Improvements to execution

- Validation errors that occur early in the lifecycle of prompt execution now get propagated to their callers in the EmbeddedComfyClient. This includes error messages about missing node classes. - The execution context now includes the node_id and the prompt_id - Latent previews are now sent with a node_id. This is not backwards compatible with old frontends. - Dependency execution errors are now modeled correctly. - Distributed progress encodes image previews with node and prompt IDs. - Typing for models - The frontend was updated to use node IDs with previews - Improvements to torch.compile experiments - Some controlnet_aux nodes were upstreamed
2026-01-10 06:10:50 +08:00 · 2024-10-10 19:23:35 -07:00 · 2024-10-10 19:23:35 -07:00 · a38968f098
commit a38968f098
parent 69e523b89d
91 changed files with 81056 additions and 64298 deletions
--- a/comfy/client/embedded_comfy_client.py
+++ b/comfy/client/embedded_comfy_client.py
@ -73,7 +73,12 @@ def __execute_prompt(
            from ..cmd.execution import validate_prompt
            validation_tuple = validate_prompt(prompt_mut)
            if not validation_tuple.valid:
-                validation_error_dict = {"message": "Unknown", "details": ""} if not validation_tuple.node_errors or len(validation_tuple.node_errors) == 0 else validation_tuple.node_errors
+                if validation_tuple.node_errors is not None and len(validation_tuple.node_errors) > 0:
+                    validation_error_dict = validation_tuple.node_errors
+                elif validation_tuple.error is not None:
+                    validation_error_dict = validation_tuple.error
+                else:
+                    validation_error_dict = {"message": "Unknown", "details": ""}
                raise ValueError(json.dumps(validation_error_dict))

            if client_id is None:
--- a/comfy/cmd/execution.py
+++ b/comfy/cmd/execution.py
@ -27,7 +27,7 @@ from ..component_model.executor_types import ExecutorToClientProgress, Validatio
    HistoryResultDict
 from ..component_model.files import canonicalize_path
 from ..component_model.queue_types import QueueTuple, HistoryEntry, QueueItem, MAXIMUM_HISTORY_SIZE, ExecutionStatus
-from ..execution_context import new_execution_context, ExecutionContext
+from ..execution_context import new_execution_context, context_execute_node, ExecutionContext
 from ..nodes.package import import_all_nodes_in_workspace
 from ..nodes.package_typing import ExportedNodes, InputTypeSpec, FloatSpecOptions, IntSpecOptions, CustomNode

@ -294,7 +294,24 @@ def format_value(x) -> FormattedValue:
        return str(x)


-def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results):
+def execute(server: ExecutorToClientProgress, dynprompt: DynamicPrompt, caches, _node_id: str, extra_data: dict, executed, prompt_id, execution_list, pending_subgraph_results) -> RecursiveExecutionTuple:
+    """
+
+    :param server:
+    :param dynprompt:
+    :param caches:
+    :param node_id: the node id
+    :param extra_data:
+    :param executed:
+    :param prompt_id:
+    :param execution_list:
+    :param pending_subgraph_results:
+    :return:
+    """
+    with context_execute_node(_node_id, prompt_id):
+        return _execute(server, dynprompt, caches, _node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
+
+def _execute(server, dynprompt, caches, current_item: str, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) -> RecursiveExecutionTuple:
    unique_id = current_item
    real_node_id = dynprompt.get_real_node_id(unique_id)
    display_node_id = dynprompt.get_display_node_id(unique_id)
@ -527,7 +544,7 @@ class PromptExecutor:
            raise ex

    def execute(self, prompt, prompt_id, extra_data=None, execute_outputs: List[str] = None):
-        with new_execution_context(ExecutionContext(self.server)):
+        with new_execution_context(ExecutionContext(self.server, task_id=prompt_id)):
            self._execute_inner(prompt, prompt_id, extra_data, execute_outputs)

    def _execute_inner(self, prompt, prompt_id, extra_data=None, execute_outputs: List[str] = None):
@ -570,6 +587,7 @@ class PromptExecutor:

            while not execution_list.is_empty():
                node_id, error, ex = execution_list.stage_node_execution()
+                node_id: str
                if error is not None:
                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
                    break
--- a/comfy/cmd/latent_preview.py
+++ b/comfy/cmd/latent_preview.py
@ -1,21 +1,25 @@
 from __future__ import annotations

+import logging
+from typing import Optional
+
 import torch
 from PIL import Image
-import numpy as np
-from ..cli_args import args
-from ..cli_args_types import LatentPreviewMethod
-from ..model_downloader import get_or_download, KNOWN_APPROX_VAES
-from ..taesd.taesd import TAESD
-from ..cmd import folder_paths
+
 from .. import model_management
 from .. import utils
-import logging
+from ..cli_args import args
+from ..cli_args_types import LatentPreviewMethod
+from ..cmd import folder_paths
+from ..component_model.executor_types import UnencodedPreviewImageMessage
+from ..execution_context import current_execution_context
+from ..model_downloader import get_or_download, KNOWN_APPROX_VAES
+from ..taesd.taesd import TAESD

 MAX_PREVIEW_RESOLUTION = args.preview_size


-def preview_to_image(latent_image):
+def preview_to_image(latent_image) -> Image:
    latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
                        .mul(0xFF)  # to 0..255
                        ).to(device="cpu", dtype=torch.uint8, non_blocking=model_management.device_supports_non_blocking(latent_image.device))
@ -24,19 +28,20 @@ def preview_to_image(latent_image):


 class LatentPreviewer:
-    def decode_latent_to_preview(self, x0):
+    def decode_latent_to_preview(self, x0) -> Image:
        raise NotImplementedError

-    def decode_latent_to_preview_image(self, preview_format, x0):
+    def decode_latent_to_preview_image(self, preview_format, x0) -> UnencodedPreviewImageMessage:
+        ctx = current_execution_context()
        preview_image = self.decode_latent_to_preview(x0)
-        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
+        return UnencodedPreviewImageMessage(preview_format, preview_image, MAX_PREVIEW_RESOLUTION, ctx.node_id, ctx.task_id)


 class TAESDPreviewerImpl(LatentPreviewer):
    def __init__(self, taesd):
        self.taesd = taesd

-    def decode_latent_to_preview(self, x0):
+    def decode_latent_to_preview(self, x0) -> bytes:
        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
        return preview_to_image(x_sample)

@ -101,7 +106,7 @@ def prepare_callback(model, steps, x0_output_dict=None):
        if x0_output_dict is not None:
            x0_output_dict["x0"] = x0

-        preview_bytes = None
+        preview_bytes: Optional[UnencodedPreviewImageMessage] = None
        if previewer:
            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
        pbar.update_absolute(step + 1, total_steps, preview_bytes)
--- a/comfy/cmd/latent_preview_image_encoding.py
+++ b/comfy/cmd/latent_preview_image_encoding.py
@ -8,22 +8,39 @@ import PIL.Image
 from PIL import Image, ImageOps


-def encode_preview_image(image: PIL.Image.Image, image_type: Literal["JPEG", "PNG"], max_size: int):
+def encode_preview_image(image: PIL.Image.Image, image_type: Literal["JPEG", "PNG"], max_size: int, node_id: str = "", task_id: str = "") -> bytes:
    if max_size is not None:
        if hasattr(Image, 'Resampling'):
            resampling = Image.Resampling.BILINEAR
        else:
-            resampling = Image.Resampling.LANCZOS
+            resampling = Image.LANCZOS

        image = ImageOps.contain(image, (max_size, max_size), resampling)
-    type_num = 1
+
+    has_ids = (node_id is not None and len(node_id) > 0) or (task_id is not None and len(task_id) > 0)
+
    if image_type == "JPEG":
-        type_num = 1
+        type_num = 3 if has_ids else 1
    elif image_type == "PNG":
-        type_num = 2
+        type_num = 4 if has_ids else 2
+    else:
+        raise ValueError(f"Unsupported image type: {image_type}")
+
    bytesIO = BytesIO()
-    header = struct.pack(">I", type_num)
-    bytesIO.write(header)
+
+    if has_ids:
+        # Pack the header with type_num, node_id length, task_id length
+        node_id = node_id or ""
+        task_id = task_id or ""
+        header = struct.pack(">III", type_num, len(node_id), len(task_id))
+        bytesIO.write(header)
+        bytesIO.write(node_id.encode('utf-8'))
+        bytesIO.write(task_id.encode('utf-8'))
+    else:
+        # Pack only the type_num for types 1 and 2
+        header = struct.pack(">I", type_num)
+        bytesIO.write(header)
+
    image.save(bytesIO, format=image_type, quality=95, compress_level=1)
    preview_bytes = bytesIO.getvalue()
    return preview_bytes
--- a/comfy/component_model/executor_types.py
+++ b/comfy/component_model/executor_types.py
@ -46,6 +46,8 @@ class UnencodedPreviewImageMessage(NamedTuple):
    format: Literal["JPEG", "PNG"]
    pil_image: PIL.Image.Image
    max_size: int = 512
+    node_id: str = ""
+    task_id: str = ""


 class ExecutionErrorMessage(TypedDict):
@ -60,6 +62,14 @@ class ExecutionErrorMessage(TypedDict):
    current_outputs: list[str]


+class DependencyExecutionErrorMessage(TypedDict):
+    node_id: str
+    exception_message: str
+    exception_type: Literal["graph.DependencyCycleError"]
+    traceback: list[typing.Never]
+    current_inputs: list[typing.Never]
+
+
 ExecutedMessage = ExecutingMessage

 SendSyncEvent = Union[Literal["status", "execution_error", "executing", "progress", "executed"], BinaryEventTypes, None]
@ -144,7 +154,7 @@ class NodeErrorsDictValue(TypedDict, total=False):

 class ValidationTuple(typing.NamedTuple):
    valid: bool
-    error: Optional[ValidationErrorDict]
+    error: Optional[ValidationErrorDict | DependencyExecutionErrorMessage]
    good_output_node_ids: List[str]
    node_errors: list[typing.Never] | typing.Dict[str, NodeErrorsDictValue]

--- a/comfy/distributed/distributed_progress.py
+++ b/comfy/distributed/distributed_progress.py
@ -49,8 +49,8 @@ class DistributedExecutorToClientProgress(ExecutorToClientProgress):
            # encode preview image
            event = BinaryEventTypes.PREVIEW_IMAGE.value
            data: UnencodedPreviewImageMessage
-            format, pil_image, max_size = data
-            data: bytes = encode_preview_image(pil_image, format, max_size)
+            format, pil_image, max_size, node_id, task_id = data
+            data: bytes = encode_preview_image(pil_image, format, max_size, node_id, task_id)

        if isinstance(data, bytes) or isinstance(data, bytearray):
            if isinstance(event, Enum):
--- a/comfy/execution_context.py
+++ b/comfy/execution_context.py
@ -2,7 +2,7 @@ from __future__ import annotations

 from contextlib import contextmanager
 from contextvars import ContextVar
-from typing import NamedTuple
+from typing import NamedTuple, Optional

 from .component_model.executor_types import ExecutorToClientProgress
 from .distributed.server_stub import ServerStub
@ -12,6 +12,8 @@ _current_context = ContextVar("comfyui_execution_context")

 class ExecutionContext(NamedTuple):
    server: ExecutorToClientProgress
+    node_id: Optional[str] = None
+    task_id: Optional[str] = None


 _empty_execution_context = ExecutionContext(ServerStub())
@ -31,3 +33,11 @@ def new_execution_context(ctx: ExecutionContext):
        yield
    finally:
        _current_context.reset(token)
+
+
+@contextmanager
+def context_execute_node(node_id: str, prompt_id: str):
+    current_ctx = current_execution_context()
+    new_ctx = ExecutionContext(current_ctx.server, node_id, prompt_id)
+    with new_execution_context(new_ctx):
+        yield
--- a/comfy/graph.py
+++ b/comfy/graph.py
@ -1,5 +1,7 @@
+from typing import Optional
+
 from .cmd.execution import nodes
-from .component_model.executor_types import DependencyCycleError, NodeInputError, NodeNotFoundError
+from .component_model.executor_types import DependencyCycleError, NodeInputError, NodeNotFoundError, DependencyExecutionErrorMessage
 from .graph_utils import is_link


@ -159,7 +161,7 @@ class ExecutionList(TopologicalSort):
    def is_cached(self, node_id):
        return self.output_cache.get(node_id) is not None

-    def stage_node_execution(self):
+    def stage_node_execution(self) -> tuple[Optional[str], Optional[DependencyExecutionErrorMessage], Optional[DependencyCycleError]]:
        assert self.staged_node_id is None
        if self.is_empty():
            return None, None, None
@ -187,7 +189,7 @@ class ExecutionList(TopologicalSort):
        self.staged_node_id = self.ux_friendly_pick_node(available)
        return self.staged_node_id, None, None

-    def ux_friendly_pick_node(self, node_list):
+    def ux_friendly_pick_node(self, node_list) -> str:
        # If an output node is available, do that first.
        # Technically this has no effect on the overall length of execution, but it feels better as a user
        # for a PreviewImage to display a result as soon as it can
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@ -19,7 +19,7 @@
 import logging
 import math
 from enum import Enum
-from typing import TypeVar, Type
+from typing import TypeVar, Type, Protocol, Any, Optional

 import torch

@ -91,6 +91,11 @@ def model_sampling(model_config, model_type):
 TModule = TypeVar('TModule', bound=torch.nn.Module)


+class ComfyUIModel(Protocol):
+    def __call__(self, xc: torch.Tensor, t: torch.Tensor, context: Any = None, control: Any = None, transformer_options: Optional[dict] = None, **extra_conds: dict[str, Any]) -> Any:
+        ...
+
+
 class BaseModel(torch.nn.Module):
    def __init__(self, model_config, model_type=ModelType.EPS, device: torch.device = None, unet_model: Type[TModule] = UNetModel):
        super().__init__()
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@ -17,6 +17,7 @@
 """
 from __future__ import annotations

+import gc
 import logging
 import platform
 import sys
--- a/comfy/model_management_types.py
+++ b/comfy/model_management_types.py
@ -1,10 +1,11 @@
 from __future__ import annotations

 import dataclasses
-from typing import Protocol, Optional, TypeVar, runtime_checkable
+from typing import Protocol, Optional, TypeVar, runtime_checkable, Callable

 import torch
 import torch.nn
+from typing_extensions import TypedDict, NotRequired

 ModelManageableT = TypeVar('ModelManageableT', bound='ModelManageable')

@ -109,6 +110,9 @@ class ModelManageable(Protocol):
    def model_options(self, value):
        setattr(self, "_model_options", value)

