str,
         persist_context: bool = False,
         model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value,
-        images: Optional[torch.Tensor] = None,
-        files: Optional[list[InputFileContent]] = None,
-        advanced_options: Optional[CreateModelResponseProperties] = None,
+        images: torch.Tensor | None = None,
+        files: list[InputFileContent] | None = None,
+        advanced_options: CreateModelResponseProperties | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
@@ -660,30 +655,7 @@ class OpenAIChatNode(IO.ComfyNode):
                 status_extractor=lambda response: response.status,
                 completed_statuses=["incomplete", "completed"]
             )
-        output_text = cls.get_text_from_message_content(cls.get_message_content_from_response(result_response))
-
-        # Update history
-        render_spec = {
-            "node_id": cls.hidden.unique_id,
-            "component": "ChatHistoryWidget",
-            "props": {
-                "history": json.dumps(
-                    [
-                        {
-                            "prompt": prompt,
-                            "response": output_text,
-                            "response_id": str(uuid.uuid4()),
-                            "timestamp": time.time(),
-                        }
-                    ]
-                ),
-            },
-        }
-        PromptServer.instance.send_sync(
-            "display_component",
-            render_spec,
-        )
-        return IO.NodeOutput(output_text)
+        return IO.NodeOutput(cls.get_text_from_message_content(cls.get_message_content_from_response(result_response)))
 
 
 class OpenAIInputFiles(IO.ComfyNode):
@@ -790,8 +762,8 @@ class OpenAIChatConfig(IO.ComfyNode):
     def execute(
         cls,
         truncation: bool,
-        instructions: Optional[str] = None,
-        max_output_tokens: Optional[int] = None,
+        instructions: str | None = None,
+        max_output_tokens: int | None = None,
     ) -> IO.NodeOutput:
         """
         Configure advanced options for the OpenAI Chat Node.

comfy_api_nodes/nodes_pika.py

@@ -92,6 +92,7 @@ class PikaImageToVideo(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -152,6 +153,7 @@ class PikaTextToVideoNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -239,6 +241,7 @@ class PikaScenes(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -323,6 +326,7 @@ class PikAdditionsNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -399,6 +403,7 @@ class PikaSwapsNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -466,6 +471,7 @@ class PikaffectsNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -515,6 +521,7 @@ class PikaStartEndFrameNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod

comfy_api_nodes/nodes_runway.py

@@ -11,12 +11,11 @@ User Guides:
 
 """
 
-from typing import Union, Optional
-from typing_extensions import override
 from enum import Enum
 
-import torch
+from typing_extensions import override
 
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
 from comfy_api_nodes.apis import (
     RunwayImageToVideoRequest,
     RunwayImageToVideoResponse,
@@ -44,8 +43,6 @@ from comfy_api_nodes.util import (
     sync_op,
     poll_op,
 )
-from comfy_api.input_impl import VideoFromFile
-from comfy_api.latest import ComfyExtension, IO
 
 PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
 PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
@@ -80,7 +77,7 @@ class RunwayGen3aAspectRatio(str, Enum):
     field_1280_768 = "1280:768"
 
 
-def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+def get_video_url_from_task_status(response: TaskStatusResponse) -> str | None:
     """Returns the video URL from the task status response if it exists."""
     if hasattr(response, "output") and len(response.output) > 0:
         return response.output[0]
@@ -89,13 +86,13 @@ def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, N
 
 def extract_progress_from_task_status(
     response: TaskStatusResponse,
-) -> Union[float, None]:
+) -> float | None:
     if hasattr(response, "progress") and response.progress is not None:
         return response.progress * 100
     return None
 
 
-def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None:
     """Returns the image URL from the task status response if it exists."""
     if hasattr(response, "output") and len(response.output) > 0:
         return response.output[0]
@@ -103,7 +100,7 @@ def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, N
 
 
 async def get_response(
-    cls: type[IO.ComfyNode], task_id: str, estimated_duration: Optional[int] = None
+    cls: type[IO.ComfyNode], task_id: str, estimated_duration: int | None = None
 ) -> TaskStatusResponse:
     """Poll the task status until it is finished then get the response."""
     return await poll_op(
@@ -119,8 +116,8 @@ async def get_response(
 async def generate_video(
     cls: type[IO.ComfyNode],
     request: RunwayImageToVideoRequest,
-    estimated_duration: Optional[int] = None,
-) -> VideoFromFile:
+    estimated_duration: int | None = None,
+) -> InputImpl.VideoFromFile:
     initial_response = await sync_op(
         cls,
         endpoint=ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
@@ -193,7 +190,7 @@ class RunwayImageToVideoNodeGen3a(IO.ComfyNode):
     async def execute(
         cls,
         prompt: str,
-        start_frame: torch.Tensor,
+        start_frame: Input.Image,
         duration: str,
         ratio: str,
         seed: int,
@@ -283,7 +280,7 @@ class RunwayImageToVideoNodeGen4(IO.ComfyNode):
     async def execute(
         cls,
         prompt: str,
-        start_frame: torch.Tensor,
+        start_frame: Input.Image,
         duration: str,
         ratio: str,
         seed: int,
@@ -381,8 +378,8 @@ class RunwayFirstLastFrameNode(IO.ComfyNode):
     async def execute(
         cls,
         prompt: str,
-        start_frame: torch.Tensor,
-        end_frame: torch.Tensor,
+        start_frame: Input.Image,
+        end_frame: Input.Image,
         duration: str,
         ratio: str,
         seed: int,
@@ -467,7 +464,7 @@ class RunwayTextToImageNode(IO.ComfyNode):
         cls,
         prompt: str,
         ratio: str,
-        reference_image: Optional[torch.Tensor] = None,
+        reference_image: Input.Image | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1)
 

comfy_api_nodes/nodes_veo2.py

@@ -3,13 +3,15 @@ from io import BytesIO
 
 from typing_extensions import override
 
-from comfy_api.input_impl.video_types import VideoFromFile
-from comfy_api.latest import IO, ComfyExtension
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
 from comfy_api_nodes.apis.veo_api import (
     VeoGenVidPollRequest,
     VeoGenVidPollResponse,
     VeoGenVidRequest,
     VeoGenVidResponse,
+    VeoRequestInstance,
+    VeoRequestInstanceImage,
+    VeoRequestParameters,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -228,7 +230,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
 
             # Check if video is provided as base64 or URL
             if hasattr(video, "bytesBase64Encoded") and video.bytesBase64Encoded:
-                return IO.NodeOutput(VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
 
             if hasattr(video, "gcsUri") and video.gcsUri:
                 return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
@@ -346,12 +348,163 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
         )
 
 
+class Veo3FirstLastFrameNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Veo3FirstLastFrameNode",
+            display_name="Google Veo 3 First-Last-Frame to Video",
+            category="api node/video/Veo",
+            description="Generate video using prompt and first and last frames.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the video",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid in the video",
+                ),
+                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["16:9", "9:16"],
+                    default="16:9",
+                    tooltip="Aspect ratio of the output video",
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=8,
+                    min=4,
+                    max=8,
+                    step=2,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Duration of the output video in seconds",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFF,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation",
+                ),
+                IO.Image.Input("first_frame", tooltip="Start frame"),
+                IO.Image.Input("last_frame", tooltip="End frame"),
+                IO.Combo.Input(
+                    "model",
+                    options=["veo-3.1-generate", "veo-3.1-fast-generate"],
+                    default="veo-3.1-fast-generate",
+                ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=True,
+                    tooltip="Generate audio for the video.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        resolution: str,
+        aspect_ratio: str,
+        duration: int,
+        seed: int,
+        first_frame: Input.Image,
+        last_frame: Input.Image,
+        model: str,
+        generate_audio: bool,
+    ):
+        model = MODELS_MAP[model]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"),
+            response_model=VeoGenVidResponse,
+            data=VeoGenVidRequest(
+                instances=[
+                    VeoRequestInstance(
+                        prompt=prompt,
+                        image=VeoRequestInstanceImage(
+                            bytesBase64Encoded=tensor_to_base64_string(first_frame), mimeType="image/png"
+                        ),
+                        lastFrame=VeoRequestInstanceImage(
+                            bytesBase64Encoded=tensor_to_base64_string(last_frame), mimeType="image/png"
+                        ),
+                    ),
+                ],
+                parameters=VeoRequestParameters(
+                    aspectRatio=aspect_ratio,
+                    personGeneration="ALLOW",
+                    durationSeconds=duration,
+                    enhancePrompt=True,  # cannot be False for Veo3
+                    seed=seed,
+                    generateAudio=generate_audio,
+                    negativePrompt=negative_prompt,
+                    resolution=resolution,
+                ),
+            ),
+        )
+        poll_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"),
+            response_model=VeoGenVidPollResponse,
+            status_extractor=lambda r: "completed" if r.done else "pending",
+            data=VeoGenVidPollRequest(
+                operationName=initial_response.name,
+            ),
+            poll_interval=5.0,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+        )
+
+        if poll_response.error:
+            raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})")
+
+        response = poll_response.response
+        filtered_count = response.raiMediaFilteredCount
+        if filtered_count:
+            reasons = response.raiMediaFilteredReasons or []
+            reason_part = f": {reasons[0]}" if reasons else ""
+            raise Exception(
+                f"Content blocked by Google's Responsible AI filters{reason_part} "
+                f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)."
+            )
+
+        if response.videos:
+            video = response.videos[0]
+            if video.bytesBase64Encoded:
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+            if video.gcsUri:
+                return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
+            raise Exception("Video returned but no data or URL was provided")
+        raise Exception("Video generation completed but no video was returned")
+
+
 class VeoExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             VeoVideoGenerationNode,
             Veo3VideoGenerationNode,
+            Veo3FirstLastFrameNode,
         ]
 
 

comfy_api_nodes/util/__init__.py

@@ -47,6 +47,7 @@ from .validation_utils import (
     validate_string,
     validate_video_dimensions,
     validate_video_duration,
+    validate_video_frame_count,
 )
 
 __all__ = [
@@ -94,6 +95,7 @@ __all__ = [
     "validate_string",
     "validate_video_dimensions",
     "validate_video_duration",
+    "validate_video_frame_count",
     # Misc functions
     "get_fs_object_size",
 ]

comfy_api_nodes/util/_helpers.py

@@ -2,8 +2,8 @@ import asyncio
 import contextlib
 import os
 import time
+from collections.abc import Callable
 from io import BytesIO
-from typing import Callable, Optional, Union
 
 from comfy.cli_args import args
 from comfy.model_management import processing_interrupted
@@ -35,12 +35,12 @@ def default_base_url() -> str:
 
 async def sleep_with_interrupt(
     seconds: float,
-    node_cls: Optional[type[IO.ComfyNode]],
-    label: Optional[str] = None,
-    start_ts: Optional[float] = None,
-    estimated_total: Optional[int] = None,
+    node_cls: type[IO.ComfyNode] | None,
+    label: str | None = None,
+    start_ts: float | None = None,
+    estimated_total: int | None = None,
     *,
-    display_callback: Optional[Callable[[type[IO.ComfyNode], str, int, Optional[int]], None]] = None,
+    display_callback: Callable[[type[IO.ComfyNode], str, int, int | None], None] | None = None,
 ):
     """
     Sleep in 1s slices while:
@@ -65,7 +65,7 @@ def mimetype_to_extension(mime_type: str) -> str:
     return mime_type.split("/")[-1].lower()
 
 
-def get_fs_object_size(path_or_object: Union[str, BytesIO]) -> int:
+def get_fs_object_size(path_or_object: str | BytesIO) -> int:
     if isinstance(path_or_object, str):
         return os.path.getsize(path_or_object)
     return len(path_or_object.getvalue())

comfy_api_nodes/util/client.py

@@ -4,10 +4,11 @@ import json
 import logging
 import time
 import uuid
+from collections.abc import Callable, Iterable
 from dataclasses import dataclass
 from enum import Enum
 from io import BytesIO
-from typing import Any, Callable, Iterable, Literal, Optional, Type, TypeVar, Union
+from typing import Any, Literal, TypeVar
 from urllib.parse import urljoin, urlparse
 
 import aiohttp
@@ -37,8 +38,8 @@ class ApiEndpoint:
         path: str,
         method: Literal["GET", "POST", "PUT", "DELETE", "PATCH"] = "GET",
         *,
-        query_params: Optional[dict[str, Any]] = None,
-        headers: Optional[dict[str, str]] = None,
+        query_params: dict[str, Any] | None = None,
+        headers: dict[str, str] | None = None,
     ):
         self.path = path
         self.method = method
@@ -52,18 +53,18 @@ class _RequestConfig:
     endpoint: ApiEndpoint
     timeout: float
     content_type: str
-    data: Optional[dict[str, Any]]
-    files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]]
-    multipart_parser: Optional[Callable]
+    data: dict[str, Any] | None
+    files: dict[str, Any] | list[tuple[str, Any]] | None
+    multipart_parser: Callable | None
     max_retries: int
     retry_delay: float
     retry_backoff: float
     wait_label: str = "Waiting"
     monitor_progress: bool = True
-    estimated_total: Optional[int] = None
-    final_label_on_success: Optional[str] = "Completed"
-    progress_origin_ts: Optional[float] = None
-    price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None
+    estimated_total: int | None = None
+    final_label_on_success: str | None = "Completed"
+    progress_origin_ts: float | None = None
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None
 
 
 @dataclass
@@ -71,10 +72,10 @@ class _PollUIState:
     started: float
     status_label: str = "Queued"
     is_queued: bool = True
-    price: Optional[float] = None
-    estimated_duration: Optional[int] = None
+    price: float | None = None
+    estimated_duration: int | None = None
     base_processing_elapsed: float = 0.0  # sum of completed active intervals
-    active_since: Optional[float] = None  # start time of current active interval (None if queued)
+    active_since: float | None = None  # start time of current active interval (None if queued)
 
