diff --git a/library/train_util.py b/library/train_util.py
index b93b8ea4..949a8206 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -4477,6 +4477,11 @@ def line_to_prompt_dict(line: str) -> dict:
                 prompt_dict['negative_prompt'] = m.group(1)
                 continue
 
+            m = re.match(r"ss (.+)", parg, re.IGNORECASE)
+            if m:  # negative prompt
+                prompt_dict['sample_sampler'] = m.group(1)
+                continue
+
             m = re.match(r"cn (.+)", parg, re.IGNORECASE)
             if m:  # negative prompt
                 prompt_dict['controlnet_image'] = m.group(1)
@@ -4540,17 +4545,19 @@ def sample_images_common(
         with open(args.sample_prompts, "r", encoding="utf-8") as f:
             prompts = json.load(f)
 
-    scheduler = get_my_scheduler(
-        sample_sampler=args.scheduler,
+    schedulers: dict = {}
+    default_scheduler = get_my_scheduler(
+        sample_sampler=args.sample_sampler,
         v_parameterization=args.v_parameterization,
     )
+    schedulers[args.sample_sampler] = default_scheduler
 
     pipeline = pipe_class(
         text_encoder=text_encoder,
         vae=vae,
         unet=unet,
         tokenizer=tokenizer,
-        scheduler=scheduler,
+        scheduler=default_scheduler,
         safety_checker=None,
         feature_extractor=None,
         requires_safety_checker=False,
@@ -4582,11 +4589,18 @@ def sample_images_common(
             seed = prompt_dict.get("seed")
             controlnet_image = prompt_dict.get("controlnet_image")
             prompt: str = prompt_dict.get("prompt", "")
+            sampler_name:str = prompt_dict.get("sample_sampler", args.sample_sampler)
 
             if seed is not None:
                 torch.manual_seed(seed)
                 torch.cuda.manual_seed(seed)
 
+            scheduler = schedulers.get(sampler_name)
+            if scheduler is None:
+                scheduler = get_my_scheduler(sample_sampler=sampler_name, v_parameterization=args.v_parameterization,)
+                schedulers[sampler_name] = scheduler
+            pipeline.scheduler = scheduler
+
             if prompt_replacement is not None:
                 prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
                 if negative_prompt is not None:
@@ -4604,6 +4618,7 @@ def sample_images_common(
             print(f"width: {width}")
             print(f"sample_steps: {sample_steps}")
             print(f"scale: {scale}")
+            print(f"sample_sampler: {sampler_name}")
             with accelerator.autocast():
                 latents = pipeline(
                     prompt=prompt,