rename test_unet.py to test_sd15_unet.py + use test_device fixture

tests/foundationals/latent_diffusion/test_sd15_unet.py

@@ -0,0 +1,31 @@
+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)