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113 lines (95 loc) · 4.52 KB
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"""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 (
ImageCompositor,
_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)
class TestCanvasEmission:
"""execute must report the document canvas so the editor sizes itself to it
rather than to the max natural size of cropped/placed layers."""
def test_explicit_document_canvas_is_emitted(self):
# A small layer placed on a large explicit canvas: the editor must learn
# the 1280x1280 canvas, not the 200x150 layer size.
doc = {
"version": 1,
"canvas": (1280, 1280),
"layers": [
{"image": _solid([1.0, 0.0, 0.0, 1.0], w=200, h=150),
"type": "raster", "x": 400, "y": 300, "z_index": 0}
],
}
ui = ImageCompositor.execute(layers=doc).ui
assert ui["compositor_canvas"] == [{"w": 1280, "h": 1280}]
def test_replay_emits_saved_canvas(self):
tensor = _solid([0.0, 1.0, 0.0, 1.0], w=4, h=4)
doc = {"version": 1, "layers": [{"image": tensor, "type": "raster"}]}
fp = ImageCompositor.execute(layers=doc).ui["compositor_inputs"]
saved = {
"version": 1,
"canvas": {"w": 640, "h": 480},
"inputs": fp,
"layers": [{
"name": "a", "visible": True, "opacity": 1.0, "blend": "normal",
"flipH": False, "flipV": False,
"transform": {"x": 0, "y": 0, "w": 4, "h": 4, "rotation": 0.0},
}],
}
ui = ImageCompositor.execute(layers=doc, compositor=saved).ui
assert ui["compositor_canvas"] == [{"w": 640, "h": 480}]
def test_no_layers_emits_no_canvas(self):
ui = ImageCompositor.execute(layers={"version": 1, "layers": []}).ui
assert "compositor_canvas" not in ui