mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Merge pull request #8718 from comfyanonymous/v3-definition-wip
V3 definition update - fix v3 node schema parsing, add missing Types
This commit is contained in:
Jedrzej Kosinski
GitHub
commit
1ad8a72fe9
@ -1,13 +1,23 @@
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from __future__ import annotations
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from typing import Any, Literal, TYPE_CHECKING, TypeVar, Callable, Optional, cast, override
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from typing import Any, Literal, TYPE_CHECKING, TypeVar, Callable, Optional, cast, TypedDict, NotRequired
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from enum import Enum
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, asdict
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from collections import Counter
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from comfy.comfy_types.node_typing import IO
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# used for type hinting
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import torch
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from spandrel import ImageModelDescriptor
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from comfy.model_patcher import ModelPatcher
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from comfy.samplers import Sampler, CFGGuider
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from comfy.sd import CLIP
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from comfy.controlnet import ControlNet
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from comfy.sd import VAE
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from comfy.sd import StyleModel as StyleModel_
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from comfy.clip_vision import ClipVisionModel
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from comfy.clip_vision import Output as ClipVisionOutput_
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from comfy_api.input import VideoInput
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from comfy.hooks import HookGroup, HookKeyframeGroup
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# from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference
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class FolderType(str, Enum):
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@ -125,7 +135,7 @@ def comfytype(io_type: str, **kwargs):
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return new_cls
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return decorator
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def Custom(io_type: IO | str) -> type[ComfyType]:
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def Custom(io_type: str) -> type[ComfyType]:
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'''Create a ComfyType for a custom io_type.'''
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@comfytype(io_type=io_type)
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class CustomComfyType(ComfyTypeIO):
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@ -271,7 +281,7 @@ class NodeStateLocal(NodeState):
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def __delitem__(self, key: str):
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del self.local_state[key]
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@comfytype(io_type=IO.BOOLEAN)
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@comfytype(io_type="BOOLEAN")
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class Boolean:
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Type = bool
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@ -294,7 +304,7 @@ class Boolean:
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class Output(OutputV3):
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...
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@comfytype(io_type=IO.INT)
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@comfytype(io_type="INT")
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class Int:
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Type = int
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@ -323,8 +333,8 @@ class Int:
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class Output(OutputV3):
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...
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@comfytype(io_type=IO.FLOAT)
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class Float:
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@comfytype(io_type="FLOAT")
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class Float(ComfyTypeIO):
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Type = float
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class Input(WidgetInputV3):
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@ -349,11 +359,8 @@ class Float:
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"display": self.display_mode,
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})
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class Output(OutputV3):
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...
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@comfytype(io_type=IO.STRING)
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class String:
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@comfytype(io_type="STRING")
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class String(ComfyTypeIO):
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Type = str
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class Input(WidgetInputV3):
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@ -372,11 +379,8 @@ class String:
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"placeholder": self.placeholder,
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})
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class Output(OutputV3):
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...
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@comfytype(io_type=IO.COMBO)
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class Combo:
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@comfytype(io_type="COMBO")
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class Combo(ComfyType):
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Type = str
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class Input(WidgetInputV3):
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'''Combo input (dropdown).'''
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@ -405,8 +409,9 @@ class Combo:
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"remote": self.remote.as_dict() if self.remote else None,
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})
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@comfytype(io_type=IO.COMBO)
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class MultiCombo:
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@comfytype(io_type="COMBO")
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class MultiCombo(ComfyType):
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'''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
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# TODO: something is wrong with the serialization, frontend does not recognize it as multiselect
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Type = list[str]
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@ -428,98 +433,278 @@ class MultiCombo:
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})
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return to_return
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@comfytype(io_type=IO.IMAGE)
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@comfytype(io_type="IMAGE")
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class Image(ComfyTypeIO):
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Type = torch.Tensor
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@comfytype(io_type=IO.MASK)
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@comfytype(io_type="MASK")
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class Mask(ComfyTypeIO):
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Type = torch.Tensor
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@comfytype(io_type=IO.LATENT)
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@comfytype(io_type="LATENT")
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class Latent(ComfyTypeIO):
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Type = Any # TODO: make Type a TypedDict
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'''Latents are stored as a dictionary.'''
