lint(api): move half-precision CUDA check before models, apply lint

api/onnx_web/convert/__main__.py

@@ -227,6 +227,11 @@ def main() -> int:
     ctx = ConversionContext(half=args.half, opset=args.opset, token=args.token)
     logger.info("Converting models in %s using %s", ctx.model_path, ctx.training_device)
 
+    if ctx.half and ctx.training_device != "cuda":
+        raise ValueError(
+            "Half precision model export is only supported on GPUs with CUDA"
+        )
+
     if not path.exists(ctx.model_path):
         logger.info("Model path does not existing, creating: %s", ctx.model_path)
         makedirs(ctx.model_path)

api/onnx_web/convert/diffusion_original.py

@@ -41,7 +41,6 @@ from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
     LDMBertConfig,
     LDMBertModel,
 )
-import sys
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
 from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from huggingface_hub import HfApi, hf_hub_download
@@ -54,7 +53,7 @@ from transformers import (
 )
 
 from .diffusion_stable import convert_diffusion_stable
-from .utils import ConversionContext, ModelDict, sanitize_name, load_yaml
+from .utils import ConversionContext, ModelDict, load_yaml, sanitize_name
 
 logger = getLogger(__name__)
 
@@ -1018,7 +1017,16 @@ def download_model(db_config: TrainingConfig, token):
         if safe_model and bin_model:
             diffusion_files.remove(bin_model)
 
-    model_file = next((x for x in model_files if ".safetensors" in x and "nonema" in x), next((x for x in model_files if "nonema" in x), next((x for x in model_files if ".safetensors" in x), model_files[0] if model_files else None)))
+    model_file = next(
+        (x for x in model_files if ".safetensors" in x and "nonema" in x),
+        next(
+            (x for x in model_files if "nonema" in x),
+            next(
+                (x for x in model_files if ".safetensors" in x),
+                model_files[0] if model_files else None
+            )
+        )
+    )
 
     files_to_fetch = None
 
@@ -1167,7 +1175,7 @@ def extract_checkpoint(
 
     # Create empty config
     db_config = TrainingConfig(ctx, model_name=new_model_name, scheduler=scheduler_type,
-                                 src=checkpoint_file if not from_hub else new_model_url)
+                               src=checkpoint_file if not from_hub else new_model_url)
 
     original_config_file = None
 
@@ -1240,7 +1248,7 @@ def extract_checkpoint(
                 if key_name in unet_dict and unet_dict[key_name].shape[-1] == 1024:
                     logger.debug("UNet using v2 parameters.")
                     v2 = True
-            except Exception as e:
+            except Exception:
                 logger.error("Exception loading unet!", traceback.format_exception(*sys.exc_info()))
 
         if v2 and not is_512:
@@ -1379,7 +1387,7 @@ def extract_checkpoint(
             tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
             pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet,
                                           scheduler=scheduler)
-    except Exception as e:
+    except Exception:
         logger.error("Exception setting up output: %s", traceback.format_exception(*sys.exc_info()))
         pipe = None
 

api/onnx_web/convert/diffusion_stable.py

@@ -76,11 +76,6 @@ def convert_diffusion_stable(
         logger.info("ONNX model already exists, skipping.")
         return
 
-    if ctx.half and ctx.training_device != "cuda":
-        raise ValueError(
-            "Half precision model export is only supported on GPUs with CUDA"
-        )
-
     pipeline = StableDiffusionPipeline.from_pretrained(
         source,
         torch_dtype=dtype,

api/onnx_web/convert/utils.py

@@ -145,7 +145,6 @@ def source_format(model: Dict) -> Optional[str]:
     return None
 
 
-
 class Config(object):
     def __init__(self, kwargs):
         self.__dict__.update(kwargs)
@@ -165,7 +164,6 @@ class Config(object):
             setattr(target, k, v)
 
 
-
 def load_yaml(file: str) -> str:
     with open(file, "r") as f:
         data = safe_load(f.read())
@@ -173,5 +171,7 @@ def load_yaml(file: str) -> str:
 
 
 safe_chars = "._-"
+
+
 def sanitize_name(name):
     return "".join(x for x in name if (x.isalnum() or x in safe_chars))