diff --git a/pipelines/generic_text_encoder.py b/pipelines/generic_text_encoder.py
index 62c392476..1c2662c11 100644
--- a/pipelines/generic_text_encoder.py
+++ b/pipelines/generic_text_encoder.py
@@ -21,6 +21,8 @@ def get_shared(cls, repo_id, subfolder=None, variant=None, shared_id: str | None
             identifiers = []
         if isinstance(identifiers, str):
             identifiers = [identifiers]
+        if not isinstance(identifiers, list):
+            identifiers = []
         identifiers = [identifier.lower() for identifier in identifiers if identifier is not None]
         shared_id = shared_id or repo_id.lower()
         if item['cls'] == cls and (not identifiers or any(identifier in shared_id for identifier in identifiers)):
diff --git a/pipelines/native_transformer.py b/pipelines/native_transformer.py
index 5e1b00321..b282f92e4 100644
--- a/pipelines/native_transformer.py
+++ b/pipelines/native_transformer.py
@@ -45,7 +45,7 @@ import os
 import json
 import time
 from dataclasses import dataclass, field
-from typing import Callable
+from typing import Callable, cast
 
 import huggingface_hub as hf
 import torch
@@ -185,7 +185,7 @@ def auto_pickup_converter(cls: type) -> Callable[[dict], dict] | None:
     fn = entry.get("checkpoint_mapping_fn")
     if fn is None or is_noop_converter(fn):
         return None
-    return fn
+    return cast('Callable[[dict], dict]', fn) # diffusers' mapping fns vary in signature (extra kwargs/config), all compatible at call sites
 
 
 def is_noop_converter(fn: Callable) -> bool:
diff --git a/scripts/differential_diffusion.py b/scripts/differential_diffusion.py
index 11eb177af..b5bc5cb1a 100644
--- a/scripts/differential_diffusion.py
+++ b/scripts/differential_diffusion.py
@@ -869,7 +869,7 @@ class StableDiffusionXLDiffImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixi
         total_time_steps = num_inference_steps
         #end diff diff change
         timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None # pylint: disable=missing-parentheses-for-call-in-test, using-constant-test
+            num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid(denoising_start) else None
         )
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
diff --git a/scripts/softfill.py b/scripts/softfill.py
index 35b2348e9..ac99afa9f 100644
--- a/scripts/softfill.py
+++ b/scripts/softfill.py
@@ -964,7 +964,7 @@ class StableDiffusionXLSoftFillPipeline(
         aesthetic_score: float = 6.0,
         negative_aesthetic_score: float = 2.5,
         clip_skip: Optional[int] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end: Optional[Callable[..., Dict]] = None, # invoked as (self, step_index, timestep, callback_kwargs) -> dict
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         **kwargs,
     ):