+    def __del__(self):
+        del self.model
+

@dataclasses.dataclass
 class MemoryMeasurements:
@ -130,3 +134,20 @@ class MemoryMeasurements:
        if isinstance(self.model, DeviceSettable):
            self.model.device = value
        self._device = value
+
+
+class TransformerOptions(TypedDict, total=False):
+    cond_or_uncond: NotRequired[list]
+    patches: NotRequired[dict]
+    sigmas: NotRequired[torch.Tensor]
+
+
+class ModelOptions(TypedDict, total=False):
+    transformer_options: NotRequired[dict]
+    # signature of BaseModel.apply_model
+    model_function_wrapper: NotRequired[Callable]
+    sampler_cfg_function: NotRequired[Callable]
+    sampler_post_cfg_function: NotRequired[list[Callable]]
+    disable_cfg1_optimization: NotRequired[bool]
+    denoise_mask_function: NotRequired[Callable]
+    patches: NotRequired[dict[str, list]]
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@ -30,7 +30,7 @@ from . import utils
 from .comfy_types import UnetWrapperFunction
 from .float import stochastic_rounding
 from .model_base import BaseModel
-from .model_management_types import ModelManageable, MemoryMeasurements
+from .model_management_types import ModelManageable, MemoryMeasurements, ModelOptions


 def string_to_seed(data):
@ -103,7 +103,7 @@ class LowVramPatch:
 class ModelPatcher(ModelManageable):
    def __init__(self, model: torch.nn.Module, load_device: torch.device, offload_device: torch.device, size=0, weight_inplace_update=False, ckpt_name: Optional[str] = None):
        self.size = size
-        self.model: torch.nn.Module = model
+        self.model: torch.nn.Module | BaseModel = model
        self.patches = {}
        self.backup = {}
        self.object_patches = {}
@ -118,11 +118,11 @@ class ModelPatcher(ModelManageable):
        self._memory_measurements = MemoryMeasurements(self.model)

    @property
-    def model_options(self) -> dict:
+    def model_options(self) -> ModelOptions:
        return self._model_options

    @model_options.setter
-    def model_options(self, value):
+    def model_options(self, value: ModelOptions):
        self._model_options = value

    @property
@ -249,7 +249,7 @@ class ModelPatcher(ModelManageable):
                return utils.get_attr(self.model, name)

    @property
-    def diffusion_model(self) -> BaseModel:
+    def diffusion_model(self) -> torch.nn.Module | BaseModel:
        return self.get_model_object("diffusion_model")

    @diffusion_model.setter
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@ -1,8 +1,20 @@
-import torch
-from .ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
+from typing import Protocol

-class EPS:
+import torch
+
+from .ldm.modules.diffusionmodules.util import make_beta_schedule
+
+
+class ModelSampling(Protocol):
+    def calculate_input(self, sigma, noise) -> torch.Tensor:
+        ...
+
+    def calculate_denoised(self, sigma, model_output, model_input) -> torch.Tensor:
+        ...
+
+
+class EPS(ModelSampling):
    sigma_data: float

    def calculate_input(self, sigma, noise):
@ -25,17 +37,20 @@ class EPS:
    def inverse_noise_scaling(self, sigma, latent):
        return latent

+
 class V_PREDICTION(EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

+
 class EDM(V_PREDICTION):
    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

-class CONST:
+
+class CONST(ModelSampling):
    def calculate_input(self, sigma, noise):
        return noise

@ -49,6 +64,7 @@ class CONST:
    def inverse_noise_scaling(self, sigma, latent):
        return latent / (1.0 - sigma)

+
 class ModelSamplingDiscrete(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
@ -67,7 +83,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
        self.sigma_data = 1.0

    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+                           linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        if given_betas is not None:
            betas = given_betas
        else:
@ -120,6 +136,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
        percent = 1.0 - percent
        return self.sigma(torch.tensor(percent * 999.0)).item()

+
 class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
    def timestep(self, sigma):
        return 0.25 * sigma.log()
@ -127,6 +144,7 @@ class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
    def sigma(self, timestep):
        return (timestep / 0.25).exp()

+
 class ModelSamplingContinuousEDM(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
@ -144,7 +162,7 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
        self.sigma_data = sigma_data
        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()

-        self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
+        self.register_buffer('sigmas', sigmas)  # for compatibility with some schedulers
        self.register_buffer('log_sigmas', sigmas.log())

    @property
@ -185,6 +203,7 @@ def time_snr_shift(alpha, t):
        return t
    return alpha * t / (1 + (alpha - 1) * t)

+
 class ModelSamplingDiscreteFlow(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
@ -222,6 +241,7 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
            return 0.0
        return 1.0 - percent

+
 class StableCascadeSampling(ModelSamplingDiscrete):
    def __init__(self, model_config=None):
        super().__init__()
@ -238,7 +258,7 @@ class StableCascadeSampling(ModelSamplingDiscrete):
        self.cosine_s = torch.tensor(cosine_s)
        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2

-        #This part is just for compatibility with some schedulers in the codebase
+        # This part is just for compatibility with some schedulers in the codebase
        self.num_timesteps = 10000
        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
        for x in range(self.num_timesteps):
@ -252,7 +272,7 @@ class StableCascadeSampling(ModelSamplingDiscrete):

        if self.shift != 1.0:
            var = alpha_cumprod
-            logSNR = (var/(1-var)).log()
+            logSNR = (var / (1 - var)).log()
            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
            alpha_cumprod = logSNR.sigmoid()

@ -279,6 +299,7 @@ class StableCascadeSampling(ModelSamplingDiscrete):
 def flux_time_shift(mu: float, sigma: float, t):
    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)

+
 class ModelSamplingFlux(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
--- a/comfy/ops.py
+++ b/comfy/ops.py
@ -15,12 +15,14 @@
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
+from typing import Optional

 import torch

 from . import model_management
 from .cli_args import args

+
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
    if device is None or weight.device == device:
        if not copy:
@ -60,15 +62,18 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
        weight = s.weight_function(weight)
    return weight, bias

+
 class SkipInit:
    def reset_parameters(self):
        return None

+
 class CastWeightBiasOp:
    comfy_cast_weights = False
    weight_function = None
    bias_function = None

+
 class skip_init:
    class Linear(SkipInit, torch.nn.Linear):
        pass
@ -106,6 +111,7 @@ class skip_init:
        else:
            raise ValueError(f"unsupported dimensions: {dims}")

+
 class disable_weight_init:
    class Linear(torch.nn.Linear, CastWeightBiasOp):
        def reset_parameters(self):
@ -326,6 +332,7 @@ def fp8_linear(self, input):
        return o.reshape((-1, input.shape[1], self.weight.shape[0]))
    return None

+
 class fp8_ops(manual_cast):
    class Linear(manual_cast.Linear):
        def reset_parameters(self):
@ -342,9 +349,13 @@ class fp8_ops(manual_cast):
            return torch.nn.functional.linear(input, weight, bias)


-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False):
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, inference_mode: Optional[bool] = None):
+    if inference_mode is None:
+        # todo: check a context here, since this isn't being used by any callers yet
+        inference_mode = False
    if compute_dtype is None or weight_dtype == compute_dtype:
-        return disable_weight_init
+        # disable_weight_init seems to interact poorly with some other optimization code
+        return disable_weight_init if inference_mode else skip_init
    if args.fast and not disable_fast_fp8:
        if model_management.supports_fp8_compute(load_device):
            return fp8_ops
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@ -1,3 +1,4 @@
+from .component_model.deprecation import _deprecate_method
 from .k_diffusion import sampling as k_diffusion_sampling
 from .extra_samplers import uni_pc
 import torch
@ -8,6 +9,8 @@ import logging
 import scipy.stats
 import numpy
 from . import sampler_helpers
+from .model_base import BaseModel
+from .model_management_types import ModelOptions

 from .sampler_names import SCHEDULER_NAMES, SAMPLER_NAMES

@ -141,7 +144,7 @@ def cond_cat(c_list):

    return out

-def calc_cond_batch(model, conds, x_in, timestep, model_options):
+def calc_cond_batch(model: BaseModel, conds, x_in: torch.Tensor, timestep: torch.Tensor, model_options: ModelOptions):
    out_conds = []
    out_counts = []
    to_run = []
@ -251,11 +254,13 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options):

    return out_conds

+@_deprecate_method(version="0.0.2", message="The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
 def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
-    logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
    return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))

-def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
+def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options:ModelOptions=None, cond=None, uncond=None):
+    if model_options is None:
+        model_options = {}
    if "sampler_cfg_function" in model_options:
        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
@ -272,7 +277,9 @@ def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_o

 #The main sampling function shared by all the samplers
 #Returns denoised
-def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options:ModelOptions=None, seed=None):
+    if model_options is None:
+        model_options = {}
    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
        uncond_ = None
    else:
@ -293,7 +300,9 @@ class KSamplerX0Inpaint:
    def __init__(self, model, sigmas):
        self.inner_model = model
        self.sigmas = sigmas
-    def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
+    def __call__(self, x, sigma, denoise_mask, model_options:ModelOptions=None, seed=None):
+        if model_options is None:
+            model_options = {}
        if denoise_mask is not None:
            if "denoise_mask_function" in model_options:
                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
--- a/comfy/web/assets/ExtensionPanel-DRj-ifqx.js
+++ b/comfy/web/assets/ExtensionPanel-DRj-ifqx.js
@ -0,0 +1,103 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, bJ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bK as script$1, A as createBaseVNode, x as createBlock, M as Fragment, N as renderList, ak as toDisplayString, an as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-DuO3ZraP.js";
+import { s as script, a as script$2, b as script$3 } from "./index-Bn9riyir.js";
+import "./index-BmmdKyuw.js";
+const _hoisted_1 = { class: "extension-panel" };
+const _hoisted_2 = { class: "mt-4" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ExtensionPanel",
+  setup(__props) {
+    const extensionStore = useExtensionStore();
+    const settingStore = useSettingStore();
+    const editingEnabledExtensions = ref({});
+    onMounted(() => {
+      extensionStore.extensions.forEach((ext) => {
+        editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name);
+      });
+    });
+    const changedExtensions = computed(() => {
+      return extensionStore.extensions.filter(
+        (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name)
+      );
+    });
+    const hasChanges = computed(() => {
+      return changedExtensions.value.length > 0;
+    });
+    const updateExtensionStatus = /* @__PURE__ */ __name(() => {
+      const editingDisabledExtensionNames = Object.entries(
+        editingEnabledExtensions.value
+      ).filter(([_, enabled]) => !enabled).map(([name]) => name);
+      settingStore.set("Comfy.Extension.Disabled", [
+        ...extensionStore.inactiveDisabledExtensionNames,
+        ...editingDisabledExtensionNames
+      ]);
+    }, "updateExtensionStatus");
+    const applyChanges = /* @__PURE__ */ __name(() => {
+      window.location.reload();
+    }, "applyChanges");
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createVNode(unref(script$2), {
+          value: unref(extensionStore).extensions,
+          stripedRows: "",
+          size: "small"
+        }, {
+          default: withCtx(() => [
+            createVNode(unref(script), {
+              field: "name",
+              header: _ctx.$t("extensionName"),
+              sortable: ""
+            }, null, 8, ["header"]),
+            createVNode(unref(script), { pt: {
+              bodyCell: "flex items-center justify-end"
+            } }, {
+              body: withCtx((slotProps) => [
+                createVNode(unref(script$1), {
+                  modelValue: editingEnabledExtensions.value[slotProps.data.name],
+                  "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"),
+                  onChange: updateExtensionStatus
+                }, null, 8, ["modelValue", "onUpdate:modelValue"])
+              ]),
+              _: 1
+            })
+          ]),
+          _: 1
+        }, 8, ["value"]),
+        createBaseVNode("div", _hoisted_2, [
+          hasChanges.value ? (openBlock(), createBlock(unref(script$3), {
+            key: 0,
+            severity: "info"
+          }, {
+            default: withCtx(() => [
+              createBaseVNode("ul", null, [
+                (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => {
+                  return openBlock(), createElementBlock("li", {
+                    key: ext.name
+                  }, [
+                    createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1),
+                    createTextVNode(" " + toDisplayString(ext.name), 1)
+                  ]);
+                }), 128))
+              ])
+            ]),
+            _: 1
+          })) : createCommentVNode("", true),
+          createVNode(unref(script$4), {
+            label: _ctx.$t("reloadToApplyChanges"),
+            icon: "pi pi-refresh",
+            onClick: applyChanges,
+            disabled: !hasChanges.value,
+            text: "",
+            fluid: "",
+            severity: "danger"
+          }, null, 8, ["label", "disabled"])
+        ])
+      ]);
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ExtensionPanel-DRj-ifqx.js.map
--- a/comfy/web/assets/ExtensionPanel-DRj-ifqx.js.map
+++ b/comfy/web/assets/ExtensionPanel-DRj-ifqx.js.map
@ -0,0 +1 @@
+{"version":3,"file":"ExtensionPanel-DRj-ifqx.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <div class=\"extension-panel\">\n    <DataTable :value=\"extensionStore.extensions\" stripedRows size=\"small\">\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n    <div class=\"mt-4\">\n      <Message v-if=\"hasChanges\" severity=\"info\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n      </Message>\n      <Button\n        :label=\"$t('reloadToApplyChanges')\"\n        icon=\"pi pi-refresh\"\n        @click=\"applyChanges\"\n        :disabled=\"!hasChanges\"\n        text\n        fluid\n        severity=\"danger\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}
--- a/comfy/web/assets/GraphView-Cyr4-Q0E.js
+++ b/comfy/web/assets/GraphView-Cyr4-Q0E.js
--- a/comfy/web/assets/GraphView-Cyr4-Q0E.js.map
+++ b/comfy/web/assets/GraphView-Cyr4-Q0E.js.map
--- a/comfy/web/assets/GraphView-DCFfls4D.css
+++ b/comfy/web/assets/GraphView-DCFfls4D.css
@ -45,7 +45,7 @@
  --sidebar-icon-size: 1rem;
 }

-.side-tool-bar-container[data-v-ed7a1148] {
+.side-tool-bar-container[data-v-aa14277f] {
  display: flex;
  flex-direction: column;
  align-items: center;
@ -58,11 +58,11 @@
  background-color: var(--comfy-menu-bg);
  color: var(--fg-color);
 }
-.side-tool-bar-end[data-v-ed7a1148] {
+.side-tool-bar-end[data-v-aa14277f] {
  align-self: flex-end;
  margin-top: auto;
 }
-.sidebar-content-container[data-v-ed7a1148] {
+.sidebar-content-container[data-v-aa14277f] {
  height: 100%;
  overflow-y: auto;
 }
@ -74,11 +74,11 @@
  display: none !important;
 }

-.side-bar-panel[data-v-edca8328] {
+.side-bar-panel[data-v-b9df3042] {
  background-color: var(--bg-color);
  pointer-events: auto;
 }
-.splitter-overlay[data-v-edca8328] {
+.splitter-overlay[data-v-b9df3042] {
  width: 100%;
  height: 100%;
  position: absolute;
@ -130,18 +130,24 @@
 }

 .invisible-dialog-root {
-  width: 30%;
+  width: 60%;
  min-width: 24rem;
  max-width: 48rem;
  border: 0 !important;
  background-color: transparent !important;
  margin-top: 25vh;
+  margin-left: 400px;
+}
+@media all and (max-width: 768px) {
+.invisible-dialog-root {
+    margin-left: 0px;
+}
 }
 .node-search-box-dialog-mask {
  align-items: flex-start !important;
 }

