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Zero
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from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Type, TYPE_CHECKING
from comfy_api.internal import ComfyAPIBase
from comfy_api.internal.singleton import ProxiedSingleton
from comfy_api.internal.async_to_sync import create_sync_class
from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
from comfy_api.latest._io import _IO as io #noqa: F401
from comfy_api.latest._ui import _UI as ui #noqa: F401
# from comfy_api.latest._resources import _RESOURCES as resources #noqa: F401
from comfy_execution.utils import get_executing_context
from comfy_execution.progress import get_progress_state, PreviewImageTuple
from PIL import Image
from comfy.cli_args import args
import numpy as np
class ComfyAPI_latest(ComfyAPIBase):
VERSION = "latest"
STABLE = False
class Execution(ProxiedSingleton):
async def set_progress(
self,
value: float,
max_value: float,
node_id: str | None = None,
preview_image: Image.Image | ImageInput | None = None,
ignore_size_limit: bool = False,
) -> None:
"""
Update the progress bar displayed in the ComfyUI interface.
This function allows custom nodes and API calls to report their progress
back to the user interface, providing visual feedback during long operations.
Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
"""
executing_context = get_executing_context()
if node_id is None and executing_context is not None:
node_id = executing_context.node_id
if node_id is None:
raise ValueError("node_id must be provided if not in executing context")
# Convert preview_image to PreviewImageTuple if needed
to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image
if to_display is not None:
# First convert to PIL Image if needed
if isinstance(to_display, ImageInput):
# Convert ImageInput (torch.Tensor) to PIL Image
# Handle tensor shape [B, H, W, C] -> get first image if batch
tensor = to_display
if len(tensor.shape) == 4:
tensor = tensor[0]
# Convert to numpy array and scale to 0-255
image_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
to_display = Image.fromarray(image_np)
if isinstance(to_display, Image.Image):
# Detect image format from PIL Image
image_format = to_display.format if to_display.format else "JPEG"
# Use None for preview_size if ignore_size_limit is True
preview_size = None if ignore_size_limit else args.preview_size
to_display = (image_format, to_display, preview_size)
get_progress_state().update_progress(
node_id=node_id,
value=value,
max_value=max_value,
image=to_display,
)
execution: Execution
class ComfyExtension(ABC):
async def on_load(self) -> None:
"""
Called when an extension is loaded.
This should be used to initialize any global resources neeeded by the extension.
"""
@abstractmethod
async def get_node_list(self) -> list[type[io.ComfyNode]]:
"""
Returns a list of nodes that this extension provides.
"""
class Input:
Image = ImageInput
Audio = AudioInput
Mask = MaskInput
Latent = LatentInput
Video = VideoInput
class InputImpl:
VideoFromFile = VideoFromFile
VideoFromComponents = VideoFromComponents
class Types:
VideoCodec = VideoCodec
VideoContainer = VideoContainer
VideoComponents = VideoComponents
ComfyAPI = ComfyAPI_latest
# Create a synchronous version of the API
if TYPE_CHECKING:
import comfy_api.latest.generated.ComfyAPISyncStub # type: ignore
ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
ComfyAPISync = create_sync_class(ComfyAPI_latest)
__all__ = [
"ComfyAPI",
"ComfyAPISync",
"Input",
"InputImpl",
"Types",
"ComfyExtension",
]
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