mirror of
https://github.com/comfyanonymous/ComfyUI.git
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feat(api-nodes): add Magnific nodes
This commit is contained in:
bigcat88
122
comfy_api_nodes/apis/magnific.py
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122
comfy_api_nodes/apis/magnific.py
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from typing import TypedDict
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from pydantic import AliasChoices, BaseModel, Field, model_validator
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class InputPortraitMode(TypedDict):
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portrait_mode: str
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portrait_style: str
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portrait_beautifier: str
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class InputAdvancedSettings(TypedDict):
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advanced_settings: str
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whites: int
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blacks: int
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brightness: int
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contrast: int
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saturation: int
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engine: str
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transfer_light_a: str
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transfer_light_b: str
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fixed_generation: bool
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class InputSkinEnhancerMode(TypedDict):
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mode: str
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skin_detail: int
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optimized_for: str
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class ImageUpscalerCreativeRequest(BaseModel):
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image: str = Field(...)
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scale_factor: str = Field(...)
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optimized_for: str = Field(...)
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prompt: str | None = Field(None)
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creativity: int = Field(...)
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hdr: int = Field(...)
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resemblance: int = Field(...)
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fractality: int = Field(...)
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engine: str = Field(...)
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class ImageUpscalerPrecisionV2Request(BaseModel):
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image: str = Field(...)
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sharpen: int = Field(...)
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smart_grain: int = Field(...)
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ultra_detail: int = Field(...)
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flavor: str = Field(...)
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scale_factor: int = Field(...)
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class ImageRelightAdvancedSettingsRequest(BaseModel):
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whites: int = Field(...)
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blacks: int = Field(...)
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brightness: int = Field(...)
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contrast: int = Field(...)
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saturation: int = Field(...)
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engine: str = Field(...)
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transfer_light_a: str = Field(...)
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transfer_light_b: str = Field(...)
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fixed_generation: bool = Field(...)
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class ImageRelightRequest(BaseModel):
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image: str = Field(...)
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prompt: str | None = Field(None)
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transfer_light_from_reference_image: str | None = Field(None)
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light_transfer_strength: int = Field(...)
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interpolate_from_original: bool = Field(...)
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change_background: bool = Field(...)
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style: str = Field(...)
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preserve_details: bool = Field(...)
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advanced_settings: ImageRelightAdvancedSettingsRequest | None = Field(...)
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class ImageStyleTransferRequest(BaseModel):
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image: str = Field(...)
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reference_image: str = Field(...)
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prompt: str | None = Field(None)
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style_strength: int = Field(...)
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structure_strength: int = Field(...)
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is_portrait: bool = Field(...)
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portrait_style: str | None = Field(...)
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portrait_beautifier: str | None = Field(...)
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flavor: str = Field(...)
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engine: str = Field(...)
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fixed_generation: bool = Field(...)
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class ImageSkinEnhancerCreativeRequest(BaseModel):
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image: str = Field(...)
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sharpen: int = Field(...)
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smart_grain: int = Field(...)
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class ImageSkinEnhancerFaithfulRequest(BaseModel):
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image: str = Field(...)
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sharpen: int = Field(...)
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smart_grain: int = Field(...)
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skin_detail: int = Field(...)
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class ImageSkinEnhancerFlexibleRequest(BaseModel):
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image: str = Field(...)
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sharpen: int = Field(...)
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smart_grain: int = Field(...)
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optimized_for: str = Field(...)
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class TaskResponse(BaseModel):
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"""Unified response model that handles both wrapped and unwrapped API responses."""
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task_id: str = Field(...)
