Conditionally add random generator to inference pipeline

handler.py

@@ -36,13 +36,13 @@ class EndpointHandler():
         width = params.pop("width", None)
         manual_seed = params.pop("manual_seed", -1)
 
-        generator = torch.Generator(device)
+        out = None
 
         if manual_seed != -1:
+            generator = torch.Generator(device='cuda')
             generator.manual_seed(manual_seed)
-
-        # run inference pipeline
-        out = self.pipe(prompt, 
+            # run inference pipeline
+            out = self.pipe(prompt, 
                         generator=generator,             
                         num_inference_steps=num_inference_steps,
                         guidance_scale=guidance_scale,
@@ -50,7 +50,18 @@ class EndpointHandler():
                         negative_prompt=negative_prompt,
                         height=height,
                         width=width
-        )
-            
-        # return first generate PIL image
+            )
+        else:
+            # run inference pipeline
+            out = self.pipe(prompt, 
+                        num_inference_steps=num_inference_steps,
+                        guidance_scale=guidance_scale,
+                        num_images_per_prompt=1,
+                        negative_prompt=negative_prompt,
+                        height=height,
+                        width=width
+            )
+
+
+        # return first generated PIL image
         return out.images[0]