mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-14 00:58:13 +00:00
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from typing import Iterator
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from refiners.fluxion import manual_seed
|
|
from refiners.fluxion.utils import no_grad
|
|
from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
|
|
from refiners.foundationals.latent_diffusion.freeu import FreeUResidualConcatenator, SDFreeUAdapter
|
|
|
|
|
|
@pytest.fixture(scope="module", params=[True, False])
|
|
def unet(request: pytest.FixtureRequest) -> Iterator[SD1UNet | SDXLUNet]:
|
|
xl: bool = request.param
|
|
unet = SDXLUNet(in_channels=4) if xl else SD1UNet(in_channels=4)
|
|
yield unet
|
|
|
|
|
|
def test_freeu_adapter(unet: SD1UNet | SDXLUNet) -> None:
|
|
freeu = SDFreeUAdapter(unet, backbone_scales=[1.2, 1.2], skip_scales=[0.9, 0.9])
|
|
|
|
assert len(list(unet.walk(FreeUResidualConcatenator))) == 0
|
|
|
|
with pytest.raises(AssertionError) as exc:
|
|
freeu.eject()
|
|
assert "could not find" in str(exc.value)
|
|
|
|
freeu.inject()
|
|
assert len(list(unet.walk(FreeUResidualConcatenator))) == 2
|
|
|
|
freeu.eject()
|
|
assert len(list(unet.walk(FreeUResidualConcatenator))) == 0
|
|
|
|
|
|
def test_freeu_adapter_too_many_scales(unet: SD1UNet | SDXLUNet) -> None:
|
|
num_blocks = len(unet.UpBlocks)
|
|
|
|
with pytest.raises(AssertionError):
|
|
SDFreeUAdapter(unet, backbone_scales=[1.2] * (num_blocks + 1), skip_scales=[0.9] * (num_blocks + 1))
|
|
|
|
|
|
def test_freeu_adapter_inconsistent_scales(unet: SD1UNet | SDXLUNet) -> None:
|
|
with pytest.raises(AssertionError):
|
|
SDFreeUAdapter(unet, backbone_scales=[1.2, 1.2], skip_scales=[0.9, 0.9, 0.9])
|
|
|
|
|
|
def test_freeu_identity_scales() -> None:
|
|
manual_seed(0)
|
|
text_embedding = torch.randn(1, 77, 768)
|
|
timestep = torch.randint(0, 999, size=(1, 1))
|
|
x = torch.randn(1, 4, 32, 32)
|
|
|
|
unet = SD1UNet(in_channels=4)
|
|
unet.set_clip_text_embedding(clip_text_embedding=text_embedding) # not flushed between forward-s
|
|
|
|
with no_grad():
|
|
unet.set_timestep(timestep=timestep)
|
|
y_1 = unet(x.clone())
|
|
|
|
freeu = SDFreeUAdapter(unet, backbone_scales=[1.0, 1.0], skip_scales=[1.0, 1.0])
|
|
freeu.inject()
|
|
|
|
with no_grad():
|
|
unet.set_timestep(timestep=timestep)
|
|
y_2 = unet(x.clone())
|
|
|
|
# The FFT -> inverse FFT sequence (skip features) introduces small numerical differences
|
|
assert torch.allclose(y_1, y_2, atol=1e-5)
|