mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-09 23:12:02 +00:00
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
This commit is contained in:
parent
83960bdbb8
commit
176807740b
|
@ -117,7 +117,7 @@ class ZeroConvolution(Passthrough):
|
||||||
device: The PyTorch device to use.
|
device: The PyTorch device to use.
|
||||||
dtype: The PyTorch data type to use.
|
dtype: The PyTorch data type to use.
|
||||||
"""
|
"""
|
||||||
self.scale = scale
|
self._scale = scale
|
||||||
|
|
||||||
super().__init__(
|
super().__init__(
|
||||||
Conv2d(
|
Conv2d(
|
||||||
|
@ -131,6 +131,15 @@ class ZeroConvolution(Passthrough):
|
||||||
ResidualAccumulator(n=residual_index),
|
ResidualAccumulator(n=residual_index),
|
||||||
)
|
)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def scale(self) -> float:
|
||||||
|
return self._scale
|
||||||
|
|
||||||
|
@scale.setter
|
||||||
|
def scale(self, value: float) -> None:
|
||||||
|
self._scale = value
|
||||||
|
self.ensure_find(Multiply).scale = value
|
||||||
|
|
||||||
|
|
||||||
class ControlLora(Passthrough):
|
class ControlLora(Passthrough):
|
||||||
"""ControlLora is a Half-UNet clone of the target UNet,
|
"""ControlLora is a Half-UNet clone of the target UNet,
|
||||||
|
|
34
tests/adapters/test_control_lora.py
Normal file
34
tests/adapters/test_control_lora.py
Normal file
|
@ -0,0 +1,34 @@
|
||||||
|
import torch
|
||||||
|
|
||||||
|
import refiners.fluxion.layers as fl
|
||||||
|
from refiners.foundationals.latent_diffusion import ControlLoraAdapter, SDXLUNet
|
||||||
|
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import ZeroConvolution
|
||||||
|
|
||||||
|
|
||||||
|
def test_inject_eject(test_device: torch.device):
|
||||||
|
unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
|
||||||
|
initial_repr = repr(unet)
|
||||||
|
adapter = ControlLoraAdapter(name="foo", target=unet)
|
||||||
|
assert repr(unet) == initial_repr
|
||||||
|
adapter.inject()
|
||||||
|
assert repr(unet) != initial_repr
|
||||||
|
adapter.eject()
|
||||||
|
assert repr(unet) == initial_repr
|
||||||
|
|
||||||
|
|
||||||
|
def test_scale(test_device: torch.device):
|
||||||
|
unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
|
||||||
|
adapter = ControlLoraAdapter(name="foo", target=unet, scale=0.75).inject()
|
||||||
|
|
||||||
|
def predicate(m: fl.Module, p: fl.Chain) -> bool:
|
||||||
|
return isinstance(p, ZeroConvolution) and isinstance(m, fl.Multiply)
|
||||||
|
|
||||||
|
for m, _ in unet.walk(predicate):
|
||||||
|
assert isinstance(m, fl.Multiply)
|
||||||
|
assert m.scale == 0.75
|
||||||
|
|
||||||
|
adapter.scale = 0.42
|
||||||
|
assert adapter.scale == 0.42
|
||||||
|
for m, _ in unet.walk(predicate):
|
||||||
|
assert isinstance(m, fl.Multiply)
|
||||||
|
assert m.scale == 0.42
|
Loading…
Reference in a new issue