Update app.py

app.py

@@ -49,7 +49,7 @@ def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta
   sd_pipe.scheduler.set_timesteps(num_diffusion_steps)
 
   # vae encode image
-  with autocast("cuda"), inference_mode():
+  with inference_mode():
       w0 = (sd_pipe.vae.encode(x0).latent_dist.mode() * 0.18215).float()
 
   # find Zs and wts - forward process
@@ -63,7 +63,7 @@ def sample(zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1):
     w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=True, zs=zs[skip:])
     
     # vae decode image
-    with autocast("cuda"), inference_mode():
+    with inference_mode():
         x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample
     if x0_dec.dim()<4:
         x0_dec = x0_dec[None,:,:,:]
@@ -107,7 +107,7 @@ def load_and_invert(
 ):
 
 
-    x0 = load_512(input_image, device=device)
+    x0 = load_512(input_image, device=device).to(torch.float16)
 
     if do_inversion or randomize_seed:
         # invert and retrieve noise maps and latent