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status: str = Field(validation_alias=AliasChoices("status", "task_status"))
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generated: list[str] | None = Field(None)
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@model_validator(mode="before")
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@classmethod
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def unwrap_data(cls, values: dict) -> dict:
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if "data" in values and isinstance(values["data"], dict):
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return values["data"]
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return values
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843
comfy_api_nodes/nodes_magnific.py
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843
comfy_api_nodes/nodes_magnific.py
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@@ -0,0 +1,843 @@
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from typing_extensions import override
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from comfy_api.latest import IO, ComfyExtension, Input
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from comfy_api_nodes.apis.magnific import (
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ImageRelightAdvancedSettingsRequest,
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ImageRelightRequest,
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ImageSkinEnhancerCreativeRequest,
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ImageSkinEnhancerFaithfulRequest,
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ImageSkinEnhancerFlexibleRequest,
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ImageStyleTransferRequest,
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ImageUpscalerCreativeRequest,
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ImageUpscalerPrecisionV2Request,
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InputAdvancedSettings,
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InputPortraitMode,
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InputSkinEnhancerMode,
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TaskResponse,
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)
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from comfy_api_nodes.util import (
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ApiEndpoint,
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download_url_to_image_tensor,
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downscale_image_tensor,
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get_image_dimensions,
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get_number_of_images,
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poll_op,
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sync_op,
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upload_images_to_comfyapi,
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validate_image_aspect_ratio,
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validate_image_dimensions,
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)
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class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="MagnificImageUpscalerCreativeNode",
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display_name="Magnific Image Upscale (Creative)",
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category="api node/image/Magnific",
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description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling.",
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inputs=[
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IO.Image.Input("image"),
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IO.String.Input("prompt", multiline=True, default=""),
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IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
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IO.Combo.Input(
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"optimized_for",
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options=[
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"standard",
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"soft_portraits",
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"hard_portraits",
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"art_n_illustration",
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"videogame_assets",
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"nature_n_landscapes",
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"films_n_photography",
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"3d_renders",
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"science_fiction_n_horror",
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],
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),
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IO.Int.Input("creativity", min=-10, max=10, default=0, display_mode=IO.NumberDisplay.slider),
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IO.Int.Input(
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"hdr",
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min=-10,
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max=10,
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default=0,
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tooltip="The level of definition and detail.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Int.Input(
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"resemblance",
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min=-10,
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max=10,
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default=0,
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tooltip="The level of resemblance to the original image.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Int.Input(
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"fractality",
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min=-10,
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max=10,
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default=0,
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tooltip="The strength of the prompt and intricacy per square pixel.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Combo.Input(
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"engine",
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options=["automatic", "magnific_illusio", "magnific_sharpy", "magnific_sparkle"],
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),
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IO.Boolean.Input(
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"auto_downscale",
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default=False,
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tooltip="Automatically downscale input image if output would exceed maximum pixel limit.",
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),
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],
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outputs=[
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IO.Image.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
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expr="""
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(
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$max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
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{"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
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)
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""",
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),
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)
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@classmethod
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async def execute(
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cls,
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image: Input.Image,
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prompt: str,
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scale_factor: str,
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optimized_for: str,
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creativity: int,
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hdr: int,
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resemblance: int,
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fractality: int,
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engine: str,
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auto_downscale: bool,
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) -> IO.NodeOutput:
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if get_number_of_images(image) != 1:
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raise ValueError("Exactly one input image is required.")
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validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
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validate_image_dimensions(image, min_height=160, min_width=160)
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max_output_pixels = 25_300_000
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height, width = get_image_dimensions(image)
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scale = int(scale_factor.rstrip("x"))
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output_pixels = height * width * scale * scale
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if output_pixels > max_output_pixels:
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if auto_downscale:
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# Calculate max input pixels to fit within output pixel limit
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max_input_pixels = max_output_pixels // (scale * scale)
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image = downscale_image_tensor(image, total_pixels=max_input_pixels)
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else:
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raise ValueError(
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f"Output size ({width * scale}x{height * scale} = {output_pixels:,} pixels) "
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f"exceeds maximum allowed size of {max_output_pixels:,} pixels. "
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f"Use a smaller input image or lower scale factor."
