856 lines
32 KiB
Python
856 lines
32 KiB
Python
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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_LAYER_MODES,
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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 nodes import MAX_RESOLUTION
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from typing_extensions import override
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MAX_LAYERS = 50
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def document_items(doc) -> list[dict]:
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if not isinstance(doc, dict):
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return []
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version = doc.get("version")
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if version is not None and version != 1:
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raise ValueError(f"LAYERS document version {version!r} is not supported")
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items = []
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for item in doc.get("layers") or []:
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if not isinstance(item, dict):
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continue
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item_type = item.get("type", "raster")
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if item_type != "raster":
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raise ValueError(f"LAYERS item type {item_type!r} is not supported yet")
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if not isinstance(item.get("image"), torch.Tensor):
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continue
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blend = item.get("blend_mode")
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if blend is not None and blend not in _LAYER_MODES:
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raise ValueError(f"LAYERS item blend_mode {blend!r} is not a known blend mode")
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items.append(item)
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return sorted(items, key=lambda item: _int(item.get("z_index"), 0))
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def document_canvas(doc) -> tuple[int, int] | None:
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if not isinstance(doc, dict):
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return None
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canvas = doc.get("canvas")
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if not isinstance(canvas, (tuple, list)) or len(canvas) != 2:
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return None
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w, h = _int(canvas[0], 0), _int(canvas[1], 0)
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return (w, h) if w > 0 and h > 0 else None
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def _int(value, default: int) -> int:
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return int(value) if isinstance(value, (int, float)) and not isinstance(value, bool) else default
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def _bbox_list(bboxes, canvas_width: int, canvas_height: int) -> list[dict]:
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if bboxes is None:
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return []
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if isinstance(bboxes, str):
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text = bboxes.strip()
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if not text:
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return []
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try:
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bboxes = json.loads(text)
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except (json.JSONDecodeError, ValueError) as exc:
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raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
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probe = bboxes if isinstance(bboxes, list) else [bboxes]
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if probe and isinstance(probe[0], list):
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probe = probe[0]
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has_elements = any(
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isinstance(box, dict) and isinstance(box.get("bbox"), (list, tuple))
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for box in probe
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)
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if has_elements and (canvas_width <= 0 or canvas_height <= 0):
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raise ValueError(
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"normalized element boxes need canvas_width and canvas_height to resolve to pixels"
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)
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return boxes_from_input(bboxes, canvas_width, canvas_height)
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def _item_mask_frame(mask, index: int) -> torch.Tensor | None:
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if not isinstance(mask, torch.Tensor):
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return None
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if mask.shape[0] == 1:
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return mask[:1]
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if index < mask.shape[0]:
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return mask[index : index + 1]
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return None
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def expand_item_frames(items: list[dict]) -> list[dict]:
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frames = []
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for item in items:
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image = item["image"]
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for index in range(image.shape[0]):
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width = _int(item.get("w"), 0)
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height = _int(item.get("h"), 0)
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rotation = item.get("rotation")
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frames.append({
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"tensor": image[index : index + 1],
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"mask": _item_mask_frame(item.get("mask"), index),
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"name": item.get("name") if isinstance(item.get("name"), str) else None,
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"x": _int(item.get("x"), 0),
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"y": _int(item.get("y"), 0),
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"w": width if width > 0 else int(image.shape[2]),
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"h": height if height > 0 else int(image.shape[1]),
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"rotation": float(rotation)
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if isinstance(rotation, (int, float)) and not isinstance(rotation, bool)
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else 0.0,
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"opacity": item.get("opacity", 1.0),
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"blend": item.get("blend_mode", "normal"),
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"visible": item.get("visible", True),
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"flip_h": bool(item.get("flip_h", False)),
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"flip_v": bool(item.get("flip_v", False)),
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})
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if len(frames) > MAX_LAYERS:
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raise ValueError(
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f"Compositor supports at most {MAX_LAYERS} layers, got {len(frames)}"
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)
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return frames
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def frame_alpha(
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tensor: torch.Tensor, mask: torch.Tensor | None
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) -> torch.Tensor | None:
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alpha = tensor[:1, :, :, 3] if tensor.shape[-1] == 4 else None
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if mask is None:
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return alpha
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h, w = tensor.shape[1], tensor.shape[2]
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m = mask[:1].to(device=tensor.device, dtype=torch.float32)
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if m.shape[1] != h or m.shape[2] != w:
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m = torch.nn.functional.interpolate(
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m.unsqueeze(1), size=(h, w), mode="bilinear"
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).squeeze(1)
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inv = torch.clamp(1.0 - m, 0.0, 1.0)
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return inv if alpha is None else alpha * inv
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def layer_preview_tensor(
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tensor: torch.Tensor, alpha: torch.Tensor | None
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) -> torch.Tensor:
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rgb = tensor[:1, :, :, :3]
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if alpha is None:
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return rgb
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return torch.cat([rgb, alpha.unsqueeze(-1)], dim=-1)
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def canvas_extent(frames: list[dict]) -> tuple[int, int]:
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right = 1
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bottom = 1
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for frame in frames:
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bx, by, bw, bh = placed_bounds(
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frame["x"], frame["y"], frame["w"], frame["h"], frame["rotation"]
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)
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right = max(right, bx + bw)
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bottom = max(bottom, by + bh)
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return (right, bottom)
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def input_fingerprints(
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frames: list[dict], alphas: list[torch.Tensor | None]
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) -> list[str]:
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fingerprints = []
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for frame, alpha in zip(frames, alphas):
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tensor = frame["tensor"]
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rgb = tensor[0, :, :, :3].detach().cpu().numpy()
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rgb8 = np.clip(np.rint(rgb * 255.0), 0, 255).astype(np.uint8)
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digest = hashlib.sha256()
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digest.update(repr(tuple(tensor.shape)).encode())
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digest.update(rgb8.tobytes())
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if alpha is not None:
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alpha8 = np.clip(
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np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
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).astype(np.uint8)
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digest.update(alpha8.tobytes())
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digest.update(
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repr((
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frame["x"],
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frame["y"],
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frame["w"],
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frame["h"],
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frame["rotation"],
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frame["opacity"],
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frame["blend"],
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bool(frame["visible"]),
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frame["flip_h"],
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frame["flip_v"],
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)).encode()
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)
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fingerprints.append(digest.hexdigest()[:16])
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return fingerprints
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def state_from_items(frames: list[dict], canvas: tuple[int, int]) -> dict:
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layers = []
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for frame in frames:
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layers.append({
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"name": frame["name"],
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"visible": bool(frame["visible"]),
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"opacity": frame["opacity"],
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"blend": frame["blend"],
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"flipH": frame["flip_h"],
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"flipV": frame["flip_v"],
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"transform": {
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"x": frame["x"],
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"y": frame["y"],
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"w": frame["w"],
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"h": frame["h"],
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"rotation": frame["rotation"],
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},
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})
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return {
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"canvas": canvas,
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"layers": layers,
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"inputs": None,
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"background": {"color": "#ffffff", "opacity": 1.0, "visible": False},
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}
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def layer_ui_entries(frames: list[dict]) -> list:
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entries = []
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for frame in frames:
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entries.append({
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"x": frame["x"],
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"y": frame["y"],
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"width": int(frame["w"]),
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"height": int(frame["h"]),
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"rotation": frame["rotation"],
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"name": frame["name"],
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"visible": bool(frame["visible"]),
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"opacity": frame["opacity"] if isinstance(frame["opacity"], (int, float)) else 1.0,
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"blend": frame["blend"] if isinstance(frame["blend"], str) else "normal",
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"flipH": frame["flip_h"],
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"flipV": frame["flip_v"],
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})
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return entries
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_HEX_DIGITS = set("0123456789abcdef")
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def _normalize_hex_color(value) -> str:
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if isinstance(value, str):
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text = value.strip().lower()
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if text.startswith("#"):
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digits = text[1:]
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if len(digits) == 3 and set(digits) <= _HEX_DIGITS:
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digits = "".join(ch * 2 for ch in digits)
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if len(digits) == 6 and set(digits) <= _HEX_DIGITS:
