mirror of
https://github.com/Comfy-Org/ComfyUI.git
synced 2026-08-15 22:16:52 +08:00
feat: ImageCompositor node with layer-state compositing
This commit is contained in:
Terry Jia
@@ -847,6 +847,24 @@ class Load3DAnimation(Load3D):
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...
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@comfytype(io_type="COMPOSITOR")
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class Compositor(ComfyTypeIO):
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class LayerState(TypedDict):
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canvas: dict
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background: NotRequired[dict]
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inputs: NotRequired[list[str]]
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layers: list[dict]
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Type = LayerState
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class Input(WidgetInput):
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def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
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socketless: bool=True, default: dict=None, advanced: bool=None):
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super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
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if default is None:
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self.default = {}
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@comfytype(io_type="PHOTOMAKER")
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class Photomaker(ComfyTypeIO):
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Type = Any
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@@ -2403,6 +2421,7 @@ __all__ = [
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"Load3DModelInfo",
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"Load3D",
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"Load3DAnimation",
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"Compositor",
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"Photomaker",
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"Point",
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"FaceAnalysis",
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308
comfy_extras/compositor_blend.py
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308
comfy_extras/compositor_blend.py
Normal file
@@ -0,0 +1,308 @@
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import math
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from typing import NamedTuple, Optional, Union
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import numpy as np
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EPSILON = 1e-6
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LUM_R = 0.2224884
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LUM_G = 0.71690369
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LUM_B = 0.06060791
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ArrayLike = Union[np.ndarray, float]
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def srgb_to_linear(c: ArrayLike) -> np.ndarray:
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c = np.asarray(c, dtype=np.float32)
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high = ((np.maximum(c, 0.0) + 0.055) / 1.055) ** 2.4
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return np.where(c <= 0.04045, c / 12.92, high).astype(np.float32)
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def linear_to_srgb(c: ArrayLike) -> np.ndarray:
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c = np.asarray(c, dtype=np.float32)
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high = 1.055 * np.maximum(c, 0.0) ** (1.0 / 2.4) - 0.055
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return np.where(c <= 0.0031308, 12.92 * c, high).astype(np.float32)
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def luminance(rgb: np.ndarray) -> np.ndarray:
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return rgb[..., 0] * LUM_R + rgb[..., 1] * LUM_G + rgb[..., 2] * LUM_B
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def safe_div(a: ArrayLike, b: ArrayLike) -> np.ndarray:
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a, b = np.broadcast_arrays(
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np.asarray(a, dtype=np.float32), np.asarray(b, dtype=np.float32)
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)
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out = np.zeros(b.shape, dtype=np.float32)
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np.divide(a, b, out=out, where=np.abs(b) >= EPSILON)
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return out
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CHANNEL_BLEND = {
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"normal": lambda i, l: l,
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"multiply": lambda i, l: i * l,
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"screen": lambda i, l: 1 - (1 - i) * (1 - l),
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"overlay": lambda i, l: np.where(i < 0.5, 2 * i * l, 1 - 2 * (1 - l) * (1 - i)),