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)
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initial_res = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler", method="POST"),
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response_model=TaskResponse,
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data=ImageUpscalerCreativeRequest(
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image=(await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=None))[0],
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scale_factor=scale_factor,
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optimized_for=optimized_for,
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creativity=creativity,
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hdr=hdr,
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resemblance=resemblance,
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fractality=fractality,
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engine=engine,
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prompt=prompt if prompt else None,
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),
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)
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final_response = await poll_op(
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cls,
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ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler/{initial_res.task_id}"),
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response_model=TaskResponse,
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status_extractor=lambda x: x.status,
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poll_interval=10.0,
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max_poll_attempts=480,
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)
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return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
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class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="MagnificImageUpscalerPreciseV2Node",
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display_name="Magnific Image Upscale (Precise V2)",
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category="api node/image/Magnific",
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description="High-fidelity upscaling with fine control over sharpness, grain, and detail.",
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inputs=[
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IO.Image.Input("image"),
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IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
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IO.Combo.Input(
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"flavor",
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options=["sublime", "photo", "photo_denoiser"],
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tooltip="Processing style: "
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"sublime for general use, photo for photographs, photo_denoiser for noisy photos.",
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),
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IO.Int.Input(
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"sharpen",
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min=0,
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max=100,
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default=7,
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tooltip="Image sharpness intensity. Higher values increase edge definition and clarity.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Int.Input(
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"smart_grain",
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min=0,
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max=100,
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default=7,
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tooltip="Intelligent grain/texture enhancement to prevent the image from "
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"looking too smooth or artificial.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Int.Input(
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"ultra_detail",
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min=0,
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max=100,
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default=30,
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tooltip="Controls fine detail, textures, and micro-details added during upscaling.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Boolean.Input(
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"auto_downscale",
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default=False,
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tooltip="Automatically downscale input image if output would exceed maximum resolution.",
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),
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],
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outputs=[
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IO.Image.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
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expr="""
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(
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$max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
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{"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
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)
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""",
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),
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)
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@classmethod
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async def execute(
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cls,
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image: Input.Image,
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scale_factor: str,
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flavor: str,
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sharpen: int,
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smart_grain: int,
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ultra_detail: int,
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auto_downscale: bool,
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) -> IO.NodeOutput:
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if get_number_of_images(image) != 1:
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raise ValueError("Exactly one input image is required.")
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validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
