apply lint

api/onnx_web/convert/diffusion/lora.py

@@ -204,7 +204,9 @@ def blend_loras(
             logger.trace("blended weight shape: %s", blended.shape)
 
             # replace the original initializer
-            updated_node = numpy_helper.from_array(blended.astype(base_weights.dtype), weight_node.name)
+            updated_node = numpy_helper.from_array(
+                blended.astype(base_weights.dtype), weight_node.name
+            )
             del base_model.graph.initializer[weight_idx]
             base_model.graph.initializer.insert(weight_idx, updated_node)
         elif matmul_key in fixed_node_names:
@@ -233,7 +235,9 @@ def blend_loras(
             logger.trace("blended weight shape: %s", blended.shape)
 
             # replace the original initializer
-            updated_node = numpy_helper.from_array(blended.astype(base_weights.dtype), matmul_node.name)
+            updated_node = numpy_helper.from_array(
+                blended.astype(base_weights.dtype), matmul_node.name
+            )
             del base_model.graph.initializer[matmul_idx]
             base_model.graph.initializer.insert(matmul_idx, updated_node)
         else:

api/onnx_web/convert/utils.py

@@ -200,13 +200,13 @@ def remove_prefix(name: str, prefix: str) -> str:
 
 def load_torch(name: str, map_location=None) -> Optional[Dict]:
     try:
-        logger.debug("loading tensor with Torch JIT: %s", name)
-        checkpoint = torch.jit.load(name)
+        logger.debug("loading tensor with Torch: %s", name)
+        checkpoint = torch.load(name, map_location=map_location)
     except Exception:
         logger.exception(
-            "error loading with Torch JIT, falling back to Torch: %s", name
+            "error loading with Torch JIT, trying with Torch JIT: %s", name
         )
-        checkpoint = torch.load(name, map_location=map_location)
+        checkpoint = torch.jit.load(name)
 
     return checkpoint
 

api/onnx_web/diffusers/load.py

@@ -360,7 +360,12 @@ class UNetWrapper(object):
         global timestep_dtype
         timestep_dtype = timestep.dtype
 
-        logger.trace("UNet parameter types: %s, %s, %s", sample.dtype, timestep.dtype, encoder_hidden_states.dtype)
+        logger.trace(
+            "UNet parameter types: %s, %s, %s",
+            sample.dtype,
+            timestep.dtype,
+            encoder_hidden_states.dtype,
+        )
         if sample.dtype != timestep.dtype:
             logger.trace("converting UNet sample to timestep dtype")
             sample = sample.astype(timestep.dtype)

api/onnx_web/server/context.py

@@ -3,7 +3,6 @@ from os import environ, path
 from typing import List, Optional
 
 import torch
-import numpy as np
 
 from ..utils import get_boolean
 from .model_cache import ModelCache