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[Partner Nodes] feat: ImageCompositor node with layer-state compositing, layer from bbox and Seedream Layer Separation node (#15317)

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Terry Jia 2026-08-07 16:25:37 -04:00 committed by GitHub
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commit 8fadc7b5be
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57
comfy_api/latest/_io.py
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@ -847,6 +847,61 @@ class Load3DAnimation(Load3D):
...
@comfytype(io_type="LAYERS")
class Layers(ComfyTypeIO):
BlendMode = Literal[
"normal", "multiply", "screen", "overlay", "darken", "lighten",
"color-dodge", "color-burn", "hard-light", "soft-light", "difference",
"exclusion", "linear-dodge", "linear-burn", "vivid-light", "pin-light",
"linear-light", "hard-mix", "subtract", "divide", "grain-extract",
"grain-merge", "hue", "saturation", "color", "luminosity",
]
class LayerItem(TypedDict):
image: torch.Tensor
type: Literal["raster"]
x: NotRequired[int]
y: NotRequired[int]
mask: NotRequired[torch.Tensor]
z_index: int
name: NotRequired[str]
opacity: NotRequired[float]
blend_mode: NotRequired["Layers.BlendMode"]
visible: NotRequired[bool]
flip_h: NotRequired[bool]
flip_v: NotRequired[bool]
rotation: NotRequired[float]
w: NotRequired[int]
h: NotRequired[int]
class Document(TypedDict):
version: int
canvas: NotRequired[tuple[int, int]]
layers: list["Layers.LayerItem"]
Type = Document
@comfytype(io_type="COMPOSITOR")
class Compositor(ComfyTypeIO):
class LayerState(TypedDict):
version: NotRequired[int]
canvas: dict
background: NotRequired[dict]
inputs: NotRequired[list[str]]
order: NotRequired[list[int]]
layers: list[dict]
Type = LayerState
class Input(WidgetInput):
def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
socketless: bool=True, default: dict=None, advanced: bool=None):
super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
if default is None:
self.default = {}
@comfytype(io_type="PHOTOMAKER")
class Photomaker(ComfyTypeIO):
Type = Any
@ -2403,6 +2458,8 @@ __all__ = [
"Load3DModelInfo",
"Load3D",
"Load3DAnimation",
"Compositor",
"Layers",
"Photomaker",
"Point",
"FaceAnalysis",

17
comfy_api_nodes/apis/bytedance.py
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@ -35,6 +35,23 @@ class Seedream4TaskCreationRequest(BaseModel):
optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
class Seedream5LayerOptimizePromptOptions(BaseModel):
mode: Literal["standard", "fast"] = Field(...)
class Seedream5LayerSeparationRequest(BaseModel):
model: str = Field(...)
prompt: str | None = Field(None)
image: str = Field(..., description="Single image URL")
size: str = Field("auto")
seed: int = Field(..., ge=0, le=2147483647)
response_format: str = Field("url")
output_format: str = Field("png")
layer_decomposition: bool = Field(True)
watermark: bool = Field(False)
optimize_prompt_options: Seedream5LayerOptimizePromptOptions | None = Field(None)
class ImageTaskCreationResponse(BaseModel):
model: str = Field(...)
created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")

370
comfy_api_nodes/nodes_bytedance.py
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@ -1,3 +1,4 @@
import asyncio
import base64
import hashlib
import logging
@ -34,6 +35,8 @@ from comfy_api_nodes.apis.bytedance import (
SeedanceVirtualLibraryCreateAssetRequest,
Seedream4Options,
Seedream4TaskCreationRequest,
Seedream5LayerOptimizePromptOptions,
Seedream5LayerSeparationRequest,
Seedream5OptimizePromptOptions,
TaskAudioContent,
TaskAudioContentUrl,
@ -75,6 +78,7 @@ from comfy_api_nodes.util import (
validate_video_dimensions,
validate_video_duration,
)
from comfy_api_nodes.util.common_exceptions import ProcessingInterrupted
from server import PromptServer
BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
@ -96,6 +100,8 @@ SEEDREAM_PRESETS = {
"seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
}
SEEDREAM_LAYER_SEPARATION_MODEL = "seedream-5-0-pro-260628"
# Long-running tasks endpoints(e.g., video)
BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id}
@ -1044,6 +1050,369 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
class ByteDanceSeedreamLayerSeparationNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceSeedreamLayerSeparationNode",
display_name="ByteDance Seedream 5.0 Pro Layer Separation",
category="partner/image/ByteDance",
search_aliases=["layer separation", "split layers", "decompose", "cutout", "RGBA layers"],
description=(
"Decompose an image into a background plate plus up to 16 repositionable transparent layers, "
"each with stacking order, bounding box, name and description."
),
inputs=[
IO.Image.Input(
"image",
tooltip=(
"The image to separate. Exactly one image, at least 512x512 pixels, aspect ratio "
"between 1:16 and 16:1. Inputs larger than about 4MP are downscaled before upload."
),
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip=(
"How to separate the image. Leave empty to auto-detect and separate all major elements. "
"Describe elements in natural language to control the separation, or target exact regions "
"with <bbox>left top right bottom</bbox> tags (0-1000 per-mille coordinates)."
),
),
IO.Combo.Input(
"size",
options=["auto", "1K", "1.5K", "2K"],
default="auto",
tooltip="Output resolution level. 'auto' follows the input image size (clamped to the 1K-2K range).",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
),
IO.Combo.Input(
"prompt_optimization",
options=["standard", "fast"],
default="standard",
optional=True,
advanced=True,
tooltip="Prompt-optimization mode: 'standard' gives higher quality, 'fast' shorter generation time.",
),
IO.Boolean.Input(
"watermark",
default=False,
optional=True,
advanced=True,
tooltip='Whether to add an "AI generated" watermark to the images.',
),
IO.Boolean.Input(
"crop_layers",
default=False,
optional=True,
label_on="minimal size",
label_off="full canvas",
tooltip=(
"Geometry of the layers/masks batch outputs (layer_stack is unaffected and always "
"tight). Full canvas: each layer on a base-sized canvas at its bounding-box position - "
"recompose directly with ImageCompositeMasked. Minimal size: each layer cropped to its "
"bounding box (padded to the largest layer for batching) - much smaller tensors; "
"rebuild placement with Layers From Bounding Boxes using the bboxes output."
),
),
],
outputs=[
IO.Image.Output(
display_name="base_image",
tooltip="The base image (background plate) the layers stack onto.",
),
IO.Mask.Output(
display_name="base_mask",
tooltip=(
"Transparency of the base image (1 = transparent, LoadImage convention); currently "
"always fully opaque."
),
),
IO.Image.Output(
display_name="layers",
tooltip=(
"Transparent layers ordered bottom to top. Full canvas mode: placed on a black "
"base-sized canvas at their bounding-box position. Minimal size mode: cropped to "
"their bounding box, anchored top-left, padded to the largest layer."
),
),
IO.Mask.Output(
display_name="masks",
tooltip=(
"Per-layer transparency, index-aligned with the layers batch (1 = transparent, "
"LoadImage convention). For ImageCompositeMasked-style compositing, add InvertMask first."
),
),
IO.BoundingBox.Output(
display_name="bboxes",
tooltip=(
"One placement box per layer, index-aligned with the layers batch (feed both, plus "
"masks, into Layers From Bounding Boxes to rebuild per-layer placement): {x, y, width, "
"height, metadata: {name, desc, z_index, native_size, content_rect, flags}}. "
"content_rect = [left, top, width, height] is the layer's content region within its "
"own frame; it lands on the canvas at the box position plus that offset."
),
),
IO.Layers.Output(
display_name="layer_stack",
tooltip=(
"Ready-to-edit layer document for Create Layered Image: the base plate plus each "
"element as its own named, tight-cropped layer at its true position and stacking "
"order. Connect directly, or extend with Add Layer."