 
 _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
@@ -87,20 +88,20 @@ async def sync_op(
     cls: type[IO.ComfyNode],
     endpoint: ApiEndpoint,
     *,
-    response_model: Type[M],
-    price_extractor: Optional[Callable[[M], Optional[float]]] = None,
-    data: Optional[BaseModel] = None,
-    files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None,
+    response_model: type[M],
+    price_extractor: Callable[[M | Any], float | None] | None = None,
+    data: BaseModel | None = None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None = None,
     content_type: str = "application/json",
     timeout: float = 3600.0,
-    multipart_parser: Optional[Callable] = None,
+    multipart_parser: Callable | None = None,
     max_retries: int = 3,
     retry_delay: float = 1.0,
     retry_backoff: float = 2.0,
     wait_label: str = "Waiting for server",
-    estimated_duration: Optional[int] = None,
-    final_label_on_success: Optional[str] = "Completed",
-    progress_origin_ts: Optional[float] = None,
+    estimated_duration: int | None = None,
+    final_label_on_success: str | None = "Completed",
+    progress_origin_ts: float | None = None,
     monitor_progress: bool = True,
 ) -> M:
     raw = await sync_op_raw(
@@ -131,22 +132,22 @@ async def poll_op(
     cls: type[IO.ComfyNode],
     poll_endpoint: ApiEndpoint,
     *,
-    response_model: Type[M],
-    status_extractor: Callable[[M], Optional[Union[str, int]]],
-    progress_extractor: Optional[Callable[[M], Optional[int]]] = None,
-    price_extractor: Optional[Callable[[M], Optional[float]]] = None,
-    completed_statuses: Optional[list[Union[str, int]]] = None,
-    failed_statuses: Optional[list[Union[str, int]]] = None,
-    queued_statuses: Optional[list[Union[str, int]]] = None,
-    data: Optional[BaseModel] = None,
+    response_model: type[M],
+    status_extractor: Callable[[M | Any], str | int | None],
+    progress_extractor: Callable[[M | Any], int | None] | None = None,
+    price_extractor: Callable[[M | Any], float | None] | None = None,
+    completed_statuses: list[str | int] | None = None,
+    failed_statuses: list[str | int] | None = None,
+    queued_statuses: list[str | int] | None = None,
+    data: BaseModel | None = None,
     poll_interval: float = 5.0,
     max_poll_attempts: int = 120,
     timeout_per_poll: float = 120.0,
     max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,
     retry_backoff_per_poll: float = 2.0,
-    estimated_duration: Optional[int] = None,
-    cancel_endpoint: Optional[ApiEndpoint] = None,
+    estimated_duration: int | None = None,
+    cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,
 ) -> M:
     raw = await poll_op_raw(
@@ -178,22 +179,22 @@ async def sync_op_raw(
     cls: type[IO.ComfyNode],
     endpoint: ApiEndpoint,
     *,
-    price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None,
-    data: Optional[Union[dict[str, Any], BaseModel]] = None,
-    files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None,
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+    data: dict[str, Any] | BaseModel | None = None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None = None,
     content_type: str = "application/json",
     timeout: float = 3600.0,
-    multipart_parser: Optional[Callable] = None,
+    multipart_parser: Callable | None = None,
     max_retries: int = 3,
     retry_delay: float = 1.0,
     retry_backoff: float = 2.0,
     wait_label: str = "Waiting for server",
-    estimated_duration: Optional[int] = None,
+    estimated_duration: int | None = None,
     as_binary: bool = False,
-    final_label_on_success: Optional[str] = "Completed",
-    progress_origin_ts: Optional[float] = None,
+    final_label_on_success: str | None = "Completed",
+    progress_origin_ts: float | None = None,
     monitor_progress: bool = True,
-) -> Union[dict[str, Any], bytes]:
+) -> dict[str, Any] | bytes:
     """
     Make a single network request.
       - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
@@ -229,21 +230,21 @@ async def poll_op_raw(
     cls: type[IO.ComfyNode],
     poll_endpoint: ApiEndpoint,
     *,
-    status_extractor: Callable[[dict[str, Any]], Optional[Union[str, int]]],
-    progress_extractor: Optional[Callable[[dict[str, Any]], Optional[int]]] = None,
-    price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None,
-    completed_statuses: Optional[list[Union[str, int]]] = None,
-    failed_statuses: Optional[list[Union[str, int]]] = None,
-    queued_statuses: Optional[list[Union[str, int]]] = None,
-    data: Optional[Union[dict[str, Any], BaseModel]] = None,
+    status_extractor: Callable[[dict[str, Any]], str | int | None],
+    progress_extractor: Callable[[dict[str, Any]], int | None] | None = None,
+    price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+    completed_statuses: list[str | int] | None = None,
+    failed_statuses: list[str | int] | None = None,
+    queued_statuses: list[str | int] | None = None,
+    data: dict[str, Any] | BaseModel | None = None,
     poll_interval: float = 5.0,
     max_poll_attempts: int = 120,
     timeout_per_poll: float = 120.0,
     max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,
     retry_backoff_per_poll: float = 2.0,
-    estimated_duration: Optional[int] = None,
-    cancel_endpoint: Optional[ApiEndpoint] = None,
+    estimated_duration: int | None = None,
+    cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,
 ) -> dict[str, Any]:
     """
@@ -261,7 +262,7 @@ async def poll_op_raw(
     consumed_attempts = 0  # counts only non-queued polls
 
     progress_bar = utils.ProgressBar(100) if progress_extractor else None
-    last_progress: Optional[int] = None
+    last_progress: int | None = None
 
     state = _PollUIState(started=started, estimated_duration=estimated_duration)
     stop_ticker = asyncio.Event()
@@ -420,10 +421,10 @@ async def poll_op_raw(
 
 def _display_text(
     node_cls: type[IO.ComfyNode],
-    text: Optional[str],
+    text: str | None,
     *,
-    status: Optional[Union[str, int]] = None,
-    price: Optional[float] = None,
+    status: str | int | None = None,
+    price: float | None = None,
 ) -> None:
     display_lines: list[str] = []
     if status:
@@ -440,13 +441,13 @@ def _display_text(
 
 def _display_time_progress(
     node_cls: type[IO.ComfyNode],
-    status: Optional[Union[str, int]],
+    status: str | int | None,
     elapsed_seconds: int,
-    estimated_total: Optional[int] = None,
+    estimated_total: int | None = None,
     *,
-    price: Optional[float] = None,
-    is_queued: Optional[bool] = None,
-    processing_elapsed_seconds: Optional[int] = None,
+    price: float | None = None,
+    is_queued: bool | None = None,
+    processing_elapsed_seconds: int | None = None,
 ) -> None:
     if estimated_total is not None and estimated_total > 0 and is_queued is False:
         pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds
@@ -488,7 +489,7 @@ def _unpack_tuple(t: tuple) -> tuple[str, Any, str]:
     raise ValueError("files tuple must be (filename, file[, content_type])")
 
 
-def _merge_params(endpoint_params: dict[str, Any], method: str, data: Optional[dict[str, Any]]) -> dict[str, Any]:
+def _merge_params(endpoint_params: dict[str, Any], method: str, data: dict[str, Any] | None) -> dict[str, Any]:
     params = dict(endpoint_params or {})
     if method.upper() == "GET" and data:
         for k, v in data.items():
@@ -534,9 +535,9 @@ def _generate_operation_id(method: str, path: str, attempt: int) -> str:
 def _snapshot_request_body_for_logging(
     content_type: str,
     method: str,
-    data: Optional[dict[str, Any]],
-    files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]],
-) -> Optional[Union[dict[str, Any], str]]:
+    data: dict[str, Any] | None,
+    files: dict[str, Any] | list[tuple[str, Any]] | None,
+) -> dict[str, Any] | str | None:
     if method.upper() == "GET":
         return None
     if content_type == "multipart/form-data":
@@ -586,13 +587,13 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
     attempt = 0
     delay = cfg.retry_delay
     operation_succeeded: bool = False
-    final_elapsed_seconds: Optional[int] = None
-    extracted_price: Optional[float] = None
+    final_elapsed_seconds: int | None = None
+    extracted_price: float | None = None
     while True:
         attempt += 1
         stop_event = asyncio.Event()
-        monitor_task: Optional[asyncio.Task] = None
-        sess: Optional[aiohttp.ClientSession] = None
+        monitor_task: asyncio.Task | None = None
+        sess: aiohttp.ClientSession | None = None
 
         operation_id = _generate_operation_id(method, cfg.endpoint.path, attempt)
         logging.debug("[DEBUG] HTTP %s %s (attempt %d)", method, url, attempt)
@@ -887,7 +888,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                 )
 
 
-def _validate_or_raise(response_model: Type[M], payload: Any) -> M:
+def _validate_or_raise(response_model: type[M], payload: Any) -> M:
     try:
         return response_model.model_validate(payload)
     except Exception as e:
@@ -902,9 +903,9 @@ def _validate_or_raise(response_model: Type[M], payload: Any) -> M:
 
 
 def _wrap_model_extractor(
-    response_model: Type[M],
-    extractor: Optional[Callable[[M], Any]],
-) -> Optional[Callable[[dict[str, Any]], Any]]:
+    response_model: type[M],
+    extractor: Callable[[M], Any] | None,
+) -> Callable[[dict[str, Any]], Any] | None:
     """Wrap a typed extractor so it can be used by the dict-based poller.
     Validates the dict into `response_model` before invoking `extractor`.
     Uses a small per-wrapper cache keyed by `id(dict)` to avoid re-validating
@@ -929,10 +930,10 @@ def _wrap_model_extractor(
     return _wrapped
 
 
-def _normalize_statuses(values: Optional[Iterable[Union[str, int]]]) -> set[Union[str, int]]:
+def _normalize_statuses(values: Iterable[str | int] | None) -> set[str | int]:
     if not values:
         return set()
-    out: set[Union[str, int]] = set()
+    out: set[str | int] = set()
     for v in values:
         nv = _normalize_status_value(v)
         if nv is not None:
@@ -940,7 +941,7 @@ def _normalize_statuses(values: Optional[Iterable[Union[str, int]]]) -> set[Unio
     return out
 
 
-def _normalize_status_value(val: Union[str, int, None]) -> Union[str, int, None]:
+def _normalize_status_value(val: str | int | None) -> str | int | None:
     if isinstance(val, str):
         return val.strip().lower()
     return val

comfy_api_nodes/util/conversions.py

@@ -4,7 +4,6 @@ import math
 import mimetypes
 import uuid
 from io import BytesIO
-from typing import Optional
 
 import av
 import numpy as np
@@ -12,8 +11,7 @@ import torch
 from PIL import Image
 
 from comfy.utils import common_upscale
-from comfy_api.latest import Input, InputImpl
-from comfy_api.util import VideoCodec, VideoContainer
+from comfy_api.latest import Input, InputImpl, Types
 
 from ._helpers import mimetype_to_extension
 
@@ -57,7 +55,7 @@ def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> to
 
 def tensor_to_bytesio(
     image: torch.Tensor,
-    name: Optional[str] = None,
+    name: str | None = None,
     total_pixels: int = 2048 * 2048,
     mime_type: str = "image/png",
 ) -> BytesIO:
@@ -177,8 +175,8 @@ def audio_to_base64_string(audio: Input.Audio, container_format: str = "mp4", co
 
 def video_to_base64_string(
     video: Input.Video,
-    container_format: VideoContainer = None,
-    codec: VideoCodec = None
+    container_format: Types.VideoContainer | None = None,
+    codec: Types.VideoCodec | None = None,
 ) -> str:
     """
     Converts a video input to a base64 string.
@@ -189,12 +187,11 @@ def video_to_base64_string(
         codec: Optional codec to use (defaults to video.codec if available)
     """
     video_bytes_io = BytesIO()
-
-    # Use provided format/codec if specified, otherwise use video's own if available
-    format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4)
-    codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264)
-
-    video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use)
+    video.save_to(
+        video_bytes_io,
+        format=container_format or getattr(video, "container", Types.VideoContainer.MP4),
+        codec=codec or getattr(video, "codec", Types.VideoCodec.H264),
+    )
     video_bytes_io.seek(0)
     return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
 

comfy_api_nodes/util/download_helpers.py

@@ -3,15 +3,15 @@ import contextlib
 import uuid
 from io import BytesIO
 from pathlib import Path
-from typing import IO, Optional, Union
+from typing import IO
 from urllib.parse import urljoin, urlparse
 
 import aiohttp
 import torch
 from aiohttp.client_exceptions import ClientError, ContentTypeError
 
-from comfy_api.input_impl import VideoFromFile
 from comfy_api.latest import IO as COMFY_IO
+from comfy_api.latest import InputImpl
 
 from . import request_logger
 from ._helpers import (
@@ -29,9 +29,9 @@ _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
 
 async def download_url_to_bytesio(
     url: str,
-    dest: Optional[Union[BytesIO, IO[bytes], str, Path]],
+    dest: BytesIO | IO[bytes] | str | Path | None,
     *,
-    timeout: Optional[float] = None,
+    timeout: float | None = None,
     max_retries: int = 5,
     retry_delay: float = 1.0,
     retry_backoff: float = 2.0,
@@ -71,10 +71,10 @@ async def download_url_to_bytesio(
 
         is_path_sink = isinstance(dest, (str, Path))
         fhandle = None
-        session: Optional[aiohttp.ClientSession] = None
-        stop_evt: Optional[asyncio.Event] = None
-        monitor_task: Optional[asyncio.Task] = None
-        req_task: Optional[asyncio.Task] = None
+        session: aiohttp.ClientSession | None = None
+        stop_evt: asyncio.Event | None = None
+        monitor_task: asyncio.Task | None = None
+        req_task: asyncio.Task | None = None
 
         try:
             with contextlib.suppress(Exception):
@@ -234,11 +234,11 @@ async def download_url_to_video_output(
     timeout: float = None,
     max_retries: int = 5,
     cls: type[COMFY_IO.ComfyNode] = None,
-) -> VideoFromFile:
+) -> InputImpl.VideoFromFile:
     """Downloads a video from a URL and returns a `VIDEO` output."""
     result = BytesIO()
     await download_url_to_bytesio(video_url, result, timeout=timeout, max_retries=max_retries, cls=cls)
-    return VideoFromFile(result)
+    return InputImpl.VideoFromFile(result)
 
 
 async def download_url_as_bytesio(

comfy_api_nodes/util/request_logger.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import datetime
 import hashlib
 import json

comfy_api_nodes/util/upload_helpers.py

@@ -4,15 +4,13 @@ import logging
 import time
 import uuid
 from io import BytesIO
-from typing import Optional, Union
 from urllib.parse import urlparse
 
 import aiohttp
 import torch
 from pydantic import BaseModel, Field
 
-from comfy_api.latest import IO, Input
-from comfy_api.util import VideoCodec, VideoContainer
+from comfy_api.latest import IO, Input, Types
 
 from . import request_logger
 from ._helpers import is_processing_interrupted, sleep_with_interrupt
@@ -32,7 +30,7 @@ from .conversions import (
 
 class UploadRequest(BaseModel):
     file_name: str = Field(..., description="Filename to upload")
-    content_type: Optional[str] = Field(
+    content_type: str | None = Field(
         None,
         description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
     )
@@ -48,22 +46,30 @@ async def upload_images_to_comfyapi(
     image: torch.Tensor,
     *,
     max_images: int = 8,
-    mime_type: Optional[str] = None,
-    wait_label: Optional[str] = "Uploading",
+    mime_type: str | None = None,
+    wait_label: str | None = "Uploading",
+    show_batch_index: bool = True,
 ) -> list[str]:
     """
     Uploads images to ComfyUI API and returns download URLs.
     To upload multiple images, stack them in the batch dimension first.
     """
-    # if batch, try to upload each file if max_images is greater than 0
+    # if batched, try to upload each file if max_images is greater than 0
     download_urls: list[str] = []
     is_batch = len(image.shape) > 3
     batch_len = image.shape[0] if is_batch else 1
+    num_to_upload = min(batch_len, max_images)
+    batch_start_ts = time.monotonic()
 
-    for idx in range(min(batch_len, max_images)):
+    for idx in range(num_to_upload):
         tensor = image[idx] if is_batch else image
         img_io = tensor_to_bytesio(tensor, mime_type=mime_type)
-        url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, wait_label)
+
+        effective_label = wait_label
+        if wait_label and show_batch_index and num_to_upload > 1:
+            effective_label = f"{wait_label} ({idx + 1}/{num_to_upload})"
+
+        url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, effective_label, batch_start_ts)
         download_urls.append(url)
     return download_urls
 
@@ -92,9 +98,10 @@ async def upload_video_to_comfyapi(
     cls: type[IO.ComfyNode],
     video: Input.Video,
     *,
-    container: VideoContainer = VideoContainer.MP4,
-    codec: VideoCodec = VideoCodec.H264,
-    max_duration: Optional[int] = None,
+    container: Types.VideoContainer = Types.VideoContainer.MP4,
+    codec: Types.VideoCodec = Types.VideoCodec.H264,
+    max_duration: int | None = None,
+    wait_label: str | None = "Uploading",
 ) -> str:
     """
     Uploads a single video to ComfyUI API and returns its download URL.
@@ -119,15 +126,16 @@ async def upload_video_to_comfyapi(
     video.save_to(video_bytes_io, format=container, codec=codec)
     video_bytes_io.seek(0)
 
-    return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type)
+    return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
 
 
 async def upload_file_to_comfyapi(
     cls: type[IO.ComfyNode],
     file_bytes_io: BytesIO,
     filename: str,
-    upload_mime_type: Optional[str],
-    wait_label: Optional[str] = "Uploading",
+    upload_mime_type: str | None,
+    wait_label: str | None = "Uploading",
+    progress_origin_ts: float | None = None,
 ) -> str:
     """Uploads a single file to ComfyUI API and returns its download URL."""
     if upload_mime_type is None:
@@ -148,6 +156,7 @@ async def upload_file_to_comfyapi(
         file_bytes_io,
         content_type=upload_mime_type,
         wait_label=wait_label,
+        progress_origin_ts=progress_origin_ts,
     )
     return create_resp.download_url
 
@@ -155,27 +164,18 @@ async def upload_file_to_comfyapi(
 async def upload_file(
     cls: type[IO.ComfyNode],
     upload_url: str,
-    file: Union[BytesIO, str],
+    file: BytesIO | str,
     *,
-    content_type: Optional[str] = None,
+    content_type: str | None = None,
     max_retries: int = 3,
     retry_delay: float = 1.0,
     retry_backoff: float = 2.0,
-    wait_label: Optional[str] = None,
+    wait_label: str | None = None,
+    progress_origin_ts: float | None = None,
 ) -> None:
     """
     Upload a file to a signed URL (e.g., S3 pre-signed PUT) with retries, Comfy progress display, and interruption.
 
-    Args:
-        cls: Node class (provides auth context + UI progress hooks).
-        upload_url: Pre-signed PUT URL.
-        file: BytesIO or path string.
-        content_type: Explicit MIME type. If None, we *suppress* Content-Type.
-        max_retries: Maximum retry attempts.
-        retry_delay: Initial delay in seconds.
-        retry_backoff: Exponential backoff factor.
-        wait_label: Progress label shown in Comfy UI.
-
     Raises:
         ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception
     """
@@ -198,7 +198,7 @@ async def upload_file(
 
     attempt = 0
     delay = retry_delay
-    start_ts = time.monotonic()
+    start_ts = progress_origin_ts if progress_origin_ts is not None else time.monotonic()
     op_uuid = uuid.uuid4().hex[:8]
     while True:
         attempt += 1
@@ -218,7 +218,7 @@ async def upload_file(
                 return
 
         monitor_task = asyncio.create_task(_monitor())
-        sess: Optional[aiohttp.ClientSession] = None
+        sess: aiohttp.ClientSession | None = None
         try:
             try:
                 request_logger.log_request_response(

comfy_api_nodes/util/validation_utils.py

@@ -1,9 +1,7 @@
 import logging
-from typing import Optional
 
 import torch
 
-from comfy_api.input.video_types import VideoInput
 from comfy_api.latest import Input
 
 
@@ -18,10 +16,10 @@ def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
 
 def validate_image_dimensions(
     image: torch.Tensor,
-    min_width: Optional[int] = None,
-    max_width: Optional[int] = None,
-    min_height: Optional[int] = None,
-    max_height: Optional[int] = None,
+    min_width: int | None = None,
+    max_width: int | None = None,
+    min_height: int | None = None,
+    max_height: int | None = None,
 ):
     height, width = get_image_dimensions(image)
 
@@ -37,8 +35,8 @@ def validate_image_dimensions(
 
 def validate_image_aspect_ratio(
     image: torch.Tensor,
-    min_ratio: Optional[tuple[float, float]] = None,  # e.g. (1, 4)
-    max_ratio: Optional[tuple[float, float]] = None,  # e.g. (4, 1)
+    min_ratio: tuple[float, float] | None = None,  # e.g. (1, 4)
+    max_ratio: tuple[float, float] | None = None,  # e.g. (4, 1)
     *,
     strict: bool = True,  # True -> (min, max); False -> [min, max]
 ) -> float:
@@ -54,8 +52,8 @@ def validate_image_aspect_ratio(
 def validate_images_aspect_ratio_closeness(
     first_image: torch.Tensor,
     second_image: torch.Tensor,
-    min_rel: float,   # e.g. 0.8
-    max_rel: float,   # e.g. 1.25
+    min_rel: float,  # e.g. 0.8
+    max_rel: float,  # e.g. 1.25
     *,
     strict: bool = False,  # True -> (min, max); False -> [min, max]
 ) -> float:
@@ -84,8 +82,8 @@ def validate_images_aspect_ratio_closeness(
 
 def validate_aspect_ratio_string(
     aspect_ratio: str,
-    min_ratio: Optional[tuple[float, float]] = None,  # e.g. (1, 4)
-    max_ratio: Optional[tuple[float, float]] = None,  # e.g. (4, 1)
+    min_ratio: tuple[float, float] | None = None,  # e.g. (1, 4)
+    max_ratio: tuple[float, float] | None = None,  # e.g. (4, 1)
     *,
     strict: bool = False,  # True -> (min, max); False -> [min, max]
 ) -> float:
@@ -97,10 +95,10 @@ def validate_aspect_ratio_string(
 
 def validate_video_dimensions(
     video: Input.Video,
-    min_width: Optional[int] = None,
-    max_width: Optional[int] = None,
-    min_height: Optional[int] = None,
-    max_height: Optional[int] = None,
+    min_width: int | None = None,
+    max_width: int | None = None,
+    min_height: int | None = None,
+    max_height: int | None = None,
 ):
     try:
         width, height = video.get_dimensions()
@@ -120,8 +118,8 @@ def validate_video_dimensions(
 
 def validate_video_duration(
     video: Input.Video,
-    min_duration: Optional[float] = None,
-    max_duration: Optional[float] = None,
+    min_duration: float | None = None,
+    max_duration: float | None = None,
 ):
     try:
         duration = video.get_duration()
@@ -136,6 +134,23 @@ def validate_video_duration(
         raise ValueError(f"Video duration must be at most {max_duration}s, got {duration}s")
 
 
+def validate_video_frame_count(
+    video: Input.Video,
+    min_frame_count: int | None = None,
+    max_frame_count: int | None = None,
+):
+    try:
+        frame_count = video.get_frame_count()
+    except Exception as e:
+        logging.error("Error getting frame count of video: %s", e)
+        return
+
+    if min_frame_count is not None and min_frame_count > frame_count:
+        raise ValueError(f"Video frame count must be at least {min_frame_count}, got {frame_count}")
+    if max_frame_count is not None and frame_count > max_frame_count:
+        raise ValueError(f"Video frame count must be at most {max_frame_count}, got {frame_count}")
+
+
 def get_number_of_images(images):
     if isinstance(images, torch.Tensor):
         return images.shape[0] if images.ndim >= 4 else 1
@@ -144,8 +159,8 @@ def get_number_of_images(images):
 
 def validate_audio_duration(
     audio: Input.Audio,
-    min_duration: Optional[float] = None,
-    max_duration: Optional[float] = None,
+    min_duration: float | None = None,
+    max_duration: float | None = None,
 ) -> None:
     sr = int(audio["sample_rate"])
     dur = int(audio["waveform"].shape[-1]) / sr
@@ -177,7 +192,7 @@ def validate_string(
         )
 
 
-def validate_container_format_is_mp4(video: VideoInput) -> None:
+def validate_container_format_is_mp4(video: Input.Video) -> None:
     """Validates video container format is MP4."""
     container_format = video.get_container_format()
     if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
@@ -194,8 +209,8 @@ def _ratio_from_tuple(r: tuple[float, float]) -> float:
 def _assert_ratio_bounds(
     ar: float,
     *,
-    min_ratio: Optional[tuple[float, float]] = None,
-    max_ratio: Optional[tuple[float, float]] = None,
+    min_ratio: tuple[float, float] | None = None,
+    max_ratio: tuple[float, float] | None = None,
     strict: bool = True,
 ) -> None:
     """Validate a numeric aspect ratio against optional min/max ratio bounds."""

comfy_execution/validation.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+from comfy_api.latest import IO
 
 
 def validate_node_input(
@@ -23,6 +24,11 @@ def validate_node_input(
     if not received_type != input_type:
         return True
 
+    # If the received type or input_type is a MatchType, we can return True immediately;
+    # validation for this is handled by the frontend
+    if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
+        return True
+
     # Not equal, and not strings
     if not isinstance(received_type, str) or not isinstance(input_type, str):
         return False

comfy_extras/nodes_audio.py

@@ -6,65 +6,80 @@ import torch
 import comfy.model_management
 import folder_paths
 import os
-import io
-import json
-import random
 import hashlib
 import node_helpers
 import logging
-from comfy.cli_args import args
-from comfy.comfy_types import FileLocator
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
 
-class EmptyLatentAudio:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
+class EmptyLatentAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyLatentAudio",
+            display_name="Empty Latent Audio",
+            category="latent/audio",
+            inputs=[
+                IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
+                IO.Int.Input(
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                ),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        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, batch_size):
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
         length = round((seconds * 44100 / 2048) / 2) * 2
-        latent = torch.zeros([batch_size, 64, length], device=self.device)
-        return ({"samples":latent, "type": "audio"}, )
+        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+        return IO.NodeOutput({"samples":latent, "type": "audio"})
 
-class ConditioningStableAudio:
+    generate = execute  # TODO: remove
+
+
+class ConditioningStableAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "seconds_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
-                             "seconds_total": ("FLOAT", {"default": 47.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
-                             }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ConditioningStableAudio",
+            category="conditioning",
+            inputs=[
+                IO.Conditioning.Input("positive"),
+                IO.Conditioning.Input("negative"),
+                IO.Float.Input("seconds_start", default=0.0, min=0.0, max=1000.0, step=0.1),
+                IO.Float.Input("seconds_total", default=47.0, min=0.0, max=1000.0, step=0.1),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
-    RETURN_NAMES = ("positive", "negative")
-
-    FUNCTION = "append"
-
-    CATEGORY = "conditioning"
-
-    def append(self, positive, negative, seconds_start, seconds_total):
+    @classmethod
+    def execute(cls, positive, negative, seconds_start, seconds_total) -> IO.NodeOutput:
         positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total})
         negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total})
-        return (positive, negative)
+        return IO.NodeOutput(positive, negative)
 
-class VAEEncodeAudio:
+    append = execute  # TODO: remove
+
+
+class VAEEncodeAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEEncodeAudio",
+            display_name="VAE Encode Audio",
+            category="latent/audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
 
-    CATEGORY = "latent/audio"
-
-    def encode(self, vae, audio):
+    @classmethod
+    def execute(cls, vae, audio) -> IO.NodeOutput:
         sample_rate = audio["sample_rate"]
         if 44100 != sample_rate:
             waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
@@ -72,213 +87,134 @@ class VAEEncodeAudio:
             waveform = audio["waveform"]
 
         t = vae.encode(waveform.movedim(1, -1))
-        return ({"samples":t}, )
+        return IO.NodeOutput({"samples":t})
 
-class VAEDecodeAudio:
+    encode = execute  # TODO: remove
+
+
+class VAEDecodeAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
-    RETURN_TYPES = ("AUDIO",)
-    FUNCTION = "decode"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeAudio",
+            display_name="VAE Decode Audio",
+            category="latent/audio",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    CATEGORY = "latent/audio"
-
-    def decode(self, vae, samples):
+    @classmethod
+    def execute(cls, vae, samples) -> IO.NodeOutput:
         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}, )
+        return IO.NodeOutput({"waveform": audio, "sample_rate": 44100})
+
+    decode = execute  # TODO: remove
 