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validate_image_dimensions(image, min_height=160, min_width=160)
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max_output_dimension = 10060
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height, width = get_image_dimensions(image)
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scale = int(scale_factor.strip("x"))
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output_width = width * scale
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output_height = height * scale
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if output_width > max_output_dimension or output_height > max_output_dimension:
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if auto_downscale:
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# Calculate max input pixels based on the largest dimension
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max_input_dim = max_output_dimension // scale
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scale_ratio = max_input_dim / max(width, height)
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max_input_pixels = int(width * height * scale_ratio * scale_ratio)
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image = downscale_image_tensor(image, total_pixels=max_input_pixels)
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else:
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raise ValueError(
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f"Output dimensions ({output_width}x{output_height}) exceed maximum allowed "
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f"resolution of {max_output_dimension}x{max_output_dimension} pixels. "
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f"Use a smaller input image or lower scale factor."
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)
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initial_res = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler-precision-v2", method="POST"),
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response_model=TaskResponse,
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data=ImageUpscalerPrecisionV2Request(
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image=(await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=None))[0],
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scale_factor=scale,
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flavor=flavor,
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sharpen=sharpen,
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smart_grain=smart_grain,
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ultra_detail=ultra_detail,
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),
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)
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final_response = await poll_op(
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cls,
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ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler-precision-v2/{initial_res.task_id}"),
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response_model=TaskResponse,
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status_extractor=lambda x: x.status,
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poll_interval=10.0,
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max_poll_attempts=480,
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)
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return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
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class MagnificImageStyleTransferNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="MagnificImageStyleTransferNode",
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display_name="Magnific Image Style Transfer",
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category="api node/image/Magnific",
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description="Transfer the style from a reference image to your input image.",
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inputs=[
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IO.Image.Input("image", tooltip="The image to apply style transfer to."),
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IO.Image.Input("reference_image", tooltip="The reference image to extract style from."),
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IO.String.Input("prompt", multiline=True, default=""),
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IO.Int.Input(
|
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"style_strength",
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min=0,
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max=100,
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default=100,
|
||||
tooltip="Percentage of style strength.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Int.Input(
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"structure_strength",
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min=0,
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max=100,
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default=50,
|
||||
tooltip="Maintains the structure of the original image.",
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display_mode=IO.NumberDisplay.slider,
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),
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IO.Combo.Input(
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"flavor",
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options=["faithful", "gen_z", "psychedelia", "detaily", "clear", "donotstyle", "donotstyle_sharp"],
|
||||
tooltip="Style transfer flavor.",
|
||||
),
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IO.Combo.Input(
|
||||
"engine",
|
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options=[
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"balanced",
|
||||
"definio",
|
||||
"illusio",
|
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"3d_cartoon",
|
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"colorful_anime",
|
||||
"caricature",
|
||||
"real",
|
||||
"super_real",
|
||||
"softy",
|
||||
],
|
||||
tooltip="Processing engine selection.",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"portrait_mode",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("disabled", []),
|
||||
IO.DynamicCombo.Option(
|
||||
"enabled",
|
||||
[
|
||||
IO.Combo.Input(
|
||||
"portrait_style",
|
||||
options=["standard", "pop", "super_pop"],
|
||||
tooltip="Visual style applied to portrait images.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"portrait_beautifier",
|
||||
options=["none", "beautify_face", "beautify_face_max"],
|
||||
tooltip="Facial beautification intensity on portraits.",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
tooltip="Enable portrait mode for facial enhancements.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"fixed_generation",
|
||||
default=True,
