Fix other Ruff complaints

main.py

@@ -402,7 +402,6 @@ def log_img(self, pl_module, batch, batch_idx, split="train"):
             # batch_idx > 5 and
             self.max_images > 0
         ):
-            logger = type(pl_module.logger)
             is_train = pl_module.training
             if is_train:
                 pl_module.eval()
@@ -691,7 +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(
@@ -839,7 +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
 
 

scripts/tests/attention.py

@@ -87,7 +87,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
     ) as prof:
         with record_function("Default detailed stats"):
             for _ in range(25):
-                o = F.scaled_dot_product_attention(query, key, value)
+                _o = F.scaled_dot_product_attention(query, key, value)
     print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
     print(
@@ -99,7 +99,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
         ) as prof:
             with record_function("Math implmentation stats"):
                 for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
+                    _o = F.scaled_dot_product_attention(query, key, value)
         print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
     with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
@@ -114,7 +114,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
         ) as prof:
             with record_function("FlashAttention stats"):
                 for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
+                    _o = F.scaled_dot_product_attention(query, key, value)
         print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
     with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
@@ -129,7 +129,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
         ) as prof:
             with record_function("EfficientAttention stats"):
                 for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
+                    _o = F.scaled_dot_product_attention(query, key, value)
         print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
 

sgm/inference/api.py

@@ -326,7 +326,6 @@ def get_discretization_config(params: SamplingParams):
 def get_sampler_config(params: SamplingParams):
     discretization_config = get_discretization_config(params)
     guider_config = get_guider_config(params)
-    sampler = None
     if params.sampler == Sampler.EULER_EDM:
         return EulerEDMSampler(
             num_steps=params.steps,

sgm/inference/helpers.py

@@ -119,7 +119,7 @@ def do_sample(
         batch2model_input = []
 
     with torch.no_grad():
-        with autocast(device) as precision_scope:
+        with autocast(device):
             with model.ema_scope():
                 num_samples = [num_samples]
                 batch, batch_uc = get_batch(
@@ -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(
@@ -255,7 +255,7 @@ def do_img2img(
     device="cuda",
 ):
     with torch.no_grad():
-        with autocast(device) as precision_scope:
+        with autocast(device):
             with model.ema_scope():
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),

sgm/models/diffusion.py

@@ -250,9 +250,9 @@ def sample(
     ):
         randn = torch.randn(batch_size, *shape).to(self.device)
 
-        denoiser = lambda input, sigma, c: self.denoiser(
-            self.model, input, sigma, c, **kwargs
-        )
+        def denoiser(input, sigma, c):
+            return self.denoiser(self.model, input, sigma, c, **kwargs)
+
         samples = self.sampler(denoiser, randn, cond, uc=uc)
         return samples
 

sgm/modules/attention.py

@@ -51,12 +51,10 @@
     import xformers.ops
 
     XFORMERS_IS_AVAILABLE = True
-except:
+except Exception:
     XFORMERS_IS_AVAILABLE = False
     logpy.warn("no module 'xformers'. Processing without...")
 
-# from .diffusionmodules.util import mixed_checkpoint as 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

@@ -9,19 +9,19 @@
 from einops import rearrange
 from packaging import version
 
+from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
+
 logpy = logging.getLogger(__name__)
 
 try:
     import xformers
     import xformers.ops
 
     XFORMERS_IS_AVAILABLE = True
-except:
+except Exception:
     XFORMERS_IS_AVAILABLE = False
     logpy.warning("no module 'xformers'. Processing without...")
 
-from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
-
 
 def get_timestep_embedding(timesteps, embedding_dim):
     """
@@ -633,8 +633,7 @@ def __init__(
         self.give_pre_end = give_pre_end
         self.tanh_out = tanh_out
 
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,) + tuple(ch_mult)
+        # compute block_in and curr_res at lowest res
         block_in = ch * ch_mult[self.num_resolutions - 1]
         curr_res = resolution // 2 ** (self.num_resolutions - 1)
         self.z_shape = (1, z_channels, curr_res, curr_res)

sgm/util.py

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