ruff check

Author: prishajain1

src/maxdiffusion/loaders/lora_conversion_utils.py

@@ -391,7 +391,7 @@ def _convert_to_ai_toolkit_cat(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
     ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
     if not is_sparse:
       # down_weight is copied to each split
-      ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
+      ait_sd.update({k: down_weight for k in ait_down_keys})
 
       # up_weight is split to each split
       ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416
@@ -534,7 +534,7 @@ def handle_qkv(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
     ait_up_keys = [k + ".lora_B.weight" for k in ait_keys]
 
     # down_weight is copied to each split
-    ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
+    ait_sd.update({k: down_weight for k in ait_down_keys})
 
     # up_weight is split to each split
     ait_sd.update({k: v for k, v in zip(ait_up_keys, torch.split(up_weight, dims, dim=0))})  # noqa: C416

src/maxdiffusion/pipelines/pipeline_flax_utils.py

@@ -473,7 +473,7 @@ def load_module(name, value):
         class_obj = import_flax_or_no_model(pipeline_module, class_name)
 
         importable_classes = ALL_IMPORTABLE_CLASSES
-        class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
+        class_candidates = {c: class_obj for c in importable_classes.keys()}
       else:
         # else we just import it from the library.