apply lint

api/onnx_web/convert/diffusion/lora.py

@@ -130,19 +130,11 @@ def fix_xl_names(keys: Dict[str, Any], nodes: List[NodeProto]) -> Dict[str, Any]
         logger.trace("searching for XL node: %s -> /%s/*/%s", root, block, suffix)
         match: Optional[str] = None
         if "conv" in suffix:
-            match = next(
-                node for node in names if node == f"{root}_Conv"
-            )
+            match = next(node for node in names if node == f"{root}_Conv")
         elif "time_emb_proj" in root:
-            match = next(
-                node for node in names if node == f"{root}_Gemm"
-            )
+            match = next(node for node in names if node == f"{root}_Gemm")
         elif block == "text_model" or simple:
-            match = next(
-                node
-                for node in names
-                if node == f"{root}_MatMul"
-            )
+            match = next(node for node in names if node == f"{root}_MatMul")
         else:
             # search in order. one side has sparse indices, so they will not match.
             match = next(
@@ -174,8 +166,9 @@ def fix_xl_names(keys: Dict[str, Any], nodes: List[NodeProto]) -> Dict[str, Any]
         names.remove(match)
 
     logger.debug(
-        "SDXL LoRA key fixup matched %s keys, %s remaining",
+        "SDXL LoRA key fixup matched %s of %s keys, %s nodes remaining",
         len(fixed.keys()),
+        len(keys.keys()),
         len(names),
     )
 

api/onnx_web/diffusers/load.py

@@ -7,12 +7,6 @@ from optimum.onnxruntime import (  # ORTStableDiffusionXLInpaintPipeline,
     ORTStableDiffusionXLImg2ImgPipeline,
     ORTStableDiffusionXLPipeline,
 )
-from optimum.onnxruntime.modeling_diffusion import (
-    ORTModelTextEncoder,
-    ORTModelUnet,
-    ORTModelVaeDecoder,
-    ORTModelVaeEncoder,
-)
 from transformers import CLIPTokenizer
 
 from ..constants import ONNX_MODEL
@@ -224,7 +218,7 @@ def load_pipeline(
                 components["vae_decoder_session"],
                 components["text_encoder_session"],
                 components["unet_session"],
-                {}, # empty config
+                {},  # empty config
                 components["tokenizer"],
                 scheduler,
                 vae_encoder_session=components.get("vae_encoder_session", None),
@@ -232,7 +226,9 @@ def load_pipeline(
                 tokenizer_2=components.get("tokenizer_2", None),
             )
         else:
-            logger.debug("loading pretrained SD pipeline for %s", pipeline_class.__name__)
+            logger.debug(
+                "loading pretrained SD pipeline for %s", pipeline_class.__name__
+            )
             pipe = pipeline_class.from_pretrained(
                 model,
                 provider=device.ort_provider(),