483 lines
16 KiB
Python
483 lines
16 KiB
Python
import importlib
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import json
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import os
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import sys
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import tempfile
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import time
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import types
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import unittest
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from unittest import mock
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class FakeImageArray:
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def __init__(self, width=8, height=6):
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self.shape = (height, width, 3)
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def cpu(self):
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return self
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def numpy(self):
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return self
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def astype(self, _dtype):
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return self
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def __rmul__(self, _value):
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return self
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class FakeTensorBatch:
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def __init__(self, width=8, height=6, count=1):
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self.image = FakeImageArray(width=width, height=height)
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self.shape = (count, height, width, 3)
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self.count = count
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def __getitem__(self, index):
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if isinstance(index, slice):
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return FakeTensorBatch(
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width=self.shape[2],
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height=self.shape[1],
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count=len(range(*index.indices(self.count))),
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)
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if index != 0:
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raise IndexError(index)
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return self.image
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class FakePILImage:
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saved_paths = []
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def save(self, path, *args, **kwargs):
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self.saved_paths.append((path, args, kwargs))
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def convert(self, _mode):
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return self
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class DumasImageNodeTests(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.temp_dir = tempfile.mkdtemp(prefix="dumas-image-node-")
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fake_numpy = types.SimpleNamespace(
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clip=lambda array, _low, _high: array,
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uint8="uint8",
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)
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fake_pil_image_module = types.SimpleNamespace(fromarray=lambda _array: FakePILImage())
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fake_pil_module = types.SimpleNamespace(Image=fake_pil_image_module)
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fake_folder_paths = types.SimpleNamespace(
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get_temp_directory=lambda: cls.temp_dir,
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get_output_directory=lambda: cls.temp_dir,
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get_save_image_path=lambda prefix, _out, _width, _height: (
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cls.temp_dir,
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prefix,
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1,
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"",
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prefix,
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),
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)
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cls._saved_modules = {
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name: sys.modules.get(name)
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for name in ("numpy", "PIL", "PIL.Image", "folder_paths")
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}
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sys.modules["numpy"] = fake_numpy
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sys.modules["PIL"] = fake_pil_module
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sys.modules["PIL.Image"] = fake_pil_image_module
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sys.modules["folder_paths"] = fake_folder_paths
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cls.image_nodes = importlib.import_module("dumas_image_nodes")
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@classmethod
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def tearDownClass(cls):
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for name, module in cls._saved_modules.items():
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if module is None:
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sys.modules.pop(name, None)
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else:
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sys.modules[name] = module
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def setUp(self):
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FakePILImage.saved_paths = []
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def test_compare_images_returns_second_input_as_new_image(self):
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node = self.image_nodes.DumasImageCompareNode()
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image1 = FakeTensorBatch()
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image2 = FakeTensorBatch()
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result = node.compare_images(image1=image1, image2=image2)
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self.assertIs(result["result"][0], image2)
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self.assertEqual([item["slot"] for item in result["ui"]["images"]], [1, 2])
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self.assertEqual(len(FakePILImage.saved_paths), 2)
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def test_compare_images_falls_back_to_first_image(self):
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node = self.image_nodes.DumasImageCompareNode()
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image1 = FakeTensorBatch()
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result = node.compare_images(image1=image1)
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self.assertIs(result["result"][0], image1)
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self.assertEqual([item["slot"] for item in result["ui"]["images"]], [1])
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def test_compare_images_handles_missing_inputs(self):
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node = self.image_nodes.DumasImageCompareNode()
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result = node.compare_images()
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self.assertIsNone(result["result"][0])
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self.assertEqual(result["ui"]["images"], [])
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def test_saved_filenames_use_dumas_prefix(self):
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node = self.image_nodes.DumasImageCompareNode()
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image1 = FakeTensorBatch()
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result = node.compare_images(image1=image1)
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self.assertTrue(result["ui"]["images"][0]["filename"].startswith("dumas_compare"))