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class LatentDict(TypedDict):
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samples: torch.Tensor
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'''Latent tensors.'''
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noise_mask: NotRequired[torch.Tensor]
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batch_index: NotRequired[list[int]]
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type: NotRequired[str]
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'''Only needed if dealing with these types: audio, hunyuan3dv2'''
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Type = LatentDict
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@comfytype(io_type=IO.CONDITIONING)
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@comfytype(io_type="CONDITIONING")
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class Conditioning(ComfyTypeIO):
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Type = Any
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class PooledDict(TypedDict):
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pooled_output: torch.Tensor
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'''Pooled output from CLIP.'''
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control: NotRequired[ControlNet]
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'''ControlNet to apply to conditioning.'''
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control_apply_to_uncond: NotRequired[bool]
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'''Whether to apply ControlNet to matching negative conditioning at sample time, if applicable.'''
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cross_attn_controlnet: NotRequired[torch.Tensor]
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'''CrossAttn from CLIP to use for controlnet only.'''
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pooled_output_controlnet: NotRequired[torch.Tensor]
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'''Pooled output from CLIP to use for controlnet only.'''
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gligen: NotRequired[tuple[str, Gligen, list[tuple[torch.Tensor, int, ...]]]]
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'''GLIGEN to apply to conditioning.'''
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area: NotRequired[tuple[int, ...] | tuple[str, float, ...]]
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'''Set area of conditioning. First half of values apply to dimensions, the second half apply to coordinates.
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By default, the dimensions are based on total pixel amount, but the first value can be set to "percentage" to use a percentage of the image size instead.
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@comfytype(io_type=IO.SAMPLER)
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(1024, 1024, 0, 0) would apply conditioning to the top-left 1024x1024 pixels.
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("percentage", 0.5, 0.5, 0, 0) would apply conditioning to the top-left 50% of the image.''' # TODO: verify its actually top-left
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strength: NotRequired[float]
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'''Strength of conditioning. Default strength is 1.0.'''
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mask: NotRequired[torch.Tensor]
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'''Mask to apply conditioning to.'''
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mask_strength: NotRequired[float]
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'''Strength of conditioning mask. Default strength is 1.0.'''
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set_area_to_bounds: NotRequired[bool]
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'''Whether conditioning mask should determine bounds of area - if set to false, latents are sampled at full resolution and result is applied in mask.'''
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concat_latent_image: NotRequired[torch.Tensor]
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'''Used for inpainting and specific models.'''
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concat_mask: NotRequired[torch.Tensor]
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'''Used for inpainting and specific models.'''
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concat_image: NotRequired[torch.Tensor]
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'''Used by SD_4XUpscale_Conditioning.'''
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noise_augmentation: NotRequired[float]
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'''Used by SD_4XUpscale_Conditioning.'''
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hooks: NotRequired[HookGroup]
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'''Applies hooks to conditioning.'''
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default: NotRequired[bool]
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'''Whether to this conditioning is 'default'; default conditioning gets applied to any areas of the image that have no masks/areas applied, assuming at least one area/mask is present during sampling.'''
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start_percent: NotRequired[float]
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'''Determines relative step to begin applying conditioning, expressed as a float between 0.0 and 1.0.'''
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end_percent: NotRequired[float]
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'''Determines relative step to end applying conditioning, expressed as a float between 0.0 and 1.0.'''
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clip_start_percent: NotRequired[float]
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'''Internal variable for conditioning scheduling - start of application, expressed as a float between 0.0 and 1.0.'''
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clip_end_percent: NotRequired[float]
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'''Internal variable for conditioning scheduling - end of application, expressed as a float between 0.0 and 1.0.'''
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attention_mask: NotRequired[torch.Tensor]
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'''Masks text conditioning; used by StyleModel among others.'''
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attention_mask_img_shape: NotRequired[tuple[int, ...]]
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'''Masks text conditioning; used by StyleModel among others.'''