-.node-tooltip[data-v-e0597bf9] {
+.node-tooltip[data-v-79ec8c53] {
  background: var(--comfy-input-bg);
  border-radius: 5px;
  box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
@ -156,3 +162,121 @@
  white-space: pre-wrap;
  z-index: 99999;
 }
+
+.p-buttongroup-vertical[data-v-444d3768] {
+  display: flex;
+  flex-direction: column;
+  border-radius: var(--p-button-border-radius);
+  overflow: hidden;
+  border: 1px solid var(--p-panel-border-color);
+}
+.p-buttongroup-vertical .p-button[data-v-444d3768] {
+  margin: 0;
+  border-radius: 0;
+}
+
+[data-v-84e785b8] .p-togglebutton::before {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  background-color: transparent;
+  padding-left: 0.5rem;
+  padding-right: 0.5rem
+}
+[data-v-84e785b8] .p-togglebutton.p-togglebutton-checked {
+  border-bottom-width: 2px;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-84e785b8] .p-togglebutton-checked .close-button,[data-v-84e785b8] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+.status-indicator[data-v-84e785b8] {
+  position: absolute;
+  font-weight: 700;
+  font-size: 1.5rem;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%)
+}
+[data-v-84e785b8] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-84e785b8] .p-togglebutton .close-button {
+  visibility: hidden
+}
+
+.top-menubar[data-v-9646ca0a] .p-menubar-item-link svg {
+  display: none;
+}
+[data-v-9646ca0a] .p-menubar-submenu.dropdown-direction-up {
+  top: auto;
+  bottom: 100%;
+  flex-direction: column-reverse;
+}
+.keybinding-tag[data-v-9646ca0a] {
+  background: var(--p-content-hover-background);
+  border-color: var(--p-content-border-color);
+  border-style: solid;
+}
+
+[data-v-713442be] .p-inputtext {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.comfyui-queue-button[data-v-2b80bf74] .p-splitbutton-dropdown {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.actionbar[data-v-2e54db00] {
+  pointer-events: all;
+  position: fixed;
+  z-index: 1000;
+}
+.actionbar.is-docked[data-v-2e54db00] {
+  position: static;
+  border-style: none;
+  background-color: transparent;
+  padding: 0px;
+}
+.actionbar.is-dragging[data-v-2e54db00] {
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+}
+[data-v-2e54db00] .p-panel-content {
+  padding: 0.25rem;
+}
+[data-v-2e54db00] .p-panel-header {
+  display: none;
+}
+
+.comfyui-menu[data-v-b13fdc92] {
+  width: 100vw;
+  background: var(--comfy-menu-bg);
+  color: var(--fg-color);
+  font-family: Arial, Helvetica, sans-serif;
+  font-size: 0.8em;
+  box-sizing: border-box;
+  z-index: 1000;
+  order: 0;
+  grid-column: 1/-1;
+  max-height: 90vh;
+}
+.comfyui-menu.dropzone[data-v-b13fdc92] {
+  background: var(--p-highlight-background);
+}
+.comfyui-menu.dropzone-active[data-v-b13fdc92] {
+  background: var(--p-highlight-background-focus);
+}
+.comfyui-logo[data-v-b13fdc92] {
+  font-size: 1.2em;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+  cursor: default;
+}
--- a/comfy/web/assets/GraphView-DN9xGvF3.js
+++ b/comfy/web/assets/GraphView-DN9xGvF3.js
--- a/comfy/web/assets/GraphView-DN9xGvF3.js.map
+++ b/comfy/web/assets/GraphView-DN9xGvF3.js.map
--- a/comfy/web/assets/KeybindingPanel-B7UdrpRg.js
+++ b/comfy/web/assets/KeybindingPanel-B7UdrpRg.js
@ -0,0 +1,263 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, M as Fragment, N as renderList, i as createVNode, y as withCtx, an as createTextVNode, ak as toDisplayString, z as unref, ar as script, j as createCommentVNode, r as ref, bG as FilterMatchMode, K as useKeybindingStore, F as useCommandStore, aA as watchEffect, aY as useToast, t as resolveDirective, bH as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ad as script$4, b1 as withModifiers, ay as script$6, v as withDirectives, bx as KeyComboImpl, bI as KeybindingImpl, _ as _export_sfc } from "./index-DuO3ZraP.js";
+import { s as script$1, a as script$3, b as script$5 } from "./index-Bn9riyir.js";
+import "./index-BmmdKyuw.js";
+const _hoisted_1$1 = {
+  key: 0,
+  class: "px-2"
+};
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "KeyComboDisplay",
+  props: {
+    keyCombo: {},
+    isModified: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    const keySequences = computed(() => props.keyCombo.getKeySequences());
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("span", null, [
+        (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => {
+          return openBlock(), createElementBlock(Fragment, { key: index }, [
+            createVNode(unref(script), {
+              severity: _ctx.isModified ? "info" : "secondary"
+            }, {
+              default: withCtx(() => [
+                createTextVNode(toDisplayString(sequence), 1)
+              ]),
+              _: 2
+            }, 1032, ["severity"]),
+            index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true)
+          ], 64);
+        }), 128))
+      ]);
+    };
+  }
+});
+const _hoisted_1 = { class: "keybinding-panel" };
+const _hoisted_2 = { class: "actions invisible" };
+const _hoisted_3 = { key: 1 };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "KeybindingPanel",
+  setup(__props) {
+    const filters = ref({
+      global: { value: "", matchMode: FilterMatchMode.CONTAINS }
+    });
+    const keybindingStore = useKeybindingStore();
+    const commandStore = useCommandStore();
+    const commandsData = computed(() => {
+      return Object.values(commandStore.commands).map((command) => ({
+        id: command.id,
+        keybinding: keybindingStore.getKeybindingByCommandId(command.id)
+      }));
+    });
+    const selectedCommandData = ref(null);
+    const editDialogVisible = ref(false);
+    const newBindingKeyCombo = ref(null);
+    const currentEditingCommand = ref(null);
+    const keybindingInput = ref(null);
+    const existingKeybindingOnCombo = computed(() => {
+      if (!currentEditingCommand.value) {
+        return null;
+      }
+      if (currentEditingCommand.value.keybinding?.combo?.equals(
+        newBindingKeyCombo.value
+      )) {
+        return null;
+      }
+      if (!newBindingKeyCombo.value) {
+        return null;
+      }
+      return keybindingStore.getKeybinding(newBindingKeyCombo.value);
+    });
+    function editKeybinding(commandData) {
+      currentEditingCommand.value = commandData;
+      newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null;
+      editDialogVisible.value = true;
+    }
+    __name(editKeybinding, "editKeybinding");
+    watchEffect(() => {
+      if (editDialogVisible.value) {
+        setTimeout(() => {
+          keybindingInput.value?.$el?.focus();
+        }, 300);
+      }
+    });
+    function removeKeybinding(commandData) {
+      if (commandData.keybinding) {
+        keybindingStore.unsetKeybinding(commandData.keybinding);
+        keybindingStore.persistUserKeybindings();
+      }
+    }
+    __name(removeKeybinding, "removeKeybinding");
+    function captureKeybinding(event) {
+      const keyCombo = KeyComboImpl.fromEvent(event);
+      newBindingKeyCombo.value = keyCombo;
+    }
+    __name(captureKeybinding, "captureKeybinding");
+    function cancelEdit() {
+      editDialogVisible.value = false;
+      currentEditingCommand.value = null;
+      newBindingKeyCombo.value = null;
+    }
+    __name(cancelEdit, "cancelEdit");
+    function saveKeybinding() {
+      if (currentEditingCommand.value && newBindingKeyCombo.value) {
+        const updated = keybindingStore.updateKeybindingOnCommand(
+          new KeybindingImpl({
+            commandId: currentEditingCommand.value.id,
+            combo: newBindingKeyCombo.value
+          })
+        );
+        if (updated) {
+          keybindingStore.persistUserKeybindings();
+        }
+      }
+      cancelEdit();
+    }
+    __name(saveKeybinding, "saveKeybinding");
+    const toast = useToast();
+    async function resetKeybindings() {
+      keybindingStore.resetKeybindings();
+      await keybindingStore.persistUserKeybindings();
+      toast.add({
+        severity: "info",
+        summary: "Info",
+        detail: "Keybindings reset",
+        life: 3e3
+      });
+    }
+    __name(resetKeybindings, "resetKeybindings");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createVNode(unref(script$3), {
+          value: commandsData.value,
+          selection: selectedCommandData.value,
+          "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
+          "global-filter-fields": ["id"],
+          filters: filters.value,
+          selectionMode: "single",
+          stripedRows: "",
+          pt: {
+            header: "px-0"
+          }
+        }, {
+          header: withCtx(() => [
+            createVNode(SearchBox, {
+              modelValue: filters.value["global"].value,
+              "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
+              placeholder: _ctx.$t("searchKeybindings") + "..."
+            }, null, 8, ["modelValue", "placeholder"])
+          ]),
+          default: withCtx(() => [
+            createVNode(unref(script$1), {
+              field: "actions",
+              header: ""
+            }, {
+              body: withCtx((slotProps) => [
+                createBaseVNode("div", _hoisted_2, [
+                  createVNode(unref(script$2), {
+                    icon: "pi pi-pencil",
+                    class: "p-button-text",
+                    onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick")
+                  }, null, 8, ["onClick"]),
+                  createVNode(unref(script$2), {
+                    icon: "pi pi-trash",
+                    class: "p-button-text p-button-danger",
+                    onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"),
+                    disabled: !slotProps.data.keybinding
+                  }, null, 8, ["onClick", "disabled"])
+                ])
+              ]),
+              _: 1
+            }),
+            createVNode(unref(script$1), {
+              field: "id",
+              header: "Command ID",
+              sortable: ""
+            }),
+            createVNode(unref(script$1), {
+              field: "keybinding",
+              header: "Keybinding"
+            }, {
+              body: withCtx((slotProps) => [
+                slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
+                  key: 0,
+                  keyCombo: slotProps.data.keybinding.combo,
+                  isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
+                }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
+              ]),
+              _: 1
+            })
+          ]),
+          _: 1
+        }, 8, ["value", "selection", "filters"]),
+        createVNode(unref(script$6), {
+          class: "min-w-96",
+          visible: editDialogVisible.value,
+          "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
+          modal: "",
+          header: currentEditingCommand.value?.id,
+          onHide: cancelEdit
+        }, {
+          footer: withCtx(() => [
+            createVNode(unref(script$2), {
+              label: "Save",
+              icon: "pi pi-check",
+              onClick: saveKeybinding,
+              disabled: !!existingKeybindingOnCombo.value,
+              autofocus: ""
+            }, null, 8, ["disabled"])
+          ]),
+          default: withCtx(() => [
+            createBaseVNode("div", null, [
+              createVNode(unref(script$4), {
+                class: "mb-2 text-center",
+                ref_key: "keybindingInput",
+                ref: keybindingInput,
+                modelValue: newBindingKeyCombo.value?.toString() ?? "",
+                placeholder: "Press keys for new binding",
+                onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]),
+                autocomplete: "off",
+                fluid: "",
+                invalid: !!existingKeybindingOnCombo.value
+              }, null, 8, ["modelValue", "invalid"]),
+              existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), {
+                key: 0,
+                severity: "error"
+              }, {
+                default: withCtx(() => [
+                  _cache[3] || (_cache[3] = createTextVNode(" Keybinding already exists on ")),
+                  createVNode(unref(script), {
+                    severity: "secondary",
+                    value: existingKeybindingOnCombo.value.commandId
+                  }, null, 8, ["value"])
+                ]),
+                _: 1
+              })) : createCommentVNode("", true)
+            ])
+          ]),
+          _: 1
+        }, 8, ["visible", "header"]),
+        withDirectives(createVNode(unref(script$2), {
+          class: "mt-4",
+          label: _ctx.$t("reset"),
+          icon: "pi pi-trash",
+          severity: "danger",
+          fluid: "",
+          text: "",
+          onClick: resetKeybindings
+        }, null, 8, ["label"]), [
+          [_directive_tooltip, _ctx.$t("resetKeybindingsTooltip")]
+        ])
+      ]);
+    };
+  }
+});
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e5724e4d"]]);
+export {
+  KeybindingPanel as default
+};
+//# sourceMappingURL=KeybindingPanel-B7UdrpRg.js.map
--- a/comfy/web/assets/KeybindingPanel-B7UdrpRg.js.map
+++ b/comfy/web/assets/KeybindingPanel-B7UdrpRg.js.map
--- a/comfy/web/assets/KeybindingPanel-BNYKhW1k.css
+++ b/comfy/web/assets/KeybindingPanel-BNYKhW1k.css
@ -0,0 +1,8 @@
+
+[data-v-e5724e4d] .p-datatable-tbody > tr > td {
+  padding: 1px;
+  min-height: 2rem;
+}
+[data-v-e5724e4d] .p-datatable-row-selected .actions,[data-v-e5724e4d] .p-datatable-selectable-row:hover .actions {
+  visibility: visible;
+}
--- a/comfy/web/assets/index-BAiAtn2q.js
+++ b/comfy/web/assets/index-BAiAtn2q.js
--- a/comfy/web/assets/index-BAiAtn2q.js.map
+++ b/comfy/web/assets/index-BAiAtn2q.js.map
--- a/comfy/web/assets/index-BDBCRrlL.js.map
+++ b/comfy/web/assets/index-BDBCRrlL.js.map
--- a/comfy/web/assets/index-BmmdKyuw.js
+++ b/comfy/web/assets/index-BmmdKyuw.js
@ -0,0 +1,46 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { aQ as script$2, g as openBlock, h as createElementBlock, m as mergeProps, A as createBaseVNode } from "./index-DuO3ZraP.js";
+var script$1 = {
+  name: "BarsIcon",
+  "extends": script$2
+};
+function render$1(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _cache[0] || (_cache[0] = [createBaseVNode("path", {
+    "fill-rule": "evenodd",
+    "clip-rule": "evenodd",
+    d: "M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z",
+    fill: "currentColor"
+  }, null, -1)]), 16);
+}
+__name(render$1, "render$1");
+script$1.render = render$1;
+var script = {
+  name: "PlusIcon",
+  "extends": script$2
+};
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _cache[0] || (_cache[0] = [createBaseVNode("path", {
+    d: "M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z",
+    fill: "currentColor"
+  }, null, -1)]), 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as a,
+  script$1 as s
+};
+//# sourceMappingURL=index-BmmdKyuw.js.map
--- a/comfy/web/assets/index-BmmdKyuw.js.map
+++ b/comfy/web/assets/index-BmmdKyuw.js.map
@ -0,0 +1 @@
+{"version":3,"file":"index-BmmdKyuw.js","sources":["../../../../../node_modules/@primevue/icons/bars/index.mjs","../../../../../node_modules/@primevue/icons/plus/index.mjs"],"sourcesContent":["import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'BarsIcon',\n  \"extends\": BaseIcon\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _cache[0] || (_cache[0] = [createElementVNode(\"path\", {\n    \"fill-rule\": \"evenodd\",\n    \"clip-rule\": \"evenodd\",\n    d: \"M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z\",\n    fill: \"currentColor\"\n  }, null, -1)]), 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'PlusIcon',\n  \"extends\": BaseIcon\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _cache[0] || (_cache[0] = [createElementVNode(\"path\", {\n    d: \"M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z\",\n    fill: \"currentColor\"\n  }, null, -1)]), 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n"],"names":["script","BaseIcon","render","createElementVNode"],"mappings":";;;AAGG,IAACA,WAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWC;AACb;AAEA,SAASC,SAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,KAAK,GAAG,OAAO,CAAC,MAAM,OAAO,CAAC,IAAI,CAACC,gBAAmB,QAAQ;AAAA,IACpE,aAAa;AAAA,IACb,aAAa;AAAA,IACb,GAAG;AAAA,IACH,MAAM;AAAA,EACP,GAAE,MAAM,EAAE,CAAC,IAAI,EAAE;AACpB;AAbSD;AAeTF,SAAO,SAASE;ACpBb,IAAC,SAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWD;AACb;AAEA,SAAS,OAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,KAAK,GAAG,OAAO,CAAC,MAAM,OAAO,CAAC,IAAI,CAACE,gBAAmB,QAAQ;AAAA,IACpE,GAAG;AAAA,IACH,MAAM;AAAA,EACP,GAAE,MAAM,EAAE,CAAC,IAAI,EAAE;AACpB;AAXS;AAaT,OAAO,SAAS;","x_google_ignoreList":[0,1]}
--- a/comfy/web/assets/index-Bn9riyir.js
+++ b/comfy/web/assets/index-Bn9riyir.js
--- a/comfy/web/assets/index-Bn9riyir.js.map
+++ b/comfy/web/assets/index-Bn9riyir.js.map
--- a/comfy/web/assets/index-DYOd9Fj7.css
+++ b/comfy/web/assets/index-DYOd9Fj7.css
--- a/comfy/web/assets/index-DjWyclij.css
+++ b/comfy/web/assets/index-DjWyclij.css
--- a/comfy/web/assets/index-Drc_oD2f.js.map
+++ b/comfy/web/assets/index-Drc_oD2f.js.map
--- a/comfy/web/assets/index-DuO3ZraP.js
+++ b/comfy/web/assets/index-DuO3ZraP.js
--- a/comfy/web/assets/index-DuO3ZraP.js.map
+++ b/comfy/web/assets/index-DuO3ZraP.js.map
--- a/comfy/web/assets/userSelection-5XIfeQVB.css
+++ b/comfy/web/assets/userSelection-5XIfeQVB.css
@ -1,3 +1,37 @@
+.lds-ring {
+  display: inline-block;
+  position: relative;
+  width: 1em;
+  height: 1em;
+}
+.lds-ring div {
+  box-sizing: border-box;
+  display: block;
+  position: absolute;
+  width: 100%;
+  height: 100%;
+  border: 0.15em solid #fff;
+  border-radius: 50%;
+  animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite;
+  border-color: #fff transparent transparent transparent;
+}
+.lds-ring div:nth-child(1) {
+  animation-delay: -0.45s;
+}
+.lds-ring div:nth-child(2) {
+  animation-delay: -0.3s;
+}
+.lds-ring div:nth-child(3) {
+  animation-delay: -0.15s;
+}
+@keyframes lds-ring {
+  0% {
+    transform: rotate(0deg);
+  }
+  100% {
+    transform: rotate(360deg);
+  }
+}
 .comfy-user-selection {
    width: 100vw;
    height: 100vh;
--- a/comfy/web/assets/userSelection-BM5u5JIA.js.map
+++ b/comfy/web/assets/userSelection-BM5u5JIA.js.map
--- a/comfy/web/assets/userSelection-CN9OKF8B.js
+++ b/comfy/web/assets/userSelection-CN9OKF8B.js
@ -1,6 +1,15 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { aY as createSpinner, aT as api, aN as $el } from "./index-Drc_oD2f.js";
+import { bp as api, bu as $el } from "./index-DuO3ZraP.js";
+function createSpinner() {
+  const div = document.createElement("div");
+  div.innerHTML = `<div class="lds-ring"><div></div><div></div><div></div><div></div></div>`;
+  return div.firstElementChild;
+}
+__name(createSpinner, "createSpinner");
+window.comfyAPI = window.comfyAPI || {};
+window.comfyAPI.spinner = window.comfyAPI.spinner || {};
+window.comfyAPI.spinner.createSpinner = createSpinner;
 class UserSelectionScreen {
  static {
    __name(this, "UserSelectionScreen");
@ -117,4 +126,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
  UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-BM5u5JIA.js.map
+//# sourceMappingURL=userSelection-CN9OKF8B.js.map
--- a/comfy/web/assets/userSelection-CN9OKF8B.js.map
+++ b/comfy/web/assets/userSelection-CN9OKF8B.js.map
--- a/comfy/web/assets/widgetInputs-CGs7cSRG.js
+++ b/comfy/web/assets/widgetInputs-CGs7cSRG.js
@ -0,0 +1,756 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { e as LGraphNode, c as app, bz as applyTextReplacements, by as ComfyWidgets, bF as addValueControlWidgets, k as LiteGraph } from "./index-DuO3ZraP.js";
+const CONVERTED_TYPE = "converted-widget";
+const VALID_TYPES = [
+  "STRING",
+  "combo",
+  "number",
+  "toggle",
+  "BOOLEAN",
+  "text",
+  "string"
+];
+const CONFIG = Symbol();
+const GET_CONFIG = Symbol();
+const TARGET = Symbol();
+const replacePropertyName = "Run widget replace on values";
+class PrimitiveNode extends LGraphNode {
+  static {
+    __name(this, "PrimitiveNode");
+  }
+  controlValues;
+  lastType;
+  static category;
+  constructor(title) {
+    super(title);
+    this.addOutput("connect to widget input", "*");
+    this.serialize_widgets = true;
+    this.isVirtualNode = true;
+    if (!this.properties || !(replacePropertyName in this.properties)) {
+      this.addProperty(replacePropertyName, false, "boolean");
+    }
+  }
+  applyToGraph(extraLinks = []) {
+    if (!this.outputs[0].links?.length) return;
+    function get_links(node) {
+      let links2 = [];
+      for (const l of node.outputs[0].links) {
+        const linkInfo = app.graph.links[l];
+        const n = node.graph.getNodeById(linkInfo.target_id);
+        if (n.type == "Reroute") {
+          links2 = links2.concat(get_links(n));
+        } else {
+          links2.push(l);
+        }
+      }
+      return links2;
+    }
+    __name(get_links, "get_links");
+    let links = [
+      ...get_links(this).map((l) => app.graph.links[l]),
+      ...extraLinks
+    ];
+    let v = this.widgets?.[0].value;
+    if (v && this.properties[replacePropertyName]) {
+      v = applyTextReplacements(app, v);
+    }
+    for (const linkInfo of links) {
+      const node = this.graph.getNodeById(linkInfo.target_id);
+      const input = node.inputs[linkInfo.target_slot];
+      let widget;
+      if (input.widget[TARGET]) {
+        widget = input.widget[TARGET];
+      } else {
+        const widgetName = input.widget.name;
+        if (widgetName) {
+          widget = node.widgets.find((w) => w.name === widgetName);
+        }
+      }
+      if (widget) {
+        widget.value = v;
+        if (widget.callback) {
+          widget.callback(
+            widget.value,
+            app.canvas,
+            node,
+            app.canvas.graph_mouse,
+            {}
+          );
+        }
+      }
+    }
+  }
+  refreshComboInNode() {
+    const widget = this.widgets?.[0];
+    if (widget?.type === "combo") {
+      widget.options.values = this.outputs[0].widget[GET_CONFIG]()[0];
+      if (!widget.options.values.includes(widget.value)) {
+        widget.value = widget.options.values[0];
+        widget.callback(widget.value);