),
),
],
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=["size"]),
expr="""
(
widgets.size in ["1k", "1.5k"]
? {
"type": "usd",
"usd": 0.032,
"format": { "suffix": " x images/Run", "approximate": true }
}
: {
"type": "range_usd",
"min_usd": 0.032,
"max_usd": 0.064,
"format": { "suffix": " x images/Run", "approximate": true }
}
)
""",
),
)
@classmethod
async def execute(
cls,
image: Input.Image,
prompt: str = "",
size: str = "auto",
seed: int = 0,
prompt_optimization: str = "standard",
watermark: bool = False,
crop_layers: bool = False,
) -> IO.NodeOutput:
if get_number_of_images(image) != 1:
raise ValueError("Only a single input image is supported.")
validate_image_aspect_ratio(image, (1, 16), (16, 1), strict=False)
validate_image_dimensions(image, min_width=512, min_height=512)
request = Seedream5LayerSeparationRequest(
model=SEEDREAM_LAYER_SEPARATION_MODEL,
prompt=prompt.strip() or None,
image=await upload_image_to_comfyapi(cls, image),
size=size,
seed=seed,
watermark=watermark,
optimize_prompt_options=Seedream5LayerOptimizePromptOptions(mode=prompt_optimization),
)
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
response_model=ImageTaskCreationResponse,
data=request,
wait_label="Separating layers",
)
if response.error:
raise RuntimeError(
f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}"
)
def z_index_of(d: dict) -> int:
v = d.get("z_index")
if isinstance(v, bool):
return 1_000_000
if isinstance(v, (int, float)):
return int(v)
if isinstance(v, str):
try:
return int(v.strip())
except ValueError:
return 1_000_000
return 1_000_000
data = [d for d in (response.data or []) if isinstance(d, dict)]
if not data or "url" not in data[0]:
raise RuntimeError("Unexpected response: no base image returned.")
base_item = data[0]
if base_item.get("bounding_box") is not None:
logging.warning(
"ByteDance layer separation: base item unexpectedly carries a bounding_box; ignoring it."
)
if z_index_of(base_item) not in (0, 1_000_000):
raise RuntimeError("Unexpected response: the first item is not the base image.")
layer_items = [d for d in data[1:] if "url" in d]
dropped = len(data) - 1 - len(layer_items)
if dropped > 0:
logging.warning(
"ByteDance layer separation: %d of %d returned elements had no 'url' and were dropped.",
dropped,
len(data) - 1,
)
if not layer_items:
raise RuntimeError("The model returned no layers. Try a different prompt or input image.")
layer_items.sort(key=z_index_of)
base_image = (await download_url_to_image_tensor(str(base_item["url"])))[..., :3].contiguous()
height, width = base_image.shape[1], base_image.shape[2]
specs = []
for item in layer_items:
flags = []
bbox = item.get("bounding_box")
absolute = bbox.get("absolute") if isinstance(bbox, dict) else None
if (
isinstance(absolute, (list, tuple))
and len(absolute) == 4
and all(isinstance(v, (int, float)) and not isinstance(v, bool) for v in absolute)
):
left, top, right, bottom = (int(round(v)) for v in absolute)
rect_w, rect_h = right - left, bottom - top # exclusive right/bottom
if rect_w > width or rect_h > height:
rect_w, rect_h = min(rect_w, width), min(rect_h, height)
flags.append("bbox_clamped")
if rect_w <= 0 or rect_h <= 0:
flags.append("bbox_degenerate")
else:
flags.append("bbox_missing")
left, top, rect_w, rect_h = 0, 0, width, height
specs.append({"item": item, "flags": flags, "left": left, "top": top,
"rect_w": rect_w, "rect_h": rect_h, "native_size": "", "stack_item": None})
if crop_layers:
canvas_w = max((s["rect_w"] for s in specs if "bbox_degenerate" not in s["flags"]), default=1)
canvas_h = max((s["rect_h"] for s in specs if "bbox_degenerate" not in s["flags"]), default=1)
else:
canvas_w, canvas_h = width, height
base_mask = torch.zeros((1, height, width))
layers = torch.zeros((len(specs), canvas_h, canvas_w, 3))
# Create Layered Image / LoadImage mask convention: 1 = transparent
masks = torch.ones((len(specs), canvas_h, canvas_w))
semaphore = asyncio.Semaphore(4)
async def fetch_and_place(i: int, spec: dict) -> None:
item, flags = spec["item"], spec["flags"]
left, top, rect_w, rect_h = spec["left"], spec["top"], spec["rect_w"], spec["rect_h"]
async with semaphore:
try:
rgba = (await download_url_to_image_tensor(str(item["url"])))[0]
except ProcessingInterrupted:
raise
except Exception as exc:
raise RuntimeError(
f"Failed to download layer {i + 1} of {len(specs)} (name={item.get('name')!r}): {exc} "
"The generation completed and was billed; the response with all layer URLs "
"is in ComfyUI/temp/api_logs/."
) from exc
spec["native_size"] = f"{rgba.shape[1]}x{rgba.shape[0]}"
if "bbox_degenerate" in flags:
return
if (rgba.shape[1], rgba.shape[0]) != (rect_w, rect_h):
# premultiply before resizing: interpolating straight alpha bleeds the undefined
# colors of transparent pixels into the anti-aliased edges
rgba = rgba.clone()
rgba[..., :3] *= rgba[..., 3:4]
rgba = (
torch.nn.functional.interpolate(
rgba.permute(2, 0, 1).unsqueeze(0),
size=(rect_h, rect_w),
mode="bilinear",
antialias=True,
)
.squeeze(0)
.permute(1, 2, 0)
)
alpha = rgba[..., 3:4]
rgba = torch.cat([rgba[..., :3] / alpha.clamp(min=1e-6), alpha], dim=-1).clamp(0, 1)
flags.append("resized_to_bbox")
# straight (unpremultiplied) RGB: downstream compositing applies the mask itself
if crop_layers:
layers[i, :rect_h, :rect_w] = rgba[..., :3]
masks[i, :rect_h, :rect_w] = 1.0 - rgba[..., 3]
else:
x0, y0 = max(left, 0), max(top, 0)
x1, y1 = min(left + rect_w, width), min(top + rect_h, height)
if x0 < x1 and y0 < y1:
patch = rgba[y0 - top : y1 - top, x0 - left : x1 - left]
layers[i, y0:y1, x0:x1] = patch[..., :3]
masks[i, y0:y1, x0:x1] = 1.0 - patch[..., 3]
else:
flags.append("bbox_out_of_canvas")
zi = z_index_of(item)
stack_item = {
"image": rgba[..., :3].unsqueeze(0).contiguous(),
"type": "raster",
"x": left,
"y": top,
"z_index": zi if zi != 1_000_000 else i + 1,
"mask": (1.0 - rgba[..., 3]).unsqueeze(0),
}
if isinstance(item.get("name"), str):
stack_item["name"] = item["name"]
spec["stack_item"] = stack_item
await asyncio.gather(*(fetch_and_place(i, s) for i, s in enumerate(specs)))
