diff --git a/comfy/ldm/chroma/layers_dct.py b/comfy/ldm/chroma/layers_dct.py
index f3130a78f..0f8b81b43 100644
--- a/comfy/ldm/chroma/layers_dct.py
+++ b/comfy/ldm/chroma/layers_dct.py
@@ -15,7 +15,7 @@ class NerfEmbedder(nn.Module):
     patch size, and enriches it with positional information before projecting
     it to a new hidden size.
     """
-    def __init__(self, in_channels, hidden_size_input, max_freqs, dtype=None, device=None, operations=None, *, embedder_dtype=None):
+    def __init__(self, in_channels, hidden_size_input, max_freqs, dtype=None, device=None, operations=None):
         """
         Initializes the NerfEmbedder.
 
@@ -29,7 +29,6 @@ class NerfEmbedder(nn.Module):
         super().__init__()
         self.max_freqs = max_freqs
         self.hidden_size_input = hidden_size_input
-        self.embedder_dtype = embedder_dtype
 
         # A linear layer to project the concatenated input features and
         # positional encodings to the final output dimension.
@@ -92,7 +91,7 @@ class NerfEmbedder(nn.Module):
 
         return dct
 
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+    def forward(self, inputs: torch.Tensor, embedder_dtype: torch.dtype) -> torch.Tensor:
         """
         Forward pass for the embedder.
 
@@ -110,13 +109,13 @@ class NerfEmbedder(nn.Module):
 
         # Possibly run the operation with a different dtype.
         orig_dtype = inputs.dtype
-        if self.embedder_dtype is not None and self.embedder_dtype != orig_dtype:
-            embedder = self.embedder.to(dtype=self.embedder_dtype)
+        if embedder_dtype != orig_dtype:
+            embedder = self.embedder.to(dtype=embedder_dtype)
         else:
             embedder = self.embedder
 
         # Fetch the pre-computed or cached positional embeddings.
-        dct = self.fetch_pos(patch_size, inputs.device, self.embedder_dtype or inputs.dtype)
+        dct = self.fetch_pos(patch_size, inputs.device, embedder_dtype)
 
         # Repeat the positional embeddings for each item in the batch.
         dct = dct.repeat(B, 1, 1)
diff --git a/comfy/ldm/chroma/model_dct.py b/comfy/ldm/chroma/model_dct.py
index a8c6a461f..e64bbba2f 100644
--- a/comfy/ldm/chroma/model_dct.py
+++ b/comfy/ldm/chroma/model_dct.py
@@ -124,7 +124,6 @@ class ChromaRadiance(chroma_model.Chroma):
             dtype=dtype,
             device=device,
             operations=operations,
-            embedder_dtype=params.nerf_embedder_dtype,
         )
 
         self.nerf_blocks = nn.ModuleList([
@@ -199,7 +198,7 @@ class ChromaRadiance(chroma_model.Chroma):
             nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
 
             # Get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct = self.nerf_image_embedder(nerf_pixels)
+            img_dct = self.nerf_image_embedder(nerf_pixels, params.nerf_embedder_dtype or nerf_pixels.dtype)
 
             # Pass through the dynamic MLP blocks (the NeRF)
             for block in self.nerf_blocks:
@@ -235,6 +234,7 @@ class ChromaRadiance(chroma_model.Chroma):
         """
         tile_size = params.nerf_tile_size
         output_tiles = []
+        embedder_dtype= params.nerf_embedder_dtype or nerf_pixels.dtype
         # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1.
         for i in range(0, num_patches, tile_size):
             end = min(i + tile_size, num_patches)
@@ -253,7 +253,7 @@ class ChromaRadiance(chroma_model.Chroma):
             nerf_pixels_tile = nerf_pixels_tile.reshape(B * num_patches_tile, C, patch_size**2).transpose(1, 2)
 
             # get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)
+            img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile, embedder_dtype)
 
             # pass through the dynamic MLP blocks (the NeRF)
             for block in self.nerf_blocks: