lint

Signed-off-by: Vladimir Mandic <mandic00@live.com>

pipelines/krea2/pipeline_krea2.py

@@ -6,8 +6,6 @@ steps mirror the reference K2 inference code. This module imports only diffusers
 so the repos can ship it for standalone use; SD.Next-specific wiring lives in the loader.
 """
 
-import os
-
 import torch
 from einops import rearrange, repeat
 

pipelines/krea2/pipeline_krea2_inpaint.py

@@ -53,7 +53,7 @@ class Krea2InpaintPipeline(Krea2Img2ImgPipeline):
     """Krea 2 inpainting pipeline."""
 
     @torch.no_grad()
-    def __call__(
+    def __call__( # pylint: disable=signature-differs
         self,
         prompt: Optional[Union[str, List[str]]] = None,
         negative_prompt: Optional[Union[str, List[str]]] = None,

test/test-krea2-transformer.py

@@ -121,8 +121,8 @@ def load_materialize_zero_init():
 
 class _FakeTokenizedBatch(SimpleNamespace):
     def to(self, device):
-        self.input_ids = self.input_ids.to(device)
-        self.attention_mask = self.attention_mask.to(device)
+        self.input_ids = self.input_ids.to(device) # pylint: disable=attribute-defined-outside-init
+        self.attention_mask = self.attention_mask.to(device) # pylint: disable=attribute-defined-outside-init
         return self
 
 
@@ -176,7 +176,7 @@ def run_dense_prompt_compaction_test():
         scheduler=fake_scheduler,
     )
     prompts = ["short", "longer prompt"]
-    hidden, mask = pipe.encode_prompt(prompts, device=torch.device("cpu"))
+    _hidden, mask = pipe.encode_prompt(prompts, device=torch.device("cpu"))
     assert mask.shape[1] < pipe.MAX_LENGTH
     assert mask.any(dim=0).all(), "Compacted prompt mask must contain no fully padded columns"
     del os.environ["SD_KREA2_DENSE"]
@@ -269,7 +269,7 @@ def run_comfy_quant_real_file():
     from pipelines.krea2 import KREA2_SPEC
 
     transformer, siblings = nt.load(local_file=path, repo_id=repo_id, spec=KREA2_SPEC, diffusers_cfg={})
-    assert siblings == {}
+    assert not siblings
 
     sdnq_layers = [m for m in transformer.modules() if m.__class__.__name__ == "SDNQLinear"]
     storage_dtypes = {m.weight.dtype for m in sdnq_layers}