|
||||
tooltip="When disabled, expect each generation to introduce a degree of randomness, "
|
||||
"leading to more diverse outcomes.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
expr="""{"type":"usd","usd":0.11}""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
image: Input.Image,
|
||||
reference_image: Input.Image,
|
||||
prompt: str,
|
||||
style_strength: int,
|
||||
structure_strength: int,
|
||||
flavor: str,
|
||||
engine: str,
|
||||
portrait_mode: InputPortraitMode,
|
||||
fixed_generation: bool,
|
||||
) -> IO.NodeOutput:
|
||||
if get_number_of_images(image) != 1:
|
||||
raise ValueError("Exactly one input image is required.")
|
||||
if get_number_of_images(reference_image) != 1:
|
||||
raise ValueError("Exactly one reference image is required.")
|
||||
validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
|
||||
validate_image_aspect_ratio(reference_image, (1, 3), (3, 1), strict=False)
|
||||
validate_image_dimensions(image, min_height=160, min_width=160)
|
||||
validate_image_dimensions(reference_image, min_height=160, min_width=160)
|
||||
|
||||
is_portrait = portrait_mode["portrait_mode"] == "enabled"
|
||||
portrait_style = portrait_mode.get("portrait_style", "standard")
|
||||
portrait_beautifier = portrait_mode.get("portrait_beautifier", "none")
|
||||
|
||||
uploaded_urls = await upload_images_to_comfyapi(cls, [image, reference_image], max_images=2)
|
||||
|
||||
initial_res = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/freepik/v1/ai/image-style-transfer", method="POST"),
|
||||
response_model=TaskResponse,
|
||||
data=ImageStyleTransferRequest(
|
||||
image=uploaded_urls[0],
|
||||
reference_image=uploaded_urls[1],
|
||||
prompt=prompt if prompt else None,
|
||||
style_strength=style_strength,
|
||||
structure_strength=structure_strength,
|
||||
is_portrait=is_portrait,
|
||||
portrait_style=portrait_style if is_portrait else None,
|
||||
portrait_beautifier=portrait_beautifier if is_portrait and portrait_beautifier != "none" else None,
|
||||
flavor=flavor,
|
||||
engine=engine,
|
||||
fixed_generation=fixed_generation,
|
||||
),
|
||||
)
|
||||
final_response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-style-transfer/{initial_res.task_id}"),
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
|
||||
class MagnificImageRelightNode(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="MagnificImageRelightNode",
|
||||
display_name="Magnific Image Relight",
|
||||
category="api node/image/Magnific",
|
||||
description="Relight an image with lighting adjustments and optional reference-based light transfer.",
|
||||
inputs=[
|
||||
IO.Image.Input("image", tooltip="The image to relight."),
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Descriptive guidance for lighting. Supports emphasis notation (1-1.4).",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"light_transfer_strength",
|
||||
min=0,
|
||||
max=100,
|
||||
default=100,
|
||||
tooltip="Intensity of light transfer application.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"style",
|
||||
options=[
|
||||
"standard",
|
||||
"darker_but_realistic",
|
||||
"clean",
|
||||
"smooth",
|
||||
"brighter",
|
||||
"contrasted_n_hdr",
|
||||
"just_composition",
|
||||
],
|
||||
tooltip="Stylistic output preference.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"interpolate_from_original",
|
||||
default=False,
|
||||
tooltip="Restricts generation freedom to match original more closely.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"change_background",
|
||||
default=True,
|
||||
tooltip="Modifies background based on prompt/reference.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"preserve_details",
|
||||
default=True,
|
||||
tooltip="Maintains texture and fine details from original.",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"advanced_settings",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("disabled", []),
|
||||
IO.DynamicCombo.Option(
|
||||
"enabled",
|
||||
[
|
||||
IO.Int.Input(
|
||||
"whites",
|
||||
min=0,
|
||||
max=100,
|
||||
default=50,
|
||||
tooltip="Adjusts the brightest tones in the image.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"blacks",
|
||||
min=0,
|
||||
max=100,
|
||||
default=50,
|
||||
tooltip="Adjusts the darkest tones in the image.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"brightness",
|
||||
min=0,
|
||||
max=100,
|
||||
default=50,
|
||||
tooltip="Overall brightness adjustment.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"contrast",
|
||||
min=0,
|
||||
max=100,
|
||||
default=50,
|
||||
tooltip="Contrast adjustment.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"saturation",
|
||||
min=0,
|
||||
max=100,
|
||||
default=50,
|
||||
tooltip="Color saturation adjustment.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"engine",
|
||||
options=[
|
||||
"automatic",
|
||||
"balanced",
|
||||
"cool",
|
||||
"real",
|
||||
"illusio",
|
||||
"fairy",
|
||||
"colorful_anime",
|
||||
"hard_transform",
|
||||
"softy",
|
||||
],
|
||||
tooltip="Processing engine selection.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"transfer_light_a",
|
||||
options=["automatic", "low", "medium", "normal", "high", "high_on_faces"],
|
||||
tooltip="The intensity of light transfer.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"transfer_light_b",
|
||||
options=[
|
||||
"automatic",
|
||||
"composition",
|
||||
"straight",
|
||||
"smooth_in",
|
||||
"smooth_out",
|
||||
"smooth_both",
|
||||
"reverse_both",
|
||||
"soft_in",
|
||||
"soft_out",
|
||||
"soft_mid",
|
||||
# "strong_mid", # Commented out because requests fail when this is set.
|
||||
"style_shift",
|
||||
"strong_shift",
|
||||
],
|
||||
tooltip="Also modifies light transfer intensity. "
|
||||
"Can be combined with the previous control for varied effects.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"fixed_generation",
|
||||
default=True,
|
||||
tooltip="Ensures consistent output with the same settings.",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
tooltip="Fine-tuning options for advanced lighting control.",
|
||||
),
|
||||
IO.Image.Input(
|
||||
"reference_image",
|
||||
optional=True,
|
||||
tooltip="Optional reference image to transfer lighting from.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
expr="""{"type":"usd","usd":0.11}""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
image: Input.Image,
|
||||
prompt: str,
|
||||
light_transfer_strength: int,
|
||||
style: str,
|
||||
interpolate_from_original: bool,
|
||||
change_background: bool,
|
||||
preserve_details: bool,
|
||||
advanced_settings: InputAdvancedSettings,
|
||||
reference_image: Input.Image | None = None,
|
||||
) -> IO.NodeOutput:
|
||||
if get_number_of_images(image) != 1:
|
||||
raise ValueError("Exactly one input image is required.")
|
||||
if reference_image is not None and get_number_of_images(reference_image) != 1:
|
||||
raise ValueError("Exactly one reference image is required.")
|
||||
validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
|
||||
validate_image_dimensions(image, min_height=160, min_width=160)
|
||||
if reference_image is not None:
|
||||
validate_image_aspect_ratio(reference_image, (1, 3), (3, 1), strict=False)
|
||||
validate_image_dimensions(reference_image, min_height=160, min_width=160)
|
||||
|
||||
image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
|
||||
reference_url = None
|
||||
if reference_image is not None:
|
||||
reference_url = (await upload_images_to_comfyapi(cls, reference_image, max_images=1))[0]
|
||||
|
||||
adv_settings = None
|
||||
if advanced_settings["advanced_settings"] == "enabled":
|
||||
adv_settings = ImageRelightAdvancedSettingsRequest(
|
||||
whites=advanced_settings["whites"],
|
||||
blacks=advanced_settings["blacks"],
|
||||
brightness=advanced_settings["brightness"],
|
||||
contrast=advanced_settings["contrast"],
|
||||
saturation=advanced_settings["saturation"],
|
||||
engine=advanced_settings["engine"],
|
||||
transfer_light_a=advanced_settings["transfer_light_a"],
|
||||
transfer_light_b=advanced_settings["transfer_light_b"],
|
||||
fixed_generation=advanced_settings["fixed_generation"],
|
||||
)
|
||||
|
||||
initial_res = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/freepik/v1/ai/image-relight", method="POST"),
|
||||
response_model=TaskResponse,
|
||||
data=ImageRelightRequest(
|
||||
image=image_url,
|
||||
prompt=prompt if prompt else None,
|
||||
transfer_light_from_reference_image=reference_url,
|
||||
light_transfer_strength=light_transfer_strength,
|
||||
interpolate_from_original=interpolate_from_original,
|
||||
change_background=change_background,
|
||||
style=style,
|
||||
preserve_details=preserve_details,
|
||||
advanced_settings=adv_settings,
|
||||
),
|
||||
)
|
||||
final_response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-relight/{initial_res.task_id}"),
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
|
||||
class MagnificImageSkinEnhancerNode(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="MagnificImageSkinEnhancerNode",
|
||||
display_name="Magnific Image Skin Enhancer",
|
||||
category="api node/image/Magnific",
|
||||
description="Skin enhancement for portraits with multiple processing modes.",
|
||||
inputs=[
|
||||
IO.Image.Input("image", tooltip="The portrait image to enhance."),
|
||||
IO.Int.Input(
|
||||
"sharpen",
|
||||
min=0,
|
||||
max=100,
|
||||
default=0,
|
||||
tooltip="Sharpening intensity level.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"smart_grain",
|
||||
min=0,
|
||||
max=100,
|
||||
default=2,
|
||||
tooltip="Smart grain intensity level.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"mode",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("creative", []),
|
||||
IO.DynamicCombo.Option(
|
||||
"faithful",
|
||||
[
|
||||
IO.Int.Input(
|
||||
"skin_detail",
|
||||
min=0,
|
||||
max=100,
|
||||
default=80,
|
||||
tooltip="Skin detail enhancement level.",
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"flexible",
|
||||
[
|
||||
IO.Combo.Input(
|
||||
"optimized_for",
|
||||
options=[
|
||||
"enhance_skin",
|
||||
"improve_lighting",
|
||||
"enhance_everything",
|
||||
"transform_to_real",
|
||||
"no_make_up",
|
||||
],
|
||||
tooltip="Enhancement optimization target.",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
tooltip="Processing mode: creative for artistic enhancement, "
|
||||
"faithful for preserving original appearance, "
|
||||
"flexible for targeted optimization.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
|
||||
expr="""
|
||||
(
|
||||
$rates := {"creative": 0.29, "faithful": 0.37, "flexible": 0.45};
|
||||
{"type":"usd","usd": $lookup($rates, widgets.mode)}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
image: Input.Image,
|
||||
sharpen: int,
|
||||
smart_grain: int,
|
||||
mode: InputSkinEnhancerMode,
|
||||
) -> IO.NodeOutput:
|
||||
if get_number_of_images(image) != 1:
|
||||
raise ValueError("Exactly one input image is required.")
|
||||
validate_image_aspect_ratio(image, (1, 3), (3, 1), strict=False)
|
||||
validate_image_dimensions(image, min_height=160, min_width=160)
|
||||
|
||||
image_url = (await upload_images_to_comfyapi(cls, image, max_images=1, total_pixels=4096 * 4096))[0]
|
||||
selected_mode = mode["mode"]
|
||||
|
||||
if selected_mode == "creative":
|
||||
endpoint = "creative"
|
||||
data = ImageSkinEnhancerCreativeRequest(
|
||||
image=image_url,
|
||||
sharpen=sharpen,
|
||||
smart_grain=smart_grain,
|
||||
)
|
||||
elif selected_mode == "faithful":
|
||||
endpoint = "faithful"
|
||||
data = ImageSkinEnhancerFaithfulRequest(
|
||||
image=image_url,
|
||||
sharpen=sharpen,
|
||||
smart_grain=smart_grain,
|
||||
skin_detail=mode["skin_detail"],
|
||||
)
|
||||
else: # flexible
|
||||
endpoint = "flexible"
|
||||
data = ImageSkinEnhancerFlexibleRequest(
|
||||
image=image_url,
|
||||
sharpen=sharpen,
|
||||
smart_grain=smart_grain,
|
||||
optimized_for=mode["optimized_for"],
|
||||
)
|
||||
|
||||
initial_res = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/freepik/v1/ai/skin-enhancer/{endpoint}", method="POST"),
|
||||
response_model=TaskResponse,
|
||||
data=data,
|
||||
)
|
||||
final_response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/freepik/v1/ai/skin-enhancer/{initial_res.task_id}"),
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
|
||||
class MagnificExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
MagnificImageUpscalerCreativeNode,
|
||||
MagnificImageUpscalerPreciseV2Node,
|
||||
MagnificImageStyleTransferNode,
|
||||
MagnificImageRelightNode,
|
||||
MagnificImageSkinEnhancerNode,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> MagnificExtension:
|
||||
return MagnificExtension()
|
||||
@@ -56,15 +56,14 @@ def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> to
|
||||
def tensor_to_bytesio(
|
||||
image: torch.Tensor,
|
||||
*,
|
||||
total_pixels: int = 2048 * 2048,
|
||||
total_pixels: int | None = 2048 * 2048,
|
||||
mime_type: str = "image/png",
|
||||
) -> BytesIO:
|
||||
"""Converts a torch.Tensor image to a named BytesIO object.
|
||||
|
||||
Args:
|
||||
image: Input torch.Tensor image.
|
||||
name: Optional filename for the BytesIO object.
|
||||
total_pixels: Maximum total pixels for potential downscaling.
|
||||
total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
|
||||
mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
|
||||
|
||||
Returns:
|
||||
@@ -79,13 +78,14 @@ def tensor_to_bytesio(
|
||||
return img_binary
|
||||
|
||||
|
||||
def tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image:
|
||||
def tensor_to_pil(image: torch.Tensor, total_pixels: int | None = 2048 * 2048) -> Image.Image:
|
||||
"""Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling."""
|
||||
if len(image.shape) > 3:
|
||||
image = image[0]
|
||||
# TODO: remove alpha if not allowed and present
|
||||
input_tensor = image.cpu()
|
||||
input_tensor = downscale_image_tensor(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze()
|
||||
if total_pixels is not None:
|
||||
input_tensor = downscale_image_tensor(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze()
|
||||
image_np = (input_tensor.numpy() * 255).astype(np.uint8)
|
||||
img = Image.fromarray(image_np)
|
||||
return img
|
||||
@@ -93,14 +93,14 @@ def tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image
|
||||
|
||||
def tensor_to_base64_string(
|
||||
image_tensor: torch.Tensor,
|
||||
total_pixels: int = 2048 * 2048,
|
||||
total_pixels: int | None = 2048 * 2048,
|
||||
mime_type: str = "image/png",
|
||||
) -> str:
|
||||
"""Convert [B, H, W, C] or [H, W, C] tensor to a base64 string.
|
||||
|
||||
Args:
|
||||
image_tensor: Input torch.Tensor image.
|
||||
total_pixels: Maximum total pixels for potential downscaling.
|
||||
total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
|
||||
mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
|
||||
|
||||
Returns:
|
||||
@@ -146,14 +146,14 @@ def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024)
|
||||
|
||||
def tensor_to_data_uri(
|
||||
image_tensor: torch.Tensor,
|
||||
total_pixels: int = 2048 * 2048,
|
||||
total_pixels: int | None = 2048 * 2048,
|
||||
mime_type: str = "image/png",
|
||||
) -> str:
|
||||
"""Converts a tensor image to a Data URI string.
|
||||
|
||||
Args:
|
||||
image_tensor: Input torch.Tensor image.
|
||||
total_pixels: Maximum total pixels for potential downscaling.
|
||||
total_pixels: Maximum total pixels for downscaling. If None, no downscaling is performed.
|
||||
mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp').
|
||||
|
||||
Returns:
|
||||
|
||||
@@ -49,7 +49,7 @@ async def upload_images_to_comfyapi(
|
||||
mime_type: str | None = None,
|
||||
wait_label: str | None = "Uploading",
|
||||
show_batch_index: bool = True,
|
||||
total_pixels: int = 2048 * 2048,
|
||||
total_pixels: int | None = 2048 * 2048,
|
||||
) -> list[str]:
|
||||
"""
|
||||
Uploads images to ComfyUI API and returns download URLs.
|
||||
|
||||
Reference in New Issue
Block a user