Update app.py

app.py

@@ -170,7 +170,7 @@ def upload_to_ftp(filename):
 def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
     filename= f'rv_C_{timestamp}.txt'
     with open(filename, "w") as f:
-        f.write(f"Realvis 5.0 (Tester D) \n")
+        f.write(f"Realvis 5.0 (Tester L) \n")
         f.write(f"Date/time: {timestamp} \n")
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
@@ -284,9 +284,8 @@ def generate_30(
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     filename = pyx.uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     #upload_to_ftp(filename) 
-    pyx.upload_to_ftp(filename) 
     #uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
-
+    sd_image_path = f"rv_L_{timestamp}.png"
     if denoising_start==0.0:
         options = {
             "prompt": [prompt],
@@ -355,6 +354,7 @@ def generate_30(
         torch.save(rv_image, sd_latent_path)
         
     if denoising_end==1.0 and denoising_start!=0.0:
+        pyx.upload_to_ftp(filename) 
         latent_file = f'rv_L_{denoising_start}.l'
         loaded_latent = torch.load(latent_file)
         print("Shape of loaded latent:", loaded_latent.shape)
@@ -423,7 +423,6 @@ def generate_30(
             options["use_resolution_binning"] = True
         batch_options = options.copy()
         rv_image = pipe(**batch_options).images[0]
-        sd_image_path = f"rv_L_{timestamp}.png"
         rv_image.save(sd_image_path,optimize=False,compress_level=0)
         pyx.upload_to_ftp(sd_image_path)    
         torch.set_float32_matmul_precision("medium")
@@ -433,7 +432,6 @@ def generate_30(
         downscale_path = f"rv_L_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
         pyx.upload_to_ftp(downscale_path) 
-
     unique_name = str(uuid.uuid4()) + ".png"  
     os.symlink(sd_image_path, unique_name)  
     return [unique_name]