Update app.py

app.py

@@ -291,12 +291,12 @@ def generate_images_30(prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width,
         upscale_2 = upscaler_2(upscale_1, tiling=True, tile_width=256, tile_height=256)
     print('-- got 4K 16-bit upscaled PIL image --')
     
-    downscaled_upscale = upscale_2.resize((upscale2.width // 8, upscale2.height // 8), Image.LANCZOS)
+    #downscaled_upscale = upscale_2.resize((upscale_2.width // 8, upscale_2.height // 8), Image.LANCZOS)
 
     torch.cuda.empty_cache()
     
     # 6. Convert the 4K 16-bit PIL back to a float32 tensor
-    upscaled_16bit_numpy = np.array(downscaled_upscale)
+    upscaled_16bit_numpy = np.array(upscale_2)
     upscaled_srgb_tensor = torch.from_numpy(upscaled_16bit_numpy).permute(2, 0, 1).unsqueeze(0).to(device, dtype=torch.float32) / 65535.0
     
     # 7. Create 10-bit HDR AVIF bytes from the 4K tensor (for GCS)
@@ -353,12 +353,12 @@ def generate_images_60(prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width,
         upscale_2 = upscaler_2(upscale_1, tiling=True, tile_width=256, tile_height=256)
     print('-- got 4K 16-bit upscaled PIL image --')
     
-    downscaled_upscale = upscale_2.resize((upscale2.width // 8, upscale2.height // 8), Image.LANCZOS)
+    #downscaled_upscale = upscale_2.resize((upscale_2.width // 8, upscale_2.height // 8), Image.LANCZOS)
 
     torch.cuda.empty_cache()
     
     # 6. Convert the 4K 16-bit PIL back to a float32 tensor
-    upscaled_16bit_numpy = np.array(downscaled_upscale)
+    upscaled_16bit_numpy = np.array(upscale_2)
     upscaled_srgb_tensor = torch.from_numpy(upscaled_16bit_numpy).permute(2, 0, 1).unsqueeze(0).to(device, dtype=torch.float32) / 65535.0
     
     # 7. Create 10-bit HDR AVIF bytes from the 4K tensor (for GCS)
@@ -408,19 +408,19 @@ def generate_images_110(prompt, neg_prompt_1, neg_prompt_2, neg_prompt_3, width,
     sd_image_pil_16bit = Image.fromarray(srgb_numpy_16bit, mode='RGB')
     print('-- got 16-bit PIL image for upscaling --')
 
-    # 5. Run the 16-bit upscaling (4x)
+   # 5. Run the 16-bit upscaling (4x)
     # We feed the high-precision 16-bit PIL image to the upscaler
     with torch.no_grad():
         upscale_1 = upscaler_2(sd_image_pil_16bit, tiling=True, tile_width=256, tile_height=256)
         upscale_2 = upscaler_2(upscale_1, tiling=True, tile_width=256, tile_height=256)
     print('-- got 4K 16-bit upscaled PIL image --')
     
-    downscaled_upscale = upscale_2.resize((upscale2.width // 8, upscale2.height // 8), Image.LANCZOS)
+    #downscaled_upscale = upscale_2.resize((upscale_2.width // 8, upscale_2.height // 8), Image.LANCZOS)
 
     torch.cuda.empty_cache()
     
     # 6. Convert the 4K 16-bit PIL back to a float32 tensor
-    upscaled_16bit_numpy = np.array(downscaled_upscale)
+    upscaled_16bit_numpy = np.array(upscale_2)
     upscaled_srgb_tensor = torch.from_numpy(upscaled_16bit_numpy).permute(2, 0, 1).unsqueeze(0).to(device, dtype=torch.float32) / 65535.0
     
     # 7. Create 10-bit HDR AVIF bytes from the 4K tensor (for GCS)