Kolors additional pipelines, community contrib

src/diffusers/loaders/lora_conversion_utils.py

@@ -433,7 +433,7 @@ 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({k: down_weight for k in ait_down_keys})
+            ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
 
             # 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
@@ -923,7 +923,7 @@ 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({k: down_weight for k in ait_down_keys})
+        ait_sd.update(dict.fromkeys(ait_down_keys, down_weight))
 
         # 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/diffusers/pipelines/pipeline_flax_utils.py

@@ -469,7 +469,7 @@ def load_module(name, value):
                 class_obj = import_flax_or_no_model(pipeline_module, class_name)
 
                 importable_classes = ALL_IMPORTABLE_CLASSES
-                class_candidates = {c: class_obj for c in importable_classes.keys()}
+                class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
             else:
                 # else we just import it from the library.
                 library = importlib.import_module(library_name)

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -341,13 +341,13 @@ def get_class_obj_and_candidates(
         pipeline_module = getattr(pipelines, library_name)
 
         class_obj = getattr(pipeline_module, class_name)
-        class_candidates = {c: class_obj for c in importable_classes.keys()}
+        class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
     elif os.path.isfile(os.path.join(component_folder, library_name + ".py")):
         # load custom component
         class_obj = get_class_from_dynamic_module(
             component_folder, module_file=library_name + ".py", class_name=class_name
         )
-        class_candidates = {c: class_obj for c in importable_classes.keys()}
+        class_candidates = dict.fromkeys(importable_classes.keys(), class_obj)
     else:
         # else we just import it from the library.
         library = importlib.import_module(library_name)

tests/pipelines/stable_diffusion_2/test_stable_diffusion_diffedit.py

@@ -205,7 +205,7 @@ def test_save_load_optional_components(self):
         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
             setattr(pipe, optional_component, None)
-        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
+        pipe.register_modules(**dict.fromkeys(pipe._optional_components))
 
         inputs = self.get_dummy_inputs(torch_device)
         output = pipe(**inputs)[0]