remove args

app.py

@@ -20,12 +20,15 @@ import argparse
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-parseargs = argparse.ArgumentParser()
-parseargs.add_argument('--pretrained_model', type=str, default='runwayml/stable-diffusion-v1-5')
-parseargs.add_argument('--controlnet', type=str, default='controlnet')
-parseargs.add_argument('--precision', type=str, default='fp32')
-args = parseargs.parse_args()
-pretrained_model = args.pretrained_model
+# parse= argparse.ArgumentParser()
+# parseadd_argument('--pretrained_model', type=str, default='runwayml/stable-diffusion-v1-5')
+# parseadd_argument('--controlnet', type=str, default='controlnet')
+# parseadd_argument('--precision', type=str, default='fp32')
+# = parseparse_)
+# pretrained_model = pretrained_model
+pretrained_model = 'runwayml/stable-diffusion-v1-5'
+controlnet = 'checkpoint-36000/controlnet'
+precision = 'bf16'
 
 # Check for different hardware architectures
 if torch.cuda.is_available():
@@ -45,18 +48,18 @@ else:
 print(f"Using device: {device}")
 
 # Load models
-if args.precision == 'fp32':
+if precision == 'fp32':
     torch_dtype = torch.float32
-elif args.precision == 'fp16':
+elif precision == 'fp16':
     torch_dtype = torch.float16
-elif args.precision == 'bf16':
+elif precision == 'bf16':
     torch_dtype = torch.bfloat16
 else:
-    raise ValueError(f"Invalid precision: {args.precision}")
+    raise ValueError(f"Invalid precision: {precision}")
 
-controlnet = ControlNetModel.from_pretrained(args.controlnet, torch_dtype=torch_dtype)
+controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch_dtype)
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    args.pretrained_model, controlnet=controlnet, torch_dtype=torch_dtype
+    pretrained_model, controlnet=controlnet, torch_dtype=torch_dtype
 )
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to(device)