diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 50816ec403..922532cfad 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -133,7 +133,8 @@ class T2I:
         full_precision=False,
         strength=0.75,  # default in scripts/img2img.py
         embedding_path=None,
-        latent_diffusion_weights=False,  # just to keep track of this parameter when regenerating prompt
+        # just to keep track of this parameter when regenerating prompt
+        latent_diffusion_weights=False,
         device='cuda',
         gfpgan=None,
     ):
@@ -175,7 +176,8 @@ class T2I:
             outdir, prompt, kwargs.get('batch_size', self.batch_size)
         )
         for r in results:
-            metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}'   # gets written into the PNG
+            # gets written into the PNG
+            metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}'
             pngwriter.write_image(r[0], r[1])
         return pngwriter.files_written
 
@@ -210,6 +212,7 @@ class T2I:
         strength=None,
         gfpgan_strength=None,
         variants=None,
+        user_sampler=None,
         **args,
     ):   # eat up additional cruft
         """
@@ -269,6 +272,10 @@ class T2I:
 
         scope = autocast if self.precision == 'autocast' else nullcontext
 
+        if user_sampler and (user_sampler != self.sampler_name):
+            self.sampler_name = user_sampler
+            self._set_sampler()
+
         tic = time.time()
         results = list()
 
@@ -305,12 +312,15 @@ class T2I:
                     iter_images = next(images_iterator)
                     for image in iter_images:
                         try:
-                            # if gfpgan strength is none or less than or equal to 0.0 then 
+                            # if gfpgan strength is none or less than or equal to 0.0 then
                             # don't even attempt to use GFPGAN.
-                            # if the user specified a value of -G that satisifies the condition and 
+                            # if the user specified a value of -G that satisifies the condition and
                             # --gfpgan wasn't specified, at startup then
                             # the net result is a message gets printed - nothing else happens.
-                            if gfpgan_strength is not None and gfpgan_strength > 0.0:
+                            if (
+                                gfpgan_strength is not None
+                                and gfpgan_strength > 0.0
+                            ):
                                 image = self._run_gfpgan(
                                     image, gfpgan_strength
                                 )
@@ -499,39 +509,38 @@ class T2I:
             except AttributeError:
                 raise SystemExit
 
-            msg = f'setting sampler to {self.sampler_name}'
-            if self.sampler_name == 'plms':
-                self.sampler = PLMSSampler(self.model, device=self.device)
-            elif self.sampler_name == 'ddim':
-                self.sampler = DDIMSampler(self.model, device=self.device)
-            elif self.sampler_name == 'k_dpm_2_a':
-                self.sampler = KSampler(
-                    self.model, 'dpm_2_ancestral', device=self.device
-                )
-            elif self.sampler_name == 'k_dpm_2':
-                self.sampler = KSampler(
-                    self.model, 'dpm_2', device=self.device
-                )
-            elif self.sampler_name == 'k_euler_a':
-                self.sampler = KSampler(
-                    self.model, 'euler_ancestral', device=self.device
-                )
-            elif self.sampler_name == 'k_euler':
-                self.sampler = KSampler(
-                    self.model, 'euler', device=self.device
-                )
-            elif self.sampler_name == 'k_heun':
-                self.sampler = KSampler(self.model, 'heun', device=self.device)
-            elif self.sampler_name == 'k_lms':
-                self.sampler = KSampler(self.model, 'lms', device=self.device)
-            else:
-                msg = f'unsupported sampler {self.sampler_name}, defaulting to plms'
-                self.sampler = PLMSSampler(self.model, device=self.device)
-
-            print(msg)
+            self._set_sampler()
 
         return self.model
 
+    def _set_sampler(self):
+        msg = f'>> Setting Sampler to {self.sampler_name}'
+        if self.sampler_name == 'plms':
+            self.sampler = PLMSSampler(self.model, device=self.device)
+        elif self.sampler_name == 'ddim':
+            self.sampler = DDIMSampler(self.model, device=self.device)
+        elif self.sampler_name == 'k_dpm_2_a':
+            self.sampler = KSampler(
+                self.model, 'dpm_2_ancestral', device=self.device
+            )
+        elif self.sampler_name == 'k_dpm_2':
+            self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
+        elif self.sampler_name == 'k_euler_a':
+            self.sampler = KSampler(
+                self.model, 'euler_ancestral', device=self.device
+            )
+        elif self.sampler_name == 'k_euler':
+            self.sampler = KSampler(self.model, 'euler', device=self.device)
+        elif self.sampler_name == 'k_heun':
+            self.sampler = KSampler(self.model, 'heun', device=self.device)
+        elif self.sampler_name == 'k_lms':
+            self.sampler = KSampler(self.model, 'lms', device=self.device)
+        else:
+            msg = f'>> Unsupported Sampler: {self.sampler_name}, Defaulting to plms'
+            self.sampler = PLMSSampler(self.model, device=self.device)
+
+        print(msg)
+
     def _load_model_from_config(self, config, ckpt):
         print(f'Loading model from {ckpt}')
         pl_sd = torch.load(ckpt, map_location='cpu')
diff --git a/scripts/dream.py b/scripts/dream.py
index 5d5e8db4a5..be72d25655 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -52,7 +52,8 @@ def main():
         weights=weights,
         full_precision=opt.full_precision,
         config=config,
-        latent_diffusion_weights=opt.laion400m,  # this is solely for recreating the prompt
+        # this is solely for recreating the prompt
+        latent_diffusion_weights=opt.laion400m,
         embedding_path=opt.embedding_path,
         device=opt.device,
     )
@@ -508,6 +509,23 @@ def create_cmd_parser():
         action='store_true',
         help='skip subprompt weight normalization',
     )
+    parser.add_argument(
+        '-m',
+        '--user_sampler',
+        default=None,
+        type=str,
+        choices=[
+            'ddim',
+            'k_dpm_2_a',
+            'k_dpm_2',
+            'k_euler_a',
+            'k_euler',
+            'k_heun',
+            'k_lms',
+            'plms',
+        ],
+        help='Change to another supported sampler using this command',
+    )
     return parser