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return "#" + digits
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return "#ffffff"
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def _parse_background(entry) -> dict | None:
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if not isinstance(entry, dict):
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return None
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return {
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"color": _normalize_hex_color(entry.get("color")),
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"opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
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"visible": bool(entry.get("visible", True)),
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}
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def _parse_order(value, layer_count: int) -> list[int] | None:
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if not isinstance(value, list) or not value:
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return None
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if not all(
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isinstance(item, int) and not isinstance(item, bool) for item in value
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):
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return None
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if sorted(value) != list(range(layer_count)):
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return None
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return value
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def layer_state_provided(raw) -> bool:
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if isinstance(raw, dict):
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return bool(raw)
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if isinstance(raw, str):
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return raw not in ("", "{}")
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return False
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def parse_layer_state(raw) -> dict | None:
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if isinstance(raw, str):
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if not raw.strip():
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return None
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try:
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raw = json.loads(raw)
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except (json.JSONDecodeError, ValueError):
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return None
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if not isinstance(raw, dict):
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return None
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state = raw
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version = state.get("version")
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if version is not None and version != 1:
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return None
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canvas = state.get("canvas")
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layers = state.get("layers")
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if not isinstance(canvas, dict) or not isinstance(layers, list) or not layers:
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return None
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try:
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w = int(round(float(canvas.get("w"))))
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h = int(round(float(canvas.get("h"))))
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except (TypeError, ValueError, OverflowError):
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return None
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if w <= 0 or h <= 0:
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return None
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inputs = state.get("inputs")
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if (
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not isinstance(inputs, list)
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or len(inputs) != len(layers)
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or not all(isinstance(entry, str) for entry in inputs)
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):
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inputs = None
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return {
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"canvas": (w, h),
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"layers": layers,
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"inputs": inputs,
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"background": _parse_background(state.get("background")),
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"order": _parse_order(state.get("order"), len(layers)),
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}
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def _number(source: dict, key: str, default: float) -> float:
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value = source.get(key, default)
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if not isinstance(value, (int, float)) or not math.isfinite(value):
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return float(default)
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return float(value)
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def _clamped_size(value: float, natural: int) -> float:
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return float(natural) if value <= 0 else min(value, float(MAX_RESOLUTION))
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def _layer_params(entry, natural_w: int, natural_h: int) -> dict:
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if not isinstance(entry, dict):
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entry = {}
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transform = entry.get("transform")
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if not isinstance(transform, dict):
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transform = {}
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blend = entry.get("blend")
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return {
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"visible": bool(entry.get("visible", True)),
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# The layer state is untrusted input: it round-trips through the saved
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# workflow and can be posted directly to /prompt. An out-of-range opacity
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# would otherwise reach blend_composite as a raw coverage multiplier and
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# produce negative or greater-than-white RGB. _parse_background already
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# clamps the same field.
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"opacity": min(max(_number(entry, "opacity", 1.0), 0.0), 1.0),
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"blend": blend if isinstance(blend, str) else "normal",
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"x": min(max(_number(transform, "x", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
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"y": min(max(_number(transform, "y", 0.0), -MAX_RESOLUTION), MAX_RESOLUTION),
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"w": _clamped_size(_number(transform, "w", natural_w), natural_w),
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"h": _clamped_size(_number(transform, "h", natural_h), natural_h),
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"rotation": _number(transform, "rotation", 0.0),
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"flip_h": bool(entry.get("flipH", False)),
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"flip_v": bool(entry.get("flipV", False)),
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}
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def _prepare_layer_bitmap(
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tensor: torch.Tensor, params: dict, alpha: torch.Tensor | None
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) -> Image.Image:
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frame = tensor[0, :, :, :3].detach().cpu().numpy()
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rgb8 = np.clip(np.rint(frame * 255.0), 0, 255).astype(np.uint8)
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if alpha is None:
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img = Image.fromarray(rgb8, "RGB").convert("RGBA")
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else:
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alpha8 = np.clip(
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np.rint(alpha[0].detach().cpu().numpy() * 255.0), 0, 255
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).astype(np.uint8)
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img = Image.fromarray(np.dstack([rgb8, alpha8]), "RGBA")
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if params["flip_h"]:
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img = img.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
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if params["flip_v"]:
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img = img.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
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target = (max(1, round(params["w"])), max(1, round(params["h"])))
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if img.size != target:
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img = img.resize(target, Image.Resampling.LANCZOS)
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if params["rotation"] != 0:
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img = img.rotate(
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-math.degrees(params["rotation"]),
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expand=True,
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resample=Image.Resampling.BICUBIC,
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fillcolor=(0, 0, 0, 0),
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)
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return img
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def _place_in_bounds(img: Image.Image, bw: int, bh: int) -> np.ndarray:
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arr = np.asarray(img, dtype=np.float32) / 255.0
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rgba = np.concatenate([srgb_to_linear(arr[..., :3]), arr[..., 3:4]], axis=-1)
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aw, ah = img.size
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buf = np.zeros((bh, bw, 4), dtype=np.float32)
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ox = (bw - aw) // 2
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oy = (bh - ah) // 2
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dx0, dy0 = max(ox, 0), max(oy, 0)
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dx1, dy1 = min(ox + aw, bw), min(oy + ah, bh)
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if dx0 < dx1 and dy0 < dy1:
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buf[dy0:dy1, dx0:dx1] = rgba[dy0 - oy : dy1 - oy, dx0 - ox : dx1 - ox]
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return buf
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def _fill_background(canvas: np.ndarray, background: dict) -> np.ndarray:
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layer = np.empty(canvas.shape, dtype=np.float32)
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layer[..., :3] = srgb_to_linear(
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np.array(hex_to_rgb(background["color"]), dtype=np.float32) / 255.0
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)
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layer[..., 3] = 1.0
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return blend_composite(
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resolve_mode("normal"), canvas, layer, background["opacity"]
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)
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def composite_from_state(
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tensors: list[torch.Tensor],
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state: dict,
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alphas: list[torch.Tensor | None],
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) -> torch.Tensor:
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cw, ch = state["canvas"]
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if cw > MAX_RESOLUTION or ch > MAX_RESOLUTION:
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raise ValueError(
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f"Compositor canvas {cw}x{ch} exceeds the maximum supported size of "
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f"{MAX_RESOLUTION}x{MAX_RESOLUTION}"
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)
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canvas = np.zeros((ch, cw, 4), dtype=np.float32)
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background = state.get("background")
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if background is not None and background["visible"] and background["opacity"] > 0:
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canvas = _fill_background(canvas, background)
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layers = state["layers"]
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order = state.get("order") or range(len(tensors))
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for index in order:
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if index < 0 or index >= len(tensors):
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continue
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tensor = tensors[index]
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entry = layers[index] if index < len(layers) else None
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params = _layer_params(entry, tensor.shape[2], tensor.shape[1])
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if not params["visible"]:
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continue
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img = _prepare_layer_bitmap(
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tensor, params, alphas[index] if index < len(alphas) else None
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)
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bx, by, bw, bh = placed_bounds(
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params["x"], params["y"], params["w"], params["h"], params["rotation"]
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)
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buf = _place_in_bounds(img, bw, bh)
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x0, y0 = max(bx, 0), max(by, 0)
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x1, y1 = min(bx + bw, cw), min(by + bh, ch)
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if x0 >= x1 or y0 >= y1:
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continue
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region = buf[y0 - by : y1 - by, x0 - bx : x1 - bx]
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mode = resolve_mode(params["blend"])
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canvas[y0:y1, x0:x1] = blend_composite(
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mode, canvas[y0:y1, x0:x1], region, params["opacity"]
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)
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rgb = linear_to_srgb(np.clip(canvas[..., :3], 0.0, 1.0))
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alpha = np.clip(canvas[..., 3:4], 0.0, 1.0)
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rgba = np.concatenate([rgb, alpha], axis=-1)
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return torch.from_numpy(rgba.astype(np.float32)).unsqueeze(0)
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OPAQUE_EPSILON = 1e-3
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def composite_outputs(out: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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if out.shape[-1] != 4:
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return out, torch.zeros(out.shape[:3], dtype=torch.float32)
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alpha = out[..., 3]
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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",
|
|
search_aliases=["compositor", "composite", "layer", "layers", "layer editor", "psd"],
|
|
is_experimental=True,
|
|
# both flags on purpose: terminal compositor graphs must execute (the
|
|
# editor needs a run to open), and cache hits must replay the layer UI
|
|
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, applied against the layers below. On the bottom layer over the default transparent background, non-normal modes produce transparency.",
|
|
),
|
|
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()
|