 
-def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"):
-
-    filename_prefix += self.prefix_append
-    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-    results: list[FileLocator] = []
-
-    # Prepare metadata dictionary
-    metadata = {}
-    if not args.disable_metadata:
-        if prompt is not None:
-            metadata["prompt"] = json.dumps(prompt)
-        if extra_pnginfo is not None:
-            for x in extra_pnginfo:
-                metadata[x] = json.dumps(extra_pnginfo[x])
-
-    # Opus supported sample rates
-    OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
-
-    for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
-        filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
-        file = f"{filename_with_batch_num}_{counter:05}_.{format}"
-        output_path = os.path.join(full_output_folder, file)
-
-        # Use original sample rate initially
-        sample_rate = audio["sample_rate"]
-
-        # Handle Opus sample rate requirements
-        if format == "opus":
-            if sample_rate > 48000:
-                sample_rate = 48000
-            elif sample_rate not in OPUS_RATES:
-                # Find the next highest supported rate
-                for rate in sorted(OPUS_RATES):
-                    if rate > sample_rate:
-                        sample_rate = rate
-                        break
-                if sample_rate not in OPUS_RATES:  # Fallback if still not supported
-                    sample_rate = 48000
-
-            # Resample if necessary
-            if sample_rate != audio["sample_rate"]:
-                waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
-
-        # Create output with specified format
-        output_buffer = io.BytesIO()
-        output_container = av.open(output_buffer, mode='w', format=format)
-
-        # Set metadata on the container
-        for key, value in metadata.items():
-            output_container.metadata[key] = value
-
-        layout = 'mono' if waveform.shape[0] == 1 else 'stereo'
-        # Set up the output stream with appropriate properties
-        if format == "opus":
-            out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
-            if quality == "64k":
-                out_stream.bit_rate = 64000
-            elif quality == "96k":
-                out_stream.bit_rate = 96000
-            elif quality == "128k":
-                out_stream.bit_rate = 128000
-            elif quality == "192k":
-                out_stream.bit_rate = 192000
-            elif quality == "320k":
-                out_stream.bit_rate = 320000
-        elif format == "mp3":
-            out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
-            if quality == "V0":
-                #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
-                out_stream.codec_context.qscale = 1
-            elif quality == "128k":
-                out_stream.bit_rate = 128000
-            elif quality == "320k":
-                out_stream.bit_rate = 320000
-        else: #format == "flac":
-            out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
-
-        frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout=layout)
-        frame.sample_rate = sample_rate
-        frame.pts = 0
-        output_container.mux(out_stream.encode(frame))
-
-        # Flush encoder
-        output_container.mux(out_stream.encode(None))
-
-        # Close containers
-        output_container.close()
-
-        # Write the output to file
-        output_buffer.seek(0)
-        with open(output_path, 'wb') as f:
-            f.write(output_buffer.getbuffer())
-
-        results.append({
-            "filename": file,
-            "subfolder": subfolder,
-            "type": self.type
-        })
-        counter += 1
-
-    return { "ui": { "audio": results } }
-
-class SaveAudio:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
+class SaveAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudio",
+            display_name="Save Audio (FLAC)",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio": ("AUDIO", ),
-                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
-                            },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
+    def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> IO.NodeOutput:
+        return IO.NodeOutput(
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format)
+        )
 
-    RETURN_TYPES = ()
-    FUNCTION = "save_flac"
+    save_flac = execute  # TODO: remove
 
-    OUTPUT_NODE = True
 
-    CATEGORY = "audio"
-
-    def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None):
-        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo)
-
-class SaveAudioMP3:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
+class SaveAudioMP3(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioMP3",
+            display_name="Save Audio (MP3)",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+                IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio": ("AUDIO", ),
-                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
-                            "quality": (["V0", "128k", "320k"], {"default": "V0"}),
-                            },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
+    def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="128k") -> IO.NodeOutput:
+        return IO.NodeOutput(
+            ui=UI.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
 
-    RETURN_TYPES = ()
-    FUNCTION = "save_mp3"
+    save_mp3 = execute  # TODO: remove
 
-    OUTPUT_NODE = True
 
-    CATEGORY = "audio"
-
-    def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"):
-        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
-
-class SaveAudioOpus:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
+class SaveAudioOpus(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioOpus",
+            display_name="Save Audio (Opus)",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+                IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio": ("AUDIO", ),
-                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
-                            "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}),
-                            },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
+    def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="V3") -> IO.NodeOutput:
+        return IO.NodeOutput(
+            ui=UI.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
 
-    RETURN_TYPES = ()
-    FUNCTION = "save_opus"
+    save_opus = execute  # TODO: remove
 
-    OUTPUT_NODE = True
 
-    CATEGORY = "audio"
-
-    def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"):
-        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
-
-class PreviewAudio(SaveAudio):
-    def __init__(self):
-        self.output_dir = folder_paths.get_temp_directory()
-        self.type = "temp"
-        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+class PreviewAudio(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewAudio",
+            display_name="Preview Audio",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"audio": ("AUDIO", ), },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
+    def execute(cls, audio) -> IO.NodeOutput:
+        return IO.NodeOutput(ui=UI.PreviewAudio(audio, cls=cls))
+
+    save_flac = execute  # TODO: remove
+
 
 def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
     """Convert audio to float 32 bits PCM format."""
@@ -316,26 +252,30 @@ def load(filepath: str) -> tuple[torch.Tensor, int]:
         wav = f32_pcm(wav)
         return wav, sr
 
-class LoadAudio:
+class LoadAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
         input_dir = folder_paths.get_input_directory()
         files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
-        return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
+        return IO.Schema(
+            node_id="LoadAudio",
+            display_name="Load Audio",
+            category="audio",
+            inputs=[
+                IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    CATEGORY = "audio"
-
-    RETURN_TYPES = ("AUDIO", )
-    FUNCTION = "load"
-
-    def load(self, audio):
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
         audio_path = folder_paths.get_annotated_filepath(audio)
         waveform, sample_rate = load(audio_path)
         audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
-        return (audio, )
+        return IO.NodeOutput(audio)
 
     @classmethod
-    def IS_CHANGED(s, audio):
+    def fingerprint_inputs(cls, audio):
         image_path = folder_paths.get_annotated_filepath(audio)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
@@ -343,46 +283,69 @@ class LoadAudio:
         return m.digest().hex()
 
     @classmethod
-    def VALIDATE_INPUTS(s, audio):
+    def validate_inputs(cls, audio):
         if not folder_paths.exists_annotated_filepath(audio):
             return "Invalid audio file: {}".format(audio)
         return True
 
-class RecordAudio:
+    load = execute  # TODO: remove
+
+
+class RecordAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"audio": ("AUDIO_RECORD", {})}}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RecordAudio",
+            display_name="Record Audio",
+            category="audio",
+            inputs=[
+                IO.Custom("AUDIO_RECORD").Input("audio"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    CATEGORY = "audio"
-
-    RETURN_TYPES = ("AUDIO", )
-    FUNCTION = "load"
-
-    def load(self, audio):
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
         audio_path = folder_paths.get_annotated_filepath(audio)
 
         waveform, sample_rate = load(audio_path)
         audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
-        return (audio, )
+        return IO.NodeOutput(audio)
+
+    load = execute  # TODO: remove
 
 
-class TrimAudioDuration:
+class TrimAudioDuration(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "audio": ("AUDIO",),
-                "start_index": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Start time in seconds, can be negative to count from the end (supports sub-seconds)."}),
-                "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "step": 0.01, "tooltip": "Duration in seconds"}),
-            },
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TrimAudioDuration",
+            display_name="Trim Audio Duration",
+            description="Trim audio tensor into chosen time range.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Float.Input(
+                    "start_index",
+                    default=0.0,
+                    min=-0xffffffffffffffff,
+                    max=0xffffffffffffffff,
+                    step=0.01,
+                    tooltip="Start time in seconds, can be negative to count from the end (supports sub-seconds).",
+                ),
+                IO.Float.Input(
+                    "duration",
+                    default=60.0,
+                    min=0.0,
+                    step=0.01,
+                    tooltip="Duration in seconds",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    FUNCTION = "trim"
-    RETURN_TYPES = ("AUDIO",)
-    CATEGORY = "audio"
-    DESCRIPTION = "Trim audio tensor into chosen time range."
-
-    def trim(self, audio, start_index, duration):
+    @classmethod
+    def execute(cls, audio, start_index, duration) -> IO.NodeOutput:
         waveform = audio["waveform"]
         sample_rate = audio["sample_rate"]
         audio_length = waveform.shape[-1]
@@ -399,23 +362,30 @@ class TrimAudioDuration:
         if start_frame >= end_frame:
             raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.")
 
-        return ({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate},)
+        return IO.NodeOutput({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate})
+
+    trim = execute  # TODO: remove
 
 
-class SplitAudioChannels:
+class SplitAudioChannels(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "audio": ("AUDIO",),
-        }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplitAudioChannels",
+            display_name="Split Audio Channels",
+            description="Separates the audio into left and right channels.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+            ],
+            outputs=[
+                IO.Audio.Output(display_name="left"),
+                IO.Audio.Output(display_name="right"),
+            ],
+        )
 
-    RETURN_TYPES = ("AUDIO", "AUDIO")
-    RETURN_NAMES = ("left", "right")
-    FUNCTION = "separate"
-    CATEGORY = "audio"
-    DESCRIPTION = "Separates the audio into left and right channels."
-
-    def separate(self, audio):
+    @classmethod
+    def execute(cls, audio) -> IO.NodeOutput:
         waveform = audio["waveform"]
         sample_rate = audio["sample_rate"]
 
@@ -425,7 +395,9 @@ class SplitAudioChannels:
         left_channel = waveform[..., 0:1, :]
         right_channel = waveform[..., 1:2, :]
 
-        return ({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+        return IO.NodeOutput({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+
+    separate = execute  # TODO: remove
 
 
 def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
@@ -443,21 +415,29 @@ def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_
     return waveform_1, waveform_2, output_sample_rate
 
 
-class AudioConcat:
+class AudioConcat(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "audio1": ("AUDIO",),
-            "audio2": ("AUDIO",),
-            "direction": (['after', 'before'], {"default": 'after', "tooltip": "Whether to append audio2 after or before audio1."}),
-        }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioConcat",
+            display_name="Audio Concat",
+            description="Concatenates the audio1 to audio2 in the specified direction.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio1"),
+                IO.Audio.Input("audio2"),
+                IO.Combo.Input(
+                    "direction",
+                    options=['after', 'before'],
+                    default="after",
+                    tooltip="Whether to append audio2 after or before audio1.",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    RETURN_TYPES = ("AUDIO",)
-    FUNCTION = "concat"
-    CATEGORY = "audio"
-    DESCRIPTION = "Concatenates the audio1 to audio2 in the specified direction."
-
-    def concat(self, audio1, audio2, direction):
+    @classmethod
+    def execute(cls, audio1, audio2, direction) -> IO.NodeOutput:
         waveform_1 = audio1["waveform"]
         waveform_2 = audio2["waveform"]
         sample_rate_1 = audio1["sample_rate"]
@@ -477,26 +457,33 @@ class AudioConcat:
         elif direction == 'before':
             concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
 
-        return ({"waveform": concatenated_audio, "sample_rate": output_sample_rate},)
+        return IO.NodeOutput({"waveform": concatenated_audio, "sample_rate": output_sample_rate})
+
+    concat = execute  # TODO: remove
 
 
-class AudioMerge:
+class AudioMerge(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "audio1": ("AUDIO",),
-                "audio2": ("AUDIO",),
-                "merge_method": (["add", "mean", "subtract", "multiply"], {"tooltip": "The method used to combine the audio waveforms."}),
-            },
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioMerge",
+            display_name="Audio Merge",
+            description="Combine two audio tracks by overlaying their waveforms.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio1"),
+                IO.Audio.Input("audio2"),
+                IO.Combo.Input(
+                    "merge_method",
+                    options=["add", "mean", "subtract", "multiply"],
+                    tooltip="The method used to combine the audio waveforms.",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    FUNCTION = "merge"
-    RETURN_TYPES = ("AUDIO",)
-    CATEGORY = "audio"
-    DESCRIPTION = "Combine two audio tracks by overlaying their waveforms."
-
-    def merge(self, audio1, audio2, merge_method):
+    @classmethod
+    def execute(cls, audio1, audio2, merge_method) -> IO.NodeOutput:
         waveform_1 = audio1["waveform"]
         waveform_2 = audio2["waveform"]
         sample_rate_1 = audio1["sample_rate"]
@@ -530,85 +517,110 @@ class AudioMerge:
         if max_val > 1.0:
             waveform = waveform / max_val
 
-        return ({"waveform": waveform, "sample_rate": output_sample_rate},)
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": output_sample_rate})
+
+    merge = execute  # TODO: remove
 
 
-class AudioAdjustVolume:
+class AudioAdjustVolume(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "audio": ("AUDIO",),
-            "volume": ("INT", {"default": 1.0, "min": -100, "max": 100, "tooltip": "Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc"}),
-        }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioAdjustVolume",
+            display_name="Audio Adjust Volume",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Int.Input(
+                    "volume",
+                    default=1,
+                    min=-100,
+                    max=100,
+                    tooltip="Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc",
+                )
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    RETURN_TYPES = ("AUDIO",)
-    FUNCTION = "adjust_volume"
-    CATEGORY = "audio"
-
-    def adjust_volume(self, audio, volume):
+    @classmethod
+    def execute(cls, audio, volume) -> IO.NodeOutput:
         if volume == 0:
-            return (audio,)
+            return IO.NodeOutput(audio)
         waveform = audio["waveform"]
         sample_rate = audio["sample_rate"]
 
         gain = 10 ** (volume / 20)
         waveform = waveform * gain
 
-        return ({"waveform": waveform, "sample_rate": sample_rate},)
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+    adjust_volume = execute  # TODO: remove
 
 
-class EmptyAudio:
+class EmptyAudio(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Duration of the empty audio clip in seconds"}),
-            "sample_rate": ("INT", {"default": 44100, "tooltip": "Sample rate of the empty audio clip."}),
-            "channels": ("INT", {"default": 2, "min": 1, "max": 2, "tooltip": "Number of audio channels (1 for mono, 2 for stereo)."}),
-        }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyAudio",
+            display_name="Empty Audio",
+            category="audio",
+            inputs=[
+                IO.Float.Input(
+                    "duration",
+                    default=60.0,
+                    min=0.0,
+                    max=0xffffffffffffffff,
+                    step=0.01,
+                    tooltip="Duration of the empty audio clip in seconds",
+                ),
+                IO.Int.Input(
+                    "sample_rate",
+                    default=44100,
+                    tooltip="Sample rate of the empty audio clip.",
+                    min=1,
+                    max=192000,
+                ),
+                IO.Int.Input(
+                    "channels",
+                    default=2,
+                    min=1,
+                    max=2,
+                    tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
 
-    RETURN_TYPES = ("AUDIO",)
-    FUNCTION = "create_empty_audio"
-    CATEGORY = "audio"
-
-    def create_empty_audio(self, duration, sample_rate, channels):
+    @classmethod
+    def execute(cls, duration, sample_rate, channels) -> IO.NodeOutput:
         num_samples = int(round(duration * sample_rate))
         waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
-        return ({"waveform": waveform, "sample_rate": sample_rate},)
+        return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+    create_empty_audio = execute  # TODO: remove
 
