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35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
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import torch
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import refiners.fluxion.layers as fl
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from refiners.foundationals.latent_diffusion import ControlLoraAdapter, SDXLUNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import ZeroConvolution
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def test_inject_eject(test_device: torch.device):
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unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
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initial_repr = repr(unet)
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adapter = ControlLoraAdapter(name="foo", target=unet)
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assert repr(unet) == initial_repr
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adapter.inject()
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assert repr(unet) != initial_repr
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adapter.eject()
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assert repr(unet) == initial_repr
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def test_scale(test_device: torch.device):
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unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
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adapter = ControlLoraAdapter(name="foo", target=unet, scale=0.75).inject()
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def predicate(m: fl.Module, p: fl.Chain) -> bool:
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return isinstance(p, ZeroConvolution) and isinstance(m, fl.Multiply)
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for m, _ in unet.walk(predicate):
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assert isinstance(m, fl.Multiply)
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assert m.scale == 0.75
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adapter.scale = 0.42
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assert adapter.scale == 0.42
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for m, _ in unet.walk(predicate):
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assert isinstance(m, fl.Multiply)
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assert m.scale == 0.42
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