diff --git a/toolkit/config_modules.py b/toolkit/config_modules.py
index b7aedd35..8fdb2670 100644
--- a/toolkit/config_modules.py
+++ b/toolkit/config_modules.py
@@ -331,7 +331,7 @@ class SliderConfig:
         print(f"Building slider targets")
         for target in targets:
             if target.shuffle:
-                target_permutations = get_slider_target_permutations(target, max_permutations=100)
+                target_permutations = get_slider_target_permutations(target, max_permutations=8)
                 self.targets = self.targets + target_permutations
             else:
                 self.targets.append(target)
diff --git a/toolkit/stable_diffusion_model.py b/toolkit/stable_diffusion_model.py
index 9ee4d995..39114ee6 100644
--- a/toolkit/stable_diffusion_model.py
+++ b/toolkit/stable_diffusion_model.py
@@ -682,15 +682,30 @@ class StableDiffusion:
         text_embeddings = text_embeddings.to(self.device_torch)
         timestep = timestep.to(self.device_torch)
 
+        # if timestep is zero dim, unsqueeze it
+        if len(timestep.shape) == 0:
+            timestep = timestep.unsqueeze(0)
+
+        # if we only have 1 timestep, we can just use the same timestep for all
+        if timestep.shape[0] == 1 and latents.shape[0] > 1:
+            # check if it is rank 1 or 2
+            if len(timestep.shape) == 1:
+                timestep = timestep.repeat(latents.shape[0])
+            else:
+                timestep = timestep.repeat(latents.shape[0], 0)
+
+
         def scale_model_input(model_input, timestep_tensor):
             mi_chunks = torch.chunk(model_input, model_input.shape[0], dim=0)
+            timestep_chunks = torch.chunk(timestep_tensor, timestep_tensor.shape[0], dim=0)
             out_chunks = []
+            # unsqueeze if timestep is zero dim
             for idx in range(model_input.shape[0]):
                 # if scheduler has step_index
                 if hasattr(self.noise_scheduler, '_step_index'):
                     self.noise_scheduler._step_index = None
                 out_chunks.append(
-                    self.noise_scheduler.scale_model_input(mi_chunks[idx], timestep_tensor[idx])
+                    self.noise_scheduler.scale_model_input(mi_chunks[idx], timestep_chunks[idx])
                 )
             return torch.cat(out_chunks, dim=0)
 
@@ -845,6 +860,10 @@ class StableDiffusion:
         latent_chunks = torch.chunk(latent_input, latent_input.shape[0], dim=0)
         timestep_chunks = torch.chunk(timestep_tensor, timestep_tensor.shape[0], dim=0)
         out_chunks = []
+        if len(timestep_chunks) == 1 and len(mi_chunks) > 1:
+            # expand timestep to match
+            timestep_chunks = timestep_chunks * len(mi_chunks)
+
         for idx in range(model_input.shape[0]):
             # Reset it so it is unique for the
             if hasattr(self.noise_scheduler, '_step_index'):