Improve deprecation warnings

src/diffusers/pipelines/pipeline_utils.py

@@ -201,24 +201,24 @@ def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLi
     # .bin, .safetensors, ...
     weight_suffixs = [w.split(".")[-1] for w in weight_names]
     # -00001-of-00002
-    transformers_index_format = "\d{5}-of-\d{5}"
+    transformers_index_format = r"\d{5}-of-\d{5}"
 
     if variant is not None:
         # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetenstors`
         variant_file_re = re.compile(
-            f"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
+            rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
         )
         # `text_encoder/pytorch_model.bin.index.fp16.json`
         variant_index_re = re.compile(
-            f"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
+            rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
         )
 
     # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetenstors`
     non_variant_file_re = re.compile(
-        f"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
+        rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
     )
     # `text_encoder/pytorch_model.bin.index.json`
-    non_variant_index_re = re.compile(f"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")
+    non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")
 
     if variant is not None:
         variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None}

tests/models/test_lora_layers.py

@@ -71,6 +71,7 @@ def get_dummy_components(self):
             beta_schedule="scaled_linear",
             clip_sample=False,
             set_alpha_to_one=False,
+            steps_offset=1,
         )
         torch.manual_seed(0)
         vae = AutoencoderKL(