 
-NODE_CLASS_MAPPINGS = {
-    "EmptyLatentAudio": EmptyLatentAudio,
-    "VAEEncodeAudio": VAEEncodeAudio,
-    "VAEDecodeAudio": VAEDecodeAudio,
-    "SaveAudio": SaveAudio,
-    "SaveAudioMP3": SaveAudioMP3,
-    "SaveAudioOpus": SaveAudioOpus,
-    "LoadAudio": LoadAudio,
-    "PreviewAudio": PreviewAudio,
-    "ConditioningStableAudio": ConditioningStableAudio,
-    "RecordAudio": RecordAudio,
-    "TrimAudioDuration": TrimAudioDuration,
-    "SplitAudioChannels": SplitAudioChannels,
-    "AudioConcat": AudioConcat,
-    "AudioMerge": AudioMerge,
-    "AudioAdjustVolume": AudioAdjustVolume,
-    "EmptyAudio": EmptyAudio,
-}
+class AudioExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            EmptyLatentAudio,
+            VAEEncodeAudio,
+            VAEDecodeAudio,
+            SaveAudio,
+            SaveAudioMP3,
+            SaveAudioOpus,
+            LoadAudio,
+            PreviewAudio,
+            ConditioningStableAudio,
+            RecordAudio,
+            TrimAudioDuration,
+            SplitAudioChannels,
+            AudioConcat,
+            AudioMerge,
+            AudioAdjustVolume,
+            EmptyAudio,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "EmptyLatentAudio": "Empty Latent Audio",
-    "VAEEncodeAudio": "VAE Encode Audio",
-    "VAEDecodeAudio": "VAE Decode Audio",
-    "PreviewAudio": "Preview Audio",
-    "LoadAudio": "Load Audio",
-    "SaveAudio": "Save Audio (FLAC)",
-    "SaveAudioMP3": "Save Audio (MP3)",
-    "SaveAudioOpus": "Save Audio (Opus)",
-    "RecordAudio": "Record Audio",
-    "TrimAudioDuration": "Trim Audio Duration",
-    "SplitAudioChannels": "Split Audio Channels",
-    "AudioConcat": "Audio Concat",
-    "AudioMerge": "Audio Merge",
-    "AudioAdjustVolume": "Audio Adjust Volume",
-    "EmptyAudio": "Empty Audio",
-}
+async def comfy_entrypoint() -> AudioExtension:
+    return AudioExtension()

comfy_extras/nodes_context_windows.py

@@ -26,6 +26,9 @@ class ContextWindowsManualNode(io.ComfyNode):
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
+                io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+                #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
+                #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with context windows applied during sampling."),
@@ -34,7 +37,8 @@ class ContextWindowsManualNode(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int) -> io.Model:
+    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
+                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
         model = model.clone()
         model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler(
             context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
@@ -43,9 +47,15 @@ class ContextWindowsManualNode(io.ComfyNode):
             context_overlap=context_overlap,
             context_stride=context_stride,
             closed_loop=closed_loop,
-            dim=dim)
+            dim=dim,
+            freenoise=freenoise,
+            cond_retain_index_list=cond_retain_index_list,
+            split_conds_to_windows=split_conds_to_windows
+        )
         # make memory usage calculation only take into account the context window latents
         comfy.context_windows.create_prepare_sampling_wrapper(model)
+        if freenoise: # no other use for this wrapper at this time
+            comfy.context_windows.create_sampler_sample_wrapper(model)
         return io.NodeOutput(model)
 
 class WanContextWindowsManualNode(ContextWindowsManualNode):
@@ -68,14 +78,18 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
                 io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+                io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+                #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
+                #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
         ]
         return schema
 
     @classmethod
-    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str) -> io.Model:
+    def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, freenoise: bool,
+                cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
         context_length = max(((context_length - 1) // 4) + 1, 1)  # at least length 1
         context_overlap = max(((context_overlap - 1) // 4) + 1, 0)  # at least overlap 0
-        return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2)
+        return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, cond_retain_index_list=cond_retain_index_list, split_conds_to_windows=split_conds_to_windows)
 
 
 class ContextWindowsExtension(ComfyExtension):

comfy_extras/nodes_freelunch.py

@@ -2,6 +2,8 @@
 
 import torch
 import logging
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO
 
 def Fourier_filter(x, threshold, scale):
     # FFT
@@ -22,21 +24,26 @@ def Fourier_filter(x, threshold, scale):
     return x_filtered.to(x.dtype)
 
 
-class FreeU:
+class FreeU(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                             "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FreeU",
+            category="model_patches/unet",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "model_patches/unet"
-
-    def patch(self, model, b1, b2, s1, s2):
+    @classmethod
+    def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
         model_channels = model.model.model_config.unet_config["model_channels"]
         scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
         on_cpu_devices = {}
@@ -59,23 +66,31 @@ class FreeU:
 
         m = model.clone()
         m.set_model_output_block_patch(output_block_patch)
-        return (m, )
+        return IO.NodeOutput(m)
 
-class FreeU_V2:
+    patch = execute  # TODO: remove
+
+
+class FreeU_V2(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                             "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FreeU_V2",
+            category="model_patches/unet",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                IO.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "model_patches/unet"
-
-    def patch(self, model, b1, b2, s1, s2):
+    @classmethod
+    def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
         model_channels = model.model.model_config.unet_config["model_channels"]
         scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
         on_cpu_devices = {}
@@ -105,9 +120,19 @@ class FreeU_V2:
 
         m = model.clone()
         m.set_model_output_block_patch(output_block_patch)
-        return (m, )
+        return IO.NodeOutput(m)
 
-NODE_CLASS_MAPPINGS = {
-    "FreeU": FreeU,
-    "FreeU_V2": FreeU_V2,
-}
+    patch = execute  # TODO: remove
+
+
+class FreelunchExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            FreeU,
+            FreeU_V2,
+        ]
+
+
+async def comfy_entrypoint() -> FreelunchExtension:
+    return FreelunchExtension()

comfy_extras/nodes_kandinsky5.py

@@ -0,0 +1,136 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.utils
+
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class Kandinsky5ImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Kandinsky5ImageToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=768, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=512, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Empty video latent"),
+                io.Latent.Output(display_name="cond_latent", tooltip="Clean encoded start images, used to replace the noisy start of the model output latents"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        cond_latent_out = {}
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            encoded = vae.encode(start_image[:, :, :, :3])
+            cond_latent_out["samples"] = encoded
+
+            mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            positive = node_helpers.conditioning_set_values(positive, {"time_dim_replace": encoded, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"time_dim_replace": encoded, "concat_mask": mask})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent, cond_latent_out)
+
+
+def adaptive_mean_std_normalization(source, reference, clump_mean_low=0.3, clump_mean_high=0.35, clump_std_low=0.35, clump_std_high=0.5):
+    source_mean = source.mean(dim=(1, 3, 4), keepdim=True)  # mean over C, H, W
+    source_std = source.std(dim=(1, 3, 4), keepdim=True)    # std over C, H, W
+
+    reference_mean = torch.clamp(reference.mean(), source_mean - clump_mean_low, source_mean + clump_mean_high)
+    reference_std = torch.clamp(reference.std(), source_std - clump_std_low, source_std + clump_std_high)
+
+    # normalization
+    normalized = (source - source_mean) / (source_std + 1e-8)
+    normalized = normalized * reference_std + reference_mean
+
+    return normalized
+
+
+class NormalizeVideoLatentStart(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="NormalizeVideoLatentStart",
+            category="conditioning/video_models",
+            description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
+            inputs=[
+                io.Latent.Input("latent"),
+                io.Int.Input("start_frame_count", default=4, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames to normalize, counted from the start"),
+                io.Int.Input("reference_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames after the start frames to use as reference"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latent, start_frame_count, reference_frame_count) -> io.NodeOutput:
+        if latent["samples"].shape[2] <= 1:
+            return io.NodeOutput(latent)
+        s = latent.copy()
+        samples = latent["samples"].clone()
+
+        first_frames = samples[:, :, :start_frame_count]
+        reference_frames_data = samples[:, :, start_frame_count:start_frame_count+min(reference_frame_count, samples.shape[2]-1)]
+        normalized_first_frames = adaptive_mean_std_normalization(first_frames, reference_frames_data)
+
+        samples[:, :, :start_frame_count] = normalized_first_frames
+        s["samples"] = samples
+        return io.NodeOutput(s)
+
+
+class CLIPTextEncodeKandinsky5(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeKandinsky5",
+            category="advanced/conditioning/kandinsky5",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("qwen25_7b", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_l, qwen25_7b) -> io.NodeOutput:
+        tokens = clip.tokenize(clip_l)
+        tokens["qwen25_7b"] = clip.tokenize(qwen25_7b)["qwen25_7b"]
+
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+class Kandinsky5Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Kandinsky5ImageToVideo,
+            NormalizeVideoLatentStart,
+            CLIPTextEncodeKandinsky5,
+        ]
+
+async def comfy_entrypoint() -> Kandinsky5Extension:
+    return Kandinsky5Extension()

comfy_extras/nodes_latent.py

@@ -4,7 +4,7 @@ import torch
 import nodes
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
-
+import logging
 
 def reshape_latent_to(target_shape, latent, repeat_batch=True):
     if latent.shape[1:] != target_shape[1:]:
@@ -388,6 +388,42 @@ class LatentOperationSharpen(io.ComfyNode):
             return luminance * sharpened
         return io.NodeOutput(sharpen)
 
+class ReplaceVideoLatentFrames(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ReplaceVideoLatentFrames",
+            category="latent/batch",
+            inputs=[
+                io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
+                io.Latent.Input("source", optional=True, tooltip="The source latent providing frames to insert into the destination latent. If not provided, the destination latent is returned unchanged."),
+                io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1, tooltip="The starting latent frame index in the destination latent where the source latent frames will be placed. Negative values count from the end."),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, destination, index, source=None) -> io.NodeOutput:
+        if source is None:
+            return io.NodeOutput(destination)
+        dest_frames = destination["samples"].shape[2]
+        source_frames = source["samples"].shape[2]
+        if index < 0:
+            index = dest_frames + index
+        if index > dest_frames:
+            logging.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
+            return io.NodeOutput(destination)
+        if index + source_frames > dest_frames:
+            logging.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
+            return io.NodeOutput(destination)
+        s = source.copy()
+        s_source = source["samples"]
+        s_destination = destination["samples"].clone()
+        s_destination[:, :, index:index + s_source.shape[2]] = s_source
+        s["samples"] = s_destination
+        return io.NodeOutput(s)
 
 class LatentExtension(ComfyExtension):
     @override
@@ -405,6 +441,7 @@ class LatentExtension(ComfyExtension):
             LatentApplyOperationCFG,
             LatentOperationTonemapReinhard,
             LatentOperationSharpen,
+            ReplaceVideoLatentFrames
         ]
 
 

comfy_extras/nodes_load_3d.py

@@ -2,8 +2,8 @@ import nodes
 import folder_paths
 import os
 
-from comfy.comfy_types import IO
-from comfy_api.input_impl import VideoFromFile
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension, InputImpl, UI
 
 from pathlib import Path
 
@@ -11,9 +11,9 @@ from pathlib import Path
 def normalize_path(path):
     return path.replace('\\', '/')
 
-class Load3D():
+class Load3D(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
         input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
 
         os.makedirs(input_dir, exist_ok=True)
@@ -26,157 +26,84 @@ class Load3D():
             for file_path in input_path.rglob("*")
             if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
         ]
+        return IO.Schema(
+            node_id="Load3D",
+            display_name="Load 3D & Animation",
+            category="3d",
+            is_experimental=True,
+            inputs=[
+                IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),
+                IO.Load3D.Input("image"),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.Image.Output(display_name="image"),
+                IO.Mask.Output(display_name="mask"),
+                IO.String.Output(display_name="mesh_path"),
+                IO.Image.Output(display_name="normal"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Video.Output(display_name="recording_video"),
+            ],
+        )
 
-        return {"required": {
-            "model_file": (sorted(files), {"file_upload": True}),
-            "image": ("LOAD_3D", {}),
-            "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-        }}
-
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video")
-
-    FUNCTION = "process"
-    EXPERIMENTAL = True
-
-    CATEGORY = "3d"
-
-    def process(self, model_file, image, **kwargs):
+    @classmethod
+    def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
         image_path = folder_paths.get_annotated_filepath(image['image'])
         mask_path = folder_paths.get_annotated_filepath(image['mask'])
         normal_path = folder_paths.get_annotated_filepath(image['normal'])
-        lineart_path = folder_paths.get_annotated_filepath(image['lineart'])
 
         load_image_node = nodes.LoadImage()
         output_image, ignore_mask = load_image_node.load_image(image=image_path)
         ignore_image, output_mask = load_image_node.load_image(image=mask_path)
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
-        lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
 
         video = None
 
         if image['recording'] != "":
             recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
 
-            video = VideoFromFile(recording_video_path)
+            video = InputImpl.VideoFromFile(recording_video_path)
 
-        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video
+        return IO.NodeOutput(output_image, output_mask, model_file, normal_image, image['camera_info'], video)
 
-class Load3DAnimation():
+    process = execute  # TODO: remove
+
+
+class Preview3D(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Preview3D",
+            display_name="Preview 3D & Animation",
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.String.Input("model_file", default="", multiline=False),
+                IO.Load3DCamera.Input("camera_info", optional=True),
+                IO.Image.Input("bg_image", optional=True),
+            ],
+            outputs=[],
+        )
 
-        os.makedirs(input_dir, exist_ok=True)
+    @classmethod
+    def execute(cls, model_file, **kwargs) -> IO.NodeOutput:
+        camera_info = kwargs.get("camera_info", None)
+        bg_image = kwargs.get("bg_image", None)
+        return IO.NodeOutput(ui=UI.PreviewUI3D(model_file, camera_info, bg_image=bg_image))
 
-        input_path = Path(input_dir)
-        base_path = Path(folder_paths.get_input_directory())
+    process = execute  # TODO: remove
 
-        files = [
-            normalize_path(str(file_path.relative_to(base_path)))
-            for file_path in input_path.rglob("*")
-            if file_path.suffix.lower() in {'.gltf', '.glb', '.fbx'}
+
+class Load3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            Load3D,
+            Preview3D,
         ]
 