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"darken": lambda i, l: np.minimum(i, l),
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"lighten": lambda i, l: np.maximum(i, l),
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"color-dodge": lambda i, l: safe_div(i, 1 - l),
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"color-burn": lambda i, l: 1 - safe_div(1 - i, l),
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"hard-light": lambda i, l: np.where(
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l > 0.5,
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np.minimum(1 - (1 - i) * (1 - (l - 0.5) * 2), 1),
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np.minimum(i * (l * 2), 1),
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),
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"soft-light": lambda i, l: (1 - i) * (i * l) + i * (1 - (1 - i) * (1 - l)),
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"difference": lambda i, l: np.abs(i - l),
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"exclusion": lambda i, l: 0.5 - 2 * (i - 0.5) * (l - 0.5),
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"linear-dodge": lambda i, l: i + l,
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"linear-burn": lambda i, l: i + l - 1,
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"vivid-light": lambda i, l: np.where(
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l <= 0.5,
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np.maximum(1 - safe_div(1 - i, 2 * l), 0),
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np.minimum(safe_div(i, 2 * (1 - l)), 1),
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),
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"pin-light": lambda i, l: np.where(
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l > 0.5, np.maximum(i, 2 * (l - 0.5)), np.minimum(i, 2 * l)
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),
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"linear-light": lambda i, l: i + 2 * l - 1,
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"hard-mix": lambda i, l: np.where(i + l < 1, 0.0, 1.0),
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"subtract": lambda i, l: np.maximum(i - l, 0),
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"divide": lambda i, l: np.clip(i / np.maximum(l, EPSILON), 0, 1),
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"grain-extract": lambda i, l: i - l + 0.5,
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"grain-merge": lambda i, l: i + l - 0.5,
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}
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def _blend_hue(i: np.ndarray, l: np.ndarray) -> np.ndarray:
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src_min = l.min(axis=-1)
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src_max = l.max(axis=-1)
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src_delta = src_max - src_min
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achromatic = src_delta <= EPSILON
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dest_max = i.max(axis=-1)
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dest_delta = dest_max - i.min(axis=-1)
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dest_s = np.where(dest_max != 0, dest_delta / np.where(dest_max != 0, dest_max, 1), 0)
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ratio = np.where(
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achromatic, 0, dest_s * dest_max / np.where(achromatic, 1, src_delta)
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)
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offset = dest_max - src_max * ratio
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return np.where(achromatic[..., None], i, l * ratio[..., None] + offset[..., None])
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def _blend_saturation(i: np.ndarray, l: np.ndarray) -> np.ndarray:
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dest_max = i.max(axis=-1)
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dest_delta = dest_max - i.min(axis=-1)
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flat = dest_delta <= EPSILON
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src_max = l.max(axis=-1)
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src_delta = src_max - l.min(axis=-1)
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src_s = np.where(src_max != 0, src_delta / np.where(src_max != 0, src_max, 1), 0)
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ratio = np.where(flat, 0, src_s * dest_max / np.where(flat, 1, dest_delta))
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offset = (1 - ratio) * dest_max
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return np.where(
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flat[..., None],
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np.broadcast_to(dest_max[..., None], i.shape),
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i * ratio[..., None] + offset[..., None],
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)
