Update app.py

app.py

@@ -6,6 +6,11 @@ from torchvision import transforms as T
 
 from boomerang import *
 
+
+HALF_PRECISION = False
+
+
+
 def main(image, prompt, percent_noise): # percent_noise = 0.5, 0.02, 0.999
 
   # Convert image to float and preprocess it.
@@ -14,7 +19,10 @@ def main(image, prompt, percent_noise): # percent_noise = 0.5, 0.02, 0.999
                         T.ConvertImageDtype(torch.float),
                         T.Normalize([0.5], [0.5])])
   
-  tensor = transform(image).half().to(pipe.device)
+  if HALF_PRECISION:
+    tensor = transform(image).half().to(pipe.device)
+  else:
+    tensor = transform(image).to(pipe.device)
   tensor = torch.unsqueeze(tensor, 0)
 
   # Project image into the latent space
@@ -25,7 +33,9 @@ def main(image, prompt, percent_noise): # percent_noise = 0.5, 0.02, 0.999
   # (this is the forward diffusion process)
   noise = torch.randn(clean_z.shape).to(pipe.device)
   timestep = torch.Tensor([int(pipe.scheduler.config.num_train_timesteps * percent_noise)]).to(pipe.device).long()
-  z = pipe.scheduler.add_noise(clean_z, noise, timestep).half()
+  z = pipe.scheduler.add_noise(clean_z, noise, timestep)
+  if HALF_PRECISION:
+    z = z.half()
 
   # Run the diffusion model
   #with autocast('cuda'):