stack_items = [{"image": base_image, "type": "raster", "x": 0, "y": 0, "z_index": 0, "name": "background"}]
boxes = []
for i, s in enumerate(specs):
abnormal = [f for f in s["flags"] if f != "resized_to_bbox"]
if abnormal:
logging.warning(
"ByteDance layer separation: layer %d (%r) flagged %s.",
i + 1,
s["item"].get("name"),
", ".join(abnormal),
)
if s["stack_item"] is not None:
stack_items.append(s["stack_item"])
zi = z_index_of(s["item"])
# placement box sized to this layer's tensor so Create Layered Image renders it 1:1;
# the true content rect travels in metadata, frame-relative
rect_x, rect_y = (0, 0) if crop_layers else (s["left"], s["top"])
boxes.append(
{
"x": s["left"] if crop_layers else 0,
"y": s["top"] if crop_layers else 0,
"width": canvas_w,
"height": canvas_h,
"metadata": {
"name": s["item"].get("name"),
"desc": s["item"].get("description"),
"z_index": zi if zi != 1_000_000 else None,
"native_size": s["native_size"],
"content_rect": [rect_x, rect_y, max(s["rect_w"], 0), max(s["rect_h"], 0)],
"flags": s["flags"],
},
}
)
# a single frame holding every box: the per-frame BOUNDING_BOX shape for boxes that
# annotate one image, as emitted and consumed by CreateBoundingBoxes
bboxes = [boxes]
layer_stack = {"version": 1, "canvas": (width, height), "layers": stack_items}
return IO.NodeOutput(base_image, base_mask, layers, masks, bboxes, layer_stack)
class ByteDanceTextToVideoNode(IO.ComfyNode):
@classmethod
@ -3036,6 +3405,7 @@ class ByteDanceExtension(ComfyExtension):
ByteDanceImageNode,
ByteDanceSeedreamNode,
ByteDanceSeedreamNodeV2,
ByteDanceSeedreamLayerSeparationNode,
ByteDanceTextToVideoNode,
ByteDanceImageToVideoNode,
ByteDanceFirstLastFrameNode,

331
comfy_extras/compositor_blend.py Normal file
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@ -0,0 +1,331 @@
import math
from typing import NamedTuple, Optional, Union
import numpy as np
EPSILON = 1e-6
LUM_R = 0.2224884
LUM_G = 0.71690369
LUM_B = 0.06060791
ArrayLike = Union[np.ndarray, float]
def srgb_to_linear(c: ArrayLike) -> np.ndarray:
c = np.asarray(c, dtype=np.float32)
high = ((np.maximum(c, 0.0) + 0.055) / 1.055) ** 2.4
return np.where(c <= 0.04045, c / 12.92, high).astype(np.float32)
def linear_to_srgb(c: ArrayLike) -> np.ndarray:
c = np.asarray(c, dtype=np.float32)
high = 1.055 * np.maximum(c, 0.0) ** (1.0 / 2.4) - 0.055
return np.where(c <= 0.0031308, 12.92 * c, high).astype(np.float32)
def luminance(rgb: np.ndarray) -> np.ndarray:
return rgb[..., 0] * LUM_R + rgb[..., 1] * LUM_G + rgb[..., 2] * LUM_B
def safe_div(a: ArrayLike, b: ArrayLike) -> np.ndarray:
a, b = np.broadcast_arrays(
np.asarray(a, dtype=np.float32), np.asarray(b, dtype=np.float32)
)
out = np.zeros(b.shape, dtype=np.float32)
np.divide(a, b, out=out, where=np.abs(b) >= EPSILON)
return out
CHANNEL_BLEND = {
"normal": lambda i, l: l,
"multiply": lambda i, l: i * l,
"screen": lambda i, l: 1 - (1 - i) * (1 - l),
"overlay": lambda i, l: np.where(i < 0.5, 2 * i * l, 1 - 2 * (1 - l) * (1 - i)),
"darken": lambda i, l: np.minimum(i, l),
"lighten": lambda i, l: np.maximum(i, l),
"color-dodge": lambda i, l: np.where(
i <= 0,
0.0,
np.where(1 - l <= EPSILON, 1.0, np.minimum(safe_div(i, 1 - l), 1.0)),
),
"color-burn": lambda i, l: np.where(
i >= 1,
1.0,
np.where(l <= EPSILON, 0.0, 1 - np.minimum(safe_div(1 - i, l), 1.0)),
),
"hard-light": lambda i, l: np.where(
l > 0.5,
np.minimum(1 - (1 - i) * (1 - (l - 0.5) * 2), 1),
np.minimum(i * (l * 2), 1),
),
"soft-light": lambda i, l: (1 - i) * (i * l) + i * (1 - (1 - i) * (1 - l)),
"difference": lambda i, l: np.abs(i - l),
"exclusion": lambda i, l: 0.5 - 2 * (i - 0.5) * (l - 0.5),
"linear-dodge": lambda i, l: i + l,
"linear-burn": lambda i, l: i + l - 1,
"vivid-light": lambda i, l: np.where(
l <= 0.5,
np.where(
i >= 1,
1.0,
np.where(
2 * l <= EPSILON,
0.0,
np.maximum(1 - safe_div(1 - i, 2 * l), 0.0),
),
),
np.where(
i <= 0,
0.0,
np.where(
2 * (1 - l) <= EPSILON,
1.0,
np.minimum(safe_div(i, 2 * (1 - l)), 1.0),
),
),
),
"pin-light": lambda i, l: np.where(
l > 0.5, np.maximum(i, 2 * (l - 0.5)), np.minimum(i, 2 * l)
),
"linear-light": lambda i, l: i + 2 * l - 1,
"hard-mix": lambda i, l: np.where(i + l < 1, 0.0, 1.0),
"subtract": lambda i, l: np.maximum(i - l, 0),
"divide": lambda i, l: np.clip(i / np.maximum(l, EPSILON), 0, 1),
"grain-extract": lambda i, l: i - l + 0.5,
"grain-merge": lambda i, l: i + l - 0.5,
}
def _blend_hue(i: np.ndarray, l: np.ndarray) -> np.ndarray:
src_min = l.min(axis=-1)
src_max = l.max(axis=-1)
src_delta = src_max - src_min
achromatic = src_delta <= EPSILON
dest_max = i.max(axis=-1)
dest_delta = dest_max - i.min(axis=-1)
dest_s = np.where(dest_max != 0, dest_delta / np.where(dest_max != 0, dest_max, 1), 0)
ratio = np.where(
achromatic, 0, dest_s * dest_max / np.where(achromatic, 1, src_delta)
)
offset = dest_max - src_max * ratio
return np.where(achromatic[..., None], i, l * ratio[..., None] + offset[..., None])
def _blend_saturation(i: np.ndarray, l: np.ndarray) -> np.ndarray:
dest_max = i.max(axis=-1)
dest_delta = dest_max - i.min(axis=-1)
flat = dest_delta <= EPSILON
src_max = l.max(axis=-1)
src_delta = src_max - l.min(axis=-1)
src_s = np.where(src_max != 0, src_delta / np.where(src_max != 0, src_max, 1), 0)
ratio = np.where(flat, 0, src_s * dest_max / np.where(flat, 1, dest_delta))
offset = (1 - ratio) * dest_max
return np.where(
flat[..., None],
np.broadcast_to(dest_max[..., None], i.shape),
i * ratio[..., None] + offset[..., None],
)
def _blend_color(i: np.ndarray, l: np.ndarray) -> np.ndarray:
dest_l = (i.min(axis=-1) + i.max(axis=-1)) / 2
src_l = (l.min(axis=-1) + l.max(axis=-1)) / 2
gray = (np.abs(src_l) <= EPSILON) | (np.abs(1 - src_l) <= EPSILON)
dest_high = dest_l > 0.5
src_high = src_l > 0.5
dl = np.minimum(dest_l, 1 - dest_l)
sl = np.minimum(src_l, 1 - src_l)
ratio = dl / np.where(gray, 1, sl)
offset = np.where(dest_high, 1 - 2 * dl, 0) + np.where(src_high, 2 * dl - ratio, 0)
return np.where(
gray[..., None],
np.broadcast_to(dest_l[..., None], i.shape),
l * ratio[..., None] + offset[..., None],
)
def _blend_luminosity(i: np.ndarray, l: np.ndarray) -> np.ndarray:
