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control_lora: fix adapter set scale
The adapter set scale did not propagate the scale to the underlying zero convolutions. The value set at CTOR time was used instead. Follow up of #285
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@ -117,7 +117,7 @@ class ZeroConvolution(Passthrough):
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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self.scale = scale
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self._scale = scale
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super().__init__(
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Conv2d(
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@ -131,6 +131,15 @@ class ZeroConvolution(Passthrough):
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ResidualAccumulator(n=residual_index),
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)
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@property
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def scale(self) -> float:
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return self._scale
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@scale.setter
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def scale(self, value: float) -> None:
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self._scale = value
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self.ensure_find(Multiply).scale = value
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class ControlLora(Passthrough):
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"""ControlLora is a Half-UNet clone of the target UNet,
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34
tests/adapters/test_control_lora.py
Normal file
34
tests/adapters/test_control_lora.py
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@ -0,0 +1,34 @@
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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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