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unclip_conditioning: NotRequired[list[dict]]
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'''Used by unCLIP.'''
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conditioning_lyrics: NotRequired[torch.Tensor]
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'''Used by AceT5Model.'''
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seconds_start: NotRequired[float]
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'''Used by StableAudio.'''
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seconds_total: NotRequired[float]
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'''Used by StableAudio.'''
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lyrics_strength: NotRequired[float]
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'''Used by AceStepAudio.'''
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width: NotRequired[int]
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'''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).'''
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height: NotRequired[int]
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'''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).'''
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aesthetic_score: NotRequired[float]
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'''Used by CLIPTextEncodeSDXL/Refiner.'''
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crop_w: NotRequired[int]
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'''Used by CLIPTextEncodeSDXL.'''
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crop_h: NotRequired[int]
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'''Used by CLIPTextEncodeSDXL.'''
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target_width: NotRequired[int]
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'''Used by CLIPTextEncodeSDXL.'''
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target_height: NotRequired[int]
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'''Used by CLIPTextEncodeSDXL.'''
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reference_latents: NotRequired[list[torch.Tensor]]
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'''Used by ReferenceLatent.'''
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guidance: NotRequired[float]
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'''Used by Flux-like models with guidance embed.'''
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guiding_frame_index: NotRequired[int]
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'''Used by Hunyuan ImageToVideo.'''
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ref_latent: NotRequired[torch.Tensor]
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'''Used by Hunyuan ImageToVideo.'''
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keyframe_idxs: NotRequired[list[int]]
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'''Used by LTXV.'''
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frame_rate: NotRequired[float]
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'''Used by LTXV.'''
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stable_cascade_prior: NotRequired[torch.Tensor]
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'''Used by StableCascade.'''
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elevation: NotRequired[list[float]]
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'''Used by SV3D.'''
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azimuth: NotRequired[list[float]]
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'''Used by SV3D.'''
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motion_bucket_id: NotRequired[int]
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'''Used by SVD-like models.'''
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fps: NotRequired[int]
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'''Used by SVD-like models.'''
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augmentation_level: NotRequired[float]
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'''Used by SVD-like models.'''
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clip_vision_output: NotRequired[ClipVisionOutput_]
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'''Used by WAN-like models.'''
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vace_frames: NotRequired[torch.Tensor]
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'''Used by WAN VACE.'''
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vace_mask: NotRequired[torch.Tensor]
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'''Used by WAN VACE.'''
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vace_strength: NotRequired[float]
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'''Used by WAN VACE.'''
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camera_conditions: NotRequired[Any] # TODO: assign proper type once defined
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'''Used by WAN Camera.'''
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time_dim_concat: NotRequired[torch.Tensor]
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'''Used by WAN Phantom Subject.'''
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CondList = list[tuple[torch.Tensor, PooledDict]]
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Type = CondList
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@comfytype(io_type="SAMPLER")
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class Sampler(ComfyTypeIO):
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Type = Any
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Type = Sampler
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@comfytype(io_type=IO.SIGMAS)
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@comfytype(io_type="SIGMAS")
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class Sigmas(ComfyTypeIO):
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Type = Any
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Type = torch.Tensor
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@comfytype(io_type=IO.NOISE)
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@comfytype(io_type="NOISE")
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class Noise(ComfyTypeIO):
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Type = Any
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Type = torch.Tensor
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@comfytype(io_type=IO.GUIDER)
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@comfytype(io_type="GUIDER")
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class Guider(ComfyTypeIO):
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Type = Any
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Type = CFGGuider
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@comfytype(io_type=IO.CLIP)
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@comfytype(io_type="CLIP")
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class Clip(ComfyTypeIO):
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Type = Any
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Type = CLIP
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@comfytype(io_type=IO.CONTROL_NET)
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@comfytype(io_type="CONTROL_NET")
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class ControlNet(ComfyTypeIO):
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Type = Any
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Type = ControlNet
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@comfytype(io_type=IO.VAE)
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@comfytype(io_type="VAE")
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class Vae(ComfyTypeIO):
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Type = Any
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Type = VAE
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@comfytype(io_type=IO.MODEL)
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@comfytype(io_type="MODEL")
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class Model(ComfyTypeIO):
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Type = ModelPatcher
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@comfytype(io_type=IO.CLIP_VISION)
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@comfytype(io_type="CLIP_VISION")
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class ClipVision(ComfyTypeIO):
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Type = Any
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Type = ClipVisionModel
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@comfytype(io_type=IO.CLIP_VISION_OUTPUT)
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@comfytype(io_type="CLIP_VISION_OUTPUT")
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class ClipVisionOutput(ComfyTypeIO):
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Type = Any
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Type = ClipVisionOutput_
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@comfytype(io_type=IO.STYLE_MODEL)
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@comfytype(io_type="STYLE_MODEL")
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class StyleModel(ComfyTypeIO):
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Type = Any
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Type = StyleModel_
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@comfytype(io_type=IO.GLIGEN)
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@comfytype(io_type="GLIGEN")
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class Gligen(ComfyTypeIO):
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Type = Any
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'''ModelPatcher that wraps around a 'Gligen' model.'''
