Fix other Ruff complaints

main.py

@@ -690,7 +690,7 @@ def init_wandb(save_dir, opt, config, group_name, name_str):
             # TODO change once leaving "swiffer" config directory
             try:
                 group_name = nowname.split(now)[-1].split("-")[1]
-            except:
+            except Exception:
                 group_name = nowname
             default_logger_cfg["params"]["group"] = group_name
             init_wandb(
@@ -838,7 +838,7 @@ def init_wandb(save_dir, opt, config, group_name, name_str):
                 print(
                     f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}"
                 )
-        except:
+        except Exception:
             print("datasets not yet initialized.")
 
         # configure learning rate

scripts/demo/detect.py

@@ -45,7 +45,7 @@ def decode(self, cv2Image, method="dwtDct", **configs):
                 bits = embed.decode(cv2Image)
                 return self.reconstruct(bits)
 
-    except:
+    except Exception:
         raise e
 
 

sgm/inference/helpers.py

@@ -131,7 +131,7 @@ def do_sample(
                     if isinstance(batch[key], torch.Tensor):
                         print(key, batch[key].shape)
                     elif isinstance(batch[key], list):
-                        print(key, [len(l) for l in batch[key]])
+                        print(key, [len(lst) for lst in batch[key]])
                     else:
                         print(key, batch[key])
                 c, uc = model.conditioner.get_unconditional_conditioning(

sgm/modules/attention.py

@@ -8,6 +8,8 @@
 from packaging import version
 from torch import nn
 
+from .diffusionmodules.util import checkpoint
+
 if version.parse(torch.__version__) >= version.parse("2.0.0"):
     SDP_IS_AVAILABLE = True
     from torch.backends.cuda import SDPBackend, sdp_kernel
@@ -46,12 +48,10 @@
     import xformers.ops
 
     XFORMERS_IS_AVAILABLE = True
-except:
+except Exception:
     XFORMERS_IS_AVAILABLE = False
     print("no module 'xformers'. Processing without...")
 
-from .diffusionmodules.util import checkpoint
-
 
 def exists(val):
     return val is not None

sgm/modules/autoencoding/lpips/loss/lpips.py

@@ -59,7 +59,7 @@ def forward(self, input, target):
             for kk in range(len(self.chns))
         ]
         val = res[0]
-        for l in range(1, len(self.chns)):
+        for l in range(1, len(self.chns)):  # noqa: E741
             val += res[l]
         return val
 

sgm/modules/diffusionmodules/model.py

@@ -8,17 +8,17 @@
 from einops import rearrange
 from packaging import version
 
+from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
+
 try:
     import xformers
     import xformers.ops
 
     XFORMERS_IS_AVAILABLE = True
-except:
+except Exception:
     XFORMERS_IS_AVAILABLE = False
     print("no module 'xformers'. Processing without...")
 
-from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
-
 
 def get_timestep_embedding(timesteps, embedding_dim):
     """

sgm/modules/diffusionmodules/openaimodel.py

@@ -646,7 +646,8 @@ def __init__(
 
         self.use_fairscale_checkpoint = False
         checkpoint_wrapper_fn = (
-            partial(checkpoint_wrapper, offload_to_cpu=offload_to_cpu)
+            # TODO: this can't work since `checkpoint_wrapper` is not defined
+            partial(checkpoint_wrapper, offload_to_cpu=offload_to_cpu)  # noqa: F821
             if self.use_fairscale_checkpoint
             else lambda x: x
         )

sgm/util.py

@@ -28,7 +28,7 @@ def get_string_from_tuple(s):
                 return t[0]
             else:
                 pass
-    except:
+    except Exception:
         pass
     return s