diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index c32f57c388..3b4cf6c54d 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 or variation_amount > 1.0:
+        if len(with_variations) > 0 or variation_amount > 0.0:
             assert seed is not None,\
                 'seed must be specified when using with_variations'
             if variation_amount == 0.0:
@@ -336,6 +336,7 @@ class T2I:
                     callback=step_callback,
                 )
             else:
+                init_latent = None
                 make_image = self._txt2img(
                     prompt,
                     steps=steps,
@@ -351,11 +352,11 @@ class T2I:
             if variation_amount > 0 or len(with_variations) > 0:
                 # use fixed initial noise plus random noise per iteration
                 seed_everything(seed)
-                initial_noise = self._get_noise(init_img,width,height)
+                initial_noise = self._get_noise(init_latent,width,height)
                 for v_seed, v_weight in with_variations:
                     seed = v_seed
                     seed_everything(seed)
-                    next_noise = self._get_noise(init_img,width,height)
+                    next_noise = self._get_noise(init_latent,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
@@ -367,7 +368,7 @@ class T2I:
                     x_T = None
                     if variation_amount > 0:
                         seed_everything(seed)
-                        target_noise = self._get_noise(init_img,width,height)
+                        target_noise = self._get_noise(init_latent,width,height)
                         x_T = self.slerp(variation_amount, initial_noise, target_noise)
                     elif initial_noise is not None:
                         # i.e. we specified particular variations
@@ -375,7 +376,7 @@ class T2I:
                     else:
                         seed_everything(seed)
                         if self.device.type == 'mps':
-                            x_T = self._get_noise(init_img,width,height)
+                            x_T = self._get_noise(init_latent,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)
@@ -606,8 +607,8 @@ 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:
+    def _get_noise(self,init_latent,width,height):
+        if init_latent is not None:
             if self.device.type == 'mps':
                 return torch.randn_like(init_latent, device='cpu').to(self.device)
             else: