Update app.py

app.py

@@ -203,19 +203,9 @@ def infer(
     # Encode the generated image into latents
     with torch.no_grad():
         generated_latents = vae.encode(generated_image_tensor.to(torch.bfloat16)).latent_dist.sample().mul_(0.18215)
-        initial_latents = pipe.prepare_latents(
-            batch_size=1,
-            num_channels_latents=pipe.transformer.in_channels,
-            height=pipe.transformer.config.sample_size[0],
-            width=pipe.transformer.config.sample_size[1],
-            dtype=pipe.transformer.dtype,
-            device=pipe.device,
-            generator=generator,
-            )
-        initial_latents += generated_latents
-        latent_path = f"sd35m_{seed}.pt"
+    latent_path = f"sd35m_{seed}.pt"
     # Save the latents to a .pt file
-    torch.save(initial_latents, latent_path)
+    torch.save(generated_latents, latent_path)
     upload_to_ftp(latent_path)
     #refiner.scheduler.set_timesteps(num_inference_steps,device)
     refine = refiner(