update

src/diffusers/pipelines/sana/pipeline_sana_controlnet.py

@@ -16,6 +16,7 @@ import html
 import inspect
 import re
 import urllib.parse as ul
+import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -1053,7 +1054,14 @@ class SanaControlNetPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             image = latents
         else:
             latents = latents.to(self.vae.dtype)
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            try:
+                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            except torch.cuda.OutOfMemoryError as e:
+                warnings.warn(
+                    f"{e}. \n"
+                    f"Try to use VAE tiling for large images. For example: \n"
+                    f"pipe.vae.enable_tiling(tile_sample_min_width=512, tile_sample_min_height=512)"
+                )
             if use_resolution_binning:
                 image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)