mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-09-19 19:05:28 +00:00
176807740b
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
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
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
|