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self.assertTrue(os.path.basename(FakePILImage.saved_paths[0][0]).startswith("dumas_compare"))
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def test_save_image_uses_second_input_token(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch(width=10, height=12)
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="shot_%input%_%input2%_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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name="alpha.png",
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name_2="beta/final",
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)
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saved_name = os.path.basename(FakePILImage.saved_paths[0][0])
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self.assertEqual(saved_name, "shot_alpha_beta_final_001.png")
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def test_save_image_resolves_date_size_and_batch_tokens(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch(width=10, height=12, count=2)
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fake_now = time.struct_time((2026, 8, 5, 13, 7, 9, 2, 217, -1))
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with mock.patch.object(self.image_nodes.time, "localtime", return_value=fake_now):
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="asset_%date:yyyy-MM-dd%_%date:hh-mm-ss%_%width%x%height%_%batch_num%_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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saved_names = [os.path.basename(path) for path, _args, _kwargs in FakePILImage.saved_paths]
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self.assertEqual(
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saved_names,
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[
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"asset_2026-08-05_13-07-09_10x12_0_001.png",
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"asset_2026-08-05_13-07-09_10x12_1_001.png",
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],
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)
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def test_save_image_counter_increments_for_existing_files(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="counter_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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open(FakePILImage.saved_paths[0][0], "a", encoding="utf-8").close()
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="counter_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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saved_names = [os.path.basename(path) for path, _args, _kwargs in FakePILImage.saved_paths]
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self.assertEqual(saved_names, ["counter_001.png", "counter_002.png"])
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def test_save_image_uses_same_counter_for_folder_and_filename(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="Char%counter%/Char%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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saved_path = FakePILImage.saved_paths[0][0].replace("\\", "/")
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self.assertTrue(saved_path.endswith("/Char001/Char001.png"))
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def test_save_image_creates_nested_directories_before_saving(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="Char_%counter%/Base",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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saved_path = FakePILImage.saved_paths[0][0]
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self.assertTrue(os.path.isdir(os.path.dirname(saved_path)))
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def test_character_helper_passes_through_images_and_formats_text(self):
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node = self.image_nodes.DumasCharacterHelperNode()
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image1 = FakeTensorBatch()
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image2 = FakeTensorBatch()
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result = node.build_character_text(
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image1=image1,
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image2=image2,
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image1_picture_id="2",
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image2_picture_id="3",
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character_id="char_dave",
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name="Dave",
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alias="The Locksmith",
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gender="male",
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age="41",
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nationality="English",
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occupation="a detective",
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height_feet="6",
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height_inches="2",
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accent="English",
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general="wears a long grey coat",
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)
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self.assertIs(result[0], image1)
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self.assertIs(result[1], image2)
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self.assertEqual(
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result[2],
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(
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"<Picture 2> and <Picture 3> reference the same character who is called Dave.\n"
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"<Picture 2> is the primary full-body reference for Dave.\n"
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"<Picture 3> is a frontal facial reference for Dave.\n"
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"Dave is also known as The Locksmith, is male, is 41 years old, is English, works as a detective, is 6 feet 2 inches tall, speaks in an English accent.\n"
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"wears a long grey coat."
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),
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)
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def test_character_helper_handles_missing_optional_fields(self):
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node = self.image_nodes.DumasCharacterHelperNode()
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image1 = FakeTensorBatch()
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image2 = FakeTensorBatch()
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result = node.build_character_text(
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image1=image1,
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image2=image2,
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image1_picture_id="4",
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image2_picture_id="6",
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character_id="",
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name="",
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alias="",
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gender="",
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age="unknown",
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nationality="",
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occupation="",
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height_feet="",
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height_inches="",
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accent="",
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general="",
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)
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self.assertEqual(
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result[2],
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(
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"<Picture 4> and <Picture 6> reference the same character.\n"
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"<Picture 4> is the primary full-body reference for the character.\n"
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"<Picture 6> is a frontal facial reference for the character."
