diff --git a/invokeai/app/invocations/latents_to_image.py b/invokeai/app/invocations/latents_to_image.py
index 35b8483f2c..90885d5c40 100644
--- a/invokeai/app/invocations/latents_to_image.py
+++ b/invokeai/app/invocations/latents_to_image.py
@@ -10,6 +10,8 @@ from diffusers.models.attention_processor import (
 )
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+from einops import rearrange
+from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
@@ -24,6 +26,9 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
@@ -53,11 +58,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
 
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-
-        vae_info = context.models.load(self.vae.vae)
+    def _vae_decode_stable_diffusion(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
@@ -113,6 +114,38 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
+        return image
+
+    def _vae_decode_flux(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        img = img.clamp(-1, 1)
+        img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
+        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+        return img_pil
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        vae_info = context.models.load(self.vae.vae)
+        if vae_info.config.base == BaseModelType.Flux:
+            if self.fp32:
+                raise NotImplementedError("FLUX VAE decode does not currently support fp32=True.")
+            if self.tiled:
+                raise NotImplementedError("FLUX VAE decode does not currently support tiled=True.")
+            image = self._vae_decode_flux(vae_info=vae_info, latents=latents)
+        elif vae_info.config.base in [
+            BaseModelType.StableDiffusion1,
+            BaseModelType.StableDiffusion2,
+            BaseModelType.StableDiffusionXL,
+        ]:
+            image = self._vae_decode_stable_diffusion(vae_info=vae_info, latents=latents)
+        else:
+            raise ValueError(f"Unsupported VAE base type: '{vae_info.config.base}'")
 
         TorchDevice.empty_cache()