-        return {"required": {
-            "model_file": (sorted(files), {"file_upload": True}),
-            "image": ("LOAD_3D_ANIMATION", {}),
-            "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-        }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video")
-
-    FUNCTION = "process"
-    EXPERIMENTAL = True
-
-    CATEGORY = "3d"
-
-    def process(self, model_file, image, **kwargs):
-        image_path = folder_paths.get_annotated_filepath(image['image'])
-        mask_path = folder_paths.get_annotated_filepath(image['mask'])
-        normal_path = folder_paths.get_annotated_filepath(image['normal'])
-
-        load_image_node = nodes.LoadImage()
-        output_image, ignore_mask = load_image_node.load_image(image=image_path)
-        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
-        normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
-
-        video = None
-
-        if image['recording'] != "":
-            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
-
-            video = VideoFromFile(recording_video_path)
-
-        return output_image, output_mask, model_file, normal_image, image['camera_info'], video
-
-class Preview3D():
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model_file": ("STRING", {"default": "", "multiline": False}),
-        },
-        "optional": {
-            "camera_info": ("LOAD3D_CAMERA", {})
-        }}
-
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-
-    CATEGORY = "3d"
-
-    FUNCTION = "process"
-    EXPERIMENTAL = True
-
-    def process(self, model_file, **kwargs):
-        camera_info = kwargs.get("camera_info", None)
-
-        return {
-            "ui": {
-                "result": [model_file, camera_info]
-            }
-        }
-
-class Preview3DAnimation():
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model_file": ("STRING", {"default": "", "multiline": False}),
-        },
-        "optional": {
-            "camera_info": ("LOAD3D_CAMERA", {})
-        }}
-
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-
-    CATEGORY = "3d"
-
-    FUNCTION = "process"
-    EXPERIMENTAL = True
-
-    def process(self, model_file, **kwargs):
-        camera_info = kwargs.get("camera_info", None)
-
-        return {
-            "ui": {
-                "result": [model_file, camera_info]
-            }
-        }
-
-NODE_CLASS_MAPPINGS = {
-    "Load3D": Load3D,
-    "Load3DAnimation": Load3DAnimation,
-    "Preview3D": Preview3D,
-    "Preview3DAnimation": Preview3DAnimation
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Load3D": "Load 3D",
-    "Load3DAnimation": "Load 3D - Animation",
-    "Preview3D": "Preview 3D",
-    "Preview3DAnimation": "Preview 3D - Animation"
-}
+async def comfy_entrypoint() -> Load3DExtension:
+    return Load3DExtension()

comfy_extras/nodes_logic.py

@@ -0,0 +1,155 @@
+from typing import TypedDict
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.latest import _io
+
+
+
+class SwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySwitchNode",
+            display_name="Switch",
+            category="logic",
+            is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
+                io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=..., on_true=...):
+        # We use ... instead of None, as None is passed for connected-but-unevaluated inputs.
+        # This trick allows us to ignore the value of the switch and still be able to run execute().
+
+        # One of the inputs may be missing, in which case we need to evaluate the other input
+        if on_false is ...:
+            return ["on_true"]
+        if on_true is ...:
+            return ["on_false"]
+        # Normal lazy switch operation
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def validate_inputs(cls, switch, on_false=..., on_true=...):
+        # This check happens before check_lazy_status(), so we can eliminate the case where
+        # both inputs are missing.
+        if on_false is ... and on_true is ...:
+            return "At least one of on_false or on_true must be connected to Switch node"
+        return True
+
+    @classmethod
+    def execute(cls, switch, on_true=..., on_false=...) -> io.NodeOutput:
+        if on_true is ...:
+            return io.NodeOutput(on_false)
+        if on_false is ...:
+            return io.NodeOutput(on_true)
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class DCTestNode(io.ComfyNode):
+    class DCValues(TypedDict):
+        combo: str
+        string: str
+        integer: int
+        image: io.Image.Type
+        subcombo: dict[str]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DCTestNode",
+            display_name="DCTest",
+            category="logic",
+            is_output_node=True,
+            inputs=[_io.DynamicCombo.Input("combo", options=[
+                _io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+                _io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+                _io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+                _io.DynamicCombo.Option("option4", [
+                    _io.DynamicCombo.Input("subcombo", options=[
+                        _io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+                        _io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+                    ])
+                ])]
+            )],
+            outputs=[io.AnyType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: DCValues) -> io.NodeOutput:
+        combo_val = combo["combo"]
+        if combo_val == "option1":
+            return io.NodeOutput(combo["string"])
+        elif combo_val == "option2":
+            return io.NodeOutput(combo["integer"])
+        elif combo_val == "option3":
+            return io.NodeOutput(combo["image"])
+        elif combo_val == "option4":
+            return io.NodeOutput(f"{combo['subcombo']}")
+        else:
+            raise ValueError(f"Invalid combo: {combo_val}")
+
+
+class AutogrowNamesTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplateNames(input=io.Float.Input("float"), names=["a", "b", "c"])
+        return io.Schema(
+            node_id="AutogrowNamesTestNode",
+            display_name="AutogrowNamesTest",
+            category="logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class AutogrowPrefixTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = _io.Autogrow.TemplatePrefix(input=io.Float.Input("float"), prefix="float", min=1, max=10)
+        return io.Schema(
+            node_id="AutogrowPrefixTestNode",
+            display_name="AutogrowPrefixTest",
+            category="logic",
+            inputs=[
+                _io.Autogrow.Input("autogrow", template=template)
+            ],
+            outputs=[io.String.Output()],
+        )
+
+    @classmethod
+    def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+        vals = list(autogrow.values())
+        combined = ",".join([str(x) for x in vals])
+        return io.NodeOutput(combined)
+
+class LogicExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            # SwitchNode,
+            # DCTestNode,
+            # AutogrowNamesTestNode,
+            # AutogrowPrefixTestNode,
+        ]
+
+async def comfy_entrypoint() -> LogicExtension:
+    return LogicExtension()

comfy_extras/nodes_mask.py

@@ -3,11 +3,10 @@ import scipy.ndimage
 import torch
 import comfy.utils
 import node_helpers
-import folder_paths
-import random
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
 
 import nodes
-from nodes import MAX_RESOLUTION
 
 def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
     source = source.to(destination.device)
@@ -46,202 +45,213 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
-class LatentCompositeMasked:
+class LatentCompositeMasked(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "destination": ("LATENT",),
-                "source": ("LATENT",),
-                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                "resize_source": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "mask": ("MASK",),
-            }
-        }
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "composite"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LatentCompositeMasked",
+            category="latent",
+            inputs=[
+                IO.Latent.Input("destination"),
+                IO.Latent.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+                IO.Boolean.Input("resize_source", default=False),
+                IO.Mask.Input("mask", optional=True),
+            ],
+            outputs=[IO.Latent.Output()],
+        )
 
-    CATEGORY = "latent"
-
-    def composite(self, destination, source, x, y, resize_source, mask = None):
+    @classmethod
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
         output = destination.copy()
         destination = destination["samples"].clone()
         source = source["samples"]
         output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
-        return (output,)
+        return IO.NodeOutput(output)
 
-class ImageCompositeMasked:
+    composite = execute  # TODO: remove
+
+
+class ImageCompositeMasked(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "destination": ("IMAGE",),
-                "source": ("IMAGE",),
-                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "resize_source": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "mask": ("MASK",),
-            }
-        }
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "composite"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageCompositeMasked",
+            category="image",
+            inputs=[
+                IO.Image.Input("destination"),
+                IO.Image.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Boolean.Input("resize_source", default=False),
+                IO.Mask.Input("mask", optional=True),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image"
-
-    def composite(self, destination, source, x, y, resize_source, mask = None):
+    @classmethod
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
         destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
-        return (output,)
+        return IO.NodeOutput(output)
 
-class MaskToImage:
+    composite = execute  # TODO: remove
+
+
+class MaskToImage(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "mask": ("MASK",),
-                }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskToImage",
+            display_name="Convert Mask to Image",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "mask_to_image"
-
-    def mask_to_image(self, mask):
+    @classmethod
+    def execute(cls, mask) -> IO.NodeOutput:
         result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
-        return (result,)
+        return IO.NodeOutput(result)
 
-class ImageToMask:
+    mask_to_image = execute  # TODO: remove
+
+
+class ImageToMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "image": ("IMAGE",),
-                    "channel": (["red", "green", "blue", "alpha"],),
-                }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageToMask",
+            display_name="Convert Image to Mask",
+            category="mask",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Combo.Input("channel", options=["red", "green", "blue", "alpha"]),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "image_to_mask"
-
-    def image_to_mask(self, image, channel):
+    @classmethod
+    def execute(cls, image, channel) -> IO.NodeOutput:
         channels = ["red", "green", "blue", "alpha"]
         mask = image[:, :, :, channels.index(channel)]
-        return (mask,)
+        return IO.NodeOutput(mask)
 
-class ImageColorToMask:
+    image_to_mask = execute  # TODO: remove
+
+
+class ImageColorToMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "image": ("IMAGE",),
-                    "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
-                }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageColorToMask",
+            category="mask",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("color", default=0, min=0, max=0xFFFFFF, step=1, display_mode=IO.NumberDisplay.number),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "image_to_mask"
-
-    def image_to_mask(self, image, color):
+    @classmethod
+    def execute(cls, image, color) -> IO.NodeOutput:
         temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
         temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
         mask = torch.where(temp == color, 1.0, 0).float()
-        return (mask,)
+        return IO.NodeOutput(mask)
 
-class SolidMask:
+    image_to_mask = execute  # TODO: remove
+
+
+class SolidMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
-                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SolidMask",
+            category="mask",
+            inputs=[
+                IO.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "solid"
-
-    def solid(self, value, width, height):
+    @classmethod
+    def execute(cls, value, width, height) -> IO.NodeOutput:
         out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
-        return (out,)
+        return IO.NodeOutput(out)
 
-class InvertMask:
+    solid = execute  # TODO: remove
+
+
+class InvertMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="InvertMask",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "invert"
-
-    def invert(self, mask):
+    @classmethod
+    def execute(cls, mask) -> IO.NodeOutput:
         out = 1.0 - mask
-        return (out,)
+        return IO.NodeOutput(out)
 
-class CropMask:
+    invert = execute  # TODO: remove
+
+
+class CropMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
-                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="CropMask",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "crop"
-
-    def crop(self, mask, x, y, width, height):
+    @classmethod
+    def execute(cls, mask, x, y, width, height) -> IO.NodeOutput:
         mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
         out = mask[:, y:y + height, x:x + width]
-        return (out,)
+        return IO.NodeOutput(out)
 
-class MaskComposite:
+    crop = execute  # TODO: remove
+
+
+class MaskComposite(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "destination": ("MASK",),
-                "source": ("MASK",),
-                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskComposite",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("destination"),
+                IO.Mask.Input("source"),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "combine"
-
-    def combine(self, destination, source, x, y, operation):
+    @classmethod
+    def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput:
         output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
         source = source.reshape((-1, source.shape[-2], source.shape[-1]))
 
@@ -267,28 +277,29 @@ class MaskComposite:
 
         output = torch.clamp(output, 0.0, 1.0)
 
-        return (output,)
+        return IO.NodeOutput(output)
 
-class FeatherMask:
+    combine = execute  # TODO: remove
+
+
+class FeatherMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="FeatherMask",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("left", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("top", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("right", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("bottom", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "feather"
-
-    def feather(self, mask, left, top, right, bottom):
+    @classmethod
+    def execute(cls, mask, left, top, right, bottom) -> IO.NodeOutput:
         output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
 
         left = min(left, output.shape[-1])
@@ -312,26 +323,28 @@ class FeatherMask:
             feather_rate = (y + 1) / bottom
             output[:, -y, :] *= feather_rate
 
-        return (output,)
+        return IO.NodeOutput(output)
 
-class GrowMask:
+    feather = execute  # TODO: remove
+
+
+class GrowMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
-                "tapered_corners": ("BOOLEAN", {"default": True}),
-            },
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GrowMask",
+            display_name="Grow Mask",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Boolean.Input("tapered_corners", default=True),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-
-    FUNCTION = "expand_mask"
-
-    def expand_mask(self, mask, expand, tapered_corners):
+    @classmethod
+    def execute(cls, mask, expand, tapered_corners) -> IO.NodeOutput:
         c = 0 if tapered_corners else 1
         kernel = np.array([[c, 1, c],
                            [1, 1, 1],
@@ -347,69 +360,74 @@ class GrowMask:
                     output = scipy.ndimage.grey_dilation(output, footprint=kernel)
             output = torch.from_numpy(output)
             out.append(output)
-        return (torch.stack(out, dim=0),)
+        return IO.NodeOutput(torch.stack(out, dim=0))
 
-class ThresholdMask:
+    expand_mask = execute  # TODO: remove
+
+
+class ThresholdMask(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "mask": ("MASK",),
-                    "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ThresholdMask",
+            category="mask",
+            inputs=[
+                IO.Mask.Input("mask"),
+                IO.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[IO.Mask.Output()],
+        )
 
-    CATEGORY = "mask"
-
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "image_to_mask"
-
-    def image_to_mask(self, mask, value):
+    @classmethod
+    def execute(cls, mask, value) -> IO.NodeOutput:
         mask = (mask > value).float()
-        return (mask,)
+        return IO.NodeOutput(mask)
+
+    image_to_mask = execute  # TODO: remove
+
 
 # Mask Preview - original implement from
 # https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
 # upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
-class MaskPreview(nodes.SaveImage):
-    def __init__(self):
-        self.output_dir = folder_paths.get_temp_directory()
-        self.type = "temp"
-        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
-        self.compress_level = 4
+class MaskPreview(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="MaskPreview",
+            display_name="Preview Mask",
+            category="mask",
+            description="Saves the input images to your ComfyUI output directory.",
+            inputs=[
+                IO.Mask.Input("mask"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"mask": ("MASK",), },
-            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-        }
-
-    FUNCTION = "execute"
-    CATEGORY = "mask"
-
-    def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
-        return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
+    def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
+        return IO.NodeOutput(ui=UI.PreviewMask(mask))
 