# Scale the backdrop so it carries the layer's luminance. Where the backdrop
# has no luminance to scale there is no hue or saturation to preserve either,
# so the result is a neutral grey at the layer's luminance - which is also the
# analytic limit of i * lum(l)/lum(i) as a grey backdrop approaches black.
# Guarding the numerator here instead (returning black) makes a luminosity
# layer disappear over dark backdrops; see tests-unit/comfy_extras_test/
# compositor_blend_golden.json.
lum_i = luminance(i)
lum_l = luminance(l)
degenerate = lum_i <= EPSILON
ratio = np.where(degenerate, 0.0, lum_l / np.where(degenerate, 1.0, lum_i))
return np.where(
degenerate[..., None],
np.broadcast_to(lum_l[..., None], i.shape),
i * ratio[..., None],
)
HSL_BLEND = {
"hue": _blend_hue,
"saturation": _blend_saturation,
"color": _blend_color,
"luminosity": _blend_luminosity,
}
def blend_pixel(blend: str, in_rgb: np.ndarray, layer_rgb: np.ndarray) -> np.ndarray:
in_rgb = np.asarray(in_rgb, dtype=np.float32)
layer_rgb = np.asarray(layer_rgb, dtype=np.float32)
hsl = HSL_BLEND.get(blend)
if hsl is not None:
return np.asarray(hsl(in_rgb, layer_rgb), dtype=np.float32)
fn = CHANNEL_BLEND.get(blend, CHANNEL_BLEND["normal"])
return np.asarray(fn(in_rgb, layer_rgb), dtype=np.float32)
def _composite_union(in_c, layer, comp, cov):
in_a = in_c[..., 3]
layer_a = layer[..., 3] * cov
new_a = layer_a + (1 - layer_a) * in_a
ratio = np.where(new_a != 0, layer_a / np.where(new_a != 0, new_a, 1), 0)
blended = (
ratio[..., None]
* (in_a[..., None] * (comp - layer[..., :3]) + layer[..., :3] - in_c[..., :3])
+ in_c[..., :3]
)
keep = (layer_a == 0) | (new_a == 0)
rgb = np.where(
keep[..., None],
in_c[..., :3],
np.where((in_a == 0)[..., None], layer[..., :3], blended),
)
return np.concatenate([rgb, new_a[..., None]], axis=-1)
def _composite_clip_to_backdrop(in_c, layer, comp, cov):
in_a = in_c[..., 3]
layer_a = layer[..., 3] * cov
mixed = comp * layer_a[..., None] + in_c[..., :3] * (1 - layer_a[..., None])
keep = (in_a == 0) | (layer_a == 0)
rgb = np.where(keep[..., None], in_c[..., :3], mixed)
return np.concatenate([rgb, in_a[..., None]], axis=-1)
def _composite_clip_to_layer(in_c, layer, comp, cov):
in_a = in_c[..., 3]
layer_a = layer[..., 3] * cov
mixed = comp * in_a[..., None] + layer[..., :3] * (1 - in_a[..., None])
rgb = np.where(
(layer_a == 0)[..., None],
in_c[..., :3],
np.where((in_a == 0)[..., None], layer[..., :3], mixed),
)
return np.concatenate([rgb, layer_a[..., None]], axis=-1)
def _composite_intersection(in_c, layer, comp, cov):
new_a = in_c[..., 3] * layer[..., 3] * cov
rgb = np.where((new_a == 0)[..., None], in_c[..., :3], comp)
return np.concatenate([rgb, new_a[..., None]], axis=-1)
_COMPOSITE = {
"union": _composite_union,
"clip-to-backdrop": _composite_clip_to_backdrop,
"clip-to-layer": _composite_clip_to_layer,
"intersection": _composite_intersection,
}
def run_composite(mode: str, in_c, layer, comp, cov) -> np.ndarray:
fn = _COMPOSITE.get(mode, _composite_union)
return fn(in_c, layer, comp, cov)
def _to_space(rgb: np.ndarray, space: str) -> np.ndarray:
return rgb if space == "linear" else linear_to_srgb(rgb)
def _from_space(rgb: np.ndarray, space: str) -> np.ndarray:
return rgb if space == "linear" else srgb_to_linear(rgb)
class EffectiveMode(NamedTuple):
blend: str
blend_space: str
composite: str
_LAYER_MODES = {
"normal": ("linear", "union"),
"multiply": ("linear", "clip-to-backdrop"),
"screen": ("perceptual", "clip-to-backdrop"),
"overlay": ("perceptual", "clip-to-backdrop"),
"darken": ("linear", "clip-to-backdrop"),
"lighten": ("linear", "clip-to-backdrop"),
"color-dodge": ("perceptual", "clip-to-backdrop"),
"color-burn": ("perceptual", "clip-to-backdrop"),
"hard-light": ("perceptual", "clip-to-backdrop"),
"soft-light": ("perceptual", "clip-to-backdrop"),
"difference": ("perceptual", "clip-to-backdrop"),
"exclusion": ("perceptual", "clip-to-backdrop"),
"linear-dodge": ("linear", "clip-to-backdrop"),
"linear-burn": ("perceptual", "clip-to-backdrop"),
"vivid-light": ("perceptual", "clip-to-backdrop"),
"pin-light": ("perceptual", "clip-to-backdrop"),
"linear-light": ("perceptual", "clip-to-backdrop"),
"hard-mix": ("perceptual", "clip-to-backdrop"),
"subtract": ("linear", "clip-to-backdrop"),
"divide": ("linear", "clip-to-backdrop"),
"grain-extract": ("perceptual", "clip-to-backdrop"),
"grain-merge": ("perceptual", "clip-to-backdrop"),
"hue": ("perceptual", "clip-to-backdrop"),
"saturation": ("perceptual", "clip-to-backdrop"),
"color": ("perceptual", "clip-to-backdrop"),
"luminosity": ("linear", "clip-to-backdrop"),
}
def resolve_mode(blend: str = "normal") -> EffectiveMode:
blend_space, composite = _LAYER_MODES.get(blend, _LAYER_MODES["normal"])
return EffectiveMode(
blend=blend,
blend_space=blend_space,
composite=composite,
)
def blend_composite(
mode: EffectiveMode,
backdrop: np.ndarray,
layer: np.ndarray,
opacity: float,
mask: Optional[ArrayLike] = None,
) -> np.ndarray:
backdrop = np.asarray(backdrop, dtype=np.float32)
layer = np.asarray(layer, dtype=np.float32)
cov = opacity * (1.0 if mask is None else mask)
in_b = _to_space(backdrop[..., :3], mode.blend_space)
layer_b = _to_space(layer[..., :3], mode.blend_space)
comp = _from_space(blend_pixel(mode.blend, in_b, layer_b), mode.blend_space)
return run_composite(mode.composite, backdrop, layer, comp, cov)
def placed_bounds(
x: float, y: float, w: float, h: float, rotation: float
) -> tuple[int, int, int, int]:
cx = x + w / 2
cy = y + h / 2
cos = math.cos(rotation)
sin = math.sin(rotation)
hw = w / 2
hh = h / 2
corners = ((-hw, -hh), (hw, -hh), (hw, hh), (-hw, hh))
xs = [cx + dx * cos - dy * sin for dx, dy in corners]
ys = [cy + dx * sin + dy * cos for dx, dy in corners]
bx = math.floor(min(xs))
by = math.floor(min(ys))
bw = max(1, math.ceil(max(xs)) - bx)
bh = max(1, math.ceil(max(ys)) - by)
return bx, by, bw, bh

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import hashlib
import json
import math
import numpy as np
import torch
from PIL import Image
from comfy_api.latest import ComfyExtension, io, UI
from comfy_extras.compositor_blend import (
_LAYER_MODES,
blend_composite,
linear_to_srgb,
placed_bounds,
resolve_mode,
srgb_to_linear,
)
from comfy_extras.color_util import hex_to_rgb
from comfy_extras.nodes_bounding_boxes import boxes_from_input
from nodes import MAX_RESOLUTION
from typing_extensions import override
MAX_LAYERS = 50
def document_items(doc) -> list[dict]:
if not isinstance(doc, dict):
return []
version = doc.get("version")
if version is not None and version != 1:
raise ValueError(f"LAYERS document version {version!r} is not supported")
items = []
for item in doc.get("layers") or []:
if not isinstance(item, dict):
continue
item_type = item.get("type", "raster")
if item_type != "raster":
raise ValueError(f"LAYERS item type {item_type!r} is not supported yet")
if not isinstance(item.get("image"), torch.Tensor):
continue
blend = item.get("blend_mode")
if blend is not None and blend not in _LAYER_MODES:
raise ValueError(f"LAYERS item blend_mode {blend!r} is not a known blend mode")
items.append(item)
return sorted(items, key=lambda item: _int(item.get("z_index"), 0))
def document_canvas(doc) -> tuple[int, int] | None:
if not isinstance(doc, dict):
return None
canvas = doc.get("canvas")
if not isinstance(canvas, (tuple, list)) or len(canvas) != 2:
return None
w, h = _int(canvas[0], 0), _int(canvas[1], 0)
return (w, h) if w > 0 and h > 0 else None
def _int(value, default: int) -> int:
return int(value) if isinstance(value, (int, float)) and not isinstance(value, bool) else default
def _bbox_list(bboxes, canvas_width: int, canvas_height: int) -> list[dict]:
if bboxes is None:
return []
if isinstance(bboxes, str):
text = bboxes.strip()
if not text:
return []
try:
bboxes = json.loads(text)
except (json.JSONDecodeError, ValueError) as exc:
raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
probe = bboxes if isinstance(bboxes, list) else [bboxes]
if probe and isinstance(probe[0], list):
probe = probe[0]
has_elements = any(
isinstance(box, dict) and isinstance(box.get("bbox"), (list, tuple))
for box in probe
)
if has_elements and (canvas_width <= 0 or canvas_height <= 0):
raise ValueError(
"normalized element boxes need canvas_width and canvas_height to resolve to pixels"
)
return boxes_from_input(bboxes, canvas_width, canvas_height)
def _item_mask_frame(mask, index: int) -> torch.Tensor | None:
if not isinstance(mask, torch.Tensor):
