apply sonar lint

api/onnx_web/chain/blend_inpaint.py

@@ -113,5 +113,5 @@ def blend_inpaint(
 
     output = process_tile_order(stage.tile_order, source, SizeChart.auto, 1, [outpaint])
 
-    logger.info("final output image size", output.size)
+    logger.info("final output image size: %s", output.size)
     return output

api/onnx_web/chain/source_noise.py

@@ -13,7 +13,7 @@ def source_noise(
     _job: JobContext,
     _server: ServerContext,
     _stage: StageParams,
-    params: ImageParams,
+    _params: ImageParams,
     source: Image.Image,
     *,
     size: Size,

api/onnx_web/convert/__main__.py

@@ -149,7 +149,7 @@ def fetch_model(
     if model_format is None:
         url = urlparse(source)
         ext = path.basename(url.path)
-        file, ext = path.splitext(ext)
+        _filename, ext = path.splitext(ext)
         if ext is not None:
             cache_name += ext
     else:

api/onnx_web/convert/diffusion_original.py

@@ -1397,8 +1397,6 @@ def extract_checkpoint(
 
     logger.info(result_status)
 
-    return
-
 
 def convert_diffusion_original(
     ctx: ConversionContext,

api/onnx_web/convert/utils.py

@@ -214,12 +214,12 @@ def load_tensor(name: str, map_location=None):
         except Exception as e:
             try:
                 logger.warning(
-                    "failed to load as safetensors file, falling back to torch", e
+                    "failed to load as safetensors file, falling back to torch: %s", e
                 )
                 checkpoint = torch.jit.load(name)
             except Exception as e:
                 logger.warning(
-                    "failed to load with Torch JIT, falling back to PyTorch", e
+                    "failed to load with Torch JIT, falling back to PyTorch: %s", e
                 )
                 checkpoint = torch.load(name, map_location=map_location)
                 checkpoint = (

api/onnx_web/diffusion/run.py

@@ -173,7 +173,6 @@ def run_inpaint_pipeline(
     fill_color: str,
     tile_order: str,
 ) -> None:
-    # device = job.get_device()
     progress = job.get_progress_callback()
     stage = StageParams(tile_order=tile_order)
 
@@ -218,7 +217,6 @@ def run_upscale_pipeline(
     upscale: UpscaleParams,
     source: Image.Image,
 ) -> None:
-    # device = job.get_device()
     progress = job.get_progress_callback()
     stage = StageParams()
 

api/onnx_web/server/device_pool.py

@@ -208,7 +208,6 @@ class DevicePoolExecutor:
             except ValueError as e:
                 logger.warning("error removing pruned job from pending: %s", e)
 
-        # self.jobs[:] = [job for job in self.jobs if not job.future.done()]
         recent_count = len(self.recent)
         if recent_count > self.recent_limit:
             logger.debug(

api/onnx_web/server/model_cache.py

@@ -31,11 +31,10 @@ class ModelCache:
 
         for i in range(len(self.cache)):
             t, k, v = self.cache[i]
-            if tag == t:
-                if key != k:
-                    logger.debug("updating model cache: %s", tag)
-                    self.cache[i] = (tag, key, value)
-                    return
+            if tag == t and key != k:
+                logger.debug("updating model cache: %s", tag)
+                self.cache[i] = (tag, key, value)
+                return
 
         logger.debug("adding new model to cache: %s", tag)
         self.cache.append((tag, key, value))