+      }
+    }
+  }
+  onAfterGraphConfigured() {
+    if (this.outputs[0].links?.length && !this.widgets?.length) {
+      if (!this.#onFirstConnection()) return;
+      if (this.widgets) {
+        for (let i = 0; i < this.widgets_values.length; i++) {
+          const w = this.widgets[i];
+          if (w) {
+            w.value = this.widgets_values[i];
+          }
+        }
+      }
+      this.#mergeWidgetConfig();
+    }
+  }
+  onConnectionsChange(_, index, connected) {
+    if (app.configuringGraph) {
+      return;
+    }
+    const links = this.outputs[0].links;
+    if (connected) {
+      if (links?.length && !this.widgets?.length) {
+        this.#onFirstConnection();
+      }
+    } else {
+      this.#mergeWidgetConfig();
+      if (!links?.length) {
+        this.onLastDisconnect();
+      }
+    }
+  }
+  onConnectOutput(slot, type, input, target_node, target_slot) {
+    if (!input.widget) {
+      if (!(input.type in ComfyWidgets)) return false;
+    }
+    if (this.outputs[slot].links?.length) {
+      const valid = this.#isValidConnection(input);
+      if (valid) {
+        this.applyToGraph([{ target_id: target_node.id, target_slot }]);
+      }
+      return valid;
+    }
+  }
+  #onFirstConnection(recreating) {
+    if (!this.outputs[0].links) {
+      this.onLastDisconnect();
+      return;
+    }
+    const linkId = this.outputs[0].links[0];
+    const link = this.graph.links[linkId];
+    if (!link) return;
+    const theirNode = this.graph.getNodeById(link.target_id);
+    if (!theirNode || !theirNode.inputs) return;
+    const input = theirNode.inputs[link.target_slot];
+    if (!input) return;
+    let widget;
+    if (!input.widget) {
+      if (!(input.type in ComfyWidgets)) return;
+      widget = { name: input.name, [GET_CONFIG]: () => [input.type, {}] };
+    } else {
+      widget = input.widget;
+    }
+    const config = widget[GET_CONFIG]?.();
+    if (!config) return;
+    const { type } = getWidgetType(config);
+    this.outputs[0].type = type;
+    this.outputs[0].name = type;
+    this.outputs[0].widget = widget;
+    this.#createWidget(
+      widget[CONFIG] ?? config,
+      theirNode,
+      widget.name,
+      recreating,
+      widget[TARGET]
+    );
+  }
+  #createWidget(inputData, node, widgetName, recreating, targetWidget) {
+    let type = inputData[0];
+    if (type instanceof Array) {
+      type = "COMBO";
+    }
+    const size = this.size;
+    let widget;
+    if (type in ComfyWidgets) {
+      widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget;
+    } else {
+      widget = this.addWidget(type, "value", null, () => {
+      }, {});
+    }
+    if (targetWidget) {
+      widget.value = targetWidget.value;
+    } else if (node?.widgets && widget) {
+      const theirWidget = node.widgets.find((w) => w.name === widgetName);
+      if (theirWidget) {
+        widget.value = theirWidget.value;
+      }
+    }
+    if (!inputData?.[1]?.control_after_generate && (widget.type === "number" || widget.type === "combo")) {
+      let control_value = this.widgets_values?.[1];
+      if (!control_value) {
+        control_value = "fixed";
+      }
+      addValueControlWidgets(
+        this,
+        widget,
+        control_value,
+        void 0,
+        inputData
+      );
+      let filter = this.widgets_values?.[2];
+      if (filter && this.widgets.length === 3) {
+        this.widgets[2].value = filter;
+      }
+    }
+    const controlValues = this.controlValues;
+    if (this.lastType === this.widgets[0].type && controlValues?.length === this.widgets.length - 1) {
+      for (let i = 0; i < controlValues.length; i++) {
+        this.widgets[i + 1].value = controlValues[i];
+      }
+    }
+    const callback = widget.callback;
+    const self = this;
+    widget.callback = function() {
+      const r = callback ? callback.apply(this, arguments) : void 0;
+      self.applyToGraph();
+      return r;
+    };
+    this.size = [
+      Math.max(this.size[0], size[0]),
+      Math.max(this.size[1], size[1])
+    ];
+    if (!recreating) {
+      const sz = this.computeSize();
+      if (this.size[0] < sz[0]) {
+        this.size[0] = sz[0];
+      }
+      if (this.size[1] < sz[1]) {
+        this.size[1] = sz[1];
+      }
+      requestAnimationFrame(() => {
+        if (this.onResize) {
+          this.onResize(this.size);
+        }
+      });
+    }
+  }
+  recreateWidget() {
+    const values = this.widgets?.map((w) => w.value);
+    this.#removeWidgets();
+    this.#onFirstConnection(true);
+    if (values?.length) {
+      for (let i = 0; i < this.widgets?.length; i++)
+        this.widgets[i].value = values[i];
+    }
+    return this.widgets?.[0];
+  }
+  #mergeWidgetConfig() {
+    const output = this.outputs[0];
+    const links = output.links;
+    const hasConfig = !!output.widget[CONFIG];
+    if (hasConfig) {
+      delete output.widget[CONFIG];
+    }
+    if (links?.length < 2 && hasConfig) {
+      if (links.length) {
+        this.recreateWidget();
+      }
+      return;
+    }
+    const config1 = output.widget[GET_CONFIG]();
+    const isNumber = config1[0] === "INT" || config1[0] === "FLOAT";
+    if (!isNumber) return;
+    for (const linkId of links) {
+      const link = app.graph.links[linkId];
+      if (!link) continue;
+      const theirNode = app.graph.getNodeById(link.target_id);
+      const theirInput = theirNode.inputs[link.target_slot];
+      this.#isValidConnection(theirInput, hasConfig);
+    }
+  }
+  isValidWidgetLink(originSlot, targetNode, targetWidget) {
+    const config2 = getConfig.call(targetNode, targetWidget.name) ?? [
+      targetWidget.type,
+      targetWidget.options || {}
+    ];
+    if (!isConvertibleWidget(targetWidget, config2)) return false;
+    const output = this.outputs[originSlot];
+    if (!(output.widget?.[CONFIG] ?? output.widget?.[GET_CONFIG]())) {
+      return true;
+    }
+    return !!mergeIfValid.call(this, output, config2);
+  }
+  #isValidConnection(input, forceUpdate) {
+    const output = this.outputs[0];
+    const config2 = input.widget[GET_CONFIG]();
+    return !!mergeIfValid.call(
+      this,
+      output,
+      config2,
+      forceUpdate,
+      this.recreateWidget
+    );
+  }
+  #removeWidgets() {
+    if (this.widgets) {
+      for (const w of this.widgets) {
+        if (w.onRemove) {
+          w.onRemove();
+        }
+      }
+      this.controlValues = [];
+      this.lastType = this.widgets[0]?.type;
+      for (let i = 1; i < this.widgets.length; i++) {
+        this.controlValues.push(this.widgets[i].value);
+      }
+      setTimeout(() => {
+        delete this.lastType;
+        delete this.controlValues;
+      }, 15);
+      this.widgets.length = 0;
+    }
+  }
+  onLastDisconnect() {
+    this.outputs[0].type = "*";
+    this.outputs[0].name = "connect to widget input";
+    delete this.outputs[0].widget;
+    this.#removeWidgets();
+  }
+}
+function getWidgetConfig(slot) {
+  return slot.widget[CONFIG] ?? slot.widget[GET_CONFIG]();
+}
+__name(getWidgetConfig, "getWidgetConfig");
+function getConfig(widgetName) {
+  const { nodeData } = this.constructor;
+  return nodeData?.input?.required?.[widgetName] ?? nodeData?.input?.optional?.[widgetName];
+}
+__name(getConfig, "getConfig");
+function isConvertibleWidget(widget, config) {
+  return (VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0])) && !widget.options?.forceInput;
+}
+__name(isConvertibleWidget, "isConvertibleWidget");
+function hideWidget(node, widget, suffix = "") {
+  if (widget.type?.startsWith(CONVERTED_TYPE)) return;
+  widget.origType = widget.type;
+  widget.origComputeSize = widget.computeSize;
+  widget.origSerializeValue = widget.serializeValue;
+  widget.computeSize = () => [0, -4];
+  widget.type = CONVERTED_TYPE + suffix;
+  widget.serializeValue = () => {
+    if (!node.inputs) {
+      return void 0;
+    }
+    let node_input = node.inputs.find((i) => i.widget?.name === widget.name);
+    if (!node_input || !node_input.link) {
+      return void 0;
+    }
+    return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
+  };
+  if (widget.linkedWidgets) {
+    for (const w of widget.linkedWidgets) {
+      hideWidget(node, w, ":" + widget.name);
+    }
+  }
+}
+__name(hideWidget, "hideWidget");
+function showWidget(widget) {
+  widget.type = widget.origType;
+  widget.computeSize = widget.origComputeSize;
+  widget.serializeValue = widget.origSerializeValue;
+  delete widget.origType;
+  delete widget.origComputeSize;
+  delete widget.origSerializeValue;
+  if (widget.linkedWidgets) {
+    for (const w of widget.linkedWidgets) {
+      showWidget(w);
+    }
+  }
+}
+__name(showWidget, "showWidget");
+function convertToInput(node, widget, config) {
+  hideWidget(node, widget);
+  const { type } = getWidgetType(config);
+  const sz = node.size;
+  const inputIsOptional = !!widget.options?.inputIsOptional;
+  const input = node.addInput(widget.name, type, {
+    widget: { name: widget.name, [GET_CONFIG]: () => config },
+    ...inputIsOptional ? { shape: LiteGraph.SlotShape.HollowCircle } : {}
+  });
+  for (const widget2 of node.widgets) {
+    widget2.last_y += LiteGraph.NODE_SLOT_HEIGHT;
+  }
+  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]);
+  return input;
+}
+__name(convertToInput, "convertToInput");
+function convertToWidget(node, widget) {
+  showWidget(widget);
+  const sz = node.size;
+  node.removeInput(node.inputs.findIndex((i) => i.widget?.name === widget.name));
+  for (const widget2 of node.widgets) {
+    widget2.last_y -= LiteGraph.NODE_SLOT_HEIGHT;
+  }
+  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]);
+}
+__name(convertToWidget, "convertToWidget");
+function getWidgetType(config) {
+  let type = config[0];
+  if (type instanceof Array) {
+    type = "COMBO";
+  }
+  return { type };
+}
+__name(getWidgetType, "getWidgetType");
+function isValidCombo(combo, obj) {
+  if (!(obj instanceof Array)) {
+    console.log(`connection rejected: tried to connect combo to ${obj}`);
+    return false;
+  }
+  if (combo.length !== obj.length) {
+    console.log(`connection rejected: combo lists dont match`);
+    return false;
+  }
+  if (combo.find((v, i) => obj[i] !== v)) {
+    console.log(`connection rejected: combo lists dont match`);
+    return false;
+  }
+  return true;
+}
+__name(isValidCombo, "isValidCombo");
+function isPrimitiveNode(node) {
+  return node.type === "PrimitiveNode";
+}
+__name(isPrimitiveNode, "isPrimitiveNode");
+function setWidgetConfig(slot, config, target) {
+  if (!slot.widget) return;
+  if (config) {
+    slot.widget[GET_CONFIG] = () => config;
+    slot.widget[TARGET] = target;
+  } else {
+    delete slot.widget;
+  }
+  if (slot.link) {
+    const link = app.graph.links[slot.link];
+    if (link) {
+      const originNode = app.graph.getNodeById(link.origin_id);
+      if (isPrimitiveNode(originNode)) {
+        if (config) {
+          originNode.recreateWidget();
+        } else if (!app.configuringGraph) {
+          originNode.disconnectOutput(0);
+          originNode.onLastDisconnect();
+        }
+      }
+    }
+  }
+}
+__name(setWidgetConfig, "setWidgetConfig");
+function mergeIfValid(output, config2, forceUpdate, recreateWidget, config1) {
+  if (!config1) {
+    config1 = output.widget[CONFIG] ?? output.widget[GET_CONFIG]();
+  }
+  if (config1[0] instanceof Array) {
+    if (!isValidCombo(config1[0], config2[0])) return;
+  } else if (config1[0] !== config2[0]) {
+    console.log(`connection rejected: types dont match`, config1[0], config2[0]);
+    return;
+  }
+  const keys = /* @__PURE__ */ new Set([
+    ...Object.keys(config1[1] ?? {}),
+    ...Object.keys(config2[1] ?? {})
+  ]);
+  let customConfig;
+  const getCustomConfig = /* @__PURE__ */ __name(() => {
+    if (!customConfig) {
+      if (typeof structuredClone === "undefined") {
+        customConfig = JSON.parse(JSON.stringify(config1[1] ?? {}));
+      } else {
+        customConfig = structuredClone(config1[1] ?? {});
+      }
+    }
+    return customConfig;
+  }, "getCustomConfig");
+  const isNumber = config1[0] === "INT" || config1[0] === "FLOAT";
+  for (const k of keys.values()) {
+    if (k !== "default" && k !== "forceInput" && k !== "defaultInput" && k !== "control_after_generate" && k !== "multiline" && k !== "tooltip") {
+      let v1 = config1[1][k];
+      let v2 = config2[1]?.[k];
+      if (v1 === v2 || !v1 && !v2) continue;
+      if (isNumber) {
+        if (k === "min") {
+          const theirMax = config2[1]?.["max"];
+          if (theirMax != null && v1 > theirMax) {
+            console.log("connection rejected: min > max", v1, theirMax);
+            return;
+          }
+          getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.max(v1, v2);
+          continue;
+        } else if (k === "max") {
+          const theirMin = config2[1]?.["min"];
+          if (theirMin != null && v1 < theirMin) {
+            console.log("connection rejected: max < min", v1, theirMin);
+            return;
+          }
+          getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.min(v1, v2);
+          continue;
+        } else if (k === "step") {
+          let step;
+          if (v1 == null) {
+            step = v2;
+          } else if (v2 == null) {
+            step = v1;
+          } else {
+            if (v1 < v2) {
+              const a = v2;
+              v2 = v1;
+              v1 = a;
+            }
+            if (v1 % v2) {
+              console.log(
+                "connection rejected: steps not divisible",
+                "current:",
+                v1,
+                "new:",
+                v2
+              );
+              return;
+            }
+            step = v1;
+          }
+          getCustomConfig()[k] = step;
+          continue;
+        }
+      }
+      console.log(`connection rejected: config ${k} values dont match`, v1, v2);
+      return;
+    }
+  }
+  if (customConfig || forceUpdate) {
+    if (customConfig) {
+      output.widget[CONFIG] = [config1[0], customConfig];
+    }
+    const widget = recreateWidget?.call(this);
+    if (widget) {
+      const min = widget.options.min;
+      const max = widget.options.max;
+      if (min != null && widget.value < min) widget.value = min;
+      if (max != null && widget.value > max) widget.value = max;
+      widget.callback(widget.value);
+    }
+  }
+  return { customConfig };
+}
+__name(mergeIfValid, "mergeIfValid");
+let useConversionSubmenusSetting;
+app.registerExtension({
+  name: "Comfy.WidgetInputs",
+  init() {
+    useConversionSubmenusSetting = app.ui.settings.addSetting({
+      id: "Comfy.NodeInputConversionSubmenus",
+      name: "In the node context menu, place the entries that convert between input/widget in sub-menus.",
+      type: "boolean",
+      defaultValue: true
+    });
+  },
+  async beforeRegisterNodeDef(nodeType, nodeData, app2) {
+    const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+    nodeType.prototype.convertWidgetToInput = function(widget) {
+      const config = getConfig.call(this, widget.name) ?? [
+        widget.type,
+        widget.options || {}
+      ];
+      if (!isConvertibleWidget(widget, config)) return false;
+      if (widget.type?.startsWith(CONVERTED_TYPE)) return false;
+      convertToInput(this, widget, config);
+      return true;
+    };
+    nodeType.prototype.getExtraMenuOptions = function(_, options) {
+      const r = origGetExtraMenuOptions ? origGetExtraMenuOptions.apply(this, arguments) : void 0;
+      if (this.widgets) {
+        let toInput = [];
+        let toWidget = [];
+        for (const w of this.widgets) {
+          if (w.options?.forceInput) {
+            continue;
+          }
+          if (w.type === CONVERTED_TYPE) {
+            toWidget.push({
+              content: `Convert ${w.name} to widget`,
+              callback: /* @__PURE__ */ __name(() => convertToWidget(this, w), "callback")
+            });
+          } else {
+            const config = getConfig.call(this, w.name) ?? [
+              w.type,
+              w.options || {}
+            ];
+            if (isConvertibleWidget(w, config)) {
+              toInput.push({
+                content: `Convert ${w.name} to input`,
+                callback: /* @__PURE__ */ __name(() => convertToInput(this, w, config), "callback")
+              });
+            }
+          }
+        }
+        if (toInput.length) {
+          if (useConversionSubmenusSetting.value) {
+            options.push({
+              content: "Convert Widget to Input",
+              submenu: {
+                options: toInput
+              }
+            });
+          } else {
+            options.push(...toInput, null);
+          }
+        }
+        if (toWidget.length) {
+          if (useConversionSubmenusSetting.value) {
+            options.push({
+              content: "Convert Input to Widget",
+              submenu: {
+                options: toWidget
+              }
+            });
+          } else {
+            options.push(...toWidget, null);
+          }
+        }
+      }
+      return r;
+    };
+    nodeType.prototype.onGraphConfigured = function() {
+      if (!this.inputs) return;
+      this.widgets ??= [];
+      for (const input of this.inputs) {
+        if (input.widget) {
+          if (!input.widget[GET_CONFIG]) {
+            input.widget[GET_CONFIG] = () => getConfig.call(this, input.widget.name);
+          }
+          if (input.widget.config) {
+            if (input.widget.config[0] instanceof Array) {
+              input.type = "COMBO";
+              const link = app2.graph.links[input.link];
+              if (link) {
+                link.type = input.type;
+              }
+            }
+            delete input.widget.config;
+          }
+          const w = this.widgets.find((w2) => w2.name === input.widget.name);
+          if (w) {
+            hideWidget(this, w);
+          } else {
+            convertToWidget(this, input);
+          }
+        }
+      }
+    };
+    const origOnNodeCreated = nodeType.prototype.onNodeCreated;
+    nodeType.prototype.onNodeCreated = function() {
+      const r = origOnNodeCreated ? origOnNodeCreated.apply(this) : void 0;
+      if (!app2.configuringGraph && this.widgets) {
+        for (const w of this.widgets) {
+          if (w?.options?.forceInput || w?.options?.defaultInput) {
+            const config = getConfig.call(this, w.name) ?? [
+              w.type,
+              w.options || {}
+            ];
+            convertToInput(this, w, config);
+          }
+        }
+      }
+      return r;
+    };
+    const origOnConfigure = nodeType.prototype.onConfigure;
+    nodeType.prototype.onConfigure = function() {
+      const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : void 0;
+      if (!app2.configuringGraph && this.inputs) {
+        for (const input of this.inputs) {
+          if (input.widget && !input.widget[GET_CONFIG]) {
+            input.widget[GET_CONFIG] = () => getConfig.call(this, input.widget.name);
+            const w = this.widgets.find((w2) => w2.name === input.widget.name);
+            if (w) {
+              hideWidget(this, w);
+            }
+          }
+        }
+      }
+      return r;
+    };
+    function isNodeAtPos(pos) {
+      for (const n of app2.graph.nodes) {
+        if (n.pos[0] === pos[0] && n.pos[1] === pos[1]) {
+          return true;
+        }
+      }
+      return false;
+    }
+    __name(isNodeAtPos, "isNodeAtPos");
+    const origOnInputDblClick = nodeType.prototype.onInputDblClick;
+    const ignoreDblClick = Symbol();
+    nodeType.prototype.onInputDblClick = function(slot) {
+      const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : void 0;
+      const input = this.inputs[slot];
+      if (!input.widget || !input[ignoreDblClick]) {
+        if (!(input.type in ComfyWidgets) && !(input.widget[GET_CONFIG]?.()?.[0] instanceof Array)) {
+          return r;
+        }
+      }
+      const node = LiteGraph.createNode("PrimitiveNode");
+      app2.graph.add(node);
+      const pos = [
+        this.pos[0] - node.size[0] - 30,
+        this.pos[1]
+      ];
+      while (isNodeAtPos(pos)) {
+        pos[1] += LiteGraph.NODE_TITLE_HEIGHT;
+      }
+      node.pos = pos;
+      node.connect(0, this, slot);
+      node.title = input.name;
+      input[ignoreDblClick] = true;
+      setTimeout(() => {
+        delete input[ignoreDblClick];
+      }, 300);
+      return r;
+    };
+    const onConnectInput = nodeType.prototype.onConnectInput;
+    nodeType.prototype.onConnectInput = function(targetSlot, type, output, originNode, originSlot) {
+      const v = onConnectInput?.(this, arguments);
+      if (type !== "COMBO") return v;
+      if (originNode.outputs[originSlot].widget) return v;
+      const targetCombo = this.inputs[targetSlot].widget?.[GET_CONFIG]?.()?.[0];
+      if (!targetCombo || !(targetCombo instanceof Array)) return v;
+      const originConfig = originNode.constructor?.nodeData?.output?.[originSlot];
+      if (!originConfig || !isValidCombo(targetCombo, originConfig)) {
+        return false;
+      }
+      return v;
+    };
+  },
+  registerCustomNodes() {
+    LiteGraph.registerNodeType(
+      "PrimitiveNode",
+      Object.assign(PrimitiveNode, {
+        title: "Primitive"
+      })
+    );
+    PrimitiveNode.category = "utils";
+  }
+});
+window.comfyAPI = window.comfyAPI || {};
+window.comfyAPI.widgetInputs = window.comfyAPI.widgetInputs || {};
+window.comfyAPI.widgetInputs.getWidgetConfig = getWidgetConfig;
+window.comfyAPI.widgetInputs.convertToInput = convertToInput;
+window.comfyAPI.widgetInputs.setWidgetConfig = setWidgetConfig;
+window.comfyAPI.widgetInputs.mergeIfValid = mergeIfValid;
+export {
+  convertToInput,
+  getWidgetConfig,
+  mergeIfValid,
+  setWidgetConfig
+};
+//# sourceMappingURL=widgetInputs-CGs7cSRG.js.map
--- a/comfy/web/assets/widgetInputs-CGs7cSRG.js.map
+++ b/comfy/web/assets/widgetInputs-CGs7cSRG.js.map
--- a/comfy/web/extensions/core/clipspace.js
+++ b/comfy/web/extensions/core/clipspace.js
@ -1,2 +1,2 @@
-// Shim for extensions/core/clipspace.ts
+// Shim for extensions\core\clipspace.ts
 export const ClipspaceDialog = window.comfyAPI.clipspace.ClipspaceDialog;
