freeu: add one more test for identity scales

It should act as a NOP when [1.0, 1.0] is used for backbone and skip
scales.

tests/foundationals/latent_diffusion/test_freeu.py

@@ -1,9 +1,11 @@
 from typing import Iterator
 
 import pytest
+import torch
 
 from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
 from refiners.foundationals.latent_diffusion.freeu import SDFreeUAdapter, FreeUResidualConcatenator
+from refiners.fluxion import manual_seed
 
 
 @pytest.fixture(scope="module", params=[True, False])
@@ -39,3 +41,27 @@ def test_freeu_adapter_too_many_scales(unet: SD1UNet | SDXLUNet) -> None:
 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 torch.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 torch.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)