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),
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)
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def test_save_image_returns_ui_entries_for_output_folder(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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result = node.save_images(
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images=image,
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folder="",
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pattern="result_%counter%",
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format="jpg",
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quality=90,
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embed_workflow=False,
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save_on_run=True,
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)
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self.assertEqual(result["ui"]["images"][0]["type"], "output")
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self.assertTrue(result["ui"]["images"][0]["filename"].endswith(".jpg"))
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def test_save_image_returns_ui_entries_for_external_folder(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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external_dir = tempfile.mkdtemp(prefix="dumas-image-node-external-")
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result = node.save_images(
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images=image,
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folder=external_dir,
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pattern="external_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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self.assertEqual(result["ui"]["images"][0]["type"], "external")
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self.assertEqual(result["ui"]["images"][0]["subfolder"], external_dir.replace("\\", "/"))
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self.assertTrue(result["ui"]["images"][0]["token"])
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def test_save_image_handles_windows_different_drive_paths(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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with mock.patch.object(
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self.image_nodes.folder_paths,
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"get_output_directory",
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return_value="C:\\ComfyUI\\output",
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), mock.patch.object(
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self.image_nodes.os.path,
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"abspath",
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return_value="D:\\renders",
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):
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result = node.save_images(
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images=image,
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folder="D:\\renders",
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pattern="drive_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=True,
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)
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self.assertEqual(result["ui"]["images"][0]["type"], "external")
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self.assertEqual(result["ui"]["images"][0]["subfolder"], "D:/renders")
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self.assertTrue(result["ui"]["images"][0]["token"])
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def test_save_image_skips_when_save_is_disabled(self):
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node = self.image_nodes.DumasSaveImageNode()
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image = FakeTensorBatch()
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result = node.save_images(
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images=image,
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folder=self.temp_dir,
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pattern="ignored_%counter%",
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format="png",
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quality=100,
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embed_workflow=False,
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save_on_run=False,
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)
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self.assertEqual(result["ui"]["images"], [])
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self.assertEqual(FakePILImage.saved_paths, [])
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def test_h3_plan_scene_images_attach_and_extract(self):
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attach_node = self.image_nodes.DumasH3PlanAttachSceneImagesNode()
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extract_node = self.image_nodes.DumasH3PlanExtractSceneImagesNode()
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plan = {"shots": [{"id": "intro"}, {"id": "middle"}]}
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image_a = FakeTensorBatch()
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image_b = FakeTensorBatch(width=10, height=10)
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attached_plan, connected = attach_node.attach(
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plan=plan,
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scene_index=2,
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image1=image_a,
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image3=image_b,
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)
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extracted = extract_node.extract(attached_plan, 2)
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self.assertEqual(connected, 2)
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self.assertEqual(attached_plan["_dumas_scene_image_bindings"]["scene_counts"], {"2": 2})
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self.assertIs(extracted[1], image_a)
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self.assertIsNone(extracted[2])
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self.assertIs(extracted[3], image_b)
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self.assertEqual(extracted[-1], 2)
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self.assertNotIn("_dumas_scene_image_bindings", plan)
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def test_h3_plan_scene_images_support_multiple_scenes(self):
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attach_node = self.image_nodes.DumasH3PlanAttachSceneImagesNode()
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extract_node = self.image_nodes.DumasH3PlanExtractSceneImagesNode()
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plan = {"shots": [{"id": "one"}, {"id": "two"}]}
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image_1 = FakeTensorBatch()
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image_2 = FakeTensorBatch(width=12, height=9)
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image_3 = FakeTensorBatch(width=8, height=8)
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plan_after_first, _connected = attach_node.attach(plan=plan, scene_index=1, image2=image_1)
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plan_after_second, _connected = attach_node.attach(
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plan=plan_after_first,
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scene_index=2,
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image6=image_2,
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image7=image_3,
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)
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scene1 = extract_node.extract(plan_after_second, 1)
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scene2 = extract_node.extract(plan_after_second, 2)
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self.assertIs(scene1[2], image_1)
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self.assertEqual(scene1[-1], 1)
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self.assertIs(scene2[6], image_2)
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self.assertIs(scene2[7], image_3)
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self.assertEqual(scene2[-1], 2)
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self.assertEqual(
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plan_after_second["_dumas_scene_image_bindings"]["scene_counts"],
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{"1": 1, "2": 2},
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)
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def test_h3_plan_scene_images_metadata_is_json_serializable(self):
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attach_node = self.image_nodes.DumasH3PlanAttachSceneImagesNode()
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plan = {"shots": [{"id": "one"}]}
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attached_plan, connected = attach_node.attach(
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plan=plan,
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scene_index=1,
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image4=FakeTensorBatch(),
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)
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self.assertEqual(connected, 1)
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json.dumps(attached_plan)
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def test_h3_plan_scene_images_can_clear_a_scene_binding(self):
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attach_node = self.image_nodes.DumasH3PlanAttachSceneImagesNode()
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extract_node = self.image_nodes.DumasH3PlanExtractSceneImagesNode()
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plan = {"shots": [{"id": "one"}]}
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attached_plan, connected = attach_node.attach(
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plan=plan,
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scene_index=1,
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image1=FakeTensorBatch(),
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)
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cleared_plan, cleared = attach_node.attach(plan=attached_plan, scene_index=1)
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extracted = extract_node.extract(cleared_plan, 1)
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self.assertEqual(connected, 1)
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self.assertEqual(cleared, 0)
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self.assertNotIn("_dumas_scene_image_bindings", cleared_plan)
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self.assertEqual(extracted[-1], 0)
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def test_h3_plan_scene_images_reject_invalid_scene_index(self):
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attach_node = self.image_nodes.DumasH3PlanAttachSceneImagesNode()
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plan = {"shots": [{"id": "one"}]}
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with self.assertRaises(ValueError):
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attach_node.attach(plan=plan, scene_index=2, image1=FakeTensorBatch())
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if __name__ == "__main__":
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unittest.main()
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