158 lines
7.0 KiB
Python
158 lines
7.0 KiB
Python
import unittest
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from unittest import mock
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try:
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import torch
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except Exception:
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torch = None
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try:
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from .test_flux2klein_helpers import (
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BrokenClipStub,
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FakeMask,
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FakeVAE,
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FakeTensor,
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StandardClipStub,
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import_plugin_submodule,
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load_plugin_package,
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)
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except ImportError:
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from test_flux2klein_helpers import (
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BrokenClipStub,
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FakeMask,
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FakeVAE,
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FakeTensor,
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StandardClipStub,
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import_plugin_submodule,
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load_plugin_package,
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)
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class TestFlux2KleinNode(unittest.TestCase):
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def setUp(self):
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plugin = load_plugin_package()
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node_cls = plugin.NODE_CLASS_MAPPINGS["LLSFlux2KleinEditTextEncode"]
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self.node = node_cls()
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self.module = import_plugin_submodule(plugin, "flux2klein.lls_flux2klein_edit_text_encode")
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def test_fallback_path_builds_conditioning_latent_and_custom_output(self):
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conditioning, latent, custom_output, main_image, output_mask = self.node.encode(
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clip=StandardClipStub(),
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vae=FakeVAE(latent_channels=4, downscale_ratio=8),
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image1=FakeTensor((1, 600, 400, 3), label="main"),
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prompt="make it cinematic",
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ref_longest_edge=1024,
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resize_mode="keep_original",
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mask_mode="use_mask",
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image2=FakeTensor((1, 256, 512, 3), label="ref2"),
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image3=None,
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mask=FakeMask((1, 300, 200), mask_bbox=(10, 10, 40, 40), mask_area_ratio=0.1, label="mask"),
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)
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self.assertEqual(
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self.module._build_flux2klein_prompt("make it cinematic", 2),
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"image1: <|vision_start|><|image_pad|><|vision_end|> "
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"image2: <|vision_start|><|image_pad|><|vision_end|> make it cinematic",
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)
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self.assertEqual(
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conditioning[0][0],
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"cond::image1: <|vision_start|><|image_pad|><|vision_end|> "
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"image2: <|vision_start|><|image_pad|><|vision_end|> make it cinematic",
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)
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self.assertEqual(len(conditioning[0][1]["reference_latents"]), 2)
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self.assertEqual(conditioning[0][1]["reference_latents"][0].shape, (1, 4, 75, 50))
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self.assertEqual(conditioning[0][1]["reference_latents"][1].shape, (1, 4, 75, 50))
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self.assertEqual(latent["samples"].shape, (1, 4, 75, 50))
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self.assertEqual(latent["noise_mask"].shape, (1, 75, 50))
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self.assertEqual(main_image.shape, (1, 600, 400, 3))
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self.assertEqual(output_mask.shape, (1, 600, 400))
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self.assertEqual(custom_output["node_name"], "LLS Flux2Klein Edit Text Encode")
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self.assertEqual(custom_output["prompt"], "make it cinematic")
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self.assertEqual(custom_output["main_image"], "image1")
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self.assertEqual(custom_output["reference_images"], ["image2"])
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self.assertEqual(custom_output["num_reference_images"], 1)
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self.assertEqual(custom_output["conditioning_backend"], "flux2klein_multivision_clip")
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self.assertTrue(custom_output["has_mask"])
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self.assertEqual(custom_output["latent_mode"], "image1_reference_latent")
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self.assertEqual(custom_output["latent_has_noise_mask"], True)
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self.assertEqual(custom_output["vision_image_count"], 2)
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self.assertEqual(custom_output["reference_latent_count"], 2)
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self.assertEqual(self.module._count_present_reference_images(main_image, None, None), 1)
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def test_longest_edge_resize_scales_images_and_inverts_mask(self):
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conditioning, latent, custom_output, main_image, output_mask = self.node.encode(
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clip=StandardClipStub(),
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vae=FakeVAE(latent_channels=16, downscale_ratio=16),
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image1=FakeTensor((1, 800, 400, 3), label="main"),
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prompt="change outfit",
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ref_longest_edge=1024,
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resize_mode="longest_edge",
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mask_mode="invert_mask",
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image2=FakeTensor((1, 400, 1600, 3), label="ref2"),
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image3=FakeTensor((1, 600, 300, 3), label="ref3"),
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mask=FakeMask((1, 800, 400), mask_bbox=(20, 20, 180, 380), mask_area_ratio=0.25, label="mask"),
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)
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self.assertEqual(
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conditioning[0][0],
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"cond::image1: <|vision_start|><|image_pad|><|vision_end|> "
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"image2: <|vision_start|><|image_pad|><|vision_end|> "
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"image3: <|vision_start|><|image_pad|><|vision_end|> change outfit",
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)
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self.assertEqual(len(conditioning[0][1]["reference_latents"]), 3)
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self.assertEqual(main_image.shape, (1, 1024, 512, 3))
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self.assertEqual(latent["samples"].shape, (1, 16, 64, 32))
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self.assertEqual(latent["noise_mask"].shape, (1, 64, 32))
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self.assertEqual(output_mask.shape, (1, 1024, 512))
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self.assertAlmostEqual(output_mask.mask_area_ratio, 0.75)
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self.assertEqual(custom_output["reference_images"], ["image2", "image3"])
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self.assertEqual(custom_output["num_reference_images"], 2)
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self.assertEqual(custom_output["resize_mode"], "longest_edge")
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self.assertEqual(custom_output["mask_mode"], "invert_mask")
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self.assertEqual(custom_output["vision_image_count"], 3)
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self.assertEqual(custom_output["reference_latent_count"], 3)
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self.assertEqual(custom_output["latent_has_noise_mask"], True)
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def test_mask_mode_none_outputs_full_mask_but_no_noise_mask(self):
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conditioning, latent, custom_output, main_image, output_mask = self.node.encode(
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clip=StandardClipStub(),
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vae=FakeVAE(latent_channels=16, downscale_ratio=16),
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image1=FakeTensor((1, 512, 512, 3), label="main"),
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prompt="replace background",
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ref_longest_edge=1024,
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resize_mode="keep_original",
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mask_mode="none",
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image2=FakeTensor((1, 512, 512, 3), label="ref2"),
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image3=None,
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mask=FakeMask((1, 512, 512), mask_bbox=(0, 0, 256, 256), mask_area_ratio=0.25, label="mask"),
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)
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self.assertEqual(main_image.shape, (1, 512, 512, 3))
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self.assertEqual(output_mask.shape, (1, 512, 512))
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if hasattr(output_mask, "mask_area_ratio"):
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self.assertEqual(output_mask.mask_area_ratio, 1.0)
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else:
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self.assertTrue(torch is not None)
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self.assertTrue(torch.all(output_mask == 1))
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self.assertNotIn("noise_mask", latent)
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self.assertEqual(custom_output["latent_has_noise_mask"], False)
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def test_missing_clip_backend_raises_clear_error(self):
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with self.assertRaisesRegex(RuntimeError, "vision-aware|text encoding backend"):
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self.node.encode(
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clip=BrokenClipStub(),
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vae=FakeVAE(),
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image1=FakeTensor((1, 512, 512, 3), label="main"),
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prompt="replace background",
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ref_longest_edge=1024,
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resize_mode="keep_original",
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mask_mode="none",
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image2=None,
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image3=None,
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mask=None,
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)
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if __name__ == "__main__":
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unittest.main()
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