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def _blend_color(i: np.ndarray, l: np.ndarray) -> np.ndarray:
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dest_l = (i.min(axis=-1) + i.max(axis=-1)) / 2
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src_l = (l.min(axis=-1) + l.max(axis=-1)) / 2
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gray = (np.abs(src_l) <= EPSILON) | (np.abs(1 - src_l) <= EPSILON)
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dest_high = dest_l > 0.5
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src_high = src_l > 0.5
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dl = np.minimum(dest_l, 1 - dest_l)
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sl = np.minimum(src_l, 1 - src_l)
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ratio = dl / np.where(gray, 1, sl)
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offset = np.where(dest_high, 1 - 2 * dl, 0) + np.where(src_high, 2 * dl - ratio, 0)
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return np.where(
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gray[..., None],
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np.broadcast_to(dest_l[..., None], i.shape),
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l * ratio[..., None] + offset[..., None],
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)
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def _blend_luminosity(i: np.ndarray, l: np.ndarray) -> np.ndarray:
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ratio = safe_div(luminance(l), luminance(i))
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return i * ratio[..., None]
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HSL_BLEND = {
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"hue": _blend_hue,
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"saturation": _blend_saturation,
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"color": _blend_color,
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"luminosity": _blend_luminosity,
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}
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def blend_pixel(blend: str, in_rgb: np.ndarray, layer_rgb: np.ndarray) -> np.ndarray:
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in_rgb = np.asarray(in_rgb, dtype=np.float32)
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layer_rgb = np.asarray(layer_rgb, dtype=np.float32)
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hsl = HSL_BLEND.get(blend)
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if hsl is not None:
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return np.asarray(hsl(in_rgb, layer_rgb), dtype=np.float32)
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fn = CHANNEL_BLEND.get(blend, CHANNEL_BLEND["normal"])
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return np.asarray(fn(in_rgb, layer_rgb), dtype=np.float32)
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def _composite_union(in_c, layer, comp, cov):
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in_a = in_c[..., 3]
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layer_a = layer[..., 3] * cov
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new_a = layer_a + (1 - layer_a) * in_a
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ratio = np.where(new_a != 0, layer_a / np.where(new_a != 0, new_a, 1), 0)
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blended = (
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ratio[..., None]
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* (in_a[..., None] * (comp - layer[..., :3]) + layer[..., :3] - in_c[..., :3])
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+ in_c[..., :3]
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)
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keep = (layer_a == 0) | (new_a == 0)
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rgb = np.where(
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keep[..., None],
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in_c[..., :3],
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np.where((in_a == 0)[..., None], layer[..., :3], blended),
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)
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return np.concatenate([rgb, new_a[..., None]], axis=-1)
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def _composite_clip_to_backdrop(in_c, layer, comp, cov):
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in_a = in_c[..., 3]
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layer_a = layer[..., 3] * cov
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mixed = comp * layer_a[..., None] + in_c[..., :3] * (1 - layer_a[..., None])
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keep = (in_a == 0) | (layer_a == 0)
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rgb = np.where(keep[..., None], in_c[..., :3], mixed)
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return np.concatenate([rgb, in_a[..., None]], axis=-1)
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def _composite_clip_to_layer(in_c, layer, comp, cov):
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in_a = in_c[..., 3]