 
-NODE_CLASS_MAPPINGS = {
-    "LatentCompositeMasked": LatentCompositeMasked,
-    "ImageCompositeMasked": ImageCompositeMasked,
-    "MaskToImage": MaskToImage,
-    "ImageToMask": ImageToMask,
-    "ImageColorToMask": ImageColorToMask,
-    "SolidMask": SolidMask,
-    "InvertMask": InvertMask,
-    "CropMask": CropMask,
-    "MaskComposite": MaskComposite,
-    "FeatherMask": FeatherMask,
-    "GrowMask": GrowMask,
-    "ThresholdMask": ThresholdMask,
-    "MaskPreview": MaskPreview
-}
+class MaskExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            LatentCompositeMasked,
+            ImageCompositeMasked,
+            MaskToImage,
+            ImageToMask,
+            ImageColorToMask,
+            SolidMask,
+            InvertMask,
+            CropMask,
+            MaskComposite,
+            FeatherMask,
+            GrowMask,
+            ThresholdMask,
+            MaskPreview,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ImageToMask": "Convert Image to Mask",
-    "MaskToImage": "Convert Mask to Image",
-}
+
+async def comfy_entrypoint() -> MaskExtension:
+    return MaskExtension()

comfy_extras/nodes_model_downscale.py

@@ -53,11 +53,6 @@ class PatchModelAddDownscale(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    # Sampling
-    "PatchModelAddDownscale": "",
-}
-
 class ModelDownscaleExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:

comfy_extras/nodes_model_patch.py

@@ -6,6 +6,7 @@ import comfy.ops
 import comfy.model_management
 import comfy.ldm.common_dit
 import comfy.latent_formats
+import comfy.ldm.lumina.controlnet
 
 
 class BlockWiseControlBlock(torch.nn.Module):
@@ -189,6 +190,35 @@ class SigLIPMultiFeatProjModel(torch.nn.Module):
 
         return embedding
 
+def z_image_convert(sd):
+    replace_keys = {".attention.to_out.0.bias": ".attention.out.bias",
+                    ".attention.norm_k.weight": ".attention.k_norm.weight",
+                    ".attention.norm_q.weight": ".attention.q_norm.weight",
+                    ".attention.to_out.0.weight": ".attention.out.weight"
+                    }
+
+    out_sd = {}
+    for k in sorted(sd.keys()):
+        w = sd[k]
+
+        k_out = k
+        if k_out.endswith(".attention.to_k.weight"):
+            cc = [w]
+            continue
+        if k_out.endswith(".attention.to_q.weight"):
+            cc = [w] + cc
+            continue
+        if k_out.endswith(".attention.to_v.weight"):
+            cc = cc + [w]
+            w = torch.cat(cc, dim=0)
+            k_out = k_out.replace(".attention.to_v.weight", ".attention.qkv.weight")
+
+        for r, rr in replace_keys.items():
+            k_out = k_out.replace(r, rr)
+        out_sd[k_out] = w
+
+    return out_sd
+
 class ModelPatchLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -211,6 +241,15 @@ class ModelPatchLoader:
         elif 'feature_embedder.mid_layer_norm.bias' in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True)
             model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+        elif 'control_all_x_embedder.2-1.weight' in sd: # alipai z image fun controlnet
+            sd = z_image_convert(sd)
+            config = {}
+            if 'control_layers.14.adaLN_modulation.0.weight' in sd:
+                config['n_control_layers'] = 15
+                config['additional_in_dim'] = 17
+                config['refiner_control'] = True
+                config['broken'] = True
+            model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast, **config)
 
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -263,6 +302,99 @@ class DiffSynthCnetPatch:
     def models(self):
         return [self.model_patch]
 
+class ZImageControlPatch:
+    def __init__(self, model_patch, vae, image, strength, inpaint_image=None, mask=None):
+        self.model_patch = model_patch
+        self.vae = vae
+        self.image = image
+        self.inpaint_image = inpaint_image
+        self.mask = mask
+        self.strength = strength
+        self.encoded_image = self.encode_latent_cond(image)
+        self.encoded_image_size = (image.shape[1], image.shape[2])
+        self.temp_data = None
+
+    def encode_latent_cond(self, control_image, inpaint_image=None):
+        latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(control_image))
+        if self.model_patch.model.additional_in_dim > 0:
+            if self.mask is None:
+                mask_ = torch.zeros_like(latent_image)[:, :1]
+            else:
+                mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none")
+            if inpaint_image is None:
+                inpaint_image = torch.ones_like(control_image) * 0.5
+
+            inpaint_image_latent = comfy.latent_formats.Flux().process_in(self.vae.encode(inpaint_image))
+
+            return torch.cat([latent_image, mask_, inpaint_image_latent], dim=1)
+        else:
+            return latent_image
+
+    def __call__(self, kwargs):
+        x = kwargs.get("x")
+        img = kwargs.get("img")
+        img_input = kwargs.get("img_input")
+        txt = kwargs.get("txt")
+        pe = kwargs.get("pe")
+        vec = kwargs.get("vec")
+        block_index = kwargs.get("block_index")
+        block_type = kwargs.get("block_type", "")
+        spacial_compression = self.vae.spacial_compression_encode()
+        if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
+            image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
+            inpaint_scaled = None
+            if self.inpaint_image is not None:
+                inpaint_scaled = comfy.utils.common_upscale(self.inpaint_image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center").movedim(1, -1)
+            loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+            self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1), inpaint_scaled)
+            self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
+            comfy.model_management.load_models_gpu(loaded_models)
+
+        cnet_blocks = self.model_patch.model.n_control_layers
+        div = round(30 / cnet_blocks)
+
+        cnet_index = (block_index // div)
+        cnet_index_float = (block_index / div)
+
+        kwargs.pop("img")  # we do ops in place
+        kwargs.pop("txt")
+
+        if cnet_index_float > (cnet_blocks - 1):
+            self.temp_data = None
+            return kwargs
+
+        if self.temp_data is None or self.temp_data[0] > cnet_index:
+            if block_type == "noise_refiner":
+                self.temp_data = (-3, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+            else:
+                self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+
+        if block_type == "noise_refiner":
+            next_layer = self.temp_data[0] + 1
+            self.temp_data = (next_layer, self.model_patch.model.forward_noise_refiner_block(block_index, self.temp_data[1][1], img_input[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+            if self.temp_data[1][0] is not None:
+                img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+        else:
+            while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+                next_layer = self.temp_data[0] + 1
+                self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img_input[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+
+            if cnet_index_float == self.temp_data[0]:
+                img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+                if cnet_blocks == self.temp_data[0] + 1:
+                    self.temp_data = None
+
+        return kwargs
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            self.encoded_image = self.encoded_image.to(device_or_dtype)
+            self.temp_data = None
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
 class QwenImageDiffsynthControlnet:
     @classmethod
     def INPUT_TYPES(s):
@@ -289,7 +421,12 @@ class QwenImageDiffsynthControlnet:
                 mask = mask.unsqueeze(2)
             mask = 1.0 - mask
 
-        model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
+        if isinstance(model_patch.model, comfy.ldm.lumina.controlnet.ZImage_Control):
+            patch = ZImageControlPatch(model_patch, vae, image, strength, mask=mask)
+            model_patched.set_model_noise_refiner_patch(patch)
+            model_patched.set_model_double_block_patch(patch)
+        else:
+            model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
         return (model_patched,)
 
 

comfy_extras/nodes_video.py

@@ -8,10 +8,7 @@ import json
 from typing import Optional
 from typing_extensions import override
 from fractions import Fraction
-from comfy_api.input import AudioInput, ImageInput, VideoInput
-from comfy_api.input_impl import VideoFromComponents, VideoFromFile
-from comfy_api.util import VideoCodec, VideoComponents, VideoContainer
-from comfy_api.latest import ComfyExtension, io, ui
+from comfy_api.latest import ComfyExtension, io, ui, Input, InputImpl, Types
 from comfy.cli_args import args
 
 class SaveWEBM(io.ComfyNode):
@@ -28,7 +25,6 @@ class SaveWEBM(io.ComfyNode):
                 io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01),
                 io.Float.Input("crf", default=32.0, min=0, max=63.0, step=1, tooltip="Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."),
             ],
-            outputs=[],
             hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
             is_output_node=True,
         )
@@ -79,16 +75,15 @@ class SaveVideo(io.ComfyNode):
             inputs=[
                 io.Video.Input("video", tooltip="The video to save."),
                 io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),
-                io.Combo.Input("format", options=VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
-                io.Combo.Input("codec", options=VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
+                io.Combo.Input("format", options=Types.VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
+                io.Combo.Input("codec", options=Types.VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
             ],
-            outputs=[],
             hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
             is_output_node=True,
         )
 
     @classmethod
-    def execute(cls, video: VideoInput, filename_prefix, format, codec) -> io.NodeOutput:
+    def execute(cls, video: Input.Video, filename_prefix, format: str, codec) -> io.NodeOutput:
         width, height = video.get_dimensions()
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
             filename_prefix,
@@ -105,10 +100,10 @@ class SaveVideo(io.ComfyNode):
                 metadata["prompt"] = cls.hidden.prompt
             if len(metadata) > 0:
                 saved_metadata = metadata
-        file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}"
+        file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}"
         video.save_to(
             os.path.join(full_output_folder, file),
-            format=format,
+            format=Types.VideoContainer(format),
             codec=codec,
             metadata=saved_metadata
         )
@@ -135,9 +130,9 @@ class CreateVideo(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, images: ImageInput, fps: float, audio: Optional[AudioInput] = None) -> io.NodeOutput:
+    def execute(cls, images: Input.Image, fps: float, audio: Optional[Input.Audio] = None) -> io.NodeOutput:
         return io.NodeOutput(
-            VideoFromComponents(VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps)))
+            InputImpl.VideoFromComponents(Types.VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps)))
         )
 
 class GetVideoComponents(io.ComfyNode):
@@ -159,11 +154,11 @@ class GetVideoComponents(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, video: VideoInput) -> io.NodeOutput:
+    def execute(cls, video: Input.Video) -> io.NodeOutput:
         components = video.get_components()
-
         return io.NodeOutput(components.images, components.audio, float(components.frame_rate))
 
+
 class LoadVideo(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -185,7 +180,7 @@ class LoadVideo(io.ComfyNode):
     @classmethod
     def execute(cls, file) -> io.NodeOutput:
         video_path = folder_paths.get_annotated_filepath(file)
-        return io.NodeOutput(VideoFromFile(video_path))
+        return io.NodeOutput(InputImpl.VideoFromFile(video_path))
 
     @classmethod
     def fingerprint_inputs(s, file):

