Update app.py

app.py

@@ -195,8 +195,9 @@ def infer(
             width=width,
             height=height,
             latents=sd_image_a,
+            output='latent',
             generator=generator
-        ).images[0]
+        ).images
     else:
         print('-- generating image --')
         #with torch.no_grad():
@@ -210,13 +211,19 @@ def infer(
             width=width,
             height=height,
          #   latents=None,
+            output='latent',
             generator=generator,
             max_sequence_length=512
-        ).images[0]
+        ).images
         print('-- got image --')
-    image_path = f"sd35m_{seed}.png"
-    sd_image.save(image_path,optimize=False,compress_level=0)
-    upload_to_ftp(image_path)
+    sd35_image = pipe.vae.decode(sd_image / 0.18215).sample
+    sd35_image = sdxl_image.cpu().permute(0, 2, 3, 1).float().detach().numpy()
+    sd35_image = (sdxl_image * 255).round().astype("uint8")
+    image_pil = Image.fromarray(sd35_image[0])
+    sd35_path = f"tst_rv_{seed}.png"
+    image_pil.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