Update app.py

app.py

@@ -220,11 +220,11 @@ def infer(
     generated_image_tensor = torch.tensor([np.array(sd_image).transpose(2, 0, 1)]).to('cuda') / 255.0
     # Encode the generated image into latents
     #with torch.no_grad():
-    generated_latents = pipe.vae.encode(generated_image_tensor.to(torch.bfloat16)).latent_dist.sample().mul_(0.18215)
-    latent_path = f"sd35m_{seed}.pt"
+    #    generated_latents = pipe.vae.encode(generated_image_tensor.to(torch.bfloat16)).latent_dist.sample().mul_(0.18215)
+    #latent_path = f"sd35m_{seed}.pt"
     # Save the latents to a .pt file
-    torch.save(generated_latents, latent_path)
-    upload_to_ftp(latent_path)
+    #torch.save(generated_latents, latent_path)
+    #upload_to_ftp(latent_path)
     #refiner.scheduler.set_timesteps(num_inference_steps,device)
     refine = refiner(
             prompt=f"{enhanced_prompt_2}, high quality masterpiece, complex details",