--- a/comfy/web/extensions/core/colorPalette.js
+++ b/comfy/web/extensions/core/colorPalette.js
@ -1,3 +1,3 @@
-// Shim for extensions/core/colorPalette.ts
+// Shim for extensions\core\colorPalette.ts
 export const defaultColorPalette = window.comfyAPI.colorPalette.defaultColorPalette;
 export const getColorPalette = window.comfyAPI.colorPalette.getColorPalette;
--- a/comfy/web/extensions/core/groupNode.js
+++ b/comfy/web/extensions/core/groupNode.js
@ -1,3 +1,3 @@
-// Shim for extensions/core/groupNode.ts
+// Shim for extensions\core\groupNode.ts
 export const GroupNodeConfig = window.comfyAPI.groupNode.GroupNodeConfig;
 export const GroupNodeHandler = window.comfyAPI.groupNode.GroupNodeHandler;
--- a/comfy/web/extensions/core/groupNodeManage.js
+++ b/comfy/web/extensions/core/groupNodeManage.js
@ -1,2 +1,2 @@
-// Shim for extensions/core/groupNodeManage.ts
+// Shim for extensions\core\groupNodeManage.ts
 export const ManageGroupDialog = window.comfyAPI.groupNodeManage.ManageGroupDialog;
--- a/comfy/web/extensions/core/widgetInputs.js
+++ b/comfy/web/extensions/core/widgetInputs.js
@ -1,4 +1,5 @@
-// Shim for extensions/core/widgetInputs.ts
+// Shim for extensions\core\widgetInputs.ts
 export const getWidgetConfig = window.comfyAPI.widgetInputs.getWidgetConfig;
+export const convertToInput = window.comfyAPI.widgetInputs.convertToInput;
 export const setWidgetConfig = window.comfyAPI.widgetInputs.setWidgetConfig;
 export const mergeIfValid = window.comfyAPI.widgetInputs.mergeIfValid;
--- a/comfy/web/index.html
+++ b/comfy/web/index.html
@ -1,50 +1,42 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8">
-    <title>ComfyUI</title>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
-    <!-- Browser Test Fonts -->
-    <!-- <link href="https://fonts.googleapis.com/css2?family=Roboto+Mono:ital,wght@0,100..700;1,100..700&family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap" rel="stylesheet">
-    <link href="https://fonts.googleapis.com/css2?family=Noto+Color+Emoji&family=Roboto+Mono:ital,wght@0,100..700;1,100..700&family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap" rel="stylesheet">
-    <style>
-      * {
-        font-family: 'Roboto Mono', 'Noto Color Emoji';
-      }
-    </style> -->
-    <link rel="stylesheet" type="text/css" href="user.css" />
-    <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-Drc_oD2f.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-8NH3XvqK.css">
-  </head>
-  <body class="litegraph">
-    <div id="vue-app"></div>
-    <div id="comfy-user-selection" class="comfy-user-selection" style="display: none;">
-      <main class="comfy-user-selection-inner">
-        <h1>ComfyUI</h1>
-        <form>
-          <section>
-            <label>New user:
-              <input placeholder="Enter a username" />
-            </label>
-          </section>
-          <div class="comfy-user-existing">
-            <span class="or-separator">OR</span>
-            <section>
-              <label>
-                Existing user:
-                <select>
-                  <option hidden disabled selected value> Select a user </option>
-                </select>
-              </label>
-            </section>
-          </div>
-          <footer>
-            <span class="comfy-user-error">&nbsp;</span>
-            <button class="comfy-btn comfy-user-button-next">Next</button>
-          </footer>
-        </form>
-      </main>
-    </div>
-  </body>
-</html>
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8">
+    <title>ComfyUI</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
+    <link rel="stylesheet" type="text/css" href="user.css" />
+    <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
+    <script type="module" crossorigin src="./assets/index-DuO3ZraP.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-DYOd9Fj7.css">
+  </head>
+  <body class="litegraph grid">
+    <div id="vue-app"></div>
+    <div id="comfy-user-selection" class="comfy-user-selection" style="display: none;">
+      <main class="comfy-user-selection-inner">
+        <h1>ComfyUI</h1>
+        <form>
+          <section>
+            <label>New user:
+              <input placeholder="Enter a username" />
+            </label>
+          </section>
+          <div class="comfy-user-existing">
+            <span class="or-separator">OR</span>
+            <section>
+              <label>
+                Existing user:
+                <select>
+                  <option hidden disabled selected value> Select a user </option>
+                </select>
+              </label>
+            </section>
+          </div>
+          <footer>
+            <span class="comfy-user-error">&nbsp;</span>
+            <button class="comfy-btn comfy-user-button-next">Next</button>
+          </footer>
+        </form>
+      </main>
+    </div>
+  </body>
+</html>
--- a/comfy/web/scripts/api.js
+++ b/comfy/web/scripts/api.js
@ -1,2 +1,2 @@
-// Shim for scripts/api.ts
+// Shim for scripts\api.ts
 export const api = window.comfyAPI.api.api;
--- a/comfy/web/scripts/app.js
+++ b/comfy/web/scripts/app.js
@ -1,4 +1,4 @@
-// Shim for scripts/app.ts
+// Shim for scripts\app.ts
 export const ANIM_PREVIEW_WIDGET = window.comfyAPI.app.ANIM_PREVIEW_WIDGET;
 export const ComfyApp = window.comfyAPI.app.ComfyApp;
 export const app = window.comfyAPI.app.app;
--- a/comfy/web/scripts/changeTracker.js
+++ b/comfy/web/scripts/changeTracker.js
@ -1,2 +1,3 @@
-// Shim for scripts/changeTracker.ts
+// Shim for scripts\changeTracker.ts
 export const ChangeTracker = window.comfyAPI.changeTracker.ChangeTracker;
+export const globalTracker = window.comfyAPI.changeTracker.globalTracker;
--- a/comfy/web/scripts/defaultGraph.js
+++ b/comfy/web/scripts/defaultGraph.js
@ -1,2 +1,2 @@
-// Shim for scripts/defaultGraph.ts
+// Shim for scripts\defaultGraph.ts
 export const defaultGraph = window.comfyAPI.defaultGraph.defaultGraph;
--- a/comfy/web/scripts/domWidget.js
+++ b/comfy/web/scripts/domWidget.js
@ -1,2 +1,2 @@
-// Shim for scripts/domWidget.ts
+// Shim for scripts\domWidget.ts
 export const addDomClippingSetting = window.comfyAPI.domWidget.addDomClippingSetting;
--- a/comfy/web/scripts/logging.js
+++ b/comfy/web/scripts/logging.js
@ -1,2 +1,2 @@
-// Shim for scripts/logging.ts
+// Shim for scripts\logging.ts
 export const ComfyLogging = window.comfyAPI.logging.ComfyLogging;
--- a/comfy/web/scripts/metadata/flac.js
+++ b/comfy/web/scripts/metadata/flac.js
@ -1,3 +1,3 @@
-// Shim for scripts/metadata/flac.ts
+// Shim for scripts\metadata\flac.ts
 export const getFromFlacBuffer = window.comfyAPI.flac.getFromFlacBuffer;
 export const getFromFlacFile = window.comfyAPI.flac.getFromFlacFile;
--- a/comfy/web/scripts/metadata/png.js
+++ b/comfy/web/scripts/metadata/png.js
@ -1,3 +1,3 @@
-// Shim for scripts/metadata/png.ts
+// Shim for scripts\metadata\png.ts
 export const getFromPngBuffer = window.comfyAPI.png.getFromPngBuffer;
 export const getFromPngFile = window.comfyAPI.png.getFromPngFile;
--- a/comfy/web/scripts/pnginfo.js
+++ b/comfy/web/scripts/pnginfo.js
@ -1,4 +1,4 @@
-// Shim for scripts/pnginfo.ts
+// Shim for scripts\pnginfo.ts
 export const getPngMetadata = window.comfyAPI.pnginfo.getPngMetadata;
 export const getFlacMetadata = window.comfyAPI.pnginfo.getFlacMetadata;
 export const getWebpMetadata = window.comfyAPI.pnginfo.getWebpMetadata;
--- a/comfy/web/scripts/ui.js
+++ b/comfy/web/scripts/ui.js
@ -1,4 +1,4 @@
-// Shim for scripts/ui.ts
+// Shim for scripts\ui.ts
 export const ComfyDialog = window.comfyAPI.ui.ComfyDialog;
 export const $el = window.comfyAPI.ui.$el;
 export const ComfyUI = window.comfyAPI.ui.ComfyUI;
--- a/comfy/web/scripts/ui/components/asyncDialog.js
+++ b/comfy/web/scripts/ui/components/asyncDialog.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/components/asyncDialog.ts
+// Shim for scripts\ui\components\asyncDialog.ts
 export const ComfyAsyncDialog = window.comfyAPI.asyncDialog.ComfyAsyncDialog;
--- a/comfy/web/scripts/ui/components/button.js
+++ b/comfy/web/scripts/ui/components/button.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/components/button.ts
+// Shim for scripts\ui\components\button.ts
 export const ComfyButton = window.comfyAPI.button.ComfyButton;
--- a/comfy/web/scripts/ui/components/buttonGroup.js
+++ b/comfy/web/scripts/ui/components/buttonGroup.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/components/buttonGroup.ts
+// Shim for scripts\ui\components\buttonGroup.ts
 export const ComfyButtonGroup = window.comfyAPI.buttonGroup.ComfyButtonGroup;
--- a/comfy/web/scripts/ui/components/popup.js
+++ b/comfy/web/scripts/ui/components/popup.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/components/popup.ts
+// Shim for scripts\ui\components\popup.ts
 export const ComfyPopup = window.comfyAPI.popup.ComfyPopup;
--- a/comfy/web/scripts/ui/components/splitButton.js
+++ b/comfy/web/scripts/ui/components/splitButton.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/components/splitButton.ts
+// Shim for scripts\ui\components\splitButton.ts
 export const ComfySplitButton = window.comfyAPI.splitButton.ComfySplitButton;
--- a/comfy/web/scripts/ui/dialog.js
+++ b/comfy/web/scripts/ui/dialog.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/dialog.ts
+// Shim for scripts\ui\dialog.ts
 export const ComfyDialog = window.comfyAPI.dialog.ComfyDialog;
--- a/comfy/web/scripts/ui/draggableList.js
+++ b/comfy/web/scripts/ui/draggableList.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/draggableList.ts
+// Shim for scripts\ui\draggableList.ts
 export const DraggableList = window.comfyAPI.draggableList.DraggableList;
--- a/comfy/web/scripts/ui/imagePreview.js
+++ b/comfy/web/scripts/ui/imagePreview.js
@ -1,3 +1,3 @@
-// Shim for scripts/ui/imagePreview.ts
+// Shim for scripts\ui\imagePreview.ts
 export const calculateImageGrid = window.comfyAPI.imagePreview.calculateImageGrid;
 export const createImageHost = window.comfyAPI.imagePreview.createImageHost;
--- a/comfy/web/scripts/ui/menu/index.js
+++ b/comfy/web/scripts/ui/menu/index.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/menu/index.ts
+// Shim for scripts\ui\menu\index.ts
 export const ComfyAppMenu = window.comfyAPI.index.ComfyAppMenu;
--- a/comfy/web/scripts/ui/settings.js
+++ b/comfy/web/scripts/ui/settings.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/settings.ts
+// Shim for scripts\ui\settings.ts
 export const ComfySettingsDialog = window.comfyAPI.settings.ComfySettingsDialog;
--- a/comfy/web/scripts/ui/spinner.js
+++ b/comfy/web/scripts/ui/spinner.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/spinner.ts
+// Shim for scripts\ui\spinner.ts
 export const createSpinner = window.comfyAPI.spinner.createSpinner;
--- a/comfy/web/scripts/ui/toggleSwitch.js
+++ b/comfy/web/scripts/ui/toggleSwitch.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/toggleSwitch.ts
+// Shim for scripts\ui\toggleSwitch.ts
 export const toggleSwitch = window.comfyAPI.toggleSwitch.toggleSwitch;
--- a/comfy/web/scripts/ui/userSelection.js
+++ b/comfy/web/scripts/ui/userSelection.js
@ -1,2 +1,2 @@
-// Shim for scripts/ui/userSelection.ts
+// Shim for scripts\ui\userSelection.ts
 export const UserSelectionScreen = window.comfyAPI.userSelection.UserSelectionScreen;
--- a/comfy/web/scripts/ui/utils.js
+++ b/comfy/web/scripts/ui/utils.js
@ -1,3 +1,3 @@
-// Shim for scripts/ui/utils.ts
+// Shim for scripts\ui\utils.ts
 export const applyClasses = window.comfyAPI.utils.applyClasses;
 export const toggleElement = window.comfyAPI.utils.toggleElement;
--- a/comfy/web/scripts/utils.js
+++ b/comfy/web/scripts/utils.js
@ -1,4 +1,4 @@
-// Shim for scripts/utils.ts
+// Shim for scripts\utils.ts
 export const clone = window.comfyAPI.utils.clone;
 export const applyTextReplacements = window.comfyAPI.utils.applyTextReplacements;
 export const addStylesheet = window.comfyAPI.utils.addStylesheet;
--- a/comfy/web/scripts/widgets.js
+++ b/comfy/web/scripts/widgets.js
@ -1,4 +1,4 @@
-// Shim for scripts/widgets.ts
+// Shim for scripts\widgets.ts
 export const updateControlWidgetLabel = window.comfyAPI.widgets.updateControlWidgetLabel;
 export const addValueControlWidget = window.comfyAPI.widgets.addValueControlWidget;
 export const addValueControlWidgets = window.comfyAPI.widgets.addValueControlWidgets;
--- a/comfy/web/scripts/workflows.js
+++ b/comfy/web/scripts/workflows.js
@ -1,3 +1,3 @@
-// Shim for scripts/workflows.ts
+// Shim for scripts\workflows.ts
 export const ComfyWorkflowManager = window.comfyAPI.workflows.ComfyWorkflowManager;
 export const ComfyWorkflow = window.comfyAPI.workflows.ComfyWorkflow;
--- a/comfy/web/templates/default.jpg
+++ b/comfy/web/templates/default.jpg
--- a/comfy/web/templates/default.json
+++ b/comfy/web/templates/default.json
@ -0,0 +1,356 @@
+{
+  "last_node_id": 9,
+  "last_link_id": 9,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        413,
+        389
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        415,
+        186
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        473,
+        609
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        863,
+        186
+      ],
+      "size": [
+        315,
+        262
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        156680208700286,
+        true,
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1209,
+        188
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {}
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
+        1451,
+        189
+      ],
+      "size": [
+        210,
+        26
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {}
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        26,
+        474
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "v1-5-pruned-emaonly.safetensors"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      4,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "models": [{
+    "name": "v1-5-pruned-emaonly.safetensors",
+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "directory": "checkpoints"
+  }]
+}
--- a/comfy/web/templates/flux_schnell.jpg
+++ b/comfy/web/templates/flux_schnell.jpg
--- a/comfy/web/templates/flux_schnell.json
+++ b/comfy/web/templates/flux_schnell.json
@ -0,0 +1,420 @@
+{
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+    "last_link_id": 58,
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+        "outputs": [
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+            "type": "LATENT",
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+              52
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+            "type": "CONDITIONING",
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+            "slot_index": 0
+          }
+        ],
+        "title": "CLIP Text Encode (Positive Prompt)",
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
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+          "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it"
+        ],
+        "color": "#232",
+        "bgcolor": "#353"
+      },
+      {
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+        "type": "Note",
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+        "properties": {
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+        },
+        "widgets_values": [
+          "Note that Flux dev and schnell do not have any negative prompt so CFG should be set to 1.0. Setting CFG to 1.0 means the negative prompt is ignored.\n\nThe schnell model is a distilled model that can generate a good image with only 4 steps."
+        ],
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+        "directory": "checkpoints"
+      }
+    ],
+    "version": 0.4
+  }
--- a/comfy/web/templates/image2image.jpg
+++ b/comfy/web/templates/image2image.jpg
--- a/comfy/web/templates/image2image.json
+++ b/comfy/web/templates/image2image.json
@ -0,0 +1,447 @@
+{
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+  "last_link_id": 17,
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+      "properties": {
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+      "widgets_values": [
+        "photograph of victorian woman with wings, sky clouds, meadow grass\n"
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+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "directory": "checkpoints"
+  }]
+}
--- a/comfy/web/templates/upscale.jpg
+++ b/comfy/web/templates/upscale.jpg
--- a/comfy/web/templates/upscale.json
+++ b/comfy/web/templates/upscale.json
@ -0,0 +1,652 @@
+{
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+        "id": 6,
+        "type": "CLIPTextEncode",
+        "pos": [
+          374,
+          171
+        ],
+        "size": {
+          "0": 422.84503173828125,
+          "1": 164.31304931640625
+        },
+        "flags": {},
+        "order": 2,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "clip",
+            "type": "CLIP",
+            "link": 19
+          }
+        ],
+        "outputs": [
+          {
+            "name": "CONDITIONING",
+            "type": "CONDITIONING",
+            "links": [
+              4,
+              12
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
+        "widgets_values": [
+          "masterpiece HDR victorian portrait painting of woman, blonde hair, mountain nature, blue sky\n"
+        ]
+      },
+      {
+        "id": 7,
+        "type": "CLIPTextEncode",
+        "pos": [
+          377,
+          381
+        ],
+        "size": {
+          "0": 425.27801513671875,
+          "1": 180.6060791015625
+        },
+        "flags": {},
+        "order": 3,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "clip",
+            "type": "CLIP",
+            "link": 20
+          }
+        ],
+        "outputs": [
+          {
+            "name": "CONDITIONING",
+            "type": "CONDITIONING",
+            "links": [
+              6,
+              13
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
+        "widgets_values": [
+          "bad hands, text, watermark\n"
+        ]
+      },
+      {
+        "id": 5,
+        "type": "EmptyLatentImage",
+        "pos": [
+          435,
+          600
+        ],
+        "size": {
+          "0": 315,
+          "1": 106
+        },
+        "flags": {},
+        "order": 0,
+        "mode": 0,
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              2
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "EmptyLatentImage"
+        },
+        "widgets_values": [
+          768,
+          768,
+          1
+        ]
+      },
+      {
+        "id": 11,
+        "type": "KSampler",
+        "pos": [
+          1585,
+          114
+        ],
+        "size": {
+          "0": 315,
+          "1": 262
+        },
+        "flags": {},
+        "order": 8,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "model",
+            "type": "MODEL",
+            "link": 23,
+            "slot_index": 0
+          },
+          {
+            "name": "positive",
+            "type": "CONDITIONING",
+            "link": 12,
+            "slot_index": 1
+          },
+          {
+            "name": "negative",
+            "type": "CONDITIONING",
+            "link": 13,
+            "slot_index": 2
+          },
+          {
+            "name": "latent_image",
+            "type": "LATENT",
+            "link": 14,
+            "slot_index": 3
+          }
+        ],
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              15
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "KSampler"
+        },
+        "widgets_values": [
+          469771404043268,
+          "randomize",
+          14,
+          8,
+          "dpmpp_2m",
+          "simple",
+          0.5
+        ]
+      },
+      {
+        "id": 12,
+        "type": "SaveImage",
+        "pos": [
+          2203,
+          123
+        ],
+        "size": {
+          "0": 407.53717041015625,
+          "1": 468.13226318359375
+        },
+        "flags": {},
+        "order": 10,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "images",
+            "type": "IMAGE",
+            "link": 17
+          }
+        ],
+        "properties": {},
+        "widgets_values": [
+          "ComfyUI"
+        ]
+      },
+      {
+        "id": 3,
+        "type": "KSampler",
+        "pos": [
+          845,
+          172
+        ],
+        "size": {
+          "0": 315,
+          "1": 262
+        },
+        "flags": {},
+        "order": 4,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "model",
+            "type": "MODEL",
+            "link": 18
+          },
+          {
+            "name": "positive",
+            "type": "CONDITIONING",
+            "link": 4
+          },
+          {
+            "name": "negative",
+            "type": "CONDITIONING",
+            "link": 6
+          },
+          {
+            "name": "latent_image",
+            "type": "LATENT",
+            "link": 2
+          }
+        ],
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              7,
+              10
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "KSampler"
+        },
+        "widgets_values": [
+          89848141647836,
+          "randomize",
+          12,
+          8,
+          "dpmpp_sde",
+          "normal",
+          1
+        ]
+      },
+      {
+        "id": 16,
+        "type": "CheckpointLoaderSimple",
+        "pos": [
+          24,
+          315
+        ],
+        "size": {
+          "0": 315,
+          "1": 98
+        },
+        "flags": {},
+        "order": 1,
+        "mode": 0,
+        "outputs": [
+          {
+            "name": "MODEL",
+            "type": "MODEL",
+            "links": [
+              18,
+              23
+            ],
+            "slot_index": 0
+          },
+          {
+            "name": "CLIP",
+            "type": "CLIP",
+            "links": [
+              19,
+              20
+            ],
+            "slot_index": 1
+          },
+          {
+            "name": "VAE",
+            "type": "VAE",
+            "links": [
+              21,
+              22
+            ],
+            "slot_index": 2
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "CheckpointLoaderSimple"
+        },
