diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py
index ccbb25822..c4e4de9d3 100644
--- a/comfy/ldm/wan/vae.py
+++ b/comfy/ldm/wan/vae.py
@@ -231,7 +231,8 @@ class Encoder3d(nn.Module):
                  num_res_blocks=2,
                  attn_scales=[],
                  temperal_downsample=[True, True, False],
-                 dropout=0.0):
+                 dropout=0.0,
+                 pruning_rate=0.0):
         super().__init__()
         self.dim = dim
         self.z_dim = z_dim
@@ -242,6 +243,7 @@ class Encoder3d(nn.Module):
 
         # dimensions
         dims = [dim * u for u in [1] + dim_mult]
+        dims = [int(d * (1 - pruning_rate)) for d in dims]
         scale = 1.0
 
         # init block
@@ -335,7 +337,8 @@ class Decoder3d(nn.Module):
                  num_res_blocks=2,
                  attn_scales=[],
                  temperal_upsample=[False, True, True],
-                 dropout=0.0):
+                 dropout=0.0,
+                 pruning_rate=0.0):
         super().__init__()
         self.dim = dim
         self.z_dim = z_dim
@@ -346,6 +349,7 @@ class Decoder3d(nn.Module):
 
         # dimensions
         dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        dims = [int(d * (1 - pruning_rate)) for d in dims]
         scale = 1.0 / 2**(len(dim_mult) - 2)
 
         # init block
@@ -449,7 +453,8 @@ class WanVAE(nn.Module):
                  num_res_blocks=2,
                  attn_scales=[],
                  temperal_downsample=[True, True, False],
-                 dropout=0.0):
+                 dropout=0.0,
+                 pruning_rate=0.0):
         super().__init__()
         self.dim = dim
         self.z_dim = z_dim
@@ -461,11 +466,11 @@ class WanVAE(nn.Module):
 
         # modules
         self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
-                                 attn_scales, self.temperal_downsample, dropout)
+                                 attn_scales, self.temperal_downsample, dropout, pruning_rate)
         self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.conv2 = CausalConv3d(z_dim, z_dim, 1)
         self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
-                                 attn_scales, self.temperal_upsample, dropout)
+                                 attn_scales, self.temperal_upsample, dropout, pruning_rate)
 
     def encode(self, x):
         conv_idx = [0]
diff --git a/comfy/sd.py b/comfy/sd.py
index 52f9fb0e4..a2f54580c 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -528,13 +528,16 @@ class VAE:
                     self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
                     self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
                 else:  # Wan 2.1 VAE
+                    pruning_rate = 0.0
+                    if sd["decoder.middle.0.residual.0.gamma"].shape[0] == 96: # lightx2v lightvae
+                        pruning_rate = 0.75
                     self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                     self.upscale_index_formula = (4, 8, 8)
                     self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                     self.downscale_index_formula = (4, 8, 8)
                     self.latent_dim = 3
                     self.latent_channels = 16
-                    ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0, "pruning_rate": pruning_rate}
                     self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                     self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)