return None
if mask.shape[0] == 1:
return mask[:1]
if index < mask.shape[0]:
return mask[index : index + 1]
return None
def expand_item_frames(items: list[dict]) -> list[dict]:
frames = []
for item in items:
image = item["image"]
for index in range(image.shape[0]):
width = _int(item.get("w"), 0)
height = _int(item.get("h"), 0)
rotation = item.get("rotation")
frames.append({
"tensor": image[index : index + 1],
"mask": _item_mask_frame(item.get("mask"), index),
"name": item.get("name") if isinstance(item.get("name"), str) else None,
"x": _int(item.get("x"), 0),
"y": _int(item.get("y"), 0),
"w": width if width > 0 else int(image.shape[2]),
"h": height if height > 0 else int(image.shape[1]),
"rotation": float(rotation)
if isinstance(rotation, (int, float)) and not isinstance(rotation, bool)
else 0.0,
"opacity": item.get("opacity", 1.0),
"blend": item.get("blend_mode", "normal"),
"visible": item.get("visible", True),
"flip_h": bool(item.get("flip_h", False)),
"flip_v": bool(item.get("flip_v", False)),
})
if len(frames) > MAX_LAYERS:
raise ValueError(
f"Compositor supports at most {MAX_LAYERS} layers, got {len(frames)}"
)
return frames
def frame_alpha(
tensor: torch.Tensor, mask: torch.Tensor | None
) -> torch.Tensor | None:
alpha = tensor[:1, :, :, 3] if tensor.shape[-1] == 4 else None
if mask is None:
return alpha
h, w = tensor.shape[1], tensor.shape[2]
m = mask[:1].to(device=tensor.device, dtype=torch.float32)
if m.shape[1] != h or m.shape[2] != w:
m = torch.nn.functional.interpolate(
m.unsqueeze(1), size=(h, w), mode="bilinear"
).squeeze(1)
inv = torch.clamp(1.0 - m, 0.0, 1.0)
return inv if alpha is None else alpha * inv
def layer_preview_tensor(
tensor: torch.Tensor, alpha: torch.Tensor | None
) -> torch.Tensor:
rgb = tensor[:1, :, :, :3]
if alpha is None:
return rgb
return torch.cat([rgb, alpha.unsqueeze(-1)], dim=-1)
def canvas_extent(frames: list[dict]) -> tuple[int, int]:
right = 1
bottom = 1
for frame in frames:
bx, by, bw, bh = placed_bounds(
frame["x"], frame["y"], frame["w"], frame["h"], frame["rotation"]
)
right = max(right, bx + bw)
bottom = max(bottom, by + bh)
return (right, bottom)
def input_fingerprints(
frames: list[dict], alphas: list[torch.Tensor | None]
) -> list[str]:
fingerprints = []
for frame, alpha in zip(frames, alphas):
tensor = frame["tensor"]
rgb = tensor[0, :, :, :3].detach().cpu().numpy()
rgb8 = np.clip(np.rint(rgb * 255.0), 0, 255).astype(np.uint8)
digest = hashlib.sha256()
digest.update(repr(tuple(tensor.shape)).encode())
digest.update(rgb8.tobytes())
if alpha is not None:
alpha8 = np.clip(
np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
).astype(np.uint8)
digest.update(alpha8.tobytes())
digest.update(
repr((
frame["x"],
frame["y"],
frame["w"],
frame["h"],
frame["rotation"],
frame["opacity"],
frame["blend"],
bool(frame["visible"]),
frame["flip_h"],
frame["flip_v"],
)).encode()
)
fingerprints.append(digest.hexdigest()[:16])
return fingerprints
def state_from_items(frames: list[dict], canvas: tuple[int, int]) -> dict:
layers = []
for frame in frames:
layers.append({
"name": frame["name"],
"visible": bool(frame["visible"]),
"opacity": frame["opacity"],
"blend": frame["blend"],
"flipH": frame["flip_h"],
"flipV": frame["flip_v"],
"transform": {
"x": frame["x"],
"y": frame["y"],
"w": frame["w"],
"h": frame["h"],
"rotation": frame["rotation"],
},
})
return {
"canvas": canvas,
"layers": layers,
"inputs": None,
"background": {"color": "#ffffff", "opacity": 1.0, "visible": False},
}
def layer_ui_entries(frames: list[dict]) -> list:
entries = []
for frame in frames:
entries.append({
"x": frame["x"],
"y": frame["y"],
"width": int(frame["w"]),
"height": int(frame["h"]),
"rotation": frame["rotation"],
"name": frame["name"],
"visible": bool(frame["visible"]),
"opacity": frame["opacity"] if isinstance(frame["opacity"], (int, float)) else 1.0,
"blend": frame["blend"] if isinstance(frame["blend"], str) else "normal",
"flipH": frame["flip_h"],
"flipV": frame["flip_v"],
})
return entries
_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 _parse_order(value, layer_count: int) -> list[int] | None:
if not isinstance(value, list) or not value:
return None
if not all(
isinstance(item, int) and not isinstance(item, bool) for item in value
):
return None
if sorted(value) != list(range(layer_count)):
return None
return value
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
version = state.get("version")
if version is not None and version != 1:
return None
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, OverflowError):
return None
if w <= 0 or h <= 0:
return None
inputs = state.get("inputs")
if (
not isinstance(inputs, list)
or len(inputs) != len(layers)
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")),
"order": _parse_order(state.get("order"), len(layers)),
}
def _number(source: dict, key: str, default: float) -> float:
value = source.get(key, default)
if not isinstance(value, (int, float)) or not math.isfinite(value):
return float(default)
return float(value)
def _clamped_size(value: float, natural: int) -> float:
return float(natural) if value <= 0 else min(value, float(MAX_RESOLUTION))
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)),
# The layer state is untrusted input: it round-trips through the saved
# workflow and can be posted directly to /prompt. An out-of-range opacity
# would otherwise reach blend_composite as a raw coverage multiplier and
# produce negative or greater-than-white RGB. _parse_background already
# clamps the same field.
"opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
"blend": blend if isinstance(blend, str) else "normal",
"x": min(max(_number(transform, "x", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
"y": min(max(_number(transform, "y", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
"w": _clamped_size(_number(transform, "w", natural_w), natural_w),
"h": _clamped_size(_number(transform, "h", natural_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, alpha: torch.Tensor | None
) -> Image.Image:
frame = tensor[0, :, :, :3].detach().cpu().numpy()
rgb8 = np.clip(np.rint(frame * 255.0), 0, 255).astype(np.uint8)
if alpha is None:
img = Image.fromarray(rgb8, "RGB").convert("RGBA")
else:
alpha8 = np.clip(
np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
).astype(np.uint8)
img = Image.fromarray(np.dstack([rgb8, alpha8]), "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,
alphas: list[torch.Tensor | None],
) -> torch.Tensor:
cw, ch = state["canvas"]
if cw > MAX_RESOLUTION or ch > MAX_RESOLUTION:
raise ValueError(
f"Compositor canvas {cw}x{ch} exceeds the maximum supported size of "
f"{MAX_RESOLUTION}x{MAX_RESOLUTION}"
)
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"]
order = state.get("order") or range(len(tensors))
for index in order:
if index < 0 or index >= len(tensors):
continue
tensor = tensors[index]
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, alphas[index] if index < len(alphas) else None
)
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))
alpha = np.clip(canvas[..., 3:4], 0.0, 1.0)
rgba = np.concatenate([rgb, alpha], axis=-1)
return torch.from_numpy(rgba.astype(np.float32)).unsqueeze(0)
OPAQUE_EPSILON = 1e-3
def composite_outputs(out: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
if out.shape[-1] != 4:
return out, torch.zeros(out.shape[:3], dtype=torch.float32)
alpha = out[..., 3]
if bool((alpha >= 1.0 - OPAQUE_EPSILON).all()):
return out[..., :3], torch.zeros_like(alpha)
return out, torch.clamp(1.0 - alpha, 0.0, 1.0)
class ImageCompositor(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ImageCompositor",
display_name="Create Layered Image",
category="image",
is_experimental=True,
is_output_node=True,
has_intermediate_output=True,
inputs=[
io.Layers.Input(
"layers",
tooltip="Layer stack to composite; build it with Add Layer. Items are stacked by z_index, batch frames inside an item expand to consecutive layers, and item placement, opacity, and blend mode define the initial composition. Without an explicit document canvas the size is a best-effort maximum extent of the placed layers. A saved composition that matches the current inputs takes priority.",
),
io.Compositor.Input(
"compositor",
tooltip="Layered composition saved by the compositor editor.",
),
],
outputs=[
io.Image.Output(
tooltip="Composited image. Carries an alpha channel when the composite has transparent areas (e.g. hidden background), otherwise plain RGB."
