Files
AnyImage/tests/anyimage/operators/test_ai_setup.py
nan 591e62085a feat(cutout): implement boundary padding for cutout textures
- Introduced a new `Boundary Padding` preference in the Cutout Tool with a default of 2 px and a configurable range of 1-4 px.
- Updated the Cutout client to establish inward mapping from the final Mask, sampling in a fixed direction and processing independent material RGBA and Depth.
- Enhanced the handling of RGBA and Depth during cutout operations, ensuring proper mapping and preservation of source data.
- Added tests to validate the new boundary padding functionality and its integration with existing cutout processes.

feat(geometry): remove overall mesh smoothing from geometry nodes

- Removed the `Smooth` input from several geometry node groups to eliminate unnecessary overall mesh smoothing.
- Retained `Smooth Weight` for depth nodes while removing it from Cutout and Relief Plane nodes.
- Ensured that boundary, normal, and fill smoothing behaviors remain intact and consistent.
- Updated tests and documentation to reflect the changes in node interfaces and behaviors.

feat(normals): scale tangent normals with depth

- Reused the existing `o_normal_reduction` attribute to adjust Tangent Normal strength based on the geometry's `Depth Scale`.
- Ensured that Tangent Normal strength is consistent with the geometry depth, preserving full strength for materials that do not provide the attribute.
- Updated tests to verify the correct behavior of normal strength adjustments in both Tangent and Object Space.
2026-09-15 00:40:24 +08:00

193 lines
6.6 KiB
Python

import importlib
import tempfile
from pathlib import Path
from types import SimpleNamespace
from unittest.mock import patch
from tests.support.blender import BlenderTestCase
class AiSetupTest(BlenderTestCase):
def test_anyimage_post_install_is_ordinary_code_using_runtime_request(self):
calls = []
fake_runtime = SimpleNamespace(
request=lambda job_type, parameters: calls.append((job_type, parameters))
)
self.runtime.post_install(fake_runtime)
self.assertEqual(
calls,
[
("download-model", {"model": "MOGE2_VITS_NORMAL"}),
("download-model", {"model": "BIREFNET_LITE"}),
(
"download-model",
{"model": "REALESRGAN_GENERAL_WDN_X4V3"},
),
],
)
def test_anyimage_post_install_names_each_failed_model(self):
calls = []
def request(_job_type, parameters):
calls.append(parameters["model"])
raise RuntimeError("network unavailable")
with self.assertRaisesRegex(
RuntimeError,
(
"MoGe-2 ViT-S Normal: network unavailable; "
"BiRefNet Lite: network unavailable; "
"Real-ESRGAN General WDN x4v3: network unavailable"
),
):
self.runtime.post_install(SimpleNamespace(request=request))
self.assertEqual(
calls,
[
"MOGE2_VITS_NORMAL",
"BIREFNET_LITE",
"REALESRGAN_GENERAL_WDN_X4V3",
],
)
def test_required_model_operator_requests_all_default_models(self):
operator = self.anyimage.operators.DownloadRequiredModels()
with patch.object(self.ai_setup, "require_environment"):
parameters = operator.request(None)
self.assertEqual(
parameters,
{
"models": [
"MOGE2_VITS_NORMAL",
"BIREFNET_LITE",
"REALESRGAN_GENERAL_WDN_X4V3",
]
},
)
def test_ai_status_checks_environment_and_required_model_files(self):
properties = self.anyimage.properties
with (
patch.object(self.anyimage.runtime, "environment_ready", return_value=True),
patch.object(
properties,
"model_catalog",
return_value=(
{"key": "MOGE2_VITS_NORMAL", "ready": True},
{"key": "BIREFNET_LITE", "ready": False},
{
"key": "REALESRGAN_GENERAL_WDN_X4V3",
"ready": True,
},
),
),
):
status = properties.ai_status()
self.assertTrue(status["environment_ready"])
self.assertEqual(status["missing_models"], ("BIREFNET_LITE",))
self.assertFalse(status["ready"])
self.assertEqual(properties.ai_setup_label(status), "Download Required Models…")
with (
patch.object(self.anyimage.runtime, "environment_ready", return_value=False),
patch.object(
properties,
"model_catalog",
return_value=tuple(
{"key": key, "ready": True}
for key in properties.shared_model_catalog.DEFAULT_MODEL_KEYS
),
),
):
status = properties.ai_status()
self.assertFalse(status["ready"])
self.assertEqual(
properties.ai_setup_label(status), "Set Up AI Server…"
)
def test_setup_ai_environment_selects_the_needed_install_step(self):
calls = []
ops = SimpleNamespace(
anyimage=SimpleNamespace(
install_environment=lambda *_args: (
calls.append("official") or {"FINISHED"}
),
install_environment_mirror=lambda *_args: (
calls.append("mirror") or {"FINISHED"}
),
download_required_models=lambda *_args: (
calls.append("models") or {"FINISHED"}
),
)
)
operator = self.ai_setup.SetupAIEnvironment()
with patch.object(self.fake_bpy, "ops", ops, create=True):
operator.use_mirror = True
with patch.object(
self.ai_setup,
"ai_status",
return_value={
"environment_ready": False,
"missing_models": ("MOGE2_VITS_NORMAL", "BIREFNET_LITE"),
"ready": False,
},
):
self.assertEqual(operator.execute(None), {"FINISHED"})
with patch.object(
self.ai_setup,
"ai_status",
return_value={
"environment_ready": True,
"missing_models": ("BIREFNET_LITE",),
"ready": False,
},
):
self.assertEqual(operator.execute(None), {"FINISHED"})
self.assertEqual(calls, ["mirror", "models"])
def test_ai_environment_ui_types_are_registered(self):
self.assertIn(self.ai_setup.SetupAIEnvironment, self.anyimage.CLASSES)
self.assertIn(
self.ai_setup.OpenAIEnvironmentSettings,
self.anyimage.CLASSES,
)
def test_ui_reads_required_model_files_without_starting_server(self):
properties = importlib.import_module("anyimage.properties")
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
moge = root / "models" / "moge-2-vits-normal-onnx"
lite = root / "models" / "birefnet-lite"
wdn = root / "models" / "realesr-general-wdn-x4v3"
moge.mkdir(parents=True)
lite.mkdir(parents=True)
wdn.mkdir(parents=True)
(moge / "model.onnx").touch()
(lite / "model.onnx").touch()
(lite / "LICENSE.txt").touch()
(wdn / "realesr-general-wdn-x4v3.onnx").touch()
with (
patch.object(
self.anyimage.runtime,
"storage_root",
return_value=root,
),
patch.object(
self.anyimage.runtime,
"resource",
side_effect=AssertionError("UI must not start the Server"),
),
):
self.assertEqual(properties.ai_status()["missing_models"], ())