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layer_a = layer[..., 3] * cov
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mixed = comp * in_a[..., None] + layer[..., :3] * (1 - in_a[..., None])
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rgb = np.where(
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(layer_a == 0)[..., None],
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in_c[..., :3],
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np.where((in_a == 0)[..., None], layer[..., :3], mixed),
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)
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return np.concatenate([rgb, layer_a[..., None]], axis=-1)
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def _composite_intersection(in_c, layer, comp, cov):
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new_a = in_c[..., 3] * layer[..., 3] * cov
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rgb = np.where((new_a == 0)[..., None], in_c[..., :3], comp)
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return np.concatenate([rgb, new_a[..., None]], axis=-1)
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_COMPOSITE = {
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"union": _composite_union,
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"clip-to-backdrop": _composite_clip_to_backdrop,
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"clip-to-layer": _composite_clip_to_layer,
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"intersection": _composite_intersection,
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}
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def run_composite(mode: str, in_c, layer, comp, cov) -> np.ndarray:
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fn = _COMPOSITE.get(mode, _composite_union)
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return fn(in_c, layer, comp, cov)
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def _to_space(rgb: np.ndarray, space: str) -> np.ndarray:
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return rgb if space == "linear" else linear_to_srgb(rgb)
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def _from_space(rgb: np.ndarray, space: str) -> np.ndarray:
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return rgb if space == "linear" else srgb_to_linear(rgb)
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class EffectiveMode(NamedTuple):
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blend: str
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blend_space: str
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composite_space: str
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composite: str
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_LAYER_MODES = {
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"normal": ("linear", "union"),
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"multiply": ("linear", "clip-to-backdrop"),
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"screen": ("perceptual", "clip-to-backdrop"),
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"overlay": ("perceptual", "clip-to-backdrop"),
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"darken": ("linear", "clip-to-backdrop"),
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"lighten": ("linear", "clip-to-backdrop"),
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"color-dodge": ("perceptual", "clip-to-backdrop"),
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"color-burn": ("perceptual", "clip-to-backdrop"),
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"hard-light": ("perceptual", "clip-to-backdrop"),
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"soft-light": ("perceptual", "clip-to-backdrop"),
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"difference": ("perceptual", "clip-to-backdrop"),
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"exclusion": ("perceptual", "clip-to-backdrop"),
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"linear-dodge": ("linear", "clip-to-backdrop"),
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"linear-burn": ("perceptual", "clip-to-backdrop"),
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"vivid-light": ("perceptual", "clip-to-backdrop"),
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"pin-light": ("perceptual", "clip-to-backdrop"),
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"linear-light": ("perceptual", "clip-to-backdrop"),
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"hard-mix": ("perceptual", "clip-to-backdrop"),
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"subtract": ("linear", "clip-to-backdrop"),
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"divide": ("linear", "clip-to-backdrop"),
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"grain-extract": ("perceptual", "clip-to-backdrop"),
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"grain-merge": ("perceptual", "clip-to-backdrop"),
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"hue": ("perceptual", "clip-to-backdrop"),
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"saturation": ("perceptual", "clip-to-backdrop"),