),
io.Mask.Output(
tooltip="Transparency of the composite (1 = fully transparent). All zeros when the composite is opaque."
),
],
)
@classmethod
def execute(cls, layers: io.Layers.Type, compositor: io.Compositor.Type = None) -> io.NodeOutput:
frames = expand_item_frames(document_items(layers))
tensors = [frame["tensor"] for frame in frames]
alphas = [frame_alpha(frame["tensor"], frame["mask"]) for frame in frames]
layer_refs = []
for tensor, alpha in zip(tensors, alphas):
layer_refs.extend(
UI.PreviewImage(layer_preview_tensor(tensor, alpha), cls=cls).values
)
fp = input_fingerprints(frames, alphas)
raw_state = compositor
state = parse_layer_state(raw_state)
replay = bool(state is not None and tensors and state["inputs"] == fp)
if replay:
out = composite_from_state(tensors, state, alphas)
elif tensors:
canvas = document_canvas(layers) or canvas_extent(frames)
out = composite_from_state(
tensors, state_from_items(frames, canvas), alphas
)
else:
out = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
state_stale = layer_state_provided(raw_state) and not replay
out, mask = composite_outputs(out)
ui_dict = UI.PreviewImage(out, cls=cls).as_dict()
ui_dict["compositor_layers"] = layer_refs
ui_dict["compositor_inputs"] = fp
ui_dict["compositor_bboxes"] = layer_ui_entries(frames)
if state_stale:
ui_dict["compositor_state_stale"] = [True]
return io.NodeOutput(out, mask, ui=ui_dict)
class AddLayer(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="AddLayer",
display_name="Add Layer",
category="image",
is_experimental=True,
inputs=[
io.Layers.Input(
"layers",
optional=True,
tooltip="Layer stack to append to. Leave unconnected to start a new stack.",
),
io.Image.Input(
"image",
tooltip="Layer content at its native size. A batch expands to consecutive layers.",
),
io.Mask.Input(
"mask",
optional=True,
tooltip="Transparency mask for this layer. Masked areas (value 1) become transparent, multiplying with any alpha channel the image already carries.",
),
io.String.Input(
"name",
optional=True,
default="",
tooltip="Layer name shown in the compositor editor.",
),
io.Int.Input(
"x",
optional=True,
default=0,
min=-MAX_RESOLUTION,
max=MAX_RESOLUTION,
tooltip="Initial horizontal placement on the canvas.",
),
io.Int.Input(
"y",
optional=True,
default=0,
min=-MAX_RESOLUTION,
max=MAX_RESOLUTION,
tooltip="Initial vertical placement on the canvas.",
),
io.Float.Input(
"opacity",
optional=True,
default=1.0,
min=0.0,
max=1.0,
step=0.01,
tooltip="Initial layer opacity.",
),
io.Combo.Input(
"blend_mode",
options=list(_LAYER_MODES),
default="normal",
optional=True,
tooltip="Initial blend mode.",
),
io.Float.Input(
"rotation",
optional=True,
default=0.0,
min=-360.0,
max=360.0,
step=1.0,
tooltip="Initial rotation in degrees, clockwise.",
),
io.Int.Input(
"width",
optional=True,
default=0,
min=0,
max=MAX_RESOLUTION,
tooltip="Initial display width. 0 keeps the image's native width.",
),
io.Int.Input(
"height",
optional=True,
default=0,
min=0,
max=MAX_RESOLUTION,
tooltip="Initial display height. 0 keeps the image's native height.",
),
io.Int.Input(
"z_index",
optional=True,
default=0,
min=-1000,
max=1000,
tooltip="Stacking override. Layers are stable-sorted by z_index; equal values keep their list order.",
),
io.Boolean.Input(
"flip_h",
optional=True,
default=False,
tooltip="Flip the layer horizontally.",
),
io.Boolean.Input(
"flip_v",
optional=True,
default=False,
tooltip="Flip the layer vertically.",
),
],
outputs=[
io.Layers.Output(tooltip="The layer stack with this layer appended."),
],
)
@classmethod
def execute(cls, image: io.Image.Type, layers: io.Layers.Type = None, mask: io.Mask.Type = None, name: str = "", x: int = 0, y: int = 0, opacity: float = 1.0, blend_mode: str = "normal", rotation: float = 0.0, width: int = 0, height: int = 0, z_index: int = 0, flip_h: bool = False, flip_v: bool = False) -> io.NodeOutput:
item: dict = {
"image": image,
"type": "raster",
"x": int(x),
"y": int(y),
"z_index": int(z_index),
}
if mask is not None:
item["mask"] = mask
if name:
item["name"] = name
if opacity != 1.0:
item["opacity"] = float(opacity)
if blend_mode != "normal":
item["blend_mode"] = blend_mode
if rotation != 0.0:
item["rotation"] = math.radians(rotation)
if width > 0:
item["w"] = int(width)
if height > 0:
item["h"] = int(height)
if flip_h:
item["flip_h"] = True
if flip_v:
item["flip_v"] = True
previous = layers if isinstance(layers, dict) else None
document: dict = {
"version": 1,
"layers": [*(previous.get("layers") or []), item] if previous else [item],
}
previous_canvas = document_canvas(previous)
if previous_canvas:
document["canvas"] = previous_canvas
return io.NodeOutput(document)
class LayersFromBoundingBoxes(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LayersFromBoundingBoxes",
display_name="Layers From Bounding Boxes",
category="image",
is_experimental=True,
description=(
"Turn an image batch plus its bounding boxes into a layer stack, one layer per frame, "
"each placed by its own box. Use this when a node emits layers as a batch - a batch "
"carries a single placement for every frame, so the individual positions are otherwise lost."
),
inputs=[
io.Image.Input(
"image",
tooltip="Image batch; each frame becomes one layer.",
),
io.MultiType.Input(
"bboxes",
[io.BoundingBox, io.Array, io.String],
tooltip=(
"Placement boxes, index-aligned with the image batch. Accepts bounding boxes "
"(x, y, width, height), normalized elements (with a 'bbox' - these need "
"canvas_width/canvas_height to resolve to pixels), or a JSON string of either. "
"Frames without a matching box are placed at the origin. A box's width/height "
"scales the layer to fit it. metadata.name (or desc) and metadata.z_index are "
"used when present, and metadata.content_rect (frame-relative) crops the frame "
"to its real content."
),
),
io.Mask.Input(
"mask",
optional=True,
tooltip=(
"Per-frame transparency, index-aligned with the image batch "
"(1 = transparent, LoadImage convention)."
),
),
io.Layers.Input(
"layers",
optional=True,
tooltip="Layer stack to append to. Leave unconnected to start a new stack.",
),
io.Boolean.Input(
"crop_to_content",
default=True,
optional=True,
tooltip=(
"Crop each frame to metadata.content_rect where present and place the content "
"at the box position plus the rect offset. Leave on for batches whose frames "
"are padded - it keeps only the real content at its true spot."