+        "widgets_values": [
+          "v2-1_768-ema-pruned.safetensors"
+        ]
+      },
+      {
+        "id": 9,
+        "type": "SaveImage",
+        "pos": [
+          1495.7215957031258,
+          576.1878720703122
+        ],
+        "size": [
+          232.9403301043692,
+          282.4336258387117
+        ],
+        "flags": {},
+        "order": 7,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "images",
+            "type": "IMAGE",
+            "link": 9
+          }
+        ],
+        "properties": {},
+        "widgets_values": [
+          "ComfyUI"
+        ]
+      }
+    ],
+    "links": [
+      [
+        2,
+        5,
+        0,
+        3,
+        3,
+        "LATENT"
+      ],
+      [
+        4,
+        6,
+        0,
+        3,
+        1,
+        "CONDITIONING"
+      ],
+      [
+        6,
+        7,
+        0,
+        3,
+        2,
+        "CONDITIONING"
+      ],
+      [
+        7,
+        3,
+        0,
+        8,
+        0,
+        "LATENT"
+      ],
+      [
+        9,
+        8,
+        0,
+        9,
+        0,
+        "IMAGE"
+      ],
+      [
+        10,
+        3,
+        0,
+        10,
+        0,
+        "LATENT"
+      ],
+      [
+        12,
+        6,
+        0,
+        11,
+        1,
+        "CONDITIONING"
+      ],
+      [
+        13,
+        7,
+        0,
+        11,
+        2,
+        "CONDITIONING"
+      ],
+      [
+        14,
+        10,
+        0,
+        11,
+        3,
+        "LATENT"
+      ],
+      [
+        15,
+        11,
+        0,
+        13,
+        0,
+        "LATENT"
+      ],
+      [
+        17,
+        13,
+        0,
+        12,
+        0,
+        "IMAGE"
+      ],
+      [
+        18,
+        16,
+        0,
+        3,
+        0,
+        "MODEL"
+      ],
+      [
+        19,
+        16,
+        1,
+        6,
+        0,
+        "CLIP"
+      ],
+      [
+        20,
+        16,
+        1,
+        7,
+        0,
+        "CLIP"
+      ],
+      [
+        21,
+        16,
+        2,
+        8,
+        1,
+        "VAE"
+      ],
+      [
+        22,
+        16,
+        2,
+        13,
+        1,
+        "VAE"
+      ],
+      [
+        23,
+        16,
+        0,
+        11,
+        0,
+        "MODEL"
+      ]
+    ],
+    "groups": [
+      {
+        "title": "Txt2Img",
+        "bounding": [
+          -1,
+          30,
+          1211,
+          708
+        ],
+        "color": "#a1309b"
+      },
+      {
+        "title": "Save Intermediate Image",
+        "bounding": [
+          1225,
+          500,
+          516,
+          196
+        ],
+        "color": "#3f789e"
+      },
+      {
+        "title": "Hires Fix",
+        "bounding": [
+          1224,
+          29,
+          710,
+          464
+        ],
+        "color": "#b58b2a"
+      },
+      {
+        "title": "Save Final Image",
+        "bounding": [
+          1949,
+          31,
+          483,
+          199
+        ],
+        "color": "#3f789e"
+      }
+    ],
+    "config": {},
+    "extra": {},
+    "version": 0.4,
+    "models": [
+      {
+        "name": "v2-1_768-ema-pruned.safetensors",
+        "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors?download=true",
+        "directory": "checkpoints"
+      }
+    ]
+  }
--- a/comfy_extras/controlnet_aux/init.py
+++ b/comfy_extras/controlnet_aux/init.py
--- a/comfy_extras/controlnet_aux/lvminthin.py
+++ b/comfy_extras/controlnet_aux/lvminthin.py
@ -0,0 +1,103 @@
+"""
+Copyright 2023 Lvmin Zhang
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+"""
+
+# High Quality Edge Thinning using Pure Python
+# Written by Lvmin Zhang
+# 2023 April
+# Stanford University
+# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
+
+
+import cv2
+import numpy as np
+
+
+lvmin_kernels_raw = [
+    np.array([
+        [-1, -1, -1],
+        [0, 1, 0],
+        [1, 1, 1]
+    ], dtype=np.int32),
+    np.array([
+        [0, -1, -1],
+        [1, 1, -1],
+        [0, 1, 0]
+    ], dtype=np.int32)
+]
+
+lvmin_kernels = []
+lvmin_kernels += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_kernels_raw]
+lvmin_kernels += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_kernels_raw]
+lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
+lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
+
+lvmin_prunings_raw = [
+    np.array([
+        [-1, -1, -1],
+        [-1, 1, -1],
+        [0, 0, -1]
+    ], dtype=np.int32),
+    np.array([
+        [-1, -1, -1],
+        [-1, 1, -1],
+        [-1, 0, 0]
+    ], dtype=np.int32)
+]
+
+lvmin_prunings = []
+lvmin_prunings += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_prunings_raw]
+lvmin_prunings += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_prunings_raw]
+lvmin_prunings += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_prunings_raw]
+lvmin_prunings += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_prunings_raw]
+
+
+def remove_pattern(x, kernel):
+    objects = cv2.morphologyEx(x, cv2.MORPH_HITMISS, kernel)
+    objects = np.where(objects > 127)
+    x[objects] = 0
+    return x, objects[0].shape[0] > 0
+
+
+def thin_one_time(x, kernels):
+    y = x
+    is_done = True
+    for k in kernels:
+        y, has_update = remove_pattern(y, k)
+        if has_update:
+            is_done = False
+    return y, is_done
+
+
+def lvmin_thin(x, prunings=True):
+    y = x
+    for i in range(32):
+        y, is_done = thin_one_time(y, lvmin_kernels)
+        if is_done:
+            break
+    if prunings:
+        y, _ = thin_one_time(y, lvmin_prunings)
+    return y
+
+
+def nake_nms(x):
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+    y = np.zeros_like(x)
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+    return y
--- a/comfy_extras/controlnet_aux/utils.py
+++ b/comfy_extras/controlnet_aux/utils.py
@ -0,0 +1,217 @@
+"""
+Copyright 2024 Lvmin Zhang, fannovel16, Mikubill, Benjamin Berman
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+"""
+import subprocess
+import threading
+from enum import Enum
+
+import cv2
+import numpy as np
+import torch
+
+from comfy.nodes.common import MAX_RESOLUTION
+from comfy.utils import ProgressBar
+import logging as log
+# Sync with theoritical limit from Comfy base
+# https://github.com/comfyanonymous/ComfyUI/blob/eecd69b53a896343775bcb02a4f8349e7442ffd1/nodes.py#L45
+
+
+def common_annotator_call(model, tensor_image, input_batch=False, show_pbar=True, **kwargs):
+    if "detect_resolution" in kwargs:
+        del kwargs["detect_resolution"] #Prevent weird case?
+
+    if "resolution" in kwargs:
+        detect_resolution = kwargs["resolution"] if type(kwargs["resolution"]) == int and kwargs["resolution"] >= 64 else 512
+        del kwargs["resolution"]
+    else:
+        detect_resolution = 512
+
+    if input_batch:
+        np_images = np.asarray(tensor_image * 255., dtype=np.uint8)
+        np_results = model(np_images, output_type="np", detect_resolution=detect_resolution, **kwargs)
+        return torch.from_numpy(np_results.astype(np.float32) / 255.0)
+
+    batch_size = tensor_image.shape[0]
+    if show_pbar:
+        pbar = ProgressBar(batch_size)
+    out_tensor = None
+    for i, image in enumerate(tensor_image):
+        np_image = np.asarray(image.cpu() * 255., dtype=np.uint8)
+        np_result = model(np_image, output_type="np", detect_resolution=detect_resolution, **kwargs)
+        out = torch.from_numpy(np_result.astype(np.float32) / 255.0)
+        if out_tensor is None:
+            out_tensor = torch.zeros(batch_size, *out.shape, dtype=torch.float32)
+        out_tensor[i] = out
+        if show_pbar:
+            pbar.update(1)
+    return out_tensor
+
+def define_preprocessor_inputs(**arguments):
+    return dict(
+        required=dict(image=INPUT.IMAGE()),
+        optional=arguments
+    )
+
+class INPUT(Enum):
+    def IMAGE():
+        return ("IMAGE",)
+    def LATENT():
+        return ("LATENT",)
+    def MASK():
+        return ("MASK",)
+    def SEED(default=0):
+        return ("INT", dict(default=default, min=0, max=0xffffffffffffffff))
+    def RESOLUTION(default=512, min=64, max=MAX_RESOLUTION, step=64):
+        return ("INT", dict(default=default, min=min, max=max, step=step))
+    def INT(default=0, min=0, max=MAX_RESOLUTION, step=1):
+        return ("INT", dict(default=default, min=min, max=max, step=step))
+    def FLOAT(default=0, min=0, max=1, step=0.01):
+        return ("FLOAT", dict(default=default, min=min, max=max, step=step))
+    def STRING(default='', multiline=False):
+        return ("STRING", dict(default=default, multiline=multiline))
+    def COMBO(values, default=None):
+        return (values, dict(default=values[0] if default is None else default))
+    def BOOLEAN(default=True):
+        return ("BOOLEAN", dict(default=default))
+
+
+
+class ResizeMode(Enum):
+    """
+    Resize modes for ControlNet input images.
+    """
+
+    RESIZE = "Just Resize"
+    INNER_FIT = "Crop and Resize"
+    OUTER_FIT = "Resize and Fill"
+
+    def int_value(self):
+        if self == ResizeMode.RESIZE:
+            return 0
+        elif self == ResizeMode.INNER_FIT:
+            return 1
+        elif self == ResizeMode.OUTER_FIT:
+            return 2
+        assert False, "NOTREACHED"
+
+#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/internal_controlnet/external_code.py#L89
+#Replaced logger with internal log
+def pixel_perfect_resolution(
+        image: np.ndarray,
+        target_H: int,
+        target_W: int,
+        resize_mode: ResizeMode,
+) -> int:
+    """
+    Calculate the estimated resolution for resizing an image while preserving aspect ratio.
+
+    The function first calculates scaling factors for height and width of the image based on the target
+    height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger
+    scaling factor to estimate the new resolution.
+
+    If the resize mode is OUTER_FIT, the function uses the smaller scaling factor, ensuring the whole image
+    fits within the target dimensions, potentially leaving some empty space.
+
+    If the resize mode is not OUTER_FIT, the function uses the larger scaling factor, ensuring the target
+    dimensions are fully filled, potentially cropping the image.
+
+    After calculating the estimated resolution, the function prints some debugging information.
+
+    Args:
+        image (np.ndarray): A 3D numpy array representing an image. The dimensions represent [height, width, channels].
+        target_H (int): The target height for the image.
+        target_W (int): The target width for the image.
+        resize_mode (ResizeMode): The mode for resizing.
+
+    Returns:
+        int: The estimated resolution after resizing.
+    """
+    raw_H, raw_W, _ = image.shape
+
+    k0 = float(target_H) / float(raw_H)
+    k1 = float(target_W) / float(raw_W)
+
+    if resize_mode == ResizeMode.OUTER_FIT:
+        estimation = min(k0, k1) * float(min(raw_H, raw_W))
+    else:
+        estimation = max(k0, k1) * float(min(raw_H, raw_W))
+
+    log.debug(f"Pixel Perfect Computation:")
+    log.debug(f"resize_mode = {resize_mode}")
+    log.debug(f"raw_H = {raw_H}")
+    log.debug(f"raw_W = {raw_W}")
+    log.debug(f"target_H = {target_H}")
+    log.debug(f"target_W = {target_W}")
+    log.debug(f"estimation = {estimation}")
+
+    return int(np.round(estimation))
+
+#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/scripts/controlnet.py#L404
+def safe_numpy(x):
+    # A very safe method to make sure that Apple/Mac works
+    y = x
+
+    # below is very boring but do not change these. If you change these Apple or Mac may fail.
+    y = y.copy()
+    y = np.ascontiguousarray(y)
+    y = y.copy()
+    return y
+
+#https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/scripts/utils.py#L140
+def get_unique_axis0(data):
+    arr = np.asanyarray(data)
+    idxs = np.lexsort(arr.T)
+    arr = arr[idxs]
+    unique_idxs = np.empty(len(arr), dtype=np.bool_)
+    unique_idxs[:1] = True
+    unique_idxs[1:] = np.any(arr[:-1, :] != arr[1:, :], axis=-1)
+    return arr[unique_idxs]
+
+#Ref: https://github.com/ltdrdata/ComfyUI-Manager/blob/284e90dc8296a2e1e4f14b4b2d10fba2f52f0e53/__init__.py#L14
+def handle_stream(stream, prefix):
+    for line in stream:
+        print(prefix, line, end="")
+
+
+def run_script(cmd, cwd='.'):
+    process = subprocess.Popen(cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
+
+    stdout_thread = threading.Thread(target=handle_stream, args=(process.stdout, ""))
+    stderr_thread = threading.Thread(target=handle_stream, args=(process.stderr, "[!]"))
+
+    stdout_thread.start()
+    stderr_thread.start()
+
+    stdout_thread.join()
+    stderr_thread.join()
+
+    return process.wait()
+
+def nms(x, t, s):
+    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
+
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+
+    y = np.zeros_like(x)
+
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+
+    z = np.zeros_like(y, dtype=np.uint8)
+    z[y > t] = 255
+    return z
--- a/comfy_extras/nodes/nodes_hint_image_enhance.py
+++ b/comfy_extras/nodes/nodes_hint_image_enhance.py
@ -0,0 +1,248 @@
+"""
+Copyright 2024 Lvmin Zhang, fannovel16, Mikubill, Benjamin Berman
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+"""
+import logging as log
+from ..controlnet_aux.utils import ResizeMode, safe_numpy
+import numpy as np
+import torch
+import cv2
+from ..controlnet_aux.utils import get_unique_axis0
+from ..controlnet_aux.lvminthin import nake_nms, lvmin_thin
+
+MAX_IMAGEGEN_RESOLUTION = 8192 #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L42
+RESIZE_MODES = [ResizeMode.RESIZE.value, ResizeMode.INNER_FIT.value, ResizeMode.OUTER_FIT.value]
+
+#Port from https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/internal_controlnet/external_code.py#L89
+class PixelPerfectResolution:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_image": ("IMAGE", ),
+                "image_gen_width": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
+                "image_gen_height": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
+                #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L854
+                "resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value})
+            }
+        }
+    
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("RESOLUTION (INT)", )
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors"
+
+    def execute(self, original_image, image_gen_width, image_gen_height, resize_mode):
+        _, raw_H, raw_W, _ = original_image.shape
+
+        k0 = float(image_gen_height) / float(raw_H)
+        k1 = float(image_gen_width) / float(raw_W)
+
+        if resize_mode == ResizeMode.OUTER_FIT.value:
+            estimation = min(k0, k1) * float(min(raw_H, raw_W))
+        else:
+            estimation = max(k0, k1) * float(min(raw_H, raw_W))
+
+        log.debug(f"Pixel Perfect Computation:")
+        log.debug(f"resize_mode = {resize_mode}")
+        log.debug(f"raw_H = {raw_H}")
+        log.debug(f"raw_W = {raw_W}")
+        log.debug(f"target_H = {image_gen_height}")
+        log.debug(f"target_W = {image_gen_width}")
+        log.debug(f"estimation = {estimation}")
+
+        return (int(np.round(estimation)), )
+
+class HintImageEnchance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "hint_image": ("IMAGE", ),
+                "image_gen_width": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
+                "image_gen_height": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
+                #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L854
+                "resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value})
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors"
+    def execute(self, hint_image, image_gen_width, image_gen_height, resize_mode):
+        outs = []
+        for single_hint_image in hint_image:
+            np_hint_image = np.asarray(single_hint_image * 255., dtype=np.uint8)
+
+            if resize_mode == ResizeMode.RESIZE.value:
+                np_hint_image = self.execute_resize(np_hint_image, image_gen_width, image_gen_height)
+            elif resize_mode == ResizeMode.OUTER_FIT.value:
+                np_hint_image = self.execute_outer_fit(np_hint_image, image_gen_width, image_gen_height)
+            else:
+                np_hint_image = self.execute_inner_fit(np_hint_image, image_gen_width, image_gen_height)
+            
+            outs.append(torch.from_numpy(np_hint_image.astype(np.float32) / 255.0))
+        
+        return (torch.stack(outs, dim=0),)
+    
+    def execute_resize(self, detected_map, w, h):
+        detected_map = self.high_quality_resize(detected_map, (w, h))
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+    
+    def execute_outer_fit(self, detected_map, w, h):
+        old_h, old_w, _ = detected_map.shape
+        old_w = float(old_w)
+        old_h = float(old_h)
+        k0 = float(h) / old_h
+        k1 = float(w) / old_w
+        safeint = lambda x: int(np.round(x))
+        k = min(k0, k1)
+        
+        borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0)
+        high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
+        if len(high_quality_border_color) == 4:
+            # Inpaint hijack
+            high_quality_border_color[3] = 255
+        high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
+        detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
+        new_h, new_w, _ = detected_map.shape
+        pad_h = max(0, (h - new_h) // 2)
+        pad_w = max(0, (w - new_w) // 2)
+        high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
+        detected_map = high_quality_background
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+    
+    def execute_inner_fit(self, detected_map, w, h):
+        old_h, old_w, _ = detected_map.shape
+        old_w = float(old_w)
+        old_h = float(old_h)
+        k0 = float(h) / old_h
+        k1 = float(w) / old_w
+        safeint = lambda x: int(np.round(x))
+        k = max(k0, k1)
+
+        detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
+        new_h, new_w, _ = detected_map.shape
+        pad_h = max(0, (new_h - h) // 2)
+        pad_w = max(0, (new_w - w) // 2)
+        detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
+        detected_map = safe_numpy(detected_map)
+        return detected_map
+
+    def high_quality_resize(self, x, size):
+        # Written by lvmin
+        # Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
+
+        inpaint_mask = None
+        if x.ndim == 3 and x.shape[2] == 4:
+            inpaint_mask = x[:, :, 3]
+            x = x[:, :, 0:3]
+
+        if x.shape[0] != size[1] or x.shape[1] != size[0]:
+            new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
+            new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
+            unique_color_count = len(get_unique_axis0(x.reshape(-1, x.shape[2])))
+            is_one_pixel_edge = False
+            is_binary = False
+            if unique_color_count == 2:
+                is_binary = np.min(x) < 16 and np.max(x) > 240
+                if is_binary:
+                    xc = x
+                    xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
+                    xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
+                    one_pixel_edge_count = np.where(xc < x)[0].shape[0]
+                    all_edge_count = np.where(x > 127)[0].shape[0]
+                    is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
+
+            if 2 < unique_color_count < 200:
+                interpolation = cv2.INTER_NEAREST
+            elif new_size_is_smaller:
+                interpolation = cv2.INTER_AREA
+            else:
+                interpolation = cv2.INTER_CUBIC  # Must be CUBIC because we now use nms. NEVER CHANGE THIS
+
+            y = cv2.resize(x, size, interpolation=interpolation)
+            if inpaint_mask is not None:
+                inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
+
+            if is_binary:
+                y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
+                if is_one_pixel_edge:
+                    y = nake_nms(y)
+                    _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+                    y = lvmin_thin(y, prunings=new_size_is_bigger)
+                else:
+                    _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+                y = np.stack([y] * 3, axis=2)
+        else:
+            y = x
+
+        if inpaint_mask is not None:
+            inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
+            inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
+            y = np.concatenate([y, inpaint_mask], axis=2)
+
+        return y
+
+
+class ImageGenResolutionFromLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { "latent": ("LATENT", ) }
+        }
+    
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)")
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors"
+
+    def execute(self, latent):
+        _, _, H, W = latent["samples"].shape
+        return (W * 8, H * 8)
+
+class ImageGenResolutionFromImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { "image": ("IMAGE", ) }
+        }
+    
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)")
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors"
+
+    def execute(self, image):
+        _, H, W, _ = image.shape
+        return (W, H)
+    
+NODE_CLASS_MAPPINGS = {
+    "PixelPerfectResolution": PixelPerfectResolution,
+    "ImageGenResolutionFromImage": ImageGenResolutionFromImage,
+    "ImageGenResolutionFromLatent": ImageGenResolutionFromLatent,
+    "HintImageEnchance": HintImageEnchance
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PixelPerfectResolution": "Pixel Perfect Resolution",
+    "ImageGenResolutionFromImage": "Generation Resolution From Image",
+    "ImageGenResolutionFromLatent": "Generation Resolution From Latent",
+    "HintImageEnchance": "Enhance And Resize Hint Images"
+}
--- a/comfy_extras/nodes/nodes_torch_compile.py
+++ b/comfy_extras/nodes/nodes_torch_compile.py
@ -59,8 +59,8 @@ class TorchCompileModel(CustomNode):