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"color": ("perceptual", "clip-to-backdrop"),
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"luminosity": ("linear", "clip-to-backdrop"),
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}
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def resolve_mode(blend: str = "normal") -> EffectiveMode:
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blend_space, composite = _LAYER_MODES.get(blend, _LAYER_MODES["normal"])
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return EffectiveMode(
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blend=blend,
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blend_space=blend_space,
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composite_space="linear",
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composite=composite,
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)
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def blend_composite(
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mode: EffectiveMode,
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backdrop: np.ndarray,
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layer: np.ndarray,
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opacity: float,
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mask: Optional[ArrayLike] = None,
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) -> np.ndarray:
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backdrop = np.asarray(backdrop, dtype=np.float32)
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layer = np.asarray(layer, dtype=np.float32)
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cov = opacity * (1.0 if mask is None else mask)
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in_b = _to_space(backdrop[..., :3], mode.blend_space)
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layer_b = _to_space(layer[..., :3], mode.blend_space)
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comp = _from_space(blend_pixel(mode.blend, in_b, layer_b), mode.blend_space)
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if mode.composite_space == "linear":
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return run_composite(mode.composite, backdrop, layer, comp, cov)
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in_c = np.concatenate(
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[_to_space(backdrop[..., :3], mode.composite_space), backdrop[..., 3:4]],
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axis=-1,
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)
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layer_c = np.concatenate(
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[_to_space(layer[..., :3], mode.composite_space), layer[..., 3:4]], axis=-1
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)
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comp_c = _to_space(comp, mode.composite_space)
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out = run_composite(mode.composite, in_c, layer_c, comp_c, cov)
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rgb = _from_space(out[..., :3], mode.composite_space)
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return np.concatenate([rgb, out[..., 3:4]], axis=-1)
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def placed_bounds(
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x: float, y: float, w: float, h: float, rotation: float
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) -> tuple[int, int, int, int]:
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cx = x + w / 2
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cy = y + h / 2
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cos = math.cos(rotation)
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sin = math.sin(rotation)
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hw = w / 2
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hh = h / 2
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corners = ((-hw, -hh), (hw, -hh), (hw, hh), (-hw, hh))
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xs = [cx + dx * cos - dy * sin for dx, dy in corners]
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ys = [cy + dx * sin + dy * cos for dx, dy in corners]
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bx = math.floor(min(xs))
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by = math.floor(min(ys))
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bw = max(1, math.ceil(max(xs)) - bx)
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bh = max(1, math.ceil(max(ys)) - by)
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return bx, by, bw, bh
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407
comfy_extras/nodes_compositor.py
Normal file
407
comfy_extras/nodes_compositor.py
Normal file
@@ -0,0 +1,407 @@
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import hashlib
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import json
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import math
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import numpy as np
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import torch
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from PIL import Image
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from comfy_api.latest import ComfyExtension, io, UI
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from comfy_extras.compositor_blend import (
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blend_composite,
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linear_to_srgb,
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placed_bounds,
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resolve_mode,
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srgb_to_linear,
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)
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from comfy_extras.color_util import hex_to_rgb
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from comfy_extras.nodes_bounding_boxes import boxes_from_input
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from typing_extensions import override
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def sort_autogrow_images(images: dict) -> list[torch.Tensor]:
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images = images or {}
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tensors = []
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for name in sorted(images, key=lambda n: int(n.rsplit("_", 1)[-1])):
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image = images[name]
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if image is None:
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continue
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tensors.append(image)
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return tensors
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def expand_batch_frames(tensors: list[torch.Tensor]) -> list[torch.Tensor]:
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frames = []
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for tensor in tensors:
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for index in range(tensor.shape[0]):
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frames.append(tensor[index : index + 1])
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return frames
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def stack_images(tensors: list[torch.Tensor]) -> torch.Tensor:
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canvas = tensors[0][:1, :, :, :3].clone()
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h, w = canvas.shape[1], canvas.shape[2]
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for layer in tensors[1:]:
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layer = layer[:1, :, :, :3]
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lh = min(layer.shape[1], h)
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lw = min(layer.shape[2], w)
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canvas[:, :lh, :lw, :] = layer[:, :lh, :lw, :]
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return canvas
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def input_fingerprints(tensors: list[torch.Tensor]) -> list[str]:
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fingerprints = []
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for tensor in tensors:
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frame = tensor[0].detach().cpu().numpy()
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frame8 = np.clip(np.rint(frame * 255.0), 0, 255).astype(np.uint8)
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digest = hashlib.sha256()
|
||||
digest.update(repr(tuple(tensor.shape)).encode())
|
||||
digest.update(frame8.tobytes())
|
||||
fingerprints.append(digest.hexdigest()[:16])
|
||||
return fingerprints
|
||||
|
||||
|
||||
def _bbox_entries(bboxes) -> list:
|
||||
if bboxes is None:
|
||||
return []
|
||||
if isinstance(bboxes, str):
|
||||
text = bboxes.strip()
|
||||
if not text:
|
||||
return []
|
||||
try:
|
||||
bboxes = json.loads(text)
|
||||
except (ValueError, TypeError) as exc:
|
||||
raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
|
||||
if isinstance(bboxes, dict):
|
||||
return [bboxes]
|
||||
if not isinstance(bboxes, list):
|
||||
raise ValueError(
|
||||
"bboxes input must be bounding boxes, elements, or a JSON string, "
|
||||
f"got {type(bboxes).__name__}"
|
||||
)
|
||||
if bboxes and isinstance(bboxes[0], list):
|
||||
return bboxes[0]
|
||||
return bboxes
|
||||
|
||||
|
||||
def layout_bboxes(bboxes, width: int, height: int) -> list:
|
||||
slots = []
|
||||
for entry in _bbox_entries(bboxes):
|
||||
try:
|
||||
boxes = boxes_from_input(entry, width, height)
|
||||
except ValueError:
|
||||
boxes = []
|
||||
slots.append(boxes[0] if boxes else None)
|
||||
return slots
|
||||
|
||||
|
||||
def bbox_layer_name(box: dict) -> str | None:
|
||||
meta = box.get("metadata")
|
||||
if not isinstance(meta, dict):
|
||||
return None
|
||||
for key in ("name", "desc"):
|
||||
value = meta.get(key)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value
|
||||
return None
|
||||
|
||||
|
||||
def _bbox_int(box: dict, key: str) -> int:
|
||||
value = box.get(key, 0)
|
||||
return int(round(value)) if isinstance(value, (int, float)) else 0
|
||||
|
||||
|
||||
def bbox_ui_entries(slots: list, count: int) -> list:
|
||||
if not slots:
|
||||
return []
|
||||
entries = []
|
||||
for index in range(count):
|
||||
box = slots[index] if index < len(slots) else None
|
||||
if box is None:
|
||||
entries.append(None)
|
||||
continue
|
||||
entries.append({
|
||||
"x": _bbox_int(box, "x"),
|
||||
"y": _bbox_int(box, "y"),
|
||||
"width": _bbox_int(box, "width"),
|
||||
"height": _bbox_int(box, "height"),
|
||||
"name": bbox_layer_name(box),
|
||||
})
|
||||
return entries
|
||||
|
||||
|
||||
def state_from_bboxes(tensors: list[torch.Tensor], slots: list) -> dict:
|
||||
layers = []
|
||||
for index in range(len(tensors)):
|
||||
box = slots[index] if index < len(slots) else None
|
||||
if box is None:
|
||||
layers.append(None)
|
||||
else:
|
||||
layers.append({
|
||||
"transform": {
|
||||
"x": box.get("x", 0),
|
||||
"y": box.get("y", 0),
|
||||
"w": box.get("width", 0),
|
||||
"h": box.get("height", 0),
|
||||
"rotation": 0,
|
||||
}
|
||||
})
|
||||
return {
|
||||
"canvas": (tensors[0].shape[2], tensors[0].shape[1]),
|
||||
"layers": layers,
|
||||
"inputs": None,
|
||||
"background": {"color": "#ffffff", "opacity": 1.0, "visible": True},
|
||||
}
|
||||
|
||||
|
||||
_HEX_DIGITS = set("0123456789abcdef")
|
||||
|
||||
|
||||
def _normalize_hex_color(value) -> str:
|
||||
if isinstance(value, str):
|
||||
text = value.strip().lower()
|
||||
if text.startswith("#"):
|
||||
digits = text[1:]
|
||||
if len(digits) == 3 and set(digits) <= _HEX_DIGITS:
|
||||
digits = "".join(ch * 2 for ch in digits)
|
||||
if len(digits) == 6 and set(digits) <= _HEX_DIGITS:
|
||||
return "#" + digits
|
||||
return "#ffffff"
|
||||
|
||||
|
||||
def _parse_background(entry) -> dict | None:
|
||||
if not isinstance(entry, dict):
|
||||
return None
|
||||
return {
|
||||
"color": _normalize_hex_color(entry.get("color")),
|
||||
"opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
|
||||
"visible": bool(entry.get("visible", True)),
|
||||
}
|
||||
|
||||
|
||||
def layer_state_provided(raw) -> bool:
|
||||
if isinstance(raw, dict):
|
||||
return bool(raw)
|
||||
if isinstance(raw, str):
|
||||
return raw not in ("", "{}")
|
||||
return False
|
||||
|
||||
|
||||
def parse_layer_state(raw) -> dict | None:
|
||||
if isinstance(raw, str):
|
||||
if not raw.strip():
|
||||
return None
|
||||
try:
|
||||
raw = json.loads(raw)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return None
|
||||
if not isinstance(raw, dict):
|
||||
return None
|
||||
state = raw
|
||||
canvas = state.get("canvas")
|
||||
layers = state.get("layers")
|
||||
if not isinstance(canvas, dict) or not isinstance(layers, list) or not layers:
|
||||
return None
|
||||
try:
|
||||
w = int(round(float(canvas.get("w"))))
|
||||
h = int(round(float(canvas.get("h"))))
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
if w <= 0 or h <= 0:
|
||||
return None
|
||||
inputs = state.get("inputs")
|
||||
if not isinstance(inputs, list) or not all(
|
||||
isinstance(entry, str) for entry in inputs
|
||||
):
|
||||
inputs = None
|
||||
return {
|
||||
"canvas": (w, h),
|
||||
"layers": layers,
|
||||
"inputs": inputs,
|
||||
"background": _parse_background(state.get("background")),
|
||||
}
|
||||
|
||||
|
||||
def _number(source: dict, key: str, default: float) -> float:
|
||||
value = source.get(key, default)
|
||||
return float(value) if isinstance(value, (int, float)) else float(default)
|
||||
|
||||
|
||||
def _layer_params(entry, natural_w: int, natural_h: int) -> dict:
|
||||
if not isinstance(entry, dict):
|
||||
entry = {}
|
||||
transform = entry.get("transform")
|
||||
if not isinstance(transform, dict):
|
||||
transform = {}
|
||||
blend = entry.get("blend")
|
||||
return {
|
||||
"visible": bool(entry.get("visible", True)),
|
||||
"opacity": _number(entry, "opacity", 1.0),
|
||||
"blend": blend if isinstance(blend, str) else "normal",
|
||||
"x": _number(transform, "x", 0.0),
|
||||
"y": _number(transform, "y", 0.0),
|
||||
"w": _number(transform, "w", natural_w),
|
||||
"h": _number(transform, "h", natural_h),
|
||||
"rotation": _number(transform, "rotation", 0.0),
|
||||
"flip_h": bool(entry.get("flipH", False)),
|
||||
"flip_v": bool(entry.get("flipV", False)),
|
||||
}
|
||||
|
||||
|
||||
def _prepare_layer_bitmap(tensor: torch.Tensor, params: dict) -> Image.Image:
|
||||
frame = tensor[0, :, :, :3].detach().cpu().numpy()
|
||||
rgb8 = np.clip(np.rint(frame * 255.0), 0, 255).astype(np.uint8)
|
||||
img = Image.fromarray(rgb8, "RGB").convert("RGBA")
|
||||
if params["flip_h"]:
|
||||
img = img.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
|
||||
if params["flip_v"]:
|
||||
img = img.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
|
||||
target = (max(1, round(params["w"])), max(1, round(params["h"])))
|
||||
if img.size != target:
|
||||
img = img.resize(target, Image.Resampling.LANCZOS)
|
||||
if params["rotation"] != 0:
|
||||
img = img.rotate(
|
||||
-math.degrees(params["rotation"]),
|
||||
expand=True,
|
||||
resample=Image.Resampling.BICUBIC,
|
||||
fillcolor=(0, 0, 0, 0),
|
||||
)
|
||||
return img
|
||||
|
||||
|
||||
def _place_in_bounds(img: Image.Image, bw: int, bh: int) -> np.ndarray:
|
||||
arr = np.asarray(img, dtype=np.float32) / 255.0
|
||||
rgba = np.concatenate([srgb_to_linear(arr[..., :3]), arr[..., 3:4]], axis=-1)
|
||||
aw, ah = img.size
|
||||
buf = np.zeros((bh, bw, 4), dtype=np.float32)
|
||||
ox = (bw - aw) // 2
|
||||
oy = (bh - ah) // 2
|
||||
dx0, dy0 = max(ox, 0), max(oy, 0)
|
||||
dx1, dy1 = min(ox + aw, bw), min(oy + ah, bh)
|
||||
if dx0 < dx1 and dy0 < dy1:
|
||||
buf[dy0:dy1, dx0:dx1] = rgba[dy0 - oy : dy1 - oy, dx0 - ox : dx1 - ox]
|
||||
return buf
|
||||
|
||||
|
||||
def _fill_background(canvas: np.ndarray, background: dict) -> np.ndarray:
|
||||
layer = np.empty(canvas.shape, dtype=np.float32)
|
||||
layer[..., :3] = srgb_to_linear(
|
||||
np.array(hex_to_rgb(background["color"]), dtype=np.float32) / 255.0
|
||||
)
|
||||
layer[..., 3] = 1.0
|
||||
return blend_composite(
|
||||
resolve_mode("normal"), canvas, layer, background["opacity"]
|
||||
)
|
||||
|
||||
|
||||
def composite_from_state(tensors: list[torch.Tensor], state: dict) -> torch.Tensor:
|
||||
cw, ch = state["canvas"]
|
||||
canvas = np.zeros((ch, cw, 4), dtype=np.float32)
|
||||
background = state.get("background")
|
||||
if background is not None and background["visible"] and background["opacity"] > 0:
|
||||
canvas = _fill_background(canvas, background)
|
||||
layers = state["layers"]
|
||||
for index, tensor in enumerate(tensors):
|
||||
entry = layers[index] if index < len(layers) else None
|
||||
params = _layer_params(entry, tensor.shape[2], tensor.shape[1])
|
||||
if not params["visible"]:
|
||||
continue
|
||||
img = _prepare_layer_bitmap(tensor, params)
|
||||
bx, by, bw, bh = placed_bounds(
|
||||
params["x"], params["y"], params["w"], params["h"], params["rotation"]
|
||||
)
|
||||
buf = _place_in_bounds(img, bw, bh)
|
||||
x0, y0 = max(bx, 0), max(by, 0)
|
||||
x1, y1 = min(bx + bw, cw), min(by + bh, ch)
|
||||
if x0 >= x1 or y0 >= y1:
|
||||
continue
|
||||
region = buf[y0 - by : y1 - by, x0 - bx : x1 - bx]
|
||||
mode = resolve_mode(params["blend"])
|
||||
canvas[y0:y1, x0:x1] = blend_composite(
|
||||
mode, canvas[y0:y1, x0:x1], region, params["opacity"]
|
||||
)
|
||||
rgb = linear_to_srgb(np.clip(canvas[..., :3], 0.0, 1.0))
|
||||
rgb = rgb * np.clip(canvas[..., 3:4], 0.0, 1.0)
|
||||
return torch.from_numpy(rgb.astype(np.float32)).unsqueeze(0)
|
||||
|
||||
|
||||
class ImageCompositor(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ImageCompositor",
|
||||
display_name="Image Compositor",
|
||||
category="image",
|
||||
is_output_node=True,
|
||||
has_intermediate_output=True,
|
||||
inputs=[
|
||||
io.Autogrow.Input(
|
||||
"images",
|
||||
template=io.Autogrow.TemplatePrefix(
|
||||
io.Image.Input("image"),
|
||||
prefix="image_",
|
||||
min=1,
|
||||
max=50,
|
||||
),
|
||||
tooltip="Layers to composite. The first input is the bottom layer; each subsequent input is stacked above the previous one.",
|
||||
),
|
||||
io.MultiType.Input(
|
||||
"bboxes",
|
||||
[io.BoundingBox, io.Array, io.String],
|
||||
optional=True,
|
||||
tooltip="Optional initial layout: bounding boxes, elements, or a JSON string, index-aligned with the image inputs (bboxes[0] places image_0). Inputs without a box keep their natural size at the origin. A saved compositor recipe that matches the current inputs takes priority.",
|
||||
),
|
||||
io.Compositor.Input(
|
||||
"compositor",
|
||||
tooltip="Layer recipe saved by the compositor editor, replayed over the current inputs",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images: io.Autogrow.Type = None, compositor: io.Compositor.Type = None, bboxes: io.MultiType.Type = None) -> io.NodeOutput:
|
||||
tensors = expand_batch_frames(sort_autogrow_images(images))
|
||||
|
||||
layer_refs = []
|
||||
for tensor in tensors:
|
||||
layer_refs.extend(UI.PreviewImage(tensor, cls=cls).values)
|
||||
|
||||
fp = input_fingerprints(tensors)
|
||||
raw_state = compositor
|
||||
state = parse_layer_state(raw_state)
|
||||
replay = bool(state is not None and tensors and state["inputs"] == fp)
|
||||
slots = (
|
||||
layout_bboxes(bboxes, tensors[0].shape[2], tensors[0].shape[1])
|
||||
if tensors
|
||||
else []
|
||||
)
|
||||
if replay:
|
||||
out = composite_from_state(tensors, state)
|
||||
elif tensors and any(slot is not None for slot in slots):
|
||||
out = composite_from_state(tensors, state_from_bboxes(tensors, slots))
|
||||
elif tensors:
|
||||
out = stack_images(tensors)
|
||||
else:
|
||||
out = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
|
||||
state_stale = layer_state_provided(raw_state) and not replay
|
||||
|
||||
ui_dict = UI.PreviewImage(out, cls=cls).as_dict()
|
||||
ui_dict["compositor_layers"] = layer_refs
|
||||
ui_dict["compositor_inputs"] = fp
|
||||
ui_dict["compositor_bboxes"] = bbox_ui_entries(slots, len(tensors))
|
||||
if state_stale:
|
||||
ui_dict["compositor_state_stale"] = [True]
|
||||
return io.NodeOutput(out, ui=ui_dict)
|
||||
|
||||
|
||||
class CompositorExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [ImageCompositor]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> CompositorExtension:
|
||||
return CompositorExtension()
|
||||
Reference in New Issue
Block a user