diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index 7be22017..b56b9832 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -18,6 +18,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from monai.data import decollate_batch
 from monai.inferers import Inferer
 from monai.transforms import CenterSpatialCrop, SpatialPad
 from monai.utils import optional_import
@@ -348,8 +349,8 @@ def __init__(
         self.ldm_latent_shape = ldm_latent_shape
         self.autoencoder_latent_shape = autoencoder_latent_shape
         if self.ldm_latent_shape is not None:
-            self.ldm_resizer = SpatialPad(spatial_size=[-1] + self.ldm_latent_shape)
-            self.autoencoder_resizer = CenterSpatialCrop(roi_size=[-1] + self.autoencoder_latent_shape)
+            self.ldm_resizer = SpatialPad(spatial_size=self.ldm_latent_shape)
+            self.autoencoder_resizer = CenterSpatialCrop(roi_size=self.autoencoder_latent_shape)
 
     def __call__(
         self,
@@ -379,7 +380,7 @@ def __call__(
             latent = autoencoder_model.encode_stage_2_inputs(inputs) * self.scale_factor
 
         if self.ldm_latent_shape is not None:
-            latent = self.ldm_resizer(latent)
+            latent = torch.stack([self.ldm_resizer(i) for i in decollate_batch(latent)], 0)
 
         call = super().__call__
         if isinstance(diffusion_model, SPADEDiffusionModelUNet):
@@ -454,14 +455,15 @@ def sample(
         else:
             latent = outputs
 
-        if self.ldm_latent_shape is not None:
-            latent = self.autoencoder_resizer(latent)
-            latent_intermediates = [self.autoencoder_resizer(l) for l in latent_intermediates]
+        if self.autoencoder_latent_shape is not None:
+            latent = torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(latent)], 0)
+            latent_intermediates = [
+                torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0) for l in latent_intermediates
+            ]
 
         decode = autoencoder_model.decode_stage_2_outputs
         if isinstance(autoencoder_model, SPADEAutoencoderKL):
             decode = partial(autoencoder_model.decode_stage_2_outputs, seg=seg)
-
         image = decode(latent / self.scale_factor)
 
         if save_intermediates:
@@ -521,7 +523,7 @@ def get_likelihood(
         latents = autoencoder_model.encode_stage_2_inputs(inputs) * self.scale_factor
 
         if self.ldm_latent_shape is not None:
-            latents = self.ldm_resizer(latents)
+            latents = torch.stack([self.ldm_resizer(i) for i in decollate_batch(latents)], 0)
 
         get_likelihood = super().get_likelihood
         if isinstance(diffusion_model, SPADEDiffusionModelUNet):
@@ -598,7 +600,7 @@ def __call__(
 
         diffuse = diffusion_model
         if isinstance(diffusion_model, SPADEDiffusionModelUNet):
-            diffuse = partial(diffusion_model, seg = seg)
+            diffuse = partial(diffusion_model, seg=seg)
 
         prediction = diffuse(
             x=noisy_image,
@@ -746,7 +748,7 @@ def get_likelihood(
 
             diffuse = diffusion_model
             if isinstance(diffusion_model, SPADEDiffusionModelUNet):
-                diffuse = partial(diffusion_model, seg = seg)
+                diffuse = partial(diffusion_model, seg=seg)
 
             if mode == "concat":
                 noisy_image = torch.cat([noisy_image, conditioning], dim=1)
@@ -832,6 +834,7 @@ def get_likelihood(
         else:
             return total_kl
 
+
 class ControlNetLatentDiffusionInferer(ControlNetDiffusionInferer):
     """
     ControlNetLatentDiffusionInferer takes a stage 1 model (VQVAE or AutoencoderKL), diffusion model, controlnet,
@@ -861,7 +864,7 @@ def __init__(
         self.ldm_latent_shape = ldm_latent_shape
         self.autoencoder_latent_shape = autoencoder_latent_shape
         if self.ldm_latent_shape is not None:
-            self.ldm_resizer = SpatialPad(spatial_size=[-1] + self.ldm_latent_shape)
+            self.ldm_resizer = SpatialPad(spatial_size=self.ldm_latent_shape)
             self.autoencoder_resizer = CenterSpatialCrop(roi_size=[-1] + self.autoencoder_latent_shape)
 
     def __call__(
@@ -896,7 +899,8 @@ def __call__(
             latent = autoencoder_model.encode_stage_2_inputs(inputs) * self.scale_factor
 
         if self.ldm_latent_shape is not None:
-            latent = self.ldm_resizer(latent)
+            latent = torch.stack([self.ldm_resizer(i) for i in decollate_batch(latent)], 0)
+
         if cn_cond.shape[2:] != latent.shape[2:]:
             cn_cond = F.interpolate(cn_cond, latent.shape[2:])
 
@@ -985,9 +989,11 @@ def sample(
         else:
             latent = outputs
 
-        if self.ldm_latent_shape is not None:
-            latent = self.autoencoder_resizer(latent)
-            latent_intermediates = [self.autoencoder_resizer(l) for l in latent_intermediates]
+        if self.autoencoder_latent_shape is not None:
+            latent = torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(latent)], 0)
+            latent_intermediates = [
+                torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0) for l in latent_intermediates
+            ]
 
         decode = autoencoder_model.decode_stage_2_outputs
         if isinstance(autoencoder_model, SPADEAutoencoderKL):
@@ -1060,7 +1066,7 @@ def get_likelihood(
             cn_cond = F.interpolate(cn_cond, latents.shape[2:])
 
         if self.ldm_latent_shape is not None:
-            latents = self.ldm_resizer(latents)
+            latents = torch.stack([self.ldm_resizer(i) for i in decollate_batch(latents)], 0)
 
         get_likelihood = super().get_likelihood
         if isinstance(diffusion_model, SPADEDiffusionModelUNet):
@@ -1085,6 +1091,7 @@ def get_likelihood(
             outputs = (outputs[0], intermediates)
         return outputs
 
+
 class VQVAETransformerInferer(Inferer):
     """
     Class to perform inference with a VQVAE + Transformer model.