diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index e7b7b524b0..c32f57c388 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -286,7 +286,7 @@ class T2I:
                 0.0 <= variation_amount <= 1.0
         ), '-v --variation_amount must be in [0.0, 1.0]'
 
-        if len(with_variations) > 0:
+        if len(with_variations) > 0 or variation_amount > 1.0:
             assert seed is not None,\
                 'seed must be specified when using with_variations'
             if variation_amount == 0.0:
@@ -324,6 +324,7 @@ class T2I:
                         self.model.encode_first_stage(init_image)
                     ) # move to latent space
 
+                print(f' DEBUG: seed at make_image time ={seed}')
                 make_image = self._img2img(
                     prompt,
                     steps=steps,
@@ -346,35 +347,15 @@ class T2I:
                     callback=step_callback,
                 )
 
-            def get_noise():
-                if init_img:
-                    if self.device.type == 'mps':
-                        return torch.randn_like(init_latent, device='cpu').to(self.device)
-                    else:
-                        return torch.randn_like(init_latent, device=self.device)
-                else:
-                    if self.device.type == 'mps':
-                        return torch.randn([1,
-                            self.latent_channels,
-                            height // self.downsampling_factor,
-                            width  // self.downsampling_factor],
-                            device='cpu').to(self.device)
-                    else:
-                        return torch.randn([1,
-                            self.latent_channels,
-                            height // self.downsampling_factor,
-                            width  // self.downsampling_factor],
-                            device=self.device)
-
             initial_noise = None
             if variation_amount > 0 or len(with_variations) > 0:
                 # use fixed initial noise plus random noise per iteration
                 seed_everything(seed)
-                initial_noise = get_noise()
+                initial_noise = self._get_noise(init_img,width,height)
                 for v_seed, v_weight in with_variations:
                     seed = v_seed
                     seed_everything(seed)
-                    next_noise = get_noise()
+                    next_noise = self._get_noise(init_img,width,height)
                     initial_noise = self.slerp(v_weight, initial_noise, next_noise)
                 if variation_amount > 0:
                     random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
@@ -386,7 +367,7 @@ class T2I:
                     x_T = None
                     if variation_amount > 0:
                         seed_everything(seed)
-                        target_noise = get_noise()
+                        target_noise = self._get_noise(init_img,width,height)
                         x_T = self.slerp(variation_amount, initial_noise, target_noise)
                     elif initial_noise is not None:
                         # i.e. we specified particular variations
@@ -394,8 +375,9 @@ class T2I:
                     else:
                         seed_everything(seed)
                         if self.device.type == 'mps':
-                            x_T = get_noise()
+                            x_T = self._get_noise(init_img,width,height)
                         # make_image will do the equivalent of get_noise itself
+                    print(f' DEBUG: seed at make_image() invocation time ={seed}')
                     image = make_image(x_T)
                     results.append([image, seed])
                     if image_callback is not None:
@@ -623,6 +605,27 @@ class T2I:
 
         return self.model
 
+    # returns a tensor filled with random numbers from a normal distribution
+    def _get_noise(self,init_img,width,height):
+        if init_img:
+            if self.device.type == 'mps':
+                return torch.randn_like(init_latent, device='cpu').to(self.device)
+            else:
+                return torch.randn_like(init_latent, device=self.device)
+        else:
+            if self.device.type == 'mps':
+                return torch.randn([1,
+                                    self.latent_channels,
+                                    height // self.downsampling_factor,
+                                    width  // self.downsampling_factor],
+                                   device='cpu').to(self.device)
+            else:
+                return torch.randn([1,
+                                    self.latent_channels,
+                                    height // self.downsampling_factor,
+                                    width  // self.downsampling_factor],
+                                   device=self.device)
+
     def _set_sampler(self):
         msg = f'>> Setting Sampler to {self.sampler_name}'
         if self.sampler_name == 'plms':