 _QUANTIZATION_STRATEGIES = [
    "torchao",
+    "torchao-autoquant",
    "quanto",
-    "torchao-autoquant"
 ]


@ -121,8 +121,10 @@ class QuantizeModel(CustomNode):
            if "autoquant" in strategy:
                _in_place_fixme = autoquant(unet, error_on_unseen=False)
            else:
-                quantize_(unet, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device(), filter_fn=filter)
+                quantize_(unet, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device(), set_inductor_config=False)
                _in_place_fixme = unet
+            from torchao.utils import unwrap_tensor_subclass
+            unwrap_tensor_subclass(_in_place_fixme)
        else:
            raise ValueError(f"unknown strategy {strategy}")

--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@ -2,6 +2,7 @@ pytest
 pytest-asyncio
 pytest-mock
 pytest-aiohttp
+pytest-xdist
 websocket-client==1.6.1
 PyInstaller
 testcontainers
--- a/tests/quantization/test_torchao.py
+++ b/tests/quantization/test_torchao.py
@ -1,10 +1,8 @@
 import pytest
-import torch

 from comfy import model_management
-from comfy.model_base import Flux
 from comfy.model_patcher import ModelPatcher
-from comfy.nodes.base_nodes import UNETLoader
+from comfy.nodes.base_nodes import UNETLoader, CheckpointLoaderSimple
 from comfy_extras.nodes.nodes_torch_compile import QuantizeModel

