Add random seeding if specified and train pipeline on base model

handler.py

@@ -8,10 +8,12 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 if device.type != 'cuda':
     raise ValueError("need to run on GPU")
 
+model_id = "stabilityai/stable-diffusion-2-1-base"
+
 class EndpointHandler():
-    def __init__(self, path=""):
+    def __init__(self):
         # load the optimized model
-        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
         self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
         self.pipe = self.pipe.to(device)
 
@@ -34,7 +36,10 @@ class EndpointHandler():
         width = params.pop("width", None)
         manual_seed = params.pop("manual_seed", -1)
 
-        generator = torch.Generator(device).manual_seed(manual_seed)
+        generator = torch.Generator(device)
+
+        if (manual_seed != -1)
+            generator.manual_seed(manual_seed)
 
         # run inference pipeline
         out = self.pipe(prompt,