diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index ee19faea..78397d75 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -4,6 +4,7 @@ class LatentFormat:
     scale_factor = 1.0
     latent_channels = 4
     latent_rgb_factors = None
+    latent_rgb_factors_bias = None
     taesd_decoder_name = None
 
     def process_in(self, latent):
diff --git a/latent_preview.py b/latent_preview.py
index e14c72ce..ae9211a2 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -36,12 +36,20 @@ class TAESDPreviewerImpl(LatentPreviewer):
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self, latent_rgb_factors):
-        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        if latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
 
     def decode_latent_to_preview(self, x0):
         self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
-        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+        if self.latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+
+        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+
         return preview_to_image(latent_image)
 
 
@@ -71,7 +79,7 @@ def get_previewer(device, latent_format):
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:
-                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
     return previewer
 
 def prepare_callback(model, steps, x0_output_dict=None):