refiners/tests/foundationals/latent_diffusion/test_sd15_unet.py
Laurent 2c0174f50e
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rename test_unet.py to test_sd15_unet.py + use test_device fixture
2024-09-10 09:16:22 +02:00

32 lines
1.1 KiB
Python

import pytest
import torch
from refiners.fluxion import manual_seed
from refiners.fluxion.utils import no_grad
from refiners.foundationals.latent_diffusion import SD1UNet
@pytest.fixture(scope="module")
def refiners_sd15_unet(test_device: torch.device) -> SD1UNet:
unet = SD1UNet(in_channels=4, device=test_device)
return unet
def test_unet_context_flush(refiners_sd15_unet: SD1UNet):
manual_seed(0)
text_embedding = torch.randn(1, 77, 768, device=refiners_sd15_unet.device, dtype=refiners_sd15_unet.dtype)
timestep = torch.randint(0, 999, size=(1, 1), device=refiners_sd15_unet.device)
x = torch.randn(1, 4, 32, 32, device=refiners_sd15_unet.device, dtype=refiners_sd15_unet.dtype)
refiners_sd15_unet.set_clip_text_embedding(clip_text_embedding=text_embedding) # not flushed between forward-s
with no_grad():
refiners_sd15_unet.set_timestep(timestep=timestep)
y_1 = refiners_sd15_unet(x.clone())
with no_grad():
refiners_sd15_unet.set_timestep(timestep=timestep)
y_2 = refiners_sd15_unet(x.clone())
assert torch.equal(y_1, y_2)