diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 69bf9be6d..8bbd8caba 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -58,14 +58,11 @@ def samples_to_images_tensor(sample, approximation=None, model=None):
 
     return x_sample
 
-lp_stream = torch.cuda.Stream()
-def single_sample_to_image(sample, approximation=None):
-    with torch.cuda.stream(lp_stream):
-        x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
-        x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
-        x_sample = 255. * x_sample.permute(1, 2, 0)
-        x_sample = x_sample.to(device='cpu', dtype=torch.uint8, non_blocking=True)
-        return x_sample
+def single_sample_to_image(sample, approximation=None, non_blocking=False):
+    x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
+    x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
+    x_sample = 255. * x_sample.permute(1, 2, 0)
+    return x_sample.to(device='cpu', dtype=torch.uint8, non_blocking=non_blocking)
 
 
 def decode_first_stage(model, x):
@@ -78,9 +75,18 @@ def sample_to_image(samples, index=0, approximation=None):
     return single_sample_to_image(samples[index], approximation)
 
 
+if torch.cuda.is_available():
+    lp_stream = torch.cuda.Stream()
+    live_preview_stream_context = torch.cuda.stream(lp_stream)
+else:
+    lp_stream = None
+    live_preview_stream_context = nullcontext()
+
 def samples_to_image_grid(samples, approximation=None):
-    sample_tensors = [single_sample_to_image(sample, approximation) for sample in samples]
-    lp_stream.synchronize()
+    with live_preview_stream_context:
+        sample_tensors = [single_sample_to_image(sample, approximation, non_blocking=True) for sample in samples]
+    if lp_stream is not None:
+        lp_stream.synchronize()
     return images.image_grid([Image.fromarray(sample.numpy()) for sample in sample_tensors])