),
),
io.Int.Input(
"canvas_width",
default=0,
min=0,
max=MAX_RESOLUTION,
optional=True,
tooltip="Document canvas width. 0 derives it from the placed layers.",
),
io.Int.Input(
"canvas_height",
default=0,
min=0,
max=MAX_RESOLUTION,
optional=True,
tooltip="Document canvas height. 0 derives it from the placed layers.",
),
],
outputs=[
io.Layers.Output(tooltip="The layer stack, ready for Create Layered Image."),
],
)
@classmethod
def execute(
cls,
image: io.Image.Type,
bboxes: io.MultiType.Type,
mask: io.Mask.Type = None,
layers: io.Layers.Type = None,
crop_to_content: bool = True,
canvas_width: int = 0,
canvas_height: int = 0,
) -> io.NodeOutput:
boxes = _bbox_list(bboxes, canvas_width, canvas_height)
previous = layers if isinstance(layers, dict) else None
items: list[dict] = list((previous.get("layers") or []) if previous else [])
base_z = max((_int(i.get("z_index"), 0) for i in items), default=-1) + 1
for index in range(image.shape[0]):
box = boxes[index] if index < len(boxes) else {}
meta = box.get("metadata") if isinstance(box.get("metadata"), dict) else {}
frame = image[index : index + 1]
frame_mask = _item_mask_frame(mask, index)
x, y = _int(box.get("x"), 0), _int(box.get("y"), 0)
box_w, box_h = _int(box.get("width"), 0), _int(box.get("height"), 0)
cropped = False
rect = meta.get("content_rect")
if crop_to_content and isinstance(rect, (list, tuple)) and len(rect) == 4:
left, top, cw, ch = (_int(v, 0) for v in rect)
left = min(max(left, 0), int(frame.shape[2]))
top = min(max(top, 0), int(frame.shape[1]))
cw = min(max(cw, 0), int(frame.shape[2]) - left)
ch = min(max(ch, 0), int(frame.shape[1]) - top)
if cw > 0 and ch > 0:
frame = frame[:, top : top + ch, left : left + cw]
if frame_mask is not None:
frame_mask = frame_mask[:, top : top + ch, left : left + cw]
x, y = x + left, y + top
cropped = True
item: dict = {
"image": frame,
"type": "raster",
"x": x,
"y": y,
"z_index": _int(meta.get("z_index"), base_z + index),
}
if not cropped:
if box_w > 0:
item["w"] = box_w
if box_h > 0:
item["h"] = box_h
if frame_mask is not None:
item["mask"] = frame_mask
name = meta.get("name")
if not (isinstance(name, str) and name):
name = meta.get("desc")
if isinstance(name, str) and name:
item["name"] = name
items.append(item)
document: dict = {"version": 1, "layers": items}
if canvas_width > 0 and canvas_height > 0:
document["canvas"] = (canvas_width, canvas_height)
else:
inherited = document_canvas(previous)
if inherited:
document["canvas"] = inherited
return io.NodeOutput(document)
class CompositorExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [ImageCompositor, AddLayer, LayersFromBoundingBoxes]
async def comfy_entrypoint() -> CompositorExtension:
return CompositorExtension()

1
nodes.py
View File

@ -2501,6 +2501,7 @@ async def init_builtin_extra_nodes():
"nodes_math.py",
"nodes_number_convert.py",
"nodes_painter.py",
"nodes_compositor.py",
"nodes_curve.py",
"nodes_bg_removal.py",
"nodes_rtdetr.py",

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@ -0,0 +1,111 @@
"""Regenerate ``compositor_blend_golden.json``.
The golden file is the *shared contract* for layer blending. Every
implementation of these 26 modes must reproduce it within ``tolerance``:
* ``comfy_extras/compositor_blend.py`` - numpy, server-side compositing
* ``layerBlend.frag`` - GLSL, the live preview in the layer editor
* any future CPU reference in the frontend
Run from the repository root::
python tests-unit/comfy_extras_test/compositor_blend_fixture_gen.py
and review the diff. A change to this file is a change to user-visible
blending behaviour in every implementation, so it should never be
regenerated just to make a test pass.
"""
import json
import os
import sys
import numpy as np
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from comfy_extras.compositor_blend import CHANNEL_BLEND, HSL_BLEND, blend_pixel # noqa: E402
GOLDEN_PATH = os.path.join(os.path.dirname(__file__), "compositor_blend_golden.json")
# Scalar grid for the per-channel modes: both endpoints, the midpoint, values
# just inside each endpoint, and values inside the 1e-6 epsilon guards.
SCALARS = [0.0, 1e-7, 0.001, 0.25, 0.5, 0.75, 0.999, 1.0 - 1e-7, 1.0]
# Colour pairs for the HSL modes, which read all three channels at once.
COLORS = [
[0.0, 0.0, 0.0],
[1.0, 1.0, 1.0],
[0.5, 0.5, 0.5],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.2, 0.4, 0.6],
[0.9, 0.1, 0.35],
[1e-7, 1e-7, 1e-7],
[1e-7, 0.0, 0.0],
[0.05, 0.05, 0.05],
]
def _round(value) -> float:
return round(float(value), 7)
def build() -> dict:
channel = {}
for mode in CHANNEL_BLEND:
rows = []
for i in SCALARS:
for l in SCALARS:
out = blend_pixel(mode, np.float32([i] * 3), np.float32([l] * 3))
rows.append([_round(i), _round(l), _round(np.asarray(out).reshape(3)[0])])
channel[mode] = rows
hsl = {}
for mode in HSL_BLEND:
rows = []
for i in COLORS:
for l in COLORS:
out = blend_pixel(mode, np.float32(i), np.float32(l))
rows.append([
[_round(v) for v in i],
[_round(v) for v in l],
[_round(v) for v in np.asarray(out).reshape(3)],
])
hsl[mode] = rows
return {
"_comment": (
"Golden blend values shared by comfy_extras/compositor_blend.py and "
"layerBlend.frag. Inputs are unpremultiplied colours already in the "
"blend space; outputs are unclamped (the compositor clamps once, at "
"the end). 'channel' rows are [i, l, out] applied per channel; 'hsl' "
"rows are [rgb_backdrop, rgb_layer, rgb_out]. Regenerate with "
"tests-unit/comfy_extras_test/compositor_blend_fixture_gen.py."
),
"tolerance": 1e-4,
"channel": channel,
"hsl": hsl,
}
def dumps(data: dict) -> str:
"""One row per line, so a behaviour change shows up as a readable diff."""
lines = ["{", f' "_comment": {json.dumps(data["_comment"])},', f' "tolerance": {data["tolerance"]},']
for section in ("channel", "hsl"):
lines.append(f' "{section}": {{')
modes = sorted(data[section])
for m_index, mode in enumerate(modes):
lines.append(f' "{mode}": [')
rows = data[section][mode]
for r_index, row in enumerate(rows):
comma = "" if r_index == len(rows) - 1 else ","
lines.append(f" {json.dumps(row)}{comma}")
lines.append(" ]" + ("" if m_index == len(modes) - 1 else ","))
lines.append(" }" + ("," if section == "channel" else ""))
lines.append("}")
return "\n".join(lines) + "\n"
if __name__ == "__main__":
with open(GOLDEN_PATH, "w") as handle:
handle.write(dumps(build()))
sys.stdout.write(f"wrote {GOLDEN_PATH}\n")

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157
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@ -0,0 +1,157 @@
"""Blend-mode parity tests for the compositor.
The compositor blends in three places: this numpy module (server-side), the
``layerBlend.frag`` GLSL shader (the live preview the user actually sees), and
anything the frontend adds later. They have diverged before, silently, and the
divergences only show up as "the render does not look like the preview".
``compositor_blend_golden.json`` is the shared contract. This file pins the
numpy implementation to it and additionally spells out, by hand, the boundary
rules that the epsilon guards exist to enforce - so a future refactor of
``safe_div`` cannot quietly re-introduce the old behaviour by regenerating the
fixture.
