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https://git.datalinker.icu/comfyanonymous/ComfyUI
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Add a new unit test for mixed lazy results
This test validates that when an output list is fed to a lazy node, the node will properly evaluate previous nodes that are needed by any inputs to the lazy node. No code in the execution model has been changed. The test already passes.
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@ -413,6 +413,23 @@ class TestExecution:
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for i in range(3):
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for i in range(3):
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assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
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assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
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def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0)
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mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1)
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3)
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output = g.node("SaveImage", images=rebatch.out(0))
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result = client.run(g)
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images = result.get_images(output)
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assert len(images) == 3, "Should have 3 image"
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assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0"
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assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5"
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assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0"
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def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
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def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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