diff --git a/pipelines/krea2/pipeline_krea2.py b/pipelines/krea2/pipeline_krea2.py index cd0caffd0..cd6562311 100644 --- a/pipelines/krea2/pipeline_krea2.py +++ b/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 diff --git a/pipelines/krea2/pipeline_krea2_inpaint.py b/pipelines/krea2/pipeline_krea2_inpaint.py index 30533a285..79979ae35 100644 --- a/pipelines/krea2/pipeline_krea2_inpaint.py +++ b/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, diff --git a/test/test-krea2-transformer.py b/test/test-krea2-transformer.py index 06132c4b8..cbc1f31c6 100644 --- a/test/test-krea2-transformer.py +++ b/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}