comfy_extras/nodes_wanmove.py

@@ -0,0 +1,535 @@
+import nodes
+import node_helpers
+import torch
+import torchvision.transforms.functional as TF
+import comfy.model_management
+import comfy.utils
+import numpy as np
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.nodes_wan import parse_json_tracks
+
+# https://github.com/ali-vilab/Wan-Move/blob/main/wan/modules/trajectory.py
+from PIL import Image, ImageDraw
+
+SKIP_ZERO = False
+
+def get_pos_emb(
+    pos_k: torch.Tensor, # A 1D tensor containing positions for which to generate embeddings.
+    pos_emb_dim: int,
+    theta_func: callable = lambda i, d: torch.pow(10000, torch.mul(2, torch.div(i.to(torch.float32), d))), #Function to compute thetas based on position and embedding dimensions.
+    device: torch.device = torch.device("cpu"),
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor: # The position embeddings (batch_size, pos_emb_dim)
+
+    assert pos_emb_dim % 2 == 0, "The dimension of position embeddings must be even."
+    pos_k = pos_k.to(device, dtype)
+    if SKIP_ZERO:
+        pos_k = pos_k + 1
+    batch_size = pos_k.size(0)
+
+    denominator = torch.arange(0, pos_emb_dim // 2, device=device, dtype=dtype)
+    # Expand denominator to match the shape needed for broadcasting
+    denominator_expanded = denominator.view(1, -1).expand(batch_size, -1)
+
+    thetas = theta_func(denominator_expanded, pos_emb_dim)
+
+    # Ensure pos_k is in the correct shape for broadcasting
+    pos_k_expanded = pos_k.view(-1, 1).to(dtype)
+    sin_thetas = torch.sin(torch.div(pos_k_expanded, thetas))
+    cos_thetas = torch.cos(torch.div(pos_k_expanded, thetas))
+
+    # Concatenate sine and cosine embeddings along the last dimension
+    pos_emb = torch.cat([sin_thetas, cos_thetas], dim=-1)
+
+    return pos_emb
+
+def create_pos_embeddings(
+    pred_tracks: torch.Tensor, # the predicted tracks, [T, N, 2]
+    pred_visibility: torch.Tensor, # the predicted visibility [T, N]
+    downsample_ratios: list[int], # the ratios for downsampling time, height, and width
+    height: int, # the height of the feature map
+    width: int, # the width of the feature map
+    track_num: int = -1, # the number of tracks to use
+    t_down_strategy: str = "sample", # the strategy for downsampling time dimension
+):
+    assert t_down_strategy in ["sample", "average"], "Invalid strategy for downsampling time dimension."
+
+    t, n, _ = pred_tracks.shape
+    t_down, h_down, w_down = downsample_ratios
+    track_pos = - torch.ones(n, (t-1) // t_down + 1, 2, dtype=torch.long)
+
+    if track_num == -1:
+        track_num = n
+
+    tracks_idx = torch.randperm(n)[:track_num]
+    tracks = pred_tracks[:, tracks_idx]
+    visibility = pred_visibility[:, tracks_idx]
+
+    for t_idx in range(0, t, t_down):
+        if t_down_strategy == "sample" or t_idx == 0:
+            cur_tracks = tracks[t_idx] # [N, 2]
+            cur_visibility = visibility[t_idx] # [N]
+        else:
+            cur_tracks = tracks[t_idx:t_idx+t_down].mean(dim=0)
+            cur_visibility = torch.any(visibility[t_idx:t_idx+t_down], dim=0)
+
+        for i in range(track_num):
+            if not cur_visibility[i] or cur_tracks[i][0] < 0 or cur_tracks[i][1] < 0 or cur_tracks[i][0] >= width or cur_tracks[i][1] >= height:
+                continue
+            x, y = cur_tracks[i]
+            x, y = int(x // w_down), int(y // h_down)
+            track_pos[i, t_idx // t_down, 0], track_pos[i, t_idx // t_down, 1] = y, x
+
+    return track_pos # the position embeddings, [N, T', 2], 2 = height, width
+
+def replace_feature(
+    vae_feature: torch.Tensor,  # [B, C', T', H', W']
+    track_pos: torch.Tensor,    # [B, N, T', 2]
+    strength: float = 1.0
+) -> torch.Tensor:
+    b, _, t, h, w = vae_feature.shape
+    assert b == track_pos.shape[0], "Batch size mismatch."
+    n = track_pos.shape[1]
+
+    # Shuffle the trajectory order
+    track_pos = track_pos[:, torch.randperm(n), :, :]
+
+    # Extract coordinates at time steps ≥ 1 and generate a valid mask
+    current_pos = track_pos[:, :, 1:, :]  # [B, N, T-1, 2]
+    mask = (current_pos[..., 0] >= 0) & (current_pos[..., 1] >= 0)  # [B, N, T-1]
+
+    # Get all valid indices
+    valid_indices = mask.nonzero(as_tuple=False)  # [num_valid, 3]
+    num_valid = valid_indices.shape[0]
+
+    if num_valid == 0:
+        return vae_feature
+
+    # Decompose valid indices into each dimension
+    batch_idx = valid_indices[:, 0]
+    track_idx = valid_indices[:, 1]
+    t_rel = valid_indices[:, 2]
+    t_target = t_rel + 1  # Convert to original time step indices
+
+    # Extract target position coordinates
+    h_target = current_pos[batch_idx, track_idx, t_rel, 0].long()  # Ensure integer indices
+    w_target = current_pos[batch_idx, track_idx, t_rel, 1].long()
+
+    # Extract source position coordinates (t=0)
+    h_source = track_pos[batch_idx, track_idx, 0, 0].long()
+    w_source = track_pos[batch_idx, track_idx, 0, 1].long()
+
+    # Get source features and assign to target positions
+    src_features = vae_feature[batch_idx, :, 0, h_source, w_source]
+    dst_features = vae_feature[batch_idx, :, t_target, h_target, w_target]
+
+    vae_feature[batch_idx, :, t_target, h_target, w_target] = dst_features + (src_features - dst_features) * strength
+
+
+    return vae_feature
+
+# Visualize functions
+
+def _draw_gradient_polyline_on_overlay(overlay, line_width, points, start_color, opacity=1.0):
+    draw = ImageDraw.Draw(overlay, 'RGBA')
+    points = points[::-1]
+
+    # Compute total length
+    total_length = 0
+    segment_lengths = []
+    for i in range(len(points) - 1):
+        dx = points[i + 1][0] - points[i][0]
+        dy = points[i + 1][1] - points[i][1]
+        length = (dx * dx + dy * dy) ** 0.5
+        segment_lengths.append(length)
+        total_length += length
+
+    if total_length == 0:
+        return
+
+    accumulated_length = 0
+
+    # Draw the gradient polyline
+    for idx, (start_point, end_point) in enumerate(zip(points[:-1], points[1:])):
+        segment_length = segment_lengths[idx]
+        steps = max(int(segment_length), 1)
+
+        for i in range(steps):
+            current_length = accumulated_length + (i / steps) * segment_length
+            ratio = current_length / total_length
+
+            alpha = int(255 * (1 - ratio) * opacity)
+            color = (*start_color, alpha)
+
+            x = int(start_point[0] + (end_point[0] - start_point[0]) * i / steps)
+            y = int(start_point[1] + (end_point[1] - start_point[1]) * i / steps)
+
+            dynamic_line_width = max(int(line_width * (1 - ratio)), 1)
+            draw.line([(x, y), (x + 1, y)], fill=color, width=dynamic_line_width)
+
+        accumulated_length += segment_length
+
+
+def add_weighted(rgb, track):
+    rgb = np.array(rgb) # [H, W, C] "RGB"
+    track = np.array(track) # [H, W, C] "RGBA"
+
+    alpha = track[:, :, 3] / 255.0
+    alpha = np.stack([alpha] * 3, axis=-1)
+    blend_img = track[:, :, :3] * alpha + rgb * (1 - alpha)
+
+    return Image.fromarray(blend_img.astype(np.uint8))
+
+def draw_tracks_on_video(video, tracks, visibility=None, track_frame=24, circle_size=12, opacity=0.5, line_width=16):
+    color_map = [(102, 153, 255), (0, 255, 255), (255, 255, 0), (255, 102, 204), (0, 255, 0)]
+
+    video = video.byte().cpu().numpy()  # (81, 480, 832, 3)
+    tracks = tracks[0].long().detach().cpu().numpy()
+    if visibility is not None:
+        visibility = visibility[0].detach().cpu().numpy()
+
+    num_frames, height, width = video.shape[:3]
+    num_tracks = tracks.shape[1]
+    alpha_opacity = int(255 * opacity)
+
+    output_frames = []
+    for t in range(num_frames):
+        frame_rgb = video[t].astype(np.float32)
+
+        # Create a single RGBA overlay for all tracks in this frame
+        overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+        draw_overlay = ImageDraw.Draw(overlay)
+
+        polyline_data = []
+
+        # Draw all circles on a single overlay
+        for n in range(num_tracks):
+            if visibility is not None and visibility[t, n] == 0:
+                continue
+
+            track_coord = tracks[t, n]
+            color = color_map[n % len(color_map)]
+            circle_color = color + (alpha_opacity,)
+
+            draw_overlay.ellipse((track_coord[0] - circle_size, track_coord[1] - circle_size, track_coord[0] + circle_size, track_coord[1] + circle_size),
+                fill=circle_color
+            )
+
+            # Store polyline data for batch processing
+            tracks_coord = tracks[max(t - track_frame, 0):t + 1, n]
+            if len(tracks_coord) > 1:
+                polyline_data.append((tracks_coord, color))
+
+        # Blend circles overlay once
+        overlay_np = np.array(overlay)
+        alpha = overlay_np[:, :, 3:4] / 255.0
+        frame_rgb = overlay_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
+
+        # Draw all polylines on a single overlay
+        if polyline_data:
+            polyline_overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+            for tracks_coord, color in polyline_data:
+                _draw_gradient_polyline_on_overlay(polyline_overlay, line_width, tracks_coord, color, opacity)
+
+            # Blend polylines overlay once
+            polyline_np = np.array(polyline_overlay)
+            alpha = polyline_np[:, :, 3:4] / 255.0
+            frame_rgb = polyline_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
+
+        output_frames.append(Image.fromarray(frame_rgb.astype(np.uint8)))
+
+    return output_frames
+
+
+class WanMoveVisualizeTracks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveVisualizeTracks",
+            category="conditioning/video_models",
+            inputs=[
+                io.Image.Input("images"),
+                io.Tracks.Input("tracks", optional=True),
+                io.Int.Input("line_resolution", default=24, min=1, max=1024),
+                io.Int.Input("circle_size", default=12, min=1, max=128),
+                io.Float.Input("opacity", default=0.75, min=0.0, max=1.0, step=0.01),
+                io.Int.Input("line_width", default=16, min=1, max=128),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, line_resolution, circle_size, opacity, line_width, tracks=None) -> io.NodeOutput:
+        if tracks is None:
+            return io.NodeOutput(images)
+
+        track_path = tracks["track_path"].unsqueeze(0)
+        track_visibility = tracks["track_visibility"].unsqueeze(0)
+        images_in = images * 255.0
+        if images_in.shape[0] != track_path.shape[1]:
+            repeat_count = track_path.shape[1] // images.shape[0]
+            images_in = images_in.repeat(repeat_count, 1, 1, 1)
+        track_video = draw_tracks_on_video(images_in, track_path, track_visibility, track_frame=line_resolution, circle_size=circle_size, opacity=opacity, line_width=line_width)
+        track_video = torch.stack([TF.to_tensor(frame) for frame in track_video], dim=0).movedim(1, -1).float()
+
+        return io.NodeOutput(track_video.to(comfy.model_management.intermediate_device()))
+
+
+class WanMoveTracksFromCoords(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveTracksFromCoords",
+            category="conditioning/video_models",
+            inputs=[
+                io.String.Input("track_coords", force_input=True, default="[]", optional=True),
+                io.Mask.Input("track_mask", optional=True),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+                io.Int.Output(display_name="track_length"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, track_coords, track_mask=None) -> io.NodeOutput:
+        device=comfy.model_management.intermediate_device()
+
+        tracks_data = parse_json_tracks(track_coords)
+        track_length = len(tracks_data[0])
+
+        track_list = [
+                [[track[frame]['x'], track[frame]['y']] for track in tracks_data]
+                for frame in range(len(tracks_data[0]))
+            ]
+        tracks = torch.tensor(track_list, dtype=torch.float32, device=device)  # [frames, num_tracks, 2]
+
+        num_tracks = tracks.shape[-2]
+        if track_mask is None:
+            track_visibility = torch.ones((track_length, num_tracks), dtype=torch.bool, device=device)
+        else:
+            track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks
+        out_track_info["track_visibility"] = track_visibility
+        return io.NodeOutput(out_track_info, track_length)
+
+
+class GenerateTracks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GenerateTracks",
+            category="conditioning/video_models",
+            inputs=[
+                io.Int.Input("width", default=832, min=16, max=4096, step=16),
+                io.Int.Input("height", default=480, min=16, max=4096, step=16),
+                io.Float.Input("start_x", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized X coordinate (0-1) for start position."),
+                io.Float.Input("start_y", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y coordinate (0-1) for start position."),
+                io.Float.Input("end_x", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized X coordinate (0-1) for end position."),
+                io.Float.Input("end_y", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y coordinate (0-1) for end position."),
+                io.Int.Input("num_frames", default=81, min=1, max=1024),
+                io.Int.Input("num_tracks", default=5, min=1, max=100),
+                io.Float.Input("track_spread", default=0.025, min=0.0, max=1.0, step=0.001, tooltip="Normalized distance between tracks. Tracks are spread perpendicular to the motion direction."),
+                io.Boolean.Input("bezier", default=False, tooltip="Enable Bezier curve path using the mid point as control point."),
+                io.Float.Input("mid_x", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Normalized X control point for Bezier curve. Only used when 'bezier' is enabled."),
+                io.Float.Input("mid_y", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y control point for Bezier curve. Only used when 'bezier' is enabled."),
+                io.Combo.Input(
+                    "interpolation",
+                    options=["linear", "ease_in", "ease_out", "ease_in_out", "constant"],
+                    tooltip="Controls the timing/speed of movement along the path.",
+                ),
+                io.Mask.Input("track_mask", optional=True, tooltip="Optional mask to indicate visible frames."),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+                io.Int.Output(display_name="track_length"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, start_x, start_y, mid_x, mid_y, end_x, end_y, num_frames, num_tracks,
+                track_spread, bezier=False, interpolation="linear", track_mask=None) -> io.NodeOutput:
+        device = comfy.model_management.intermediate_device()
+        track_length = num_frames
+
+        # normalized coordinates to pixel coordinates
+        start_x_px = start_x * width
+        start_y_px = start_y * height
+        mid_x_px = mid_x * width
+        mid_y_px = mid_y * height
+        end_x_px = end_x * width
+        end_y_px = end_y * height
+
+        track_spread_px = track_spread * (width + height) / 2 # Use average of width/height for spread to keep it proportional
+
+        t = torch.linspace(0, 1, num_frames, device=device)
+        if interpolation == "constant": # All points stay at start position
+            interp_values = torch.zeros_like(t)
+        elif interpolation == "linear":
+            interp_values = t
+        elif interpolation == "ease_in":
+            interp_values = t ** 2
+        elif interpolation == "ease_out":
+            interp_values = 1 - (1 - t) ** 2
+        elif interpolation == "ease_in_out":
+            interp_values = t * t * (3 - 2 * t)
+
+        if bezier: # apply interpolation to t for timing control along the bezier path
+            t_interp = interp_values
+            one_minus_t = 1 - t_interp
+            x_positions = one_minus_t ** 2 * start_x_px + 2 * one_minus_t * t_interp * mid_x_px + t_interp ** 2 * end_x_px
+            y_positions = one_minus_t ** 2 * start_y_px + 2 * one_minus_t * t_interp * mid_y_px + t_interp ** 2 * end_y_px
+            tangent_x = 2 * one_minus_t * (mid_x_px - start_x_px) + 2 * t_interp * (end_x_px - mid_x_px)
+            tangent_y = 2 * one_minus_t * (mid_y_px - start_y_px) + 2 * t_interp * (end_y_px - mid_y_px)
+        else: # calculate base x and y positions for each frame (center track)
+            x_positions = start_x_px + (end_x_px - start_x_px) * interp_values
+            y_positions = start_y_px + (end_y_px - start_y_px) * interp_values
+            # For non-bezier, tangent is constant (direction from start to end)
+            tangent_x = torch.full_like(t, end_x_px - start_x_px)
+            tangent_y = torch.full_like(t, end_y_px - start_y_px)
+
+        track_list = []
+        for frame_idx in range(num_frames):
+            # Calculate perpendicular direction at this frame
+            tx = tangent_x[frame_idx].item()
+            ty = tangent_y[frame_idx].item()
+            length = (tx ** 2 + ty ** 2) ** 0.5
+
+            if length > 0: # Perpendicular unit vector (rotate 90 degrees)
+                perp_x = -ty / length
+                perp_y = tx / length
+            else: # If tangent is zero, spread horizontally
+                perp_x = 1.0
+                perp_y = 0.0
+
+            frame_tracks = []
+            for track_idx in range(num_tracks): # center tracks around the main path offset ranges from -(num_tracks-1)/2 to +(num_tracks-1)/2
+                offset = (track_idx - (num_tracks - 1) / 2) * track_spread_px
+                track_x = x_positions[frame_idx].item() + perp_x * offset
+                track_y = y_positions[frame_idx].item() + perp_y * offset
+                frame_tracks.append([track_x, track_y])
+            track_list.append(frame_tracks)
+
+        tracks = torch.tensor(track_list, dtype=torch.float32, device=device)  # [frames, num_tracks, 2]
+
+        if track_mask is None:
+            track_visibility = torch.ones((track_length, num_tracks), dtype=torch.bool, device=device)
+        else:
+            track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks
+        out_track_info["track_visibility"] = track_visibility
+        return io.NodeOutput(out_track_info, track_length)
+
+
+class WanMoveConcatTrack(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveConcatTrack",
+            category="conditioning/video_models",
+            inputs=[
+                io.Tracks.Input("tracks_1"),
+                io.Tracks.Input("tracks_2", optional=True),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, tracks_1=None, tracks_2=None) -> io.NodeOutput:
+        if tracks_2 is None:
+            return io.NodeOutput(tracks_1)
+
+        tracks_out = torch.cat([tracks_1["track_path"], tracks_2["track_path"]], dim=1)  # Concatenate along the track dimension
+        mask_out = torch.cat([tracks_1["track_visibility"], tracks_2["track_visibility"]], dim=-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks_out
+        out_track_info["track_visibility"] = mask_out
+        return io.NodeOutput(out_track_info)
+
+
+class WanMoveTrackToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveTrackToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Tracks.Input("tracks", optional=True),
+                io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01, tooltip="Strength of the track conditioning."),
+                io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image"),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, strength, tracks=None, start_image=None, clip_vision_output=None) -> io.NodeOutput:
+        device=comfy.model_management.intermediate_device()
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=device)
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5
+            image[:start_image.shape[0]] = start_image
+
+            concat_latent_image = vae.encode(image[:, :, :, :3])
+            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            if tracks is not None and strength > 0.0:
+                tracks_path = tracks["track_path"][:length]  # [T, N, 2]
+                num_tracks = tracks_path.shape[-2]
+
+                track_visibility = tracks.get("track_visibility", torch.ones((length, num_tracks), dtype=torch.bool, device=device))
+
+                track_pos = create_pos_embeddings(tracks_path, track_visibility, [4, 8, 8], height, width, track_num=num_tracks)
+                track_pos = comfy.utils.resize_to_batch_size(track_pos.unsqueeze(0), batch_size)
+                concat_latent_image_pos = replace_feature(concat_latent_image, track_pos, strength)
+            else:
+                concat_latent_image_pos = concat_latent_image
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image_pos, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+
+class WanMoveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            WanMoveTrackToVideo,
+            WanMoveTracksFromCoords,
+            WanMoveConcatTrack,
+            WanMoveVisualizeTracks,
+            GenerateTracks,
+        ]
+
+async def comfy_entrypoint() -> WanMoveExtension:
+    return WanMoveExtension()