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Type = ModelPatcher
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@comfytype(io_type=IO.UPSCALE_MODEL)
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@comfytype(io_type="UPSCALE_MODEL")
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class UpscaleModel(ComfyTypeIO):
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Type = Any
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Type = ImageModelDescriptor
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@comfytype(io_type=IO.AUDIO)
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@comfytype(io_type="AUDIO")
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class Audio(ComfyTypeIO):
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Type = Any
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class AudioDict(TypedDict):
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waveform: torch.Tensor
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sampler_rate: int
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Type = AudioDict
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@comfytype(io_type=IO.POINT)
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class Point(ComfyTypeIO):
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Type = Any
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@comfytype(io_type=IO.FACE_ANALYSIS)
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class FaceAnalysis(ComfyTypeIO):
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Type = Any
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@comfytype(io_type=IO.BBOX)
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class BBOX(ComfyTypeIO):
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Type = Any
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@comfytype(io_type=IO.SEGS)
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class SEGS(ComfyTypeIO):
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Type = Any
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@comfytype(io_type=IO.VIDEO)
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@comfytype(io_type="VIDEO")
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class Video(ComfyTypeIO):
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Type = Any
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Type = VideoInput
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@comfytype(io_type="SVG")
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class SVG(ComfyTypeIO):
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Type = Any # TODO: SVG class is defined in comfy_extras/nodes_images.py, causing circular reference; should be moved to somewhere else before referenced directly in v3
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@comfytype(io_type="LORA_MODEL")
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class LoraModel(ComfyTypeIO):
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Type = dict[str, torch.Tensor]
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@comfytype(io_type="LOSS_MAP")
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class LossMap(ComfyTypeIO):
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class LossMapDict(TypedDict):
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loss: list[torch.Tensor]
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Type = LossMapDict
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@comfytype(io_type="VOXEL")
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class Voxel(ComfyTypeIO):
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Type = Any # TODO: VOXEL class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3
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@comfytype(io_type="MESH")
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class Mesh(ComfyTypeIO):
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Type = Any # TODO: MESH class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3
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@comfytype(io_type="HOOKS")
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class Hooks(ComfyTypeIO):
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Type = HookGroup
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@comfytype(io_type="HOOK_KEYFRAMES")
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class HookKeyframes(ComfyTypeIO):
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Type = HookKeyframeGroup
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@comfytype(io_type="TIMESTEPS_RANGE")
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class TimestepsRange(ComfyTypeIO):
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'''Range defined by start and endpoint, between 0.0 and 1.0.'''
