Update app.py

app.py

@@ -485,26 +485,26 @@ def generate_30c(
         "num_inference_steps": num_inference_steps,
         "generator": generator,
         "output_type": "pil",
-        "callback_on_step_end": pyx.scheduler_swap_callback,
+        "callback_on_step_end": scheduler_swap_callback,
     }
     if use_resolution_binning:
         options["use_resolution_binning"] = True
     images = []
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = pyx.uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
-    pyx.upload_to_ftp(filename) 
+    filename = uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
+    upload_to_ftp(filename) 
     batch_options = options.copy()
     rv_image = pipe(**batch_options).images[0]
     sd_image_path = f"rv_C_{timestamp}.png"
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
-    pyx.upload_to_ftp(sd_image_path)    
+    upload_to_ftp(sd_image_path)    
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
         upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
     downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
     downscale_path = f"rv50_upscale_{timestamp}.png"
     downscale1.save(downscale_path,optimize=False,compress_level=0)
-    pyx.upload_to_ftp(downscale_path) 
+    upload_to_ftp(downscale_path) 
     unique_name = str(uuid.uuid4()) + ".png"  
     os.symlink(sd_image_path, unique_name)  
     return [unique_name]