Clean some codes and refract the tests

tests/requirements.txt

+# additional requirements for testing
 pytest
 datasets
 torchmetrics

tests/sana/test_t2i.py

+import pytest
 import torch
 from diffusers import SanaPAGPipeline, SanaPipeline
 
 from nunchaku import NunchakuSanaTransformer2DModel
+from nunchaku.utils import get_precision, is_turing
 
 
+@pytest.mark.skipif(is_turing() or get_precision() == "fp4", reason="Skip tests due to Turing GPUs")
 def test_sana():
     transformer = NunchakuSanaTransformer2DModel.from_pretrained("mit-han-lab/svdq-int4-sana-1600m")
     pipe = SanaPipeline.from_pretrained(
@@ -28,6 +31,7 @@ def test_sana():
     image.save("sana_1600m.png")
 
 
+@pytest.mark.skipif(is_turing() or get_precision() == "fp4", reason="Skip tests due to Turing GPUs")
 def test_sana_pag():
     transformer = NunchakuSanaTransformer2DModel.from_pretrained("mit-han-lab/svdq-int4-sana-1600m", pag_layers=8)
     pipe = SanaPAGPipeline.from_pretrained(

tests/utils.py

@@ -59,7 +59,7 @@ class MultiImageDataset(data.Dataset):
 
 
 def compute_lpips(
-    ref_dirpath: str, gen_dirpath: str, batch_size: int = 64, num_workers: int = 8, device: str | torch.device = "cuda"
+    ref_dirpath: str, gen_dirpath: str, batch_size: int = 4, num_workers: int = 8, device: str | torch.device = "cuda"
 ) -> float:
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
     metric = LearnedPerceptualImagePatchSimilarity(normalize=True).to(device)