Update app.py

app.py

@@ -203,6 +203,9 @@ def infer(
           #  output='latent',
             generator=generator
         ).images[0]
+        rv_path = f"sd35_{seed}.png"
+        sd_image[0].save(rv_path,optimize=False,compress_level=0)
+        upload_to_ftp(rv_path)
     else:
         print('-- generating image --')
         #with torch.no_grad():
@@ -221,20 +224,17 @@ def infer(
             max_sequence_length=512
         ).images[0]
         print('-- got image --')
-
         sd35_image_image = pipe.vae.decode(sd_image / 0.18215).sample
         sd35_image = sd35_image.cpu().permute(0, 2, 3, 1).float().detach().numpy()
         sd35_image = (sd35_image * 255).round().astype("uint8")
         image_pil = Image.fromarray(sd35_image[0])
-        sd35_path = f"tst_rv_{seed}.png"
+        sd35_path = f"sd35_{seed}.png"
         image_pil.save(sd35_path,optimize=False,compress_level=0)
         upload_to_ftp(sd35_path)
 
-
     #sd35_path = f"sd35_{seed}.png"
     #sd_image.save(sd35_path,optimize=False,compress_level=0)
     #upload_to_ftp(sd35_path)
-    
         # Convert the generated image to a tensor
     #generated_image_tensor = torch.tensor([np.array(sd_image).transpose(2, 0, 1)]).to('cuda') / 255.0
     # Encode the generated image into latents
@@ -253,7 +253,7 @@ def infer(
             image=sd_image,
             generator=generator,
     ).images[0]
-    refine_path = f"sd35m_refine_{seed}.png"
+    refine_path = f"sd35_refine_{seed}.png"
     refine.save(refine_path,optimize=False,compress_level=0)
     upload_to_ftp(refine_path)
     return refine, seed, enhanced_prompt