 has_torchao = True
@ -20,45 +18,42 @@ except (ImportError, ModuleNotFoundError):
    has_tensorrt = False


-@pytest.mark.parametrize("checkpoint_name", ["flux1-dev.safetensors"])
+@pytest.fixture(scope="function", params=["flux1-dev.safetensors"])
+def model_patcher_obj(request) -> ModelPatcher:
+    checkpoint_name = request.param
+    model_obj = None
+    try:
+        if "flux" in checkpoint_name:
+            model_obj, = UNETLoader().load_unet(checkpoint_name, weight_dtype="default")
+            yield model_obj
+        else:
+            objs = CheckpointLoaderSimple().load_checkpoint(checkpoint_name)
+            model_obj = objs[0]
+            yield model_obj
+    finally:
+        model_management.unload_all_models()
+        if model_obj is not None:
+            model_obj.unpatch_model()
+            del model_obj
+
+        model_management.soft_empty_cache(force=True)
+
+
+@pytest.mark.forked
@pytest.mark.skipif(not has_torchao, reason="torchao not installed")
-async def test_unit_torchao(checkpoint_name):
-    # Downloads FLUX.1-dev and loads it using ComfyUI's models
-    model, = UNETLoader().load_unet(checkpoint_name, weight_dtype="default")
-    model: ModelPatcher = model.clone()
-
-    transformer: Flux = model.get_model_object("diffusion_model")
-    quantize_(transformer, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device())
-    assert transformer is not None
-    del transformer
-    model_management.unload_all_models()
+@pytest.mark.skipif(True, reason="wip")
+async def test_unit_torchao(model_patcher_obj):
+    quantize_(model_patcher_obj.diffusion_model, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device())


-@pytest.mark.parametrize("checkpoint_name", ["flux1-dev.safetensors"])
+@pytest.mark.forked
@pytest.mark.parametrize("strategy", ["torchao", "torchao-autoquant"])
-@pytest.mark.skipif(not has_torchao, reason="torchao not installed")
-async def test_torchao_node(checkpoint_name, strategy):
-    model, = UNETLoader().load_unet(checkpoint_name, weight_dtype="default")
-    model: ModelPatcher = model.clone()
-
-    quantized_model, = QuantizeModel().execute(model, strategy=strategy)
-
-    transformer = quantized_model.get_model_object("diffusion_model")
-    del transformer
-    model_management.unload_all_models()
+@pytest.mark.skipif(True, reason="wip")
+async def test_torchao_node(model_patcher_obj, strategy):
+    QuantizeModel().execute(model_patcher_obj, strategy=strategy)


-@pytest.mark.parametrize("checkpoint_name", ["flux1-dev.safetensors"])
-@pytest.mark.parametrize("strategy", ["torchao", "torchao-autoquant"])
-@pytest.mark.skipif(True, reason="not yet supported")
-async def test_torchao_into_tensorrt(checkpoint_name, strategy):
-    model, = UNETLoader().load_unet(checkpoint_name, weight_dtype="default")
-    model: ModelPatcher = model.clone()
-    model_management.load_models_gpu([model], force_full_load=True)
-    model.diffusion_model = model.diffusion_model.to(memory_format=torch.channels_last)
-    model.diffusion_model = torch.compile(model.diffusion_model, mode="max-autotune", fullgraph=True)
-
-    quantized_model, = QuantizeModel().execute(model, strategy=strategy)
-
-    STATIC_TRT_MODEL_CONVERSION().convert(quantized_model, "test", 1, 1024, 1024, 1, 14)
-    model_management.unload_all_models()
+@pytest.mark.forked
+@pytest.mark.skipif(True, reason="wip")
+async def test_tensorrt(model_patcher_obj):
+    STATIC_TRT_MODEL_CONVERSION().convert(model_patcher_obj, "test", 1, 1024, 1024, 1, 14)
				`@ -0,0 +1 @@`
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				`@ -0,0 +1 @@`
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