"""
import json
import os
import numpy as np
import pytest
from comfy_extras.compositor_blend import (
CHANNEL_BLEND,
HSL_BLEND,
EffectiveMode,
blend_composite,
blend_pixel,
resolve_mode,
)
GOLDEN_PATH = os.path.join(os.path.dirname(__file__), "compositor_blend_golden.json")
with open(GOLDEN_PATH) as _handle:
GOLDEN = json.load(_handle)
TOLERANCE = GOLDEN["tolerance"]
def _blend(mode: str, i, l) -> np.ndarray:
return np.asarray(
blend_pixel(mode, np.float32(i), np.float32(l)), dtype=np.float64
).reshape(3)
def test_golden_covers_every_mode():
"""A new blend mode must arrive with golden values, not silently."""
assert set(GOLDEN["channel"]) == set(CHANNEL_BLEND)
assert set(GOLDEN["hsl"]) == set(HSL_BLEND)
@pytest.mark.parametrize("mode", sorted(CHANNEL_BLEND))
def test_channel_modes_match_golden(mode):
for i, l, expected in GOLDEN["channel"][mode]:
actual = _blend(mode, [i] * 3, [l] * 3)
assert actual == pytest.approx([expected] * 3, abs=TOLERANCE), (
f"{mode}(i={i}, l={l}) -> {actual.tolist()}, golden {expected}"
)
@pytest.mark.parametrize("mode", sorted(HSL_BLEND))
def test_hsl_modes_match_golden(mode):
for i, l, expected in GOLDEN["hsl"][mode]:
actual = _blend(mode, i, l)
assert actual == pytest.approx(expected, abs=TOLERANCE), (
f"{mode}(i={i}, l={l}) -> {actual.tolist()}, golden {expected}"
)
@pytest.mark.parametrize("mode", sorted(set(CHANNEL_BLEND) | set(HSL_BLEND)))
def test_no_mode_produces_nan_or_inf(mode):
edges = [0.0, 1e-7, 1e-6, 0.5, 1.0 - 1e-7, 1.0]
for i in edges:
for l in edges:
out = _blend(mode, [i, 0.0, 1.0], [l, 1.0, 0.0])
assert np.all(np.isfinite(out)), f"{mode}(i={i}, l={l}) -> {out.tolist()}"
class TestBoundaryRules:
"""The rules the epsilon guards encode, written out independently of the fixture."""
def test_color_dodge_full_layer_is_white_not_black(self):
# Guarding the denominator returns 0 here, which reads as "the dodge
# layer turned the image black" - the exact inversion CodeRabbit flagged.
assert _blend("color-dodge", [0.5] * 3, [1.0] * 3) == pytest.approx([1.0] * 3)
def test_color_dodge_black_backdrop_stays_black(self):
assert _blend("color-dodge", [0.0] * 3, [1.0] * 3) == pytest.approx([0.0] * 3)
def test_color_dodge_is_clamped(self):
assert _blend("color-dodge", [0.6] * 3, [0.9] * 3) == pytest.approx([1.0] * 3)
def test_color_burn_empty_layer_is_black_not_white(self):
assert _blend("color-burn", [0.5] * 3, [0.0] * 3) == pytest.approx([0.0] * 3)
def test_color_burn_white_backdrop_stays_white(self):
assert _blend("color-burn", [1.0] * 3, [0.0] * 3) == pytest.approx([1.0] * 3)
def test_vivid_light_boundaries(self):
assert _blend("vivid-light", [0.5] * 3, [0.0] * 3) == pytest.approx([0.0] * 3)
assert _blend("vivid-light", [0.5] * 3, [1.0] * 3) == pytest.approx([1.0] * 3)
assert _blend("vivid-light", [1.0] * 3, [0.0] * 3) == pytest.approx([1.0] * 3)
assert _blend("vivid-light", [0.0] * 3, [1.0] * 3) == pytest.approx([0.0] * 3)
def test_divide_by_zero_is_clamped_to_one(self):
assert _blend("divide", [0.5] * 3, [0.0] * 3) == pytest.approx([1.0] * 3)
def test_luminosity_over_black_takes_the_layer_luminance(self):
# A luminosity layer over a black backdrop must not vanish. There is no
# hue or saturation in the backdrop to preserve, so the result is a
# neutral grey at the layer's luminance.
assert _blend("luminosity", [0.0] * 3, [1.0] * 3) == pytest.approx([1.0] * 3)
assert _blend("luminosity", [0.0] * 3, [0.5] * 3) == pytest.approx([0.5] * 3)
def test_luminosity_is_continuous_approaching_black(self):
near = _blend("luminosity", [1e-7] * 3, [1.0] * 3)
at = _blend("luminosity", [0.0] * 3, [1.0] * 3)
assert near == pytest.approx(at, abs=TOLERANCE)
def test_luminosity_preserves_backdrop_chroma(self):
out = _blend("luminosity", [0.4, 0.2, 0.1], [0.5] * 3)
assert out[0] > out[1] > out[2]
class TestCompositeAndModeTable:
def test_unknown_blend_mode_falls_back_to_normal(self):
unknown = resolve_mode("not-a-mode")
assert (unknown.blend_space, unknown.composite) == (
resolve_mode("normal").blend_space,
resolve_mode("normal").composite,
)
assert _blend("not-a-mode", [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]) == pytest.approx(
_blend("normal", [0.1, 0.2, 0.3], [0.4, 0.5, 0.6])
)
def test_every_blend_mode_has_a_composite_entry(self):
for mode in set(CHANNEL_BLEND) | set(HSL_BLEND):
resolved = resolve_mode(mode)
assert isinstance(resolved, EffectiveMode)
assert resolved.blend == mode
assert resolved.blend_space in ("linear", "perceptual")
assert resolved.composite in (
"union",
"clip-to-backdrop",
"clip-to-layer",
"intersection",
)
def test_normal_over_transparent_backdrop_keeps_the_layer(self):
backdrop = np.zeros((1, 1, 4), dtype=np.float32)
layer = np.float32([[[0.25, 0.5, 0.75, 1.0]]])
out = blend_composite(resolve_mode("normal"), backdrop, layer, 1.0)
assert out[0, 0].tolist() == pytest.approx([0.25, 0.5, 0.75, 1.0])
def test_zero_opacity_is_a_no_op(self):
backdrop = np.float32([[[0.1, 0.2, 0.3, 1.0]]])
layer = np.float32([[[1.0, 1.0, 1.0, 1.0]]])
out = blend_composite(resolve_mode("multiply"), backdrop, layer, 0.0)
assert out[0, 0].tolist() == pytest.approx([0.1, 0.2, 0.3, 1.0])

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@ -0,0 +1,72 @@
"""Regression tests for ImageCompositor's handling of untrusted layer state.
The compositor's `compositor` widget value is persisted into the saved workflow
and is accepted verbatim on `POST /prompt`, so every field in it is untrusted
input, not an internal invariant.
"""
import numpy as np
import pytest
import torch
from comfy_extras.nodes_compositor import (
_layer_params,
composite_from_state,
expand_item_frames,
state_from_items,
)
def _solid(color, w=4, h=4) -> torch.Tensor:
frame = np.zeros((h, w, len(color)), dtype=np.float32)
frame[:] = color
return torch.from_numpy(frame).unsqueeze(0)
class TestLayerOpacity:
@pytest.mark.parametrize(
("raw", "expected"),
[(-0.5, 0.0), (0.0, 0.0), (0.25, 0.25), (1.0, 1.0), (3.0, 1.0)],
)
def test_opacity_is_clamped(self, raw, expected):
assert _layer_params({"opacity": raw}, 4, 4)["opacity"] == expected
def test_opacity_defaults_to_opaque(self):
assert _layer_params({}, 4, 4)["opacity"] == 1.0
def test_out_of_range_opacity_does_not_leak_into_the_next_layer(self):
# The canvas is only clamped once, after every layer has been composited,
# so an out-of-range coverage multiplier on one layer changes the *blend*
# of the layer above it. White at opacity 3.0 over black leaves the canvas
# at 3.0; the multiply above it then reads 3.0 as its backdrop and the
# result is visibly lighter than the same stack at opacity 1.0.
def run(opacity):
state = {
"canvas": (2, 2),
"layers": [{"opacity": opacity}, {"opacity": 1.0, "blend": "multiply"}],
"inputs": None,
"background": {"color": "#000000", "opacity": 1.0, "visible": True},
"order": None,
}
tensors = [_solid([1.0, 1.0, 1.0], 2, 2), _solid([0.5, 0.5, 0.5], 2, 2)]
return composite_from_state(tensors, state, [None, None])[0, 0, 0, :3]
assert run(3.0).tolist() == pytest.approx(run(1.0).tolist(), abs=1e-6)
class TestGraphOnlyBackground:
def test_default_layout_background_is_hidden(self):
# A visible white background here would make every graph-only run emit a
# white matte instead of transparency.
frames = expand_item_frames([{"image": _solid([1.0, 0.0, 0.0])}])
state = state_from_items(frames, (4, 4))
assert state["background"]["visible"] is False
def test_uncovered_canvas_stays_transparent(self):
tensors = [_solid([1.0, 0.0, 0.0], w=2, h=2)]
frames = expand_item_frames([{"image": tensors[0]}])
state = state_from_items(frames, (4, 4))
out = composite_from_state(tensors, state, [None])[0]
assert out.shape[-1] == 4
assert float(out[0, 0, 3]) == pytest.approx(1.0)
assert float(out[3, 3, 3]) == pytest.approx(0.0)