moving accelerator into process

app.py

@@ -27,6 +27,7 @@ import k_diffusion as K
 
 # test natten import:
 import natten
+import accelerate
 
 zero = torch.Tensor([0]).cuda()
 print("Zero Device = ",zero.device," <-- this probably says cpu") # <-- 'cpu' 🤔
@@ -98,7 +99,12 @@ def grab_dense_gen(init_img,
 
 @spaces.GPU
 def process_image(image, repaint, busyness):
+
     print("Process Image: Zero Device = ",zero.device)
+    accelerator = accelerate.Accelerator()
+    device = accelerator.device
+    print('Accelerator device:', device, flush=True)
+
     # get image ready and execute sampler
     print("image = ",image)
     image = image['composite']
@@ -112,7 +118,7 @@ def process_image(image, repaint, busyness):
     print("Saving masked image file to ", masked_img_file)
     image.save(masked_img_file)
     num = 64 # number of images to generate; we'll take the one with the most notes in the masked region
-    bs = num
+    bs = numx
     repaint = repaint
     seed_scale = 1.0
     CT_HOME = '.'