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Type = tuple[int, int]
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@comfytype(io_type="LATENT_OPERATION")
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class LatentOperation(ComfyTypeIO):
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Type = Callable[[torch.Tensor], torch.Tensor]
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@comfytype(io_type="FLOW_CONTROL")
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class FlowControl(ComfyTypeIO):
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# NOTE: only used in testing_nodes right now
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Type = tuple[str, Any]
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@comfytype(io_type="ACCUMULATION")
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class Accumulation(ComfyTypeIO):
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# NOTE: only used in testing_nodes right now
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class AccumulationDict(TypedDict):
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accum: list[Any]
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Type = AccumulationDict
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@comfytype(io_type="LOAD3D_CAMERA")
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class Load3DCamera(ComfyTypeIO):
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Type = Any # TODO: figure out type for this; in code, only described as image['camera_info'], gotten from a LOAD_3D or LOAD_3D_ANIMATION type
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@comfytype(io_type="POINT")
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class Point(ComfyTypeIO):
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Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
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@comfytype(io_type="FACE_ANALYSIS")
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class FaceAnalysis(ComfyTypeIO):
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Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
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@comfytype(io_type="BBOX")
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class BBOX(ComfyTypeIO):
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Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
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@comfytype(io_type="SEGS")
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class SEGS(ComfyTypeIO):
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Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist?
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@comfytype(io_type="COMFY_MULTITYPED_V3")
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class MultiType:
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@ -528,7 +713,7 @@ class MultiType:
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'''
|
||||
Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values.
|
||||
'''
|
||||
def __init__(self, id: str | ComfyType.Input, types: list[type[ComfyType] | ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
|
||||
def __init__(self, id: str | InputV3, types: list[type[ComfyType] | ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
|
||||
# if id is an Input, then use that Input with overridden values
|
||||
self.input_override = None
|
||||
if isinstance(id, InputV3):
|
||||
@ -561,7 +746,6 @@ class MultiType:
|
||||
str_types.insert(0, self.input_override.get_io_type_V1())
|
||||
return ",".join(list(dict.fromkeys(str_types)))
|
||||
|
||||
@override
|
||||
def as_dict_V1(self):
|
||||
if self.input_override is not None:
|
||||
return self.input_override.as_dict_V1() | super().as_dict_V1()
|
||||
@ -754,7 +938,7 @@ class SchemaV3:
|
||||
raise ValueError("\n".join(issues))
|
||||
|
||||
class Serializer:
|
||||
def __init_subclass__(cls, io_type: IO | str, **kwargs):
|
||||
def __init_subclass__(cls, io_type: str, **kwargs):
|
||||
cls.io_type = io_type
|
||||
super().__init_subclass__(**kwargs)
|
||||
|
||||
@ -1134,7 +1318,7 @@ class TestNode(ComfyNodeV3):
|
||||
if __name__ == "__main__":
|
||||
print("hello there")
|
||||
inputs: list[InputV3] = [
|
||||
Int.Input("tessfes", widgetType=IO.STRING),
|
||||
Int.Input("tessfes", widgetType=String.io_type),
|
||||
Int.Input("my_int"),
|
||||
Custom("XYZ").Input("xyz"),
|
||||
Custom("MODEL_M").Input("model1"),
|
||||
|
||||
@ -36,7 +36,7 @@ class V3TestNode(io.ComfyNodeV3):
|
||||
io.Image.Input("image", display_name="new_image"),
|
||||
XYZ.Input("xyz", optional=True),
|
||||
io.Custom("JKL").Input("jkl", optional=True),
|
||||
io.Mask.Input("mask", optional=True),
|
||||
io.Mask.Input("mask", display_name="mask haha", optional=True),
|
||||
io.Int.Input("some_int", display_name="new_name", min=0, max=127, default=42,
|
||||
tooltip="My tooltip 😎", display_mode=io.NumberDisplay.slider),
|
||||
io.Combo.Input("combo", options=["a", "b", "c"], tooltip="This is a combo input"),
|
||||
|
||||
@ -555,7 +555,7 @@ class PromptServer():
|
||||
|
||||
def node_info(node_class):
|
||||
obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
|
||||
if isinstance(obj_class, ComfyNodeV3):
|
||||
if issubclass(obj_class, ComfyNodeV3):
|
||||
return obj_class.GET_NODE_INFO_V1()
|
||||
info = {}
|
||||
info['input'] = obj_class.INPUT_TYPES()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user